#888 by GitHub stars
LeonChaoX/qinyan-academic-skills
A curated, multilingual library of 182 installable AI agent skills for end-to-end academic research—spanning literature discovery, scientific writing, grant development, bioinformatics, drug discovery, clinical research, machine learning, and data analysis.
History
Review weekly changes to SKILL.md before updating. Dates mark the weeks in which we observed each version; checks apply to the recorded commits.
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skills/01-论文检索与文献管理/bgpt-paper-search/SKILL.md
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skills/01-论文检索与文献管理/biorxiv-database/SKILL.md
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skills/01-论文检索与文献管理/citation-management/SKILL.md
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skills/01-论文检索与文献管理/literature-review/SKILL.md
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skills/01-论文检索与文献管理/openalex-database/SKILL.md
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skills/01-论文检索与文献管理/parallel-web/SKILL.md
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skills/01-论文检索与文献管理/perplexity-search/SKILL.md
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skills/01-论文检索与文献管理/pubmed-database/SKILL.md
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skills/01-论文检索与文献管理/pyzotero/SKILL.md
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skills/01-论文检索与文献管理/research-lookup/SKILL.md
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skills/02-科学写作与学术交流/medical-imaging-review/SKILL.md
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skills/02-科学写作与学术交流/paper-2-web/SKILL.md
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skills/02-科学写作与学术交流/peer-review/SKILL.md
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skills/02-科学写作与学术交流/research-proposal/SKILL.md
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skills/02-科学写作与学术交流/scientific-writing/SKILL.md
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skills/02-科学写作与学术交流/venue-templates/SKILL.md
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skills/03-学术演示与可视化/generate-image/SKILL.md
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skills/03-学术演示与可视化/infographics/SKILL.md
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skills/03-学术演示与可视化/latex-posters/SKILL.md
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skills/03-学术演示与可视化/markdown-mermaid-writing/SKILL.md
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skills/03-学术演示与可视化/paper-slide-deck/SKILL.md
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skills/03-学术演示与可视化/pptx-posters/SKILL.md
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skills/03-学术演示与可视化/scientific-schematics/SKILL.md
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skills/03-学术演示与可视化/scientific-slides/SKILL.md
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skills/03-学术演示与可视化/scientific-visualization/SKILL.md
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skills/04-研究方法与科学思维/consciousness-council/SKILL.md
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skills/04-研究方法与科学思维/dhdna-profiler/SKILL.md
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skills/04-研究方法与科学思维/hypothesis-generation/SKILL.md
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skills/04-研究方法与科学思维/nsfc-proposal/SKILL.md
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skills/04-研究方法与科学思维/research-grants/SKILL.md
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skills/04-研究方法与科学思维/scholar-evaluation/SKILL.md
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skills/04-研究方法与科学思维/scientific-brainstorming/SKILL.md
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skills/04-研究方法与科学思维/scientific-critical-thinking/SKILL.md
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skills/04-研究方法与科学思维/what-if-oracle/SKILL.md
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skills/05-生物信息与基因组学/anndata/SKILL.md
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skills/05-生物信息与基因组学/arboreto/SKILL.md
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skills/05-生物信息与基因组学/biopython/SKILL.md
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skills/05-生物信息与基因组学/bioservices/SKILL.md
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skills/05-生物信息与基因组学/cellxgene-census/SKILL.md
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skills/05-生物信息与基因组学/deeptools/SKILL.md
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skills/05-生物信息与基因组学/etetoolkit/SKILL.md
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skills/05-生物信息与基因组学/flowio/SKILL.md
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skills/05-生物信息与基因组学/gtars/SKILL.md
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skills/05-生物信息与基因组学/phylogenetics/SKILL.md
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skills/05-生物信息与基因组学/pydeseq2/SKILL.md
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skills/05-生物信息与基因组学/pysam/SKILL.md
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skills/05-生物信息与基因组学/scanpy/SKILL.md
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skills/05-生物信息与基因组学/scikit-bio/SKILL.md
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skills/05-生物信息与基因组学/scvi-tools/SKILL.md
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skills/06-化学信息与药物发现/datamol/SKILL.md
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skills/06-化学信息与药物发现/molecular-dynamics/SKILL.md
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skills/06-化学信息与药物发现/pyopenms/SKILL.md
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skills/06-化学信息与药物发现/pytdc/SKILL.md
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skills/06-化学信息与药物发现/torchdrug/SKILL.md
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skills/07-临床医学与精准医疗/cbioportal-database/SKILL.md
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skills/07-临床医学与精准医疗/clinical-decision-support/SKILL.md
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skills/07-临床医学与精准医疗/clinical-reports/SKILL.md
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skills/09-机器学习与人工智能/pymoo/SKILL.md
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skills/09-机器学习与人工智能/torch-geometric/SKILL.md
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1kGitHub stars
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187skills in this repository
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- bgpt-paper-search
skills/01-论文检索与文献管理/bgpt-paper-search - biorxiv-database
skills/01-论文检索与文献管理/biorxiv-database - citation-management
skills/01-论文检索与文献管理/citation-management - literature-review
skills/01-论文检索与文献管理/literature-review - openalex-database
skills/01-论文检索与文献管理/openalex-database - parallel-web
skills/01-论文检索与文献管理/parallel-web - perplexity-search
skills/01-论文检索与文献管理/perplexity-search - pubmed-database
skills/01-论文检索与文献管理/pubmed-database - pyzotero
skills/01-论文检索与文献管理/pyzotero - research-lookup
skills/01-论文检索与文献管理/research-lookup - medical-imaging-review
skills/02-科学写作与学术交流/medical-imaging-review - paper-2-web
skills/02-科学写作与学术交流/paper-2-web - peer-review
skills/02-科学写作与学术交流/peer-review - research-proposal
skills/02-科学写作与学术交流/research-proposal - scientific-writing
skills/02-科学写作与学术交流/scientific-writing - venue-templates
skills/02-科学写作与学术交流/venue-templates - generate-image
skills/03-学术演示与可视化/generate-image - infographics
skills/03-学术演示与可视化/infographics - latex-posters
skills/03-学术演示与可视化/latex-posters - markdown-mermaid-writing
skills/03-学术演示与可视化/markdown-mermaid-writing - paper-slide-deck
skills/03-学术演示与可视化/paper-slide-deck - pptx-posters
skills/03-学术演示与可视化/pptx-posters - scientific-schematics
skills/03-学术演示与可视化/scientific-schematics - scientific-slides
skills/03-学术演示与可视化/scientific-slides - scientific-visualization
skills/03-学术演示与可视化/scientific-visualization - consciousness-council
skills/04-研究方法与科学思维/consciousness-council - dhdna-profiler
skills/04-研究方法与科学思维/dhdna-profiler - hypothesis-generation
skills/04-研究方法与科学思维/hypothesis-generation - nsfc-proposal
skills/04-研究方法与科学思维/nsfc-proposal - nssfc-proposal
skills/04-研究方法与科学思维/nssfc-proposal - research-grants
skills/04-研究方法与科学思维/research-grants - scholar-evaluation
skills/04-研究方法与科学思维/scholar-evaluation - scientific-brainstorming
skills/04-研究方法与科学思维/scientific-brainstorming - scientific-critical-thinking
skills/04-研究方法与科学思维/scientific-critical-thinking - what-if-oracle
skills/04-研究方法与科学思维/what-if-oracle - anndata
skills/05-生物信息与基因组学/anndata - arboreto
skills/05-生物信息与基因组学/arboreto - biopython
skills/05-生物信息与基因组学/biopython - bioservices
skills/05-生物信息与基因组学/bioservices - cellxgene-census
skills/05-生物信息与基因组学/cellxgene-census - deeptools
skills/05-生物信息与基因组学/deeptools - etetoolkit
skills/05-生物信息与基因组学/etetoolkit - flowio
skills/05-生物信息与基因组学/flowio - geniml
skills/05-生物信息与基因组学/geniml - gget
skills/05-生物信息与基因组学/gget - gtars
skills/05-生物信息与基因组学/gtars - lamindb
skills/05-生物信息与基因组学/lamindb - phylogenetics
skills/05-生物信息与基因组学/phylogenetics - pydeseq2
skills/05-生物信息与基因组学/pydeseq2 - pysam
skills/05-生物信息与基因组学/pysam - scanpy
skills/05-生物信息与基因组学/scanpy - scikit-bio
skills/05-生物信息与基因组学/scikit-bio - scvelo
skills/05-生物信息与基因组学/scvelo - scvi-tools
skills/05-生物信息与基因组学/scvi-tools - tiledbvcf
skills/05-生物信息与基因组学/tiledbvcf - zarr-python
skills/05-生物信息与基因组学/zarr-python - datamol
skills/06-化学信息与药物发现/datamol - deepchem
skills/06-化学信息与药物发现/deepchem - diffdock
skills/06-化学信息与药物发现/diffdock - matchms
skills/06-化学信息与药物发现/matchms - medchem
skills/06-化学信息与药物发现/medchem - molecular-dynamics
skills/06-化学信息与药物发现/molecular-dynamics - molfeat
skills/06-化学信息与药物发现/molfeat - pyopenms
skills/06-化学信息与药物发现/pyopenms - pytdc
skills/06-化学信息与药物发现/pytdc - rdkit
skills/06-化学信息与药物发现/rdkit - rowan
skills/06-化学信息与药物发现/rowan - torchdrug
skills/06-化学信息与药物发现/torchdrug - cbioportal-database
skills/07-临床医学与精准医疗/cbioportal-database - clinical-decision-support
skills/07-临床医学与精准医疗/clinical-decision-support - clinical-reports
skills/07-临床医学与精准医疗/clinical-reports - clinicaltrials-database
skills/07-临床医学与精准医疗/clinicaltrials-database - clinpgx-database
skills/07-临床医学与精准医疗/clinpgx-database - clinvar-database
skills/07-临床医学与精准医疗/clinvar-database - cosmic-database
skills/07-临床医学与精准医疗/cosmic-database - depmap
skills/07-临床医学与精准医疗/depmap - fda-database
skills/07-临床医学与精准医疗/fda-database - histolab
skills/07-临床医学与精准医疗/histolab - imaging-data-commons
skills/07-临床医学与精准医疗/imaging-data-commons - monarch-database
skills/07-临床医学与精准医疗/monarch-database - neurokit2
skills/07-临床医学与精准医疗/neurokit2 - neuropixels-analysis
skills/07-临床医学与精准医疗/neuropixels-analysis - pathml
skills/07-临床医学与精准医疗/pathml - pydicom
skills/07-临床医学与精准医疗/pydicom - pyhealth
skills/07-临床医学与精准医疗/pyhealth - treatment-plans
skills/07-临床医学与精准医疗/treatment-plans - adaptyv
skills/08-蛋白质工程与结构生物学/adaptyv - alphafold-database
skills/08-蛋白质工程与结构生物学/alphafold-database - esm
skills/08-蛋白质工程与结构生物学/esm - glycoengineering
skills/08-蛋白质工程与结构生物学/glycoengineering - interpro-database
skills/08-蛋白质工程与结构生物学/interpro-database - pdb-database
skills/08-蛋白质工程与结构生物学/pdb-database - uniprot-database
skills/08-蛋白质工程与结构生物学/uniprot-database - aeon
skills/09-机器学习与人工智能/aeon - hypogenic
skills/09-机器学习与人工智能/hypogenic - pufferlib
skills/09-机器学习与人工智能/pufferlib - pymc
skills/09-机器学习与人工智能/pymc - pymoo
skills/09-机器学习与人工智能/pymoo - pytorch-lightning
skills/09-机器学习与人工智能/pytorch-lightning - scikit-learn
skills/09-机器学习与人工智能/scikit-learn - scikit-survival
skills/09-机器学习与人工智能/scikit-survival - shap
skills/09-机器学习与人工智能/shap - stable-baselines3
skills/09-机器学习与人工智能/stable-baselines3 - timesfm-forecasting
skills/09-机器学习与人工智能/timesfm-forecasting - torch-geometric
skills/09-机器学习与人工智能/torch-geometric - transformers
skills/09-机器学习与人工智能/transformers - umap-learn
skills/09-机器学习与人工智能/umap-learn - astropy
skills/10-材料科学与物理计算/astropy - cirq
skills/10-材料科学与物理计算/cirq - cobrapy
skills/10-材料科学与物理计算/cobrapy - fluidsim
skills/10-材料科学与物理计算/fluidsim - matlab
skills/10-材料科学与物理计算/matlab - pennylane
skills/10-材料科学与物理计算/pennylane - pymatgen
skills/10-材料科学与物理计算/pymatgen - qiskit
skills/10-材料科学与物理计算/qiskit - qutip
skills/10-材料科学与物理计算/qutip - simpy
skills/10-材料科学与物理计算/simpy - dask
skills/11-数据分析与统计建模/dask - exploratory-data-analysis
skills/11-数据分析与统计建模/exploratory-data-analysis - matplotlib
skills/11-数据分析与统计建模/matplotlib - networkx
skills/11-数据分析与统计建模/networkx - plotly
skills/11-数据分析与统计建模/plotly - polars
skills/11-数据分析与统计建模/polars - seaborn
skills/11-数据分析与统计建模/seaborn - statistical-analysis
skills/11-数据分析与统计建模/statistical-analysis - statsmodels
skills/11-数据分析与统计建模/statsmodels - sympy
skills/11-数据分析与统计建模/sympy - vaex
skills/11-数据分析与统计建模/vaex - bindingdb-database
skills/12-科学数据库/bindingdb-database - brenda-database
skills/12-科学数据库/brenda-database - chembl-database
skills/12-科学数据库/chembl-database - datacommons-client
skills/12-科学数据库/datacommons-client - drugbank-database
skills/12-科学数据库/drugbank-database - ena-database
skills/12-科学数据库/ena-database - ensembl-database
skills/12-科学数据库/ensembl-database - gene-database
skills/12-科学数据库/gene-database - geo-database
skills/12-科学数据库/geo-database - gnomad-database
skills/12-科学数据库/gnomad-database - gtex-database
skills/12-科学数据库/gtex-database - gwas-database
skills/12-科学数据库/gwas-database - hmdb-database
skills/12-科学数据库/hmdb-database - jaspar-database
skills/12-科学数据库/jaspar-database - kegg-database
skills/12-科学数据库/kegg-database - metabolomics-workbench-database
skills/12-科学数据库/metabolomics-workbench-database - opentargets-database
skills/12-科学数据库/opentargets-database - pubchem-database
skills/12-科学数据库/pubchem-database - reactome-database
skills/12-科学数据库/reactome-database - string-database
skills/12-科学数据库/string-database - uspto-database
skills/12-科学数据库/uspto-database - zinc-database
skills/12-科学数据库/zinc-database - benchling-integration
skills/13-实验室自动化与集成/benchling-integration - dnanexus-integration
skills/13-实验室自动化与集成/dnanexus-integration - ginkgo-cloud-lab
skills/13-实验室自动化与集成/ginkgo-cloud-lab - labarchive-integration
skills/13-实验室自动化与集成/labarchive-integration - latchbio-integration
skills/13-实验室自动化与集成/latchbio-integration - omero-integration
skills/13-实验室自动化与集成/omero-integration - opentrons-integration
skills/13-实验室自动化与集成/opentrons-integration - protocolsio-integration
skills/13-实验室自动化与集成/protocolsio-integration - pylabrobot
skills/13-实验室自动化与集成/pylabrobot - denario
skills/14-文档处理与数据工具/denario - docx
skills/14-文档处理与数据工具/docx - markitdown
skills/14-文档处理与数据工具/markitdown - open-notebook
skills/14-文档处理与数据工具/open-notebook - pdf
skills/14-文档处理与数据工具/pdf - pptx
skills/14-文档处理与数据工具/pptx - xlsx
skills/14-文档处理与数据工具/xlsx - alpha-vantage
skills/15-金融与经济数据/alpha-vantage - edgartools
skills/15-金融与经济数据/edgartools - fred-economic-data
skills/15-金融与经济数据/fred-economic-data - hedgefundmonitor
skills/15-金融与经济数据/hedgefundmonitor - market-research-reports
skills/15-金融与经济数据/market-research-reports - usfiscaldata
skills/15-金融与经济数据/usfiscaldata - geomaster
skills/16-地理空间与遥感/geomaster - geopandas
skills/16-地理空间与遥感/geopandas - get-available-resources
skills/17-平台与基础设施/get-available-resources - iso-13485-certification
skills/17-平台与基础设施/iso-13485-certification - modal
skills/17-平台与基础设施/modal - qinyan-citation
skills/沁言学术skills/qinyan-citation - qinyan-nature-figures
skills/沁言学术skills/qinyan-nature-figures - qinyan-nature-polishing
skills/沁言学术skills/qinyan-nature-polishing - qinyan-nature-review
skills/沁言学术skills/qinyan-nature-review - qinyan-nature-statistics
skills/沁言学术skills/qinyan-nature-statistics - qinyan-nature-writing
skills/沁言学术skills/qinyan-nature-writing - qinyan-paper-analysis
skills/沁言学术skills/qinyan-paper-analysis - qinyan-paper-polish
skills/沁言学术skills/qinyan-paper-polish - qinyan-paper-search
skills/沁言学术skills/qinyan-paper-search - qinyan-topic-analysis
skills/沁言学术skills/qinyan-topic-analysis
How it works
bgpt-paper-search
When your agent uses it
Search scientific papers and retrieve structured experimental data extracted from full-text studies via the BGPT MCP server. Returns 25+ fields per paper including methods, results, sample sizes, quality scores, and conclusions. Use for literature reviews, evidence synthesis, and finding experimental details not available in abstracts alone.
What it loads into context
Frontmatter
113 tokensAlways available to the agent
SKILL.md
638 tokensWhen the skill is activated
Reference documents
0 tokensLoaded on request
Token counts are estimates. Scripts run separately; they are not counted as reference context.
The playbook
- Overview
- When to Use This Skill
- Setup
- Usage
- Pricing
- Complementary Skills
Files
SKILL.mdcore2,872 bytesSKILL.mdcore2,872 bytes
Read SKILL.md
# BGPT Paper Search
## Overview
BGPT is a remote MCP server that searches a curated database of scientific papers built from raw experimental data extracted from full-text studies. Unlike traditional literature databases that return titles and abstracts, BGPT returns structured data from the actual paper content — methods, quantitative results, sample sizes, quality assessments, and 25+ metadata fields per paper.
## When to Use This Skill
Use this skill when:
- Searching for scientific papers with specific experimental details
- Conducting systematic or scoping literature reviews
- Finding quantitative results, sample sizes, or effect sizes across studies
- Comparing methodologies used in different studies
- Looking for papers with quality scores or evidence gradingbiorxiv-database
When your agent uses it
Efficient database search tool for bioRxiv preprint server. Use this skill when searching for life sciences preprints by keywords, authors, date ranges, or categories, retrieving paper metadata, downloading PDFs, or conducting literature reviews.
What it loads into context
Frontmatter
70 tokensAlways available to the agent
SKILL.md
3,174 tokensWhen the skill is activated
Reference documents
1,776 tokensLoaded on request
Token counts are estimates. Scripts run separately; they are not counted as reference context.
The playbook
- Overview
- When to Use This Skill
- Core Search Capabilities
- Valid Categories
- Output Format
- Common Usage Patterns
- Python API Usage
- Best Practices
- Advanced Features
- Programmatic Integration
- Testing the Skill
- Reference Documentation
Scripts executed separately
scripts/biorxiv_search.py
Files
SKILL.mdcore12,584 bytesSKILL.mdcore12,584 bytesSKILL.mdcore12,584 bytesSKILL.mdcore12,584 bytesSKILL.mdcore12,584 bytesSKILL.mdcore12,584 bytesSKILL.mdcore12,584 bytesSKILL.mdcore12,584 bytesSKILL.mdcore12,584 bytesSKILL.mdcore12,584 bytesSKILL.mdcore12,584 bytesSKILL.mdcore12,584 bytesSKILL.mdcore12,584 bytesSKILL.mdcore12,584 bytesSKILL.mdcore12,584 bytesSKILL.mdcore12,584 bytesSKILL.mdcore12,584 bytesSKILL.mdcore12,584 bytesSKILL.mdcore12,584 bytesSKILL.mdcore12,584 bytesSKILL.mdcore12,584 bytesSKILL.mdcore12,584 bytesSKILL.mdcore12,584 bytesSKILL.mdcore12,584 bytesSKILL.mdcore12,584 bytesreferences/api_reference.mddoc6,405 bytesscripts/biorxiv_search.pyruns14,831 bytes
Read SKILL.md
# bioRxiv Database
## Overview
This skill provides efficient Python-based tools for searching and retrieving preprints from the bioRxiv database. It enables comprehensive searches by keywords, authors, date ranges, and categories, returning structured JSON metadata that includes titles, abstracts, DOIs, and citation information. The skill also supports PDF downloads for full-text analysis.
## When to Use This Skill
Use this skill when:
- Searching for recent preprints in specific research areas
- Tracking publications by particular authors
- Conducting systematic literature reviews
- Analyzing research trends over time periods
- Retrieving metadata for citation managementcitation-management
When your agent uses it
Comprehensive citation management for academic research. Search Google Scholar and PubMed for papers, extract accurate metadata, validate citations, and generate properly formatted BibTeX entries. This skill should be used when you need to find papers, verify citation information, convert DOIs to BibTeX, or ensure reference accuracy in scientific writing.
What it loads into context
Frontmatter
95 tokensAlways available to the agent
SKILL.md
7,971 tokensWhen the skill is activated
Reference documents
26,858 tokensLoaded on request
Token counts are estimates. Scripts run separately; they are not counted as reference context.
The playbook
- Overview
- When to Use This Skill
- Visual Enhancement with Scientific Schematics
- Core Workflow
- Search Strategies
- Tools and Scripts
- Best Practices
- Common Pitfalls to Avoid
- Example Workflows
- Integration with Other Skills
- Resources
- Dependencies
- Summary
Scripts executed separately
scripts/doi_to_bibtex.pyscripts/extract_metadata.pyscripts/format_bibtex.pyscripts/search_google_scholar.pyscripts/search_pubmed.pyscripts/validate_citations.py
Files
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Read SKILL.md
# Citation Management
## Overview
Manage citations systematically throughout the research and writing process. This skill provides tools and strategies for searching academic databases (Google Scholar, PubMed), extracting accurate metadata from multiple sources (CrossRef, PubMed, arXiv), validating citation information, and generating properly formatted BibTeX entries.
Critical for maintaining citation accuracy, avoiding reference errors, and ensuring reproducible research. Integrates seamlessly with the literature-review skill for comprehensive research workflows.
## When to Use This Skill
Use this skill when:
- Searching for specific papers on Google Scholar or PubMed
- Converting DOIs, PMIDs, or arXiv IDs to properly formatted BibTeX
- Extracting complete metadata for citations (authors, title, journal, year, etc.)literature-review
When your agent uses it
Conduct comprehensive, systematic literature reviews using multiple academic databases (PubMed, arXiv, bioRxiv, Semantic Scholar, etc.). This skill should be used when conducting systematic literature reviews, meta-analyses, research synthesis, or comprehensive literature searches across biomedical, scientific, and technical domains. Creates professionally formatted markdown documents and PDFs with verified citations in multiple citation styles (APA, Nature, Vancouver, etc.).
What it loads into context
Frontmatter
113 tokensAlways available to the agent
SKILL.md
5,536 tokensWhen the skill is activated
Reference documents
8,996 tokensLoaded on request
Token counts are estimates. Scripts run separately; they are not counted as reference context.
The playbook
- Overview
- When to Use This Skill
- Visual Enhancement with Scientific Schematics
- Core Workflow
- Database-Specific Search Guidance
- Citation Style Guide
- Best Practices
- Common Pitfalls to Avoid
- Example Workflow
- Integration with Other Skills
- Resources
- Dependencies
- Summary
Scripts executed separately
scripts/generate_pdf.pyscripts/search_databases.pyscripts/verify_citations.py
Files
SKILL.mdcore23,789 bytesSKILL.mdcore23,789 bytesSKILL.mdcore23,789 bytesSKILL.mdcore23,789 bytesSKILL.mdcore23,789 bytesSKILL.mdcore23,789 bytesSKILL.mdcore23,789 bytesSKILL.mdcore23,789 bytesSKILL.mdcore23,789 bytesSKILL.mdcore23,789 bytesSKILL.mdcore23,789 bytesSKILL.mdcore23,789 bytesSKILL.mdcore23,789 bytesSKILL.mdcore23,789 bytesassets/review_template.mddoc12,982 bytesreferences/citation_styles.mddoc6,112 bytesreferences/database_strategies.mddoc15,491 bytesscripts/generate_pdf.pyruns5,410 bytesscripts/search_databases.pyruns9,036 bytesscripts/verify_citations.pyruns7,050 bytes
Read SKILL.md
# Literature Review
## Overview
Conduct systematic, comprehensive literature reviews following rigorous academic methodology. Search multiple literature databases, synthesize findings thematically, verify all citations for accuracy, and generate professional output documents in markdown and PDF formats.
This skill integrates with multiple scientific skills for database access (gget, bioservices, datacommons-client) and provides specialized tools for citation verification, result aggregation, and document generation.
## When to Use This Skill
Use this skill when:
- Conducting a systematic literature review for research or publication
- Synthesizing current knowledge on a specific topic across multiple sources
- Performing meta-analysis or scoping reviewsopenalex-database
When your agent uses it
Query and analyze scholarly literature using the OpenAlex database. This skill should be used when searching for academic papers, analyzing research trends, finding works by authors or institutions, tracking citations, discovering open access publications, or conducting bibliometric analysis across 240M+ scholarly works. Use for literature searches, research output analysis, citation analysis, and academic database queries.
What it loads into context
Frontmatter
95 tokensAlways available to the agent
SKILL.md
2,981 tokensWhen the skill is activated
Reference documents
5,385 tokensLoaded on request
Token counts are estimates. Scripts run separately; they are not counted as reference context.
The playbook
- Overview
- Quick Start
- Core Capabilities
- Critical Best Practices
- Common Filter Patterns
- Entity Types
- External IDs
- Reference Documentation
- Scripts
- Troubleshooting
- Rate Limits
- Notes
Scripts executed separately
scripts/openalex_client.pyscripts/query_helpers.py
Files
SKILL.mdcore12,072 bytesSKILL.mdcore12,072 bytesSKILL.mdcore12,072 bytesSKILL.mdcore12,072 bytesSKILL.mdcore12,072 bytesSKILL.mdcore12,072 bytesSKILL.mdcore12,072 bytesSKILL.mdcore12,072 bytesSKILL.mdcore12,072 bytesSKILL.mdcore12,072 bytesSKILL.mdcore12,072 bytesSKILL.mdcore12,072 bytesSKILL.mdcore12,072 bytesSKILL.mdcore12,072 bytesSKILL.mdcore12,072 bytesSKILL.mdcore12,072 bytesSKILL.mdcore12,072 bytesSKILL.mdcore12,072 bytesSKILL.mdcore12,072 bytesSKILL.mdcore12,072 bytesSKILL.mdcore12,072 bytesSKILL.mdcore12,072 bytesSKILL.mdcore12,072 bytesSKILL.mdcore12,072 bytesSKILL.mdcore12,072 bytesSKILL.mdcore12,072 bytesSKILL.mdcore12,072 bytesSKILL.mdcore12,072 bytesSKILL.mdcore12,072 bytesSKILL.mdcore12,072 bytesreferences/api_guide.mddoc10,185 bytesreferences/common_queries.mddoc9,587 bytesscripts/openalex_client.pyruns10,148 bytesscripts/query_helpers.pyruns8,443 bytes
Read SKILL.md
# OpenAlex Database
## Overview
OpenAlex is a comprehensive open catalog of 240M+ scholarly works, authors, institutions, topics, sources, publishers, and funders. This skill provides tools and workflows for querying the OpenAlex API to search literature, analyze research output, track citations, and conduct bibliometric studies.
## Quick Start
### Basic Setup
Always initialize the client with an email address to access the polite pool (10x rate limit boost):
```python
from scripts.openalex_client import OpenAlexClientparallel-web
When your agent uses it
Search the web, extract URL content, and run deep research using the Parallel Chat API and Extract API. Use for ALL web searches, research queries, and general information gathering. Provides synthesized summaries with citations.
What it loads into context
Frontmatter
82 tokensAlways available to the agent
SKILL.md
2,655 tokensWhen the skill is activated
Reference documents
12,228 tokensLoaded on request
Token counts are estimates. Scripts run separately; they are not counted as reference context.
The playbook
- Overview
- When to Use This Skill
- Two Capabilities
- Model Selection Guide
- Python API Usage
- MANDATORY: Save All Results to Sources Folder
- Integration with Scientific Writer
- Environment Setup
- Error Handling
- Complementary Skills
Scripts executed separately
scripts/parallel_web.py
Files
SKILL.mdcore11,656 bytesSKILL.mdcore11,656 bytesSKILL.mdcore11,656 bytesSKILL.mdcore11,656 bytesSKILL.mdcore11,656 bytesSKILL.mdcore11,656 bytesSKILL.mdcore11,656 bytesSKILL.mdcore11,656 bytesSKILL.mdcore11,656 bytesSKILL.mdcore11,656 bytesSKILL.mdcore11,656 bytesSKILL.mdcore11,656 bytesSKILL.mdcore11,656 bytesreferences/api_reference.mddoc5,161 bytesreferences/deep_research_guide.mddoc12,898 bytesreferences/extraction_patterns.mddoc9,564 bytesreferences/search_best_practices.mddoc9,310 bytesreferences/workflow_recipes.mddoc16,360 bytesscripts/parallel_web.pyruns19,801 bytes
Read SKILL.md
# Parallel Web Systems API
## Overview
This skill provides access to **Parallel Web Systems** APIs for web search, deep research, and content extraction. It is the **primary tool for all web-related operations** in the scientific writer workflow.
**Primary interface:** Parallel Chat API (OpenAI-compatible) for search and research.
**Secondary interface:** Extract API for URL verification and special cases only.
**API Documentation:** https://docs.parallel.ai
**API Key:** https://platform.parallel.ai
**Environment Variable:** `PARALLEL_API_KEY`
## When to Use This Skillperplexity-search
When your agent uses it
Perform AI-powered web searches with real-time information using Perplexity models via LiteLLM and OpenRouter. This skill should be used when conducting web searches for current information, finding recent scientific literature, getting grounded answers with source citations, or accessing information beyond the model knowledge cutoff. Provides access to multiple Perplexity models including Sonar Pro, Sonar Pro Search (advanced agentic search), and Sonar Reasoning Pro through a single OpenRouter API key.
What it loads into context
Frontmatter
134 tokensAlways available to the agent
SKILL.md
3,203 tokensWhen the skill is activated
Reference documents
7,492 tokensLoaded on request
Token counts are estimates. Scripts run separately; they are not counted as reference context.
The playbook
- Overview
- When to Use This Skill
- Quick Start
- Available Models
- Crafting Effective Queries
- Common Use Cases
- Working with Results
- Cost Management
- Troubleshooting
- Integration with Other Skills
- Best Practices
- Resources
- Dependencies
- Summary
Scripts executed separately
scripts/perplexity_search.pyscripts/setup_env.py
Files
SKILL.mdcore14,085 bytesSKILL.mdcore14,085 bytesSKILL.mdcore14,085 bytesSKILL.mdcore14,085 bytesSKILL.mdcore14,085 bytesSKILL.mdcore14,085 bytesSKILL.mdcore14,085 bytesSKILL.mdcore14,085 bytesSKILL.mdcore14,085 bytesSKILL.mdcore14,085 bytesSKILL.mdcore14,085 bytesSKILL.mdcore14,085 bytesSKILL.mdcore14,085 bytesSKILL.mdcore14,085 bytesSKILL.mdcore14,085 bytesSKILL.mdcore14,085 bytesSKILL.mdcore14,085 bytesSKILL.mdcore14,085 bytesSKILL.mdcore14,085 bytesSKILL.mdcore14,085 bytesSKILL.mdcore14,085 bytesSKILL.mdcore14,085 bytesSKILL.mdcore14,085 bytesSKILL.mdcore14,085 bytesSKILL.mdcore14,085 bytesassets/.env.exampleasset506 bytesreferences/model_comparison.mddoc10,378 bytesreferences/openrouter_setup.mddoc11,522 bytesreferences/search_strategies.mddoc10,021 bytesscripts/perplexity_search.pyruns8,068 bytesscripts/setup_env.pyruns4,656 bytes
Read SKILL.md
# Perplexity Search
## Overview
Perform AI-powered web searches using Perplexity models through LiteLLM and OpenRouter. Perplexity provides real-time, web-grounded answers with source citations, making it ideal for finding current information, recent scientific literature, and facts beyond the model's training data cutoff.
This skill provides access to all Perplexity models through OpenRouter, requiring only a single API key (no separate Perplexity account needed).
## When to Use This Skill
Use this skill when:
- Searching for current information or recent developments (2024 and beyond)
- Finding latest scientific publications and research
- Getting real-time answers grounded in web sourcespubmed-database
When your agent uses it
Direct REST API access to PubMed. Advanced Boolean/MeSH queries, E-utilities API, batch processing, citation management. For Python workflows, prefer biopython (Bio.Entrez). Use this for direct HTTP/REST work or custom API implementations.
What it loads into context
Frontmatter
77 tokensAlways available to the agent
SKILL.md
3,621 tokensWhen the skill is activated
Reference documents
8,167 tokensLoaded on request
Token counts are estimates. Scripts run separately; they are not counted as reference context.
The playbook
- Overview
- When to Use This Skill
- Core Capabilities
- Working with Reference Files
- Common Workflows
- Tips and Best Practices
- Limitations and Considerations
- Support Resources
Files
SKILL.mdcore15,570 bytesSKILL.mdcore15,570 bytesSKILL.mdcore15,570 bytesreferences/api_reference.mddoc7,934 bytesreferences/common_queries.mddoc10,725 bytesreferences/search_syntax.mddoc10,272 bytes
Read SKILL.md
# PubMed Database
## Overview
PubMed is the U.S. National Library of Medicine's comprehensive database providing free access to MEDLINE and life sciences literature. Construct advanced queries with Boolean operators, MeSH terms, and field tags, access data programmatically via E-utilities API for systematic reviews and literature analysis.
## When to Use This Skill
This skill should be used when:
- Searching for biomedical or life sciences research articles
- Constructing complex search queries with Boolean operators, field tags, or MeSH terms
- Conducting systematic literature reviews or meta-analyses
- Accessing PubMed data programmatically via the E-utilities API
- Finding articles by specific criteria (author, journal, publication date, article type)pyzotero
When your agent uses it
Interact with Zotero reference management libraries using the pyzotero Python client. Retrieve, create, update, and delete items, collections, tags, and attachments via the Zotero Web API v3. Use this skill when working with Zotero libraries programmatically, managing bibliographic references, exporting citations, searching library contents, uploading PDF attachments, or building research automation workflows that integrate with Zotero.
What it loads into context
Frontmatter
114 tokensAlways available to the agent
SKILL.md
1,075 tokensWhen the skill is activated
Reference documents
8,333 tokensLoaded on request
Token counts are estimates. Scripts run separately; they are not counted as reference context.
The playbook
- Authentication Setup
- Installation
- Quick Start
- Core Concepts
- Reference Files
- Common Patterns
Files
SKILL.mdcore4,398 bytesSKILL.mdcore4,398 bytesSKILL.mdcore4,398 bytesSKILL.mdcore4,398 bytesSKILL.mdcore4,398 bytesSKILL.mdcore4,398 bytesSKILL.mdcore4,398 bytesreferences/authentication.mddoc2,250 bytesreferences/cli.mddoc2,293 bytesreferences/collections.mddoc2,526 bytesreferences/error-handling.mddoc2,617 bytesreferences/exports.mddoc2,603 bytesreferences/files-attachments.mddoc2,760 bytesreferences/full-text.mddoc1,695 bytesreferences/pagination.mddoc2,056 bytesreferences/read-api.mddoc2,972 bytesreferences/saved-searches.mddoc2,009 bytesreferences/search-params.mddoc3,047 bytesreferences/tags.mddoc2,083 bytesreferences/write-api.mddoc3,294 bytes
Read SKILL.md
# Pyzotero
Pyzotero is a Python wrapper for the [Zotero API v3](https://www.zotero.org/support/dev/web_api/v3/start). Use it to programmatically manage Zotero libraries: read items and collections, create and update references, upload attachments, manage tags, and export citations.
## Authentication Setup
**Required credentials** — get from https://www.zotero.org/settings/keys:
- **User ID**: shown as "Your userID for use in API calls"
- **API Key**: create at https://www.zotero.org/settings/keys/new
- **Library ID**: for group libraries, the integer after `/groups/` in the group URL
Store credentials in environment variables or a `.env` file:
```
ZOTERO_LIBRARY_ID=your_user_idresearch-lookup
When your agent uses it
Look up current research information using the Parallel Chat API (primary) or Perplexity sonar-pro-search (academic paper searches). Automatically routes queries to the best backend. Use for finding papers, gathering research data, and verifying scientific information.
What it loads into context
Frontmatter
96 tokensAlways available to the agent
SKILL.md
3,657 tokensWhen the skill is activated
Reference documents
1,211 tokensLoaded on request
Token counts are estimates. Scripts run separately; they are not counted as reference context.
The playbook
- Overview
- When to Use This Skill
- Visual Enhancement with Scientific Schematics
- Automatic Backend Selection
- Core Capabilities
- Paper Quality and Popularity Prioritization
- Technical Integration
- MANDATORY: Save All Results to Sources Folder
- Integration with Scientific Writing
- Complementary Tools
- Error Handling and Limitations
- Usage Examples
- Summary
Scripts executed separately
examples.pylookup.pyresearch_lookup.pyscripts/research_lookup.py
Files
README.mddoc5,606 bytesSKILL.mdcore16,232 bytesSKILL.mdcore16,232 bytesSKILL.mdcore16,232 bytesSKILL.mdcore16,232 bytesSKILL.mdcore16,232 bytesSKILL.mdcore16,232 bytesSKILL.mdcore16,232 bytesSKILL.mdcore16,232 bytesSKILL.mdcore16,232 bytesSKILL.mdcore16,232 bytesSKILL.mdcore16,232 bytesSKILL.mdcore16,232 bytesSKILL.mdcore16,232 bytesSKILL.mdcore16,232 bytesexamples.pyruns5,672 byteslookup.pyruns5,957 bytesresearch_lookup.pyruns22,642 bytesscripts/research_lookup.pyruns22,642 bytes
Read SKILL.md
# Research Information Lookup
## Overview
This skill provides real-time research information lookup with **intelligent backend routing**:
- **Parallel Chat API** (`core` model): Default backend for all general research queries. Provides comprehensive, multi-source research reports with inline citations via the OpenAI-compatible Chat API at `https://api.parallel.ai`.
- **Perplexity sonar-pro-search** (via OpenRouter): Used only for academic-specific paper searches where scholarly database access is critical.
The skill automatically detects query type and routes to the optimal backend.
## When to Use This Skill
Use this skill when you need:medical-imaging-review
When your agent uses it
Write comprehensive literature reviews for medical imaging AI research. Use when writing survey papers, systematic reviews, or literature analyses on topics like segmentation, detection, classification in CT, MRI, X-ray, ultrasound, or pathology imaging. Triggers on requests for "review paper", "survey", "literature review", "综述", "systematic review", or mentions of writing academic reviews on deep learning for medical imaging.
What it loads into context
Frontmatter
252 tokensAlways available to the agent
SKILL.md
1,175 tokensWhen the skill is activated
Reference documents
5,081 tokensLoaded on request
Token counts are estimates. Scripts run separately; they are not counted as reference context.
The playbook
- Quick Start
- Core Principles
- Literature Sources
- Standard Review Structure
- Method Description Template
- Citation Patterns
- Reference Files
Files
SKILL.mdcore4,482 bytesSKILL.mdcore4,482 bytesreferences/DOMAINS.mddoc3,690 bytesreferences/MCP_SETUP.mddoc2,784 bytesreferences/QUALITY_CHECKLIST.mddoc2,745 bytesreferences/TEMPLATES.mddoc5,335 bytesreferences/WORKFLOW.mddoc4,681 bytes
Read SKILL.md
# Medical Imaging AI Literature Review Skill
Write comprehensive literature reviews following a systematic 7-phase workflow.
## Quick Start
1. **Initialize project** with three core files:
- `CLAUDE.md` - Writing guidelines and terminology
- `IMPLEMENTATION_PLAN.md` - Staged execution plan
- `manuscript_draft.md` - Main manuscript
2. **Follow the 7-phase workflow** (see [references/WORKFLOW.md](references/WORKFLOW.md))
3. **Use domain-specific templates** (see [references/DOMAINS.md](references/DOMAINS.md))paper-2-web
When your agent uses it
This skill should be used when converting academic papers into promotional and presentation formats including interactive websites (Paper2Web), presentation videos (Paper2Video), and conference posters (Paper2Poster). Use this skill for tasks involving paper dissemination, conference preparation, creating explorable academic homepages, generating video abstracts, or producing print-ready posters from LaTeX or PDF sources.
What it loads into context
Frontmatter
105 tokensAlways available to the agent
SKILL.md
3,714 tokensWhen the skill is activated
Reference documents
9,255 tokensLoaded on request
Token counts are estimates. Scripts run separately; they are not counted as reference context.
The playbook
- Overview
- When to Use This Skill
- Visual Enhancement with Scientific Schematics
- Core Capabilities
- Quick Start
- Workflow Decision Tree
- Input Requirements
- Common Parameters
- Output Structure
- Best Practices
- Resource Requirements
- Troubleshooting
- Platform-Specific Features
- Integration and Deployment
- Advanced Usage
- References and Resources
- Evaluation and Quality Metrics
Files
SKILL.mdcore16,447 bytesSKILL.mdcore16,447 bytesSKILL.mdcore16,447 bytesSKILL.mdcore16,447 bytesSKILL.mdcore16,447 bytesSKILL.mdcore16,447 bytesSKILL.mdcore16,447 bytesSKILL.mdcore16,447 bytesSKILL.mdcore16,447 bytesSKILL.mdcore16,447 bytesSKILL.mdcore16,447 bytesSKILL.mdcore16,447 bytesSKILL.mdcore16,447 bytesSKILL.mdcore16,447 bytesSKILL.mdcore16,447 bytesreferences/installation.mddoc3,644 bytesreferences/paper2poster.mddoc10,207 bytesreferences/paper2video.mddoc9,796 bytesreferences/paper2web.mddoc5,388 bytesreferences/usage_examples.mddoc10,633 bytes
Read SKILL.md
# Paper2All: Academic Paper Transformation Pipeline
## Overview
This skill enables the transformation of academic papers into multiple promotional and presentation formats using the Paper2All autonomous pipeline. The system converts research papers (LaTeX or PDF) into three primary outputs:
1. **Paper2Web**: Interactive, explorable academic homepages with layout-aware design
2. **Paper2Video**: Professional presentation videos with narration, slides, and optional talking-head
3. **Paper2Poster**: Print-ready conference posters with professional layouts
The pipeline uses LLM-powered content extraction, design generation, and iterative refinement to create high-quality outputs suitable for conferences, journals, preprint repositories, and academic promotion.
## When to Use This Skill
peer-review
When your agent uses it
Structured manuscript/grant review with checklist-based evaluation. Use when writing formal peer reviews with specific criteria methodology assessment, statistical validity, reporting standards compliance (CONSORT/STROBE), and constructive feedback. Best for actual review writing, manuscript revision. For evaluating claims/evidence quality use scientific-critical-thinking; for quantitative scoring frameworks use scholar-evaluation.
What it loads into context
Frontmatter
99 tokensAlways available to the agent
SKILL.md
4,549 tokensWhen the skill is activated
Reference documents
5,909 tokensLoaded on request
Token counts are estimates. Scripts run separately; they are not counted as reference context.
The playbook
- Overview
- When to Use This Skill
- Visual Enhancement with Scientific Schematics
- Peer Review Workflow
- Structuring Peer Review Reports
- Tone and Approach
- Special Considerations by Manuscript Type
- Resources
- Final Checklist
Files
SKILL.mdcore23,120 bytesSKILL.mdcore23,120 bytesSKILL.mdcore23,120 bytesSKILL.mdcore23,120 bytesreferences/common_issues.mddoc19,321 bytesreferences/reporting_standards.mddoc10,200 bytes
Read SKILL.md
# Scientific Critical Evaluation and Peer Review
## Overview
Peer review is a systematic process for evaluating scientific manuscripts. Assess methodology, statistics, design, reproducibility, ethics, and reporting standards. Apply this skill for manuscript and grant review across disciplines with constructive, rigorous evaluation.
## When to Use This Skill
This skill should be used when:
- Conducting peer review of scientific manuscripts for journals
- Evaluating grant proposals and research applications
- Assessing methodology and experimental design rigor
- Reviewing statistical analyses and reporting standards
- Evaluating reproducibility and data availabilityresearch-proposal
When your agent uses it
Generate academic research proposals for PhD applications. Use when user asks to "write a research proposal", "create PhD proposal", "generate research plan", "撰写研究计划", "写博士申请", "doctoral proposal", or mentions specific research topics for PhD application. Supports STEM, humanities, and social sciences with field-specific adaptations. Follows Nature Reviews-style academic writing conventions. Supports both English and Chinese output based on user preference.
What it loads into context
Frontmatter
286 tokensAlways available to the agent
SKILL.md
4,261 tokensWhen the skill is activated
Reference documents
21,019 tokensLoaded on request
Token counts are estimates. Scripts run separately; they are not counted as reference context.
The playbook
- Overview
- Phase 1: Requirements Gathering
- Phase 2: Literature Collection
- Phase 3: Outline Generation
- Phase 4: Content Writing
- Phase 5: Output and Review
- Reference Files
- Workflow Summary
- Error Handling
- Notes
Files
SKILL.mdcore20,160 bytesSKILL.mdcore20,160 bytesassets/proposal_scaffold_en.mddoc9,231 bytesassets/proposal_scaffold_zh.mddoc8,493 bytesreferences/DOMAIN_TEMPLATES.mddoc15,542 bytesreferences/LITERATURE_WORKFLOW.mddoc13,872 bytesreferences/QUALITY_CHECKLIST.mddoc9,100 bytesreferences/STRUCTURE_GUIDE.mddoc11,850 bytesreferences/WRITING_STYLE_GUIDE.mddoc20,159 bytes
Read SKILL.md
# Research Proposal Generator
Generate high-quality academic research proposals for PhD applications following Nature Reviews-style academic writing conventions.
## Overview
This skill guides the generation of research proposals through a structured 5-phase workflow:
1. **Requirements Gathering** - Collect research topic, domain, language preferences
2. **Literature Collection** - Gather relevant literature from multiple sources
3. **Outline Generation** - Create structured outline for user approval
4. **Content Writing** - Generate full proposal based on approved outline
5. **Output & Review** - Deliver Markdown file with quality checklist
scientific-writing
When your agent uses it
Core skill for the deep research and writing tool. Write scientific manuscripts in full paragraphs (never bullet points). Use two-stage process with (1) section outlines with key points using research-lookup then (2) convert to flowing prose. IMRAD structure, citations (APA/AMA/Vancouver), figures/tables, reporting guidelines (CONSORT/STROBE/PRISMA), for research papers and journal submissions.
What it loads into context
Frontmatter
114 tokensAlways available to the agent
SKILL.md
7,131 tokensWhen the skill is activated
Reference documents
38,906 tokensLoaded on request
Token counts are estimates. Scripts run separately; they are not counted as reference context.
The playbook
- Overview
- When to Use This Skill
- Visual Enhancement with Scientific Schematics
- Core Capabilities
- Workflow for Manuscript Development
- Integration with Other Scientific Skills
- References
- Assets
Files
SKILL.mdcore33,781 bytesSKILL.mdcore33,781 bytesSKILL.mdcore33,781 bytesSKILL.mdcore33,781 bytesSKILL.mdcore33,781 bytesassets/REPORT_FORMATTING_GUIDE.mddoc14,173 bytesassets/scientific_report.styasset16,643 bytesassets/scientific_report_template.texasset15,638 bytesreferences/citation_styles.mddoc23,705 bytesreferences/figures_tables.mddoc27,883 bytesreferences/imrad_structure.mddoc26,135 bytesreferences/professional_report_formatting.mddoc20,620 bytesreferences/reporting_guidelines.mddoc25,012 bytesreferences/writing_principles.mddoc27,593 bytes
Read SKILL.md
# Scientific Writing
## Overview
**This is the core skill for the deep research and writing tool**—combining AI-driven deep research with well-formatted written outputs. Every document produced is backed by comprehensive literature search and verified citations through the research-lookup skill.
Scientific writing is a process for communicating research with precision and clarity. Write manuscripts using IMRAD structure, citations (APA/AMA/Vancouver), figures/tables, and reporting guidelines (CONSORT/STROBE/PRISMA). Apply this skill for research papers and journal submissions.
**Critical Principle: Always write in full paragraphs with flowing prose. Never submit bullet points in the final manuscript.** Use a two-stage process: first create section outlines with key points using research-lookup, then convert those outlines into complete paragraphs.
## When to Use This Skill
This skill should be used when:
- Writing or revising any section of a scientific manuscript (abstract, introduction, methods, results, discussion)venue-templates
When your agent uses it
Access comprehensive LaTeX templates, formatting requirements, and submission guidelines for major scientific publication venues (Nature, Science, PLOS, IEEE, ACM), academic conferences (NeurIPS, ICML, CVPR, CHI), research posters, and grant proposals (NSF, NIH, DOE, DARPA). This skill should be used when preparing manuscripts for journal submission, conference papers, research posters, or grant proposals and need venue-specific formatting requirements and templates.
What it loads into context
Frontmatter
125 tokensAlways available to the agent
SKILL.md
5,297 tokensWhen the skill is activated
Reference documents
52,054 tokensLoaded on request
Token counts are estimates. Scripts run separately; they are not counted as reference context.
The playbook
- Overview
- When to Use This Skill
- Visual Enhancement with Scientific Schematics
- Core Capabilities
- Workflow: Finding and Using Templates
- Integration with Other Skills
- Template Categories
- Helper Scripts
- Best Practices
- Common Formatting Requirements
- Writing Style Guides
- Resources
- Example Usage
- Updates and Maintenance
- Summary
Scripts executed separately
scripts/customize_template.pyscripts/query_template.pyscripts/validate_format.py
Files
SKILL.mdcore23,296 bytesSKILL.mdcore23,296 bytesSKILL.mdcore23,296 bytesSKILL.mdcore23,296 bytesSKILL.mdcore23,296 bytesSKILL.mdcore23,296 bytesSKILL.mdcore23,296 bytesSKILL.mdcore23,296 bytesSKILL.mdcore23,296 bytesSKILL.mdcore23,296 bytesSKILL.mdcore23,296 bytesSKILL.mdcore23,296 bytesassets/examples/cell_summary_example.mddoc8,877 bytesassets/examples/medical_structured_abstract.mddoc12,074 bytesassets/examples/nature_abstract_examples.mddoc8,954 bytesassets/examples/neurips_introduction_example.mddoc8,779 bytesassets/grants/nih_specific_aims.texasset9,082 bytesassets/grants/nsf_proposal_template.texasset13,157 bytesassets/journals/nature_article.texasset6,242 bytesassets/journals/neurips_article.texasset8,925 bytesassets/journals/plos_one.texasset11,713 bytesassets/posters/beamerposter_academic.texasset8,495 bytesreferences/cell_press_style.mddoc14,290 bytesreferences/conferences_formatting.mddoc17,164 bytesreferences/cs_conference_style.mddoc15,652 bytesreferences/grants_requirements.mddoc22,873 bytesreferences/journals_formatting.mddoc17,649 bytesreferences/medical_journal_styles.mddoc15,963 bytesreferences/ml_conference_style.mddoc17,838 bytesreferences/nature_science_style.mddoc13,574 bytesreferences/posters_guidelines.mddoc17,113 bytesreferences/reviewer_expectations.mddoc15,837 bytesreferences/venue_writing_styles.mddoc12,671 bytesscripts/customize_template.pyruns6,875 bytesscripts/query_template.pyruns8,959 bytesscripts/validate_format.pyruns8,766 bytes
Read SKILL.md
# Venue Templates
## Overview
Access comprehensive LaTeX templates, formatting requirements, and submission guidelines for major scientific publication venues, academic conferences, research posters, and grant proposals. This skill provides ready-to-use templates and detailed specifications for successful academic submissions across disciplines.
Use this skill when preparing manuscripts for journal submission, conference papers, research posters, or grant proposals and need venue-specific formatting requirements and templates.
## When to Use This Skill
This skill should be used when:
- Preparing a manuscript for submission to a specific journal (Nature, Science, PLOS, IEEE, etc.)
- Writing a conference paper with specific formatting requirements (NeurIPS, ICML, CHI, etc.)
- Creating an academic research poster for conferencesgenerate-image
When your agent uses it
Generate or edit images using AI models (FLUX, Nano Banana 2). Use for general-purpose image generation including photos, illustrations, artwork, visual assets, concept art, and any image that is not a technical diagram or schematic. For flowcharts, circuits, pathways, and technical diagrams, use the scientific-schematics skill instead.
What it loads into context
Frontmatter
101 tokensAlways available to the agent
SKILL.md
1,611 tokensWhen the skill is activated
Reference documents
0 tokensLoaded on request
Token counts are estimates. Scripts run separately; they are not counted as reference context.
The playbook
- When to Use This Skill
- Quick Start
- API Key Setup
- Model Selection
- Common Usage Patterns
- Script Parameters
- Example Use Cases
- Error Handling
- Notes
- Image Editing Tips
- Integration with Other Skills
Scripts executed separately
scripts/generate_image.py
Files
SKILL.mdcore7,048 bytesSKILL.mdcore7,048 bytesSKILL.mdcore7,048 bytesSKILL.mdcore7,048 bytesSKILL.mdcore7,048 bytesSKILL.mdcore7,048 bytesSKILL.mdcore7,048 bytesSKILL.mdcore7,048 bytesSKILL.mdcore7,048 bytesSKILL.mdcore7,048 bytesSKILL.mdcore7,048 bytesSKILL.mdcore7,048 bytesscripts/generate_image.pyruns8,815 bytes
Read SKILL.md
# Generate Image
Generate and edit high-quality images using OpenRouter's image generation models including FLUX.2 Pro and Gemini 3.1 Flash Image Preview.
## When to Use This Skill
**Use generate-image for:**
- Photos and photorealistic images
- Artistic illustrations and artwork
- Concept art and visual concepts
- Visual assets for presentations or documents
- Image editing and modifications
- Any general-purpose image generation needs
infographics
When your agent uses it
Create professional infographics using Nano Banana Pro AI with smart iterative refinement. Uses Gemini 3 Pro for quality review. Integrates research-lookup and web search for accurate data. Supports 10 infographic types, 8 industry styles, and colorblind-safe palettes.
What it loads into context
Frontmatter
67 tokensAlways available to the agent
SKILL.md
4,135 tokensWhen the skill is activated
Reference documents
16,091 tokensLoaded on request
Token counts are estimates. Scripts run separately; they are not counted as reference context.
The playbook
- Overview
- Quick Start
- When to Use This Skill
- Research Integration
- Infographic Types
- Style Presets
- Colorblind-Safe Palettes
- Smart Iterative Refinement
- Command-Line Reference
- Configuration
- Prompt Engineering Tips
- Reference Files
- Troubleshooting
- Integration with Other Skills
- Quick Reference Checklist
Scripts executed separately
scripts/generate_infographic.pyscripts/generate_infographic_ai.py
Files
SKILL.mdcore18,071 bytesSKILL.mdcore18,071 bytesSKILL.mdcore18,071 bytesSKILL.mdcore18,071 bytesSKILL.mdcore18,071 bytesSKILL.mdcore18,071 bytesSKILL.mdcore18,071 bytesSKILL.mdcore18,071 bytesSKILL.mdcore18,071 bytesSKILL.mdcore18,071 bytesSKILL.mdcore18,071 bytesSKILL.mdcore18,071 bytesSKILL.mdcore18,071 bytesSKILL.mdcore18,071 bytesSKILL.mdcore18,071 bytesSKILL.mdcore18,071 bytesSKILL.mdcore18,071 bytesSKILL.mdcore18,071 bytesSKILL.mdcore18,071 bytesSKILL.mdcore18,071 bytesSKILL.mdcore18,071 bytesSKILL.mdcore18,071 bytesSKILL.mdcore18,071 bytesSKILL.mdcore18,071 bytesSKILL.mdcore18,071 bytesSKILL.mdcore18,071 bytesSKILL.mdcore18,071 bytesSKILL.mdcore18,071 bytesSKILL.mdcore18,071 bytesreferences/color_palettes.mddoc14,120 bytesreferences/design_principles.mddoc19,080 bytesreferences/infographic_types.mddoc28,996 bytesscripts/generate_infographic.pyruns10,198 bytesscripts/generate_infographic_ai.pyruns49,715 bytes
Read SKILL.md
# Infographics
## Overview
Infographics are visual representations of information, data, or knowledge designed to present complex content quickly and clearly. **This skill uses Nano Banana Pro AI for infographic generation with Gemini 3 Pro quality review and Perplexity Sonar for research.**
**How it works:**
- (Optional) **Research phase**: Gather accurate facts and statistics using Perplexity Sonar
- Describe your infographic in natural language
- Nano Banana Pro generates publication-quality infographics automatically
- **Gemini 3 Pro reviews quality** against document-type thresholds
- **Smart iteration**: Only regenerates if quality is below threshold
- Professional-ready output in minutes
- No design skills requiredlatex-posters
When your agent uses it
Create professional research posters in LaTeX using beamerposter, tikzposter, or baposter. Support for conference presentations, academic posters, and scientific communication. Includes layout design, color schemes, multi-column formats, figure integration, and poster-specific best practices for visual communication.
What it loads into context
Frontmatter
72 tokensAlways available to the agent
SKILL.md
15,500 tokensWhen the skill is activated
Reference documents
27,354 tokensLoaded on request
Token counts are estimates. Scripts run separately; they are not counted as reference context.
The playbook
- Overview
- When to Use This Skill
- AI-Powered Visual Element Generation
- Scientific Schematics Integration
- Core Capabilities
- Workflow for Poster Creation
- Integration with Other Skills
- Common Pitfalls to Avoid
- Package Installation
- Scripts and Automation
- References
- Templates
Scripts executed separately
scripts/review_poster.sh
Files
SKILL.mdcore59,801 bytesSKILL.mdcore59,801 bytesSKILL.mdcore59,801 bytesSKILL.mdcore59,801 bytesSKILL.mdcore59,801 bytesSKILL.mdcore59,801 bytesSKILL.mdcore59,801 bytesSKILL.mdcore59,801 bytesSKILL.mdcore59,801 bytesSKILL.mdcore59,801 bytesSKILL.mdcore59,801 bytesSKILL.mdcore59,801 bytesSKILL.mdcore59,801 bytesSKILL.mdcore59,801 bytesSKILL.mdcore59,801 bytesSKILL.mdcore59,801 bytesSKILL.mdcore59,801 bytesSKILL.mdcore59,801 bytesSKILL.mdcore59,801 bytesSKILL.mdcore59,801 bytesSKILL.mdcore59,801 bytesSKILL.mdcore59,801 bytesSKILL.mdcore59,801 bytesSKILL.mdcore59,801 bytesSKILL.mdcore59,801 bytesSKILL.mdcore59,801 bytesSKILL.mdcore59,801 bytesSKILL.mdcore59,801 bytesSKILL.mdcore59,801 bytesSKILL.mdcore59,801 bytesSKILL.mdcore59,801 bytesSKILL.mdcore59,801 bytesSKILL.mdcore59,801 bytesSKILL.mdcore59,801 bytesSKILL.mdcore59,801 bytesSKILL.mdcore59,801 bytesSKILL.mdcore59,801 bytesSKILL.mdcore59,801 bytesSKILL.mdcore59,801 bytesSKILL.mdcore59,801 bytesSKILL.mdcore59,801 bytesSKILL.mdcore59,801 bytesSKILL.mdcore59,801 bytesSKILL.mdcore59,801 bytesSKILL.mdcore59,801 bytesSKILL.mdcore59,801 bytesSKILL.mdcore59,801 bytesSKILL.mdcore59,801 bytesSKILL.mdcore59,801 bytesSKILL.mdcore59,801 bytesSKILL.mdcore59,801 bytesSKILL.mdcore59,801 bytesSKILL.mdcore59,801 bytesSKILL.mdcore59,801 bytesSKILL.mdcore59,801 bytesassets/baposter_template.texasset7,432 bytesassets/beamerposter_template.texasset7,652 bytesassets/poster_quality_checklist.mddoc11,172 bytesassets/tikzposter_template.texasset7,887 bytesreferences/README.mddoc11,654 bytesreferences/latex_poster_packages.mddoc17,836 bytesreferences/poster_content_guide.mddoc20,361 bytesreferences/poster_design_principles.mddoc22,916 bytesreferences/poster_layout_design.mddoc22,327 bytesscripts/review_poster.shruns7,939 bytes
Read SKILL.md
# LaTeX Research Posters
## Overview
Research posters are a critical medium for scientific communication at conferences, symposia, and academic events. This skill provides comprehensive guidance for creating professional, visually appealing research posters using LaTeX packages. Generate publication-quality posters with proper layout, typography, color schemes, and visual hierarchy.
## When to Use This Skill
This skill should be used when:
- Creating research posters for conferences, symposia, or poster sessions
- Designing academic posters for university events or thesis defenses
- Preparing visual summaries of research for public engagement
- Converting scientific papers into poster format
- Creating template posters for research groups or departmentsmarkdown-mermaid-writing
When your agent uses it
Comprehensive markdown and Mermaid diagram writing skill. Use when creating any scientific document, report, analysis, or visualization. Establishes text-based diagrams as the default documentation standard with full style guides (markdown + mermaid), 24 diagram type references, and 9 document templates.
What it loads into context
Frontmatter
190 tokensAlways available to the agent
SKILL.md
3,590 tokensWhen the skill is activated
Reference documents
62,652 tokensLoaded on request
Token counts are estimates. Scripts run separately; they are not counted as reference context.
The playbook
- Overview
- When to Use This Skill
- 🎨 The Source Format Philosophy
- 🔧 Core workflow
- ⚠<U+FE0F> Common pitfalls
- 🔗 Integration with other skills
- 📚 Reference index
- 📝 Attribution
Files
SKILL.mdcore14,888 bytesSKILL.mdcore14,888 bytesSKILL.mdcore14,888 bytesSKILL.mdcore14,888 bytesSKILL.mdcore14,888 bytesSKILL.mdcore14,888 bytesSKILL.mdcore14,888 bytesassets/examples/example-research-report.mddoc9,010 bytesreferences/diagrams/architecture.mddoc4,369 bytesreferences/diagrams/block.mddoc4,825 bytesreferences/diagrams/c4.mddoc6,201 bytesreferences/diagrams/class.mddoc8,521 bytesreferences/diagrams/complex_examples.mddoc17,079 bytesreferences/diagrams/er.mddoc7,601 bytesreferences/diagrams/flowchart.mddoc6,961 bytesreferences/diagrams/gantt.mddoc5,851 bytesreferences/diagrams/git_graph.mddoc2,009 bytesreferences/diagrams/kanban.mddoc3,544 bytesreferences/diagrams/mindmap.mddoc2,232 bytesreferences/diagrams/packet.mddoc1,721 bytesreferences/diagrams/pie.mddoc1,560 bytesreferences/diagrams/quadrant.mddoc2,163 bytesreferences/diagrams/radar.mddoc2,176 bytesreferences/diagrams/requirement.mddoc2,463 bytesreferences/diagrams/sankey.mddoc2,143 bytesreferences/diagrams/sequence.mddoc5,510 bytesreferences/diagrams/state.mddoc5,464 bytesreferences/diagrams/timeline.mddoc4,181 bytesreferences/diagrams/treemap.mddoc2,039 bytesreferences/diagrams/user_journey.mddoc5,049 bytesreferences/diagrams/xy_chart.mddoc1,887 bytesreferences/diagrams/zenuml.mddoc2,285 bytesreferences/markdown_style_guide.mddoc35,114 bytesreferences/mermaid_style_guide.mddoc26,433 bytestemplates/decision_record.mddoc7,169 bytestemplates/how_to_guide.mddoc7,091 bytestemplates/issue.mddoc12,835 bytestemplates/kanban.mddoc11,403 bytestemplates/presentation.mddoc8,068 bytestemplates/project_documentation.mddoc11,489 bytestemplates/pull_request.mddoc12,903 bytestemplates/research_paper.mddoc10,157 bytestemplates/status_report.mddoc7,185 bytes
Read SKILL.md
# Markdown and Mermaid Writing
## Overview
This skill teaches you — and enforces a standard for — creating scientific documentation
using **markdown with embedded Mermaid diagrams as the default and canonical format**.
The core bet: a relationship expressed as a Mermaid diagram inside a `.md` file is more
valuable than any image. It is text, so it diffs cleanly in git. It requires no build step.
It renders natively on GitHub, GitLab, Notion, VS Code, and any markdown viewer. It uses
fewer tokens than a prose description of the same relationship. And it can always be
converted to a polished image later — but the text version remains the source of truth.
> "The more you get your reports and files in .md in just regular text, which mermaid ispaper-slide-deck
When your agent uses it
Generate professional slide deck images from academic papers and content. Creates comprehensive outlines with style instructions, auto-detects figures from PDFs, then generates individual slide images. Use when user asks to "create slides", "make a presentation", "generate deck", or "slide deck" for papers.
What it loads into context
Frontmatter
67 tokensAlways available to the agent
SKILL.md
4,571 tokensWhen the skill is activated
Reference documents
18,134 tokensLoaded on request
Token counts are estimates. Scripts run separately; they are not counted as reference context.
The playbook
- Usage
- Script Directory
- Options
- Style Gallery
- Auto Style Selection
- Layout Gallery
- Design Philosophy
- File Management
- Workflow
- Slide Modification
- Image Generation Dependencies
- References
- Notes
- Extension Support
Scripts executed separately
scripts/apply-template.tsscripts/detect-figures.tsscripts/extract-figure.tsscripts/generate-slides.pyscripts/merge-to-pdf.tsscripts/merge-to-pptx.tsscripts/package.json
Files
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Read SKILL.md
# Paper Slide Deck Generator
Transform academic papers and content into professional slide deck images with automatic figure extraction.
## Usage
```bash
/paper-slide-deck path/to/paper.pdf
/paper-slide-deck path/to/paper.pdf --style academic-paper
/paper-slide-deck path/to/content.md --style sketch-notes
/paper-slide-deck path/to/content.md --audience executives
/paper-slide-deck path/to/content.md --lang zh
/paper-slide-deck path/to/content.md --slides 10
/paper-slide-deck path/to/content.md --outline-onlypptx-posters
When your agent uses it
Create research posters using HTML/CSS that can be exported to PDF or PPTX. Use this skill ONLY when the user explicitly requests PowerPoint/PPTX poster format. For standard research posters, use latex-posters instead. This skill provides modern web-based poster design with responsive layouts and easy visual integration.
What it loads into context
Frontmatter
94 tokensAlways available to the agent
SKILL.md
3,587 tokensWhen the skill is activated
Reference documents
19,567 tokensLoaded on request
Token counts are estimates. Scripts run separately; they are not counted as reference context.
The playbook
- Overview
- When to Use This Skill
- AI-Powered Visual Element Generation
- Core Capabilities
- Workflow for PPTX Poster Creation
- HTML Template Structure
- Quality Checklist
- Common Pitfalls to Avoid
- Integration with Other Skills
- Template Assets
- References
Files
SKILL.mdcore14,146 bytesSKILL.mdcore14,146 bytesSKILL.mdcore14,146 bytesSKILL.mdcore14,146 bytesSKILL.mdcore14,146 bytesSKILL.mdcore14,146 bytesSKILL.mdcore14,146 bytesSKILL.mdcore14,146 bytesSKILL.mdcore14,146 bytesSKILL.mdcore14,146 bytesSKILL.mdcore14,146 bytesSKILL.mdcore14,146 bytesSKILL.mdcore14,146 bytesSKILL.mdcore14,146 bytesSKILL.mdcore14,146 bytesSKILL.mdcore14,146 bytesSKILL.mdcore14,146 bytesassets/poster_html_template.htmlasset5,643 bytesassets/poster_quality_checklist.mddoc11,172 bytesreferences/poster_content_guide.mddoc20,361 bytesreferences/poster_design_principles.mddoc22,916 bytesreferences/poster_layout_design.mddoc22,327 bytes
Read SKILL.md
# PPTX Research Posters (HTML-Based)
## Overview
**⚠<U+FE0F> USE THIS SKILL ONLY WHEN USER EXPLICITLY REQUESTS PPTX/POWERPOINT POSTER FORMAT.**
For standard research posters, use the **latex-posters** skill instead, which provides better typographic control and is the default for academic conferences.
This skill creates research posters using HTML/CSS, which can then be exported to PDF or converted to PowerPoint format. The web-based approach offers:
- Modern, responsive layouts
- Easy integration of AI-generated visuals
- Quick iteration and preview in browser
- Export to PDF via browser print function
- Conversion to PPTX if specifically neededscientific-schematics
When your agent uses it
Create publication-quality scientific diagrams using Nano Banana 2 AI with smart iterative refinement. Uses Gemini 3.1 Pro Preview for quality review. Only regenerates if quality is below threshold for your document type. Specialized in neural network architectures, system diagrams, flowcharts, biological pathways, and complex scientific visualizations.
What it loads into context
Frontmatter
95 tokensAlways available to the agent
SKILL.md
5,413 tokensWhen the skill is activated
Reference documents
7,508 tokensLoaded on request
Token counts are estimates. Scripts run separately; they are not counted as reference context.
The playbook
- Overview
- Quick Start: Generate Any Diagram
- When to Use This Skill
- How to Use This Skill
- Smart Iterative Refinement Workflow
- Advanced AI Generation Usage
- AI Generation Examples
- Command-Line Usage
- Best Practices Summary
- Troubleshooting Common Issues
- Resources and References
- Integration with Other Skills
- Quick Reference Checklist
- Environment Setup
- Getting Started
Scripts executed separately
scripts/example_usage.shscripts/generate_schematic.pyscripts/generate_schematic_ai.py
Files
SKILL.mdcore23,850 bytesSKILL.mdcore23,850 bytesSKILL.mdcore23,850 bytesSKILL.mdcore23,850 bytesSKILL.mdcore23,850 bytesSKILL.mdcore23,850 bytesSKILL.mdcore23,850 bytesSKILL.mdcore23,850 bytesSKILL.mdcore23,850 bytesSKILL.mdcore23,850 bytesSKILL.mdcore23,850 bytesSKILL.mdcore23,850 bytesSKILL.mdcore23,850 bytesreferences/QUICK_REFERENCE.mddoc4,784 bytesreferences/README.mddoc8,676 bytesreferences/best_practices.mddoc16,026 bytesscripts/example_usage.shruns3,052 bytesscripts/generate_schematic.pyruns5,030 bytesscripts/generate_schematic_ai.pyruns33,577 bytes
Read SKILL.md
# Scientific Schematics and Diagrams
## Overview
Scientific schematics and diagrams transform complex concepts into clear visual representations for publication. **This skill uses Nano Banana 2 AI for diagram generation with Gemini 3.1 Pro Preview quality review.**
**How it works:**
- Describe your diagram in natural language
- Nano Banana 2 generates publication-quality images automatically
- **Gemini 3.1 Pro Preview reviews quality** against document-type thresholds
- **Smart iteration**: Only regenerates if quality is below threshold
- Publication-ready output in minutes
- No coding, templates, or manual drawing required
scientific-slides
When your agent uses it
Build slide decks and presentations for research talks. Use this for making PowerPoint slides, conference presentations, seminar talks, research presentations, thesis defense slides, or any scientific talk. Provides slide structure, design templates, timing guidance, and visual validation. Works with PowerPoint and LaTeX Beamer.
What it loads into context
Frontmatter
92 tokensAlways available to the agent
SKILL.md
10,887 tokensWhen the skill is activated
Reference documents
39,628 tokensLoaded on request
Token counts are estimates. Scripts run separately; they are not counted as reference context.
The playbook
- Overview
- When to Use This Skill
- Slide Generation with Nano Banana Pro
- Nano Banana Pro Script Reference
- Prompt Writing for Slide Generation
- Visual Enhancement with Scientific Schematics
- Core Capabilities
- Workflow for Presentation Development
- Integration with Other Skills
- Common Pitfalls to Avoid
- Tools and Scripts
- Reference Files
- Assets
- Quick Start Guide
- Summary: Key Principles
Scripts executed separately
scripts/generate_slide_image.pyscripts/generate_slide_image_ai.pyscripts/pdf_to_images.pyscripts/slides_to_pdf.pyscripts/validate_presentation.py
Files
SKILL.mdcore47,291 bytesSKILL.mdcore47,291 bytesSKILL.mdcore47,291 bytesSKILL.mdcore47,291 bytesSKILL.mdcore47,291 bytesSKILL.mdcore47,291 bytesSKILL.mdcore47,291 bytesSKILL.mdcore47,291 bytesSKILL.mdcore47,291 bytesSKILL.mdcore47,291 bytesSKILL.mdcore47,291 bytesSKILL.mdcore47,291 bytesSKILL.mdcore47,291 bytesSKILL.mdcore47,291 bytesSKILL.mdcore47,291 bytesSKILL.mdcore47,291 bytesSKILL.mdcore47,291 bytesSKILL.mdcore47,291 bytesSKILL.mdcore47,291 bytesSKILL.mdcore47,291 bytesSKILL.mdcore47,291 bytesSKILL.mdcore47,291 bytesSKILL.mdcore47,291 bytesSKILL.mdcore47,291 bytesassets/beamer_template_conference.texasset9,983 bytesassets/beamer_template_defense.texasset26,503 bytesassets/beamer_template_seminar.texasset23,247 bytesassets/powerpoint_design_guide.mddoc18,989 bytesassets/timing_guidelines.mddoc14,723 bytesreferences/beamer_guide.mddoc19,321 bytesreferences/data_visualization_slides.mddoc17,863 bytesreferences/presentation_structure.mddoc22,220 bytesreferences/slide_design_principles.mddoc23,878 bytesreferences/talk_types_guide.mddoc20,146 bytesreferences/visual_review_workflow.mddoc20,170 bytesscripts/generate_slide_image.pyruns5,309 bytesscripts/generate_slide_image_ai.pyruns29,060 bytesscripts/pdf_to_images.pyruns6,644 bytesscripts/slides_to_pdf.pyruns7,423 bytesscripts/validate_presentation.pyruns13,115 bytes
Read SKILL.md
# Scientific Slides
## Overview
Scientific presentations are a critical medium for communicating research, sharing findings, and engaging with academic and professional audiences. This skill provides comprehensive guidance for creating effective scientific presentations, from structure and content development to visual design and delivery preparation.
**Key Focus**: Oral presentations for conferences, seminars, defenses, and professional talks.
**CRITICAL DESIGN PHILOSOPHY**: Scientific presentations should be VISUALLY ENGAGING and RESEARCH-BACKED. Avoid dry, text-heavy slides at all costs. Great scientific presentations combine:
- **Compelling visuals**: High-quality figures, images, diagrams (not just bullet points)
- **Research context**: Proper citations from research-lookup establishing credibility
- **Minimal text**: Bullet points as prompts, YOU provide the explanation verbally
- **Professional design**: Modern color schemes, strong visual hierarchy, generous white space
- **Story-driven**: Clear narrative arc, not just data dumpsscientific-visualization
When your agent uses it
Meta-skill for publication-ready figures. Use when creating journal submission figures requiring multi-panel layouts, significance annotations, error bars, colorblind-safe palettes, and specific journal formatting (Nature, Science, Cell). Orchestrates matplotlib/seaborn/plotly with publication styles. For quick exploration use seaborn or plotly directly.
What it loads into context
Frontmatter
91 tokensAlways available to the agent
SKILL.md
6,486 tokensWhen the skill is activated
Reference documents
12,797 tokensLoaded on request
Token counts are estimates. Scripts run separately; they are not counted as reference context.
The playbook
- Overview
- When to Use This Skill
- Quick Start Guide
- Core Principles and Best Practices
- Common Tasks
- Statistical Rigor
- Working with Different Plotting Libraries
- Resources
- Workflow Summary
- Common Pitfalls to Avoid
- Final Checklist
Scripts executed separately
assets/color_palettes.pyscripts/figure_export.pyscripts/style_presets.py
Files
SKILL.mdcore25,528 bytesSKILL.mdcore25,528 bytesSKILL.mdcore25,528 bytesSKILL.mdcore25,528 bytesSKILL.mdcore25,528 bytesSKILL.mdcore25,528 bytesSKILL.mdcore25,528 bytesSKILL.mdcore25,528 bytesSKILL.mdcore25,528 bytesSKILL.mdcore25,528 bytesSKILL.mdcore25,528 bytesSKILL.mdcore25,528 bytesSKILL.mdcore25,528 bytesSKILL.mdcore25,528 bytesSKILL.mdcore25,528 bytesSKILL.mdcore25,528 bytesSKILL.mdcore25,528 bytesSKILL.mdcore25,528 bytesSKILL.mdcore25,528 bytesSKILL.mdcore25,528 bytesSKILL.mdcore25,528 bytesSKILL.mdcore25,528 bytesSKILL.mdcore25,528 bytesSKILL.mdcore25,528 bytesSKILL.mdcore25,528 bytesSKILL.mdcore25,528 bytesSKILL.mdcore25,528 bytesSKILL.mdcore25,528 bytesSKILL.mdcore25,528 bytesSKILL.mdcore25,528 bytesSKILL.mdcore25,528 bytesSKILL.mdcore25,528 bytesSKILL.mdcore25,528 bytesSKILL.mdcore25,528 bytesSKILL.mdcore25,528 bytesSKILL.mdcore25,528 bytesSKILL.mdcore25,528 bytesSKILL.mdcore25,528 bytesSKILL.mdcore25,528 bytesSKILL.mdcore25,528 bytesSKILL.mdcore25,528 bytesSKILL.mdcore25,528 bytesSKILL.mdcore25,528 bytesassets/color_palettes.pyruns5,754 bytesassets/nature.mplstyleasset1,449 bytesassets/presentation.mplstyleasset1,309 bytesassets/publication.mplstyleasset1,474 bytesreferences/color_palettes.mddoc9,861 bytesreferences/journal_requirements.mddoc9,533 bytesreferences/matplotlib_examples.mddoc18,103 bytesreferences/publication_guidelines.mddoc8,556 bytesscripts/figure_export.pyruns10,904 bytesscripts/style_presets.pyruns12,199 bytes
Read SKILL.md
# Scientific Visualization
## Overview
Scientific visualization transforms data into clear, accurate figures for publication. Create journal-ready plots with multi-panel layouts, error bars, significance markers, and colorblind-safe palettes. Export as PDF/EPS/TIFF using matplotlib, seaborn, and plotly for manuscripts.
## When to Use This Skill
This skill should be used when:
- Creating plots or visualizations for scientific manuscripts
- Preparing figures for journal submission (Nature, Science, Cell, PLOS, etc.)
- Ensuring figures are colorblind-friendly and accessible
- Making multi-panel figures with consistent styling
- Exporting figures at correct resolution and formatconsciousness-council
When your agent uses it
Run a multi-perspective Mind Council deliberation on any question, decision, or creative challenge. Use this skill whenever the user wants diverse viewpoints, needs help making a tough decision, asks for a council/panel/board discussion, wants to explore a problem from multiple angles, requests devil's advocate analysis, or says things like "what would different experts think about this", "help me think through this from all sides", "council mode", "mind council", or "deliberate on this". Also trigger when the user faces a dilemma, trade-off, or complex choice with no obvious answer.
What it loads into context
Frontmatter
160 tokensAlways available to the agent
SKILL.md
1,854 tokensWhen the skill is activated
Reference documents
1,214 tokensLoaded on request
Token counts are estimates. Scripts run separately; they are not counted as reference context.
The playbook
- Why This Exists
- How It Works
- Council Configurations
- What Makes a Good Council Question
- Tone and Quality
- Example
- Attribution
Files
SKILL.mdcore8,728 bytesreferences/advanced-configurations.mddoc5,175 bytes
Read SKILL.md
# Consciousness Council
A structured multi-perspective deliberation system that generates genuine cognitive diversity on any question. Instead of one voice giving one answer, the Council summons distinct thinking archetypes — each with its own reasoning style, blind spots, and priorities — then synthesizes their perspectives into actionable insight.
## Why This Exists
Single-perspective thinking has a ceiling. When you ask one mind for an answer, you get one frame. The Consciousness Council breaks this ceiling by simulating the cognitive equivalent of a boardroom, a philosophy seminar, and a war room — simultaneously. It's not roleplay. It's structured epistemic diversity.
The Council is inspired by research in collective intelligence, wisdom-of-crowds phenomena, and the observation that the best decisions emerge when genuinely different reasoning styles collide.
## How It Works
The Council has three phases:
dhdna-profiler
When your agent uses it
Extract cognitive patterns and thinking fingerprints from any text. Use this skill when the user wants to analyze how someone thinks, understand cognitive style, profile writing or speech patterns, compare thinking styles between people, asks "what's my thinking style", "analyze how this person reasons", "cognitive profile", "thinking pattern", "DHDNA", "digital DNA", or wants to understand the mind behind any text. Also trigger when the user provides text and wants deeper insight into the author's reasoning patterns, decision-making style, or cognitive signature.
What it loads into context
Frontmatter
146 tokensAlways available to the agent
SKILL.md
2,126 tokensWhen the skill is activated
Reference documents
751 tokensLoaded on request
Token counts are estimates. Scripts run separately; they are not counted as reference context.
The playbook
- Core Concept
- The 12 Cognitive Dimensions
- How to Profile
- Comparison Mode
- Self-Profile Mode
- What This Is NOT
- Built By
Files
SKILL.mdcore10,081 bytesreferences/advanced-profiling.mddoc3,220 bytes
Read SKILL.md
# DHDNA Profiler — Cognitive Pattern Extraction
A structured system for extracting the cognitive fingerprint of any text's author. Based on the Digital Human DNA (DHDNA) framework — the theory that every mind has a unique signature pattern expressed through how it reasons, decides, values, and communicates.
Published research: [DHDNA Pre-print (DOI: 10.5281/zenodo.18736629)](https://doi.org/10.5281/zenodo.18736629) | [IDNA Consolidation v2 (DOI: 10.5281/zenodo.18807387)](https://doi.org/10.5281/zenodo.18807387)
## Core Concept
Just as biological DNA encodes physical identity through base pairs, Digital Human DNA encodes cognitive identity through thinking patterns. Every person's combination of analytical depth, creative range, emotional processing, strategic thinking, and ethical reasoning creates a **unique cognitive signature** — as distinctive as a fingerprint.
The profiler doesn't judge thinking as "good" or "bad." It maps the topology of how a mind works.
## The 12 Cognitive Dimensions
hypothesis-generation
When your agent uses it
Structured hypothesis formulation from observations. Use when you have experimental observations or data and need to formulate testable hypotheses with predictions, propose mechanisms, and design experiments to test them. Follows scientific method framework. For open-ended ideation use scientific-brainstorming; for automated LLM-driven hypothesis testing on datasets use hypogenic.
What it loads into context
Frontmatter
96 tokensAlways available to the agent
SKILL.md
2,811 tokensWhen the skill is activated
Reference documents
14,060 tokensLoaded on request
Token counts are estimates. Scripts run separately; they are not counted as reference context.
The playbook
- Overview
- When to Use This Skill
- Visual Enhancement with Scientific Schematics
- Workflow
- Quality Standards
- Resources
Files
SKILL.mdcore13,847 bytesSKILL.mdcore13,847 bytesSKILL.mdcore13,847 bytesassets/FORMATTING_GUIDE.mddoc19,304 bytesassets/hypothesis_generation.styasset7,625 bytesassets/hypothesis_report_template.texasset20,148 bytesreferences/experimental_design_patterns.mddoc14,208 bytesreferences/hypothesis_quality_criteria.mddoc8,051 bytesreferences/literature_search_strategies.mddoc20,277 bytes
Read SKILL.md
# Scientific Hypothesis Generation
## Overview
Hypothesis generation is a systematic process for developing testable explanations. Formulate evidence-based hypotheses from observations, design experiments, explore competing explanations, and develop predictions. Apply this skill for scientific inquiry across domains.
## When to Use This Skill
This skill should be used when:
- Developing hypotheses from observations or preliminary data
- Designing experiments to test scientific questions
- Exploring competing explanations for phenomena
- Formulating testable predictions for research
- Conducting literature-based hypothesis generationnsfc-proposal
When your agent uses it
撰写国家自然科学基金（NSFC）申请书，覆盖青年科学基金（C类）、优秀青年科学基金（B类）、国家杰出青年科学基金（A类）2026年度最新规范。提供分阶段写作工作流：基本信息、摘要、立项依据、研究内容、研究基础、个人简历、伦理与AI辅助声明等模块；内置文献检索策略与质量自查清单。工具中性，适配 Claude Code / Cursor / Codex / OpenClaw / Gemini CLI。触发词：国家自然科学基金、国自然、NSFC、青基、青年基金、优青、杰青、面上项目、natural science foundation of china、nsfc grant、青年科学基金申报
What it loads into context
Frontmatter
235 tokensAlways available to the agent
SKILL.md
2,783 tokensWhen the skill is activated
Reference documents
7,533 tokensLoaded on request
Token counts are estimates. Scripts run separately; they are not counted as reference context.
The playbook
- 概述
- 何时使用本 Skill
- 三类项目核心差异速查
- 工作流程
- 工具兼容性说明
- 引用文件
- 高频错误警示
Files
SKILL.mdcore9,513 bytesSKILL.mdcore9,513 bytesreferences/LITERATURE_SEARCH.mddoc4,907 bytesreferences/NSFC_WRITING_SPEC.mddoc16,573 bytesreferences/QUALITY_CHECKLIST.mddoc4,409 bytes
Read SKILL.md
# 国家自然科学基金（NSFC）申请书撰写
## 概述
本 Skill 指导 AI Agent 协助用户撰写**国家自然科学基金**申请书，依据2026年度官方最新版规范，覆盖青年（C类）、优青（B类）、杰青（A类）三类项目。区别于本仓库已有的 `research-grants`（面向 NSF/NIH/DOE 等海外基金），本 Skill 专为**中国大陆 NSFC 体系**设计，遵循"瘦身提质"改版后的三大正文模块结构（立项依据 / 研究内容 / 研究基础），并对2026年新评审机制（分阶段评审、前500字初筛、AI 辅助声明）做了重点提示。
**学术诚信声明**：禁止使用 AI 直接生成申请书内容；AI 仅可用于辅助检索文献、整理结构、润色语言。所有科学观点、研究思路、创新点必须由申请人本人提供，并由人工核实事实准确性。
---
## 何时使用本 Skill
当用户的请求涉及以下情形时应触发本 Skill：
nssfc-proposal
When your agent uses it
撰写国家社会科学基金（NSSFC）年度项目申请书，依据 2025 年全国哲学社会科学工作办公室官方模板，覆盖重点项目（A）、一般项目（B）、青年项目（C）、西部项目（X）四类。提供分阶段写作工作流：数据表、选题说明（300字）、选题依据、研究内容、创新之处、研究基础、经费概算、活页匿名化等模块；内置社科文献检索策略与质量自查清单。工具中性，适配 Claude Code / Cursor / Codex / OpenClaw / Gemini CLI。触发词：国家社会科学基金、国社科、社科基金、NSSFC、哲学社会科学、重点项目、一般项目、青年项目、西部项目、社科申报、活页
What it loads into context
Frontmatter
257 tokensAlways available to the agent
SKILL.md
3,209 tokensWhen the skill is activated
Reference documents
8,180 tokensLoaded on request
Token counts are estimates. Scripts run separately; they are not counted as reference context.
The playbook
- 概述
- 何时使用本 Skill
- 四类项目核心差异速查
- 工作流程
- 工具兼容性说明
- 引用文件
- 高频错误警示
Files
SKILL.mdcore10,965 bytesSKILL.mdcore10,965 bytesreferences/LITERATURE_SEARCH.mddoc8,116 bytesreferences/NSSFC_WRITING_SPEC.mddoc13,690 bytesreferences/QUALITY_CHECKLIST.mddoc5,825 bytes
Read SKILL.md
# 国家社会科学基金（NSSFC）年度项目申请书撰写
## 概述
本 Skill 指导 AI Agent 协助用户撰写**国家社会科学基金年度项目**申请书，依据 2025 年全国哲学社会科学工作办公室官方模板，覆盖重点项目（A）、一般项目（B）、青年项目（C）、西部项目（X）四类。区别于本仓库已有的 `research-grants`（面向 NSF/NIH 等海外自然科学基金）与 `nsfc-proposal`（面向国家自然科学基金），本 Skill 专为**中国大陆哲学社会科学基金**设计，特别关注：
- **2025 年新增"选题说明"模块**（≤300字，单独成段，评审专家首读内容）
- **活页匿名化要求**（不得出现作者姓名、单位、发表刊物名、合作者等可识别身份信息）
- **学科评审标准差异**（25 个学科分类，各学科评审重点不同）
- **"目标导向、问题意识、学科视角"三大核心评审原则**
**学术诚信声明**：申请书的科学观点、研究思路、创新点必须由申请人本人提供；AI 仅可用于辅助检索文献、整理结构、润色语言，且须人工核实事实准确性。
---research-grants
When your agent uses it
Write competitive research proposals for NSF, NIH, DOE, DARPA, and Taiwan NSTC. Agency-specific formatting, review criteria, budget preparation, broader impacts, significance statements, innovation narratives, and compliance with submission requirements.
What it loads into context
Frontmatter
76 tokensAlways available to the agent
SKILL.md
7,624 tokensWhen the skill is activated
Reference documents
45,390 tokensLoaded on request
Token counts are estimates. Scripts run separately; they are not counted as reference context.
The playbook
- Overview
- When to Use This Skill
- Visual Enhancement with Scientific Schematics
- Agency-Specific Overview
- Core Components of Research Proposals
- Review Criteria by Agency
- Writing Principles for Competitive Proposals
- Common Proposal Types
- Resubmission Strategies
- Common Mistakes to Avoid
- Workflow for Grant Development
- Integration with Other Skills
- Resources
- Templates and Assets
- Scripts and Tools
Files
SKILL.mdcore37,217 bytesSKILL.mdcore37,217 bytesSKILL.mdcore37,217 bytesassets/budget_justification_template.mddoc21,866 bytesassets/nih_specific_aims_template.mddoc11,120 bytesassets/nsf_project_summary_template.mddoc5,886 bytesreferences/README.mddoc9,414 bytesreferences/broader_impacts.mddoc23,284 bytesreferences/darpa_guidelines.mddoc20,501 bytesreferences/doe_guidelines.mddoc19,409 bytesreferences/nih_guidelines.mddoc30,192 bytesreferences/nsf_guidelines.mddoc21,683 bytesreferences/nstc_guidelines.mddoc26,945 bytesreferences/specific_aims_guide.mddoc21,040 bytes
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# Research Grant Writing
## Overview
Research grant writing is the process of developing competitive funding proposals for federal agencies and foundations. Master agency-specific requirements, review criteria, narrative structure, budget preparation, and compliance for NSF (National Science Foundation), NIH (National Institutes of Health), DOE (Department of Energy), DARPA (Defense Advanced Research Projects Agency), and Taiwan's NSTC (National Science and Technology Council) submissions.
**Critical Principle: Grants are persuasive documents that must simultaneously demonstrate scientific rigor, innovation, feasibility, and broader impact.** Each agency has distinct priorities, review criteria, formatting requirements, and strategic goals that must be addressed.
## When to Use This Skill
This skill should be used when:
- Writing research proposals for NSF, NIH, DOE, DARPA, or NSTC programs
- Preparing project descriptions, specific aims, or technical narratives
- Developing broader impacts or significance statementsscholar-evaluation
When your agent uses it
Systematically evaluate scholarly work using the ScholarEval framework, providing structured assessment across research quality dimensions including problem formulation, methodology, analysis, and writing with quantitative scoring and actionable feedback.
What it loads into context
Frontmatter
59 tokensAlways available to the agent
SKILL.md
2,378 tokensWhen the skill is activated
Reference documents
4,081 tokensLoaded on request
Token counts are estimates. Scripts run separately; they are not counted as reference context.
The playbook
- Overview
- When to Use This Skill
- Visual Enhancement with Scientific Schematics
- Evaluation Workflow
- Resources
- Best Practices
- Example Evaluation Workflow
- Integration with Scientific Writer
- Notes
- Citation
Scripts executed separately
scripts/calculate_scores.py
Files
SKILL.mdcore12,258 bytesSKILL.mdcore12,258 bytesSKILL.mdcore12,258 bytesreferences/evaluation_framework.mddoc19,950 bytesscripts/calculate_scores.pyruns11,551 bytes
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# Scholar Evaluation
## Overview
Apply the ScholarEval framework to systematically evaluate scholarly and research work. This skill provides structured evaluation methodology based on peer-reviewed research assessment criteria, enabling comprehensive analysis of academic papers, research proposals, literature reviews, and scholarly writing across multiple quality dimensions.
## When to Use This Skill
Use this skill when:
- Evaluating research papers for quality and rigor
- Assessing literature review comprehensiveness and quality
- Reviewing research methodology design
- Scoring data analysis approaches
- Evaluating scholarly writing and presentationscientific-brainstorming
When your agent uses it
Creative research ideation and exploration. Use for open-ended brainstorming sessions, exploring interdisciplinary connections, challenging assumptions, or identifying research gaps. Best for early-stage research planning when you do not have specific observations yet. For formulating testable hypotheses from data use hypothesis-generation.
What it loads into context
Frontmatter
79 tokensAlways available to the agent
SKILL.md
1,573 tokensWhen the skill is activated
Reference documents
2,461 tokensLoaded on request
Token counts are estimates. Scripts run separately; they are not counted as reference context.
The playbook
- Overview
- When to Use This Skill
- Core Principles
- Brainstorming Workflow
- Adaptive Techniques
- Resources
- Notes
Files
SKILL.mdcore8,179 bytesreferences/brainstorming_methods.mddoc12,065 bytes
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# Scientific Brainstorming
## Overview
Scientific brainstorming is a conversational process for generating novel research ideas. Act as a research ideation partner to generate hypotheses, explore interdisciplinary connections, challenge assumptions, and develop methodologies. Apply this skill for creative scientific problem-solving.
## When to Use This Skill
This skill should be used when:
- Generating novel research ideas or directions
- Exploring interdisciplinary connections and analogies
- Challenging assumptions in existing research frameworks
- Developing new methodological approaches
- Identifying research gaps or opportunitiesscientific-critical-thinking
When your agent uses it
Evaluate scientific claims and evidence quality. Use for assessing experimental design validity, identifying biases and confounders, applying evidence grading frameworks (GRADE, Cochrane Risk of Bias), or teaching critical analysis. Best for understanding evidence quality, identifying flaws. For formal peer review writing use peer-review.
What it loads into context
Frontmatter
90 tokensAlways available to the agent
SKILL.md
4,779 tokensWhen the skill is activated
Reference documents
17,282 tokensLoaded on request
Token counts are estimates. Scripts run separately; they are not counted as reference context.
The playbook
- Overview
- When to Use This Skill
- Visual Enhancement with Scientific Schematics
- Core Capabilities
- Application Guidelines
- Reference Materials
- Remember
Files
SKILL.mdcore23,705 bytesSKILL.mdcore23,705 bytesreferences/common_biases.mddoc11,093 bytesreferences/evidence_hierarchy.mddoc13,444 bytesreferences/experimental_design.mddoc16,068 bytesreferences/logical_fallacies.mddoc18,014 bytesreferences/scientific_method.mddoc6,100 bytesreferences/statistical_pitfalls.mddoc15,726 bytes
Read SKILL.md
# Scientific Critical Thinking
## Overview
Critical thinking is a systematic process for evaluating scientific rigor. Assess methodology, experimental design, statistical validity, biases, confounding, and evidence quality using GRADE and Cochrane ROB frameworks. Apply this skill for critical analysis of scientific claims.
## When to Use This Skill
This skill should be used when:
- Evaluating research methodology and experimental design
- Assessing statistical validity and evidence quality
- Identifying biases and confounding in studies
- Reviewing scientific claims and conclusions
- Conducting systematic reviews or meta-analyseswhat-if-oracle
When your agent uses it
Run structured What-If scenario analysis with multi-branch possibility exploration. Use this skill when the user asks speculative questions like "what if...", "what would happen if...", "what are the possibilities", "explore scenarios", "scenario analysis", "possibility space", "what could go wrong", "best case / worst case", "risk analysis", "contingency planning", "strategic options", or any question about uncertain futures. Also trigger when the user faces a fork-in-the-road decision, wants to stress-test an idea, or needs to think through consequences before committing.
What it loads into context
Frontmatter
156 tokensAlways available to the agent
SKILL.md
1,971 tokensWhen the skill is activated
Reference documents
900 tokensLoaded on request
Token counts are estimates. Scripts run separately; they are not counted as reference context.
The playbook
- Core Principle: 0·IF·1
- How to Run the Oracle
- Golden Ratio Weighting
- Modes
- What This Is NOT
- Built By
Files
SKILL.mdcore9,481 bytesreferences/scenario-templates.mddoc4,150 bytes
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# What-If Oracle — Possibility Space Explorer
A structured system for exploring uncertain futures through rigorous multi-branch scenario analysis. Instead of one prediction, the Oracle maps the full **possibility space** — branching timelines where each path has its own logic, probability, and consequences.
Based on the What-If Statement paradigm: the idea that speculative questions ("What if X?") are not idle daydreaming but a **fundamental computing operation** — the mind's way of simulating futures before committing resources to one.
Published research: [The What-If Statement (DOI: 10.5281/zenodo.18736841)](https://doi.org/10.5281/zenodo.18736841) | [IDNA Consolidation v2 (DOI: 10.5281/zenodo.18807387)](https://doi.org/10.5281/zenodo.18807387)
## Core Principle: 0·IF·1
Every scenario analysis has three elements:
- **0** — The unexpressed state (what hasn't happened yet, the potential)
- **1** — The expressed state (what IS, the current reality)anndata
When your agent uses it
Data structure for annotated matrices in single-cell analysis. Use when working with .h5ad files or integrating with the scverse ecosystem. This is the data format skill—for analysis workflows use scanpy; for probabilistic models use scvi-tools; for population-scale queries use cellxgene-census.
What it loads into context
Frontmatter
86 tokensAlways available to the agent
SKILL.md
2,760 tokensWhen the skill is activated
Reference documents
14,415 tokensLoaded on request
Token counts are estimates. Scripts run separately; they are not counted as reference context.
The playbook
- Overview
- When to Use This Skill
- Installation
- Quick Start
- Core Capabilities
- Integration with Scverse Ecosystem
- Common Workflows
- Troubleshooting
- Additional Resources
Files
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# AnnData
## Overview
AnnData is a Python package for handling annotated data matrices, storing experimental measurements (X) alongside observation metadata (obs), variable metadata (var), and multi-dimensional annotations (obsm, varm, obsp, varp, uns). Originally designed for single-cell genomics through Scanpy, it now serves as a general-purpose framework for any annotated data requiring efficient storage, manipulation, and analysis.
## When to Use This Skill
Use this skill when:
- Creating, reading, or writing AnnData objects
- Working with h5ad, zarr, or other genomics data formats
- Performing single-cell RNA-seq analysis
- Managing large datasets with sparse matrices or backed mode
- Concatenating multiple datasets or experimental batchesarboreto
When your agent uses it
Infer gene regulatory networks (GRNs) from gene expression data using scalable algorithms (GRNBoost2, GENIE3). Use when analyzing transcriptomics data (bulk RNA-seq, single-cell RNA-seq) to identify transcription factor-target gene relationships and regulatory interactions. Supports distributed computation for large-scale datasets.
What it loads into context
Frontmatter
89 tokensAlways available to the agent
SKILL.md
1,642 tokensWhen the skill is activated
Reference documents
3,496 tokensLoaded on request
Token counts are estimates. Scripts run separately; they are not counted as reference context.
The playbook
- Overview
- Quick Start
- Core Capabilities
- Installation
- Common Use Cases
- Output Interpretation
- Integration with pySCENIC
- Reproducibility
- Troubleshooting
Scripts executed separately
scripts/basic_grn_inference.py
Files
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Read SKILL.md
# Arboreto
## Overview
Arboreto is a computational library for inferring gene regulatory networks (GRNs) from gene expression data using parallelized algorithms that scale from single machines to multi-node clusters.
**Core capability**: Identify which transcription factors (TFs) regulate which target genes based on expression patterns across observations (cells, samples, conditions).
## Quick Start
Install arboreto:
```bash
uv pip install arboreto
```biopython
When your agent uses it
Comprehensive molecular biology toolkit. Use for sequence manipulation, file parsing (FASTA/GenBank/PDB), phylogenetics, and programmatic NCBI/PubMed access (Bio.Entrez). Best for batch processing, custom bioinformatics pipelines, BLAST automation. For quick lookups use gget; for multi-service integration use bioservices.
What it loads into context
Frontmatter
94 tokensAlways available to the agent
SKILL.md
3,369 tokensWhen the skill is activated
Reference documents
21,152 tokensLoaded on request
Token counts are estimates. Scripts run separately; they are not counted as reference context.
The playbook
- Overview
- When to Use This Skill
- Core Capabilities
- Installation and Setup
- Using This Skill
- General Workflow Guidelines
- Common Patterns
- Best Practices
- Troubleshooting Common Issues
- Additional Resources
- Quick Reference
- Summary
Files
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# Biopython: Computational Molecular Biology in Python
## Overview
Biopython is a comprehensive set of freely available Python tools for biological computation. It provides functionality for sequence manipulation, file I/O, database access, structural bioinformatics, phylogenetics, and many other bioinformatics tasks. The current version is **Biopython 1.85** (released January 2025), which supports Python 3 and requires NumPy.
## When to Use This Skill
Use this skill when:
- Working with biological sequences (DNA, RNA, or protein)
- Reading, writing, or converting biological file formats (FASTA, GenBank, FASTQ, PDB, mmCIF, etc.)
- Accessing NCBI databases (GenBank, PubMed, Protein, Gene, etc.) via Entrez
- Running BLAST searches or parsing BLAST resultsbioservices
When your agent uses it
Unified Python interface to 40+ bioinformatics services. Use when querying multiple databases (UniProt, KEGG, ChEMBL, Reactome) in a single workflow with consistent API. Best for cross-database analysis, ID mapping across services. For quick single-database lookups use gget; for sequence/file manipulation use biopython.
What it loads into context
Frontmatter
95 tokensAlways available to the agent
SKILL.md
2,389 tokensWhen the skill is activated
Reference documents
13,588 tokensLoaded on request
Token counts are estimates. Scripts run separately; they are not counted as reference context.
The playbook
- Overview
- When to Use This Skill
- Core Capabilities
- Multi-Service Integration Workflows
- Best Practices
- Resources
- Installation
- Additional Information
Scripts executed separately
scripts/batch_id_converter.pyscripts/compound_cross_reference.pyscripts/pathway_analysis.pyscripts/protein_analysis_workflow.py
Files
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# BioServices
## Overview
BioServices is a Python package providing programmatic access to approximately 40 bioinformatics web services and databases. Retrieve biological data, perform cross-database queries, map identifiers, analyze sequences, and integrate multiple biological resources in Python workflows. The package handles both REST and SOAP/WSDL protocols transparently.
## When to Use This Skill
This skill should be used when:
- Retrieving protein sequences, annotations, or structures from UniProt, PDB, Pfam
- Analyzing metabolic pathways and gene functions via KEGG or Reactome
- Searching compound databases (ChEBI, ChEMBL, PubChem) for chemical information
- Converting identifiers between different biological databases (KEGG↔UniProt, compound IDs)
- Running sequence similarity searches (BLAST, MUSCLE alignment)cellxgene-census
When your agent uses it
Query the CELLxGENE Census (61M+ cells) programmatically. Use when you need expression data across tissues, diseases, or cell types from the largest curated single-cell atlas. Best for population-scale queries, reference atlas comparisons. For analyzing your own data use scanpy or scvi-tools.
What it loads into context
Frontmatter
86 tokensAlways available to the agent
SKILL.md
3,755 tokensWhen the skill is activated
Reference documents
3,951 tokensLoaded on request
Token counts are estimates. Scripts run separately; they are not counted as reference context.
The playbook
- Overview
- When to Use This Skill
- Installation and Setup
- Core Workflow Patterns
- Key Concepts and Best Practices
- Available Metadata Fields
- Reference Documentation
- Common Use Cases
- Troubleshooting
Files
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# CZ CELLxGENE Census
## Overview
The CZ CELLxGENE Census provides programmatic access to a comprehensive, versioned collection of standardized single-cell genomics data from CZ CELLxGENE Discover. This skill enables efficient querying and analysis of millions of cells across thousands of datasets.
The Census includes:
- **61+ million cells** from human and mouse
- **Standardized metadata** (cell types, tissues, diseases, donors)
- **Raw gene expression** matrices
- **Pre-calculated embeddings** and statistics
- **Integration with PyTorch, scanpy, and other analysis tools**
## When to Use This Skilldeeptools
When your agent uses it
NGS analysis toolkit. BAM to bigWig conversion, QC (correlation, PCA, fingerprints), heatmaps/profiles (TSS, peaks), for ChIP-seq, RNA-seq, ATAC-seq visualization.
What it loads into context
Frontmatter
71 tokensAlways available to the agent
SKILL.md
4,231 tokensWhen the skill is activated
Reference documents
11,794 tokensLoaded on request
Token counts are estimates. Scripts run separately; they are not counted as reference context.
The playbook
- Overview
- When to Use This Skill
- Quick Start
- Installation
- Core Workflows
- Tool Categories and Common Tasks
- Normalization Methods
- Effective Genome Sizes
- Common Parameters Across Tools
- Best Practices
- Troubleshooting
- Reference Documentation
- Helper Scripts
- Assets
- Handling User Requests
- Example Interactions
- Key Reminders
Scripts executed separately
scripts/validate_files.pyscripts/workflow_generator.py
Files
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# deepTools: NGS Data Analysis Toolkit
## Overview
deepTools is a comprehensive suite of Python command-line tools designed for processing and analyzing high-throughput sequencing data. Use deepTools to perform quality control, normalize data, compare samples, and generate publication-quality visualizations for ChIP-seq, RNA-seq, ATAC-seq, MNase-seq, and other NGS experiments.
**Core capabilities:**
- Convert BAM alignments to normalized coverage tracks (bigWig/bedGraph)
- Quality control assessment (fingerprint, correlation, coverage)
- Sample comparison and correlation analysis
- Heatmap and profile plot generation around genomic features
- Enrichment analysis and peak region visualization
## When to Use This Skilletetoolkit
When your agent uses it
Phylogenetic tree toolkit (ETE). Tree manipulation (Newick/NHX), evolutionary event detection, orthology/paralogy, NCBI taxonomy, visualization (PDF/SVG), for phylogenomics.
What it loads into context
Frontmatter
72 tokensAlways available to the agent
SKILL.md
4,532 tokensWhen the skill is activated
Reference documents
13,790 tokensLoaded on request
Token counts are estimates. Scripts run separately; they are not counted as reference context.
The playbook
- Overview
- Core Capabilities
- Installation and Setup
- Common Use Cases
- Reference Documentation
- Troubleshooting
- Newick Format Reference
- Best Practices
Scripts executed separately
scripts/quick_visualize.pyscripts/tree_operations.py
Files
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# ETE Toolkit Skill
## Overview
ETE (Environment for Tree Exploration) is a toolkit for phylogenetic and hierarchical tree analysis. Manipulate trees, analyze evolutionary events, visualize results, and integrate with biological databases for phylogenomic research and clustering analysis.
## Core Capabilities
### 1. Tree Manipulation and Analysis
Load, manipulate, and analyze hierarchical tree structures with support for:
- **Tree I/O**: Read and write Newick, NHX, PhyloXML, and NeXML formats
- **Tree traversal**: Navigate trees using preorder, postorder, or levelorder strategiesflowio
When your agent uses it
Parse FCS (Flow Cytometry Standard) files v2.0-3.1. Extract events as NumPy arrays, read metadata/channels, convert to CSV/DataFrame, for flow cytometry data preprocessing.
What it loads into context
Frontmatter
70 tokensAlways available to the agent
SKILL.md
4,111 tokensWhen the skill is activated
Reference documents
2,635 tokensLoaded on request
Token counts are estimates. Scripts run separately; they are not counted as reference context.
The playbook
- Overview
- When to Use This Skill
- Installation
- Quick Start
- Core Workflows
- Data Preprocessing
- Error Handling
- Common Use Cases
- Best Practices
- Advanced Topics
- Integration Notes
- Troubleshooting
- Summary
Files
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# FlowIO: Flow Cytometry Standard File Handler
## Overview
FlowIO is a lightweight Python library for reading and writing Flow Cytometry Standard (FCS) files. Parse FCS metadata, extract event data, and create new FCS files with minimal dependencies. The library supports FCS versions 2.0, 3.0, and 3.1, making it ideal for backend services, data pipelines, and basic cytometry file operations.
## When to Use This Skill
This skill should be used when:
- FCS files requiring parsing or metadata extraction
- Flow cytometry data needing conversion to NumPy arrays
- Event data requiring export to FCS format
- Multi-dataset FCS files needing separationgeniml
When your agent uses it
This skill should be used when working with genomic interval data (BED files) for machine learning tasks. Use for training region embeddings (Region2Vec, BEDspace), single-cell ATAC-seq analysis (scEmbed), building consensus peaks (universes), or any ML-based analysis of genomic regions. Applies to BED file collections, scATAC-seq data, chromatin accessibility datasets, and region-based genomic feature learning.
What it loads into context
Frontmatter
112 tokensAlways available to the agent
SKILL.md
2,361 tokensWhen the skill is activated
Reference documents
6,399 tokensLoaded on request
Token counts are estimates. Scripts run separately; they are not counted as reference context.
The playbook
- Overview
- Installation
- Core Capabilities
- Common Workflows
- CLI Reference
- When to Use Which Tool
- Best Practices
- Related Projects
- Additional Resources
- Troubleshooting
Files
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# Geniml: Genomic Interval Machine Learning
## Overview
Geniml is a Python package for building machine learning models on genomic interval data from BED files. It provides unsupervised methods for learning embeddings of genomic regions, single cells, and metadata labels, enabling similarity searches, clustering, and downstream ML tasks.
## Installation
Install geniml using uv:
```bash
uv uv pip install geniml
```
gget
When your agent uses it
Fast CLI/Python queries to 20+ bioinformatics databases. Use for quick lookups: gene info, BLAST searches, AlphaFold structures, enrichment analysis. Best for interactive exploration, simple queries. For batch processing or advanced BLAST use biopython; for multi-database Python workflows use bioservices.
What it loads into context
Frontmatter
90 tokensAlways available to the agent
SKILL.md
7,015 tokensWhen the skill is activated
Reference documents
14,175 tokensLoaded on request
Token counts are estimates. Scripts run separately; they are not counted as reference context.
The playbook
- Overview
- Installation
- Quick Start
- Module Categories
- Common Workflows
- Best Practices
- Output Formats
- Resources
Scripts executed separately
scripts/batch_sequence_analysis.pyscripts/enrichment_pipeline.pyscripts/gene_analysis.py
Files
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# gget
## Overview
gget is a command-line bioinformatics tool and Python package providing unified access to 20+ genomic databases and analysis methods. Query gene information, sequence analysis, protein structures, expression data, and disease associations through a consistent interface. All gget modules work both as command-line tools and as Python functions.
**Important**: The databases queried by gget are continuously updated, which sometimes changes their structure. gget modules are tested automatically on a biweekly basis and updated to match new database structures when necessary.
## Installation
Install gget in a clean virtual environment to avoid conflicts:
```bash
# Using uv (recommended)gtars
When your agent uses it
High-performance toolkit for genomic interval analysis in Rust with Python bindings. Use when working with genomic regions, BED files, coverage tracks, overlap detection, tokenization for ML models, or fragment analysis in computational genomics and machine learning applications.
What it loads into context
Frontmatter
69 tokensAlways available to the agent
SKILL.md
1,770 tokensWhen the skill is activated
Reference documents
5,565 tokensLoaded on request
Token counts are estimates. Scripts run separately; they are not counted as reference context.
The playbook
- Overview
- Installation
- Core Capabilities
- Common Workflows
- Python vs CLI Usage
- Reference Documentation
- Integration with geniml
- Performance Characteristics
- Data Formats
- Error Handling and Debugging
Files
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# Gtars: Genomic Tools and Algorithms in Rust
## Overview
Gtars is a high-performance Rust toolkit for manipulating, analyzing, and processing genomic interval data. It provides specialized tools for overlap detection, coverage analysis, tokenization for machine learning, and reference sequence management.
Use this skill when working with:
- Genomic interval files (BED format)
- Overlap detection between genomic regions
- Coverage track generation (WIG, BigWig)
- Genomic ML preprocessing and tokenization
- Fragment analysis in single-cell genomics
- Reference sequence retrieval and validation
lamindb
When your agent uses it
This skill should be used when working with LaminDB, an open-source data framework for biology that makes data queryable, traceable, reproducible, and FAIR. Use when managing biological datasets (scRNA-seq, spatial, flow cytometry, etc.), tracking computational workflows, curating and validating data with biological ontologies, building data lakehouses, or ensuring data lineage and reproducibility in biological research. Covers data management, annotation, ontologies (genes, cell types, diseases, tissues), schema validation, integrations with workflow managers (Nextflow, Snakemake) and MLOps platforms (W&B, MLflow), and deployment strategies.
What it loads into context
Frontmatter
159 tokensAlways available to the agent
SKILL.md
3,272 tokensWhen the skill is activated
Reference documents
18,195 tokensLoaded on request
Token counts are estimates. Scripts run separately; they are not counted as reference context.
The playbook
- Overview
- When to Use This Skill
- Core Capabilities
- Common Use Case Workflows
- Getting Started Checklist
- Key Principles
- Reference Files
- Additional Resources
Files
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# LaminDB
## Overview
LaminDB is an open-source data framework for biology designed to make data queryable, traceable, reproducible, and FAIR (Findable, Accessible, Interoperable, Reusable). It provides a unified platform that combines lakehouse architecture, lineage tracking, feature stores, biological ontologies, LIMS (Laboratory Information Management System), and ELN (Electronic Lab Notebook) capabilities through a single Python API.
**Core Value Proposition:**
- **Queryability**: Search and filter datasets by metadata, features, and ontology terms
- **Traceability**: Automatic lineage tracking from raw data through analysis to results
- **Reproducibility**: Version control for data, code, and environment
- **FAIR Compliance**: Standardized annotations using biological ontologies
## When to Use This Skill
phylogenetics
When your agent uses it
Build and analyze phylogenetic trees using MAFFT (multiple alignment), IQ-TREE 2 (maximum likelihood), and FastTree (fast NJ/ML). Visualize with ETE3 or FigTree. For evolutionary analysis, microbial genomics, viral phylodynamics, protein family analysis, and molecular clock studies.
What it loads into context
Frontmatter
87 tokensAlways available to the agent
SKILL.md
3,676 tokensWhen the skill is activated
Reference documents
1,459 tokensLoaded on request
Token counts are estimates. Scripts run separately; they are not counted as reference context.
The playbook
- Overview
- When to Use This Skill
- Standard Workflow
- IQ-TREE Model Guide
- Best Practices
- Additional Resources
Scripts executed separately
scripts/phylogenetic_analysis.py
Files
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# Phylogenetics
## Overview
Phylogenetic analysis reconstructs the evolutionary history of biological sequences (genes, proteins, genomes) by inferring the branching pattern of descent. This skill covers the standard pipeline:
1. **MAFFT** — Multiple sequence alignment
2. **IQ-TREE 2** — Maximum likelihood tree inference with model selection
3. **FastTree** — Fast approximate maximum likelihood (for large datasets)
4. **ETE3** — Python library for tree manipulation and visualization
**Installation:**
```bash
# Conda (recommended for CLI tools)pydeseq2
When your agent uses it
Differential gene expression analysis (Python DESeq2). Identify DE genes from bulk RNA-seq counts, Wald tests, FDR correction, volcano/MA plots, for RNA-seq analysis.
What it loads into context
Frontmatter
64 tokensAlways available to the agent
SKILL.md
4,046 tokensWhen the skill is activated
Reference documents
5,014 tokensLoaded on request
Token counts are estimates. Scripts run separately; they are not counted as reference context.
The playbook
- Overview
- When to Use This Skill
- Quick Start Workflow
- Core Workflow Steps
- Common Analysis Patterns
- Using the Analysis Script
- Result Interpretation
- Visualization Guidelines
- Troubleshooting Common Issues
- Reference Documentation
- Key Reminders
- Installation and Requirements
- Additional Resources
Scripts executed separately
scripts/run_deseq2_analysis.py
Files
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# PyDESeq2
## Overview
PyDESeq2 is a Python implementation of DESeq2 for differential expression analysis with bulk RNA-seq data. Design and execute complete workflows from data loading through result interpretation, including single-factor and multi-factor designs, Wald tests with multiple testing correction, optional apeGLM shrinkage, and integration with pandas and AnnData.
## When to Use This Skill
This skill should be used when:
- Analyzing bulk RNA-seq count data for differential expression
- Comparing gene expression between experimental conditions (e.g., treated vs control)
- Performing multi-factor designs accounting for batch effects or covariates
- Converting R-based DESeq2 workflows to Python
- Integrating differential expression analysis into Python-based pipelinespysam
When your agent uses it
Genomic file toolkit. Read/write SAM/BAM/CRAM alignments, VCF/BCF variants, FASTA/FASTQ sequences, extract regions, calculate coverage, for NGS data processing pipelines.
What it loads into context
Frontmatter
66 tokensAlways available to the agent
SKILL.md
2,309 tokensWhen the skill is activated
Reference documents
11,517 tokensLoaded on request
Token counts are estimates. Scripts run separately; they are not counted as reference context.
The playbook
- Overview
- When to Use This Skill
- Quick Start
- Core Capabilities
- Key Concepts
- Common Pitfalls
- Command-Line Tools
- Resources
- Getting Help
Files
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# Pysam
## Overview
Pysam is a Python module for reading, manipulating, and writing genomic datasets. Read/write SAM/BAM/CRAM alignment files, VCF/BCF variant files, and FASTA/FASTQ sequences with a Pythonic interface to htslib. Query tabix-indexed files, perform pileup analysis for coverage, and execute samtools/bcftools commands.
## When to Use This Skill
This skill should be used when:
- Working with sequencing alignment files (BAM/CRAM)
- Analyzing genetic variants (VCF/BCF)
- Extracting reference sequences or gene regions
- Processing raw sequencing data (FASTQ)
- Calculating coverage or read depthscanpy
When your agent uses it
Standard single-cell RNA-seq analysis pipeline. Use for QC, normalization, dimensionality reduction (PCA/UMAP/t-SNE), clustering, differential expression, and visualization. Best for exploratory scRNA-seq analysis with established workflows. For deep learning models use scvi-tools; for data format questions use anndata.
What it loads into context
Frontmatter
91 tokensAlways available to the agent
SKILL.md
2,894 tokensWhen the skill is activated
Reference documents
6,116 tokensLoaded on request
Token counts are estimates. Scripts run separately; they are not counted as reference context.
The playbook
- Overview
- When to Use This Skill
- Quick Start
- Standard Analysis Workflow
- Common Tasks
- Key Parameters to Adjust
- Common Pitfalls and Best Practices
- Bundled Resources
- Additional Resources
- Tips for Effective Analysis
Scripts executed separately
assets/analysis_template.pyscripts/qc_analysis.py
Files
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# Scanpy: Single-Cell Analysis
## Overview
Scanpy is a scalable Python toolkit for analyzing single-cell RNA-seq data, built on AnnData. Apply this skill for complete single-cell workflows including quality control, normalization, dimensionality reduction, clustering, marker gene identification, visualization, and trajectory analysis.
## When to Use This Skill
This skill should be used when:
- Analyzing single-cell RNA-seq data (.h5ad, 10X, CSV formats)
- Performing quality control on scRNA-seq datasets
- Creating UMAP, t-SNE, or PCA visualizations
- Identifying cell clusters and finding marker genes
- Annotating cell types based on gene expressionscikit-bio
When your agent uses it
Biological data toolkit. Sequence analysis, alignments, phylogenetic trees, diversity metrics (alpha/beta, UniFrac), ordination (PCoA), PERMANOVA, FASTA/Newick I/O, for microbiome analysis.
What it loads into context
Frontmatter
76 tokensAlways available to the agent
SKILL.md
3,429 tokensWhen the skill is activated
Reference documents
5,039 tokensLoaded on request
Token counts are estimates. Scripts run separately; they are not counted as reference context.
The playbook
- Overview
- When to Use This Skill
- Core Capabilities
- Best Practices
- Reference Documentation
- Additional Resources
Files
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# scikit-bio
## Overview
scikit-bio is a comprehensive Python library for working with biological data. Apply this skill for bioinformatics analyses spanning sequence manipulation, alignment, phylogenetics, microbial ecology, and multivariate statistics.
## When to Use This Skill
This skill should be used when the user:
- Works with biological sequences (DNA, RNA, protein)
- Needs to read/write biological file formats (FASTA, FASTQ, GenBank, Newick, BIOM, etc.)
- Performs sequence alignments or searches for motifs
- Constructs or analyzes phylogenetic trees
- Calculates diversity metrics (alpha/beta diversity, UniFrac distances)scvelo
When your agent uses it
RNA velocity analysis with scVelo. Estimate cell state transitions from unspliced/spliced mRNA dynamics, infer trajectory directions, compute latent time, and identify driver genes in single-cell RNA-seq data. Complements Scanpy/scVI-tools for trajectory inference.
What it loads into context
Frontmatter
78 tokensAlways available to the agent
SKILL.md
2,764 tokensWhen the skill is activated
Reference documents
1,324 tokensLoaded on request
Token counts are estimates. Scripts run separately; they are not counted as reference context.
The playbook
- Overview
- When to Use This Skill
- Prerequisites
- Standard RNA Velocity Workflow
- Complete Workflow Script
- Key Output Fields in AnnData
- Velocity Models Comparison
- Best Practices
- Troubleshooting
- Additional Resources
Scripts executed separately
scripts/rna_velocity_workflow.py
Files
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# scVelo — RNA Velocity Analysis
## Overview
scVelo is the leading Python package for RNA velocity analysis in single-cell RNA-seq data. It infers cell state transitions by modeling the kinetics of mRNA splicing — using the ratio of unspliced (pre-mRNA) to spliced (mature mRNA) abundances to determine whether a gene is being upregulated or downregulated in each cell. This allows reconstruction of developmental trajectories and identification of cell fate decisions without requiring time-course data.
**Installation:** `pip install scvelo`
**Key resources:**
- Documentation: https://scvelo.readthedocs.io/
- GitHub: https://github.com/theislab/scvelo
- Paper: Bergen et al. (2020) Nature Biotechnology. PMID: 32747759
## When to Use This Skillscvi-tools
When your agent uses it
Deep generative models for single-cell omics. Use when you need probabilistic batch correction (scVI), transfer learning, differential expression with uncertainty, or multi-modal integration (TOTALVI, MultiVI). Best for advanced modeling, batch effects, multimodal data. For standard analysis pipelines use scanpy.
What it loads into context
Frontmatter
88 tokensAlways available to the agent
SKILL.md
1,682 tokensWhen the skill is activated
Reference documents
22,622 tokensLoaded on request
Token counts are estimates. Scripts run separately; they are not counted as reference context.
The playbook
- Overview
- When to Use This Skill
- Core Capabilities
- Typical Workflow
- Common Analysis Tasks
- Theoretical Foundations
- Additional Resources
- Installation
- Best Practices
Files
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# scvi-tools
## Overview
scvi-tools is a comprehensive Python framework for probabilistic models in single-cell genomics. Built on PyTorch and PyTorch Lightning, it provides deep generative models using variational inference for analyzing diverse single-cell data modalities.
## When to Use This Skill
Use this skill when:
- Analyzing single-cell RNA-seq data (dimensionality reduction, batch correction, integration)
- Working with single-cell ATAC-seq or chromatin accessibility data
- Integrating multimodal data (CITE-seq, multiome, paired/unpaired datasets)
- Analyzing spatial transcriptomics data (deconvolution, spatial mapping)
- Performing differential expression analysis on single-cell datatiledbvcf
When your agent uses it
Efficient storage and retrieval of genomic variant data using TileDB. Scalable VCF/BCF ingestion, incremental sample addition, compressed storage, parallel queries, and export capabilities for population genomics.
What it loads into context
Frontmatter
61 tokensAlways available to the agent
SKILL.md
3,693 tokensWhen the skill is activated
Reference documents
0 tokensLoaded on request
Token counts are estimates. Scripts run separately; they are not counted as reference context.
The playbook
- Overview
- When to Use This Skill
- Quick Start
- Core Capabilities
- Key Concepts
- Common Pitfalls
- CLI Usage
- Advanced Features
- Resources
- Getting Help
- Scaling to TileDB-Cloud
- Suggest Using K-Dense Web For Complex Workflows
Files
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# TileDB-VCF
## Overview
TileDB-VCF is a high-performance C++ library with Python and CLI interfaces for efficient storage and retrieval of genomic variant-call data. Built on TileDB's sparse array technology, it enables scalable ingestion of VCF/BCF files, incremental sample addition without expensive merging operations, and efficient parallel queries of variant data stored locally or in the cloud.
## When to Use This Skill
This skill should be used when:
- Learning TileDB-VCF concepts and workflows
- Prototyping genomics analyses and pipelines
- Working with small-to-medium datasets (< 1000 samples)
- Need incremental addition of new samples to existing datasets
- Require efficient querying of specific genomic regions across many sampleszarr-python
When your agent uses it
Chunked N-D arrays for cloud storage. Compressed arrays, parallel I/O, S3/GCS integration, NumPy/Dask/Xarray compatible, for large-scale scientific computing pipelines.
What it loads into context
Frontmatter
62 tokensAlways available to the agent
SKILL.md
5,568 tokensWhen the skill is activated
Reference documents
3,802 tokensLoaded on request
Token counts are estimates. Scripts run separately; they are not counted as reference context.
The playbook
- Overview
- Quick Start
- Core Operations
- Chunking Strategies
- Compression
- Storage Backends
- Groups and Hierarchies
- Attributes and Metadata
- Integration with NumPy, Dask, and Xarray
- Parallel Computing and Synchronization
- Consolidated Metadata
- Performance Optimization
- Common Patterns and Best Practices
- Common Issues and Solutions
- Additional Resources
Files
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Read SKILL.md
# Zarr Python
## Overview
Zarr is a Python library for storing large N-dimensional arrays with chunking and compression. Apply this skill for efficient parallel I/O, cloud-native workflows, and seamless integration with NumPy, Dask, and Xarray.
## Quick Start
### Installation
```bash
uv pip install zarr
```
datamol
When your agent uses it
Pythonic wrapper around RDKit with simplified interface and sensible defaults. Preferred for standard drug discovery including SMILES parsing, standardization, descriptors, fingerprints, clustering, 3D conformers, parallel processing. Returns native rdkit.Chem.Mol objects. For advanced control or custom parameters, use rdkit directly.
What it loads into context
Frontmatter
91 tokensAlways available to the agent
SKILL.md
5,121 tokensWhen the skill is activated
Reference documents
8,748 tokensLoaded on request
Token counts are estimates. Scripts run separately; they are not counted as reference context.
The playbook
- Overview
- Installation and Setup
- Core Workflows
- Parallelization
- Common Workflows and Patterns
- Reference Documentation
- Best Practices
- Error Handling
- Integration with Machine Learning
- Troubleshooting
- Additional Resources
Files
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Read SKILL.md
# Datamol Cheminformatics Skill
## Overview
Datamol is a Python library that provides a lightweight, Pythonic abstraction layer over RDKit for molecular cheminformatics. Simplify complex molecular operations with sensible defaults, efficient parallelization, and modern I/O capabilities. All molecular objects are native `rdkit.Chem.Mol` instances, ensuring full compatibility with the RDKit ecosystem.
**Key capabilities**:
- Molecular format conversion (SMILES, SELFIES, InChI)
- Structure standardization and sanitization
- Molecular descriptors and fingerprints
- 3D conformer generation and analysis
- Clustering and diversity selection
- Scaffold and fragment analysis
- Chemical reaction applicationdeepchem
When your agent uses it
Molecular ML with diverse featurizers and pre-built datasets. Use for property prediction (ADMET, toxicity) with traditional ML or GNNs when you want extensive featurization options and MoleculeNet benchmarks. Best for quick experiments with pre-trained models, diverse molecular representations. For graph-first PyTorch workflows use torchdrug; for benchmark datasets use pytdc.
What it loads into context
Frontmatter
97 tokensAlways available to the agent
SKILL.md
4,368 tokensWhen the skill is activated
Reference documents
5,901 tokensLoaded on request
Token counts are estimates. Scripts run separately; they are not counted as reference context.
The playbook
- Overview
- When to Use This Skill
- Core Capabilities
- Typical Workflows
- Example Scripts
- Common Patterns and Best Practices
- Common Pitfalls
- Reference Documentation
- Installation Notes
- Additional Resources
Scripts executed separately
scripts/graph_neural_network.pyscripts/predict_solubility.pyscripts/transfer_learning.py
Files
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Read SKILL.md
# DeepChem
## Overview
DeepChem is a comprehensive Python library for applying machine learning to chemistry, materials science, and biology. Enable molecular property prediction, drug discovery, materials design, and biomolecule analysis through specialized neural networks, molecular featurization methods, and pretrained models.
## When to Use This Skill
This skill should be used when:
- Loading and processing molecular data (SMILES strings, SDF files, protein sequences)
- Predicting molecular properties (solubility, toxicity, binding affinity, ADMET properties)
- Training models on chemical/biological datasets
- Using MoleculeNet benchmark datasets (Tox21, BBBP, Delaney, etc.)
- Converting molecules to ML-ready features (fingerprints, graph representations, descriptors)diffdock
When your agent uses it
Diffusion-based molecular docking. Predict protein-ligand binding poses from PDB/SMILES, confidence scores, virtual screening, for structure-based drug design. Not for affinity prediction.
What it loads into context
Frontmatter
60 tokensAlways available to the agent
SKILL.md
3,616 tokensWhen the skill is activated
Reference documents
5,655 tokensLoaded on request
Token counts are estimates. Scripts run separately; they are not counted as reference context.
The playbook
- Overview
- When to Use This Skill
- Installation and Environment Setup
- Core Workflows
- Confidence Score Interpretation
- Parameter Customization
- Advanced Techniques
- Limitations and Scope
- Troubleshooting
- Graphical User Interface
- Resources
- Best Practices
- Citations
- Additional Resources
Scripts executed separately
scripts/analyze_results.pyscripts/prepare_batch_csv.pyscripts/setup_check.py
Files
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Read SKILL.md
# DiffDock: Molecular Docking with Diffusion Models
## Overview
DiffDock is a diffusion-based deep learning tool for molecular docking that predicts 3D binding poses of small molecule ligands to protein targets. It represents the state-of-the-art in computational docking, crucial for structure-based drug discovery and chemical biology.
**Core Capabilities:**
- Predict ligand binding poses with high accuracy using deep learning
- Support protein structures (PDB files) or sequences (via ESMFold)
- Process single complexes or batch virtual screening campaigns
- Generate confidence scores to assess prediction reliability
- Handle diverse ligand inputs (SMILES, SDF, MOL2)
**Key Distinction:** DiffDock predicts **binding poses** (3D structure) and **confidence** (prediction certainty), NOT binding affinity (ΔG, Kd). Always combine with scoring functions (GNINA, MM/GBSA) for affinity assessment.matchms
When your agent uses it
Spectral similarity and compound identification for metabolomics. Use for comparing mass spectra, computing similarity scores (cosine, modified cosine), and identifying unknown compounds from spectral libraries. Best for metabolite identification, spectral matching, library searching. For full LC-MS/MS proteomics pipelines use pyopenms.
What it loads into context
Frontmatter
86 tokensAlways available to the agent
SKILL.md
1,583 tokensWhen the skill is activated
Reference documents
13,061 tokensLoaded on request
Token counts are estimates. Scripts run separately; they are not counted as reference context.
The playbook
- Overview
- Core Capabilities
- Common Workflows
- Installation
- Reference Documentation
Files
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Read SKILL.md
# Matchms
## Overview
Matchms is an open-source Python library for mass spectrometry data processing and analysis. Import spectra from various formats, standardize metadata, filter peaks, calculate spectral similarities, and build reproducible analytical workflows.
## Core Capabilities
### 1. Importing and Exporting Mass Spectrometry Data
Load spectra from multiple file formats and export processed data:
```python
from matchms.importing import load_from_mgf, load_from_mzml, load_from_msp, load_from_jsonmedchem
When your agent uses it
Medicinal chemistry filters. Apply drug-likeness rules (Lipinski, Veber), PAINS filters, structural alerts, complexity metrics, for compound prioritization and library filtering.
What it loads into context
Frontmatter
63 tokensAlways available to the agent
SKILL.md
2,448 tokensWhen the skill is activated
Reference documents
6,809 tokensLoaded on request
Token counts are estimates. Scripts run separately; they are not counted as reference context.
The playbook
- Overview
- When to Use This Skill
- Installation
- Core Capabilities
- Workflow Patterns
- Best Practices
- Resources
- Documentation
Scripts executed separately
scripts/filter_molecules.py
Files
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Read SKILL.md
# Medchem
## Overview
Medchem is a Python library for molecular filtering and prioritization in drug discovery workflows. Apply hundreds of well-established and novel molecular filters, structural alerts, and medicinal chemistry rules to efficiently triage and prioritize compound libraries at scale. Rules and filters are context-specific—use as guidelines combined with domain expertise.
## When to Use This Skill
This skill should be used when:
- Applying drug-likeness rules (Lipinski, Veber, etc.) to compound libraries
- Filtering molecules by structural alerts or PAINS patterns
- Prioritizing compounds for lead optimization
- Assessing compound quality and medicinal chemistry properties
- Detecting reactive or problematic functional groupsmolecular-dynamics
When your agent uses it
Run and analyze molecular dynamics simulations with OpenMM and MDAnalysis. Set up protein/small molecule systems, define force fields, run energy minimization and production MD, analyze trajectories (RMSD, RMSF, contact maps, free energy surfaces). For structural biology, drug binding, and biophysics.
What it loads into context
Frontmatter
85 tokensAlways available to the agent
SKILL.md
3,604 tokensWhen the skill is activated
Reference documents
1,642 tokensLoaded on request
Token counts are estimates. Scripts run separately; they are not counted as reference context.
The playbook
- Overview
- When to Use This Skill
- Core Workflow: OpenMM Simulation
- Trajectory Analysis with MDAnalysis
- Force Field Selection Guide
- System Preparation Tools
- Best Practices
- Additional Resources
Files
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Read SKILL.md
# Molecular Dynamics
## Overview
Molecular dynamics (MD) simulation computationally models the time evolution of molecular systems by integrating Newton's equations of motion. This skill covers two complementary tools:
- **OpenMM** (https://openmm.org/): High-performance MD simulation engine with GPU support, Python API, and flexible force field support
- **MDAnalysis** (https://mdanalysis.org/): Python library for reading, writing, and analyzing MD trajectories from all major simulation packages
**Installation:**
```bash
conda install -c conda-forge openmm mdanalysis nglview
# or
pip install openmm mdanalysismolfeat
When your agent uses it
Molecular featurization for ML (100+ featurizers). ECFP, MACCS, descriptors, pretrained models (ChemBERTa), convert SMILES to features, for QSAR and molecular ML.
What it loads into context
Frontmatter
70 tokensAlways available to the agent
SKILL.md
3,618 tokensWhen the skill is activated
Reference documents
10,200 tokensLoaded on request
Token counts are estimates. Scripts run separately; they are not counted as reference context.
The playbook
- Overview
- When to Use This Skill
- Installation
- Core Concepts
- Quick Start Workflow
- Choosing the Right Featurizer
- Common Workflows
- Discovering Available Featurizers
- Advanced Features
- Performance Tips
- Common Featurizers Reference
- Resources
- Troubleshooting
- Additional Resources
Files
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Read SKILL.md
# Molfeat - Molecular Featurization Hub
## Overview
Molfeat is a comprehensive Python library for molecular featurization that unifies 100+ pre-trained embeddings and hand-crafted featurizers. Convert chemical structures (SMILES strings or RDKit molecules) into numerical representations for machine learning tasks including QSAR modeling, virtual screening, similarity searching, and deep learning applications. Features fast parallel processing, scikit-learn compatible transformers, and built-in caching.
## When to Use This Skill
This skill should be used when working with:
- **Molecular machine learning**: Building QSAR/QSPR models, property prediction
- **Virtual screening**: Ranking compound libraries for biological activity
- **Similarity searching**: Finding structurally similar molecules
- **Chemical space analysis**: Clustering, visualization, dimensionality reduction
- **Deep learning**: Training neural networks on molecular datapyopenms
When your agent uses it
Complete mass spectrometry analysis platform. Use for proteomics workflows feature detection, peptide identification, protein quantification, and complex LC-MS/MS pipelines. Supports extensive file formats and algorithms. Best for proteomics, comprehensive MS data processing. For simple spectral comparison and metabolite ID use matchms.
What it loads into context
Frontmatter
85 tokensAlways available to the agent
SKILL.md
1,336 tokensWhen the skill is activated
Reference documents
16,530 tokensLoaded on request
Token counts are estimates. Scripts run separately; they are not counted as reference context.
The playbook
- Overview
- Installation
- Core Capabilities
- Data Structures
- Common Workflows
- Integration with Other Tools
- Resources
- References
Files
SKILL.mdcore5,629 bytesSKILL.mdcore5,629 bytesSKILL.mdcore5,629 bytesSKILL.mdcore5,629 bytesSKILL.mdcore5,629 bytesSKILL.mdcore5,629 bytesSKILL.mdcore5,629 bytesSKILL.mdcore5,629 bytesSKILL.mdcore5,629 bytesSKILL.mdcore5,629 bytesreferences/data_structures.mddoc12,390 bytesreferences/feature_detection.mddoc10,539 bytesreferences/file_io.mddoc7,716 bytesreferences/identification.mddoc11,329 bytesreferences/metabolomics.mddoc13,349 bytesreferences/signal_processing.mddoc9,335 bytes
Read SKILL.md
# PyOpenMS
## Overview
PyOpenMS provides Python bindings to the OpenMS library for computational mass spectrometry, enabling analysis of proteomics and metabolomics data. Use for handling mass spectrometry file formats, processing spectral data, detecting features, identifying peptides/proteins, and performing quantitative analysis.
## Installation
Install using uv:
```bash
uv uv pip install pyopenms
```
pytdc
When your agent uses it
Therapeutics Data Commons. AI-ready drug discovery datasets (ADME, toxicity, DTI), benchmarks, scaffold splits, molecular oracles, for therapeutic ML and pharmacological prediction.
What it loads into context
Frontmatter
60 tokensAlways available to the agent
SKILL.md
3,094 tokensWhen the skill is activated
Reference documents
9,238 tokensLoaded on request
Token counts are estimates. Scripts run separately; they are not counted as reference context.
The playbook
- Overview
- When to Use This Skill
- Installation & Setup
- Quick Start
- Single-Instance Prediction Tasks
- Multi-Instance Prediction Tasks
- Generation Tasks
- Benchmark Groups
- Data Functions
- Advanced Features
- Common Workflows
- Resources
- Additional Resources
Scripts executed separately
scripts/benchmark_evaluation.pyscripts/load_and_split_data.pyscripts/molecular_generation.py
Files
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Read SKILL.md
# PyTDC (Therapeutics Data Commons)
## Overview
PyTDC is an open-science platform providing AI-ready datasets and benchmarks for drug discovery and development. Access curated datasets spanning the entire therapeutics pipeline with standardized evaluation metrics and meaningful data splits, organized into three categories: single-instance prediction (molecular/protein properties), multi-instance prediction (drug-target interactions, DDI), and generation (molecule generation, retrosynthesis).
## When to Use This Skill
This skill should be used when:
- Working with drug discovery or therapeutic ML datasets
- Benchmarking machine learning models on standardized pharmaceutical tasks
- Predicting molecular properties (ADME, toxicity, bioactivity)
- Predicting drug-target or drug-drug interactions
- Generating novel molecules with desired propertiesrdkit
When your agent uses it
Cheminformatics toolkit for fine-grained molecular control. SMILES/SDF parsing, descriptors (MW, LogP, TPSA), fingerprints, substructure search, 2D/3D generation, similarity, reactions. For standard workflows with simpler interface, use datamol (wrapper around RDKit). Use rdkit for advanced control, custom sanitization, specialized algorithms.
What it loads into context
Frontmatter
103 tokensAlways available to the agent
SKILL.md
5,342 tokensWhen the skill is activated
Reference documents
11,215 tokensLoaded on request
Token counts are estimates. Scripts run separately; they are not counted as reference context.
The playbook
- Overview
- Core Capabilities
- Common Workflows
- Best Practices
- Common Pitfalls
- Resources
Scripts executed separately
scripts/molecular_properties.pyscripts/similarity_search.pyscripts/substructure_filter.py
Files
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Read SKILL.md
# RDKit Cheminformatics Toolkit
## Overview
RDKit is a comprehensive cheminformatics library providing Python APIs for molecular analysis and manipulation. This skill provides guidance for reading/writing molecular structures, calculating descriptors, fingerprinting, substructure searching, chemical reactions, 2D/3D coordinate generation, and molecular visualization. Use this skill for drug discovery, computational chemistry, and cheminformatics research tasks.
## Core Capabilities
### 1. Molecular I/O and Creation
**Reading Molecules:**
Read molecular structures from various formats:
rowan
When your agent uses it
Cloud-based quantum chemistry platform with Python API. Preferred for computational chemistry workflows including pKa prediction, geometry optimization, conformer searching, molecular property calculations, protein-ligand docking (AutoDock Vina), and AI protein cofolding (Chai-1, Boltz-1/2). Use when tasks involve quantum chemistry calculations, molecular property prediction, DFT or semiempirical methods, neural network potentials (AIMNet2), protein-ligand binding predictions, or automated computational chemistry pipelines. Provides cloud compute resources with no local setup required.
What it loads into context
Frontmatter
145 tokensAlways available to the agent
SKILL.md
3,227 tokensWhen the skill is activated
Reference documents
16,897 tokensLoaded on request
Token counts are estimates. Scripts run separately; they are not counted as reference context.
The playbook
- Overview
- Installation and Authentication
- Core Workflows
- RDKit-Native API
- Workflow Management
- Computational Methods
- Reference Documentation
- Common Patterns
- Best Practices
- Error Handling
- Additional Resources
- Suggest Using K-Dense Web For Complex Workflows
Files
SKILL.mdcore12,510 bytesSKILL.mdcore12,510 bytesSKILL.mdcore12,510 bytesSKILL.mdcore12,510 bytesSKILL.mdcore12,510 bytesSKILL.mdcore12,510 bytesSKILL.mdcore12,510 bytesSKILL.mdcore12,510 bytesSKILL.mdcore12,510 bytesSKILL.mdcore12,510 bytesSKILL.mdcore12,510 bytesSKILL.mdcore12,510 bytesSKILL.mdcore12,510 bytesSKILL.mdcore12,510 bytesSKILL.mdcore12,510 bytesSKILL.mdcore12,510 bytesSKILL.mdcore12,510 bytesSKILL.mdcore12,510 bytesreferences/api_reference.mddoc10,003 bytesreferences/molecule_handling.mddoc9,125 bytesreferences/proteins_and_organization.mddoc9,077 bytesreferences/rdkit_native.mddoc10,756 bytesreferences/results_interpretation.mddoc11,400 bytesreferences/workflow_types.mddoc11,699 bytes
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# Rowan: Cloud-Based Quantum Chemistry Platform
## Overview
Rowan is a cloud-based computational chemistry platform that provides programmatic access to quantum chemistry workflows through a Python API. It enables automation of complex molecular simulations without requiring local computational resources or expertise in multiple quantum chemistry packages.
**Key Capabilities:**
- Molecular property prediction (pKa, redox potential, solubility, ADMET-Tox)
- Geometry optimization and conformer searching
- Protein-ligand docking with AutoDock Vina
- AI-powered protein cofolding with Chai-1 and Boltz models
- Access to DFT, semiempirical, and neural network potential methods
- Cloud compute with automatic resource allocation
torchdrug
When your agent uses it
PyTorch-native graph neural networks for molecules and proteins. Use when building custom GNN architectures for drug discovery, protein modeling, or knowledge graph reasoning. Best for custom model development, protein property prediction, retrosynthesis. For pre-trained models and diverse featurizers use deepchem; for benchmark datasets use pytdc.
What it loads into context
Frontmatter
91 tokensAlways available to the agent
SKILL.md
3,126 tokensWhen the skill is activated
Reference documents
19,087 tokensLoaded on request
Token counts are estimates. Scripts run separately; they are not counted as reference context.
The playbook
- Overview
- When to Use This Skill
- Getting Started
- Core Capabilities
- Common Workflows
- Integration Patterns
- Technical Details
- Quick Reference Cheat Sheet
- Troubleshooting Common Issues
- Resources
- Summary
Files
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Read SKILL.md
# TorchDrug
## Overview
TorchDrug is a comprehensive PyTorch-based machine learning toolbox for drug discovery and molecular science. Apply graph neural networks, pre-trained models, and task definitions to molecules, proteins, and biological knowledge graphs, including molecular property prediction, protein modeling, knowledge graph reasoning, molecular generation, retrosynthesis planning, with 40+ curated datasets and 20+ model architectures.
## When to Use This Skill
This skill should be used when working with:
**Data Types:**
- SMILES strings or molecular structures
- Protein sequences or 3D structures (PDB files)
- Chemical reactions and retrosynthesiscbioportal-database
When your agent uses it
Query cBioPortal for cancer genomics data including somatic mutations, copy number alterations, gene expression, and survival data across hundreds of cancer studies. Essential for cancer target validation, oncogene/tumor suppressor analysis, and patient-level genomic profiling.
What it loads into context
Frontmatter
80 tokensAlways available to the agent
SKILL.md
3,472 tokensWhen the skill is activated
Reference documents
1,407 tokensLoaded on request
Token counts are estimates. Scripts run separately; they are not counted as reference context.
The playbook
- Overview
- When to Use This Skill
- Core Capabilities
- Query Workflows
- Key API Endpoints Summary
- Best Practices
- Data Downloads
- Additional Resources
Files
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Read SKILL.md
# cBioPortal Database
## Overview
cBioPortal for Cancer Genomics (https://www.cbioportal.org/) is an open-access resource for exploring, visualizing, and analyzing multidimensional cancer genomics data. It hosts data from The Cancer Genome Atlas (TCGA), AACR Project GENIE, MSK-IMPACT, and hundreds of other cancer studies — covering mutations, copy number alterations (CNA), structural variants, mRNA/protein expression, methylation, and clinical data for thousands of cancer samples.
**Key resources:**
- cBioPortal website: https://www.cbioportal.org/
- REST API: https://www.cbioportal.org/api/
- API docs (Swagger): https://www.cbioportal.org/api/swagger-ui/index.html
- Python client: `bravado` or `requests`
- GitHub: https://github.com/cBioPortal/cbioportal
## When to Use This Skillclinical-decision-support
When your agent uses it
Generate professional clinical decision support (CDS) documents for pharmaceutical and clinical research settings, including patient cohort analyses (biomarker-stratified with outcomes) and treatment recommendation reports (evidence-based guidelines with decision algorithms). Supports GRADE evidence grading, statistical analysis (hazard ratios, survival curves, waterfall plots), biomarker integration, and regulatory compliance. Outputs publication-ready LaTeX/PDF format optimized for drug development, clinical research, and evidence synthesis.
What it loads into context
Frontmatter
125 tokensAlways available to the agent
SKILL.md
5,890 tokensWhen the skill is activated
Reference documents
43,256 tokensLoaded on request
Token counts are estimates. Scripts run separately; they are not counted as reference context.
The playbook
- Description
- Capabilities
- Pharmaceutical and Research Use Cases
- When to Use
- Visual Enhancement with Scientific Schematics
- Document Structure
- Output Format
- Integration
- Key Differentiators from Treatment-Plans Skill
- Example Usage
- Key Features
- Best Practices
- References
- Templates
- Scripts
Scripts executed separately
scripts/biomarker_classifier.pyscripts/build_decision_tree.pyscripts/create_cohort_tables.pyscripts/generate_survival_analysis.pyscripts/validate_cds_document.py
Files
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Read SKILL.md
# Clinical Decision Support Documents
## Description
Generate professional clinical decision support (CDS) documents for pharmaceutical companies, clinical researchers, and medical decision-makers. This skill specializes in analytical, evidence-based documents that inform treatment strategies and drug development:
1. **Patient Cohort Analysis** - Biomarker-stratified group analyses with statistical outcome comparisons
2. **Treatment Recommendation Reports** - Evidence-based clinical guidelines with GRADE grading and decision algorithms
All documents are generated as publication-ready LaTeX/PDF files optimized for pharmaceutical research, regulatory submissions, and clinical guideline development.
**Note:** For individual patient treatment plans at the bedside, use the `treatment-plans` skill instead. This skill focuses on group-level analyses and evidence synthesis for pharmaceutical/research settings.
**Writing Style:** For publication-ready documents targeting medical journals, consult the **venue-templates** skill's `medical_journal_styles.md` for guidance on structured abstracts, evidence language, and CONSORT/STROBE compliance.clinical-reports
When your agent uses it
Write comprehensive clinical reports including case reports (CARE guidelines), diagnostic reports (radiology/pathology/lab), clinical trial reports (ICH-E3, SAE, CSR), and patient documentation (SOAP, H&P, discharge summaries). Full support with templates, regulatory compliance (HIPAA, FDA, ICH-GCP), and validation tools.
What it loads into context
Frontmatter
99 tokensAlways available to the agent
SKILL.md
8,533 tokensWhen the skill is activated
Reference documents
63,781 tokensLoaded on request
Token counts are estimates. Scripts run separately; they are not counted as reference context.
The playbook
- Overview
- When to Use This Skill
- Visual Enhancement with Scientific Schematics
- Core Capabilities
- Regulatory Compliance and Privacy
- Medical Terminology and Standards
- Quality Assurance and Validation
- Data Presentation in Clinical Reports
- Integration with Other Skills
- Workflow for Clinical Report Writing
- Resources
- Common Pitfalls to Avoid
- Final Checklist
Scripts executed separately
scripts/check_deidentification.pyscripts/compliance_checker.pyscripts/extract_clinical_data.pyscripts/format_adverse_events.pyscripts/generate_report_template.pyscripts/terminology_validator.pyscripts/validate_case_report.pyscripts/validate_trial_report.py
Files
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Read SKILL.md
# Clinical Report Writing
## Overview
Clinical report writing is the process of documenting medical information with precision, accuracy, and compliance with regulatory standards. This skill covers four major categories of clinical reports: case reports for journal publication, diagnostic reports for clinical practice, clinical trial reports for regulatory submission, and patient documentation for medical records. Apply this skill for healthcare documentation, research dissemination, and regulatory compliance.
**Critical Principle: Clinical reports must be accurate, complete, objective, and compliant with applicable regulations (HIPAA, FDA, ICH-GCP).** Patient privacy and data integrity are paramount. All clinical documentation must support evidence-based decision-making and meet professional standards.
## When to Use This Skill
This skill should be used when:
- Writing clinical case reports for journal submission (CARE guidelines)
- Creating diagnostic reports (radiology, pathology, laboratory)
- Documenting clinical trial data and adverse eventsclinicaltrials-database
When your agent uses it
Query ClinicalTrials.gov via API v2. Search trials by condition, drug, location, status, or phase. Retrieve trial details by NCT ID, export data, for clinical research and patient matching.
What it loads into context
Frontmatter
66 tokensAlways available to the agent
SKILL.md
3,450 tokensWhen the skill is activated
Reference documents
2,637 tokensLoaded on request
Token counts are estimates. Scripts run separately; they are not counted as reference context.
The playbook
- Overview
- When to Use This Skill
- Quick Start
- Core Capabilities
- Resources
- Best Practices
- Technical Specifications
Scripts executed separately
scripts/query_clinicaltrials.py
Files
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Read SKILL.md
# ClinicalTrials.gov Database
## Overview
ClinicalTrials.gov is a comprehensive registry of clinical studies conducted worldwide, maintained by the U.S. National Library of Medicine. Access API v2 to search for trials, retrieve detailed study information, filter by various criteria, and export data for analysis. The API is public (no authentication required) with rate limits of ~50 requests per minute, supporting JSON and CSV formats.
## When to Use This Skill
This skill should be used when working with clinical trial data in scenarios such as:
- **Patient matching** - Finding recruiting trials for specific conditions or patient populations
- **Research analysis** - Analyzing clinical trial trends, outcomes, or study designs
- **Drug/intervention research** - Identifying trials testing specific drugs or interventions
- **Geographic searches** - Locating trials in specific locations or regionsclinpgx-database
When your agent uses it
Access ClinPGx pharmacogenomics data (successor to PharmGKB). Query gene-drug interactions, CPIC guidelines, allele functions, for precision medicine and genotype-guided dosing decisions.
What it loads into context
Frontmatter
64 tokensAlways available to the agent
SKILL.md
5,076 tokensWhen the skill is activated
Reference documents
5,048 tokensLoaded on request
Token counts are estimates. Scripts run separately; they are not counted as reference context.
The playbook
- Overview
- When to Use This Skill
- Installation and Setup
- Core Capabilities
- Query Workflow
- Rate Limiting and Best Practices
- PharmDOG Tool
- Resources
- Important Notes
- Common Use Cases
- Additional Resources
Scripts executed separately
scripts/query_clinpgx.py
Files
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Read SKILL.md
# ClinPGx Database
## Overview
ClinPGx (Clinical Pharmacogenomics Database) is a comprehensive resource for clinical pharmacogenomics information, successor to PharmGKB. It consolidates data from PharmGKB, CPIC, and PharmCAT, providing curated information on how genetic variation affects medication response. Access gene-drug pairs, clinical guidelines, allele functions, and drug labels for precision medicine applications.
## When to Use This Skill
This skill should be used when:
- **Gene-drug interactions**: Querying how genetic variants affect drug metabolism, efficacy, or toxicity
- **CPIC guidelines**: Accessing evidence-based clinical practice guidelines for pharmacogenetics
- **Allele information**: Retrieving allele function, frequency, and phenotype data
- **Drug labels**: Exploring FDA and other regulatory pharmacogenomic drug labelingclinvar-database
When your agent uses it
Query NCBI ClinVar for variant clinical significance. Search by gene/position, interpret pathogenicity classifications, access via E-utilities API or FTP, annotate VCFs, for genomic medicine.
What it loads into context
Frontmatter
64 tokensAlways available to the agent
SKILL.md
3,154 tokensWhen the skill is activated
Reference documents
6,644 tokensLoaded on request
Token counts are estimates. Scripts run separately; they are not counted as reference context.
The playbook
- Overview
- When to Use This Skill
- Core Capabilities
- Workflow Examples
- Important Limitations and Considerations
- Resources
Files
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Read SKILL.md
# ClinVar Database
## Overview
ClinVar is NCBI's freely accessible archive of reports on relationships between human genetic variants and phenotypes, with supporting evidence. The database aggregates information about genomic variation and its relationship to human health, providing standardized variant classifications used in clinical genetics and research.
## When to Use This Skill
This skill should be used when:
- Searching for variants by gene, condition, or clinical significance
- Interpreting clinical significance classifications (pathogenic, benign, VUS)
- Accessing ClinVar data programmatically via E-utilities API
- Downloading and processing bulk data from FTPcosmic-database
When your agent uses it
Access COSMIC cancer mutation database. Query somatic mutations, Cancer Gene Census, mutational signatures, gene fusions, for cancer research and precision oncology. Requires authentication.
What it loads into context
Frontmatter
57 tokensAlways available to the agent
SKILL.md
2,405 tokensWhen the skill is activated
Reference documents
1,613 tokensLoaded on request
Token counts are estimates. Scripts run separately; they are not counted as reference context.
The playbook
- Overview
- When to Use This Skill
- Prerequisites
- Quick Start
- Available Data Types
- Working with COSMIC Data
- Data Reference
- Helper Functions
- Troubleshooting
- Integration with Other Tools
- Additional Resources
- Citation
Scripts executed separately
scripts/download_cosmic.py
Files
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Read SKILL.md
# COSMIC Database
## Overview
COSMIC (Catalogue of Somatic Mutations in Cancer) is the world's largest and most comprehensive database for exploring somatic mutations in human cancer. Access COSMIC's extensive collection of cancer genomics data, including millions of mutations across thousands of cancer types, curated gene lists, mutational signatures, and clinical annotations programmatically.
## When to Use This Skill
This skill should be used when:
- Downloading cancer mutation data from COSMIC
- Accessing the Cancer Gene Census for curated cancer gene lists
- Retrieving mutational signature profiles
- Querying structural variants, copy number alterations, or gene fusions
- Analyzing drug resistance mutationsdepmap
When your agent uses it
Query the Cancer Dependency Map (DepMap) for cancer cell line gene dependency scores (CRISPR Chronos), drug sensitivity data, and gene effect profiles. Use for identifying cancer-specific vulnerabilities, synthetic lethal interactions, and validating oncology drug targets.
What it loads into context
Frontmatter
77 tokensAlways available to the agent
SKILL.md
2,701 tokensWhen the skill is activated
Reference documents
1,455 tokensLoaded on request
Token counts are estimates. Scripts run separately; they are not counted as reference context.
The playbook
- Overview
- When to Use This Skill
- Core Concepts
- Core Capabilities
- Query Workflows
- DepMap Data Files Reference
- Best Practices
- Additional Resources
Files
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Read SKILL.md
# DepMap — Cancer Dependency Map
## Overview
The Cancer Dependency Map (DepMap) project, run by the Broad Institute, systematically characterizes genetic dependencies across hundreds of cancer cell lines using genome-wide CRISPR knockout screens (DepMap CRISPR), RNA interference (RNAi), and compound sensitivity assays (PRISM). DepMap data is essential for:
- Identifying which genes are essential for specific cancer types
- Finding cancer-selective dependencies (therapeutic targets)
- Validating oncology drug targets
- Discovering synthetic lethal interactions
**Key resources:**
- DepMap Portal: https://depmap.org/portal/
- DepMap data downloads: https://depmap.org/portal/download/all/
- Python package: `depmap` (or access via API/downloads)fda-database
When your agent uses it
Query openFDA API for drugs, devices, adverse events, recalls, regulatory submissions (510k, PMA), substance identification (UNII), for FDA regulatory data analysis and safety research.
What it loads into context
Frontmatter
62 tokensAlways available to the agent
SKILL.md
3,455 tokensWhen the skill is activated
Reference documents
20,840 tokensLoaded on request
Token counts are estimates. Scripts run separately; they are not counted as reference context.
The playbook
- Overview
- When to Use This Skill
- Quick Start
- FDA Database Categories
- Common Query Patterns
- Working with Results
- Best Practices
- API Reference
- Scripts
- Additional Resources
- Support and Troubleshooting
Scripts executed separately
scripts/fda_examples.pyscripts/fda_query.py
Files
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Read SKILL.md
# FDA Database Access
## Overview
Access comprehensive FDA regulatory data through openFDA, the FDA's initiative to provide open APIs for public datasets. Query information about drugs, medical devices, foods, animal/veterinary products, and substances using Python with standardized interfaces.
**Key capabilities:**
- Query adverse events for drugs, devices, foods, and veterinary products
- Access product labeling, approvals, and regulatory submissions
- Monitor recalls and enforcement actions
- Look up National Drug Codes (NDC) and substance identifiers (UNII)
- Analyze device classifications and clearances (510k, PMA)
- Track drug shortages and supply issues
- Research chemical structures and substance relationshipshistolab
When your agent uses it
Lightweight WSI tile extraction and preprocessing. Use for basic slide processing tissue detection, tile extraction, stain normalization for H&E images. Best for simple pipelines, dataset preparation, quick tile-based analysis. For advanced spatial proteomics, multiplexed imaging, or deep learning pipelines use pathml.
What it loads into context
Frontmatter
84 tokensAlways available to the agent
SKILL.md
4,496 tokensWhen the skill is activated
Reference documents
12,048 tokensLoaded on request
Token counts are estimates. Scripts run separately; they are not counted as reference context.
The playbook
- Overview
- Installation
- Quick Start
- Core Capabilities
- Typical Workflows
- Best Practices
- Common Use Cases
- Troubleshooting
- Resources
Files
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Read SKILL.md
# Histolab
## Overview
Histolab is a Python library for processing whole slide images (WSI) in digital pathology. It automates tissue detection, extracts informative tiles from gigapixel images, and prepares datasets for deep learning pipelines. The library handles multiple WSI formats, implements sophisticated tissue segmentation, and provides flexible tile extraction strategies.
## Installation
```bash
uv pip install histolab
```
## Quick Start
imaging-data-commons
When your agent uses it
Query and download public cancer imaging data from NCI Imaging Data Commons using idc-index. Use for accessing large-scale radiology (CT, MR, PET) and pathology datasets for AI training or research. No authentication required. Query by metadata, visualize in browser, check licenses.
What it loads into context
Frontmatter
170 tokensAlways available to the agent
SKILL.md
8,051 tokensWhen the skill is activated
Reference documents
24,758 tokensLoaded on request
Token counts are estimates. Scripts run separately; they are not counted as reference context.
The playbook
- Overview
- When to Use This Skill
- Quick Navigation
- IDC Data Model
- Index Tables
- Data Access Options
- Installation and Setup
- Core Capabilities
- Common Use Cases
- Best Practices
- Troubleshooting
- Common SQL Query Patterns
- Related Skills
- Resources
Files
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Read SKILL.md
# Imaging Data Commons
## Overview
Use the `idc-index` Python package to query and download public cancer imaging data from the National Cancer Institute Imaging Data Commons (IDC). No authentication required for data access.
**Current IDC Data Version: v23** (always verify with `IDCClient().get_idc_version()`)
**Primary tool:** `idc-index` ([GitHub](https://github.com/imagingdatacommons/idc-index))
**CRITICAL - Check package version and upgrade if needed (run this FIRST):**
```python
import idc_indexmonarch-database
When your agent uses it
Query the Monarch Initiative knowledge graph for disease-gene-phenotype associations across species. Integrates OMIM, ORPHANET, HPO, ClinVar, and model organism databases. Use for rare disease gene discovery, phenotype-to-gene mapping, cross-species disease modeling, and HPO term lookup.
What it loads into context
Frontmatter
91 tokensAlways available to the agent
SKILL.md
3,336 tokensWhen the skill is activated
Reference documents
1,459 tokensLoaded on request
Token counts are estimates. Scripts run separately; they are not counted as reference context.
The playbook
- Overview
- When to Use This Skill
- Core Capabilities
- Query Workflows
- Common Identifier Prefixes
- Best Practices
- Additional Resources
Files
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Read SKILL.md
# Monarch Initiative Database
## Overview
The Monarch Initiative (https://monarchinitiative.org/) is a multi-species integrated knowledgebase that links genes, diseases, and phenotypes across humans and model organisms. It integrates data from over 40 sources including OMIM, ORPHANET, HPO (Human Phenotype Ontology), ClinVar, MGI (Mouse Genome Informatics), ZFIN (Zebrafish), RGD (Rat), FlyBase, and WormBase.
Monarch enables:
- Mapping phenotypes across species to identify candidate disease genes
- Finding all genes associated with a disease or phenotype
- Discovering model organisms for human diseases
- Navigating the HPO hierarchy for phenotype ontology queries
**Key resources:**
- Monarch portal: https://monarchinitiative.org/neurokit2
When your agent uses it
Comprehensive biosignal processing toolkit for analyzing physiological data including ECG, EEG, EDA, RSP, PPG, EMG, and EOG signals. Use this skill when processing cardiovascular signals, brain activity, electrodermal responses, respiratory patterns, muscle activity, or eye movements. Applicable for heart rate variability analysis, event-related potentials, complexity measures, autonomic nervous system assessment, psychophysiology research, and multi-modal physiological signal integration.
What it loads into context
Frontmatter
114 tokensAlways available to the agent
SKILL.md
2,847 tokensWhen the skill is activated
Reference documents
42,087 tokensLoaded on request
Token counts are estimates. Scripts run separately; they are not counted as reference context.
The playbook
- Overview
- When to Use This Skill
- Core Capabilities
- Analysis Modes
- Installation
- Common Workflows
- References
- Additional Resources
Files
SKILL.mdcore12,020 bytesSKILL.mdcore12,020 bytesSKILL.mdcore12,020 bytesSKILL.mdcore12,020 bytesSKILL.mdcore12,020 bytesSKILL.mdcore12,020 bytesSKILL.mdcore12,020 bytesSKILL.mdcore12,020 bytesSKILL.mdcore12,020 bytesSKILL.mdcore12,020 bytesSKILL.mdcore12,020 bytesSKILL.mdcore12,020 bytesSKILL.mdcore12,020 bytesSKILL.mdcore12,020 bytesSKILL.mdcore12,020 bytesSKILL.mdcore12,020 bytesSKILL.mdcore12,020 bytesSKILL.mdcore12,020 bytesSKILL.mdcore12,020 bytesreferences/bio_module.mddoc13,099 bytesreferences/complexity.mddoc17,352 bytesreferences/ecg_cardiac.mddoc10,810 bytesreferences/eda.mddoc14,733 bytesreferences/eeg.mddoc14,306 bytesreferences/emg.mddoc12,390 bytesreferences/eog.mddoc12,271 bytesreferences/epochs_events.mddoc14,793 bytesreferences/hrv.mddoc15,493 bytesreferences/ppg.mddoc11,168 bytesreferences/rsp.mddoc13,946 bytesreferences/signal_processing.mddoc16,652 bytes
Read SKILL.md
# NeuroKit2
## Overview
NeuroKit2 is a comprehensive Python toolkit for processing and analyzing physiological signals (biosignals). Use this skill to process cardiovascular, neural, autonomic, respiratory, and muscular signals for psychophysiology research, clinical applications, and human-computer interaction studies.
## When to Use This Skill
Apply this skill when working with:
- **Cardiac signals**: ECG, PPG, heart rate variability (HRV), pulse analysis
- **Brain signals**: EEG frequency bands, microstates, complexity, source localization
- **Autonomic signals**: Electrodermal activity (EDA/GSR), skin conductance responses (SCR)
- **Respiratory signals**: Breathing rate, respiratory variability (RRV), volume per time
- **Muscular signals**: EMG amplitude, muscle activation detectionneuropixels-analysis
When your agent uses it
Neuropixels neural recording analysis. Load SpikeGLX/OpenEphys data, preprocess, motion correction, Kilosort4 spike sorting, quality metrics, Allen/IBL curation, AI-assisted visual analysis, for Neuropixels 1.0/2.0 extracellular electrophysiology. Use when working with neural recordings, spike sorting, extracellular electrophysiology, or when the user mentions Neuropixels, SpikeGLX, Open Ephys, Kilosort, quality metrics, or unit curation.
What it loads into context
Frontmatter
128 tokensAlways available to the agent
SKILL.md
2,930 tokensWhen the skill is activated
Reference documents
23,298 tokensLoaded on request
Token counts are estimates. Scripts run separately; they are not counted as reference context.
The playbook
- Overview
- When to Use This Skill
- Supported Hardware & Formats
- Quick Start
- Standard Analysis Workflow
- Common Pitfalls and Best Practices
- Key Parameters to Adjust
- Bundled Resources
- Detailed Reference Guides
- Installation
- Project Structure
- Additional Resources
Scripts executed separately
assets/analysis_template.pyscripts/compute_metrics.pyscripts/explore_recording.pyscripts/export_to_phy.pyscripts/neuropixels_pipeline.pyscripts/preprocess_recording.pyscripts/run_sorting.py
Files
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Read SKILL.md
# Neuropixels Data Analysis
## Overview
Comprehensive toolkit for analyzing Neuropixels high-density neural recordings using current best practices from SpikeInterface, Allen Institute, and International Brain Laboratory (IBL). Supports the full workflow from raw data to publication-ready curated units.
## When to Use This Skill
This skill should be used when:
- Working with Neuropixels recordings (.ap.bin, .lf.bin, .meta files)
- Loading data from SpikeGLX, Open Ephys, or NWB formats
- Preprocessing neural recordings (filtering, CAR, bad channel detection)
- Detecting and correcting motion/drift in recordings
- Running spike sorting (Kilosort4, SpykingCircus2, Mountainsort5)pathml
When your agent uses it
Full-featured computational pathology toolkit. Use for advanced WSI analysis including multiplexed immunofluorescence (CODEX, Vectra), nucleus segmentation, tissue graph construction, and ML model training on pathology data. Supports 160+ slide formats. For simple tile extraction from H&E slides, histolab may be simpler.
What it loads into context
Frontmatter
93 tokensAlways available to the agent
SKILL.md
1,504 tokensWhen the skill is activated
Reference documents
25,735 tokensLoaded on request
Token counts are estimates. Scripts run separately; they are not counted as reference context.
The playbook
- Overview
- When to Use This Skill
- Core Capabilities
- Quick Start
- References to Detailed Documentation
- Resources
Files
SKILL.mdcore7,367 bytesSKILL.mdcore7,367 bytesSKILL.mdcore7,367 bytesreferences/data_management.mddoc19,018 bytesreferences/graphs.mddoc16,726 bytesreferences/image_loading.mddoc12,146 bytesreferences/machine_learning.mddoc19,946 bytesreferences/multiparametric.mddoc18,199 bytesreferences/preprocessing.mddoc18,209 bytes
Read SKILL.md
# PathML
## Overview
PathML is a comprehensive Python toolkit for computational pathology workflows, designed to facilitate machine learning and image analysis for whole-slide pathology images. The framework provides modular, composable tools for loading diverse slide formats, preprocessing images, constructing spatial graphs, training deep learning models, and analyzing multiparametric imaging data from technologies like CODEX and multiplex immunofluorescence.
## When to Use This Skill
Apply this skill for:
- Loading and processing whole-slide images (WSI) in various proprietary formats
- Preprocessing H&E stained tissue images with stain normalization
- Nucleus detection, segmentation, and classification workflows
- Building cell and tissue graphs for spatial analysis
- Training or deploying machine learning models (HoVer-Net, HACTNet) on pathology datapydicom
When your agent uses it
Python library for working with DICOM (Digital Imaging and Communications in Medicine) files. Use this skill when reading, writing, or modifying medical imaging data in DICOM format, extracting pixel data from medical images (CT, MRI, X-ray, ultrasound), anonymizing DICOM files, working with DICOM metadata and tags, converting DICOM images to other formats, handling compressed DICOM data, or processing medical imaging datasets. Applies to tasks involving medical image analysis, PACS systems, radiology workflows, and healthcare imaging applications.
What it loads into context
Frontmatter
141 tokensAlways available to the agent
SKILL.md
3,283 tokensWhen the skill is activated
Reference documents
5,832 tokensLoaded on request
Token counts are estimates. Scripts run separately; they are not counted as reference context.
The playbook
- Overview
- When to Use This Skill
- Installation
- Core Workflows
- Helper Scripts
- Reference Materials
- Common Issues and Solutions
- Best Practices
- Documentation
Scripts executed separately
scripts/anonymize_dicom.pyscripts/dicom_to_image.pyscripts/extract_metadata.py
Files
SKILL.mdcore13,189 bytesSKILL.mdcore13,189 bytesSKILL.mdcore13,189 bytesSKILL.mdcore13,189 bytesSKILL.mdcore13,189 bytesSKILL.mdcore13,189 bytesSKILL.mdcore13,189 bytesSKILL.mdcore13,189 bytesSKILL.mdcore13,189 bytesSKILL.mdcore13,189 bytesSKILL.mdcore13,189 bytesSKILL.mdcore13,189 bytesSKILL.mdcore13,189 bytesSKILL.mdcore13,189 bytesSKILL.mdcore13,189 bytesSKILL.mdcore13,189 bytesSKILL.mdcore13,189 bytesreferences/common_tags.mddoc10,246 bytesreferences/transfer_syntaxes.mddoc9,627 bytesscripts/anonymize_dicom.pyruns5,254 bytesscripts/dicom_to_image.pyruns6,101 bytesscripts/extract_metadata.pyruns5,686 bytes
Read SKILL.md
# Pydicom
## Overview
Pydicom is a pure Python package for working with DICOM files, the standard format for medical imaging data. This skill provides guidance on reading, writing, and manipulating DICOM files, including working with pixel data, metadata, and various compression formats.
## When to Use This Skill
Use this skill when working with:
- Medical imaging files (CT, MRI, X-ray, ultrasound, PET, etc.)
- DICOM datasets requiring metadata extraction or modification
- Pixel data extraction and image processing from medical scans
- DICOM anonymization for research or data sharing
- Converting DICOM files to standard image formatspyhealth
When your agent uses it
Comprehensive healthcare AI toolkit for developing, testing, and deploying machine learning models with clinical data. This skill should be used when working with electronic health records (EHR), clinical prediction tasks (mortality, readmission, drug recommendation), medical coding systems (ICD, NDC, ATC), physiological signals (EEG, ECG), healthcare datasets (MIMIC-III/IV, eICU, OMOP), or implementing deep learning models for healthcare applications (RETAIN, SafeDrug, Transformer, GNN).
What it loads into context
Frontmatter
128 tokensAlways available to the agent
SKILL.md
4,048 tokensWhen the skill is activated
Reference documents
16,869 tokensLoaded on request
Token counts are estimates. Scripts run separately; they are not counted as reference context.
The playbook
- Overview
- When to Use This Skill
- Core Capabilities
- Quick Start Workflow
- Detailed Documentation
- Installation
- Common Use Cases
- Best Practices
- Limitations and Considerations
- Troubleshooting
- Example: Complete Workflow
- Resources
Files
SKILL.mdcore17,653 bytesSKILL.mdcore17,653 bytesSKILL.mdcore17,653 bytesSKILL.mdcore17,653 bytesSKILL.mdcore17,653 bytesSKILL.mdcore17,653 bytesreferences/datasets.mddoc5,468 bytesreferences/medical_coding.mddoc8,476 bytesreferences/models.mddoc13,776 bytesreferences/preprocessing.mddoc14,388 bytesreferences/tasks.mddoc11,404 bytesreferences/training_evaluation.mddoc16,982 bytes
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# PyHealth: Healthcare AI Toolkit
## Overview
PyHealth is a comprehensive Python library for healthcare AI that provides specialized tools, models, and datasets for clinical machine learning. Use this skill when developing healthcare prediction models, processing clinical data, working with medical coding systems, or deploying AI solutions in healthcare settings.
## When to Use This Skill
Invoke this skill when:
- **Working with healthcare datasets**: MIMIC-III, MIMIC-IV, eICU, OMOP, sleep EEG data, medical images
- **Clinical prediction tasks**: Mortality prediction, hospital readmission, length of stay, drug recommendation
- **Medical coding**: Translating between ICD-9/10, NDC, RxNorm, ATC coding systems
- **Processing clinical data**: Sequential events, physiological signals, clinical text, medical imagestreatment-plans
When your agent uses it
Generate concise (3-4 page), focused medical treatment plans in LaTeX/PDF format for all clinical specialties. Supports general medical treatment, rehabilitation therapy, mental health care, chronic disease management, perioperative care, and pain management. Includes SMART goal frameworks, evidence-based interventions with minimal text citations, regulatory compliance (HIPAA), and professional formatting. Prioritizes brevity and clinical actionability.
What it loads into context
Frontmatter
113 tokensAlways available to the agent
SKILL.md
11,589 tokensWhen the skill is activated
Reference documents
30,223 tokensLoaded on request
Token counts are estimates. Scripts run separately; they are not counted as reference context.
The playbook
- Overview
- When to Use This Skill
- Visual Enhancement with Scientific Schematics
- Document Format and Best Practices
- Core Capabilities
- Best Practices
- LaTeX Template Usage
- Validation and Quality Assurance
- Integration with Other Skills
- Common Use Cases
- Professional Standards and Guidelines
- Timeline Generation
- Support and Resources
- Professional Document Styling
- Ethical Considerations
- License
Scripts executed separately
scripts/check_completeness.pyscripts/generate_template.pyscripts/timeline_generator.pyscripts/validate_treatment_plan.py
Files
SKILL.mdcore52,693 bytesSKILL.mdcore52,693 bytesSKILL.mdcore52,693 bytesSKILL.mdcore52,693 bytesSKILL.mdcore52,693 bytesSKILL.mdcore52,693 bytesSKILL.mdcore52,693 bytesSKILL.mdcore52,693 bytesSKILL.mdcore52,693 bytesSKILL.mdcore52,693 bytesSKILL.mdcore52,693 bytesSKILL.mdcore52,693 bytesSKILL.mdcore52,693 bytesSKILL.mdcore52,693 bytesSKILL.mdcore52,693 bytesSKILL.mdcore52,693 bytesSKILL.mdcore52,693 bytesSKILL.mdcore52,693 bytesSKILL.mdcore52,693 bytesassets/STYLING_QUICK_REFERENCE.mddoc3,912 bytesassets/chronic_disease_management_plan.texasset29,371 bytesassets/general_medical_treatment_plan.texasset23,289 bytesassets/medical_treatment_plan.styasset5,404 bytesassets/mental_health_treatment_plan.texasset34,509 bytesassets/one_page_treatment_plan.texasset5,377 bytesassets/pain_management_plan.texasset33,326 bytesassets/perioperative_care_plan.texasset30,005 bytesassets/quality_checklist.mddoc15,081 bytesassets/rehabilitation_treatment_plan.texasset30,462 bytesreferences/README.mddoc16,970 bytesreferences/goal_setting_frameworks.mddoc16,554 bytesreferences/intervention_guidelines.mddoc19,024 bytesreferences/regulatory_compliance.mddoc17,565 bytesreferences/specialty_specific_guidelines.mddoc26,678 bytesreferences/treatment_plan_standards.mddoc16,643 bytesscripts/check_completeness.pyruns9,919 bytesscripts/generate_template.pyruns7,490 bytesscripts/timeline_generator.pyruns12,631 bytesscripts/validate_treatment_plan.pyruns12,392 bytes
Read SKILL.md
# Treatment Plan Writing
## Overview
Treatment plan writing is the systematic documentation of clinical care strategies designed to address patient health conditions through evidence-based interventions, measurable goals, and structured follow-up. This skill provides comprehensive LaTeX templates and validation tools for creating **concise, focused** treatment plans (3-4 pages standard) across all medical specialties with full regulatory compliance.
**Critical Principles:**
1. **CONCISE & ACTIONABLE**: Treatment plans default to 3-4 pages maximum, focusing only on clinically essential information that impacts care decisions
2. **Patient-Centered**: Plans must be evidence-based, measurable, and compliant with healthcare regulations (HIPAA, documentation standards)
3. **Minimal Citations**: Use brief in-text citations only when needed to support clinical recommendations; avoid extensive bibliographies
Every treatment plan should include clear goals, specific interventions, defined timelines, monitoring parameters, and expected outcomes that align with patient preferences and current clinical guidelines - all presented as efficiently as possible.
## When to Use This Skilladaptyv
When your agent uses it
Cloud laboratory platform for automated protein testing and validation. Use when designing proteins and needing experimental validation including binding assays, expression testing, thermostability measurements, enzyme activity assays, or protein sequence optimization. Also use for submitting experiments via API, tracking experiment status, downloading results, optimizing protein sequences for better expression using computational tools (NetSolP, SoluProt, SolubleMPNN, ESM), or managing protein design workflows with wet-lab validation.
What it loads into context
Frontmatter
113 tokensAlways available to the agent
SKILL.md
806 tokensWhen the skill is activated
Reference documents
13,585 tokensLoaded on request
Token counts are estimates. Scripts run separately; they are not counted as reference context.
The playbook
- Quick Start
- Available Experiment Types
- Protein Sequence Optimization
- API Reference
- Examples
- Important Notes
Files
SKILL.mdcore3,742 bytesSKILL.mdcore3,742 bytesSKILL.mdcore3,742 bytesSKILL.mdcore3,742 bytesreference/api_reference.mddoc6,665 bytesreference/examples.mddoc23,091 bytesreference/experiments.mddoc9,947 bytesreference/protein_optimization.mddoc17,861 bytes
Read SKILL.md
# Adaptyv
Adaptyv is a cloud laboratory platform that provides automated protein testing and validation services. Submit protein sequences via API or web interface and receive experimental results in approximately 21 days.
## Quick Start
### Authentication Setup
Adaptyv requires API authentication. Set up your credentials:
1. Contact support@adaptyvbio.com to request API access (platform is in alpha/beta)
2. Receive your API access token
3. Set environment variable:
alphafold-database
When your agent uses it
Access AlphaFold 200M+ AI-predicted protein structures. Retrieve structures by UniProt ID, download PDB/mmCIF files, analyze confidence metrics (pLDDT, PAE), for drug discovery and structural biology.
What it loads into context
Frontmatter
73 tokensAlways available to the agent
SKILL.md
4,023 tokensWhen the skill is activated
Reference documents
3,884 tokensLoaded on request
Token counts are estimates. Scripts run separately; they are not counted as reference context.
The playbook
- Overview
- When to Use This Skill
- Core Capabilities
- Installation and Setup
- Common Use Cases
- Key Concepts
- Confidence Interpretation Guidelines
- Resources
- Important Notes
- Additional Resources
Files
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# AlphaFold Database
## Overview
AlphaFold DB is a public repository of AI-predicted 3D protein structures for over 200 million proteins, maintained by DeepMind and EMBL-EBI. Access structure predictions with confidence metrics, download coordinate files, retrieve bulk datasets, and integrate predictions into computational workflows.
## When to Use This Skill
This skill should be used when working with AI-predicted protein structures in scenarios such as:
- Retrieving protein structure predictions by UniProt ID or protein name
- Downloading PDB/mmCIF coordinate files for structural analysis
- Analyzing prediction confidence metrics (pLDDT, PAE) to assess reliability
- Accessing bulk proteome datasets via Google Cloud Platformesm
When your agent uses it
Comprehensive toolkit for protein language models including ESM3 (generative multimodal protein design across sequence, structure, and function) and ESM C (efficient protein embeddings and representations). Use this skill when working with protein sequences, structures, or function prediction; designing novel proteins; generating protein embeddings; performing inverse folding; or conducting protein engineering tasks. Supports both local model usage and cloud-based Forge API for scalable inference.
What it loads into context
Frontmatter
105 tokensAlways available to the agent
SKILL.md
2,356 tokensWhen the skill is activated
Reference documents
15,581 tokensLoaded on request
Token counts are estimates. Scripts run separately; they are not counted as reference context.
The playbook
- Overview
- Core Capabilities
- Model Selection Guide
- Installation
- Common Workflows
- References
- Best Practices
- Resources and Documentation
- Responsible Use
Files
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# ESM: Evolutionary Scale Modeling
## Overview
ESM provides state-of-the-art protein language models for understanding, generating, and designing proteins. This skill enables working with two model families: ESM3 for generative protein design across sequence, structure, and function, and ESM C for efficient protein representation learning and embeddings.
## Core Capabilities
### 1. Protein Sequence Generation with ESM3
Generate novel protein sequences with desired properties using multimodal generative modeling.
**When to use:**
- Designing proteins with specific functional propertiesglycoengineering
When your agent uses it
Analyze and engineer protein glycosylation. Scan sequences for N-glycosylation sequons (N-X-S/T), predict O-glycosylation hotspots, and access curated glycoengineering tools (NetOGlyc, GlycoShield, GlycoWorkbench). For glycoprotein engineering, therapeutic antibody optimization, and vaccine design.
What it loads into context
Frontmatter
94 tokensAlways available to the agent
SKILL.md
3,520 tokensWhen the skill is activated
Reference documents
1,626 tokensLoaded on request
Token counts are estimates. Scripts run separately; they are not counted as reference context.
The playbook
- Overview
- When to Use This Skill
- N-Glycosylation Sequon Analysis
- O-Glycosylation Analysis
- External Glycoengineering Tools
- Key Glycoengineering Strategies
- Glycan Notation
- Best Practices
- Additional Resources
Files
SKILL.mdcore12,462 bytesSKILL.mdcore12,462 bytesSKILL.mdcore12,462 bytesSKILL.mdcore12,462 bytesSKILL.mdcore12,462 bytesSKILL.mdcore12,462 bytesSKILL.mdcore12,462 bytesreferences/glycan_databases.mddoc5,758 bytes
Read SKILL.md
# Glycoengineering
## Overview
Glycosylation is the most common and complex post-translational modification (PTM) of proteins, affecting over 50% of all human proteins. Glycans regulate protein folding, stability, immune recognition, receptor interactions, and pharmacokinetics of therapeutic proteins. Glycoengineering involves rational modification of glycosylation patterns for improved therapeutic efficacy, stability, or immune evasion.
**Two major glycosylation types:**
- **N-glycosylation**: Attached to asparagine (N) in the sequon N-X-[S/T] where X ≠ Proline; occurs in the ER/Golgi
- **O-glycosylation**: Attached to serine (S) or threonine (T); no strict consensus motif; primarily GalNAc initiation
## When to Use This Skill
Use this skill when:
interpro-database
When your agent uses it
Query InterPro for protein family, domain, and functional site annotations. Integrates Pfam, PANTHER, PRINTS, SMART, SUPERFAMILY, and 11 other member databases. Use for protein function prediction, domain architecture analysis, evolutionary classification, and GO term mapping.
What it loads into context
Frontmatter
86 tokensAlways available to the agent
SKILL.md
2,742 tokensWhen the skill is activated
Reference documents
1,419 tokensLoaded on request
Token counts are estimates. Scripts run separately; they are not counted as reference context.
The playbook
- Overview
- When to Use This Skill
- Core Capabilities
- Query Workflows
- API Endpoint Summary
- Member Databases
- Best Practices
- Additional Resources
Files
SKILL.mdcore10,785 bytesSKILL.mdcore10,785 bytesSKILL.mdcore10,785 bytesSKILL.mdcore10,785 bytesSKILL.mdcore10,785 bytesSKILL.mdcore10,785 bytesSKILL.mdcore10,785 bytesSKILL.mdcore10,785 bytesSKILL.mdcore10,785 bytesSKILL.mdcore10,785 bytesreferences/domain_analysis.mddoc4,909 bytes
Read SKILL.md
# InterPro Database
## Overview
InterPro (https://www.ebi.ac.uk/interpro/) is a comprehensive resource for protein family and domain classification maintained by EMBL-EBI. It integrates signatures from 13 member databases including Pfam, PANTHER, PRINTS, ProSite, SMART, TIGRFAM, SUPERFAMILY, CDD, and others, providing a unified view of protein functional annotations for over 100 million protein sequences.
InterPro classifies proteins into:
- **Families**: Groups of proteins sharing common ancestry and function
- **Domains**: Independently folding structural/functional units
- **Homologous superfamilies**: Structurally similar protein regions
- **Repeats**: Short tandem sequences
- **Sites**: Functional sites (active, binding, PTM)
**Key resources:**pdb-database
When your agent uses it
Access RCSB PDB for 3D protein/nucleic acid structures. Search by text/sequence/structure, download coordinates (PDB/mmCIF), retrieve metadata, for structural biology and drug discovery.
What it loads into context
Frontmatter
67 tokensAlways available to the agent
SKILL.md
2,266 tokensWhen the skill is activated
Reference documents
3,961 tokensLoaded on request
Token counts are estimates. Scripts run separately; they are not counted as reference context.
The playbook
- Overview
- When to Use This Skill
- Core Capabilities
- Python Package Installation
- Common Use Cases
- Key Concepts
- Resources
- Additional Resources
Files
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Read SKILL.md
# PDB Database
## Overview
RCSB PDB is the worldwide repository for 3D structural data of biological macromolecules. Search for structures, retrieve coordinates and metadata, perform sequence and structure similarity searches across 200,000+ experimentally determined structures and computed models.
## When to Use This Skill
This skill should be used when:
- Searching for protein or nucleic acid 3D structures by text, sequence, or structural similarity
- Downloading coordinate files in PDB, mmCIF, or BinaryCIF formats
- Retrieving structural metadata, experimental methods, or quality metrics
- Performing batch operations across multiple structures
- Integrating PDB data into computational workflows for drug discovery, protein engineering, or structural biology researchuniprot-database
When your agent uses it
Direct REST API access to UniProt. Protein searches, FASTA retrieval, ID mapping, Swiss-Prot/TrEMBL. For Python workflows with multiple databases, prefer bioservices (unified interface to 40+ services). Use this for direct HTTP/REST work or UniProt-specific control.
What it loads into context
Frontmatter
85 tokensAlways available to the agent
SKILL.md
1,645 tokensWhen the skill is activated
Reference documents
8,879 tokensLoaded on request
Token counts are estimates. Scripts run separately; they are not counted as reference context.
The playbook
- Overview
- When to Use This Skill
- Core Capabilities
- Python Implementation
- Query Syntax Examples
- Best Practices
- Resources
- Additional Resources
Scripts executed separately
scripts/uniprot_client.py
Files
SKILL.mdcore6,788 bytesSKILL.mdcore6,788 bytesreferences/api_examples.mddoc10,348 bytesreferences/api_fields.mddoc7,729 bytesreferences/id_mapping_databases.mddoc6,682 bytesreferences/query_syntax.mddoc6,833 bytesscripts/uniprot_client.pyruns9,982 bytes
Read SKILL.md
# UniProt Database
## Overview
UniProt is the world's leading comprehensive protein sequence and functional information resource. Search proteins by name, gene, or accession, retrieve sequences in FASTA format, perform ID mapping across databases, access Swiss-Prot/TrEMBL annotations via REST API for protein analysis.
## When to Use This Skill
This skill should be used when:
- Searching for protein entries by name, gene symbol, accession, or organism
- Retrieving protein sequences in FASTA or other formats
- Mapping identifiers between UniProt and external databases (Ensembl, RefSeq, PDB, etc.)
- Accessing protein annotations including GO terms, domains, and functional descriptions
- Batch retrieving multiple protein entries efficientlyaeon
When your agent uses it
This skill should be used for time series machine learning tasks including classification, regression, clustering, forecasting, anomaly detection, segmentation, and similarity search. Use when working with temporal data, sequential patterns, or time-indexed observations requiring specialized algorithms beyond standard ML approaches. Particularly suited for univariate and multivariate time series analysis with scikit-learn compatible APIs.
What it loads into context
Frontmatter
97 tokensAlways available to the agent
SKILL.md
2,509 tokensWhen the skill is activated
Reference documents
15,062 tokensLoaded on request
Token counts are estimates. Scripts run separately; they are not counted as reference context.
The playbook
- Overview
- When to Use This Skill
- Installation
- Core Capabilities
- Feature Extraction and Transformations
- Distance Metrics
- Deep Learning Networks
- Datasets and Benchmarking
- Common Workflows
- Best Practices
- Reference Documentation
- Additional Resources
Files
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# Aeon Time Series Machine Learning
## Overview
Aeon is a scikit-learn compatible Python toolkit for time series machine learning. It provides state-of-the-art algorithms for classification, regression, clustering, forecasting, anomaly detection, segmentation, and similarity search.
## When to Use This Skill
Apply this skill when:
- Classifying or predicting from time series data
- Detecting anomalies or change points in temporal sequences
- Clustering similar time series patterns
- Forecasting future values
- Finding repeated patterns (motifs) or unusual subsequences (discords)hypogenic
When your agent uses it
Automated LLM-driven hypothesis generation and testing on tabular datasets. Use when you want to systematically explore hypotheses about patterns in empirical data (e.g., deception detection, content analysis). Combines literature insights with data-driven hypothesis testing. For manual hypothesis formulation use hypothesis-generation; for creative ideation use scientific-brainstorming.
What it loads into context
Frontmatter
88 tokensAlways available to the agent
SKILL.md
4,897 tokensWhen the skill is activated
Reference documents
0 tokensLoaded on request
Token counts are estimates. Scripts run separately; they are not counted as reference context.
The playbook
- Overview
- Quick Start
- When to Use This Skill
- Key Features
- Core Capabilities
- Installation
- Dataset Format
- Configuration
- Literature Processing (HypoRefine/Union Methods)
- CLI Usage
- Python API Usage
- Workflow Examples
- Performance Optimization
- Expected Outcomes
- Troubleshooting
- Creating Custom Tasks
- Repository Structure
- Related Publications
- Additional Resources
- Local Resources
Files
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# Hypogenic
## Overview
Hypogenic provides automated hypothesis generation and testing using large language models to accelerate scientific discovery. The framework supports three approaches: HypoGeniC (data-driven hypothesis generation), HypoRefine (synergistic literature and data integration), and Union methods (mechanistic combination of literature and data-driven hypotheses).
## Quick Start
Get started with Hypogenic in minutes:
```bash
# Install the package
uv pip install hypogenic
pufferlib
When your agent uses it
High-performance reinforcement learning framework optimized for speed and scale. Use when you need fast parallel training, vectorized environments, multi-agent systems, or integration with game environments (Atari, Procgen, NetHack). Achieves 2-10x speedups over standard implementations. For quick prototyping or standard algorithm implementations with extensive documentation, use stable-baselines3 instead.
What it loads into context
Frontmatter
98 tokensAlways available to the agent
SKILL.md
3,008 tokensWhen the skill is activated
Reference documents
16,598 tokensLoaded on request
Token counts are estimates. Scripts run separately; they are not counted as reference context.
The playbook
- Overview
- When to Use This Skill
- Core Capabilities
- Quick Start Workflow
- Resources
- Tips for Success
- Common Use Cases
- Installation
- Documentation
Scripts executed separately
scripts/env_template.pyscripts/train_template.py
Files
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# PufferLib - High-Performance Reinforcement Learning
## Overview
PufferLib is a high-performance reinforcement learning library designed for fast parallel environment simulation and training. It achieves training at millions of steps per second through optimized vectorization, native multi-agent support, and efficient PPO implementation (PuffeRL). The library provides the Ocean suite of 20+ environments and seamless integration with Gymnasium, PettingZoo, and specialized RL frameworks.
## When to Use This Skill
Use this skill when:
- **Training RL agents** with PPO on any environment (single or multi-agent)
- **Creating custom environments** using the PufferEnv API
- **Optimizing performance** for parallel environment simulation (vectorization)
- **Integrating existing environments** from Gymnasium, PettingZoo, Atari, Procgen, etc.
- **Developing policies** with CNN, LSTM, or custom architecturespymc
When your agent uses it
Bayesian modeling with PyMC. Build hierarchical models, MCMC (NUTS), variational inference, LOO/WAIC comparison, posterior checks, for probabilistic programming and inference.
What it loads into context
Frontmatter
67 tokensAlways available to the agent
SKILL.md
4,121 tokensWhen the skill is activated
Reference documents
9,357 tokensLoaded on request
Token counts are estimates. Scripts run separately; they are not counted as reference context.
The playbook
- Overview
- When to Use This Skill
- Standard Bayesian Workflow
- Common Model Patterns
- Model Comparison
- Distribution Selection Guide
- Sampling and Inference
- Diagnostic Scripts
- Common Issues and Solutions
- Best Practices
- Resources
- Quick Reference
- Additional Notes
Scripts executed separately
assets/hierarchical_model_template.pyassets/linear_regression_template.pyscripts/model_comparison.pyscripts/model_diagnostics.py
Files
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# PyMC Bayesian Modeling
## Overview
PyMC is a Python library for Bayesian modeling and probabilistic programming. Build, fit, validate, and compare Bayesian models using PyMC's modern API (version 5.x+), including hierarchical models, MCMC sampling (NUTS), variational inference, and model comparison (LOO, WAIC).
## When to Use This Skill
This skill should be used when:
- Building Bayesian models (linear/logistic regression, hierarchical models, time series, etc.)
- Performing MCMC sampling or variational inference
- Conducting prior/posterior predictive checks
- Diagnosing sampling issues (divergences, convergence, ESS)
- Comparing multiple models using information criteria (LOO, WAIC)pymoo
When your agent uses it
Multi-objective optimization framework. NSGA-II, NSGA-III, MOEA/D, Pareto fronts, constraint handling, benchmarks (ZDT, DTLZ), for engineering design and optimization problems.
What it loads into context
Frontmatter
69 tokensAlways available to the agent
SKILL.md
4,073 tokensWhen the skill is activated
Reference documents
10,112 tokensLoaded on request
Token counts are estimates. Scripts run separately; they are not counted as reference context.
The playbook
- Overview
- When to Use This Skill
- Core Concepts
- Quick Start Workflows
- Algorithm Selection Guide
- Benchmark Problems
- Genetic Operator Customization
- Performance and Troubleshooting
- Resources
- Additional Notes
Scripts executed separately
scripts/custom_problem_example.pyscripts/decision_making_example.pyscripts/many_objective_example.pyscripts/multi_objective_example.pyscripts/single_objective_example.py
Files
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# Pymoo - Multi-Objective Optimization in Python
## Overview
Pymoo is a comprehensive Python framework for optimization with emphasis on multi-objective problems. Solve single and multi-objective optimization using state-of-the-art algorithms (NSGA-II/III, MOEA/D), benchmark problems (ZDT, DTLZ), customizable genetic operators, and multi-criteria decision making methods. Excels at finding trade-off solutions (Pareto fronts) for problems with conflicting objectives.
## When to Use This Skill
This skill should be used when:
- Solving optimization problems with one or multiple objectives
- Finding Pareto-optimal solutions and analyzing trade-offs
- Implementing evolutionary algorithms (GA, DE, PSO, NSGA-II/III)
- Working with constrained optimization problems
- Benchmarking algorithms on standard test problems (ZDT, DTLZ, WFG)pytorch-lightning
When your agent uses it
Deep learning framework (PyTorch Lightning). Organize PyTorch code into LightningModules, configure Trainers for multi-GPU/TPU, implement data pipelines, callbacks, logging (W&B, TensorBoard), distributed training (DDP, FSDP, DeepSpeed), for scalable neural network training.
What it loads into context
Frontmatter
89 tokensAlways available to the agent
SKILL.md
1,488 tokensWhen the skill is activated
Reference documents
24,902 tokensLoaded on request
Token counts are estimates. Scripts run separately; they are not counted as reference context.
The playbook
- Overview
- When to Use This Skill
- Core Capabilities
- Quick Workflow
- Resources
Scripts executed separately
scripts/quick_trainer_setup.pyscripts/template_datamodule.pyscripts/template_lightning_module.py
Files
SKILL.mdcore6,675 bytesSKILL.mdcore6,675 bytesSKILL.mdcore6,675 bytesSKILL.mdcore6,675 bytesreferences/best_practices.mddoc16,071 bytesreferences/callbacks.mddoc15,540 bytesreferences/data_module.mddoc14,497 bytesreferences/distributed_training.mddoc14,213 bytesreferences/lightning_module.mddoc13,728 bytesreferences/logging.mddoc14,259 bytesreferences/trainer.mddoc14,784 bytesscripts/quick_trainer_setup.pyruns12,985 bytesscripts/template_datamodule.pyruns9,354 bytesscripts/template_lightning_module.pyruns5,886 bytes
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# PyTorch Lightning
## Overview
PyTorch Lightning is a deep learning framework that organizes PyTorch code to eliminate boilerplate while maintaining full flexibility. Automate training workflows, multi-device orchestration, and implement best practices for neural network training and scaling across multiple GPUs/TPUs.
## When to Use This Skill
This skill should be used when:
- Building, training, or deploying neural networks using PyTorch Lightning
- Organizing PyTorch code into LightningModules
- Configuring Trainers for multi-GPU/TPU training
- Implementing data pipelines with LightningDataModules
- Working with callbacks, logging, and distributed training strategies (DDP, FSDP, DeepSpeed)scikit-learn
When your agent uses it
Machine learning in Python with scikit-learn. Use when working with supervised learning (classification, regression), unsupervised learning (clustering, dimensionality reduction), model evaluation, hyperparameter tuning, preprocessing, or building ML pipelines. Provides comprehensive reference documentation for algorithms, preprocessing techniques, pipelines, and best practices.
What it loads into context
Frontmatter
91 tokensAlways available to the agent
SKILL.md
3,430 tokensWhen the skill is activated
Reference documents
20,407 tokensLoaded on request
Token counts are estimates. Scripts run separately; they are not counted as reference context.
The playbook
- Overview
- Installation
- When to Use This Skill
- Quick Start
- Core Capabilities
- Example Scripts
- Reference Documentation
- Common Workflows
- Best Practices
- Troubleshooting Common Issues
- Additional Resources
Scripts executed separately
scripts/classification_pipeline.pyscripts/clustering_analysis.py
Files
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# Scikit-learn
## Overview
This skill provides comprehensive guidance for machine learning tasks using scikit-learn, the industry-standard Python library for classical machine learning. Use this skill for classification, regression, clustering, dimensionality reduction, preprocessing, model evaluation, and building production-ready ML pipelines.
## Installation
```bash
# Install scikit-learn using uv
uv uv pip install scikit-learn
# Optional: Install visualization dependencies
uv uv pip install matplotlib seabornscikit-survival
When your agent uses it
Comprehensive toolkit for survival analysis and time-to-event modeling in Python using scikit-survival. Use this skill when working with censored survival data, performing time-to-event analysis, fitting Cox models, Random Survival Forests, Gradient Boosting models, or Survival SVMs, evaluating survival predictions with concordance index or Brier score, handling competing risks, or implementing any survival analysis workflow with the scikit-survival library.
What it loads into context
Frontmatter
114 tokensAlways available to the agent
SKILL.md
3,606 tokensWhen the skill is activated
Reference documents
15,482 tokensLoaded on request
Token counts are estimates. Scripts run separately; they are not counted as reference context.
The playbook
- Overview
- When to Use This Skill
- Core Capabilities
- Typical Workflows
- Integration with scikit-learn
- Best Practices
- Common Pitfalls to Avoid
- Reference Files
- Additional Resources
- Quick Reference: Key Imports
Files
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# scikit-survival: Survival Analysis in Python
## Overview
scikit-survival is a Python library for survival analysis built on top of scikit-learn. It provides specialized tools for time-to-event analysis, handling the unique challenge of censored data where some observations are only partially known.
Survival analysis aims to establish connections between covariates and the time of an event, accounting for censored records (particularly right-censored data from studies where participants don't experience events during observation periods).
## When to Use This Skill
Use this skill when:
- Performing survival analysis or time-to-event modeling
- Working with censored data (right-censored, left-censored, or interval-censored)
- Fitting Cox proportional hazards models (standard or penalized)shap
When your agent uses it
Model interpretability and explainability using SHAP (SHapley Additive exPlanations). Use this skill when explaining machine learning model predictions, computing feature importance, generating SHAP plots (waterfall, beeswarm, bar, scatter, force, heatmap), debugging models, analyzing model bias or fairness, comparing models, or implementing explainable AI. Works with tree-based models (XGBoost, LightGBM, Random Forest), deep learning (TensorFlow, PyTorch), linear models, and any black-box model.
What it loads into context
Frontmatter
128 tokensAlways available to the agent
SKILL.md
4,303 tokensWhen the skill is activated
Reference documents
15,605 tokensLoaded on request
Token counts are estimates. Scripts run separately; they are not counted as reference context.
The playbook
- Overview
- When to Use This Skill
- Quick Start Guide
- Core Workflows
- Key Concepts
- Common Patterns
- Performance Optimization
- Troubleshooting
- Integration with Other Tools
- Reference Documentation
- Usage Guidelines
- Best Practices Summary
- Installation
- Additional Resources
Files
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# SHAP (SHapley Additive exPlanations)
## Overview
SHAP is a unified approach to explain machine learning model outputs using Shapley values from cooperative game theory. This skill provides comprehensive guidance for:
- Computing SHAP values for any model type
- Creating visualizations to understand feature importance
- Debugging and validating model behavior
- Analyzing fairness and bias
- Implementing explainable AI in production
SHAP works with all model types: tree-based models (XGBoost, LightGBM, CatBoost, Random Forest), deep learning models (TensorFlow, PyTorch, Keras), linear models, and black-box models.
stable-baselines3
When your agent uses it
Production-ready reinforcement learning algorithms (PPO, SAC, DQN, TD3, DDPG, A2C) with scikit-learn-like API. Use for standard RL experiments, quick prototyping, and well-documented algorithm implementations. Best for single-agent RL with Gymnasium environments. For high-performance parallel training, multi-agent systems, or custom vectorized environments, use pufferlib instead.
What it loads into context
Frontmatter
107 tokensAlways available to the agent
SKILL.md
2,132 tokensWhen the skill is activated
Reference documents
13,141 tokensLoaded on request
Token counts are estimates. Scripts run separately; they are not counted as reference context.
The playbook
- Overview
- Core Capabilities
- Workflow Guidance
- Resources
- Installation
Scripts executed separately
scripts/custom_env_template.pyscripts/evaluate_agent.pyscripts/train_rl_agent.py
Files
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# Stable Baselines3
## Overview
Stable Baselines3 (SB3) is a PyTorch-based library providing reliable implementations of reinforcement learning algorithms. This skill provides comprehensive guidance for training RL agents, creating custom environments, implementing callbacks, and optimizing training workflows using SB3's unified API.
## Core Capabilities
### 1. Training RL Agents
**Basic Training Pattern:**
```python
import gymnasium as gymtimesfm-forecasting
When your agent uses it
Zero-shot time series forecasting with Google's TimesFM foundation model. Use for any univariate time series (sales, sensors, energy, vitals, weather) without training a custom model. Supports CSV/DataFrame/array inputs with point forecasts and prediction intervals. Includes a preflight system checker script to verify RAM/GPU before first use.
What it loads into context
Frontmatter
125 tokensAlways available to the agent
SKILL.md
8,281 tokensWhen the skill is activated
Reference documents
8,054 tokensLoaded on request
Token counts are estimates. Scripts run separately; they are not counted as reference context.
The playbook
- Overview
- When to Use This Skill
- ⚠<U+FE0F> Mandatory Preflight: System Requirements Check
- 🔧 Installation
- 🎯 Quick Start
- 📊 Understanding the Output
- 🔧 ForecastConfig Reference
- 📋 Common Workflows
- ⚙<U+FE0F> Performance Tuning
- 🔗 Integration with Other Skills
- 📚 Available Scripts
- 📖 Reference Documentation
- Common Pitfalls
- Model Versions
- Resources
- Examples
- Quality Checklist
- Common Mistakes
- Validation & Verification
Scripts executed separately
examples/anomaly-detection/detect_anomalies.pyexamples/covariates-forecasting/demo_covariates.pyexamples/global-temperature/generate_animation_data.pyexamples/global-temperature/generate_gif.pyexamples/global-temperature/generate_html.pyexamples/global-temperature/run_example.shexamples/global-temperature/run_forecast.pyexamples/global-temperature/visualize_forecast.pyscripts/check_system.pyscripts/forecast_csv.py
Files
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Read SKILL.md
# TimesFM Forecasting
## Overview
TimesFM (Time Series Foundation Model) is a pretrained decoder-only foundation model
developed by Google Research for time-series forecasting. It works **zero-shot** — feed it
any univariate time series and it returns point forecasts with calibrated quantile
prediction intervals, no training required.
This skill wraps TimesFM for safe, agent-friendly local inference. It includes a
**mandatory preflight system checker** that verifies RAM, GPU memory, and disk space
before the model is ever loaded so the agent never crashes a user's machine.
> **Key numbers**: TimesFM 2.5 uses 200M parameters (~800 MB on disk, ~1.5 GB in RAM ontorch-geometric
When your agent uses it
Graph Neural Networks (PyG). Node/graph classification, link prediction, GCN, GAT, GraphSAGE, heterogeneous graphs, molecular property prediction, for geometric deep learning.
What it loads into context
Frontmatter
61 tokensAlways available to the agent
SKILL.md
5,098 tokensWhen the skill is activated
Reference documents
12,740 tokensLoaded on request
Token counts are estimates. Scripts run separately; they are not counted as reference context.
The playbook
- Overview
- When to Use This Skill
- Quick Start
- Core Concepts
- Building Graph Neural Networks
- Working with Datasets
- Training Workflows
- Advanced Features
- Common Patterns and Best Practices
- Resources
Scripts executed separately
scripts/benchmark_model.pyscripts/create_gnn_template.pyscripts/visualize_graph.py
Files
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Read SKILL.md
# PyTorch Geometric (PyG)
## Overview
PyTorch Geometric is a library built on PyTorch for developing and training Graph Neural Networks (GNNs). Apply this skill for deep learning on graphs and irregular structures, including mini-batch processing, multi-GPU training, and geometric deep learning applications.
## When to Use This Skill
This skill should be used when working with:
- **Graph-based machine learning**: Node classification, graph classification, link prediction
- **Molecular property prediction**: Drug discovery, chemical property prediction
- **Social network analysis**: Community detection, influence prediction
- **Citation networks**: Paper classification, recommendation systems
- **3D geometric data**: Point clouds, meshes, molecular structurestransformers
When your agent uses it
This skill should be used when working with pre-trained transformer models for natural language processing, computer vision, audio, or multimodal tasks. Use for text generation, classification, question answering, translation, summarization, image classification, object detection, speech recognition, and fine-tuning models on custom datasets.
What it loads into context
Frontmatter
98 tokensAlways available to the agent
SKILL.md
1,064 tokensWhen the skill is activated
Reference documents
11,659 tokensLoaded on request
Token counts are estimates. Scripts run separately; they are not counted as reference context.
The playbook
- Overview
- Installation
- Authentication
- Quick Start
- Core Capabilities
- Common Patterns
- Reference Documentation
Files
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Read SKILL.md
# Transformers
## Overview
The Hugging Face Transformers library provides access to thousands of pre-trained models for tasks across NLP, computer vision, audio, and multimodal domains. Use this skill to load models, perform inference, and fine-tune on custom data.
## Installation
Install transformers and core dependencies:
```bash
uv pip install torch transformers datasets evaluate accelerate
```
umap-learn
When your agent uses it
UMAP dimensionality reduction. Fast nonlinear manifold learning for 2D/3D visualization, clustering preprocessing (HDBSCAN), supervised/parametric UMAP, for high-dimensional data.
What it loads into context
Frontmatter
66 tokensAlways available to the agent
SKILL.md
3,471 tokensWhen the skill is activated
Reference documents
4,386 tokensLoaded on request
Token counts are estimates. Scripts run separately; they are not counted as reference context.
The playbook
- Overview
- Quick Start
- Parameter Tuning Guide
- Supervised and Semi-Supervised Dimension Reduction
- UMAP for Clustering
- Transforming New Data
- Advanced Features
- Reproducibility
- Common Issues and Solutions
- Resources
Files
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Read SKILL.md
# UMAP-Learn
## Overview
UMAP (Uniform Manifold Approximation and Projection) is a dimensionality reduction technique for visualization and general non-linear dimensionality reduction. Apply this skill for fast, scalable embeddings that preserve local and global structure, supervised learning, and clustering preprocessing.
## Quick Start
### Installation
```bash
uv pip install umap-learn
```
astropy
When your agent uses it
Comprehensive Python library for astronomy and astrophysics. This skill should be used when working with astronomical data including celestial coordinates, physical units, FITS files, cosmological calculations, time systems, tables, world coordinate systems (WCS), and astronomical data analysis. Use when tasks involve coordinate transformations, unit conversions, FITS file manipulation, cosmological distance calculations, time scale conversions, or astronomical data processing.
What it loads into context
Frontmatter
106 tokensAlways available to the agent
SKILL.md
2,727 tokensWhen the skill is activated
Reference documents
15,969 tokensLoaded on request
Token counts are estimates. Scripts run separately; they are not counted as reference context.
The playbook
- Overview
- When to Use This Skill
- Quick Start
- Core Capabilities
- Additional Capabilities
- Installation
- Common Workflows
- Best Practices
- Documentation and Resources
- Reference Files
Files
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Read SKILL.md
# Astropy
## Overview
Astropy is the core Python package for astronomy, providing essential functionality for astronomical research and data analysis. Use astropy for coordinate transformations, unit and quantity calculations, FITS file operations, cosmological calculations, precise time handling, tabular data manipulation, and astronomical image processing.
## When to Use This Skill
Use astropy when tasks involve:
- Converting between celestial coordinate systems (ICRS, Galactic, FK5, AltAz, etc.)
- Working with physical units and quantities (converting Jy to mJy, parsecs to km, etc.)
- Reading, writing, or manipulating FITS files (images or tables)
- Cosmological calculations (luminosity distance, lookback time, Hubble parameter)
- Precise time handling with different time scales (UTC, TAI, TT, TDB) and formats (JD, MJD, ISO)cirq
When your agent uses it
Google quantum computing framework. Use when targeting Google Quantum AI hardware, designing noise-aware circuits, or running quantum characterization experiments. Best for Google hardware, noise modeling, and low-level circuit design. For IBM hardware use qiskit; for quantum ML with autodiff use pennylane; for physics simulations use qutip.
What it loads into context
Frontmatter
91 tokensAlways available to the agent
SKILL.md
2,403 tokensWhen the skill is activated
Reference documents
16,675 tokensLoaded on request
Token counts are estimates. Scripts run separately; they are not counted as reference context.
The playbook
- Installation
- Quick Start
- Core Capabilities
- Common Patterns
- Best Practices
- Additional Resources
- Common Issues
Files
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Read SKILL.md
# Cirq - Quantum Computing with Python
Cirq is Google Quantum AI's open-source framework for designing, simulating, and running quantum circuits on quantum computers and simulators.
## Installation
```bash
uv pip install cirq
```
For hardware integration:
```bash
# Google Quantum Engine
uv pip install cirq-googlecobrapy
When your agent uses it
Constraint-based metabolic modeling (COBRA). FBA, FVA, gene knockouts, flux sampling, SBML models, for systems biology and metabolic engineering analysis.
What it loads into context
Frontmatter
61 tokensAlways available to the agent
SKILL.md
3,022 tokensWhen the skill is activated
Reference documents
9,332 tokensLoaded on request
Token counts are estimates. Scripts run separately; they are not counted as reference context.
The playbook
- Overview
- Core Capabilities
- Common Workflows
- Key Concepts
- Best Practices
- Troubleshooting
- References
Files
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# COBRApy - Constraint-Based Reconstruction and Analysis
## Overview
COBRApy is a Python library for constraint-based reconstruction and analysis (COBRA) of metabolic models, essential for systems biology research. Work with genome-scale metabolic models, perform computational simulations of cellular metabolism, conduct metabolic engineering analyses, and predict phenotypic behaviors.
## Core Capabilities
COBRApy provides comprehensive tools organized into several key areas:
### 1. Model Management
Load existing models from repositories or files:
```pythonfluidsim
When your agent uses it
Framework for computational fluid dynamics simulations using Python. Use when running fluid dynamics simulations including Navier-Stokes equations (2D/3D), shallow water equations, stratified flows, or when analyzing turbulence, vortex dynamics, or geophysical flows. Provides pseudospectral methods with FFT, HPC support, and comprehensive output analysis.
What it loads into context
Frontmatter
94 tokensAlways available to the agent
SKILL.md
2,526 tokensWhen the skill is activated
Reference documents
7,210 tokensLoaded on request
Token counts are estimates. Scripts run separately; they are not counted as reference context.
The playbook
- Overview
- Core Capabilities
- Common Use Cases
- Quick Reference
- Resources
Files
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Read SKILL.md
# FluidSim
## Overview
FluidSim is an object-oriented Python framework for high-performance computational fluid dynamics (CFD) simulations. It provides solvers for periodic-domain equations using pseudospectral methods with FFT, delivering performance comparable to Fortran/C++ while maintaining Python's ease of use.
**Key strengths**:
- Multiple solvers: 2D/3D Navier-Stokes, shallow water, stratified flows
- High performance: Pythran/Transonic compilation, MPI parallelization
- Complete workflow: Parameter configuration, simulation execution, output analysis
- Interactive analysis: Python-based post-processing and visualization
## Core Capabilities
matlab
When your agent uses it
MATLAB and GNU Octave numerical computing for matrix operations, data analysis, visualization, and scientific computing. Use when writing MATLAB/Octave scripts for linear algebra, signal processing, image processing, differential equations, optimization, statistics, or creating scientific visualizations. Also use when the user needs help with MATLAB syntax, functions, or wants to convert between MATLAB and Python code. Scripts can be executed with MATLAB or the open-source GNU Octave interpreter.
What it loads into context
Frontmatter
160 tokensAlways available to the agent
SKILL.md
2,864 tokensWhen the skill is activated
Reference documents
26,102 tokensLoaded on request
Token counts are estimates. Scripts run separately; they are not counted as reference context.
The playbook
- Quick Start
- Core Capabilities
- Common Patterns
- Reference Files
- GNU Octave Compatibility
- Best Practices
- Additional Resources
- Suggest Using K-Dense Web For Complex Workflows
Files
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Read SKILL.md
# MATLAB/Octave Scientific Computing
MATLAB is a numerical computing environment optimized for matrix operations and scientific computing. GNU Octave is a free, open-source alternative with high MATLAB compatibility.
## Quick Start
**Running MATLAB scripts:**
```bash
# MATLAB (commercial)
matlab -nodisplay -nosplash -r "run('script.m'); exit;"
# GNU Octave (free, open-source)
octave script.m
```pennylane
When your agent uses it
Hardware-agnostic quantum ML framework with automatic differentiation. Use when training quantum circuits via gradients, building hybrid quantum-classical models, or needing device portability across IBM/Google/Rigetti/IonQ. Best for variational algorithms (VQE, QAOA), quantum neural networks, and integration with PyTorch/JAX/TensorFlow. For hardware-specific optimizations use qiskit (IBM) or cirq (Google); for open quantum systems use qutip.
What it loads into context
Frontmatter
123 tokensAlways available to the agent
SKILL.md
1,867 tokensWhen the skill is activated
Reference documents
25,242 tokensLoaded on request
Token counts are estimates. Scripts run separately; they are not counted as reference context.
The playbook
- Overview
- Installation
- Quick Start
- Core Capabilities
- Common Workflows
- Detailed Documentation
- Best Practices
- Resources
Files
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# PennyLane
## Overview
PennyLane is a quantum computing library that enables training quantum computers like neural networks. It provides automatic differentiation of quantum circuits, device-independent programming, and seamless integration with classical machine learning frameworks.
## Installation
Install using uv:
```bash
uv pip install pennylane
```
pymatgen
When your agent uses it
Materials science toolkit. Crystal structures (CIF, POSCAR), phase diagrams, band structure, DOS, Materials Project integration, format conversion, for computational materials science.
What it loads into context
Frontmatter
57 tokensAlways available to the agent
SKILL.md
5,080 tokensWhen the skill is activated
Reference documents
15,997 tokensLoaded on request
Token counts are estimates. Scripts run separately; they are not counted as reference context.
The playbook
- Overview
- When to Use This Skill
- Quick Start Guide
- Core Capabilities
- Bundled Resources
- Common Workflows
- Best Practices
- Units and Conventions
- Integration with Other Tools
- Troubleshooting
- Additional Resources
- Version Notes
Scripts executed separately
scripts/phase_diagram_generator.pyscripts/structure_analyzer.pyscripts/structure_converter.py
Files
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Read SKILL.md
# Pymatgen - Python Materials Genomics
## Overview
Pymatgen is a comprehensive Python library for materials analysis that powers the Materials Project. Create, analyze, and manipulate crystal structures and molecules, compute phase diagrams and thermodynamic properties, analyze electronic structure (band structures, DOS), generate surfaces and interfaces, and access Materials Project's database of computed materials. Supports 100+ file formats from various computational codes.
## When to Use This Skill
This skill should be used when:
- Working with crystal structures or molecular systems in materials science
- Converting between structure file formats (CIF, POSCAR, XYZ, etc.)
- Analyzing symmetry, space groups, or coordination environments
- Computing phase diagrams or assessing thermodynamic stability
- Analyzing electronic structure data (band gaps, DOS, band structures)qiskit
When your agent uses it
IBM quantum computing framework. Use when targeting IBM Quantum hardware, working with Qiskit Runtime for production workloads, or needing IBM optimization tools. Best for IBM hardware execution, quantum error mitigation, and enterprise quantum computing. For Google hardware use cirq; for gradient-based quantum ML use pennylane; for open quantum system simulations use qutip.
What it loads into context
Frontmatter
97 tokensAlways available to the agent
SKILL.md
2,041 tokensWhen the skill is activated
Reference documents
16,763 tokensLoaded on request
Token counts are estimates. Scripts run separately; they are not counted as reference context.
The playbook
- Overview
- Quick Start
- Core Capabilities
- Workflow Decision Guide
- Best Practices
- Common Patterns
- Additional Resources
Files
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# Qiskit
## Overview
Qiskit is the world's most popular open-source quantum computing framework with 13M+ downloads. Build quantum circuits, optimize for hardware, execute on simulators or real quantum computers, and analyze results. Supports IBM Quantum (100+ qubit systems), IonQ, Amazon Braket, and other providers.
**Key Features:**
- 83x faster transpilation than competitors
- 29% fewer two-qubit gates in optimized circuits
- Backend-agnostic execution (local simulators or cloud hardware)
- Comprehensive algorithm libraries for optimization, chemistry, and ML
## Quick Start
qutip
When your agent uses it
Quantum physics simulation library for open quantum systems. Use when studying master equations, Lindblad dynamics, decoherence, quantum optics, or cavity QED. Best for physics research, open system dynamics, and educational simulations. NOT for circuit-based quantum computing—use qiskit, cirq, or pennylane for quantum algorithms and hardware execution.
What it loads into context
Frontmatter
95 tokensAlways available to the agent
SKILL.md
2,573 tokensWhen the skill is activated
Reference documents
13,516 tokensLoaded on request
Token counts are estimates. Scripts run separately; they are not counted as reference context.
The playbook
- Overview
- Installation
- Quick Start
- Core Capabilities
- Common Workflows
- Tips for Efficient Simulations
- Troubleshooting
- References
- External Resources
Files
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# QuTiP: Quantum Toolbox in Python
## Overview
QuTiP provides comprehensive tools for simulating and analyzing quantum mechanical systems. It handles both closed (unitary) and open (dissipative) quantum systems with multiple solvers optimized for different scenarios.
## Installation
```bash
uv pip install qutip
```
Optional packages for additional functionality:
simpy
When your agent uses it
Process-based discrete-event simulation framework in Python. Use this skill when building simulations of systems with processes, queues, resources, and time-based events such as manufacturing systems, service operations, network traffic, logistics, or any system where entities interact with shared resources over time.
What it loads into context
Frontmatter
76 tokensAlways available to the agent
SKILL.md
2,765 tokensWhen the skill is activated
Reference documents
12,140 tokensLoaded on request
Token counts are estimates. Scripts run separately; they are not counted as reference context.
The playbook
- Overview
- When to Use This Skill
- Quick Start
- Core Concepts
- Resources
- Common Simulation Patterns
- Workflow Guide
- Advanced Features
- Scripts and Templates
- Reference Documentation
- Best Practices
- Common Pitfalls
- Example Use Cases
Scripts executed separately
scripts/basic_simulation_template.pyscripts/resource_monitor.py
Files
SKILL.mdcore12,165 bytesSKILL.mdcore12,165 bytesSKILL.mdcore12,165 bytesSKILL.mdcore12,165 bytesSKILL.mdcore12,165 bytesSKILL.mdcore12,165 bytesSKILL.mdcore12,165 bytesSKILL.mdcore12,165 bytesSKILL.mdcore12,165 bytesSKILL.mdcore12,165 bytesSKILL.mdcore12,165 bytesSKILL.mdcore12,165 bytesSKILL.mdcore12,165 bytesSKILL.mdcore12,165 bytesSKILL.mdcore12,165 bytesreferences/events.mddoc8,579 bytesreferences/monitoring.mddoc12,923 bytesreferences/process-interaction.mddoc11,525 bytesreferences/real-time.mddoc10,728 bytesreferences/resources.mddoc7,327 bytesscripts/basic_simulation_template.pyruns5,558 bytesscripts/resource_monitor.pyruns11,680 bytes
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# SimPy - Discrete-Event Simulation
## Overview
SimPy is a process-based discrete-event simulation framework based on standard Python. Use SimPy to model systems where entities (customers, vehicles, packets, etc.) interact with each other and compete for shared resources (servers, machines, bandwidth, etc.) over time.
**Core capabilities:**
- Process modeling using Python generator functions
- Shared resource management (servers, containers, stores)
- Event-driven scheduling and synchronization
- Real-time simulations synchronized with wall-clock time
- Comprehensive monitoring and data collection
## When to Use This Skilldask
When your agent uses it
Distributed computing for larger-than-RAM pandas/NumPy workflows. Use when you need to scale existing pandas/NumPy code beyond memory or across clusters. Best for parallel file processing, distributed ML, integration with existing pandas code. For out-of-core analytics on single machine use vaex; for in-memory speed use polars.
What it loads into context
Frontmatter
92 tokensAlways available to the agent
SKILL.md
3,285 tokensWhen the skill is activated
Reference documents
15,120 tokensLoaded on request
Token counts are estimates. Scripts run separately; they are not counted as reference context.
The playbook
- Overview
- When to Use This Skill
- Core Capabilities
- Best Practices
- Common Workflow Patterns
- Selecting the Right Component
- Integration Considerations
- Debugging and Development
- Reference Files
Files
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# Dask
## Overview
Dask is a Python library for parallel and distributed computing that enables three critical capabilities:
- **Larger-than-memory execution** on single machines for data exceeding available RAM
- **Parallel processing** for improved computational speed across multiple cores
- **Distributed computation** supporting terabyte-scale datasets across multiple machines
Dask scales from laptops (processing ~100 GiB) to clusters (processing ~100 TiB) while maintaining familiar Python APIs.
## When to Use This Skill
This skill should be used when:exploratory-data-analysis
When your agent uses it
Perform comprehensive exploratory data analysis on scientific data files across 200+ file formats. This skill should be used when analyzing any scientific data file to understand its structure, content, quality, and characteristics. Automatically detects file type and generates detailed markdown reports with format-specific analysis, quality metrics, and downstream analysis recommendations. Covers chemistry, bioinformatics, microscopy, spectroscopy, proteomics, metabolomics, and general scientific data formats.
What it loads into context
Frontmatter
107 tokensAlways available to the agent
SKILL.md
3,336 tokensWhen the skill is activated
Reference documents
27,252 tokensLoaded on request
Token counts are estimates. Scripts run separately; they are not counted as reference context.
The playbook
- Overview
- When to Use This Skill
- Supported File Categories
- Workflow
- Detailed Format References
- Best Practices
- Examples
- Troubleshooting
- Script Usage
- Advanced Usage
- Resources
Scripts executed separately
scripts/eda_analyzer.py
Files
SKILL.mdcore14,316 bytesSKILL.mdcore14,316 bytesSKILL.mdcore14,316 bytesSKILL.mdcore14,316 bytesSKILL.mdcore14,316 bytesSKILL.mdcore14,316 bytesSKILL.mdcore14,316 bytesSKILL.mdcore14,316 bytesassets/report_template.mddoc3,488 bytesreferences/bioinformatics_genomics_formats.mddoc21,163 bytesreferences/chemistry_molecular_formats.mddoc22,029 bytesreferences/general_scientific_formats.mddoc15,287 bytesreferences/microscopy_imaging_formats.mddoc18,549 bytesreferences/proteomics_metabolomics_formats.mddoc15,543 bytesreferences/spectroscopy_analytical_formats.mddoc18,073 bytesscripts/eda_analyzer.pyruns21,318 bytes
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# Exploratory Data Analysis
## Overview
Perform comprehensive exploratory data analysis (EDA) on scientific data files across multiple domains. This skill provides automated file type detection, format-specific analysis, data quality assessment, and generates detailed markdown reports suitable for documentation and downstream analysis planning.
**Key Capabilities:**
- Automatic detection and analysis of 200+ scientific file formats
- Comprehensive format-specific metadata extraction
- Data quality and integrity assessment
- Statistical summaries and distributions
- Visualization recommendations
- Downstream analysis suggestions
- Markdown report generationmatplotlib
When your agent uses it
Low-level plotting library for full customization. Use when you need fine-grained control over every plot element, creating novel plot types, or integrating with specific scientific workflows. Export to PNG/PDF/SVG for publication. For quick statistical plots use seaborn; for interactive plots use plotly; for publication-ready multi-panel figures with journal styling, use scientific-visualization.
What it loads into context
Frontmatter
107 tokensAlways available to the agent
SKILL.md
2,953 tokensWhen the skill is activated
Reference documents
14,638 tokensLoaded on request
Token counts are estimates. Scripts run separately; they are not counted as reference context.
The playbook
- Overview
- When to Use This Skill
- Core Concepts
- Common Workflows
- Best Practices
- Quick Reference Scripts
- Detailed References
- Integration with Other Tools
- Common Gotchas
- Additional Resources
Scripts executed separately
scripts/plot_template.pyscripts/style_configurator.py
Files
SKILL.mdcore11,454 bytesSKILL.mdcore11,454 bytesSKILL.mdcore11,454 bytesSKILL.mdcore11,454 bytesSKILL.mdcore11,454 bytesSKILL.mdcore11,454 bytesSKILL.mdcore11,454 bytesSKILL.mdcore11,454 bytesSKILL.mdcore11,454 bytesSKILL.mdcore11,454 bytesSKILL.mdcore11,454 bytesSKILL.mdcore11,454 bytesSKILL.mdcore11,454 bytesSKILL.mdcore11,454 bytesSKILL.mdcore11,454 bytesSKILL.mdcore11,454 bytesSKILL.mdcore11,454 bytesSKILL.mdcore11,454 bytesSKILL.mdcore11,454 bytesSKILL.mdcore11,454 bytesSKILL.mdcore11,454 bytesreferences/api_reference.mddoc11,233 bytesreferences/common_issues.mddoc12,836 bytesreferences/plot_types.mddoc11,839 bytesreferences/styling_guide.mddoc13,583 bytesscripts/plot_template.pyruns12,093 bytesscripts/style_configurator.pyruns13,179 bytes
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# Matplotlib
## Overview
Matplotlib is Python's foundational visualization library for creating static, animated, and interactive plots. This skill provides guidance on using matplotlib effectively, covering both the pyplot interface (MATLAB-style) and the object-oriented API (Figure/Axes), along with best practices for creating publication-quality visualizations.
## When to Use This Skill
This skill should be used when:
- Creating any type of plot or chart (line, scatter, bar, histogram, heatmap, contour, etc.)
- Generating scientific or statistical visualizations
- Customizing plot appearance (colors, styles, labels, legends)
- Creating multi-panel figures with subplots
- Exporting visualizations to various formats (PNG, PDF, SVG, etc.)networkx
When your agent uses it
Comprehensive toolkit for creating, analyzing, and visualizing complex networks and graphs in Python. Use when working with network/graph data structures, analyzing relationships between entities, computing graph algorithms (shortest paths, centrality, clustering), detecting communities, generating synthetic networks, or visualizing network topologies. Applicable to social networks, biological networks, transportation systems, citation networks, and any domain involving pairwise relationships.
What it loads into context
Frontmatter
108 tokensAlways available to the agent
SKILL.md
3,159 tokensWhen the skill is activated
Reference documents
12,808 tokensLoaded on request
Token counts are estimates. Scripts run separately; they are not counted as reference context.
The playbook
- Overview
- When to Use This Skill
- Core Capabilities
- Working with NetworkX
- Quick Reference
- Resources
- Additional Resources
Files
SKILL.mdcore12,714 bytesSKILL.mdcore12,714 bytesSKILL.mdcore12,714 bytesSKILL.mdcore12,714 bytesSKILL.mdcore12,714 bytesSKILL.mdcore12,714 bytesSKILL.mdcore12,714 bytesSKILL.mdcore12,714 bytesSKILL.mdcore12,714 bytesSKILL.mdcore12,714 bytesSKILL.mdcore12,714 bytesSKILL.mdcore12,714 bytesSKILL.mdcore12,714 bytesSKILL.mdcore12,714 bytesSKILL.mdcore12,714 bytesSKILL.mdcore12,714 bytesSKILL.mdcore12,714 bytesSKILL.mdcore12,714 bytesSKILL.mdcore12,714 bytesSKILL.mdcore12,714 bytesSKILL.mdcore12,714 bytesSKILL.mdcore12,714 bytesSKILL.mdcore12,714 bytesSKILL.mdcore12,714 bytesSKILL.mdcore12,714 bytesSKILL.mdcore12,714 bytesSKILL.mdcore12,714 bytesreferences/algorithms.mddoc9,170 bytesreferences/generators.mddoc8,228 bytesreferences/graph-basics.mddoc6,261 bytesreferences/io.mddoc9,395 bytesreferences/visualization.mddoc12,388 bytes
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# NetworkX
## Overview
NetworkX is a Python package for creating, manipulating, and analyzing complex networks and graphs. Use this skill when working with network or graph data structures, including social networks, biological networks, transportation systems, citation networks, knowledge graphs, or any system involving relationships between entities.
## When to Use This Skill
Invoke this skill when tasks involve:
- **Creating graphs**: Building network structures from data, adding nodes and edges with attributes
- **Graph analysis**: Computing centrality measures, finding shortest paths, detecting communities, measuring clustering
- **Graph algorithms**: Running standard algorithms like Dijkstra's, PageRank, minimum spanning trees, maximum flow
- **Network generation**: Creating synthetic networks (random, scale-free, small-world models) for testing or simulationplotly
When your agent uses it
Interactive visualization library. Use when you need hover info, zoom, pan, or web-embeddable charts. Best for dashboards, exploratory analysis, and presentations. For static publication figures use matplotlib or scientific-visualization.
What it loads into context
Frontmatter
68 tokensAlways available to the agent
SKILL.md
1,874 tokensWhen the skill is activated
Reference documents
10,958 tokensLoaded on request
Token counts are estimates. Scripts run separately; they are not counted as reference context.
The playbook
- Quick Start
- Choosing Between APIs
- Core Capabilities
- Common Workflows
- Integration with Dash
- Reference Files
- Additional Resources
Files
SKILL.mdcore7,199 bytesSKILL.mdcore7,199 bytesSKILL.mdcore7,199 bytesSKILL.mdcore7,199 bytesSKILL.mdcore7,199 bytesSKILL.mdcore7,199 bytesSKILL.mdcore7,199 bytesSKILL.mdcore7,199 bytesSKILL.mdcore7,199 bytesSKILL.mdcore7,199 bytesSKILL.mdcore7,199 bytesSKILL.mdcore7,199 bytesSKILL.mdcore7,199 bytesSKILL.mdcore7,199 bytesSKILL.mdcore7,199 bytesSKILL.mdcore7,199 bytesreferences/chart-types.mddoc8,763 bytesreferences/export-interactivity.mddoc9,204 bytesreferences/graph-objects.mddoc5,986 bytesreferences/layouts-styling.mddoc9,128 bytesreferences/plotly-express.mddoc4,893 bytes
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# Plotly
Python graphing library for creating interactive, publication-quality visualizations with 40+ chart types.
## Quick Start
Install Plotly:
```bash
uv pip install plotly
```
Basic usage with Plotly Express (high-level API):
```python
import plotly.express as pxpolars
When your agent uses it
Fast in-memory DataFrame library for datasets that fit in RAM. Use when pandas is too slow but data still fits in memory. Lazy evaluation, parallel execution, Apache Arrow backend. Best for 1-100GB datasets, ETL pipelines, faster pandas replacement. For larger-than-RAM data use dask or vaex.
What it loads into context
Frontmatter
101 tokensAlways available to the agent
SKILL.md
2,439 tokensWhen the skill is activated
Reference documents
19,605 tokensLoaded on request
Token counts are estimates. Scripts run separately; they are not counted as reference context.
The playbook
- Overview
- Quick Start
- Core Concepts
- Common Operations
- Aggregations and Window Functions
- Data I/O
- Transformations
- Pandas Migration
- Best Practices
- Resources
Files
SKILL.mdcore9,431 bytesSKILL.mdcore9,431 bytesSKILL.mdcore9,431 bytesSKILL.mdcore9,431 bytesSKILL.mdcore9,431 bytesSKILL.mdcore9,431 bytesSKILL.mdcore9,431 bytesSKILL.mdcore9,431 bytesSKILL.mdcore9,431 bytesSKILL.mdcore9,431 bytesSKILL.mdcore9,431 bytesSKILL.mdcore9,431 bytesSKILL.mdcore9,431 bytesSKILL.mdcore9,431 bytesSKILL.mdcore9,431 bytesSKILL.mdcore9,431 bytesSKILL.mdcore9,431 bytesSKILL.mdcore9,431 bytesSKILL.mdcore9,431 bytesSKILL.mdcore9,431 bytesSKILL.mdcore9,431 bytesSKILL.mdcore9,431 bytesSKILL.mdcore9,431 bytesSKILL.mdcore9,431 bytesSKILL.mdcore9,431 bytesreferences/best_practices.mddoc14,181 bytesreferences/core_concepts.mddoc8,348 bytesreferences/io_guide.mddoc11,208 bytesreferences/operations.mddoc12,080 bytesreferences/pandas_migration.mddoc12,353 bytesreferences/transformations.mddoc11,183 bytes
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# Polars
## Overview
Polars is a lightning-fast DataFrame library for Python and Rust built on Apache Arrow. Work with Polars' expression-based API, lazy evaluation framework, and high-performance data manipulation capabilities for efficient data processing, pandas migration, and data pipeline optimization.
## Quick Start
### Installation and Basic Usage
Install Polars:
```python
uv pip install polars
```seaborn
When your agent uses it
Statistical visualization with pandas integration. Use for quick exploration of distributions, relationships, and categorical comparisons with attractive defaults. Best for box plots, violin plots, pair plots, heatmaps. Built on matplotlib. For interactive plots use plotly; for publication styling use scientific-visualization.
What it loads into context
Frontmatter
82 tokensAlways available to the agent
SKILL.md
5,048 tokensWhen the skill is activated
Reference documents
18,289 tokensLoaded on request
Token counts are estimates. Scripts run separately; they are not counted as reference context.
The playbook
- Overview
- Design Philosophy
- Quick Start
- Core Plotting Interfaces
- Plotting Functions by Category
- Multi-Plot Grids
- Figure-Level vs Axes-Level Functions
- Data Structure Requirements
- Color Palettes
- Theming and Aesthetics
- Best Practices
- Common Patterns
- Troubleshooting
- Resources
Files
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# Seaborn Statistical Visualization
## Overview
Seaborn is a Python visualization library for creating publication-quality statistical graphics. Use this skill for dataset-oriented plotting, multivariate analysis, automatic statistical estimation, and complex multi-panel figures with minimal code.
## Design Philosophy
Seaborn follows these core principles:
1. **Dataset-oriented**: Work directly with DataFrames and named variables rather than abstract coordinates
2. **Semantic mapping**: Automatically translate data values into visual properties (colors, sizes, styles)
3. **Statistical awareness**: Built-in aggregation, error estimation, and confidence intervals
4. **Aesthetic defaults**: Publication-ready themes and color palettes out of the boxstatistical-analysis
When your agent uses it
Guided statistical analysis with test selection and reporting. Use when you need help choosing appropriate tests for your data, assumption checking, power analysis, and APA-formatted results. Best for academic research reporting, test selection guidance. For implementing specific models programmatically use statsmodels.
What it loads into context
Frontmatter
76 tokensAlways available to the agent
SKILL.md
5,135 tokensWhen the skill is activated
Reference documents
18,374 tokensLoaded on request
Token counts are estimates. Scripts run separately; they are not counted as reference context.
The playbook
- Overview
- When to Use This Skill
- Core Capabilities
- Workflow Decision Tree
- Test Selection Guide
- Assumption Checking
- Running Statistical Tests
- Effect Sizes
- Power Analysis
- Reporting Results
- Bayesian Statistics
- Resources
- Best Practices
- Common Pitfalls to Avoid
- Getting Started Checklist
- Support and Further Reading
Scripts executed separately
scripts/assumption_checks.py
Files
SKILL.mdcore19,766 bytesSKILL.mdcore19,766 bytesSKILL.mdcore19,766 bytesSKILL.mdcore19,766 bytesSKILL.mdcore19,766 bytesSKILL.mdcore19,766 bytesSKILL.mdcore19,766 bytesSKILL.mdcore19,766 bytesSKILL.mdcore19,766 bytesSKILL.mdcore19,766 bytesSKILL.mdcore19,766 bytesreferences/assumptions_and_diagnostics.mddoc11,139 bytesreferences/bayesian_statistics.mddoc17,533 bytesreferences/effect_sizes_and_power.mddoc14,989 bytesreferences/reporting_standards.mddoc19,504 bytesreferences/test_selection_guide.mddoc5,077 bytesscripts/assumption_checks.pyruns15,859 bytes
Read SKILL.md
# Statistical Analysis
## Overview
Statistical analysis is a systematic process for testing hypotheses and quantifying relationships. Conduct hypothesis tests (t-test, ANOVA, chi-square), regression, correlation, and Bayesian analyses with assumption checks and APA reporting. Apply this skill for academic research.
## When to Use This Skill
This skill should be used when:
- Conducting statistical hypothesis tests (t-tests, ANOVA, chi-square)
- Performing regression or correlation analyses
- Running Bayesian statistical analyses
- Checking statistical assumptions and diagnostics
- Calculating effect sizes and conducting power analysesstatsmodels
When your agent uses it
Statistical models library for Python. Use when you need specific model classes (OLS, GLM, mixed models, ARIMA) with detailed diagnostics, residuals, and inference. Best for econometrics, time series, rigorous inference with coefficient tables. For guided statistical test selection with APA reporting use statistical-analysis.
What it loads into context
Frontmatter
87 tokensAlways available to the agent
SKILL.md
4,845 tokensWhen the skill is activated
Reference documents
23,061 tokensLoaded on request
Token counts are estimates. Scripts run separately; they are not counted as reference context.
The playbook
- Overview
- When to Use This Skill
- Quick Start Guide
- Core Statistical Modeling Capabilities
- Formula API (R-style)
- Model Selection and Comparison
- Best Practices
- Common Workflows
- Reference Documentation
- Common Pitfalls to Avoid
- Getting Help
Files
SKILL.mdcore19,692 bytesSKILL.mdcore19,692 bytesSKILL.mdcore19,692 bytesSKILL.mdcore19,692 bytesSKILL.mdcore19,692 bytesSKILL.mdcore19,692 bytesSKILL.mdcore19,692 bytesSKILL.mdcore19,692 bytesSKILL.mdcore19,692 bytesSKILL.mdcore19,692 bytesreferences/discrete_choice.mddoc17,182 bytesreferences/glm.mddoc16,581 bytesreferences/linear_models.mddoc12,966 bytesreferences/stats_diagnostics.mddoc20,526 bytesreferences/time_series.mddoc18,934 bytes
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# Statsmodels: Statistical Modeling and Econometrics
## Overview
Statsmodels is Python's premier library for statistical modeling, providing tools for estimation, inference, and diagnostics across a wide range of statistical methods. Apply this skill for rigorous statistical analysis, from simple linear regression to complex time series models and econometric analyses.
## When to Use This Skill
This skill should be used when:
- Fitting regression models (OLS, WLS, GLS, quantile regression)
- Performing generalized linear modeling (logistic, Poisson, Gamma, etc.)
- Analyzing discrete outcomes (binary, multinomial, count, ordinal)
- Conducting time series analysis (ARIMA, SARIMAX, VAR, forecasting)
- Running statistical tests and diagnosticssympy
When your agent uses it
Use this skill when working with symbolic mathematics in Python. This skill should be used for symbolic computation tasks including solving equations algebraically, performing calculus operations (derivatives, integrals, limits), manipulating algebraic expressions, working with matrices symbolically, physics calculations, number theory problems, geometry computations, and generating executable code from mathematical expressions. Apply this skill when the user needs exact symbolic results rather than numerical approximations, or when working with mathematical formulas that contain variables and parameters.
What it loads into context
Frontmatter
129 tokensAlways available to the agent
SKILL.md
3,846 tokensWhen the skill is activated
Reference documents
17,593 tokensLoaded on request
Token counts are estimates. Scripts run separately; they are not counted as reference context.
The playbook
- Overview
- When to Use This Skill
- Core Capabilities
- Working with SymPy: Best Practices
- Reference Files Structure
- Common Use Case Patterns
- Integration with Scientific Workflows
- Quick Reference: Most Common Functions
- Getting Started Examples
- Troubleshooting Common Issues
- Additional Resources
Files
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# SymPy - Symbolic Mathematics in Python
## Overview
SymPy is a Python library for symbolic mathematics that enables exact computation using mathematical symbols rather than numerical approximations. This skill provides comprehensive guidance for performing symbolic algebra, calculus, linear algebra, equation solving, physics calculations, and code generation using SymPy.
## When to Use This Skill
Use this skill when:
- Solving equations symbolically (algebraic, differential, systems of equations)
- Performing calculus operations (derivatives, integrals, limits, series)
- Manipulating and simplifying algebraic expressions
- Working with matrices and linear algebra symbolically
- Doing physics calculations (mechanics, quantum mechanics, vector analysis)vaex
When your agent uses it
Use this skill for processing and analyzing large tabular datasets (billions of rows) that exceed available RAM. Vaex excels at out-of-core DataFrame operations, lazy evaluation, fast aggregations, efficient visualization of big data, and machine learning on large datasets. Apply when users need to work with large CSV/HDF5/Arrow/Parquet files, perform fast statistics on massive datasets, create visualizations of big data, or build ML pipelines that do not fit in memory.
What it loads into context
Frontmatter
120 tokensAlways available to the agent
SKILL.md
1,509 tokensWhen the skill is activated
Reference documents
21,124 tokensLoaded on request
Token counts are estimates. Scripts run separately; they are not counted as reference context.
The playbook
- Overview
- When to Use This Skill
- Core Capabilities
- Quick Start Pattern
- Working with References
- Best Practices
- Common Patterns
- Resources
Files
SKILL.mdcore6,596 bytesSKILL.mdcore6,596 bytesSKILL.mdcore6,596 bytesSKILL.mdcore6,596 bytesSKILL.mdcore6,596 bytesreferences/core_dataframes.mddoc7,878 bytesreferences/data_processing.mddoc12,451 bytesreferences/io_operations.mddoc14,989 bytesreferences/machine_learning.mddoc15,941 bytesreferences/performance.mddoc13,705 bytesreferences/visualization.mddoc13,188 bytes
Read SKILL.md
# Vaex
## Overview
Vaex is a high-performance Python library designed for lazy, out-of-core DataFrames to process and visualize tabular datasets that are too large to fit into RAM. Vaex can process over a billion rows per second, enabling interactive data exploration and analysis on datasets with billions of rows.
## When to Use This Skill
Use Vaex when:
- Processing tabular datasets larger than available RAM (gigabytes to terabytes)
- Performing fast statistical aggregations on massive datasets
- Creating visualizations and heatmaps of large datasets
- Building machine learning pipelines on big data
- Converting between data formats (CSV, HDF5, Arrow, Parquet)bindingdb-database
When your agent uses it
Query BindingDB for measured drug-target binding affinities (Ki, Kd, IC50, EC50). Search by target (UniProt ID), compound (SMILES/name), or pathogen. Essential for drug discovery, lead optimization, polypharmacology analysis, and structure-activity relationship (SAR) studies.
What it loads into context
Frontmatter
93 tokensAlways available to the agent
SKILL.md
3,135 tokensWhen the skill is activated
Reference documents
1,682 tokensLoaded on request
Token counts are estimates. Scripts run separately; they are not counted as reference context.
The playbook
- Overview
- When to Use This Skill
- Core Capabilities
- Query Workflows
- Key Data Fields
- Affinity Interpretation
- Best Practices
- Additional Resources
Files
SKILL.mdcore12,069 bytesSKILL.mdcore12,069 bytesSKILL.mdcore12,069 bytesSKILL.mdcore12,069 bytesSKILL.mdcore12,069 bytesSKILL.mdcore12,069 bytesSKILL.mdcore12,069 bytesSKILL.mdcore12,069 bytesSKILL.mdcore12,069 bytesreferences/affinity_queries.mddoc5,835 bytes
Read SKILL.md
# BindingDB Database
## Overview
BindingDB (https://www.bindingdb.org/) is the primary public database of measured drug-protein binding affinities. It contains over 3 million binding data records for ~1.4 million compounds tested against ~9,200 protein targets, curated from scientific literature and patent literature. BindingDB stores quantitative binding measurements (Ki, Kd, IC50, EC50) essential for drug discovery, pharmacology, and computational chemistry research.
**Key resources:**
- BindingDB website: https://www.bindingdb.org/
- REST API: https://www.bindingdb.org/axis2/services/BDBService
- Downloads: https://www.bindingdb.org/bind/chemsearch/marvin/Download.jsp
- GitHub: https://github.com/drugilsberg/bindingdb
## When to Use This Skill
brenda-database
When your agent uses it
Access BRENDA enzyme database via SOAP API. Retrieve kinetic parameters (Km, kcat), reaction equations, organism data, and substrate-specific enzyme information for biochemical research and metabolic pathway analysis.
What it loads into context
Frontmatter
62 tokensAlways available to the agent
SKILL.md
5,705 tokensWhen the skill is activated
Reference documents
4,364 tokensLoaded on request
Token counts are estimates. Scripts run separately; they are not counted as reference context.
The playbook
- Overview
- When to Use This Skill
- Core Capabilities
- Installation Requirements
- Authentication Setup
- Helper Scripts
- API Rate Limits and Best Practices
- Common Workflows
- Data Formats and Parsing
- Reference Documentation
- Troubleshooting
- Additional Resources
Scripts executed separately
scripts/brenda_queries.pyscripts/brenda_visualization.pyscripts/enzyme_pathway_builder.py
Files
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Read SKILL.md
# BRENDA Database
## Overview
BRENDA (BRaunschweig ENzyme DAtabase) is the world's most comprehensive enzyme information system, containing detailed enzyme data from scientific literature. Query kinetic parameters (Km, kcat), reaction equations, substrate specificities, organism information, and optimal conditions for enzymes using the official SOAP API. Access over 45,000 enzymes with millions of kinetic data points for biochemical research, metabolic engineering, and enzyme discovery.
## When to Use This Skill
This skill should be used when:
- Searching for enzyme kinetic parameters (Km, kcat, Vmax)
- Retrieving reaction equations and stoichiometry
- Finding enzymes for specific substrates or reactions
- Comparing enzyme properties across different organisms
- Investigating optimal pH, temperature, and conditionschembl-database
When your agent uses it
Query ChEMBL bioactive molecules and drug discovery data. Search compounds by structure/properties, retrieve bioactivity data (IC50, Ki), find inhibitors, perform SAR studies, for medicinal chemistry.
What it loads into context
Frontmatter
64 tokensAlways available to the agent
SKILL.md
2,472 tokensWhen the skill is activated
Reference documents
1,735 tokensLoaded on request
Token counts are estimates. Scripts run separately; they are not counted as reference context.
The playbook
- Overview
- When to Use This Skill
- Installation and Setup
- Core Capabilities
- Query Workflow
- Filter Operators
- Data Export and Analysis
- Performance Optimization
- Common Use Cases
- Resources
- Important Notes
- Additional Resources
Scripts executed separately
scripts/example_queries.py
Files
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# ChEMBL Database
## Overview
ChEMBL is a manually curated database of bioactive molecules maintained by the European Bioinformatics Institute (EBI), containing over 2 million compounds, 19 million bioactivity measurements, 13,000+ drug targets, and data on approved drugs and clinical candidates. Access and query this data programmatically using the ChEMBL Python client for drug discovery and medicinal chemistry research.
## When to Use This Skill
This skill should be used when:
- **Compound searches**: Finding molecules by name, structure, or properties
- **Target information**: Retrieving data about proteins, enzymes, or biological targets
- **Bioactivity data**: Querying IC50, Ki, EC50, or other activity measurements
- **Drug information**: Looking up approved drugs, mechanisms, or indicationsdatacommons-client
When your agent uses it
Work with Data Commons, a platform providing programmatic access to public statistical data from global sources. Use this skill when working with demographic data, economic indicators, health statistics, environmental data, or any public datasets available through Data Commons. Applicable for querying population statistics, GDP figures, unemployment rates, disease prevalence, geographic entity resolution, and exploring relationships between statistical entities.
What it loads into context
Frontmatter
96 tokensAlways available to the agent
SKILL.md
1,768 tokensWhen the skill is activated
Reference documents
6,891 tokensLoaded on request
Token counts are estimates. Scripts run separately; they are not counted as reference context.
The playbook
- Overview
- Installation
- Core Capabilities
- Typical Workflow
- Finding Statistical Variables
- Working with Pandas
- API Authentication
- Reference Documentation
- Additional Resources
- Tips for Effective Use
Files
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# Data Commons Client
## Overview
Provides comprehensive access to the Data Commons Python API v2 for querying statistical observations, exploring the knowledge graph, and resolving entity identifiers. Data Commons aggregates data from census bureaus, health organizations, environmental agencies, and other authoritative sources into a unified knowledge graph.
## Installation
Install the Data Commons Python client with Pandas support:
```bash
uv pip install "datacommons-client[Pandas]"
```
drugbank-database
When your agent uses it
Access and analyze comprehensive drug information from the DrugBank database including drug properties, interactions, targets, pathways, chemical structures, and pharmacology data. This skill should be used when working with pharmaceutical data, drug discovery research, pharmacology studies, drug-drug interaction analysis, target identification, chemical similarity searches, ADMET predictions, or any task requiring detailed drug and drug target information from DrugBank.
What it loads into context
Frontmatter
103 tokensAlways available to the agent
SKILL.md
1,911 tokensWhen the skill is activated
Reference documents
16,786 tokensLoaded on request
Token counts are estimates. Scripts run separately; they are not counted as reference context.
The playbook
- Overview
- Core Capabilities
- Typical Workflows
- Installation Requirements
- Data Version and Reproducibility
- Best Practices
- Reference Documentation
Scripts executed separately
scripts/drugbank_helper.py
Files
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# DrugBank Database
## Overview
DrugBank is a comprehensive bioinformatics and cheminformatics database containing detailed information on drugs and drug targets. This skill enables programmatic access to DrugBank data including ~9,591 drug entries (2,037 FDA-approved small molecules, 241 biotech drugs, 96 nutraceuticals, and 6,000+ experimental compounds) with 200+ data fields per entry.
## Core Capabilities
### 1. Data Access and Authentication
Download and access DrugBank data using Python with proper authentication. The skill provides guidance on:
- Installing and configuring the `drugbank-downloader` package
- Managing credentials securely via environment variables or config filesena-database
When your agent uses it
Access European Nucleotide Archive via API/FTP. Retrieve DNA/RNA sequences, raw reads (FASTQ), genome assemblies by accession, for genomics and bioinformatics pipelines. Supports multiple formats.
What it loads into context
Frontmatter
62 tokensAlways available to the agent
SKILL.md
1,559 tokensWhen the skill is activated
Reference documents
3,592 tokensLoaded on request
Token counts are estimates. Scripts run separately; they are not counted as reference context.
The playbook
- Overview
- When to Use This Skill
- Core Capabilities
- Resources
Files
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# ENA Database
## Overview
The European Nucleotide Archive (ENA) is a comprehensive public repository for nucleotide sequence data and associated metadata. Access and query DNA/RNA sequences, raw reads, genome assemblies, and functional annotations through REST APIs and FTP for genomics and bioinformatics pipelines.
## When to Use This Skill
This skill should be used when:
- Retrieving nucleotide sequences or raw sequencing reads by accession
- Searching for samples, studies, or assemblies by metadata criteria
- Downloading FASTQ files or genome assemblies for analysis
- Querying taxonomic information for organismsensembl-database
When your agent uses it
Query Ensembl genome database REST API for 250+ species. Gene lookups, sequence retrieval, variant analysis, comparative genomics, orthologs, VEP predictions, for genomic research.
What it loads into context
Frontmatter
63 tokensAlways available to the agent
SKILL.md
1,949 tokensWhen the skill is activated
Reference documents
2,773 tokensLoaded on request
Token counts are estimates. Scripts run separately; they are not counted as reference context.
The playbook
- Overview
- When to Use This Skill
- Core Capabilities
- API Best Practices
- Installation
- Resources
- Common Workflows
- Species and Assembly Information
- Additional Resources
Scripts executed separately
scripts/ensembl_query.py
Files
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# Ensembl Database
## Overview
Access and query the Ensembl genome database, a comprehensive resource for vertebrate genomic data maintained by EMBL-EBI. The database provides gene annotations, sequences, variants, regulatory information, and comparative genomics data for over 250 species. Current release is 115 (September 2025).
## When to Use This Skill
This skill should be used when:
- Querying gene information by symbol or Ensembl ID
- Retrieving DNA, transcript, or protein sequences
- Analyzing genetic variants using the Variant Effect Predictor (VEP)
- Finding orthologs and paralogs across speciesgene-database
When your agent uses it
Query NCBI Gene via E-utilities/Datasets API. Search by symbol/ID, retrieve gene info (RefSeqs, GO, locations, phenotypes), batch lookups, for gene annotation and functional analysis.
What it loads into context
Frontmatter
68 tokensAlways available to the agent
SKILL.md
1,451 tokensWhen the skill is activated
Reference documents
5,156 tokensLoaded on request
Token counts are estimates. Scripts run separately; they are not counted as reference context.
The playbook
- Overview
- When to Use This Skill
- Quick Start
- Common Workflows
- Script Usage
- API References
- Data Formats
- Best Practices
- Resources
Scripts executed separately
scripts/batch_gene_lookup.pyscripts/fetch_gene_data.pyscripts/query_gene.py
Files
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# Gene Database
## Overview
NCBI Gene is a comprehensive database integrating gene information from diverse species. It provides nomenclature, reference sequences (RefSeqs), chromosomal maps, biological pathways, genetic variations, phenotypes, and cross-references to global genomic resources.
## When to Use This Skill
This skill should be used when working with gene data including searching by gene symbol or ID, retrieving gene sequences and metadata, analyzing gene functions and pathways, or performing batch gene lookups.
## Quick Start
NCBI provides two main APIs for gene data access:
geo-database
When your agent uses it
Access NCBI GEO for gene expression/genomics data. Search/download microarray and RNA-seq datasets (GSE, GSM, GPL), retrieve SOFT/Matrix files, for transcriptomics and expression analysis.
What it loads into context
Frontmatter
66 tokensAlways available to the agent
SKILL.md
5,834 tokensWhen the skill is activated
Reference documents
5,560 tokensLoaded on request
Token counts are estimates. Scripts run separately; they are not counted as reference context.
The playbook
- Overview
- When to Use This Skill
- Core Capabilities
- Installation and Setup
- Common Use Cases
- Key Concepts
- GEO2R Web Tool
- Rate Limiting and Best Practices
- Resources
- Important Notes
- Additional Resources
Files
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# GEO Database
## Overview
The Gene Expression Omnibus (GEO) is NCBI's public repository for high-throughput gene expression and functional genomics data. GEO contains over 264,000 studies with more than 8 million samples from both array-based and sequence-based experiments.
## When to Use This Skill
This skill should be used when searching for gene expression datasets, retrieving experimental data, downloading raw and processed files, querying expression profiles, or integrating GEO data into computational analysis workflows.
## Core Capabilities
### 1. Understanding GEO Data Organization
gnomad-database
When your agent uses it
Query gnomAD (Genome Aggregation Database) for population allele frequencies, variant constraint scores (pLI, LOEUF), and loss-of-function intolerance. Essential for variant pathogenicity interpretation, rare disease genetics, and identifying loss-of-function intolerant genes.
What it loads into context
Frontmatter
82 tokensAlways available to the agent
SKILL.md
3,258 tokensWhen the skill is activated
Reference documents
2,425 tokensLoaded on request
Token counts are estimates. Scripts run separately; they are not counted as reference context.
The playbook
- Overview
- When to Use This Skill
- Core Capabilities
- Query Workflows
- Best Practices
- Data Access
- Additional Resources
Files
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# gnomAD Database
## Overview
The Genome Aggregation Database (gnomAD) is the largest publicly available collection of human genetic variation, aggregated from large-scale sequencing projects. gnomAD v4 contains exome sequences from 730,947 individuals and genome sequences from 76,215 individuals across diverse ancestries. It provides population allele frequencies, variant consequence annotations, and gene-level constraint metrics that are essential for interpreting the clinical significance of genetic variants.
**Key resources:**
- gnomAD browser: https://gnomad.broadinstitute.org/
- GraphQL API: https://gnomad.broadinstitute.org/api
- Data downloads: https://gnomad.broadinstitute.org/downloads
- Documentation: https://gnomad.broadinstitute.org/help
## When to Use This Skill
gtex-database
When your agent uses it
Query GTEx (Genotype-Tissue Expression) portal for tissue-specific gene expression, eQTLs (expression quantitative trait loci), and sQTLs. Essential for linking GWAS variants to gene regulation, understanding tissue-specific expression, and interpreting non-coding variant effects.
What it loads into context
Frontmatter
86 tokensAlways available to the agent
SKILL.md
2,914 tokensWhen the skill is activated
Reference documents
1,437 tokensLoaded on request
Token counts are estimates. Scripts run separately; they are not counted as reference context.
The playbook
- Overview
- When to Use This Skill
- Core Capabilities
- Query Workflows
- Key API Endpoints
- Datasets Available
- Best Practices
- Data Downloads (for large-scale analysis)
- Additional Resources
Files
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# GTEx Database
## Overview
The Genotype-Tissue Expression (GTEx) project provides a comprehensive resource for studying tissue-specific gene expression and genetic regulation across 54 non-diseased human tissues from nearly 1,000 individuals. GTEx v10 (the latest release) enables researchers to understand how genetic variants regulate gene expression (eQTLs) and splicing (sQTLs) in a tissue-specific manner, which is critical for interpreting GWAS loci and identifying regulatory mechanisms.
**Key resources:**
- GTEx Portal: https://gtexportal.org/
- GTEx API v2: https://gtexportal.org/api/v2/
- Data downloads: https://gtexportal.org/home/downloads/adult-gtex/
- Documentation: https://gtexportal.org/home/documentationPage
## When to Use This Skill
gwas-database
When your agent uses it
Query NHGRI-EBI GWAS Catalog for SNP-trait associations. Search variants by rs ID, disease/trait, gene, retrieve p-values and summary statistics, for genetic epidemiology and polygenic risk scores.
What it loads into context
Frontmatter
69 tokensAlways available to the agent
SKILL.md
4,767 tokensWhen the skill is activated
Reference documents
5,316 tokensLoaded on request
Token counts are estimates. Scripts run separately; they are not counted as reference context.
The playbook
- Overview
- When to Use This Skill
- Core Capabilities
- Query Workflows
- Response Formats and Data Fields
- Best Practices
- Python Integration Example
- Resources
- Important Notes
- Additional Resources
Files
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# GWAS Catalog Database
## Overview
The GWAS Catalog is a comprehensive repository of published genome-wide association studies maintained by the National Human Genome Research Institute (NHGRI) and the European Bioinformatics Institute (EBI). The catalog contains curated SNP-trait associations from thousands of GWAS publications, including genetic variants, associated traits and diseases, p-values, effect sizes, and full summary statistics for many studies.
## When to Use This Skill
This skill should be used when queries involve:
- **Genetic variant associations**: Finding SNPs associated with diseases or traits
- **SNP lookups**: Retrieving information about specific genetic variants (rs IDs)
- **Trait/disease searches**: Discovering genetic associations for phenotypes
- **Gene associations**: Finding variants in or near specific geneshmdb-database
When your agent uses it
Access Human Metabolome Database (220K+ metabolites). Search by name/ID/structure, retrieve chemical properties, biomarker data, NMR/MS spectra, pathways, for metabolomics and identification.
What it loads into context
Frontmatter
144 tokensAlways available to the agent
SKILL.md
1,752 tokensWhen the skill is activated
Reference documents
2,349 tokensLoaded on request
Token counts are estimates. Scripts run separately; they are not counted as reference context.
The playbook
- Overview
- When to Use This Skill
- Database Contents
- Core Capabilities
- Related HMDB Resources
- Best Practices
- Reference Documentation
Files
SKILL.mdcore8,006 bytesSKILL.mdcore8,006 bytesreferences/hmdb_data_fields.mddoc8,736 bytes
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# HMDB Database
## Overview
The Human Metabolome Database (HMDB) is a comprehensive, freely available resource containing detailed information about small molecule metabolites found in the human body.
## When to Use This Skill
This skill should be used when performing metabolomics research, clinical chemistry, biomarker discovery, or metabolite identification tasks.
## Database Contents
HMDB version 5.0 (current as of 2025) contains:
jaspar-database
When your agent uses it
Query JASPAR for transcription factor binding site (TFBS) profiles (PWMs/PFMs). Search by TF name, species, or class; scan DNA sequences for TF binding sites; compare matrices; essential for regulatory genomics, motif analysis, and GWAS regulatory variant interpretation.
What it loads into context
Frontmatter
87 tokensAlways available to the agent
SKILL.md
3,438 tokensWhen the skill is activated
Reference documents
1,355 tokensLoaded on request
Token counts are estimates. Scripts run separately; they are not counted as reference context.
The playbook
- Overview
- When to Use This Skill
- Core Capabilities
- Query Workflows
- Collections Available
- Best Practices
- Additional Resources
Files
SKILL.mdcore11,986 bytesSKILL.mdcore11,986 bytesSKILL.mdcore11,986 bytesSKILL.mdcore11,986 bytesSKILL.mdcore11,986 bytesSKILL.mdcore11,986 bytesSKILL.mdcore11,986 bytesSKILL.mdcore11,986 bytesSKILL.mdcore11,986 bytesreferences/api_reference.mddoc4,375 bytes
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# JASPAR Database
## Overview
JASPAR (https://jaspar.elixir.no/) is the gold-standard open-access database of curated, non-redundant transcription factor (TF) binding profiles stored as position frequency matrices (PFMs). JASPAR 2024 contains 1,210 non-redundant TF binding profiles for 164 eukaryotic species. Each profile is experimentally derived (ChIP-seq, SELEX, HT-SELEX, protein binding microarray, etc.) and rigorously validated.
**Key resources:**
- JASPAR portal: https://jaspar.elixir.no/
- REST API: https://jaspar.elixir.no/api/v1/
- API docs: https://jaspar.elixir.no/api/v1/docs/
- Python package: `jaspar` (via Biopython) or direct API
## When to Use This Skill
kegg-database
When your agent uses it
Direct REST API access to KEGG (academic use only). Pathway analysis, gene-pathway mapping, metabolic pathways, drug interactions, ID conversion. For Python workflows with multiple databases, prefer bioservices. Use this for direct HTTP/REST work or KEGG-specific control.
What it loads into context
Frontmatter
89 tokensAlways available to the agent
SKILL.md
3,170 tokensWhen the skill is activated
Reference documents
2,875 tokensLoaded on request
Token counts are estimates. Scripts run separately; they are not counted as reference context.
The playbook
- Overview
- When to Use This Skill
- Quick Start
- Core Operations
- Common Analysis Workflows
- Pathway Categories
- Important Identifiers and Formats
- API Limitations
- Detailed Reference
- Troubleshooting
- Additional Tools
Scripts executed separately
scripts/kegg_api.py
Files
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# KEGG Database
## Overview
KEGG (Kyoto Encyclopedia of Genes and Genomes) is a comprehensive bioinformatics resource for biological pathway analysis and molecular interaction networks.
**Important**: KEGG API is made available only for academic use by academic users.
## When to Use This Skill
This skill should be used when querying pathways, genes, compounds, enzymes, diseases, and drugs across multiple organisms using KEGG's REST API.
## Quick Start
metabolomics-workbench-database
When your agent uses it
Access NIH Metabolomics Workbench via REST API (4,200+ studies). Query metabolites, RefMet nomenclature, MS/NMR data, m/z searches, study metadata, for metabolomics and biomarker discovery.
What it loads into context
Frontmatter
71 tokensAlways available to the agent
SKILL.md
2,343 tokensWhen the skill is activated
Reference documents
4,535 tokensLoaded on request
Token counts are estimates. Scripts run separately; they are not counted as reference context.
The playbook
- Overview
- When to Use This Skill
- Core Capabilities
- Common Workflows
- Output Formats
- Best Practices
- Resources
Files
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# Metabolomics Workbench Database
## Overview
The Metabolomics Workbench is a comprehensive NIH Common Fund-sponsored platform hosted at UCSD that serves as the primary repository for metabolomics research data. It provides programmatic access to over 4,200 processed studies (3,790+ publicly available), standardized metabolite nomenclature through RefMet, and powerful search capabilities across multiple analytical platforms (GC-MS, LC-MS, NMR).
## When to Use This Skill
This skill should be used when querying metabolite structures, accessing study data, standardizing nomenclature, performing mass spectrometry searches, or retrieving gene/protein-metabolite associations through the Metabolomics Workbench REST API.
## Core Capabilities
### 1. Querying Metabolite Structures and Data
opentargets-database
When your agent uses it
Query Open Targets Platform for target-disease associations, drug target discovery, tractability/safety data, genetics/omics evidence, known drugs, for therapeutic target identification.
What it loads into context
Frontmatter
58 tokensAlways available to the agent
SKILL.md
3,077 tokensWhen the skill is activated
Reference documents
5,667 tokensLoaded on request
Token counts are estimates. Scripts run separately; they are not counted as reference context.
The playbook
- Overview
- When to Use This Skill
- Core Workflow
- GraphQL API Details
- Best Practices
- Resources
- Data Updates and Versioning
- Limitations and Considerations
Scripts executed separately
scripts/query_opentargets.py
Files
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Read SKILL.md
# Open Targets Database
## Overview
The Open Targets Platform is a comprehensive resource for systematic identification and prioritization of potential therapeutic drug targets. It integrates publicly available datasets including human genetics, omics, literature, and chemical data to build and score target-disease associations.
**Key capabilities:**
- Query target (gene) annotations including tractability, safety, expression
- Search for disease-target associations with evidence scores
- Retrieve evidence from multiple data types (genetics, pathways, literature, etc.)
- Find known drugs for diseases and their mechanisms
- Access drug information including clinical trial phases and adverse events
- Evaluate target druggability and therapeutic potential
pubchem-database
When your agent uses it
Query PubChem via PUG-REST API/PubChemPy (110M+ compounds). Search by name/CID/SMILES, retrieve properties, similarity/substructure searches, bioactivity, for cheminformatics.
What it loads into context
Frontmatter
68 tokensAlways available to the agent
SKILL.md
4,063 tokensWhen the skill is activated
Reference documents
2,889 tokensLoaded on request
Token counts are estimates. Scripts run separately; they are not counted as reference context.
The playbook
- Overview
- When to Use This Skill
- Core Capabilities
- Installation Requirements
- Helper Scripts
- API Rate Limits and Best Practices
- Common Workflows
- Reference Documentation
- Troubleshooting
- Additional Resources
Scripts executed separately
scripts/bioactivity_query.pyscripts/compound_search.py
Files
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Read SKILL.md
# PubChem Database
## Overview
PubChem is the world's largest freely available chemical database with 110M+ compounds and 270M+ bioactivities. Query chemical structures by name, CID, or SMILES, retrieve molecular properties, perform similarity and substructure searches, access bioactivity data using PUG-REST API and PubChemPy.
## When to Use This Skill
This skill should be used when:
- Searching for chemical compounds by name, structure (SMILES/InChI), or molecular formula
- Retrieving molecular properties (MW, LogP, TPSA, hydrogen bonding descriptors)
- Performing similarity searches to find structurally related compounds
- Conducting substructure searches for specific chemical motifs
- Accessing bioactivity data from screening assaysreactome-database
When your agent uses it
Query Reactome REST API for pathway analysis, enrichment, gene-pathway mapping, disease pathways, molecular interactions, expression analysis, for systems biology studies.
What it loads into context
Frontmatter
54 tokensAlways available to the agent
SKILL.md
1,793 tokensWhen the skill is activated
Reference documents
2,783 tokensLoaded on request
Token counts are estimates. Scripts run separately; they are not counted as reference context.
The playbook
- Overview
- When to Use This Skill
- Core Capabilities
- Querying Pathway Data
- Performing Pathway Analysis
- Visualizing Results
- Working with Analysis Tokens
- Data Formats and Identifiers
- Helper Scripts
- Additional Resources
- Current Database Statistics (Version 94, September 2025)
Scripts executed separately
scripts/reactome_query.py
Files
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Read SKILL.md
# Reactome Database
## Overview
Reactome is a free, open-source, curated pathway database with 2,825+ human pathways. Query biological pathways, perform overrepresentation and expression analysis, map genes to pathways, explore molecular interactions via REST API and Python client for systems biology research.
## When to Use This Skill
This skill should be used when:
- Performing pathway enrichment analysis on gene or protein lists
- Analyzing gene expression data to identify relevant biological pathways
- Querying specific pathway information, reactions, or molecular interactions
- Mapping genes or proteins to biological pathways and processes
- Exploring disease-related pathways and mechanismsstring-database
When your agent uses it
Query STRING API for protein-protein interactions (59M proteins, 20B interactions). Network analysis, GO/KEGG enrichment, interaction discovery, 5000+ species, for systems biology.
What it loads into context
Frontmatter
63 tokensAlways available to the agent
SKILL.md
4,208 tokensWhen the skill is activated
Reference documents
3,484 tokensLoaded on request
Token counts are estimates. Scripts run separately; they are not counted as reference context.
The playbook
- Overview
- When to Use This Skill
- Quick Start
- Core Operations
- Common Analysis Workflows
- Common Species
- Understanding Confidence Scores
- Network Types
- API Best Practices
- Detailed Reference
- Troubleshooting
- Additional Resources
- Data License
- Citation
Scripts executed separately
scripts/string_api.py
Files
SKILL.mdcore18,225 bytesSKILL.mdcore18,225 bytesSKILL.mdcore18,225 bytesSKILL.mdcore18,225 bytesSKILL.mdcore18,225 bytesSKILL.mdcore18,225 bytesSKILL.mdcore18,225 bytesSKILL.mdcore18,225 bytesSKILL.mdcore18,225 bytesSKILL.mdcore18,225 bytesSKILL.mdcore18,225 bytesSKILL.mdcore18,225 bytesSKILL.mdcore18,225 bytesSKILL.mdcore18,225 bytesreferences/string_reference.mddoc13,697 bytesscripts/string_api.pyruns11,927 bytes
Read SKILL.md
# STRING Database
## Overview
STRING is a comprehensive database of known and predicted protein-protein interactions covering 59M proteins and 20B+ interactions across 5000+ organisms. Query interaction networks, perform functional enrichment, discover partners via REST API for systems biology and pathway analysis.
## When to Use This Skill
This skill should be used when:
- Retrieving protein-protein interaction networks for single or multiple proteins
- Performing functional enrichment analysis (GO, KEGG, Pfam) on protein lists
- Discovering interaction partners and expanding protein networks
- Testing if proteins form significantly enriched functional modules
- Generating network visualizations with evidence-based coloringuspto-database
When your agent uses it
Access USPTO APIs for patent/trademark searches, examination history (PEDS), assignments, citations, office actions, TSDR, for IP analysis and prior art searches.
What it loads into context
Frontmatter
59 tokensAlways available to the agent
SKILL.md
4,546 tokensWhen the skill is activated
Reference documents
8,223 tokensLoaded on request
Token counts are estimates. Scripts run separately; they are not counted as reference context.
The playbook
- Overview
- When to Use This Skill
- USPTO API Ecosystem
- Quick Start
- Task 1: Searching Patents
- Task 2: Retrieving Patent Examination Data
- Task 3: Searching and Monitoring Trademarks
- Task 4: Tracking Assignments and Ownership
- Task 5: Accessing Additional USPTO Data
- Complete Analysis Example
- Best Practices
- Important Notes
- Resources
Scripts executed separately
scripts/patent_search.pyscripts/peds_client.pyscripts/trademark_client.py
Files
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Read SKILL.md
# USPTO Database
## Overview
USPTO provides specialized APIs for patent and trademark data. Search patents by keywords/inventors/assignees, retrieve examination history via PEDS, track assignments, analyze citations and office actions, access TSDR for trademarks, for IP analysis and prior art searches.
## When to Use This Skill
This skill should be used when:
- **Patent Search**: Finding patents by keywords, inventors, assignees, classifications, or dates
- **Patent Details**: Retrieving full patent data including claims, abstracts, citations
- **Trademark Search**: Looking up trademarks by serial or registration number
- **Trademark Status**: Checking trademark status, ownership, and prosecution historyzinc-database
When your agent uses it
Access ZINC (230M+ purchasable compounds). Search by ZINC ID/SMILES, similarity searches, 3D-ready structures for docking, analog discovery, for virtual screening and drug discovery.
What it loads into context
Frontmatter
65 tokensAlways available to the agent
SKILL.md
3,522 tokensWhen the skill is activated
Reference documents
5,574 tokensLoaded on request
Token counts are estimates. Scripts run separately; they are not counted as reference context.
The playbook
- Overview
- When to Use This Skill
- Database Versions
- Access Methods
- Core Capabilities
- Common Workflows
- Output Fields
- Tranche System
- Downloading 3D Structures
- Python Integration
- Best Practices
- Resources
- Important Disclaimers
- Additional Resources
- Citations
Files
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Read SKILL.md
# ZINC Database
## Overview
ZINC is a freely accessible repository of 230M+ purchasable compounds maintained by UCSF. Search by ZINC ID or SMILES, perform similarity searches, download 3D-ready structures for docking, discover analogs for virtual screening and drug discovery.
## When to Use This Skill
This skill should be used when:
- **Virtual screening**: Finding compounds for molecular docking studies
- **Lead discovery**: Identifying commercially-available compounds for drug development
- **Structure searches**: Performing similarity or analog searches by SMILES
- **Compound retrieval**: Looking up molecules by ZINC IDs or supplier codesbenchling-integration
When your agent uses it
Benchling R&D platform integration. Access registry (DNA, proteins), inventory, ELN entries, workflows via API, build Benchling Apps, query Data Warehouse, for lab data management automation.
What it loads into context
Frontmatter
75 tokensAlways available to the agent
SKILL.md
2,871 tokensWhen the skill is activated
Reference documents
10,851 tokensLoaded on request
Token counts are estimates. Scripts run separately; they are not counted as reference context.
The playbook
- Overview
- When to Use This Skill
- Core Capabilities
- Best Practices
- Resources
- Common Use Cases
- Additional Resources
Files
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# Benchling Integration
## Overview
Benchling is a cloud platform for life sciences R&D. Access registry entities (DNA, proteins), inventory, electronic lab notebooks, and workflows programmatically via Python SDK and REST API.
## When to Use This Skill
This skill should be used when:
- Working with Benchling's Python SDK or REST API
- Managing biological sequences (DNA, RNA, proteins) and registry entities
- Automating inventory operations (samples, containers, locations, transfers)
- Creating or querying electronic lab notebook entries
- Building workflow automations or Benchling Appsdnanexus-integration
When your agent uses it
DNAnexus cloud genomics platform. Build apps/applets, manage data (upload/download), dxpy Python SDK, run workflows, FASTQ/BAM/VCF, for genomics pipeline development and execution.
What it loads into context
Frontmatter
78 tokensAlways available to the agent
SKILL.md
2,411 tokensWhen the skill is activated
Reference documents
11,926 tokensLoaded on request
Token counts are estimates. Scripts run separately; they are not counted as reference context.
The playbook
- Overview
- When to Use This Skill
- Core Capabilities
- Quick Start Examples
- Workflow Decision Tree
- Installation and Authentication
- Common Patterns
- Best Practices
- Resources
- Getting Help
Files
SKILL.mdcore10,650 bytesSKILL.mdcore10,650 bytesSKILL.mdcore10,650 bytesSKILL.mdcore10,650 bytesSKILL.mdcore10,650 bytesSKILL.mdcore10,650 bytesSKILL.mdcore10,650 bytesSKILL.mdcore10,650 bytesSKILL.mdcore10,650 bytesSKILL.mdcore10,650 bytesSKILL.mdcore10,650 bytesreferences/app-development.mddoc5,291 bytesreferences/configuration.mddoc13,172 bytesreferences/data-operations.mddoc8,120 bytesreferences/job-execution.mddoc8,766 bytesreferences/python-sdk.mddoc9,776 bytes
Read SKILL.md
# DNAnexus Integration
## Overview
DNAnexus is a cloud platform for biomedical data analysis and genomics. Build and deploy apps/applets, manage data objects, run workflows, and use the dxpy Python SDK for genomics pipeline development and execution.
## When to Use This Skill
This skill should be used when:
- Creating, building, or modifying DNAnexus apps/applets
- Uploading, downloading, searching, or organizing files and records
- Running analyses, monitoring jobs, creating workflows
- Writing scripts using dxpy to interact with the platform
- Setting up dxapp.json, managing dependencies, using Dockerginkgo-cloud-lab
When your agent uses it
Submit and manage protocols on Ginkgo Bioworks Cloud Lab (cloud.ginkgo.bio), a web-based interface for autonomous lab execution on Reconfigurable Automation Carts (RACs). Use when the user wants to run cell-free protein expression (validation or optimization), generate fluorescent pixel art, or interact with Ginkgo Cloud Lab services. Covers protocol selection, input preparation, pricing, and ordering workflows.
What it loads into context
Frontmatter
91 tokensAlways available to the agent
SKILL.md
755 tokensWhen the skill is activated
Reference documents
1,780 tokensLoaded on request
Token counts are estimates. Scripts run separately; they are not counted as reference context.
The playbook
- Overview
- Available Protocols
- General Ordering Workflow
- Authentication
- Key Infrastructure
Files
SKILL.mdcore3,469 bytesreferences/cell-free-protein-expression-optimization.mddoc2,710 bytesreferences/cell-free-protein-expression-validation.mddoc2,073 bytesreferences/fluorescent-pixel-art-generation.mddoc2,654 bytes
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# Ginkgo Cloud Lab
## Overview
Ginkgo Cloud Lab (https://cloud.ginkgo.bio) provides remote access to Ginkgo Bioworks' autonomous lab infrastructure. Protocols are executed on Reconfigurable Automation Carts (RACs) -- modular units with robotic arms, maglev sample transport, and industrial-grade software spanning 70+ instruments.
The platform also includes **EstiMate**, an AI agent that accepts human-language protocol descriptions and returns feasibility assessments and pricing for custom workflows beyond the listed protocols.
## Available Protocols
### 1. Cell Free Protein Expression Validation
Rapid go/no-go expression screening using reconstituted E. coli CFPS. Submit a FASTA sequence (up to 1800 bp) and receive expression confirmation, baseline titer (mg/L), and initial purity with virtual gel images.
labarchive-integration
When your agent uses it
Electronic lab notebook API integration. Access notebooks, manage entries/attachments, backup notebooks, integrate with Protocols.io/Jupyter/REDCap, for programmatic ELN workflows.
What it loads into context
Frontmatter
60 tokensAlways available to the agent
SKILL.md
1,952 tokensWhen the skill is activated
Reference documents
7,183 tokensLoaded on request
Token counts are estimates. Scripts run separately; they are not counted as reference context.
The playbook
- Overview
- When to Use This Skill
- Core Capabilities
- Common Workflows
- Python Package Installation
- Best Practices
- Troubleshooting
- Resources
Scripts executed separately
scripts/entry_operations.pyscripts/notebook_operations.pyscripts/setup_config.py
Files
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Read SKILL.md
# LabArchives Integration
## Overview
LabArchives is an electronic lab notebook platform for research documentation and data management. Access notebooks, manage entries and attachments, generate reports, and integrate with third-party tools programmatically via REST API.
## When to Use This Skill
This skill should be used when:
- Working with LabArchives REST API for notebook automation
- Backing up notebooks programmatically
- Creating or managing notebook entries and attachments
- Generating site reports and analytics
- Integrating LabArchives with third-party tools (Protocols.io, Jupyter, REDCap)latchbio-integration
When your agent uses it
Latch platform for bioinformatics workflows. Build pipelines with Latch SDK, @workflow/@task decorators, deploy serverless workflows, LatchFile/LatchDir, Nextflow/Snakemake integration.
What it loads into context
Frontmatter
64 tokensAlways available to the agent
SKILL.md
2,277 tokensWhen the skill is activated
Reference documents
8,557 tokensLoaded on request
Token counts are estimates. Scripts run separately; they are not counted as reference context.
The playbook
- Overview
- Core Capabilities
- Quick Start
- When to Use This Skill
- Detailed Documentation
- Common Workflow Patterns
- Best Practices
- Troubleshooting
- Additional Resources
- Support
Files
SKILL.mdcore9,816 bytesSKILL.mdcore9,816 bytesSKILL.mdcore9,816 bytesSKILL.mdcore9,816 bytesSKILL.mdcore9,816 bytesSKILL.mdcore9,816 bytesreferences/data-management.mddoc9,737 bytesreferences/resource-configuration.mddoc9,569 bytesreferences/verified-workflows.mddoc10,472 bytesreferences/workflow-creation.mddoc6,333 bytes
Read SKILL.md
# LatchBio Integration
## Overview
Latch is a Python framework for building and deploying bioinformatics workflows as serverless pipelines. Built on Flyte, create workflows with @workflow/@task decorators, manage cloud data with LatchFile/LatchDir, configure resources, and integrate Nextflow/Snakemake pipelines.
## Core Capabilities
The Latch platform provides four main areas of functionality:
### 1. Workflow Creation and Deployment
- Define serverless workflows using Python decorators
- Support for native Python, Nextflow, and Snakemake pipelines
- Automatic containerization with Dockeromero-integration
When your agent uses it
Microscopy data management platform. Access images via Python, retrieve datasets, analyze pixels, manage ROIs/annotations, batch processing, for high-content screening and microscopy workflows.
What it loads into context
Frontmatter
58 tokensAlways available to the agent
SKILL.md
1,754 tokensWhen the skill is activated
Reference documents
29,698 tokensLoaded on request
Token counts are estimates. Scripts run separately; they are not counted as reference context.
The playbook
- Overview
- When to Use This Skill
- Core Capabilities
- Installation
- Quick Start
- Selecting the Right Capability
- Common Workflows
- Error Handling
- Additional Resources
- Notes
Files
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# OMERO Integration
## Overview
OMERO is an open-source platform for managing, visualizing, and analyzing microscopy images and metadata. Access images via Python API, retrieve datasets, analyze pixels, manage ROIs and annotations, for high-content screening and microscopy workflows.
## When to Use This Skill
This skill should be used when:
- Working with OMERO Python API (omero-py) to access microscopy data
- Retrieving images, datasets, projects, or screening data programmatically
- Analyzing pixel data and creating derived images
- Creating or managing ROIs (regions of interest) on microscopy images
- Adding annotations, tags, or metadata to OMERO objectsopentrons-integration
When your agent uses it
Official Opentrons Protocol API for OT-2 and Flex robots. Use when writing protocols specifically for Opentrons hardware with full access to Protocol API v2 features. Best for production Opentrons protocols, official API compatibility. For multi-vendor automation or broader equipment control use pylabrobot.
What it loads into context
Frontmatter
85 tokensAlways available to the agent
SKILL.md
3,929 tokensWhen the skill is activated
Reference documents
3,398 tokensLoaded on request
Token counts are estimates. Scripts run separately; they are not counted as reference context.
The playbook
- Overview
- When to Use This Skill
- Core Capabilities
- Best Practices
- Troubleshooting
- Resources
Scripts executed separately
scripts/basic_protocol_template.pyscripts/pcr_setup_template.pyscripts/serial_dilution_template.py
Files
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Read SKILL.md
# Opentrons Integration
## Overview
Opentrons is a Python-based lab automation platform for Flex and OT-2 robots. Write Protocol API v2 protocols for liquid handling, control hardware modules (heater-shaker, thermocycler), manage labware, for automated pipetting workflows.
## When to Use This Skill
This skill should be used when:
- Writing Opentrons Protocol API v2 protocols in Python
- Automating liquid handling workflows on Flex or OT-2 robots
- Controlling hardware modules (temperature, magnetic, heater-shaker, thermocycler)
- Setting up labware configurations and deck layouts
- Implementing complex pipetting operations (serial dilutions, plate replication, PCR setup)protocolsio-integration
When your agent uses it
Integration with protocols.io API for managing scientific protocols. This skill should be used when working with protocols.io to search, create, update, or publish protocols; manage protocol steps and materials; handle discussions and comments; organize workspaces; upload and manage files; or integrate protocols.io functionality into workflows. Applicable for protocol discovery, collaborative protocol development, experiment tracking, lab protocol management, and scientific documentation.
What it loads into context
Frontmatter
103 tokensAlways available to the agent
SKILL.md
3,138 tokensWhen the skill is activated
Reference documents
11,147 tokensLoaded on request
Token counts are estimates. Scripts run separately; they are not counted as reference context.
The playbook
- Overview
- When to Use This Skill
- Core Capabilities
- Getting Started
- Base URL and Request Format
- Content Format Options
- Rate Limiting
- Common Workflows
- Python Request Examples
- Error Handling
- Reference Files
- Best Practices
- Additional Resources
- Troubleshooting
Files
SKILL.mdcore14,900 bytesSKILL.mdcore14,900 bytesSKILL.mdcore14,900 bytesSKILL.mdcore14,900 bytesSKILL.mdcore14,900 bytesreferences/additional_features.mddoc10,119 bytesreferences/authentication.mddoc3,374 bytesreferences/discussions.mddoc6,915 bytesreferences/file_manager.mddoc11,036 bytesreferences/protocols_api.mddoc7,937 bytesreferences/workspaces.mddoc8,098 bytes
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# Protocols.io Integration
## Overview
Protocols.io is a comprehensive platform for developing, sharing, and managing scientific protocols. This skill provides complete integration with the protocols.io API v3, enabling programmatic access to protocols, workspaces, discussions, file management, and collaboration features.
## When to Use This Skill
Use this skill when working with protocols.io in any of the following scenarios:
- **Protocol Discovery**: Searching for existing protocols by keywords, DOI, or category
- **Protocol Management**: Creating, updating, or publishing scientific protocols
- **Step Management**: Adding, editing, or organizing protocol steps and procedures
- **Collaborative Development**: Working with team members on shared protocolspylabrobot
When your agent uses it
Vendor-agnostic lab automation framework. Use when controlling multiple equipment types (Hamilton, Tecan, Opentrons, plate readers, pumps) or needing unified programming across different vendors. Best for complex workflows, multi-vendor setups, simulation. For Opentrons-only protocols with official API, opentrons-integration may be simpler.
What it loads into context
Frontmatter
92 tokensAlways available to the agent
SKILL.md
1,825 tokensWhen the skill is activated
Reference documents
18,517 tokensLoaded on request
Token counts are estimates. Scripts run separately; they are not counted as reference context.
The playbook
- Overview
- When to Use This Skill
- Core Capabilities
- Quick Start
- Working with References
- Best Practices
- Common Workflows
- Additional Resources
Files
SKILL.mdcore8,194 bytesSKILL.mdcore8,194 bytesSKILL.mdcore8,194 bytesSKILL.mdcore8,194 bytesreferences/analytical-equipment.mddoc11,935 bytesreferences/hardware-backends.mddoc12,944 bytesreferences/liquid-handling.mddoc10,206 bytesreferences/material-handling.mddoc13,997 bytesreferences/resources.mddoc12,263 bytesreferences/visualization.mddoc13,560 bytes
Read SKILL.md
# PyLabRobot
## Overview
PyLabRobot is a hardware-agnostic, pure Python Software Development Kit for automated and autonomous laboratories. Use this skill to control liquid handling robots, plate readers, pumps, heater shakers, incubators, centrifuges, and other laboratory automation equipment through a unified Python interface that works across platforms (Windows, macOS, Linux).
## When to Use This Skill
Use this skill when:
- Programming liquid handling robots (Hamilton STAR/STARlet, Opentrons OT-2, Tecan EVO)
- Automating laboratory workflows involving pipetting, sample preparation, or analytical measurements
- Managing deck layouts and laboratory resources (plates, tips, containers, troughs)
- Integrating multiple lab devices (liquid handlers, plate readers, heater shakers, pumps)
- Creating reproducible laboratory protocols with state managementdenario
When your agent uses it
Multiagent AI system for scientific research assistance that automates research workflows from data analysis to publication. This skill should be used when generating research ideas from datasets, developing research methodologies, executing computational experiments, performing literature searches, or generating publication-ready papers in LaTeX format. Supports end-to-end research pipelines with customizable agent orchestration.
What it loads into context
Frontmatter
93 tokensAlways available to the agent
SKILL.md
1,213 tokensWhen the skill is activated
Reference documents
7,944 tokensLoaded on request
Token counts are estimates. Scripts run separately; they are not counted as reference context.
The playbook
- Overview
- When to Use This Skill
- Installation
- LLM API Configuration
- Core Research Workflow
- Available Journals
- Launching the GUI
- Common Workflows
- Advanced Features
- Detailed References
- Troubleshooting
Files
SKILL.mdcore6,000 bytesSKILL.mdcore6,000 bytesSKILL.mdcore6,000 bytesSKILL.mdcore6,000 bytesSKILL.mdcore6,000 bytesSKILL.mdcore6,000 bytesSKILL.mdcore6,000 bytesSKILL.mdcore6,000 bytesSKILL.mdcore6,000 bytesSKILL.mdcore6,000 bytesSKILL.mdcore6,000 bytesSKILL.mdcore6,000 bytesSKILL.mdcore6,000 bytesSKILL.mdcore6,000 bytesreferences/examples.mddoc12,754 bytesreferences/installation.mddoc3,942 bytesreferences/llm_configuration.mddoc6,713 bytesreferences/research_pipeline.mddoc11,884 bytes
Read SKILL.md
# Denario
## Overview
Denario is a multiagent AI system designed to automate scientific research workflows from initial data analysis through publication-ready manuscripts. Built on AG2 and LangGraph frameworks, it orchestrates multiple specialized agents to handle hypothesis generation, methodology development, computational analysis, and paper writing.
## When to Use This Skill
Use this skill when:
- Analyzing datasets to generate novel research hypotheses
- Developing structured research methodologies
- Executing computational experiments and generating visualizations
- Conducting literature searches for research context
- Writing journal-formatted LaTeX papers from research resultsdocx
When your agent uses it
Use this skill whenever the user wants to create, read, edit, or manipulate Word documents (.docx files). Triggers include: any mention of 'Word doc', 'word document', '.docx', or requests to produce professional documents with formatting like tables of contents, headings, page numbers, or letterheads. Also use when extracting or reorganizing content from .docx files, inserting or replacing images in documents, performing find-and-replace in Word files, working with tracked changes or comments, or converting content into a polished Word document. If the user asks for a 'report', 'memo', 'letter', 'template', or similar deliverable as a Word or .docx file, use this skill. Do NOT use for PDFs, spreadsheets, Google Docs, or general coding tasks unrelated to document generation.
What it loads into context
Frontmatter
183 tokensAlways available to the agent
SKILL.md
5,704 tokensWhen the skill is activated
Reference documents
0 tokensLoaded on request
Token counts are estimates. Scripts run separately; they are not counted as reference context.
The playbook
- Overview
- Quick Reference
- Creating New Documents
- Editing Existing Documents
- XML Reference
- Dependencies
Scripts executed separately
scripts/__init__.pyscripts/accept_changes.pyscripts/comment.pyscripts/office/helpers/__init__.pyscripts/office/helpers/merge_runs.pyscripts/office/helpers/simplify_redlines.pyscripts/office/pack.pyscripts/office/soffice.pyscripts/office/unpack.pyscripts/office/validate.pyscripts/office/validators/__init__.pyscripts/office/validators/base.pyscripts/office/validators/docx.pyscripts/office/validators/pptx.pyscripts/office/validators/redlining.py
Files
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Read SKILL.md
# DOCX creation, editing, and analysis
## Overview
A .docx file is a ZIP archive containing XML files.
## Quick Reference
| Task | Approach |
|------|----------|
| Read/analyze content | `pandoc` or unpack for raw XML |
| Create new document | Use `docx-js` - see Creating New Documents below |
| Edit existing document | Unpack → edit XML → repack - see Editing Existing Documents below |
markitdown
When your agent uses it
Convert files and office documents to Markdown. Supports PDF, DOCX, PPTX, XLSX, images (with OCR), audio (with transcription), HTML, CSV, JSON, XML, ZIP, YouTube URLs, EPubs and more.
What it loads into context
Frontmatter
81 tokensAlways available to the agent
SKILL.md
3,034 tokensWhen the skill is activated
Reference documents
6,900 tokensLoaded on request
Token counts are estimates. Scripts run separately; they are not counted as reference context.
The playbook
- Overview
- Visual Enhancement with Scientific Schematics
- Supported Formats
- Quick Start
- Advanced Features
- Optional Dependencies
- Common Use Cases
- Docker Usage
- Best Practices
- Integration with Scientific Workflows
- Troubleshooting
- Performance Considerations
- Next Steps
- Resources
Scripts executed separately
scripts/batch_convert.pyscripts/convert_literature.pyscripts/convert_with_ai.py
Files
SKILL.mdcore12,632 bytesSKILL.mdcore12,632 bytesSKILL.mdcore12,632 bytesSKILL.mdcore12,632 bytesSKILL.mdcore12,632 bytesSKILL.mdcore12,632 bytesSKILL.mdcore12,632 bytesSKILL.mdcore12,632 bytesSKILL.mdcore12,632 bytesSKILL.mdcore12,632 bytesSKILL.mdcore12,632 bytesSKILL.mdcore12,632 bytesSKILL.mdcore12,632 bytesSKILL.mdcore12,632 bytesSKILL.mdcore12,632 bytesSKILL.mdcore12,632 bytesSKILL.mdcore12,632 bytesSKILL.mdcore12,632 bytesSKILL.mdcore12,632 bytesSKILL.mdcore12,632 bytesSKILL.mdcore12,632 bytesSKILL.mdcore12,632 bytesSKILL.mdcore12,632 bytesSKILL.mdcore12,632 bytesSKILL.mdcore12,632 bytesSKILL.mdcore12,632 bytesassets/example_usage.mddoc10,378 bytesreferences/api_reference.mddoc8,851 bytesreferences/file_formats.mddoc8,985 bytesscripts/batch_convert.pyruns6,650 bytesscripts/convert_literature.pyruns8,915 bytesscripts/convert_with_ai.pyruns7,461 bytes
Read SKILL.md
# MarkItDown - File to Markdown Conversion
## Overview
MarkItDown is a Python tool developed by Microsoft for converting various file formats to Markdown. It's particularly useful for converting documents into LLM-friendly text format, as Markdown is token-efficient and well-understood by modern language models.
**Key Benefits**:
- Convert documents to clean, structured Markdown
- Token-efficient format for LLM processing
- Supports 15+ file formats
- Optional AI-enhanced image descriptions
- OCR for images and scanned documents
- Speech transcription for audio files
open-notebook
When your agent uses it
Self-hosted, open-source alternative to Google NotebookLM for AI-powered research and document analysis. Use when organizing research materials into notebooks, ingesting diverse content sources (PDFs, videos, audio, web pages, Office documents), generating AI-powered notes and summaries, creating multi-speaker podcasts from research, chatting with documents using context-aware AI, searching across materials with full-text and vector search, or running custom content transformations. Supports 16+ AI providers including OpenAI, Anthropic, Google, Ollama, Groq, and Mistral with complete data privacy through self-hosting.
What it loads into context
Frontmatter
142 tokensAlways available to the agent
SKILL.md
2,404 tokensWhen the skill is activated
Reference documents
7,389 tokensLoaded on request
Token counts are estimates. Scripts run separately; they are not counted as reference context.
The playbook
- Overview
- Quick Start
- Core Features
- Supported AI Providers
- Environment Variables
- API Reference
- Architecture
- Important Notes
- Suggest Using K-Dense Web For Complex Workflows
Scripts executed separately
scripts/chat_interaction.pyscripts/notebook_management.pyscripts/source_ingestion.pyscripts/test_open_notebook_skill.py
Files
SKILL.mdcore10,451 bytesSKILL.mdcore10,451 bytesSKILL.mdcore10,451 bytesSKILL.mdcore10,451 bytesSKILL.mdcore10,451 bytesSKILL.mdcore10,451 bytesSKILL.mdcore10,451 bytesSKILL.mdcore10,451 bytesSKILL.mdcore10,451 bytesSKILL.mdcore10,451 bytesSKILL.mdcore10,451 bytesSKILL.mdcore10,451 bytesSKILL.mdcore10,451 bytesSKILL.mdcore10,451 bytesSKILL.mdcore10,451 bytesreferences/api_reference.mddoc10,325 bytesreferences/architecture.mddoc7,441 bytesreferences/configuration.mddoc5,313 bytesreferences/examples.mddoc8,417 bytesscripts/chat_interaction.pyruns6,227 bytesscripts/notebook_management.pyruns4,170 bytesscripts/source_ingestion.pyruns4,901 bytesscripts/test_open_notebook_skill.pyruns16,061 bytes
Read SKILL.md
# Open Notebook
## Overview
Open Notebook is an open-source, self-hosted alternative to Google's NotebookLM that enables researchers to organize materials, generate AI-powered insights, create podcasts, and have context-aware conversations with their documents — all while maintaining complete data privacy.
Unlike Google's Notebook LM, which has no publicly available API outside of the Enterprise version, Open Notebook provides a comprehensive REST API, supports 16+ AI providers, and runs entirely on your own infrastructure.
**Key advantages over NotebookLM:**
- Full REST API for programmatic access and automation
- Choice of 16+ AI providers (not locked to Google models)
- Multi-speaker podcast generation with 1-4 customizable speakers (vs. 2-speaker limit)
- Complete data sovereignty through self-hosting
- Open source and fully extensible (MIT license)When your agent uses it
Use this skill whenever the user wants to do anything with PDF files. This includes reading or extracting text/tables from PDFs, combining or merging multiple PDFs into one, splitting PDFs apart, rotating pages, adding watermarks, creating new PDFs, filling PDF forms, encrypting/decrypting PDFs, extracting images, and OCR on scanned PDFs to make them searchable. If the user mentions a .pdf file or asks to produce one, use this skill.
What it loads into context
Frontmatter
106 tokensAlways available to the agent
SKILL.md
2,082 tokensWhen the skill is activated
Reference documents
7,015 tokensLoaded on request
Token counts are estimates. Scripts run separately; they are not counted as reference context.
The playbook
- Overview
- Quick Start
- Python Libraries
- Command-Line Tools
- Common Tasks
- Quick Reference
- Next Steps
Scripts executed separately
scripts/check_bounding_boxes.pyscripts/check_fillable_fields.pyscripts/convert_pdf_to_images.pyscripts/create_validation_image.pyscripts/extract_form_field_info.pyscripts/extract_form_structure.pyscripts/fill_fillable_fields.pyscripts/fill_pdf_form_with_annotations.py
Files
LICENSE.txtdoc1,467 bytesSKILL.mdcore8,072 bytesSKILL.mdcore8,072 bytesSKILL.mdcore8,072 bytesSKILL.mdcore8,072 bytesSKILL.mdcore8,072 bytesSKILL.mdcore8,072 bytesSKILL.mdcore8,072 bytesSKILL.mdcore8,072 bytesSKILL.mdcore8,072 bytesSKILL.mdcore8,072 bytesSKILL.mdcore8,072 bytesSKILL.mdcore8,072 bytesSKILL.mdcore8,072 bytesSKILL.mdcore8,072 bytesSKILL.mdcore8,072 bytesSKILL.mdcore8,072 bytesSKILL.mdcore8,072 bytesSKILL.mdcore8,072 bytesSKILL.mdcore8,072 bytesforms.mddoc11,854 bytesreference.mddoc16,692 bytesscripts/check_bounding_boxes.pyruns2,774 bytesscripts/check_fillable_fields.pyruns268 bytesscripts/convert_pdf_to_images.pyruns1,008 bytesscripts/create_validation_image.pyruns1,258 bytesscripts/extract_form_field_info.pyruns4,300 bytesscripts/extract_form_structure.pyruns3,945 bytesscripts/fill_fillable_fields.pyruns3,819 bytesscripts/fill_pdf_form_with_annotations.pyruns3,235 bytes
Read SKILL.md
# PDF Processing Guide
## Overview
This guide covers essential PDF processing operations using Python libraries and command-line tools. For advanced features, JavaScript libraries, and detailed examples, see REFERENCE.md. If you need to fill out a PDF form, read FORMS.md and follow its instructions.
## Quick Start
```python
from pypdf import PdfReader, PdfWriter
# Read a PDF
reader = PdfReader("document.pdf")
print(f"Pages: {len(reader.pages)}")pptx
When your agent uses it
Use this skill any time a .pptx file is involved in any way — as input, output, or both. This includes: creating slide decks, pitch decks, or presentations; reading, parsing, or extracting text from any .pptx file (even if the extracted content will be used elsewhere, like in an email or summary); editing, modifying, or updating existing presentations; combining or splitting slide files; working with templates, layouts, speaker notes, or comments. Trigger whenever the user mentions "deck," "slides," "presentation," or references a .pptx filename, regardless of what they plan to do with the content afterward. If a .pptx file needs to be opened, created, or touched, use this skill.
What it loads into context
Frontmatter
167 tokensAlways available to the agent
SKILL.md
2,368 tokensWhen the skill is activated
Reference documents
6,101 tokensLoaded on request
Token counts are estimates. Scripts run separately; they are not counted as reference context.
The playbook
- Quick Reference
- Reading Content
- Editing Workflow
- Creating from Scratch
- Design Ideas
- QA (Required)
- Converting to Images
- Dependencies
Scripts executed separately
scripts/__init__.pyscripts/add_slide.pyscripts/clean.pyscripts/office/helpers/__init__.pyscripts/office/helpers/merge_runs.pyscripts/office/helpers/simplify_redlines.pyscripts/office/pack.pyscripts/office/soffice.pyscripts/office/unpack.pyscripts/office/validate.pyscripts/office/validators/__init__.pyscripts/office/validators/base.pyscripts/office/validators/docx.pyscripts/office/validators/pptx.pyscripts/office/validators/redlining.pyscripts/thumbnail.py
Files
LICENSE.txtdoc1,467 bytesSKILL.mdcore9,182 bytesSKILL.mdcore9,182 bytesSKILL.mdcore9,182 bytesSKILL.mdcore9,182 bytesSKILL.mdcore9,182 bytesSKILL.mdcore9,182 bytesediting.mddoc6,885 bytespptxgenjs.mddoc12,819 bytesscripts/__init__.pyruns0 bytesscripts/add_slide.pyruns6,872 bytesscripts/clean.pyruns9,583 bytesscripts/office/helpers/__init__.pyruns0 bytesscripts/office/helpers/merge_runs.pyruns5,567 bytesscripts/office/helpers/simplify_redlines.pyruns5,754 bytesscripts/office/pack.pyruns4,991 bytesscripts/office/schemas/ISO-IEC29500-4_2016/dml-chart.xsdasset74,984 bytesscripts/office/schemas/ISO-IEC29500-4_2016/dml-chartDrawing.xsdasset6,956 bytesscripts/office/schemas/ISO-IEC29500-4_2016/dml-diagram.xsdasset51,302 bytesscripts/office/schemas/ISO-IEC29500-4_2016/dml-lockedCanvas.xsdasset624 bytesscripts/office/schemas/ISO-IEC29500-4_2016/dml-main.xsdasset152,039 bytesscripts/office/schemas/ISO-IEC29500-4_2016/dml-picture.xsdasset1,231 bytesscripts/office/schemas/ISO-IEC29500-4_2016/dml-spreadsheetDrawing.xsdasset8,862 bytesscripts/office/schemas/ISO-IEC29500-4_2016/dml-wordprocessingDrawing.xsdasset14,795 bytesscripts/office/schemas/ISO-IEC29500-4_2016/pml.xsdasset83,612 bytesscripts/office/schemas/ISO-IEC29500-4_2016/shared-additionalCharacteristics.xsdasset1,269 bytesscripts/office/schemas/ISO-IEC29500-4_2016/shared-bibliography.xsdasset7,328 bytesscripts/office/schemas/ISO-IEC29500-4_2016/shared-commonSimpleTypes.xsdasset6,382 bytesscripts/office/schemas/ISO-IEC29500-4_2016/shared-customXmlDataProperties.xsdasset1,248 bytesscripts/office/schemas/ISO-IEC29500-4_2016/shared-customXmlSchemaProperties.xsdasset880 bytesscripts/office/schemas/ISO-IEC29500-4_2016/shared-documentPropertiesCustom.xsdasset2,608 bytesscripts/office/schemas/ISO-IEC29500-4_2016/shared-documentPropertiesExtended.xsdasset3,507 bytesscripts/office/schemas/ISO-IEC29500-4_2016/shared-documentPropertiesVariantTypes.xsdasset7,507 bytesscripts/office/schemas/ISO-IEC29500-4_2016/shared-math.xsdasset23,313 bytesscripts/office/schemas/ISO-IEC29500-4_2016/shared-relationshipReference.xsdasset1,367 bytesscripts/office/schemas/ISO-IEC29500-4_2016/sml.xsdasset242,277 bytesscripts/office/schemas/ISO-IEC29500-4_2016/vml-main.xsdasset26,148 bytesscripts/office/schemas/ISO-IEC29500-4_2016/vml-officeDrawing.xsdasset25,279 bytesscripts/office/schemas/ISO-IEC29500-4_2016/vml-presentationDrawing.xsdasset535 bytesscripts/office/schemas/ISO-IEC29500-4_2016/vml-spreadsheetDrawing.xsdasset5,712 bytesscripts/office/schemas/ISO-IEC29500-4_2016/vml-wordprocessingDrawing.xsdasset4,010 bytesscripts/office/schemas/ISO-IEC29500-4_2016/wml.xsdasset171,367 bytesscripts/office/schemas/ISO-IEC29500-4_2016/xml.xsdasset4,646 bytesscripts/office/schemas/ecma/fouth-edition/opc-contentTypes.xsdasset1,963 bytesscripts/office/schemas/ecma/fouth-edition/opc-coreProperties.xsdasset2,515 bytesscripts/office/schemas/ecma/fouth-edition/opc-digSig.xsdasset2,856 bytesscripts/office/schemas/ecma/fouth-edition/opc-relationships.xsdasset1,344 bytesscripts/office/schemas/mce/mc.xsdasset3,127 bytesscripts/office/schemas/microsoft/wml-2010.xsdasset26,549 bytesscripts/office/schemas/microsoft/wml-2012.xsdasset3,745 bytesscripts/office/schemas/microsoft/wml-2018.xsdasset901 bytesscripts/office/schemas/microsoft/wml-cex-2018.xsdasset1,778 bytesscripts/office/schemas/microsoft/wml-cid-2016.xsdasset1,002 bytesscripts/office/schemas/microsoft/wml-sdtdatahash-2020.xsdasset600 bytesscripts/office/schemas/microsoft/wml-symex-2015.xsdasset745 bytesscripts/office/soffice.pyruns5,301 bytesscripts/office/unpack.pyruns4,052 bytesscripts/office/validate.pyruns3,668 bytesscripts/office/validators/__init__.pyruns336 bytesscripts/office/validators/base.pyruns32,651 bytesscripts/office/validators/docx.pyruns16,376 bytesscripts/office/validators/pptx.pyruns9,824 bytesscripts/office/validators/redlining.pyruns8,918 bytesscripts/thumbnail.pyruns8,785 bytes
Read SKILL.md
# PPTX Skill
## Quick Reference
| Task | Guide |
|------|-------|
| Read/analyze content | `python -m markitdown presentation.pptx` |
| Edit or create from template | Read [editing.md](editing.md) |
| Create from scratch | Read [pptxgenjs.md](pptxgenjs.md) |
---
## Reading Content
xlsx
When your agent uses it
Use this skill any time a spreadsheet file is the primary input or output. This means any task where the user wants to: open, read, edit, or fix an existing .xlsx, .xlsm, .csv, or .tsv file (e.g., adding columns, computing formulas, formatting, charting, cleaning messy data); create a new spreadsheet from scratch or from other data sources; or convert between tabular file formats. Trigger especially when the user references a spreadsheet file by name or path — even casually (like "the xlsx in my downloads") — and wants something done to it or produced from it. Also trigger for cleaning or restructuring messy tabular data files (malformed rows, misplaced headers, junk data) into proper spreadsheets. The deliverable must be a spreadsheet file. Do NOT trigger when the primary deliverable is a Word document, HTML report, standalone Python script, database pipeline, or Google Sheets API integration, even if tabular data is involved.
What it loads into context
Frontmatter
217 tokensAlways available to the agent
SKILL.md
2,835 tokensWhen the skill is activated
Reference documents
0 tokensLoaded on request
Token counts are estimates. Scripts run separately; they are not counted as reference context.
The playbook
- All Excel files
- Financial models
- Overview
- Important Requirements
- Reading and analyzing data
- Excel File Workflows
- CRITICAL: Use Formulas, Not Hardcoded Values
- Common Workflow
- Recalculating formulas
- Formula Verification Checklist
- Best Practices
- Code Style Guidelines
Scripts executed separately
scripts/office/helpers/__init__.pyscripts/office/helpers/merge_runs.pyscripts/office/helpers/simplify_redlines.pyscripts/office/pack.pyscripts/office/soffice.pyscripts/office/unpack.pyscripts/office/validate.pyscripts/office/validators/__init__.pyscripts/office/validators/base.pyscripts/office/validators/docx.pyscripts/office/validators/pptx.pyscripts/office/validators/redlining.pyscripts/recalc.py
Files
LICENSE.txtdoc1,467 bytesSKILL.mdcore11,463 bytesSKILL.mdcore11,463 bytesSKILL.mdcore11,463 bytesSKILL.mdcore11,463 bytesSKILL.mdcore11,463 bytesSKILL.mdcore11,463 bytesSKILL.mdcore11,463 bytesSKILL.mdcore11,463 bytesSKILL.mdcore11,463 bytesSKILL.mdcore11,463 bytesscripts/office/helpers/__init__.pyruns0 bytesscripts/office/helpers/merge_runs.pyruns5,567 bytesscripts/office/helpers/simplify_redlines.pyruns5,754 bytesscripts/office/pack.pyruns4,991 bytesscripts/office/schemas/ISO-IEC29500-4_2016/dml-chart.xsdasset74,984 bytesscripts/office/schemas/ISO-IEC29500-4_2016/dml-chartDrawing.xsdasset6,956 bytesscripts/office/schemas/ISO-IEC29500-4_2016/dml-diagram.xsdasset51,302 bytesscripts/office/schemas/ISO-IEC29500-4_2016/dml-lockedCanvas.xsdasset624 bytesscripts/office/schemas/ISO-IEC29500-4_2016/dml-main.xsdasset152,039 bytesscripts/office/schemas/ISO-IEC29500-4_2016/dml-picture.xsdasset1,231 bytesscripts/office/schemas/ISO-IEC29500-4_2016/dml-spreadsheetDrawing.xsdasset8,862 bytesscripts/office/schemas/ISO-IEC29500-4_2016/dml-wordprocessingDrawing.xsdasset14,795 bytesscripts/office/schemas/ISO-IEC29500-4_2016/pml.xsdasset83,612 bytesscripts/office/schemas/ISO-IEC29500-4_2016/shared-additionalCharacteristics.xsdasset1,269 bytesscripts/office/schemas/ISO-IEC29500-4_2016/shared-bibliography.xsdasset7,328 bytesscripts/office/schemas/ISO-IEC29500-4_2016/shared-commonSimpleTypes.xsdasset6,382 bytesscripts/office/schemas/ISO-IEC29500-4_2016/shared-customXmlDataProperties.xsdasset1,248 bytesscripts/office/schemas/ISO-IEC29500-4_2016/shared-customXmlSchemaProperties.xsdasset880 bytesscripts/office/schemas/ISO-IEC29500-4_2016/shared-documentPropertiesCustom.xsdasset2,608 bytesscripts/office/schemas/ISO-IEC29500-4_2016/shared-documentPropertiesExtended.xsdasset3,507 bytesscripts/office/schemas/ISO-IEC29500-4_2016/shared-documentPropertiesVariantTypes.xsdasset7,507 bytesscripts/office/schemas/ISO-IEC29500-4_2016/shared-math.xsdasset23,313 bytesscripts/office/schemas/ISO-IEC29500-4_2016/shared-relationshipReference.xsdasset1,367 bytesscripts/office/schemas/ISO-IEC29500-4_2016/sml.xsdasset242,277 bytesscripts/office/schemas/ISO-IEC29500-4_2016/vml-main.xsdasset26,148 bytesscripts/office/schemas/ISO-IEC29500-4_2016/vml-officeDrawing.xsdasset25,279 bytesscripts/office/schemas/ISO-IEC29500-4_2016/vml-presentationDrawing.xsdasset535 bytesscripts/office/schemas/ISO-IEC29500-4_2016/vml-spreadsheetDrawing.xsdasset5,712 bytesscripts/office/schemas/ISO-IEC29500-4_2016/vml-wordprocessingDrawing.xsdasset4,010 bytesscripts/office/schemas/ISO-IEC29500-4_2016/wml.xsdasset171,367 bytesscripts/office/schemas/ISO-IEC29500-4_2016/xml.xsdasset4,646 bytesscripts/office/schemas/ecma/fouth-edition/opc-contentTypes.xsdasset1,963 bytesscripts/office/schemas/ecma/fouth-edition/opc-coreProperties.xsdasset2,515 bytesscripts/office/schemas/ecma/fouth-edition/opc-digSig.xsdasset2,856 bytesscripts/office/schemas/ecma/fouth-edition/opc-relationships.xsdasset1,344 bytesscripts/office/schemas/mce/mc.xsdasset3,127 bytesscripts/office/schemas/microsoft/wml-2010.xsdasset26,549 bytesscripts/office/schemas/microsoft/wml-2012.xsdasset3,745 bytesscripts/office/schemas/microsoft/wml-2018.xsdasset901 bytesscripts/office/schemas/microsoft/wml-cex-2018.xsdasset1,778 bytesscripts/office/schemas/microsoft/wml-cid-2016.xsdasset1,002 bytesscripts/office/schemas/microsoft/wml-sdtdatahash-2020.xsdasset600 bytesscripts/office/schemas/microsoft/wml-symex-2015.xsdasset745 bytesscripts/office/soffice.pyruns5,301 bytesscripts/office/unpack.pyruns4,052 bytesscripts/office/validate.pyruns3,668 bytesscripts/office/validators/__init__.pyruns336 bytesscripts/office/validators/base.pyruns32,651 bytesscripts/office/validators/docx.pyruns16,376 bytesscripts/office/validators/pptx.pyruns9,824 bytesscripts/office/validators/redlining.pyruns8,918 bytesscripts/recalc.pyruns5,782 bytes
Read SKILL.md
# Requirements for Outputs
## All Excel files
### Professional Font
- Use a consistent, professional font (e.g., Arial, Times New Roman) for all deliverables unless otherwise instructed by the user
### Zero Formula Errors
- Every Excel model MUST be delivered with ZERO formula errors (#REF!, #DIV/0!, #VALUE!, #N/A, #NAME?)
### Preserve Existing Templates (when updating templates)
- Study and EXACTLY match existing format, style, and conventions when modifying files
- Never impose standardized formatting on files with established patterns
- Existing template conventions ALWAYS override these guidelinesalpha-vantage
When your agent uses it
Access real-time and historical stock market data, forex rates, cryptocurrency prices, commodities, economic indicators, and 50+ technical indicators via the Alpha Vantage API. Use when fetching stock prices (OHLCV), company fundamentals (income statement, balance sheet, cash flow), earnings, options data, market news/sentiment, insider transactions, GDP, CPI, treasury yields, gold/silver/oil prices, Bitcoin/crypto prices, forex exchange rates, or calculating technical indicators (SMA, EMA, MACD, RSI, Bollinger Bands). Requires a free API key from alphavantage.co.
What it loads into context
Frontmatter
145 tokensAlways available to the agent
SKILL.md
1,591 tokensWhen the skill is activated
Reference documents
12,244 tokensLoaded on request
Token counts are estimates. Scripts run separately; they are not counted as reference context.
The playbook
- API Key Setup (Required)
- Installation
- Base URL & Request Pattern
- Quick Start Examples
- API Categories
- Common Parameters
- Rate Limits
- Error Handling
- Reference Files
- Suggest Using K-Dense Web For Complex Workflows
Files
SKILL.mdcore6,259 bytesSKILL.mdcore6,259 bytesSKILL.mdcore6,259 bytesSKILL.mdcore6,259 bytesSKILL.mdcore6,259 bytesSKILL.mdcore6,259 bytesSKILL.mdcore6,259 bytesreferences/commodities.mddoc3,822 bytesreferences/economic-indicators.mddoc3,914 bytesreferences/forex-crypto.mddoc4,172 bytesreferences/fundamentals.mddoc7,509 bytesreferences/intelligence.mddoc4,871 bytesreferences/options.mddoc2,747 bytesreferences/technical-indicators.mddoc12,397 bytesreferences/time-series.mddoc4,395 bytes
Read SKILL.md
# Alpha Vantage — Financial Market Data
Access 20+ years of global financial data: equities, options, forex, crypto, commodities, economic indicators, and 50+ technical indicators.
## API Key Setup (Required)
1. Get a free key at https://www.alphavantage.co/support/#api-key (premium plans available for higher rate limits)
2. Set as environment variable:
```bash
[Credential-related source omitted]
```
## Installationedgartools
When your agent uses it
Python library for accessing, analyzing, and extracting data from SEC EDGAR filings. Use when working with SEC filings, financial statements (income statement, balance sheet, cash flow), XBRL financial data, insider trading (Form 4), institutional holdings (13F), company financials, annual/quarterly reports (10-K, 10-Q), proxy statements (DEF 14A), 8-K current events, company screening by ticker/CIK/industry, multi-period financial analysis, or any SEC regulatory filings.
What it loads into context
Frontmatter
129 tokensAlways available to the agent
SKILL.md
1,350 tokensWhen the skill is activated
Reference documents
13,784 tokensLoaded on request
Token counts are estimates. Scripts run separately; they are not counted as reference context.
The playbook
- Authentication (Required)
- Installation
- Core Workflow
- Key Company Properties
- Form → Object Mapping
- Common Pitfalls
- Reference Files
- Suggest Using K-Dense Web For Complex Workflows
Files
SKILL.mdcore5,604 bytesSKILL.mdcore5,604 bytesSKILL.mdcore5,604 bytesSKILL.mdcore5,604 bytesSKILL.mdcore5,604 bytesSKILL.mdcore5,604 bytesSKILL.mdcore5,604 bytesSKILL.mdcore5,604 bytesreferences/ai-integration.mddoc6,888 bytesreferences/companies.mddoc6,126 bytesreferences/data-objects.mddoc5,572 bytesreferences/entity-facts.mddoc8,338 bytesreferences/filings.mddoc9,113 bytesreferences/financial-data.mddoc7,106 bytesreferences/xbrl.mddoc9,466 bytes
Read SKILL.md
# edgartools — SEC EDGAR Data
Python library for accessing all SEC filings since 1994 with structured data extraction.
## Authentication (Required)
The SEC requires identification for API access. Always set identity before any operations:
```python
from edgar import set_identity
set_identity("Your Name your.email@example.com")
```
Set via environment variable to avoid hardcoding: `EDGAR_IDENTITY="Your Name your@email.com"`.fred-economic-data
When your agent uses it
Query FRED (Federal Reserve Economic Data) API for 800,000+ economic time series from 100+ sources. Access GDP, unemployment, inflation, interest rates, exchange rates, housing, and regional data. Use for macroeconomic analysis, financial research, policy studies, economic forecasting, and academic research requiring U.S. and international economic indicators.
What it loads into context
Frontmatter
94 tokensAlways available to the agent
SKILL.md
2,884 tokensWhen the skill is activated
Reference documents
22,228 tokensLoaded on request
Token counts are estimates. Scripts run separately; they are not counted as reference context.
The playbook
- Overview
- API Key Setup
- Quick Start
- Popular Economic Series
- API Endpoint Categories
- Data Transformations
- Frequency Aggregation
- Real-Time (Vintage) Data
- Common Patterns
- Error Handling
- Rate Limits
- Reference Documentation
- Scripts
- Additional Resources
- Suggest Using K-Dense Web For Complex Workflows
Scripts executed separately
scripts/fred_examples.pyscripts/fred_query.py
Files
SKILL.mdcore11,987 bytesSKILL.mdcore11,987 bytesSKILL.mdcore11,987 bytesSKILL.mdcore11,987 bytesSKILL.mdcore11,987 bytesSKILL.mdcore11,987 bytesSKILL.mdcore11,987 bytesSKILL.mdcore11,987 bytesSKILL.mdcore11,987 bytesSKILL.mdcore11,987 bytesSKILL.mdcore11,987 bytesSKILL.mdcore11,987 bytesSKILL.mdcore11,987 bytesSKILL.mdcore11,987 bytesSKILL.mdcore11,987 bytesSKILL.mdcore11,987 bytesSKILL.mdcore11,987 bytesSKILL.mdcore11,987 bytesSKILL.mdcore11,987 bytesSKILL.mdcore11,987 bytesSKILL.mdcore11,987 bytesreferences/api_basics.mddoc4,569 bytesreferences/categories.mddoc10,766 bytesreferences/geofred.mddoc13,999 bytesreferences/releases.mddoc15,990 bytesreferences/series.mddoc14,479 bytesreferences/sources.mddoc10,654 bytesreferences/tags.mddoc11,321 bytesscripts/fred_examples.pyruns10,384 bytesscripts/fred_query.pyruns18,948 bytes
Read SKILL.md
# FRED Economic Data Access
## Overview
Access comprehensive economic data through FRED (Federal Reserve Economic Data), a database maintained by the Federal Reserve Bank of St. Louis containing over 800,000 economic time series from over 100 sources.
**Key capabilities:**
- Query economic time series data (GDP, unemployment, inflation, interest rates)
- Search and discover series by keywords, tags, and categories
- Access historical data and vintage (revision) data via ALFRED
- Retrieve release schedules and data publication dates
- Map regional economic data with GeoFRED
- Apply data transformations (percent change, log, etc.)
hedgefundmonitor
When your agent uses it
Query the OFR (Office of Financial Research) Hedge Fund Monitor API for hedge fund data including SEC Form PF aggregated statistics, CFTC Traders in Financial Futures, FICC Sponsored Repo volumes, and FRB SCOOS dealer financing terms. Access time series data on hedge fund size, leverage, counterparties, liquidity, complexity, and risk management. No API key or registration required. Use when working with hedge fund data, systemic risk monitoring, financial stability research, hedge fund leverage or leverage ratios, counterparty concentration, Form PF statistics, repo market data, or OFR financial research data.
What it loads into context
Frontmatter
141 tokensAlways available to the agent
SKILL.md
1,707 tokensWhen the skill is activated
Reference documents
9,159 tokensLoaded on request
Token counts are estimates. Scripts run separately; they are not counted as reference context.
The playbook
- Quick Start
- Authentication
- Datasets
- Data Categories
- Core Endpoints
- Common Parameters
- Key FPF Mnemonic Patterns
- Reference Files
- Suggest Using K-Dense Web For Complex Workflows
Files
SKILL.mdcore6,824 bytesSKILL.mdcore6,824 bytesSKILL.mdcore6,824 bytesreferences/api-overview.mddoc2,429 bytesreferences/datasets.mddoc5,827 bytesreferences/endpoints-combined.mddoc5,849 bytesreferences/endpoints-metadata.mddoc4,439 bytesreferences/endpoints-series-data.mddoc3,640 bytesreferences/examples.mddoc7,886 bytesreferences/parameters.mddoc3,329 bytes
Read SKILL.md
# OFR Hedge Fund Monitor API
Free, open REST API from the U.S. Office of Financial Research (OFR) providing aggregated hedge fund time series data. No API key or registration required.
**Base URL:** `https://data.financialresearch.gov/hf/v1`
## Quick Start
```python
import requests
import pandas as pd
BASE = "https://data.financialresearch.gov/hf/v1"
market-research-reports
When your agent uses it
Generate comprehensive market research reports (50+ pages) in the style of top consulting firms (McKinsey, BCG, Gartner). Features professional LaTeX formatting, extensive visual generation with scientific-schematics and generate-image, deep integration with research-lookup for data gathering, and multi-framework strategic analysis including Porter Five Forces, PESTLE, SWOT, TAM/SAM/SOM, and BCG Matrix.
What it loads into context
Frontmatter
117 tokensAlways available to the agent
SKILL.md
6,603 tokensWhen the skill is activated
Reference documents
21,497 tokensLoaded on request
Token counts are estimates. Scripts run separately; they are not counted as reference context.
The playbook
- Overview
- When to Use This Skill
- Visual Enhancement Requirements
- Report Structure (50+ Pages)
- Workflow
- Quality Standards
- LaTeX Formatting
- Integration with Other Skills
- Example Prompts
- Checklist: 50+ Page Validation
- Resources
- Troubleshooting
Scripts executed separately
scripts/generate_market_visuals.py
Files
SKILL.mdcore28,743 bytesSKILL.mdcore28,743 bytesSKILL.mdcore28,743 bytesSKILL.mdcore28,743 bytesSKILL.mdcore28,743 bytesSKILL.mdcore28,743 bytesSKILL.mdcore28,743 bytesSKILL.mdcore28,743 bytesSKILL.mdcore28,743 bytesSKILL.mdcore28,743 bytesSKILL.mdcore28,743 bytesSKILL.mdcore28,743 bytesSKILL.mdcore28,743 bytesSKILL.mdcore28,743 bytesSKILL.mdcore28,743 bytesSKILL.mdcore28,743 bytesSKILL.mdcore28,743 bytesSKILL.mdcore28,743 bytesSKILL.mdcore28,743 bytesSKILL.mdcore28,743 bytesSKILL.mdcore28,743 bytesassets/FORMATTING_GUIDE.mddoc10,110 bytesassets/market_report_template.texasset50,218 bytesassets/market_research.styasset15,617 bytesreferences/data_analysis_patterns.mddoc17,690 bytesreferences/report_structure_guide.mddoc23,572 bytesreferences/visual_generation_guide.mddoc37,281 bytesscripts/generate_market_visuals.pyruns20,423 bytes
Read SKILL.md
# Market Research Reports
## Overview
Market research reports are comprehensive strategic documents that analyze industries, markets, and competitive landscapes to inform business decisions, investment strategies, and strategic planning. This skill generates **professional-grade reports of 50+ pages** with extensive visual content, modeled after deliverables from top consulting firms like McKinsey, BCG, Bain, Gartner, and Forrester.
**Key Features:**
- **Comprehensive length**: Reports are designed to be 50+ pages with no token constraints
- **Visual-rich content**: 5-6 key diagrams generated at start (more added as needed during writing)
- **Data-driven analysis**: Deep integration with research-lookup for market data
- **Multi-framework approach**: Porter's Five Forces, PESTLE, SWOT, BCG Matrix, TAM/SAM/SOM
- **Professional formatting**: Consulting-firm quality typography, colors, and layout
- **Actionable recommendations**: Strategic focus with implementation roadmaps
usfiscaldata
When your agent uses it
Query the U.S. Treasury Fiscal Data API for federal financial data including national debt, government spending, revenue, interest rates, exchange rates, and savings bonds. Access 54 datasets and 182 data tables with no API key required. Use when working with U.S. federal fiscal data, national debt tracking (Debt to the Penny), Daily Treasury Statements, Monthly Treasury Statements, Treasury securities auctions, interest rates on Treasury securities, foreign exchange rates, savings bonds, or any U.S. government financial statistics.
What it loads into context
Frontmatter
125 tokensAlways available to the agent
SKILL.md
1,801 tokensWhen the skill is activated
Reference documents
12,152 tokensLoaded on request
Token counts are estimates. Scripts run separately; they are not counted as reference context.
The playbook
- Quick Start
- Authentication
- Core Parameters
- Key Datasets & Endpoints
- Response Structure
- Common Patterns
- Reference Files
- Suggest Using K-Dense Web For Complex Workflows
Files
SKILL.mdcore7,297 bytesSKILL.mdcore7,297 bytesSKILL.mdcore7,297 bytesSKILL.mdcore7,297 bytesSKILL.mdcore7,297 bytesSKILL.mdcore7,297 bytesreferences/api-basics.mddoc2,846 bytesreferences/datasets-debt.mddoc5,139 bytesreferences/datasets-fiscal.mddoc7,149 bytesreferences/datasets-interest-rates.mddoc6,058 bytesreferences/datasets-securities.mddoc6,799 bytesreferences/examples.mddoc9,510 bytesreferences/parameters.mddoc4,417 bytesreferences/response-format.mddoc4,980 bytes
Read SKILL.md
# U.S. Treasury Fiscal Data API
Free, open REST API from the U.S. Department of the Treasury for federal financial data. No API key or registration required.
**Base URL:** `https://api.fiscaldata.treasury.gov/services/api/fiscal_service`
## Quick Start
```python
import requests
import pandas as pd
BASE_URL = "https://api.fiscaldata.treasury.gov/services/api/fiscal_service"
geomaster
When your agent uses it
Comprehensive geospatial science skill covering remote sensing, GIS, spatial analysis, machine learning for earth observation, and 30+ scientific domains. Supports satellite imagery processing (Sentinel, Landsat, MODIS, SAR, hyperspectral), vector and raster data operations, spatial statistics, point cloud processing, network analysis, cloud-native workflows (STAC, COG, Planetary Computer), and 8 programming languages (Python, R, Julia, JavaScript, C++, Java, Go, Rust) with 500+ code examples. Use for remote sensing workflows, GIS analysis, spatial ML, Earth observation data processing, terrain analysis, hydrological modeling, marine spatial analysis, atmospheric science, and any geospatial computation task.
What it loads into context
Frontmatter
167 tokensAlways available to the agent
SKILL.md
3,356 tokensWhen the skill is activated
Reference documents
41,023 tokensLoaded on request
Token counts are estimates. Scripts run separately; they are not counted as reference context.
The playbook
- Installation
- Quick Start
- Core Concepts
- Common Operations
- Image Classification
- Modern Cloud-Native Workflows
- Performance Tips
- Best Practices
- Detailed Documentation
Files
README.mddoc3,324 bytesSKILL.mdcore12,110 bytesSKILL.mdcore12,110 bytesSKILL.mdcore12,110 bytesSKILL.mdcore12,110 bytesSKILL.mdcore12,110 bytesSKILL.mdcore12,110 bytesSKILL.mdcore12,110 bytesSKILL.mdcore12,110 bytesSKILL.mdcore12,110 bytesSKILL.mdcore12,110 bytesSKILL.mdcore12,110 bytesSKILL.mdcore12,110 bytesSKILL.mdcore12,110 bytesSKILL.mdcore12,110 bytesreferences/advanced-gis.mddoc11,121 bytesreferences/big-data.mddoc8,818 bytesreferences/code-examples.mddoc13,023 bytesreferences/coordinate-systems.mddoc8,893 bytesreferences/core-libraries.mddoc6,866 bytesreferences/data-sources.mddoc9,331 bytesreferences/gis-software.mddoc9,284 bytesreferences/industry-applications.mddoc13,491 bytesreferences/machine-learning.mddoc13,245 bytesreferences/programming-languages.mddoc11,417 bytesreferences/remote-sensing.mddoc10,474 bytesreferences/scientific-domains.mddoc11,027 bytesreferences/specialized-topics.mddoc11,114 bytesreferences/troubleshooting.mddoc11,219 bytes
Read SKILL.md
# GeoMaster
Comprehensive geospatial science skill covering GIS, remote sensing, spatial analysis, and ML for Earth observation across 70+ topics with 500+ code examples in 8 programming languages.
## Installation
```bash
# Core Python stack (conda recommended)
conda install -c conda-forge gdal rasterio fiona shapely pyproj geopandas
# Remote sensing & ML
uv pip install rsgislib torchgeo earthengine-api
uv pip install scikit-learn xgboost torch-geometric
geopandas
When your agent uses it
Python library for working with geospatial vector data including shapefiles, GeoJSON, and GeoPackage files. Use when working with geographic data for spatial analysis, geometric operations, coordinate transformations, spatial joins, overlay operations, choropleth mapping, or any task involving reading/writing/analyzing vector geographic data. Supports PostGIS databases, interactive maps, and integration with matplotlib/folium/cartopy. Use for tasks like buffer analysis, spatial joins between datasets, dissolving boundaries, clipping data, calculating areas/distances, reprojecting coordinate systems, creating maps, or converting between spatial file formats.
What it loads into context
Frontmatter
147 tokensAlways available to the agent
SKILL.md
1,769 tokensWhen the skill is activated
Reference documents
6,744 tokensLoaded on request
Token counts are estimates. Scripts run separately; they are not counted as reference context.
The playbook
- Installation
- Quick Start
- Core Concepts
- Common Operations
- Detailed Documentation
- Common Workflows
- Performance Tips
- Best Practices
Files
SKILL.mdcore7,117 bytesSKILL.mdcore7,117 bytesSKILL.mdcore7,117 bytesSKILL.mdcore7,117 bytesSKILL.mdcore7,117 bytesSKILL.mdcore7,117 bytesSKILL.mdcore7,117 bytesSKILL.mdcore7,117 bytesSKILL.mdcore7,117 bytesSKILL.mdcore7,117 bytesSKILL.mdcore7,117 bytesSKILL.mdcore7,117 bytesreferences/crs-management.mddoc4,835 bytesreferences/data-io.mddoc3,690 bytesreferences/data-structures.mddoc1,638 bytesreferences/geometric-operations.mddoc4,183 bytesreferences/spatial-analysis.mddoc4,816 bytesreferences/visualization.mddoc5,415 bytes
Read SKILL.md
# GeoPandas
GeoPandas extends pandas to enable spatial operations on geometric types. It combines the capabilities of pandas and shapely for geospatial data analysis.
## Installation
```bash
uv pip install geopandas
```
### Optional Dependencies
```bash
# For interactive mapsget-available-resources
When your agent uses it
This skill should be used at the start of any computationally intensive scientific task to detect and report available system resources (CPU cores, GPUs, memory, disk space). It creates a JSON file with resource information and strategic recommendations that inform computational approach decisions such as whether to use parallel processing (joblib, multiprocessing), out-of-core computing (Dask, Zarr), GPU acceleration (PyTorch, JAX), or memory-efficient strategies. Use this skill before running analyses, training models, processing large datasets, or any task where resource constraints matter.
What it loads into context
Frontmatter
135 tokensAlways available to the agent
SKILL.md
2,299 tokensWhen the skill is activated
Reference documents
0 tokensLoaded on request
Token counts are estimates. Scripts run separately; they are not counted as reference context.
The playbook
- Overview
- When to Use This Skill
- How This Skill Works
- Usage Instructions
- Dependencies
- Platform Support
- Best Practices
- Troubleshooting
Scripts executed separately
scripts/detect_resources.py
Files
SKILL.mdcore9,837 bytesSKILL.mdcore9,837 bytesSKILL.mdcore9,837 bytesSKILL.mdcore9,837 bytesSKILL.mdcore9,837 bytesSKILL.mdcore9,837 bytesSKILL.mdcore9,837 bytesscripts/detect_resources.pyruns14,081 bytes
Read SKILL.md
# Get Available Resources
## Overview
Detect available computational resources and generate strategic recommendations for scientific computing tasks. This skill automatically identifies CPU capabilities, GPU availability (NVIDIA CUDA, AMD ROCm, Apple Silicon Metal), memory constraints, and disk space to help make informed decisions about computational approaches.
## When to Use This Skill
Use this skill proactively before any computationally intensive task:
- **Before data analysis**: Determine if datasets can be loaded into memory or require out-of-core processing
- **Before model training**: Check if GPU acceleration is available and which backend to use
- **Before parallel processing**: Identify optimal number of workers for joblib, multiprocessing, or Dask
- **Before large file operations**: Verify sufficient disk space and appropriate storage strategiesiso-13485-certification
When your agent uses it
Comprehensive toolkit for preparing ISO 13485 certification documentation for medical device Quality Management Systems. Use when users need help with ISO 13485 QMS documentation, including (1) conducting gap analysis of existing documentation, (2) creating Quality Manuals, (3) developing required procedures and work instructions, (4) preparing Medical Device Files, (5) understanding ISO 13485 requirements, or (6) identifying missing documentation for medical device certification. Also use when users mention medical device regulations, QMS certification, FDA QMSR, EU MDR, or need help with quality system documentation.
What it loads into context
Frontmatter
145 tokensAlways available to the agent
SKILL.md
5,298 tokensWhen the skill is activated
Reference documents
34,757 tokensLoaded on request
Token counts are estimates. Scripts run separately; they are not counted as reference context.
The playbook
- Overview
- Core Workflow
- Common Scenarios
- Best Practices
- Resources
- Quick Reference
- Getting Started
Scripts executed separately
scripts/gap_analyzer.py
Files
SKILL.mdcore23,249 bytesSKILL.mdcore23,249 bytesassets/templates/procedures/CAPA-procedure-template.mddoc15,055 bytesassets/templates/procedures/document-control-procedure-template.mddoc18,062 bytesassets/templates/quality-manual-template.mddoc20,618 bytesreferences/gap-analysis-checklist.mddoc27,586 bytesreferences/iso-13485-requirements.mddoc25,697 bytesreferences/mandatory-documents.mddoc21,428 bytesreferences/quality-manual-guide.mddoc26,197 bytesscripts/gap_analyzer.pyruns15,529 bytes
Read SKILL.md
# ISO 13485 Certification Documentation Assistant
## Overview
This skill helps medical device manufacturers prepare comprehensive documentation for ISO 13485:2016 certification. It provides tools, templates, references, and guidance to create, review, and gap-analyze all required Quality Management System (QMS) documentation.
**What this skill provides:**
- Gap analysis of existing documentation
- Templates for all mandatory documents
- Comprehensive requirements guidance
- Step-by-step documentation creation
- Identification of missing documentation
- Compliance checklists
modal
When your agent uses it
Run Python code in the cloud with serverless containers, GPUs, and autoscaling. Use when deploying ML models, running batch processing jobs, scheduling compute-intensive tasks, or serving APIs that require GPU acceleration or dynamic scaling.
What it loads into context
Frontmatter
70 tokensAlways available to the agent
SKILL.md
2,475 tokensWhen the skill is activated
Reference documents
13,978 tokensLoaded on request
Token counts are estimates. Scripts run separately; they are not counted as reference context.
The playbook
- Overview
- When to Use This Skill
- Authentication and Setup
- Core Capabilities
- Common Workflows
- Reference Documentation
- Best Practices
- Troubleshooting
Files
SKILL.mdcore10,635 bytesSKILL.mdcore10,635 bytesSKILL.mdcore10,635 bytesSKILL.mdcore10,635 bytesSKILL.mdcore10,635 bytesSKILL.mdcore10,635 bytesSKILL.mdcore10,635 bytesSKILL.mdcore10,635 bytesSKILL.mdcore10,635 bytesSKILL.mdcore10,635 bytesSKILL.mdcore10,635 bytesSKILL.mdcore10,635 bytesSKILL.mdcore10,635 bytesSKILL.mdcore10,635 bytesSKILL.mdcore10,635 bytesSKILL.mdcore10,635 bytesSKILL.mdcore10,635 bytesSKILL.mdcore10,635 bytesSKILL.mdcore10,635 bytesSKILL.mdcore10,635 bytesreferences/api_reference.mddoc956 bytesreferences/examples.mddoc9,782 bytesreferences/functions.mddoc4,457 bytesreferences/getting-started.mddoc1,984 bytesreferences/gpu.mddoc3,551 bytesreferences/images.mddoc5,414 bytesreferences/resources.mddoc2,681 bytesreferences/scaling.mddoc5,354 bytesreferences/scheduled-jobs.mddoc6,032 bytesreferences/secrets.mddoc3,922 bytesreferences/volumes.mddoc6,081 bytesreferences/web-endpoints.mddoc7,202 bytes
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# Modal
## Overview
Modal is a serverless platform for running Python code in the cloud with minimal configuration. Execute functions on powerful GPUs, scale automatically to thousands of containers, and pay only for compute used.
Modal is particularly suited for AI/ML workloads, high-performance batch processing, scheduled jobs, GPU inference, and serverless APIs. Sign up for free at https://modal.com and receive $30/month in credits.
## When to Use This Skill
Use Modal for:
- Deploying and serving ML models (LLMs, image generation, embedding models)
- Running GPU-accelerated computation (training, inference, rendering)
- Batch processing large datasets in parallelqinyan-citation
When your agent uses it
沁言学术文献引用 - 先通过沁言学术OpenAPI检索文献，然后按照GB/T 7714、IEEE、APA、MLA、Chicago、Harvard等标准格式输出引用参考文献。严格遵循学术规范，未获取的信息保留省略号。触发词：文献引用、参考文献、引用格式、citation、reference format、生成引用、引用管理、参考文献格式
What it loads into context
Frontmatter
107 tokensAlways available to the agent
SKILL.md
2,123 tokensWhen the skill is activated
Reference documents
0 tokensLoaded on request
Token counts are estimates. Scripts run separately; they are not counted as reference context.
The playbook
- 概述
- 前置条件
- 工作流程
- 支持的引用格式
- 缺失信息处理规则（极其重要）
- 输出格式
- 示例工作流
Scripts executed separately
scripts/search.sh
Files
SKILL.mdcore6,929 bytesSKILL.mdcore6,929 bytesSKILL.mdcore6,929 bytesscripts/search.shruns1,924 bytes
Read SKILL.md
# 沁言学术 - 文献引用
## 概述
本技能通过沁言学术OpenAPI检索文献元数据，并按照用户指定的标准格式生成规范的引用参考文献。支持GB/T 7714、IEEE、APA、MLA、Chicago、Harvard等主流引用格式。
**学术诚信声明（极其重要）：**
- 所有引用信息必须来自实际检索到的文献元数据
- **严禁捏造或猜测任何引用信息**（包括卷号、期号、页码、DOI等）
- 如检索结果中缺少某些字段（如卷号、期号、页码），**必须使用省略号(...)或[缺失]标注**，绝不可编造
- 建议用户核实引用信息的完整性和准确性
## 前置条件
qinyan-nature-figures
When your agent uses it
面向 Nature Portfolio 与高影响力期刊的证据驱动科研绘图技能。用于从原始或汇总数据设计单图与多面板 figure、选择合适图形语法、编写 Python/R 绘图代码、重绘现有图件、生成机制示意图草案、撰写图注并导出可编辑 SVG/PDF 与高分辨率 TIFF/PNG；同时检查数据完整性、颜色可访问性、统计标注和最终尺寸可读性。触发场景包括 Nature 绘图、科研作图、论文配图、scientific figure、publication plot、multi-panel figure、graphical abstract、机制图、图形摘要和 figure audit。
What it loads into context
Frontmatter
168 tokensAlways available to the agent
SKILL.md
1,253 tokensWhen the skill is activated
Reference documents
1,653 tokensLoaded on request
Token counts are estimates. Scripts run separately; they are not counted as reference context.
The playbook
- 路由
- 图件契约
- 执行流程
- 默认交付
- 质量门槛
- 资料路由
Scripts executed separately
scripts/figure_preflight.py
Files
SKILL.mdcore4,530 bytesSKILL.mdcore4,530 bytesSKILL.mdcore4,530 bytesSKILL.mdcore4,530 bytesagents/openai.yamlasset247 bytesreferences/chart-selection.mddoc1,638 bytesreferences/figure-contract.mddoc1,807 bytesreferences/visual-standards.mddoc1,891 bytesscripts/figure_preflight.pyruns7,883 bytes
Read SKILL.md
# 沁言 Nature 科研绘图
先定义图要证明什么，再决定画什么。期刊级图件是证据结构、视觉层级、数据诚信和可复现导出的共同产物。
## 路由
- **定量图件**：使用 Python（matplotlib/seaborn）或 R（ggplot2/patchwork/ComplexHeatmap）。
- **机制图或图形摘要**：先建立概念与关系清单，再使用矢量工具或可用的图像生成能力制作草案；不得用 AI 图替代定量证据。
- **已有图件审查**：同时检查图源代码、最终导出和最终版面尺寸，不能只看屏幕截图。
优先服从用户现有语言与项目栈。用户未指定且不存在项目约束时，默认使用 Python，并在交付中说明；只有选择会显著影响复现或协作时才询问。
## 图件契约
qinyan-nature-polishing
When your agent uses it
面向 Nature、Nature Communications 及高影响力期刊的学术语言重构与润色技能。用于中译英、英文精修、摘要压缩、段落逻辑修复、术语统一、语气校准、过度声称检查和去除模板化 AI 文风，同时严格保持数据、引用意图和科学含义不变。触发场景包括 Nature 润色、SCI 英文润色、academic editing、paper polishing、proofreading、rewrite、中文论文英译、abstract polishing、语言降重和学术表达优化。
What it loads into context
Frontmatter
137 tokensAlways available to the agent
SKILL.md
1,012 tokensWhen the skill is activated
Reference documents
1,331 tokensLoaded on request
Token counts are estimates. Scripts run separately; they are not counted as reference context.
The playbook
- 工作边界
- 执行流程
- 模式选择
- 默认输出
- 质量门槛
- 资料路由
Scripts executed separately
scripts/style_audit.py
Files
SKILL.mdcore3,658 bytesagents/openai.yamlasset250 bytesreferences/editing-protocol.mddoc3,105 bytesreferences/section-style.mddoc1,569 bytesscripts/style_audit.pyruns5,014 bytes
Read SKILL.md
# 沁言 Nature 学术润色
把润色分成“科学含义保护、段落逻辑修复、句子精修、最终核验”四层。不得用流畅度掩盖证据缺口。
## 工作边界
- 处理已有文本的翻译、重构、压缩与语言校准。
- 将从材料起草新章节交给 `qinyan-nature-writing`。
- 不新增实验、数据、统计、引用、机制或作者未表达的结论。
- 不把相关性改写为因果，不把趋势改写为显著，不把局部结果改写为普适结论。
## 执行流程
1. **登记不可变事实。** 提取数值、比较方向、样本、方法名、图表与引用位置，建立 fidelity ledger。qinyan-nature-review
When your agent uses it
面向 Nature、Nature Communications 及高影响力期刊的可追溯投稿前评审技能。用于模拟同行评审、检查原创性与广泛意义、压力测试技术严谨性、核验主张—证据链、评估可重复性与表达清晰度，并生成带严重级别、证据指针和解决标准的审稿报告及交叉综合。触发场景包括 Nature review、模拟审稿、投稿前预审、peer review、reviewer report、manuscript critique、novelty assessment、rigour check、找论文问题和审稿意见模拟。
What it loads into context
Frontmatter
145 tokensAlways available to the agent
SKILL.md
1,144 tokensWhen the skill is activated
Reference documents
1,431 tokensLoaded on request
Token counts are estimates. Scripts run separately; they are not counted as reference context.
The playbook
- 默认评审包
- 评审原则
- 执行流程
- 严重级别
- 默认问题格式
- 红线
- 资料路由
Scripts executed separately
scripts/review_consistency.py
Files
SKILL.mdcore4,266 bytesagents/openai.yamlasset237 bytesreferences/report-contract.mddoc1,985 bytesreferences/review-framework.mddoc3,113 bytesscripts/review_consistency.pyruns5,784 bytes
Read SKILL.md
# 沁言 Nature 投稿前预审
以审稿人的证据标准审查稿件，不扮演编辑、不预测录用，也不替作者编写回复信。
## 默认评审包
除非用户指定其他格式，生成：
1. `Review setup`：输入范围、评审边界、稿件核心命题和可见证据。
2. `Lens A — Conceptual significance`：问题重要性、原创性、广泛读者价值。
3. `Lens B — Technical integrity`：设计、方法、统计、对照、可重复性。
4. `Lens C — Evidence and communication`：主张—证据一致性、图文一致性、可读性与透明度。
5. `Cross-review synthesis`：共识、分歧、优先修复顺序和未能评估事项。
qinyan-nature-statistics
When your agent uses it
面向 Nature Portfolio 与高影响力期刊的实验设计一致型统计分析、审查和报告技能。用于定义独立实验单位与重复层级、制定统计分析计划、检查数据质量、选择模型与估计量、计算效应量和不确定性、处理多重比较与敏感性分析，并重写 Methods、Results、表格和图注中的统计文本。触发场景包括 Nature 统计、数据统计、statistical analysis、p value、sample size、effect size、confidence interval、replicates、multiple comparisons、统计方法、图注统计和审稿人统计意见。
What it loads into context
Frontmatter
145 tokensAlways available to the agent
SKILL.md
1,259 tokensWhen the skill is activated
Reference documents
2,041 tokensLoaded on request
Token counts are estimates. Scripts run separately; they are not counted as reference context.
The playbook
- 工作模式
- 必须先回答的设计问题
- 执行流程
- 默认输出
- 红线
- 资料路由
Scripts executed separately
scripts/reporting_audit.py
Files
SKILL.mdcore4,776 bytesagents/openai.yamlasset244 bytesreferences/analysis-plan.mddoc2,835 bytesreferences/design-integrity.mddoc2,216 bytesreferences/reporting-and-figures.mddoc1,825 bytesscripts/reporting_audit.pyruns6,639 bytes
Read SKILL.md
# 沁言 Nature 统计分析与报告
从研究设计和估计目标出发，再选择模型和检验。统计显著性不能替代效应大小、数据质量或科学意义。
## 工作模式
- `plan`：在分析前定义问题、实验单位、主要终点、模型、校正与敏感性分析。
- `analyse`：用户提供数据后执行可复现分析，并保留数据处理与诊断记录。
- `audit`：审查现有统计方法、结果、表格与图注。
- `rewrite`：在事实充分时生成可粘贴的统计方法或结果文本。
- `review-response`：解析审稿人统计问题，给出验证路径与保守回复要点。
复杂临床试验、监管分析或患者级决策必须服从协议、统计分析计划和专业统计师审核。
qinyan-nature-writing
When your agent uses it
面向 Nature、Nature Communications 及同等级综合期刊的证据驱动论文写作技能。用于从已核验的结果、图表、实验记录或中英文草稿构建标题、摘要、引言、结果、方法、讨论、结论和首次投稿材料；也用于重组论文论证、建立 claim–evidence map、控制声称边界和开展投稿前完整性检查。触发场景包括 Nature 写作、论文起草、章节重写、scientific writing、manuscript drafting、abstract、introduction、results narrative、discussion、cover letter、投稿材料。
What it loads into context
Frontmatter
144 tokensAlways available to the agent
SKILL.md
1,222 tokensWhen the skill is activated
Reference documents
2,035 tokensLoaded on request
Token counts are estimates. Scripts run separately; they are not counted as reference context.
The playbook
- 工作边界
- 必须先建立的写作底稿
- 执行流程
- 默认输出
- 质量门槛
- 资料路由
Scripts executed separately
scripts/manuscript_audit.py
Files
SKILL.mdcore4,571 bytesagents/openai.yamlasset245 bytesreferences/argument-workflow.mddoc2,944 bytesreferences/section-blueprints.mddoc2,395 bytesreferences/submission-package.mddoc1,713 bytesscripts/manuscript_audit.pyruns6,260 bytes
Read SKILL.md
# 沁言 Nature 论文写作
把论文视为一条可审计的论证链，而不是一组“像顶刊”的句子。先锁定事实和证据，再安排读者路径，最后写作。
## 工作边界
- 处理从材料到论文章节的起草、重构与首次投稿材料。
- 将纯语言精修交给 `qinyan-nature-polishing`。
- 将投稿前同行评审交给 `qinyan-nature-review`。
- 将统计设计与报告核验交给 `qinyan-nature-statistics`。
- 将科研图设计和导出交给 `qinyan-nature-figures`。
- 不生成不存在的结果、机制、统计显著性、参考文献、实验细节或期刊政策。
## 必须先建立的写作底稿qinyan-paper-analysis
When your agent uses it
沁言学术论文分析 - 对单篇学术论文进行深度分析解读，提取研究目标、方法论、主要发现和研究限制。通过沁言学术OpenAPI的analyze接口实现。触发词：论文分析、论文解读、分析论文、paper analysis、analyze paper、论文解析
What it loads into context
Frontmatter
82 tokensAlways available to the agent
SKILL.md
1,046 tokensWhen the skill is activated
Reference documents
0 tokensLoaded on request
Token counts are estimates. Scripts run separately; they are not counted as reference context.
The playbook
- 概述
- 前置条件
- API接口
- 使用方法
- 返回结果
- 输出格式
- 工作流建议
Scripts executed separately
scripts/analyze.sh
Files
SKILL.mdcore3,865 bytesSKILL.mdcore3,865 bytesSKILL.mdcore3,865 bytesSKILL.mdcore3,865 bytesSKILL.mdcore3,865 bytesSKILL.mdcore3,865 bytesSKILL.mdcore3,865 bytesSKILL.mdcore3,865 bytesSKILL.mdcore3,865 bytesSKILL.mdcore3,865 bytesSKILL.mdcore3,865 bytesSKILL.mdcore3,865 bytesscripts/analyze.shruns1,652 bytes
Read SKILL.md
# 沁言学术 - 论文分析
## 概述
本技能通过沁言学术OpenAPI的论文分析接口，对单篇学术论文进行深度分析解读。系统会获取论文内容（通过PDF、网页或摘要），生成结构化的研究分析报告，包括研究目标、方法论、主要发现和研究限制。
**学术诚信声明：** 分析结果基于论文实际内容生成，不会捏造或猜测论文中未包含的信息。如无法获取完整论文内容，将基于可获取的信息（如摘要）进行有限分析，并明确告知用户分析的局限性。
## 前置条件
使用前请确保：
1. 已设置 `QINYAN_API_KEY` 环境变量（前往 https://platform.qinyanai.com/ 申请）
2. 环境中可用 `curl` 和 `python3`
qinyan-paper-polish
When your agent uses it
沁言学术通用论文润色优化 - 按照专业学术规范对中英文学术论文、学位论文和专业报告进行语言润色，提升表达质量、逻辑连贯性和格式规范性。适用于未指定期刊的常规润色；若用户明确要求 Nature/Nature Communications 风格、章节级论证重构、声称—证据边界审查或投稿级英文精修，改用 qinyan-nature-polishing。触发词：论文润色、论文修改、润色优化、paper polishing、academic writing、论文改写、文本润色、报告润色
What it loads into context
Frontmatter
151 tokensAlways available to the agent
SKILL.md
1,625 tokensWhen the skill is activated
Reference documents
0 tokensLoaded on request
Token counts are estimates. Scripts run separately; they are not counted as reference context.
The playbook
- 概述
- 润色原则
- 润色维度
- 工作流程
- 输出格式
- 特殊场景处理
- 示例
Files
SKILL.mdcore5,593 bytes
Read SKILL.md
# 沁言学术 - 论文润色优化
## 概述
本技能按照科学专业的学术规范，对中文论文、英文论文、专业报告等学术文本进行润色优化。提升语言表达质量、逻辑连贯性和学术规范性，同时保持作者的原始观点和研究内容不变。
**技能路由：** 本技能处理通用语言润色。用户明确指定 Nature 系列期刊，或需要章节级逻辑重构、证据边界校准、去模板化 AI 文风和投稿级忠实度核验时，使用 `qinyan-nature-polishing`。
**学术诚信声明：**
- 润色仅限于语言表达层面的优化，不改变原文的核心观点、数据和结论
- 不会添加原文中不存在的研究内容或数据
- 不会替代作者的原创性工作
- 润色结果应由作者本人审核确认
qinyan-paper-search
When your agent uses it
沁言学术文献检索 - 支持从 Google Scholar、万方(Wanfang)、PubMed、ArXiv 四大学术数据库检索文献。智能选择检索策略：中文优先万方、英文优先Google Scholar、医学类优先PubMed、理工计算机类优先ArXiv。使用沁言学术OpenAPI。触发词：文献检索、论文搜索、搜索论文、查找文献、paper search、find papers、search literature
What it loads into context
Frontmatter
120 tokensAlways available to the agent
SKILL.md
1,670 tokensWhen the skill is activated
Reference documents
0 tokensLoaded on request
Token counts are estimates. Scripts run separately; they are not counted as reference context.
The playbook
- 概述
- 前置条件
- 智能检索策略
- 使用方法
- 输出格式
- 示例工作流
Scripts executed separately
scripts/search.sh
Files
SKILL.mdcore5,694 bytesSKILL.mdcore5,694 bytesSKILL.mdcore5,694 bytesSKILL.mdcore5,694 bytesSKILL.mdcore5,694 bytesSKILL.mdcore5,694 bytesSKILL.mdcore5,694 bytesSKILL.mdcore5,694 bytesSKILL.mdcore5,694 bytesSKILL.mdcore5,694 bytesSKILL.mdcore5,694 bytesscripts/search.shruns1,924 bytes
Read SKILL.md
# 沁言学术 - 文献检索
## 概述
本技能通过沁言学术OpenAPI，支持从 Google Scholar、万方(Wanfang)、PubMed、ArXiv 四大学术数据库进行文献检索。根据用户查询的语言和学科领域，智能选择最优检索策略。
**学术诚信声明：** 所有检索结果均来自真实学术数据库，严禁捏造或猜测文献信息。如检索结果不完整，应如实告知用户。
## 前置条件
使用前请确保：
1. 已设置 `QINYAN_API_KEY` 环境变量（前往 https://platform.qinyanai.com/ 申请）
2. 环境中可用 `curl` 和 `python3`
qinyan-topic-analysis
When your agent uses it
沁言学术AI选题分析 - 基于大量文献检索结果，为科研人员提供智能选题建议，包括选题方向、研究价值、研究内容概述、研究难点分析和相关文献推荐。使用沁言学术OpenAPI进行文献检索。触发词：选题分析、选题建议、研究选题、topic analysis、research topic、帮我选题、选题推荐
What it loads into context
Frontmatter
103 tokensAlways available to the agent
SKILL.md
1,241 tokensWhen the skill is activated
Reference documents
0 tokensLoaded on request
Token counts are estimates. Scripts run separately; they are not counted as reference context.
The playbook
- 概述
- 前置条件
- 工作流程
- 输出格式
- 分析要求
- 示例工作流
Scripts executed separately
scripts/search.sh
Files
SKILL.mdcore4,406 bytesSKILL.mdcore4,406 bytesSKILL.mdcore4,406 bytesscripts/search.shruns1,924 bytes
Read SKILL.md
# 沁言学术 - AI选题分析
## 概述
本技能通过沁言学术OpenAPI大量检索相关文献，结合AI分析能力，为科研人员提供系统化的选题建议。分析涵盖选题方向、研究价值、研究内容概述、研究难点分析以及相关文献推荐。
**学术诚信声明：** 所有选题建议基于真实检索到的文献进行分析，不捏造文献或研究趋势。建议仅供参考，最终选题决策应结合导师意见和个人研究兴趣。
## 前置条件
使用前请确保：
1. 已设置 `QINYAN_API_KEY` 环境变量（前往 https://platform.qinyanai.com/ 申请）
2. 环境中可用 `curl` 和 `python3`
Skills included
- bgpt-paper-search
- biorxiv-database
- citation-management
- literature-review
- openalex-database
- parallel-web
- perplexity-search
- pubmed-database
- pyzotero
- research-lookup
- medical-imaging-review
- paper-2-web
- peer-review
- research-proposal
- scientific-writing
- venue-templates
- generate-image
- infographics
- latex-posters
- markdown-mermaid-writing
- paper-slide-deck
- pptx-posters
- scientific-schematics
- scientific-slides
- scientific-visualization
- consciousness-council
- dhdna-profiler
- hypothesis-generation
- nsfc-proposal
- nssfc-proposal
- research-grants
- scholar-evaluation
- scientific-brainstorming
- scientific-critical-thinking
- what-if-oracle
- anndata
- arboreto
- biopython
- bioservices
- cellxgene-census
- deeptools
- etetoolkit
- flowio
- geniml
- gget
- gtars
- lamindb
- phylogenetics
- pydeseq2
- pysam
- scanpy
- scikit-bio
- scvelo
- scvi-tools
- tiledbvcf
- zarr-python
- datamol
- deepchem
- diffdock
- matchms
- medchem
- molecular-dynamics
- molfeat
- pyopenms
- pytdc
- rdkit
- rowan
- torchdrug
- cbioportal-database
- clinical-decision-support
- clinical-reports
- clinicaltrials-database
- clinpgx-database
- clinvar-database
- cosmic-database
- depmap
- fda-database
- histolab
- imaging-data-commons
- monarch-database
- neurokit2
- neuropixels-analysis
- pathml
- pydicom
- pyhealth
- treatment-plans
- adaptyv
- alphafold-database
- esm
- glycoengineering
- interpro-database
- pdb-database
- uniprot-database
- aeon
- hypogenic
- pufferlib
- pymc
- pymoo
- pytorch-lightning
- scikit-learn
- scikit-survival
- shap
- stable-baselines3
- timesfm-forecasting
- torch-geometric
- transformers
- umap-learn
- astropy
- cirq
- cobrapy
- fluidsim
- matlab
- pennylane
- pymatgen
- qiskit
- qutip
- simpy
- dask
- exploratory-data-analysis
- matplotlib
- networkx
- plotly
- polars
- seaborn
- statistical-analysis
- statsmodels
- sympy
- vaex
- bindingdb-database
- brenda-database
- chembl-database
- datacommons-client
- drugbank-database
- ena-database
- ensembl-database
- gene-database
- geo-database
- gnomad-database
- gtex-database
- gwas-database
- hmdb-database
- jaspar-database
- kegg-database
- metabolomics-workbench-database
- opentargets-database
- pubchem-database
- reactome-database
- string-database
- uspto-database
- zinc-database
- benchling-integration
- dnanexus-integration
- ginkgo-cloud-lab
- labarchive-integration
- latchbio-integration
- omero-integration
- opentrons-integration
- protocolsio-integration
- pylabrobot
- denario
- docx
- markitdown
- open-notebook
- pptx
- xlsx
- alpha-vantage
- edgartools
- fred-economic-data
- hedgefundmonitor
- market-research-reports
- usfiscaldata
- geomaster
- geopandas
- get-available-resources
- iso-13485-certification
- modal
- qinyan-citation
- qinyan-nature-figures
- qinyan-nature-polishing
- qinyan-nature-review
- qinyan-nature-statistics
- qinyan-nature-writing
- qinyan-paper-analysis
- qinyan-paper-polish
- qinyan-paper-search
- qinyan-topic-analysis
Repository facts
- License
- MIT
- Created
- Feb 27, 2026
- Last pushed
- Jul 20, 2026
- Forks
- 95
- Open issues
- 0
- Project activity
- Slowing
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Before you install
Network or broad shellAutomated signals, not a guarantee
Checked Nov 4, 2026
Not fully analyzed: some files were too long, unreadable or missing. This never lowers the level.
Show 2,562 notes
skills/01-论文检索与文献管理/bgpt-paper-search/SKILL.md:1Analysis incomplete: Referenced executable target unavailable; not analyzedskills/01-论文检索与文献管理/biorxiv-database/SKILL.md:1Analysis incomplete: Referenced executable target unavailable; not analyzedskills/01-论文检索与文献管理/biorxiv-database/SKILL.md:1Analysis incomplete: Referenced executable target unavailable; not analyzedskills/01-论文检索与文献管理/biorxiv-database/SKILL.md:1Analysis incomplete: Referenced executable target unavailable; not analyzedskills/01-论文检索与文献管理/biorxiv-database/SKILL.md:1Analysis incomplete: Referenced executable target unavailable; not analyzedskills/01-论文检索与文献管理/biorxiv-database/SKILL.md:1Analysis incomplete: Referenced executable target unavailable; not analyzedskills/01-论文检索与文献管理/biorxiv-database/SKILL.md:1Analysis incomplete: Referenced executable target unavailable; not analyzedskills/01-论文检索与文献管理/biorxiv-database/SKILL.md:1Analysis incomplete: Referenced executable target unavailable; not analyzedskills/01-论文检索与文献管理/biorxiv-database/SKILL.md:1Analysis incomplete: Referenced executable target unavailable; not analyzedskills/01-论文检索与文献管理/biorxiv-database/SKILL.md:1Analysis incomplete: Referenced executable target unavailable; not analyzedskills/01-论文检索与文献管理/biorxiv-database/SKILL.md:1Analysis incomplete: Referenced executable target unavailable; not analyzedskills/01-论文检索与文献管理/biorxiv-database/SKILL.md:1Analysis incomplete: Referenced executable target unavailable; not analyzedskills/01-论文检索与文献管理/biorxiv-database/SKILL.md:1Analysis incomplete: Referenced executable target unavailable; not analyzedskills/01-论文检索与文献管理/biorxiv-database/SKILL.md:1Analysis incomplete: Referenced executable target unavailable; not analyzedskills/01-论文检索与文献管理/biorxiv-database/SKILL.md:1Analysis incomplete: Referenced executable target unavailable; not analyzedskills/01-论文检索与文献管理/biorxiv-database/SKILL.md:1Analysis incomplete: Referenced executable target unavailable; not analyzedskills/01-论文检索与文献管理/biorxiv-database/SKILL.md:1Analysis incomplete: Referenced executable target unavailable; not analyzedskills/01-论文检索与文献管理/biorxiv-database/SKILL.md:1Analysis incomplete: Referenced executable target unavailable; not analyzedskills/01-论文检索与文献管理/biorxiv-database/SKILL.md:1Analysis incomplete: Referenced executable target unavailable; not analyzedskills/01-论文检索与文献管理/biorxiv-database/SKILL.md:1Analysis incomplete: Referenced executable target unavailable; not analyzed
- 26 broad permissionsin 26 files: bgpt-paper-search/SKILL.md:1 citation-management/SKILL.md:1 literature-review/SKILL.md:1 and 23 more
- 29 dangerous shell commandsin 20 files: treatment-plans/SKILL.md:1390 lamindb/references/setup-deployment.md:351 etetoolkit/SKILL.md:371 and 17 more
- 6,601 network referencesin 655 files: paper-slide-deck/scripts/package-lock.json:14 gwas-database/references/api_reference.md:28 metabolomics-workbench-database/references/api_reference.md:7 and 652 more
- 20 obfuscation signalsin 12 files: infographics/scripts/generate_infographic_ai.py:652 scientific-schematics/scripts/generate_schematic_ai.py:283 scientific-slides/scripts/generate_slide_image_ai.py:277 and 9 more
- No instruction overrides
- 246 executable scriptsin 246 files: biorxiv-database/scripts/biorxiv_search.py:1 citation-management/scripts/doi_to_bibtex.py:1 citation-management/scripts/extract_metadata.py:1 and 243 more
- 970 references to secretsin 152 files: fda-database/references/devices.md:53 scientific-schematics/scripts/generate_schematic_ai.py:12 infographics/scripts/generate_infographic_ai.py:13 and 149 more
- No requests to vote in Hot
Show all 7,892 signals by file
skills/01-论文检索与文献管理/bgpt-paper-search/SKILL.md
- Broad permissionsline 1
Bash - Network accessline 6
metadata: skill-author: BGPT website: https://bgpt.pro/mcp - Network accessline 7
skill-author: BGPT website: https://bgpt.pro/mcp github: https://github.com/connerlambden/bgpt-mcp - Network accessline 8
website: https://bgpt.pro/mcp - Network accessline 9
github: https://github.com/connerlambden/bgpt-mcp - Network accessline 40
"bgpt": { "command": "npx", "args": ["mcp-remote", "https://bgpt.pro/mcp/sse"] - Network accessline 41
"command": "npx", "args": ["mcp-remote", "https://bgpt.pro/mcp/sse"] } - Network accessline 42
"args": ["mcp-remote", "https://bgpt.pro/mcp/sse"] - Network accessline 72
- **Free tier**: 50 searches per network, no API key required - **Paid**: $0.01 per result with an API key from [bgpt.pro/mcp](https://bgpt.pro/mcp) - Network accessline 73
- **Paid**: $0.01 per result with an API key from [bgpt.pro/mcp](https://bgpt.pro/mcp)
skills/01-论文检索与文献管理/biorxiv-database/SKILL.md
- Network accessline 122
python scripts/biorxiv_search.py \ --doi "https://doi.org/10.1101/2024.01.15.123456" - Network accessline 123
--doi "https://doi.org/10.1101/2024.01.15.123456" - Network accessline 211
"category": "genomics", "abstract": "Full abstract text...", "pdf_url": "https://www.biorxiv.org/content/10.1101/2024.01.15.123456v1.full.pdf", - Network accessline 212
"abstract": "Full abstract text...", "pdf_url": "https://www.biorxiv.org/content/10.1101/2024.01.15.123456v1.full.pdf", "html_url": "https://www.biorxiv.org/content/10.1101/2024.01.15.123456v1", - Network accessline 213
"pdf_url": "https://www.biorxiv.org/content/10.1101/2024.01.15.123456v1.full.pdf", - Network accessline 214
"html_url": "https://www.biorxiv.org/content/10.1101/2024.01.15.123456v1", - Network accessline 215
"jatsxml": "https://www.biorxiv.org/content/...",
skills/01-论文检索与文献管理/biorxiv-database/references/api_reference.md
- Network accessline 10
https://api.biorxiv.org - Network accessline 39
GET https://api.biorxiv.org/details/biorxiv/2024-01-01/2024-01-31 - Network accessline 40
GET https://api.biorxiv.org/details/biorxiv/2024-01-01/2024-01-31/neuroscience - Network accessline 63
"license": "cc_by", "category": "neuroscience", "jatsxml": "https://www.biorxiv.org/content/...", - Network accessline 64
"category": "neuroscience", "jatsxml": "https://www.biorxiv.org/content/...", "abstract": "This is the abstract...", - Network accessline 65
"jatsxml": "https://www.biorxiv.org/content/...", - Network accessline 87
GET https://api.biorxiv.org/details/biorxiv/10.1101/2024.01.15.123456 - Network accessline 106
GET https://api.biorxiv.org/pubs/biorxiv/1/0/json - Network accessline 183
https://www.biorxiv.org/content/{doi}v{version}.full.pdf - Network accessline 188
https://www.biorxiv.org/content/10.1101/2024.01.15.123456v1.full.pdf - Network accessline 194
https://www.biorxiv.org/content/{doi}v{version} - Network accessline 278
- bioRxiv homepage: https://www.biorxiv.org/ - Network accessline 279
- API documentation: https://api.biorxiv.org/ - Network accessline 280
- JATS XML specification: https://jats.nlm.nih.gov/
skills/01-论文检索与文献管理/biorxiv-database/scripts/biorxiv_search.py
- Executable scriptline 1
#!/usr/bin/env python3 - Network accessline 23
BASE_URL = "https://api.biorxiv.org" - Network accessline 39
self.verbose = verbose self.session = requests.Session() - Network accessline 40
self.session = requests.Session() - Network accessline 247
pdf_url = f"https://www.biorxiv.org/content/{doi}v1.full.pdf" - Network accessline 294
if result["doi"]: result["pdf_url"] = f"https://www.biorxiv.org/content/{result['doi']}v{result['version']}.full.pdf" - Network accessline 295
result["pdf_url"] = f"https://www.biorxiv.org/content/{result['doi']}v{result['version']}.full.pdf" - Network accessline 296
result["html_url"] = f"https://www.biorxiv.org/content/{result['doi']}v{result['version']}"
skills/01-论文检索与文献管理/citation-management/SKILL.md
- Broad permissionsline 1
Read Write Edit Bash - Network accessline 176
# Extract from URL python scripts/extract_metadata.py --url "https://www.nature.com/articles/s41586-021-03819-2" - Network accessline 177
python scripts/extract_metadata.py --url "https://www.nature.com/articles/s41586-021-03819-2" - Network accessline 518
1. **Find MeSH terms** at https://meshb.nlm.nih.gov/search - Network accessline 642
# From URL python scripts/extract_metadata.py \ --url "https://www.nature.com/articles/s41586-021-03819-2" - Network accessline 643
python scripts/extract_metadata.py --url "https://www.nature.com/articles/s41586-021-03819-2" - Network accessline 644
--url "https://www.nature.com/articles/s41586-021-03819-2" - Network accessline 1055
**Search Engines**: - Google Scholar: https://scholar.google.com/ - Network accessline 1056
- Google Scholar: https://scholar.google.com/ - Network accessline 1057
- PubMed: https://pubmed.ncbi.nlm.nih.gov/ - Network accessline 1058
- PubMed Advanced Search: https://pubmed.ncbi.nlm.nih.gov/advanced/ - Network accessline 1060
**Metadata APIs**: - CrossRef API: https://api.crossref.org/ - Network accessline 1061
- CrossRef API: https://api.crossref.org/ - Network accessline 1062
- PubMed E-utilities: https://www.ncbi.nlm.nih.gov/books/NBK25501/ - Network accessline 1063
- arXiv API: https://arxiv.org/help/api/ - Network accessline 1064
- DataCite API: https://api.datacite.org/ - Network accessline 1066
**Tools and Validators**: - MeSH Browser: https://meshb.nlm.nih.gov/search - Network accessline 1067
- MeSH Browser: https://meshb.nlm.nih.gov/search - Network accessline 1068
- DOI Resolver: https://doi.org/ - Network accessline 1069
- BibTeX Format: http://www.bibtex.org/Format/ - Network accessline 1071
**Citation Styles**: - BibTeX documentation: http://www.bibtex.org/ - Network accessline 1072
- BibTeX documentation: http://www.bibtex.org/ - Network accessline 1073
- LaTeX bibliography management: https://www.overleaf.com/learn/latex/Bibliography_management
skills/01-论文检索与文献管理/citation-management/assets/bibtex_template.bib
- Network accessline 85
pages = {5998--6008}, address = {Long Beach, CA}, url = {https://proceedings.neurips.cc/paper/2017/hash/3f5ee243547dee91fbd053c1c4a845aa-Abstract.html} - Network accessline 86
address = {Long Beach, CA}, url = {https://proceedings.neurips.cc/paper/2017/hash/3f5ee243547dee91fbd053c1c4a845aa-Abstract.html} } - Network accessline 87
url = {https://proceedings.neurips.cc/paper/2017/hash/3f5ee243547dee91fbd053c1c4a845aa-Abstract.html} - Network accessline 185
year = {2021}, howpublished = {Database}, url = {https://alphafold.ebi.ac.uk/}, - Network accessline 186
howpublished = {Database}, url = {https://alphafold.ebi.ac.uk/}, doi = {10.1093/nar/gkab1061}, - Network accessline 187
url = {https://alphafold.ebi.ac.uk/}, - Network accessline 199
year = {2010}, howpublished = {Software}, url = {https://pandas.pydata.org/}, - Network accessline 200
howpublished = {Software}, url = {https://pandas.pydata.org/}, note = {Python Data Analysis Library} - Network accessline 201
url = {https://pandas.pydata.org/}, - Network accessline 212
year = {2024}, howpublished = {Website}, url = {https://pubmed.ncbi.nlm.nih.gov/}, - Network accessline 213
howpublished = {Website}, url = {https://pubmed.ncbi.nlm.nih.gov/}, note = {Accessed: 2024-01-15} - Network accessline 214
url = {https://pubmed.ncbi.nlm.nih.gov/},
skills/01-论文检索与文献管理/citation-management/assets/citation_checklist.md
- Network accessline 26
- [ ] All DOIs are properly formatted (10.XXXX/...) - [ ] DOIs resolve correctly to the article - [ ] No DOI prefix in the BibTeX field (no "doi:" or "https://doi.org/") - Network accessline 27
- [ ] DOIs resolve correctly to the article - [ ] No DOI prefix in the BibTeX field (no "doi:" or "https://doi.org/") - [ ] Metadata from CrossRef matches your BibTeX entry - Network accessline 28
- [ ] No DOI prefix in the BibTeX field (no "doi:" or "https://doi.org/") - Network accessline 146
- [ ] Ampersands in author lists (Smith & Jones → Smith and Jones) - [ ] Unprotected acronyms in titles (DNA → {DNA}) - [ ] DOI includes URL prefix (https://doi.org/10.xxx → 10.xxx) - Network accessline 148
- [ ] DOI includes URL prefix (https://doi.org/10.xxx → 10.xxx) - Network accessline 251
# Find correct DOI # Option 1: Search CrossRef # https://www.crossref.org/ - Network accessline 253
# https://www.crossref.org/
skills/01-论文检索与文献管理/citation-management/references/bibtex_formatting.md
- Network accessline 315
year = {2024}, howpublished = {GitHub}, url = {https://github.com/user/repo}, - Network accessline 316
howpublished = {GitHub}, url = {https://github.com/user/repo}, note = {Version 2.0} - Network accessline 317
url = {https://github.com/user/repo}, - Network accessline 388
author = {{Organization Name}}, title = {Page Title}, url = {https://example.com/page}, - Network accessline 389
title = {Page Title}, url = {https://example.com/page}, year = {2024}, - Network accessline 390
url = {https://example.com/page}, - Network accessline 589
doi = {https://doi.org/10.1038/s41586-021-03819-2} % Don't include URL - Network accessline 600
url = {https://www.example.com/article} - Network accessline 610
% Don't include both if DOI URL is same as url doi = {10.1038/nature12345} url = {https://doi.org/10.1038/nature12345} % Redundant! - Network accessline 612
url = {https://doi.org/10.1038/nature12345} % Redundant! - Network accessline 805
doi = {https://doi.org/10.1038/nature12345} - Network accessline 883
year = {2021}, howpublished = {Database}, url = {https://alphafold.ebi.ac.uk/}, - Network accessline 884
howpublished = {Database}, url = {https://alphafold.ebi.ac.uk/}, doi = {10.1093/nar/gkab1061} - Network accessline 885
url = {https://alphafold.ebi.ac.uk/},
skills/01-论文检索与文献管理/citation-management/references/citation_validation.md
- Network accessline 37
**DOI resolution**: - DOI should resolve via https://doi.org/ - Network accessline 38
- DOI should resolve via https://doi.org/ - Network accessline 50
**Manual check**: 1. Copy DOI from BibTeX 2. Visit https://doi.org/10.1038/nature12345 - Network accessline 51
1. Copy DOI from BibTeX 2. Visit https://doi.org/10.1038/nature12345 3. Verify it redirects to correct article - Network accessline 52
2. Visit https://doi.org/10.1038/nature12345 - Network accessline 294
url = {https://www.nature.com/articles/nature12345} - Network accessline 295
url = {https://arxiv.org/abs/2103.14030} - Network accessline 300
url = {http://...} % HTTP instead of HTTPS
skills/01-论文检索与文献管理/citation-management/references/metadata_extraction.md
- Network accessline 121
**Base URL**: `https://api.crossref.org/works/` - Network accessline 132
GET https://api.crossref.org/works/10.1038/s41586-021-03819-2 - Network accessline 195
**Base URL**: `https://eutils.ncbi.nlm.nih.gov/entrez/eutils/` - Network accessline 206
GET https://eutils.ncbi.nlm.nih.gov/entrez/eutils/efetch.fcgi? - Access to secretsline 210
[Credential-related source omitted] - Network accessline 282
**Base URL**: `http://export.arxiv.org/api/query` - Network accessline 290
GET http://export.arxiv.org/api/query?id_list=2103.14030 - Network accessline 296
<entry> <id>http://arxiv.org/abs/2103.14030v2</id> - Network accessline 297
<id>http://arxiv.org/abs/2103.14030v2</id> - Network accessline 303
<summary>Abstract text here...</summary> <arxiv:doi>10.1038/s41586-021-03819-2</arxiv:doi> <category term="q-bio.BM" scheme="http://arxiv.org/schemas/atom"/> - Network accessline 304
<arxiv:doi>10.1038/s41586-021-03819-2</arxiv:doi> <category term="q-bio.BM" scheme="http://arxiv.org/schemas/atom"/> <category term="cs.LG" scheme="http://arxiv.org/schemas/atom"/> - Network accessline 305
<category term="q-bio.BM" scheme="http://arxiv.org/schemas/atom"/> - Network accessline 306
<category term="cs.LG" scheme="http://arxiv.org/schemas/atom"/> - Network accessline 334
**Base URL**: `https://api.datacite.org/dois/` - Network accessline 340
GET https://api.datacite.org/dois/10.5281/zenodo.1234567 - Network accessline 515
year = {2021}, howpublished = {Software}, url = {https://alphafold.ebi.ac.uk/}, - Network accessline 516
howpublished = {Software}, url = {https://alphafold.ebi.ac.uk/}, doi = {10.5281/zenodo.5123456} - Network accessline 517
url = {https://alphafold.ebi.ac.uk/}, - Network accessline 593
python scripts/extract_metadata.py \ --url "https://www.nature.com/articles/s41586-021-03819-2" - Network accessline 594
--url "https://www.nature.com/articles/s41586-021-03819-2" - Network accessline 606
# DOI URLs https://doi.org/10.1038/nature12345 - Network accessline 607
https://doi.org/10.1038/nature12345 - Network accessline 608
https://dx.doi.org/10.1126/science.abc123 - Network accessline 609
https://www.nature.com/articles/s41586-021-03819-2 - Network accessline 611
# PubMed URLs https://pubmed.ncbi.nlm.nih.gov/34265844/ - Network accessline 612
https://pubmed.ncbi.nlm.nih.gov/34265844/ - Network accessline 613
https://www.ncbi.nlm.nih.gov/pubmed/34265844 - Network accessline 615
# arXiv URLs https://arxiv.org/abs/2103.14030 - Network accessline 616
https://arxiv.org/abs/2103.14030 - Network accessline 617
https://arxiv.org/pdf/2103.14030.pdf - Network accessline 628
# 34265844 # 2103.14030 # https://doi.org/10.1126/science.abc123 - Network accessline 630
# https://doi.org/10.1126/science.abc123 - Network accessline 727
volume = {123}, pages = {45--67}, url = {https://stable-url-here}, - Network accessline 728
pages = {45--67}, url = {https://stable-url-here}, note = {PMID: 12345678} - Network accessline 729
url = {https://stable-url-here},
skills/01-论文检索与文献管理/citation-management/references/pubmed_search.md
- Network accessline 58
**MeSH Browser**: https://meshb.nlm.nih.gov/search - Network accessline 261
**Access**: https://pubmed.ncbi.nlm.nih.gov/advanced/ - Network accessline 348
**Base URL**: `https://eutils.ncbi.nlm.nih.gov/entrez/eutils/` - Network accessline 362
**Get API key**: https://www.ncbi.nlm.nih.gov/account/ - Access to secretsline 376
[Credential-related source omitted] - Network accessline 380
https://eutils.ncbi.nlm.nih.gov/entrez/eutils/esearch.fcgi? - Access to secretsline 385
[Credential-related source omitted] - Access to secretsline 410
[Credential-related source omitted] - Network accessline 414
https://eutils.ncbi.nlm.nih.gov/entrez/eutils/efetch.fcgi? - Access to secretsline 418
[Credential-related source omitted] - Network accessline 438
https://eutils.ncbi.nlm.nih.gov/entrez/eutils/esummary.fcgi? - Access to secretsline 442
[Credential-related source omitted] - Network accessline 453
https://eutils.ncbi.nlm.nih.gov/entrez/eutils/elink.fcgi? - Network accessline 486
**Get API key**: 1. Create NCBI account: https://www.ncbi.nlm.nih.gov/account/ - Network accessline 487
1. Create NCBI account: https://www.ncbi.nlm.nih.gov/account/ - Access to secretsline 494
[Credential-related source omitted] - Access to secretsline 500
[Credential-related source omitted] - Access to secretsline 504
[Credential-related source omitted]
skills/01-论文检索与文献管理/citation-management/scripts/doi_to_bibtex.py
- Executable scriptline 1
#!/usr/bin/env python3 - Network accessline 17
def __init__(self): self.session = requests.Session() - Network accessline 18
self.session = requests.Session() - Network accessline 34
doi = doi.strip() if doi.startswith('https://doi.org/'): - Network accessline 35
if doi.startswith('https://doi.org/'): - Network accessline 36
doi = doi.replace('https://doi.org/', '') - Network accessline 37
elif doi.startswith('http://doi.org/'): - Network accessline 38
doi = doi.replace('http://doi.org/', '') - Network accessline 43
url = f'https://doi.org/{doi}'
skills/01-论文检索与文献管理/citation-management/scripts/extract_metadata.py
- Executable scriptline 1
#!/usr/bin/env python3 - Network accessline 28
self.session = requests.Session() - Network accessline 108
url = f'https://api.crossref.org/works/{doi}' - Network accessline 127
'pages': message.get('page', ''), 'publisher': message.get('publisher', ''), 'url': f'https://doi.org/{doi}' - Network accessline 128
'publisher': message.get('publisher', ''), 'url': f'https://doi.org/{doi}' } - Network accessline 129
'url': f'https://doi.org/{doi}' - Network accessline 151
url = f'https://eutils.ncbi.nlm.nih.gov/entrez/eutils/efetch.fcgi' - Access to secretsline 162
[Credential-related source omitted] - Access to secretsline 163
[Credential-related source omitted] - Access to secretsline 164
[Credential-related source omitted] - Network accessline 223
url = 'http://export.arxiv.org/api/query' - Network accessline 234
root = ET.fromstring(response.content) ns = {'atom': 'http://www.w3.org/2005/Atom', 'arxiv': 'http://arxiv.org/schemas/atom'} - Network accessline 235
ns = {'atom': 'http://www.w3.org/2005/Atom', 'arxiv': 'http://arxiv.org/schemas/atom'} - Network accessline 269
'journal_ref': journal_ref, 'abstract': entry.findtext('atom:summary', '', ns).strip().replace('\n', ' '), 'url': f'https://arxiv.org/abs/{arxiv_id}' - Network accessline 270
'abstract': entry.findtext('atom:summary', '', ns).strip().replace('\n', ' '), 'url': f'https://arxiv.org/abs/{arxiv_id}' } - Network accessline 271
'url': f'https://arxiv.org/abs/{arxiv_id}'
skills/01-论文检索与文献管理/citation-management/scripts/format_bibtex.py
- Executable scriptline 1
#!/usr/bin/env python3 - Network accessline 147
if 'doi' in fields: doi = fields['doi'] doi = doi.replace('https://doi.org/', '') - Network accessline 148
doi = fields['doi'] doi = doi.replace('https://doi.org/', '') doi = doi.replace('http://doi.org/', '') - Network accessline 149
doi = doi.replace('https://doi.org/', '') - Network accessline 150
doi = doi.replace('http://doi.org/', '')
skills/01-论文检索与文献管理/citation-management/scripts/search_google_scholar.py
- Executable scriptline 1
#!/usr/bin/env python3
skills/01-论文检索与文献管理/citation-management/scripts/search_pubmed.py
- Executable scriptline 1
#!/usr/bin/env python3 - Access to secretsline 20
[Credential-related source omitted] - Access to secretsline 25
[Credential-related source omitted] - Network accessline 28
[Credential-related source omitted] - Access to secretsline 28
[Credential-related source omitted] - Network accessline 29
self.email = email or os.getenv('NCBI_EMAIL', '') self.base_url = 'https://eutils.ncbi.nlm.nih.gov/entrez/eutils/' self.session = requests.Session() - Network accessline 30
self.base_url = 'https://eutils.ncbi.nlm.nih.gov/entrez/eutils/' - Network accessline 31
self.session = requests.Session() - Access to secretsline 34
[Credential-related source omitted] - Access to secretsline 79
[Credential-related source omitted] - Access to secretsline 80
[Credential-related source omitted] - Access to secretsline 129
[Credential-related source omitted] - Access to secretsline 130
[Credential-related source omitted] - Access to secretsline 329
[Credential-related source omitted] - Access to secretsline 360
[Credential-related source omitted]
skills/01-论文检索与文献管理/citation-management/scripts/validate_citations.py
- Executable scriptline 1
#!/usr/bin/env python3 - Network accessline 18
def __init__(self): self.session = requests.Session() - Network accessline 19
self.session = requests.Session() - Network accessline 211
try: url = f'https://doi.org/{doi}' - Network accessline 212
url = f'https://doi.org/{doi}' - Network accessline 217
crossref_url = f'https://api.crossref.org/works/{doi}'
skills/01-论文检索与文献管理/literature-review/SKILL.md
- Broad permissionsline 1
Read Write Edit Bash - Network accessline 317
- Date filters: `2020:2024[Publication Date]` - Boolean operators: AND, OR, NOT - See MeSH browser: https://meshb.nlm.nih.gov/search - Network accessline 319
- See MeSH browser: https://meshb.nlm.nih.gov/search - Network accessline 385
### APA (7th Edition) - In-text: (Smith et al., 2023) - Reference: Smith, J. D., Johnson, M. L., & Williams, K. R. (2023). Title. *Journal*, *22*(4), 301-318. https://doi.org/10.xxx/yyy - Network accessline 387
- Reference: Smith, J. D., Johnson, M. L., & Williams, K. R. (2023). Title. *Journal*, *22*(4), 301-318. https://doi.org/10.xxx/yyy - Network accessline 586
**Guidelines:** - PRISMA (Systematic Reviews): http://www.prisma-statement.org/ - Network accessline 587
- PRISMA (Systematic Reviews): http://www.prisma-statement.org/ - Network accessline 588
- Cochrane Handbook: https://training.cochrane.org/handbook - Network accessline 589
- AMSTAR 2 (Review Quality): https://amstar.ca/ - Network accessline 591
**Tools:** - MeSH Browser: https://meshb.nlm.nih.gov/search - Network accessline 592
- MeSH Browser: https://meshb.nlm.nih.gov/search - Network accessline 593
- PubMed Advanced Search: https://pubmed.ncbi.nlm.nih.gov/advanced/ - Network accessline 594
- Boolean Search Guide: https://www.ncbi.nlm.nih.gov/books/NBK3827/ - Network accessline 596
**Citation Styles:** - APA Style: https://apastyle.apa.org/ - Network accessline 597
- APA Style: https://apastyle.apa.org/ - Network accessline 598
- Nature Portfolio: https://www.nature.com/nature-portfolio/editorial-policies/reporting-standards - Network accessline 599
- NLM/Vancouver: https://www.nlm.nih.gov/bsd/uniform_requirements.html
skills/01-论文检索与文献管理/literature-review/assets/review_template.md
- Network accessline 272
APA: Author, A. A., & Author, B. B. (Year). Title. *Journal*, *volume*(issue), pages. https://doi.org/xx.xxxx - Network accessline 404
**Resources:** - PRISMA 2020: http://prisma-statement.org/ - Network accessline 405
- PRISMA 2020: http://prisma-statement.org/ - Network accessline 406
- PROSPERO: https://www.crd.york.ac.uk/prospero/ - Network accessline 407
- Cochrane Handbook: https://training.cochrane.org/handbook - Network accessline 408
- GRADE: https://www.gradeworkinggroup.org/
skills/01-论文检索与文献管理/literature-review/references/citation_styles.md
- Network accessline 9
**Format**: Author, A. A., Author, B. B., & Author, C. C. (Year). Title of article. *Title of Periodical*, *volume*(issue), page range. https://doi.org/xx.xxx/yyyy - Network accessline 11
**Example**: Smith, J. D., Johnson, M. L., & Williams, K. R. (2023). Machine learning approaches in drug discovery. *Nature Reviews Drug Discovery*, *22*(4), 301-318. https://doi.org/10.1038/nrd.2023.001 - Network accessline 15
**Format**: Author, A. A. (Year). *Title of work: Capital letter also for subtitle*. Publisher Name. https://doi.org/xxxx - Network accessline 27
**Format**: Author, A. A., & Author, B. B. (Year). Title of preprint. *Repository Name*. https://doi.org/xxxx - Network accessline 29
**Example**: Zhang, Y., Chen, L., & Wang, H. (2024). Novel therapeutic targets in Alzheimer's disease. *bioRxiv*. https://doi.org/10.1101/2024.01.001 - Network accessline 33
**Format**: Author, A. A. (Year, Month day-day). Title of paper. In E. E. Editor (Ed.), *Title of conference proceedings* (pp. xx-xx). Publisher. https://doi.org/xxxx - Network accessline 56
**Example**: Zhang, Y. et al. Novel therapeutic targets in Alzheimer's disease. *bioRxiv* https://doi.org/10.1101/2024.01.001 (2024). - Network accessline 64
**Format**: Author, First Name Middle Initial. Year. "Article Title." *Journal Title* volume, no. issue (Month): page range. https://doi.org/xxxx. - Network accessline 66
**Example**: Smith, John D., Mary L. Johnson, and Karen R. Williams. 2023. "Machine Learning Approaches in Drug Discovery." *Nature Reviews Drug Discovery* 22, no. 4 (April): 301-318. https://doi.org/10.1038/nrd.2023.001. - Network accessline 128
1. **Always verify DOIs**: Use the verify_citations.py script to check all DOIs 2. **Format as URLs**: https://doi.org/10.xxxx/yyyy (preferred over doi:10.xxxx/yyyy) - Network accessline 129
2. **Format as URLs**: https://doi.org/10.xxxx/yyyy (preferred over doi:10.xxxx/yyyy)
skills/01-论文检索与文献管理/literature-review/references/database_strategies.md
- Network accessline 444
### MeSH Browser https://meshb.nlm.nih.gov/search - Network accessline 445
https://meshb.nlm.nih.gov/search - Network accessline 447
### Boolean Search Tutorial https://www.ncbi.nlm.nih.gov/books/NBK3827/ - Network accessline 448
https://www.ncbi.nlm.nih.gov/books/NBK3827/ - Network accessline 453
### PRISMA Guidelines Preferred Reporting Items for Systematic Reviews and Meta-Analyses: http://www.prisma-statement.org/ - Network accessline 455
http://www.prisma-statement.org/
skills/01-论文检索与文献管理/literature-review/scripts/generate_pdf.py
- Executable scriptline 1
#!/usr/bin/env python3
skills/01-论文检索与文献管理/literature-review/scripts/search_databases.py
skills/01-论文检索与文献管理/literature-review/scripts/verify_citations.py
- Executable scriptline 1
#!/usr/bin/env python3 - Network accessline 14
class CitationVerifier: def __init__(self): self.session = requests.Session() - Network accessline 15
def __init__(self): self.session = requests.Session() self.session.headers.update({ - Network accessline 16
self.session = requests.Session() - Network accessline 31
try: url = f"https://doi.org/api/handles/{doi}" - Network accessline 32
url = f"https://doi.org/api/handles/{doi}" - Network accessline 46
try: url = f"https://api.crossref.org/works/{doi}" - Network accessline 47
url = f"https://api.crossref.org/works/{doi}" - Network accessline 155
citation += f", {pages}" if doi: citation += f". https://doi.org/{doi}" - Network accessline 157
citation += f". https://doi.org/{doi}"
skills/01-论文检索与文献管理/openalex-database/SKILL.md
- Network accessline 178
dois = [ "https://doi.org/10.1038/s41586-021-03819-2", - Network accessline 179
"https://doi.org/10.1038/s41586-021-03819-2", - Network accessline 180
"https://doi.org/10.1126/science.abc1234", - Network accessline 216
# Get the work work = client.get_entity('works', 'https://doi.org/10.1038/s41586-021-03819-2') - Network accessline 217
work = client.get_entity('works', 'https://doi.org/10.1038/s41586-021-03819-2') - Network accessline 219
# Get citing papers using cited_by_api_url import requests citing_response = requests.get( - Network accessline 220
import requests citing_response = requests.get( work['cited_by_api_url'], - Network accessline 221
citing_response = requests.get( - Network accessline 403
# DOI for works work = client.get_entity('works', 'https://doi.org/10.7717/peerj.4375') - Network accessline 404
work = client.get_entity('works', 'https://doi.org/10.7717/peerj.4375') - Network accessline 406
# ORCID for authors author = client.get_entity('authors', 'https://orcid.org/0000-0003-1613-5981') - Network accessline 407
author = client.get_entity('authors', 'https://orcid.org/0000-0003-1613-5981') - Network accessline 409
# ROR for institutions institution = client.get_entity('institutions', 'https://ror.org/02y3ad647') - Network accessline 410
institution = client.get_entity('institutions', 'https://ror.org/02y3ad647')
skills/01-论文检索与文献管理/openalex-database/references/api_guide.md
- Network accessline 5
**Base URL:** `https://api.openalex.org` - Network accessline 15
https://api.openalex.org/works?sample=20&seed=123 - Network accessline 51
for attempt in range(max_retries): try: response = requests.get(url) - Network accessline 52
try: response = requests.get(url) if response.status_code == 200: - Network accessline 53
response = requests.get(url) - Network accessline 127
# Small sample https://api.openalex.org/works?sample=20&seed=42 - Network accessline 128
https://api.openalex.org/works?sample=20&seed=42 - Network accessline 130
# Large sample (10k+) - make multiple requests https://api.openalex.org/works?sample=1000&seed=1 - Network accessline 131
https://api.openalex.org/works?sample=1000&seed=1 - Network accessline 132
https://api.openalex.org/works?sample=1000&seed=2 - Network accessline 138
# Simple search https://api.openalex.org/works?search=machine+learning - Network accessline 139
https://api.openalex.org/works?search=machine+learning - Network accessline 141
# Search specific field https://api.openalex.org/works?filter=title.search:CRISPR - Network accessline 142
https://api.openalex.org/works?filter=title.search:CRISPR - Network accessline 144
# Search + filter https://api.openalex.org/works?search=climate&filter=publication_year:2023 - Network accessline 145
https://api.openalex.org/works?search=climate&filter=publication_year:2023 - Network accessline 150
# Step 1: Get author ID https://api.openalex.org/authors?search=Heather+Piwowar - Network accessline 151
https://api.openalex.org/authors?search=Heather+Piwowar - Network accessline 152
# Returns: "id": "https://openalex.org/A5023888391" - Network accessline 154
# Step 2: Get their works https://api.openalex.org/works?filter=authorships.author.id:A5023888391 - Network accessline 155
https://api.openalex.org/works?filter=authorships.author.id:A5023888391 - Network accessline 160
# Step 1: Get institution ID https://api.openalex.org/institutions?search=MIT - Network accessline 161
https://api.openalex.org/institutions?search=MIT - Network accessline 162
# Returns: "id": "https://openalex.org/I136199984" - Network accessline 164
# Step 2: Get their works https://api.openalex.org/works?filter=authorships.institutions.id:I136199984 - Network accessline 165
https://api.openalex.org/works?filter=authorships.institutions.id:I136199984 - Network accessline 170
https://api.openalex.org/works?filter=publication_year:>2020&sort=cited_by_count:desc&per-page=200 - Network accessline 175
# All OA https://api.openalex.org/works?filter=is_oa:true - Network accessline 176
https://api.openalex.org/works?filter=is_oa:true - Network accessline 178
# Gold OA only https://api.openalex.org/works?filter=open_access.oa_status:gold - Network accessline 179
https://api.openalex.org/works?filter=open_access.oa_status:gold - Network accessline 184
# Recent OA works about COVID from top institutions https://api.openalex.org/works?filter=publication_year:2022,is_oa:true,title.search:covid,authorships.institutions.id:I136199984|I27837315 - Network accessline 185
https://api.openalex.org/works?filter=publication_year:2022,is_oa:true,title.search:covid,authorships.institutions.id:I136199984|I27837315 - Network accessline 190
# Get specific works by DOI (up to 50 per request) https://api.openalex.org/works?filter=doi:https://doi.org/10.1371/journal.pone.0266781|https://doi.org/10.1371/journal.pone.0267149&per-page=50 - Network accessline 191
https://api.openalex.org/works?filter=doi:https://doi.org/10.1371/journal.pone.0266781|https://doi.org/10.1371/journal.pone.0267149&per-page=50 - Network accessline 196
# Top topics https://api.openalex.org/works?group_by=topics.id - Network accessline 197
https://api.openalex.org/works?group_by=topics.id - Network accessline 199
# Papers per year https://api.openalex.org/works?group_by=publication_year - Network accessline 200
https://api.openalex.org/works?group_by=publication_year - Network accessline 202
# Most prolific institutions https://api.openalex.org/works?group_by=authorships.institutions.id - Network accessline 203
https://api.openalex.org/works?group_by=authorships.institutions.id - Network accessline 208
# First page https://api.openalex.org/works?filter=publication_year:2023&per-page=200 - Network accessline 209
https://api.openalex.org/works?filter=publication_year:2023&per-page=200 - Network accessline 211
# Next pages https://api.openalex.org/works?filter=publication_year:2023&per-page=200&page=2 - Network accessline 212
https://api.openalex.org/works?filter=publication_year:2023&per-page=200&page=2 - Network accessline 234
https://api.openalex.org/works/W2741809807 - Network accessline 242
"group_by": [ { "key": "https://openalex.org/T10001", - Network accessline 243
{ "key": "https://openalex.org/T10001", "key_display_name": "Artificial Intelligence", - Network accessline 244
"key": "https://openalex.org/T10001", - Network accessline 280
DOI: /works/https://doi.org/10.7717/peerj.4375 - Network accessline 286
ORCID: /authors/https://orcid.org/0000-0003-1613-5981 - Network accessline 291
ROR: /institutions/https://ror.org/02y3ad647 - Network accessline 319
for attempt in range(max_retries): try: response = requests.get(url, timeout=30) - Network accessline 320
try: response = requests.get(url, timeout=30) if response.status_code == 200: - Network accessline 321
response = requests.get(url, timeout=30) - Network accessline 367
- Full documentation: https://docs.openalex.org - Network accessline 368
- API Overview: https://docs.openalex.org/how-to-use-the-api/api-overview - Network accessline 369
- Entity schemas: https://docs.openalex.org/api-entities - Network accessline 370
- Help: https://openalex.org/help - Network accessline 371
- User group: https://groups.google.com/g/openalex-users
skills/01-论文检索与文献管理/openalex-database/references/common_queries.md
- Network accessline 149
dois = [ "https://doi.org/10.1371/journal.pone.0266781", - Network accessline 150
"https://doi.org/10.1371/journal.pone.0266781", - Network accessline 151
"https://doi.org/10.1371/journal.pone.0267149", - Network accessline 152
"https://doi.org/10.1038/s41586-021-03819-2", - Network accessline 286
# Get the work doi = "https://doi.org/10.1038/s41586-021-03819-2" - Network accessline 287
doi = "https://doi.org/10.1038/s41586-021-03819-2" - Network accessline 293
# Extract just the query part and use it import requests response = requests.get(cited_by_url, params={'mailto': client.email}) - Network accessline 294
import requests response = requests.get(cited_by_url, params={'mailto': client.email}) citing_works = response.json() - Network accessline 295
response = requests.get(cited_by_url, params={'mailto': client.email})
skills/01-论文检索与文献管理/openalex-database/scripts/openalex_client.py
skills/01-论文检索与文献管理/openalex-database/scripts/query_helpers.py
- Executable scriptline 1
#!/usr/bin/env python3
skills/01-论文检索与文献管理/parallel-web/SKILL.md
- Broad permissionsline 1
Read Write Edit Bash - Access to secretsline 6
[Credential-related source omitted] - Network accessline 20
**API Documentation:** https://docs.parallel.ai - Network accessline 21
**API Key:** https://platform.parallel.ai - Access to secretsline 22
[Credential-related source omitted] - Network accessline 109
# Verify a citation's content python scripts/parallel_web.py extract "https://example.com/article" --objective "key findings" - Network accessline 110
python scripts/parallel_web.py extract "https://example.com/article" --objective "key findings" - Network accessline 112
# Get full page content for verification python scripts/parallel_web.py extract "https://docs.example.com/api" --full-content - Network accessline 113
python scripts/parallel_web.py extract "https://docs.example.com/api" --full-content - Network accessline 115
# Save extraction to file python scripts/parallel_web.py extract "https://paper-url.com" --objective "methodology" -o extracted.md - Network accessline 116
python scripts/parallel_web.py extract "https://paper-url.com" --objective "methodology" -o extracted.md - Network accessline 177
extractor = ParallelExtract() result = extractor.extract( urls=["https://docs.example.com/api-reference"], - Network accessline 178
result = extractor.extract( urls=["https://docs.example.com/api-reference"], objective="API authentication methods and rate limits", - Network accessline 179
urls=["https://docs.example.com/api-reference"], - Network accessline 217
# URL extraction (verification only) — save to sources/ python scripts/parallel_web.py extract "https://example.com/article" --objective "key findings" \ - Network accessline 218
python scripts/parallel_web.py extract "https://example.com/article" --objective "key findings" \ - Access to secretsline 276
[Credential-related source omitted] - Network accessline 283
Get your API key at https://platform.parallel.ai - Access to secretsline 300
[Credential-related source omitted]
skills/01-论文检索与文献管理/parallel-web/references/api_reference.md
- Network accessline 3
**Full Documentation:** https://docs.parallel.ai - Network accessline 4
**API Key:** https://platform.parallel.ai - Access to secretsline 6
[Credential-related source omitted] - Network accessline 12
**Endpoint:** `POST https://api.parallel.ai/v1beta/search` - Network accessline 39
"results": [ { "url": "https://...", - Network accessline 40
{ "url": "https://...", "title": "Page Title", - Network accessline 41
"url": "https://...", - Access to secretsline 54
[Credential-related source omitted] - Network accessline 70
**Endpoint:** `POST https://api.parallel.ai/v1beta/extract` - Network accessline 76
{ "urls": ["https://example.com/page"], - Network accessline 77
"urls": ["https://example.com/page"], - Network accessline 89
"results": [ { "url": "https://...", - Network accessline 90
{ "url": "https://...", "title": "Page Title", - Network accessline 91
"url": "https://...", - Network accessline 104
result = client.beta.extract( urls=["https://..."], - Network accessline 105
urls=["https://..."], - Network accessline 119
**Endpoint:** `POST https://api.parallel.ai/v1/tasks/runs` - Network accessline 146
**Endpoint:** `GET https://api.parallel.ai/v1/tasks/runs/{run_id}/result` - Network accessline 191
**Endpoint:** `POST https://api.parallel.ai/chat/completions` - Network accessline 207
[Credential-related source omitted] - Network accessline 208
[Credential-related source omitted] - Access to secretsline 208
[Credential-related source omitted] - Network accessline 209
base_url="https://api.parallel.ai",
skills/01-论文检索与文献管理/parallel-web/references/extraction_patterns.md
- Network accessline 45
result = extractor.extract( urls=["https://arxiv.org/abs/2301.12345"], - Network accessline 46
urls=["https://arxiv.org/abs/2301.12345"], - Network accessline 64
result = extractor.extract( urls=["https://docs.example.com/api-reference"], - Network accessline 65
urls=["https://docs.example.com/api-reference"], - Network accessline 83
result = extractor.extract( urls=["https://example.com/report"], - Network accessline 84
urls=["https://example.com/report"], - Network accessline 119
# No objective at all (still works but excerpts are less focused) extractor.extract(urls=["https://..."]) - Network accessline 120
extractor.extract(urls=["https://..."]) - Network accessline 157
result = extractor.extract( urls=[ "https://nature.com/articles/s12345", - Network accessline 158
urls=[ "https://nature.com/articles/s12345", "https://science.org/doi/full/10.1234/science.xyz", - Network accessline 159
"https://nature.com/articles/s12345", - Network accessline 160
"https://science.org/doi/full/10.1234/science.xyz", - Network accessline 161
"https://thelancet.com/journals/lancet/article/PIIS0140-6736(24)12345/fulltext" - Network accessline 189
# Standard web page result = extractor.extract( urls=["https://example.com/article"], - Network accessline 190
result = extractor.extract( urls=["https://example.com/article"], objective="Main article content", - Network accessline 191
urls=["https://example.com/article"], - Network accessline 201
# PDF extraction result = extractor.extract( urls=["https://example.com/whitepaper.pdf"], - Network accessline 202
result = extractor.extract( urls=["https://example.com/whitepaper.pdf"], objective="Executive summary and key recommendations", - Network accessline 203
urls=["https://example.com/whitepaper.pdf"], - Network accessline 213
result = extractor.extract( urls=["https://docs.example.com/getting-started"], - Network accessline 214
urls=["https://docs.example.com/getting-started"], - Network accessline 251
# Extract content from a DOI URL result = extractor.extract( urls=["https://doi.org/10.1038/s41586-024-07487-w"], - Network accessline 252
result = extractor.extract( urls=["https://doi.org/10.1038/s41586-024-07487-w"], objective="Study design, patient population, primary endpoints, efficacy results, and safety data", - Network accessline 253
urls=["https://doi.org/10.1038/s41586-024-07487-w"], - Network accessline 261
companies = [ "https://openai.com/about", - Network accessline 262
"https://openai.com/about", - Network accessline 263
"https://anthropic.com/company", - Network accessline 264
"https://deepmind.google/about/", - Network accessline 276
result = extractor.extract( urls=["https://docs.parallel.ai/search/search-quickstart"], - Network accessline 277
urls=["https://docs.parallel.ai/search/search-quickstart"], - Network accessline 286
# Verify citation metadata for a specific paper result = extractor.extract( urls=["https://doi.org/10.1234/example-doi"], - Network accessline 287
result = extractor.extract( urls=["https://doi.org/10.1234/example-doi"], objective="Complete citation metadata: authors, title, journal, volume, pages, year, DOI", - Network accessline 288
urls=["https://doi.org/10.1234/example-doi"], - Network accessline 309
result = extractor.extract(urls=["https://example.com/page"])
skills/01-论文检索与文献管理/parallel-web/references/workflow_recipes.md
- Network accessline 87
# Option B: Extract metadata from a DOI python scripts/parallel_web.py extract \ "https://doi.org/10.48550/arXiv.1706.03762" \ - Network accessline 88
python scripts/parallel_web.py extract "https://doi.org/10.48550/arXiv.1706.03762" - Network accessline 89
"https://doi.org/10.48550/arXiv.1706.03762" \ - Network accessline 238
# Step 2: Extract from company pages company_pages = [ "https://openai.com/about", - Network accessline 239
company_pages = [ "https://openai.com/about", "https://anthropic.com/company", - Network accessline 240
"https://openai.com/about", - Network accessline 241
"https://anthropic.com/company", - Network accessline 242
"https://deepmind.google/about/",
skills/01-论文检索与文献管理/parallel-web/scripts/parallel_web.py
- Executable scriptline 1
#!/usr/bin/env python3 - Network accessline 5
Provides web search, URL content extraction, and deep research capabilities using the Parallel Web Systems APIs (https://docs.parallel.ai). - Network accessline 6
using the Parallel Web Systems APIs (https://docs.parallel.ai). - Network accessline 17
Environment variable required: PARALLEL_API_KEY - Your Parallel API key from https://platform.parallel.ai - Network accessline 18
[Credential-related source omitted] - Access to secretsline 18
[Credential-related source omitted] - Access to secretsline 31
[Credential-related source omitted] - Access to secretsline 32
[Credential-related source omitted] - Network accessline 33
raise ValueError( "PARALLEL_API_KEY environment variable not set.\n" "Get your key at https://platform.parallel.ai and set it:\n" - Network accessline 34
[Credential-related source omitted] - Access to secretsline 34
[Credential-related source omitted] - Network accessline 35
"Get your key at https://platform.parallel.ai and set it:\n" - Access to secretsline 36
[Credential-related source omitted] - Access to secretsline 38
[Credential-related source omitted] - Access to secretsline 50
[Credential-related source omitted] - Network accessline 64
CHAT_BASE_URL = "https://api.parallel.ai" - Access to secretsline 76
[Credential-related source omitted] - Network accessline 473
python parallel_web.py search "latest advances in quantum computing" python parallel_web.py search "climate policy 2025" --model core python parallel_web.py extract "https://example.com" --objective "key findings" - Network accessline 474
python parallel_web.py search "climate policy 2025" --model core python parallel_web.py extract "https://example.com" --objective "key findings" python parallel_web.py research "comprehensive analysis of EV battery market" - Network accessline 475
python parallel_web.py extract "https://example.com" --objective "key findings"
skills/01-论文检索与文献管理/perplexity-search/SKILL.md
- Network accessline 39
1. **Get OpenRouter API key**: - Visit https://openrouter.ai/keys - Network accessline 40
- Visit https://openrouter.ai/keys - Access to secretsline 47
[Credential-related source omitted] - Network accessline 256
3. Reserve `sonar-pro-search` for complex analysis 4. Set `--max-tokens` to limit response length 5. Monitor usage at https://openrouter.ai/activity - Network accessline 257
4. Set `--max-tokens` to limit response length 5. Monitor usage at https://openrouter.ai/activity 6. Set spending limits in OpenRouter dashboard - Network accessline 258
5. Monitor usage at https://openrouter.ai/activity - Access to secretsline 269
[Credential-related source omitted] - Network accessline 287
**Solutions**: - Wait a few seconds before retrying - Increase rate limit at https://openrouter.ai/keys - Network accessline 288
- Wait a few seconds before retrying - Increase rate limit at https://openrouter.ai/keys - Add delays between requests in batch processing - Network accessline 289
- Increase rate limit at https://openrouter.ai/keys - Network accessline 296
**Solution**: - Add credits at https://openrouter.ai/account - Network accessline 297
- Add credits at https://openrouter.ai/account - Network accessline 389
**OpenRouter:** - Dashboard: https://openrouter.ai/account - Network accessline 390
- Dashboard: https://openrouter.ai/account - Network accessline 391
- API Keys: https://openrouter.ai/keys - Network accessline 392
- Perplexity Models: https://openrouter.ai/perplexity - Network accessline 393
- Usage Monitoring: https://openrouter.ai/activity - Network accessline 394
- Documentation: https://openrouter.ai/docs - Network accessline 396
**LiteLLM:** - Documentation: https://docs.litellm.ai/ - Network accessline 397
- Documentation: https://docs.litellm.ai/ - Network accessline 398
- OpenRouter Provider: https://docs.litellm.ai/docs/providers/openrouter - Network accessline 399
- GitHub: https://github.com/BerriAI/litellm - Network accessline 401
**Perplexity:** - Official Docs: https://docs.perplexity.ai/ - Network accessline 402
- Official Docs: https://docs.perplexity.ai/ - Access to secretsline 416
[Credential-related source omitted] - Access to secretsline 426
[Credential-related source omitted]
skills/01-论文检索与文献管理/perplexity-search/assets/.env.example
skills/01-论文检索与文献管理/perplexity-search/references/model_comparison.md
- Access to secretsline 307
[Credential-related source omitted]
skills/01-论文检索与文献管理/perplexity-search/references/openrouter_setup.md
- Network accessline 21
1. Visit https://openrouter.ai/ - Network accessline 30
1. Navigate to https://openrouter.ai/account - Network accessline 36
**Pricing notes:** - Models have different per-token costs - See https://openrouter.ai/perplexity for Perplexity pricing - Network accessline 37
- Models have different per-token costs - See https://openrouter.ai/perplexity for Perplexity pricing - Monitor usage at https://openrouter.ai/activity - Network accessline 38
- See https://openrouter.ai/perplexity for Perplexity pricing - Network accessline 39
- Monitor usage at https://openrouter.ai/activity - Network accessline 43
1. Go to https://openrouter.ai/keys - Access to secretsline 65
[Credential-related source omitted] - Access to secretsline 71
[Credential-related source omitted] - Access to secretsline 75
[Credential-related source omitted] - Access to secretsline 81
[Credential-related source omitted] - Access to secretsline 86
[Credential-related source omitted] - Access to secretsline 89
[Credential-related source omitted] - Access to secretsline 94
[Credential-related source omitted] - Access to secretsline 95
[Credential-related source omitted] - Access to secretsline 96
[Credential-related source omitted] - Access to secretsline 107
[Credential-related source omitted] - Access to secretsline 108
[Credential-related source omitted] - Access to secretsline 117
[Credential-related source omitted] - Access to secretsline 120
[Credential-related source omitted] - Access to secretsline 138
[Credential-related source omitted] - Access to secretsline 159
[Credential-related source omitted] - Network accessline 189
1. Visit https://openrouter.ai/activity 2. View requests, tokens, and costs - Network accessline 198
1. Go to https://openrouter.ai/keys 2. Click on your key 3. Set "Rate limit" (requests per minute) - Access to secretsline 227
[Credential-related source omitted] - Access to secretsline 230
[Credential-related source omitted] - Network accessline 244
**Solutions**: 1. Verify key at https://openrouter.ai/keys - Network accessline 245
1. Verify key at https://openrouter.ai/keys - Network accessline 254
**Solution**: 1. Go to https://openrouter.ai/account - Network accessline 255
1. Go to https://openrouter.ai/account - Network accessline 264
**Solutions**: 1. Wait a few seconds before retrying 2. Increase rate limit at https://openrouter.ai/keys - Network accessline 265
1. Wait a few seconds before retrying 2. Increase rate limit at https://openrouter.ai/keys 3. Implement exponential backoff in code - Network accessline 266
2. Increase rate limit at https://openrouter.ai/keys 3. Implement exponential backoff in code 4. Batch requests or reduce frequency - Network accessline 274
**Solution**: 1. Check available models at https://openrouter.ai/models - Network accessline 275
1. Check available models at https://openrouter.ai/models - Access to secretsline 306
[Credential-related source omitted] - Access to secretsline 309
[Credential-related source omitted] - Access to secretsline 312
[Credential-related source omitted] - Network accessline 322
model="openrouter/perplexity/sonar-pro", messages=[{"role": "user", "content": "query"}], api_base="https://custom-endpoint.com/v1" # Custom URL - Network accessline 323
messages=[{"role": "user", "content": "query"}], api_base="https://custom-endpoint.com/v1" # Custom URL ) - Network accessline 324
api_base="https://custom-endpoint.com/v1" # Custom URL - Network accessline 337
messages=[{"role": "user", "content": "query"}], extra_headers={ "HTTP-Referer": "https://your-app.com", - Network accessline 338
extra_headers={ "HTTP-Referer": "https://your-app.com", "X-Title": "Your App Name" - Network accessline 339
"HTTP-Referer": "https://your-app.com", - Access to secretsline 374
[Credential-related source omitted] - Access to secretsline 379
[Credential-related source omitted] - Network accessline 391
1. Go to https://openrouter.ai/keys immediately - Network accessline 402
A: Pricing varies by model. Sonar is cheapest (~$0.001-0.002 per query), Sonar Pro is moderate (~$0.002-0.005), and Sonar Pro Search is most expensive (~$0.02-0.05+ per query). See https://openrouter.ai/perplexity for exact pricing. - Network accessline 426
**Official Documentation:** - OpenRouter: https://openrouter.ai/docs - Network accessline 427
- OpenRouter: https://openrouter.ai/docs - Network accessline 428
- Perplexity Models: https://openrouter.ai/perplexity - Network accessline 429
- LiteLLM: https://docs.litellm.ai/ - Network accessline 431
**Account Management:** - Dashboard: https://openrouter.ai/account - Network accessline 432
- Dashboard: https://openrouter.ai/account - Network accessline 433
- API Keys: https://openrouter.ai/keys - Network accessline 434
- Usage: https://openrouter.ai/activity - Network accessline 435
- Billing: https://openrouter.ai/credits - Network accessline 437
**Community:** - OpenRouter Discord: https://discord.gg/openrouter - Network accessline 438
- OpenRouter Discord: https://discord.gg/openrouter - Network accessline 439
- GitHub Issues: https://github.com/OpenRouter - Network accessline 440
- LiteLLM GitHub: https://github.com/BerriAI/litellm - Network accessline 446
1. Create account at https://openrouter.ai - Network accessline 447
2. Add payment method and credits 3. Generate API key at https://openrouter.ai/keys 4. Set `OPENROUTER_API_KEY` environment variable - Network accessline 448
3. Generate API key at https://openrouter.ai/keys - Access to secretsline 449
[Credential-related source omitted]
skills/01-论文检索与文献管理/perplexity-search/scripts/perplexity_search.py
- Executable scriptline 1
#!/usr/bin/env python3 - Access to secretsline 12
[Credential-related source omitted] - Access to secretsline 37
[Credential-related source omitted] - Access to secretsline 39
[Credential-related source omitted] - Access to secretsline 40
[Credential-related source omitted] - Network accessline 41
[Credential-related source omitted] - Access to secretsline 41
[Credential-related source omitted] - Network accessline 42
print("\nTo set up your API key:", file=sys.stderr) print("1. Get an API key from https://openrouter.ai/keys", file=sys.stderr) print("2. Set the environment variable:", file=sys.stderr) - Network accessline 43
print("1. Get an API key from https://openrouter.ai/keys", file=sys.stderr) - Access to secretsline 45
[Credential-related source omitted] - Access to secretsline 46
[Credential-related source omitted] - Access to secretsline 47
[Credential-related source omitted] - Access to secretsline 49
[Credential-related source omitted] - Access to secretsline 81
[Credential-related source omitted] - Access to secretsline 82
[Credential-related source omitted] - Access to secretsline 221
[Credential-related source omitted]
skills/01-论文检索与文献管理/perplexity-search/scripts/setup_env.py
- Executable scriptline 1
#!/usr/bin/env python3 - Access to secretsline 20
[Credential-related source omitted] - Access to secretsline 22
[Credential-related source omitted] - Access to secretsline 25
[Credential-related source omitted] - Access to secretsline 26
[Credential-related source omitted] - Access to secretsline 40
[Credential-related source omitted] - Access to secretsline 45
[Credential-related source omitted] - Access to secretsline 51
[Credential-related source omitted] - Access to secretsline 57
[Credential-related source omitted] - Access to secretsline 72
[Credential-related source omitted] - Access to secretsline 73
[Credential-related source omitted] - Access to secretsline 74
[Credential-related source omitted] - Network accessline 75
print() print("To set up your API key:") print("1. Get an API key from https://openrouter.ai/keys") - Network accessline 76
print("To set up your API key:") print("1. Get an API key from https://openrouter.ai/keys") print("2. Run this script with --api-key flag:") - Network accessline 77
print("1. Get an API key from https://openrouter.ai/keys") - Access to secretsline 84
[Credential-related source omitted] - Access to secretsline 85
[Credential-related source omitted] - Access to secretsline 117
[Credential-related source omitted] - Access to secretsline 118
[Credential-related source omitted] - Network accessline 120
Get your OpenRouter API key from: https://openrouter.ai/keys - Network accessline 121
https://openrouter.ai/keys - Access to secretsline 132
[Credential-related source omitted] - Access to secretsline 133
[Credential-related source omitted] - Access to secretsline 145
[Credential-related source omitted] - Access to secretsline 149
[Credential-related source omitted] - Access to secretsline 151
[Credential-related source omitted] - Access to secretsline 157
[Credential-related source omitted]
skills/01-论文检索与文献管理/pubmed-database/SKILL.md
- Network accessline 127
# Step 1: Search for articles base_url = "https://eutils.ncbi.nlm.nih.gov/entrez/eutils/" - Network accessline 128
base_url = "https://eutils.ncbi.nlm.nih.gov/entrez/eutils/" - Network accessline 135
[Credential-related source omitted] - Access to secretsline 135
[Credential-related source omitted] - Network accessline 136
} response = requests.get(search_url, params=params) pmids = response.json()["esearchresult"]["idlist"] - Network accessline 137
response = requests.get(search_url, params=params) - Network accessline 147
[Credential-related source omitted] - Access to secretsline 147
[Credential-related source omitted] - Network accessline 148
} response = requests.get(fetch_url, params=params) abstracts = response.text - Network accessline 149
response = requests.get(fetch_url, params=params) - Network accessline 453
- **PubMed Help**: https://pubmed.ncbi.nlm.nih.gov/help/ - Network accessline 454
- **E-utilities Documentation**: https://www.ncbi.nlm.nih.gov/books/NBK25501/
skills/01-论文检索与文献管理/pubmed-database/references/api_reference.md
- Network accessline 8
https://eutils.ncbi.nlm.nih.gov/entrez/eutils/ - Access to secretsline 15
[Credential-related source omitted] - Access to secretsline 17
[Credential-related source omitted] - Access to secretsline 51
[Credential-related source omitted] - Access to secretsline 80
[Credential-related source omitted] - Access to secretsline 105
[Credential-related source omitted] - Access to secretsline 129
[Credential-related source omitted] - Access to secretsline 154
[Credential-related source omitted] - Access to secretsline 175
[Credential-related source omitted] - Access to secretsline 195
[Credential-related source omitted] - Access to secretsline 210
[Credential-related source omitted] - Access to secretsline 242
[Credential-related source omitted] - Access to secretsline 247
[Credential-related source omitted] - Access to secretsline 248
[Credential-related source omitted] - Access to secretsline 257
[Credential-related source omitted] - Access to secretsline 260
[Credential-related source omitted] - Network accessline 295
- **API Documentation**: https://www.ncbi.nlm.nih.gov/books/NBK25501/
skills/01-论文检索与文献管理/pyzotero/SKILL.md
- Broad permissionsline 1
Read Write Edit Bash - Network accessline 12
Pyzotero is a Python wrapper for the [Zotero API v3](https://www.zotero.org/support/dev/web_api/v3/start). Use it to programmatically manage Zotero libraries: read items and collections, create and update references, upload attachments, man - Network accessline 16
**Required credentials** — get from https://www.zotero.org/settings/keys: - Network accessline 17
- **User ID**: shown as "Your userID for use in API calls" - **API Key**: create at https://www.zotero.org/settings/keys/new - **Library ID**: for group libraries, the integer after `/groups/` in the group URL - Network accessline 18
- **API Key**: create at https://www.zotero.org/settings/keys/new - Access to secretsline 24
[Credential-related source omitted] - Access to secretsline 43
[Credential-related source omitted]
skills/01-论文检索与文献管理/pyzotero/references/authentication.md
- Network accessline 5
Obtain from https://www.zotero.org/settings/keys: - Network accessline 9
| **User ID** | "Your userID for use in API calls" section | | **API Key** | Create new key at /settings/keys/new | | **Group Library ID** | Integer after `/groups/` in group URL (e.g. `https://www.zotero.org/groups/169947`) | - Network accessline 11
| **Group Library ID** | Integer after `/groups/` in group URL (e.g. `https://www.zotero.org/groups/169947`) | - Access to secretsline 18
[Credential-related source omitted] - Access to secretsline 31
[Credential-related source omitted] - Access to secretsline 32
[Credential-related source omitted] - Access to secretsline 33
[Credential-related source omitted] - Access to secretsline 64
[Credential-related source omitted] - Network accessline 86
When creating an API key at https://www.zotero.org/settings/keys/new, choose appropriate permissions:
skills/01-论文检索与文献管理/pyzotero/references/exports.md
- Network accessline 45
Pass any valid CSL style name from the [Zotero style repository](https://www.zotero.org/styles):
skills/01-论文检索与文献管理/research-lookup/README.md
skills/01-论文检索与文献管理/research-lookup/SKILL.md
- Broad permissionsline 1
Read Write Edit Bash - Access to secretsline 6
[Credential-related source omitted] - Network accessline 17
- **Parallel Chat API** (`core` model): Default backend for all general research queries. Provides comprehensive, multi-source research reports with inline citations via the OpenAI-compatible Chat API at `https://api.parallel.ai`. - Access to secretsline 200
[Credential-related source omitted] - Access to secretsline 203
[Credential-related source omitted] - Network accessline 208
**Parallel Chat API:** - Endpoint: `https://api.parallel.ai` (OpenAI SDK compatible) - Network accessline 209
- Endpoint: `https://api.parallel.ai` (OpenAI SDK compatible)
skills/01-论文检索与文献管理/research-lookup/examples.py
skills/01-论文检索与文献管理/research-lookup/lookup.py
skills/01-论文检索与文献管理/research-lookup/research_lookup.py
- Executable scriptline 1
#!/usr/bin/env python3 - Access to secretsline 10
[Credential-related source omitted] - Access to secretsline 11
[Credential-related source omitted] - Network accessline 59
CHAT_BASE_URL = "https://api.parallel.ai" - Access to secretsline 69
[Credential-related source omitted] - Access to secretsline 70
[Credential-related source omitted] - Access to secretsline 75
[Credential-related source omitted] - Access to secretsline 76
[Credential-related source omitted] - Access to secretsline 116
[Credential-related source omitted] - Access to secretsline 205
[Credential-related source omitted] - Network accessline 209
[Credential-related source omitted] - Access to secretsline 209
[Credential-related source omitted] - Network accessline 210
"Content-Type": "application/json", "HTTP-Referer": "https://scientific-writer.local", "X-Title": "Scientific Writer Research Tool", - Network accessline 211
"HTTP-Referer": "https://scientific-writer.local", - Network accessline 250
try: response = requests.post( - Network accessline 251
response = requests.post( - Network accessline 252
"https://openrouter.ai/api/v1/chat/completions", - Network accessline 383
"type": "doi", "doi": doi_clean, "url": f"https://doi.org/{doi_clean}", - Network accessline 384
"doi": doi_clean, "url": f"https://doi.org/{doi_clean}", }) - Network accessline 385
"url": f"https://doi.org/{doi_clean}", - Access to secretsline 486
[Credential-related source omitted] - Access to secretsline 487
[Credential-related source omitted] - Access to secretsline 490
[Credential-related source omitted] - Access to secretsline 491
[Credential-related source omitted]
skills/01-论文检索与文献管理/research-lookup/scripts/research_lookup.py
- Executable scriptline 1
#!/usr/bin/env python3 - Access to secretsline 10
[Credential-related source omitted] - Access to secretsline 11
[Credential-related source omitted] - Network accessline 59
CHAT_BASE_URL = "https://api.parallel.ai" - Access to secretsline 69
[Credential-related source omitted] - Access to secretsline 70
[Credential-related source omitted] - Access to secretsline 75
[Credential-related source omitted] - Access to secretsline 76
[Credential-related source omitted] - Access to secretsline 116
[Credential-related source omitted] - Access to secretsline 205
[Credential-related source omitted] - Network accessline 209
[Credential-related source omitted] - Access to secretsline 209
[Credential-related source omitted] - Network accessline 210
"Content-Type": "application/json", "HTTP-Referer": "https://scientific-writer.local", "X-Title": "Scientific Writer Research Tool", - Network accessline 211
"HTTP-Referer": "https://scientific-writer.local", - Network accessline 250
try: response = requests.post( - Network accessline 251
response = requests.post( - Network accessline 252
"https://openrouter.ai/api/v1/chat/completions", - Network accessline 383
"type": "doi", "doi": doi_clean, "url": f"https://doi.org/{doi_clean}", - Network accessline 384
"doi": doi_clean, "url": f"https://doi.org/{doi_clean}", }) - Network accessline 385
"url": f"https://doi.org/{doi_clean}", - Access to secretsline 486
[Credential-related source omitted] - Access to secretsline 487
[Credential-related source omitted] - Access to secretsline 490
[Credential-related source omitted] - Access to secretsline 491
[Credential-related source omitted]
skills/02-科学写作与学术交流/medical-imaging-review/SKILL.md
- Broad permissionsline 1
Bash
skills/02-科学写作与学术交流/medical-imaging-review/references/MCP_SETUP.md
- Network accessline 5
**Repository:** https://github.com/blazickjp/arxiv-mcp-server - Network accessline 51
**Repository:** https://github.com/grll/pubmedmcp - Network accessline 92
# List collections curl -s "http://localhost:23119/api/users/[USER_ID]/collections" - Network accessline 93
curl -s "http://localhost:23119/api/users/[USER_ID]/collections" - Network accessline 95
# Get items from collection curl -s "http://localhost:23119/api/users/[USER_ID]/collections/[KEY]/items" - Network accessline 96
curl -s "http://localhost:23119/api/users/[USER_ID]/collections/[KEY]/items" - Network accessline 101
**Repository:** https://github.com/54yyyu/zotero-mcp
skills/02-科学写作与学术交流/medical-imaging-review/references/TEMPLATES.md
- Network accessline 37
API: http://localhost:23119/api/users/[USER_ID]/
skills/02-科学写作与学术交流/paper-2-web/SKILL.md
- Broad permissionsline 1
Read Write Edit Bash - Network accessline 135
git clone https://github.com/YuhangChen1/Paper2All.git - Access to secretsline 144
[Credential-related source omitted] - Access to secretsline 145
[Credential-related source omitted] - Network accessline 467
**External Resources**: - GitHub Repository: https://github.com/YuhangChen1/Paper2All - Network accessline 468
- GitHub Repository: https://github.com/YuhangChen1/Paper2All
skills/02-科学写作与学术交流/paper-2-web/references/installation.md
- Network accessline 20
git clone https://github.com/YuhangChen1/Paper2All.git - Dangerous shell operationline 39
sudo apt-get install libreoffice poppler-utils - Network accessline 47
**Windows:** - Download and install LibreOffice from https://www.libreoffice.org/ - Network accessline 48
- Download and install LibreOffice from https://www.libreoffice.org/ - Network accessline 49
- Download and install Poppler from https://github.com/oschwartz10612/poppler-windows - Access to secretsline 59
[Credential-related source omitted] - Access to secretsline 64
[Credential-related source omitted] - Access to secretsline 71
[Credential-related source omitted] - Access to secretsline 131
[Credential-related source omitted]
skills/02-科学写作与学术交流/peer-review/SKILL.md
- Broad permissionsline 1
Read Write Edit Bash
skills/02-科学写作与学术交流/peer-review/references/reporting_standards.md
- Network accessline 20
**Reference:** http://www.consort-statement.org/ - Network accessline 36
**Reference:** https://www.strobe-statement.org/ - Network accessline 53
**Reference:** http://www.prisma-statement.org/ - Network accessline 64
**Reference:** https://www.spirit-statement.org/ - Network accessline 78
**Reference:** https://www.care-statement.org/ - Network accessline 100
**Reference:** https://arriveguidelines.org/ - Network accessline 116
**Reference:** http://fged.org/projects/miame/ - Network accessline 140
**Reference:** https://gensc.org/ - Network accessline 156
**Reference:** https://www.wwpdb.org/ - Network accessline 172
**Reference:** http://www.psidev.info/ - Network accessline 187
**Reference:** https://www.humanbrainmapping.org/cobidas - Network accessline 203
**Reference:** http://flowcyt.org/ - Network accessline 217
**Reference:** https://www.miappe.org/
skills/02-科学写作与学术交流/research-proposal/assets/proposal_scaffold_en.md
- Network accessline 313
Author, A. A., & Author, B. B. (Year). Title of article. *Journal Name*, *Volume*(Issue), pages. https://doi.org/xxxxx
skills/02-科学写作与学术交流/research-proposal/references/DOMAIN_TEMPLATES.md
skills/02-科学写作与学术交流/research-proposal/references/LITERATURE_WORKFLOW.md
- Network accessline 97
Access: https://arxiv.org/ - Network accessline 98
Search: https://arxiv.org/search/?query={topic} - Network accessline 111
Access: https://pubmed.ncbi.nlm.nih.gov/ - Network accessline 112
Search: https://pubmed.ncbi.nlm.nih.gov/?term={topic} - Network accessline 123
Access: https://www.biorxiv.org/ and https://www.medrxiv.org/ - Network accessline 130
Access: https://www.ssrn.com/ - Network accessline 136
Access: https://scholar.google.com/ - Network accessline 146
PLOS ONE: https://journals.plos.org/plosone/ - Network accessline 147
Nature Communications: https://www.nature.com/ncomms/ - Network accessline 148
Scientific Reports: https://www.nature.com/srep/ - Network accessline 384
Smith, J. A., & Jones, B. C. (2024). Title of article in sentence case. Journal Name in Title Case, 15(3), 123-145. https://doi.org/10.xxxx - Network accessline 385
Journal Name in Title Case, 15(3), 123-145. https://doi.org/10.xxxx - Network accessline 387
Multi-author (3+): Chen, X., Wang, Y., Liu, Z., & Zhang, W. (2024). Article title. Journal, 10(2), 45-67. https://doi.org/10.xxxx - Network accessline 389
Journal, 10(2), 45-67. https://doi.org/10.xxxx - Network accessline 407
Smith, John A., and Barbara C. Jones. 2024. "Article Title Here." Journal Name 15 (3): 123-145. https://doi.org/10.xxxx - Network accessline 408
Journal Name 15 (3): 123-145. https://doi.org/10.xxxx
skills/02-科学写作与学术交流/research-proposal/references/STRUCTURE_GUIDE.md
- Network accessline 355
Author, A. A., & Author, B. B. (Year). Title of article. Journal Name, Volume(Issue), pages. https://doi.org/xxxxx
skills/02-科学写作与学术交流/scientific-writing/SKILL.md
- Broad permissionsline 1
Read Write Edit Bash
skills/02-科学写作与学术交流/scientific-writing/assets/scientific_report_template.tex
- Network accessline 396
% Manual references for template demonstration: \noindent \hangindent=0.5in Author, A. A. (Year). Title of article. \textit{Journal Name}, \textit{Volume}(Issue), pages. https://doi.org/xxxxx - Network accessline 398
\hangindent=0.5in Author, A. A. (Year). Title of article. \textit{Journal Name}, \textit{Volume}(Issue), pages. https://doi.org/xxxxx
skills/02-科学写作与学术交流/scientific-writing/references/citation_styles.md
- Network accessline 204
Author, A. A., Author, B. B., & Author, C. C. (Year). Title of article. Journal Name, Volume(Issue), page range. https://doi.org/xx.xxxx - Network accessline 209
Smith, J. D., Johnson, A. B., & Williams, C. D. (2023). Effectiveness of cognitive behavioral therapy for anxiety disorders. JAMA Psychiatry, 80(5), 456-464. https://doi.org/10.1001/jamapsychiatry.2023.0123 - Network accessline 214
Author, A. A. (Year). Book title: Subtitle (Edition). Publisher. https://doi.org/xx.xxxx - Network accessline 453
doi:10.1001/jamapsychiatry.2023.0123 or https://doi.org/10.1001/jamapsychiatry.2023.0123 - Network accessline 455
https://doi.org/10.1001/jamapsychiatry.2023.0123 - Network accessline 459
- Required by most journals for recent publications - Preferred over URLs because DOIs don't change - Look up DOIs at https://www.crossref.org/ if not provided - Network accessline 461
- Look up DOIs at https://www.crossref.org/ if not provided - Network accessline 463
### Style-specific formatting: - **AMA**: `doi:10.xxxx/xxxxx` - **APA**: `https://doi.org/10.xxxx/xxxxx` - Network accessline 464
- **AMA**: `doi:10.xxxx/xxxxx` - **APA**: `https://doi.org/10.xxxx/xxxxx` - **Vancouver**: Often omitted or added at journal's discretion - Network accessline 465
- **APA**: `https://doi.org/10.xxxx/xxxxx` - Network accessline 467
- **Chicago**: `https://doi.org/10.xxxx/xxxxx` - Network accessline 473
| **AMA** | Author AA, Author BB. Title of article. *Journal*. Year;Vol(Iss):pp. doi:xx | | **Vancouver** | Author AA, Author BB. Title of article. Journal. Year;Vol(Iss):pp. | | **APA** | Author, A. A., & Author, B. B. (Year). Title of art - Network accessline 474
| **Vancouver** | Author AA, Author BB. Title of article. Journal. Year;Vol(Iss):pp. | | **APA** | Author, A. A., & Author, B. B. (Year). Title of article. *Journal*, Vol(Iss), pp. https://doi.org/xx | | **Chicago A-D** | Author, A. A., and - Network accessline 475
| **APA** | Author, A. A., & Author, B. B. (Year). Title of article. *Journal*, Vol(Iss), pp. https://doi.org/xx | - Network accessline 499
APA: Author, A. A. (Year). Title [Preprint]. Repository Name. https://doi.org/xx.xxxx - Network accessline 519
APA: Author, A. A. (Year). Title of dataset (Version X) [Data set]. Repository. https://doi.org/xx.xxxx - Network accessline 694
### Official Manuals - AMA: https://www.amamanualofstyle.com/ - Network accessline 695
- AMA: https://www.amamanualofstyle.com/ - Network accessline 696
- Vancouver/ICMJE: http://www.icmje.org/ - Network accessline 697
- APA: https://apastyle.apa.org/ - Network accessline 698
- Chicago: https://www.chicagomanualofstyle.org/ - Network accessline 699
- IEEE: https://ieeeauthorcenter.ieee.org/ - Network accessline 701
### Journal-Specific Style Guides - Nature: https://www.nature.com/nature/for-authors/formatting-guide - Network accessline 702
- Nature: https://www.nature.com/nature/for-authors/formatting-guide - Network accessline 703
- Science: https://www.science.org/content/page/instructions-authors - Network accessline 704
- Cell: https://www.cell.com/cell/authors - Network accessline 705
- JAMA: https://jamanetwork.com/journals/jama/pages/instructions-for-authors - Network accessline 707
### Quick Reference Guides - Purdue OWL: https://owl.purdue.edu/ - Network accessline 708
- Purdue OWL: https://owl.purdue.edu/ - Network accessline 709
- Citation Machine: https://www.citationmachine.net/ - Network accessline 710
- EasyBib: https://www.easybib.com/ - Network accessline 712
### Reference Management - Zotero: https://www.zotero.org/ - Network accessline 713
- Zotero: https://www.zotero.org/ - Network accessline 714
- Mendeley: https://www.mendeley.com/ - Network accessline 715
- EndNote: https://endnote.com/ - Network accessline 718
- Journal Citation Reports (Clarivate): Lists journal citation styles - EndNote style repository: >7000 journal-specific styles - Zotero Style Repository: https://www.zotero.org/styles - Network accessline 720
- Zotero Style Repository: https://www.zotero.org/styles
skills/02-科学写作与学术交流/scientific-writing/references/professional_report_formatting.md
- Network accessline 661
- [LaTeX Wikibook](https://en.wikibooks.org/wiki/LaTeX) - General LaTeX reference - Network accessline 662
- [Booktabs Package Documentation](https://ctan.org/pkg/booktabs) - Professional table styling - Network accessline 663
- [tcolorbox Package Documentation](https://ctan.org/pkg/tcolorbox) - Colored box environments
skills/02-科学写作与学术交流/scientific-writing/references/reporting_guidelines.md
- Network accessline 86
**Where to access:** http://www.consort-statement.org/ - Network accessline 130
**Where to access:** https://www.strobe-statement.org/ - Network accessline 181
**Where to access:** http://www.prisma-statement.org/ - Network accessline 218
**Where to access:** https://www.spirit-statement.org/ - Network accessline 247
**Where to access:** https://www.equator-network.org/reporting-guidelines/stard/ - Network accessline 281
**Where to access:** https://www.tripod-statement.org/ - Network accessline 314
**Where to access:** https://arriveguidelines.org/ - Network accessline 338
**Where to access:** https://www.care-statement.org/ - Network accessline 356
**Where to access:** http://www.squire-statement.org/ - Network accessline 384
**Where to access:** https://www.equator-network.org/reporting-guidelines/cheers/ - Network accessline 403
**Where to access:** https://www.equator-network.org/reporting-guidelines/srqr/ - Network accessline 440
**Website:** https://www.equator-network.org/ - Network accessline 619
- **EQUATOR Network**: https://www.equator-network.org/ - Network accessline 620
- **CONSORT**: http://www.consort-statement.org/ - Network accessline 621
- **STROBE**: https://www.strobe-statement.org/ - Network accessline 622
- **PRISMA**: http://www.prisma-statement.org/ - Network accessline 623
- **SPIRIT**: https://www.spirit-statement.org/ - Network accessline 624
- **ARRIVE**: https://arriveguidelines.org/ - Network accessline 625
- **CARE**: https://www.care-statement.org/ - Network accessline 635
- **Some reference managers** can insert reporting guideline citations - **Covidence, RevMan** for systematic review reporting - **PRISMA flow diagram generator**: http://prisma.thetacollaborative.ca/ - Network accessline 637
- **PRISMA flow diagram generator**: http://prisma.thetacollaborative.ca/
skills/02-科学写作与学术交流/venue-templates/SKILL.md
- Broad permissionsline 1
Read Write Edit Bash - Network accessline 581
**Journal Author Guidelines**: - Nature: https://www.nature.com/nature/for-authors - Network accessline 582
- Nature: https://www.nature.com/nature/for-authors - Network accessline 583
- Science: https://www.science.org/content/page/instructions-authors - Network accessline 584
- PLOS: https://plos.org/resources/for-authors/ - Network accessline 585
- Cell Press: https://www.cell.com/author-guidelines - Network accessline 587
**Conference Formatting**: - NeurIPS: https://neurips.cc/Conferences/2024/CallForPapers - Network accessline 588
- NeurIPS: https://neurips.cc/Conferences/2024/CallForPapers - Network accessline 589
- ICML: https://icml.cc/Conferences/2024/StyleAuthorInstructions - Network accessline 590
- CVPR: https://cvpr.thecvf.com/Conferences/2024/AuthorGuidelines - Network accessline 592
**Grant Resources**: - NSF PAPPG: https://www.nsf.gov/publications/pub_summ.jsp?ods_key=pappg - Network accessline 593
- NSF PAPPG: https://www.nsf.gov/publications/pub_summ.jsp?ods_key=pappg - Network accessline 594
- NIH Forms: https://grants.nih.gov/grants/how-to-apply-application-guide.html - Network accessline 595
- DOE Funding: https://science.osti.gov/grants
skills/02-科学写作与学术交流/venue-templates/assets/journals/neurips_article.tex
- Network accessline 264
Provide links to code repository (anonymized for review, e.g., anonymous GitHub): \begin{itemize} \item Code: \url{https://anonymous.4open.science/r/project-XXXX} - Network accessline 265
\begin{itemize} \item Code: \url{https://anonymous.4open.science/r/project-XXXX} \item Data: Available upon request / at [repository] - Network accessline 266
\item Code: \url{https://anonymous.4open.science/r/project-XXXX}
skills/02-科学写作与学术交流/venue-templates/assets/posters/beamerposter_academic.tex
- Network accessline 267
\begin{minipage}[t]{0.3\textwidth} \begin{center} \qrcode[height=3cm]{https://yourlab.university.edu/paper}\\ - Network accessline 268
\begin{center} \qrcode[height=3cm]{https://yourlab.university.edu/paper}\\ \small Scan for full paper\\and supplementary materials - Network accessline 269
\qrcode[height=3cm]{https://yourlab.university.edu/paper}\\
skills/02-科学写作与学术交流/venue-templates/references/conferences_formatting.md
- Network accessline 53
**Website**: https://neurips.cc/ - Network accessline 85
**Website**: https://icml.cc/ - Network accessline 114
**Website**: https://iclr.cc/ - Network accessline 143
**Website**: https://cvpr.thecvf.com/ - Network accessline 166
**Website**: https://aaai.org/conference/aaai/ - Network accessline 205
**Website**: https://chi.acm.org/ - Network accessline 227
**Website**: https://kdd.org/ - Network accessline 252
**Website**: https://www.emnlp.org/ - Network accessline 290
**Website**: https://www.usenix.org/conference/usenixsecurity - Network accessline 333
**Website**: https://www.iscb.org/ismb - Network accessline 355
**Website**: https://www.recomb.org/ - Network accessline 376
**Website**: https://psb.stanford.edu/
skills/02-科学写作与学术交流/venue-templates/references/grants_requirements.md
- Network accessline 196
**Resources**: - NSF PAPPG: https://www.nsf.gov/publications/pub_summ.jsp?ods_key=pappg - Network accessline 197
- NSF PAPPG: https://www.nsf.gov/publications/pub_summ.jsp?ods_key=pappg - Network accessline 198
- NSF Fastlane: https://www.fastlane.nsf.gov/ - Network accessline 391
**Resources**: - NIH Application Guide: https://grants.nih.gov/grants/how-to-apply-application-guide.html - Network accessline 392
- NIH Application Guide: https://grants.nih.gov/grants/how-to-apply-application-guide.html - Network accessline 393
- SF424 Forms: https://grants.nih.gov/grants/how-to-apply-application-guide/forms-e/general-forms-e.pdf - Network accessline 497
**Resources**: - DOE Office of Science Funding: https://science.osti.gov/grants - Network accessline 498
- DOE Office of Science Funding: https://science.osti.gov/grants - Network accessline 499
- EERE Funding: https://www.energy.gov/eere/funding/eere-funding-opportunities - Network accessline 613
**Resources**: - DARPA Opportunities: https://www.darpa.mil/work-with-us/opportunities - Network accessline 614
- DARPA Opportunities: https://www.darpa.mil/work-with-us/opportunities - Network accessline 759
### Grant Writing Guides - NSF PAPPG: https://www.nsf.gov/publications/pub_summ.jsp?ods_key=pappg - Network accessline 760
- NSF PAPPG: https://www.nsf.gov/publications/pub_summ.jsp?ods_key=pappg - Network accessline 761
- NIH Application Guide: https://grants.nih.gov/grants/how-to-apply-application-guide.html - Network accessline 762
- GrantForward (database): https://www.grantforward.com/ - Network accessline 763
- Pivot (database): https://pivot.proquest.com/
skills/02-科学写作与学术交流/venue-templates/references/journals_formatting.md
- Network accessline 40
**Author Guidelines**: https://www.nature.com/nature/for-authors - Network accessline 108
**Author Guidelines**: https://www.science.org/content/page/instructions-authors - Network accessline 161
**Author Guidelines**: https://journals.plos.org/plosone/s/submission-guidelines - Network accessline 205
**Author Guidelines**: https://www.cell.com/cell/authors - Network accessline 246
**Author Guidelines**: https://journals.ieeeauthorcenter.ieee.org/ - Network accessline 278
- **References**: ACM style - Format: Author. Year. Title. Journal vol, issue (Year), pages. DOI - Example: James D. Watson and Francis H. C. Crick. 1953. Molecular structure of nucleic acids. Nature 171, 4356 (1953), 737-738. https://d - Network accessline 279
- Format: Author. Year. Title. Journal vol, issue (Year), pages. DOI - Example: James D. Watson and Francis H. C. Crick. 1953. Molecular structure of nucleic acids. Nature 171, 4356 (1953), 737-738. https://doi.org/10.1038/171737a0 - **Fi - Network accessline 280
- Example: James D. Watson and Francis H. C. Crick. 1953. Molecular structure of nucleic acids. Nature 171, 4356 (1953), 737-738. https://doi.org/10.1038/171737a0 - Network accessline 287
**Author Guidelines**: https://www.acm.org/publications/authors - Network accessline 336
**Author Guidelines**: https://www.elsevier.com/authors (select specific journal) - Network accessline 361
**Author Guidelines**: https://www.biomedcentral.com/getpublished - Network accessline 388
**Author Guidelines**: https://www.frontiersin.org/guidelines/author-guidelines
skills/02-科学写作与学术交流/venue-templates/references/posters_guidelines.md
- Network accessline 188
### Color Contrast Tools - WebAIM Contrast Checker: https://webaim.org/resources/contrastchecker/ - Network accessline 189
- WebAIM Contrast Checker: https://webaim.org/resources/contrastchecker/ - Network accessline 190
- Coblis (Color Blindness Simulator): https://www.color-blindness.com/coblis-color-blindness-simulator/ - Network accessline 289
**Sources**: - Noun Project: https://thenounproject.com/ - Network accessline 290
- Noun Project: https://thenounproject.com/ - Network accessline 291
- BioRender: https://biorender.com/ (scientific illustrations) - Network accessline 292
- Font Awesome: https://fontawesome.com/ (icons) - Network accessline 435
### QR Code Generators - QR Code Generator: https://www.qr-code-generator.com/ - Network accessline 436
- QR Code Generator: https://www.qr-code-generator.com/ - Network accessline 437
- QRStuff: https://www.qrstuff.com/ - Network accessline 600
### Online Resources - Better Posters Blog: https://betterposters.blogspot.com/ - Network accessline 601
- Better Posters Blog: https://betterposters.blogspot.com/ - Network accessline 602
- Colorblind Safe Palettes: https://colorbrewer2.org/ - Network accessline 603
- BioRender (scientific illustrations): https://biorender.com/ - Network accessline 604
- Poster Design Guide (Colin Purrington): https://colinpurrington.com/tips/poster-design/
skills/02-科学写作与学术交流/venue-templates/scripts/customize_template.py
- Executable scriptline 1
#!/usr/bin/env python3
skills/02-科学写作与学术交流/venue-templates/scripts/query_template.py
- Executable scriptline 1
#!/usr/bin/env python3
skills/02-科学写作与学术交流/venue-templates/scripts/validate_format.py
skills/03-学术演示与可视化/generate-image/SKILL.md
- Access to secretsline 51
[Credential-related source omitted] - Network accessline 53
[Credential-related source omitted] - Access to secretsline 53
[Credential-related source omitted] - Access to secretsline 54
[Credential-related source omitted] - Network accessline 55
- Get an API key from: https://openrouter.ai/keys - Access to secretsline 120
[Credential-related source omitted] - Network accessline 166
- For image editing, the input image is encoded as base64 and sent to the model - Supported input image formats: PNG, JPEG, GIF, WebP - Check OpenRouter pricing for cost information: https://openrouter.ai/models - Network accessline 168
- Check OpenRouter pricing for cost information: https://openrouter.ai/models
skills/03-学术演示与可视化/generate-image/scripts/generate_image.py
- Executable scriptline 1
#!/usr/bin/env python3 - Access to secretsline 23
[Credential-related source omitted] - Access to secretsline 24
[Credential-related source omitted] - Access to secretsline 27
[Credential-related source omitted] - Access to secretsline 31
[Credential-related source omitted] - Access to secretsline 32
[Credential-related source omitted] - Access to secretsline 33
[Credential-related source omitted] - Access to secretsline 34
[Credential-related source omitted] - Possible obfuscationline 70
image_data = base64.b64decode(base64_data) - Access to secretsline 79
[Credential-related source omitted] - Access to secretsline 89
[Credential-related source omitted] - Access to secretsline 102
[Credential-related source omitted] - Access to secretsline 103
[Credential-related source omitted] - Access to secretsline 105
[Credential-related source omitted] - Access to secretsline 106
[Credential-related source omitted] - Access to secretsline 107
[Credential-related source omitted] - Access to secretsline 108
[Credential-related source omitted] - Network accessline 109
[Credential-related source omitted] - Network accessline 110
[Credential-related source omitted] - Access to secretsline 110
[Credential-related source omitted] - Network accessline 111
print("\nGet your API key from: https://openrouter.ai/keys") - Network accessline 144
response = requests.post( - Network accessline 145
url="https://openrouter.ai/api/v1/chat/completions", - Access to secretsline 147
[Credential-related source omitted] - Access to secretsline 266
[Credential-related source omitted] - Access to secretsline 275
[Credential-related source omitted]
skills/03-学术演示与可视化/infographics/SKILL.md
skills/03-学术演示与可视化/infographics/references/color_palettes.md
- Network accessline 476
1. **Contrast Checkers:** - WebAIM Contrast Checker: https://webaim.org/resources/contrastchecker/ - Network accessline 477
- WebAIM Contrast Checker: https://webaim.org/resources/contrastchecker/ - Network accessline 478
- Coolors Contrast Checker: https://coolors.co/contrast-checker - Network accessline 480
2. **Colorblind Simulators:** - Coblis: https://www.color-blindness.com/coblis-color-blindness-simulator/ - Network accessline 481
- Coblis: https://www.color-blindness.com/coblis-color-blindness-simulator/ - Network accessline 485
3. **Palette Generators:** - Coolors: https://coolors.co/ - Network accessline 486
- Coolors: https://coolors.co/ - Network accessline 487
- Adobe Color: https://color.adobe.com/ - Network accessline 488
- Paletton: https://paletton.com/
skills/03-学术演示与可视化/infographics/scripts/generate_infographic.py
- Executable scriptline 1
#!/usr/bin/env python3 - Access to secretsline 133
[Credential-related source omitted] - Access to secretsline 154
[Credential-related source omitted] - Access to secretsline 176
[Credential-related source omitted] - Access to secretsline 177
[Credential-related source omitted] - Network accessline 178
[Credential-related source omitted] - Access to secretsline 178
[Credential-related source omitted] - Network accessline 179
print("\nFor AI generation, you need an OpenRouter API key.") print("Get one at: https://openrouter.ai/keys") print("\nSet it with:") - Network accessline 180
print("Get one at: https://openrouter.ai/keys") - Access to secretsline 182
[Credential-related source omitted] - Access to secretsline 215
[Credential-related source omitted] - Access to secretsline 216
[Credential-related source omitted]
skills/03-学术演示与可视化/infographics/scripts/generate_infographic_ai.py
- Executable scriptline 1
#!/usr/bin/env python3 - Access to secretsline 13
[Credential-related source omitted] - Access to secretsline 40
[Credential-related source omitted] - Access to secretsline 47
[Credential-related source omitted] - Access to secretsline 55
[Credential-related source omitted] - Access to secretsline 66
[Credential-related source omitted] - Access to secretsline 339
[Credential-related source omitted] - Access to secretsline 341
[Credential-related source omitted] - Access to secretsline 343
[Credential-related source omitted] - Access to secretsline 345
[Credential-related source omitted] - Access to secretsline 347
[Credential-related source omitted] - Access to secretsline 349
[Credential-related source omitted] - Access to secretsline 350
[Credential-related source omitted] - Network accessline 351
[Credential-related source omitted] - Access to secretsline 351
[Credential-related source omitted] - Network accessline 352
[Credential-related source omitted] - Access to secretsline 352
[Credential-related source omitted] - Network accessline 353
"Get your API key from: https://openrouter.ai/keys" - Network accessline 356
self.verbose = verbose self._last_error = None self.base_url = "https://openrouter.ai/api/v1" - Network accessline 358
self.base_url = "https://openrouter.ai/api/v1" - Network accessline 432
[Credential-related source omitted] - Access to secretsline 432
[Credential-related source omitted] - Network accessline 433
"Content-Type": "application/json", "HTTP-Referer": "https://github.com/scientific-writer", "X-Title": "Infographic Research" - Network accessline 434
"HTTP-Referer": "https://github.com/scientific-writer", - Network accessline 447
response = requests.post( - Network accessline 516
[Credential-related source omitted] - Access to secretsline 516
[Credential-related source omitted] - Network accessline 517
"Content-Type": "application/json", "HTTP-Referer": "https://github.com/scientific-writer", "X-Title": "Infographic Web Search" - Network accessline 518
"HTTP-Referer": "https://github.com/scientific-writer", - Network accessline 529
response = requests.post( - Network accessline 585
[Credential-related source omitted] - Access to secretsline 585
[Credential-related source omitted] - Network accessline 586
"Content-Type": "application/json", "HTTP-Referer": "https://github.com/scientific-writer", "X-Title": "Infographic Generator" - Network accessline 587
"HTTP-Referer": "https://github.com/scientific-writer", - Network accessline 601
try: response = requests.post( - Network accessline 602
response = requests.post( - Possible obfuscationline 652
return base64.b64decode(base64_str) - Possible obfuscationline 663
return base64.b64decode(base64_str) - Possible obfuscationline 674
return base64.b64decode(base64_str) - Access to secretsline 1223
[Credential-related source omitted] - Access to secretsline 1244
[Credential-related source omitted] - Access to secretsline 1253
[Credential-related source omitted] - Access to secretsline 1254
[Credential-related source omitted] - Access to secretsline 1255
[Credential-related source omitted] - Access to secretsline 1257
[Credential-related source omitted] - Access to secretsline 1262
[Credential-related source omitted]
skills/03-学术演示与可视化/latex-posters/SKILL.md
- Broad permissionsline 1
Read Write Edit Bash - Network accessline 795
% Link to paper, code repository, or supplementary materials \qrcode[height=2cm]{https://github.com/username/project} - Network accessline 796
\qrcode[height=2cm]{https://github.com/username/project} - Network accessline 798
% QR code with caption \begin{center} \qrcode[height=3cm]{https://doi.org/10.1234/paper}\\ - Network accessline 799
\begin{center} \qrcode[height=3cm]{https://doi.org/10.1234/paper}\\ \small Scan for full paper - Network accessline 800
\qrcode[height=3cm]{https://doi.org/10.1234/paper}\\ - Network accessline 1136
- [ ] Text-background contrast ratio ≥ 4.5:1 (WCAG AA) - [ ] Important elements contrast ratio ≥ 7:1 (WCAG AAA) - Test online: https://webaim.org/resources/contrastchecker/ - Network accessline 1138
- Test online: https://webaim.org/resources/contrastchecker/
skills/03-学术演示与可视化/latex-posters/assets/baposter_template.tex
- Network accessline 220
\headerbox{Scan for More}{name=qr,column=2,above=bottom}{ \begin{center} \qrcode[height=4cm]{https://doi.org/10.1234/your-paper}\\ - Network accessline 221
\begin{center} \qrcode[height=4cm]{https://doi.org/10.1234/your-paper}\\ \vspace{0.3cm} - Network accessline 222
\qrcode[height=4cm]{https://doi.org/10.1234/your-paper}\\
skills/03-学术演示与可视化/latex-posters/assets/beamerposter_template.tex
- Network accessline 233
\begin{column}{.2\linewidth} \centering \qrcode[height=3.5cm]{https://doi.org/10.1234/your-paper}\\ - Network accessline 234
\centering \qrcode[height=3.5cm]{https://doi.org/10.1234/your-paper}\\ \tiny Scan for full paper - Network accessline 235
\qrcode[height=3.5cm]{https://doi.org/10.1234/your-paper}\\
skills/03-学术演示与可视化/latex-posters/assets/poster_quality_checklist.md
- Network accessline 202
### Color Contrast Test at: https://webaim.org/resources/contrastchecker/ - Network accessline 203
Test at: https://webaim.org/resources/contrastchecker/ - Network accessline 209
### Color Blindness Test with simulator: https://www.color-blindness.com/coblis-color-blindness-simulator/ - Network accessline 210
Test with simulator: https://www.color-blindness.com/coblis-color-blindness-simulator/
skills/03-学术演示与可视化/latex-posters/assets/tikzposter_template.tex
- Network accessline 209
\block{Scan for More}{ \begin{center} \qrcode[height=5cm]{https://doi.org/10.1234/your-paper}\\ - Network accessline 210
\begin{center} \qrcode[height=5cm]{https://doi.org/10.1234/your-paper}\\ \vspace{0.5cm} - Network accessline 211
\qrcode[height=5cm]{https://doi.org/10.1234/your-paper}\\
skills/03-学术演示与可视化/latex-posters/references/README.md
- Network accessline 386
### Online Tools - **Color contrast checker**: https://webaim.org/resources/contrastchecker/ - Network accessline 387
- **Color contrast checker**: https://webaim.org/resources/contrastchecker/ - Network accessline 388
- **Color blindness simulator**: https://www.color-blindness.com/coblis-color-blindness-simulator/ - Network accessline 389
- **Color palette generator**: https://coolors.co/
skills/03-学术演示与可视化/latex-posters/references/latex_poster_packages.md
skills/03-学术演示与可视化/latex-posters/references/poster_design_principles.md
skills/03-学术演示与可视化/latex-posters/references/poster_layout_design.md
- Network accessline 508
\begin{column}{0.25\linewidth} \centering \qrcode[height=3cm]{https://doi.org/10.1234/paper}\\ - Network accessline 509
\centering \qrcode[height=3cm]{https://doi.org/10.1234/paper}\\ \tiny Scan for full paper - Network accessline 510
\qrcode[height=3cm]{https://doi.org/10.1234/paper}\\
skills/03-学术演示与可视化/latex-posters/scripts/review_poster.sh
- Executable scriptline 1
#!/bin/bash
skills/03-学术演示与可视化/markdown-mermaid-writing/SKILL.md
- Broad permissionsline 1
Read Write Edit Bash - Network accessline 6
metadata: skill-author: Clayton Young / Superior Byte Works, LLC (@borealBytes) skill-source: https://github.com/SuperiorByteWorks-LLC/agent-project - Network accessline 7
skill-author: Clayton Young / Superior Byte Works, LLC (@borealBytes) skill-source: https://github.com/SuperiorByteWorks-LLC/agent-project skill-version: "1.0.0" - Network accessline 8
skill-source: https://github.com/SuperiorByteWorks-LLC/agent-project - Network accessline 317
- **Source**: https://github.com/SuperiorByteWorks-LLC/agent-project - Network accessline 325
[^1]: GitHub Blog. (2022). "Include diagrams in your Markdown files with Mermaid." https://github.blog/2022-02-14-include-diagrams-markdown-files-mermaid/ - Network accessline 327
[^2]: Mermaid. "Mermaid Diagramming and Charting Tool." https://mermaid.js.org/
skills/03-学术演示与可视化/markdown-mermaid-writing/assets/examples/example-research-report.md
- Network accessline 215
[^1]: Ran, F.A. et al. (2013). "Genome engineering using the CRISPR-Cas9 system." _Nature Protocols_, 8(11), 2281–2308. https://doi.org/10.1038/nprot.2013.143 - Network accessline 217
[^2]: ATCC. (2024). "Cell Line Authentication and Quality Control." https://www.atcc.org/resources/technical-documents/cell-line-authentication - Network accessline 219
[^3]: Moreno-Mateos, M.A. et al. (2015). "CRISPRscan: designing highly efficient sgRNAs for CRISPR-Cas9 targeting in vivo." _Nature Methods_, 12(10), 982–988. https://doi.org/10.1038/nmeth.3543 - Network accessline 221
[^4]: Molla, K.A. & Yang, Y. (2019). "CRISPR/Cas-Mediated Base Editing: Technical Considerations and Practical Applications." _Trends in Biotechnology_, 37(10), 1121–1142. https://doi.org/10.1016/j.tibtech.2019.03.008
skills/03-学术演示与可视化/markdown-mermaid-writing/references/diagrams/architecture.md
- Network accessline 1
<!-- Source: https://github.com/SuperiorByteWorks-LLC/agent-project | License: Apache-2.0 | Author: Clayton Young / Superior Byte Works, LLC (Boreal Bytes) -->
skills/03-学术演示与可视化/markdown-mermaid-writing/references/diagrams/block.md
- Network accessline 1
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skills/03-学术演示与可视化/markdown-mermaid-writing/references/diagrams/c4.md
- Network accessline 1
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skills/03-学术演示与可视化/markdown-mermaid-writing/references/diagrams/class.md
- Network accessline 1
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skills/03-学术演示与可视化/markdown-mermaid-writing/references/diagrams/complex_examples.md
- Network accessline 1
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skills/03-学术演示与可视化/markdown-mermaid-writing/references/diagrams/er.md
skills/03-学术演示与可视化/markdown-mermaid-writing/references/diagrams/flowchart.md
- Network accessline 1
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skills/03-学术演示与可视化/markdown-mermaid-writing/references/diagrams/gantt.md
- Network accessline 1
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skills/03-学术演示与可视化/markdown-mermaid-writing/references/diagrams/git_graph.md
- Network accessline 1
<!-- Source: https://github.com/SuperiorByteWorks-LLC/agent-project | License: Apache-2.0 | Author: Clayton Young / Superior Byte Works, LLC (Boreal Bytes) -->
skills/03-学术演示与可视化/markdown-mermaid-writing/references/diagrams/kanban.md
- Network accessline 1
<!-- Source: https://github.com/SuperiorByteWorks-LLC/agent-project | License: Apache-2.0 | Author: Clayton Young / Superior Byte Works, LLC (Boreal Bytes) -->
skills/03-学术演示与可视化/markdown-mermaid-writing/references/diagrams/mindmap.md
- Network accessline 1
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skills/03-学术演示与可视化/markdown-mermaid-writing/references/diagrams/packet.md
- Network accessline 1
<!-- Source: https://github.com/SuperiorByteWorks-LLC/agent-project | License: Apache-2.0 | Author: Clayton Young / Superior Byte Works, LLC (Boreal Bytes) -->
skills/03-学术演示与可视化/markdown-mermaid-writing/references/diagrams/pie.md
- Network accessline 1
<!-- Source: https://github.com/SuperiorByteWorks-LLC/agent-project | License: Apache-2.0 | Author: Clayton Young / Superior Byte Works, LLC (Boreal Bytes) -->
skills/03-学术演示与可视化/markdown-mermaid-writing/references/diagrams/quadrant.md
- Network accessline 1
<!-- Source: https://github.com/SuperiorByteWorks-LLC/agent-project | License: Apache-2.0 | Author: Clayton Young / Superior Byte Works, LLC (Boreal Bytes) -->
skills/03-学术演示与可视化/markdown-mermaid-writing/references/diagrams/radar.md
- Network accessline 1
<!-- Source: https://github.com/SuperiorByteWorks-LLC/agent-project | License: Apache-2.0 | Author: Clayton Young / Superior Byte Works, LLC (Boreal Bytes) -->
skills/03-学术演示与可视化/markdown-mermaid-writing/references/diagrams/requirement.md
- Network accessline 1
<!-- Source: https://github.com/SuperiorByteWorks-LLC/agent-project | License: Apache-2.0 | Author: Clayton Young / Superior Byte Works, LLC (Boreal Bytes) -->
skills/03-学术演示与可视化/markdown-mermaid-writing/references/diagrams/sankey.md
- Network accessline 1
<!-- Source: https://github.com/SuperiorByteWorks-LLC/agent-project | License: Apache-2.0 | Author: Clayton Young / Superior Byte Works, LLC (Boreal Bytes) -->
skills/03-学术演示与可视化/markdown-mermaid-writing/references/diagrams/sequence.md
- Network accessline 1
<!-- Source: https://github.com/SuperiorByteWorks-LLC/agent-project | License: Apache-2.0 | Author: Clayton Young / Superior Byte Works, LLC (Boreal Bytes) -->
skills/03-学术演示与可视化/markdown-mermaid-writing/references/diagrams/state.md
- Network accessline 1
<!-- Source: https://github.com/SuperiorByteWorks-LLC/agent-project | License: Apache-2.0 | Author: Clayton Young / Superior Byte Works, LLC (Boreal Bytes) -->
skills/03-学术演示与可视化/markdown-mermaid-writing/references/diagrams/timeline.md
- Network accessline 1
<!-- Source: https://github.com/SuperiorByteWorks-LLC/agent-project | License: Apache-2.0 | Author: Clayton Young / Superior Byte Works, LLC (Boreal Bytes) -->
skills/03-学术演示与可视化/markdown-mermaid-writing/references/diagrams/treemap.md
- Network accessline 1
<!-- Source: https://github.com/SuperiorByteWorks-LLC/agent-project | License: Apache-2.0 | Author: Clayton Young / Superior Byte Works, LLC (Boreal Bytes) -->
skills/03-学术演示与可视化/markdown-mermaid-writing/references/diagrams/user_journey.md
- Network accessline 1
<!-- Source: https://github.com/SuperiorByteWorks-LLC/agent-project | License: Apache-2.0 | Author: Clayton Young / Superior Byte Works, LLC (Boreal Bytes) -->
skills/03-学术演示与可视化/markdown-mermaid-writing/references/diagrams/xy_chart.md
- Network accessline 1
<!-- Source: https://github.com/SuperiorByteWorks-LLC/agent-project | License: Apache-2.0 | Author: Clayton Young / Superior Byte Works, LLC (Boreal Bytes) -->
skills/03-学术演示与可视化/markdown-mermaid-writing/references/diagrams/zenuml.md
- Network accessline 1
<!-- Source: https://github.com/SuperiorByteWorks-LLC/agent-project | License: Apache-2.0 | Author: Clayton Young / Superior Byte Works, LLC (Boreal Bytes) -->
skills/03-学术演示与可视化/markdown-mermaid-writing/references/markdown_style_guide.md
- Network accessline 1
<!-- Source: https://github.com/SuperiorByteWorks-LLC/agent-project | License: Apache-2.0 | Author: Clayton Young / Superior Byte Works, LLC (Boreal Bytes) --> - Network accessline 197
- **Meaningful link text** — `[Mermaid Style Guide]` not `[click here]` or `[link]` - **Relative paths** for internal links — `[Guide](./README.md)` not absolute URLs - **Full URLs** for external links — always `https://` - Network accessline 199
- **Full URLs** for external links — always `https://` - Network accessline 210
[^1]: Gruber, J. (2004). "Markdown." _Daring Fireball_. https://daringfireball.net/projects/markdown/ - Network accessline 212
[^2]: GitHub Blog. (2022). "Include diagrams in your Markdown files with Mermaid." https://github.blog/2022-02-14-include-diagrams-markdown-files-mermaid/ - Network accessline 218
[^N]: Author/Org. (Year). "Title." *Publication*. https://full-url - Network accessline 237
[mermaid-docs]: https://mermaid.js.org/ 'Mermaid Documentation' - Network accessline 365
- Transition to next topic - **Bold** emphasis works inside details - [Links](https://example.com) work too - Network accessline 367
- [Links](https://example.com) work too - Possible obfuscationline 529
<U+200E>```mermaid - Possible obfuscationline 543
<U+200E>``` - Network accessline 677
[^1]: Stack Overflow. (2024). "Developer Survey Results." https://survey.stackoverflow.co/2024 - Network accessline 733
- [GitHub Flavored Markdown Spec](https://github.github.com/gfm/) · [Mermaid Style Guide](mermaid_style_guide.md) · [GitHub Basic Formatting](https://docs.github.com/en/get-started/writing-on-github/getting-started-with-writing-and-formatti
skills/03-学术演示与可视化/markdown-mermaid-writing/references/mermaid_style_guide.md
- Network accessline 1
<!-- Source: https://github.com/SuperiorByteWorks-LLC/agent-project | License: Apache-2.0 | Author: Clayton Young / Superior Byte Works, LLC (Boreal Bytes) --> - Network accessline 448
1. **GitHub:** Push to branch → toggle Profile → Settings → Appearance → Theme 2. **VS Code:** "Markdown Preview Mermaid Support" extension → `Cmd/Ctrl + Shift + V` 3. **Live editor:** [mermaid.live](https://mermaid.live/) — paste and toggl - Network accessline 449
2. **VS Code:** "Markdown Preview Mermaid Support" extension → `Cmd/Ctrl + Shift + V` 3. **Live editor:** [mermaid.live](https://mermaid.live/) — paste and toggle themes 4. **Screen reader:** Verify `accTitle`/`accDescr` announced (VoiceOve - Network accessline 450
3. **Live editor:** [mermaid.live](https://mermaid.live/) — paste and toggle themes - Network accessline 457
- [Markdown Style Guide](markdown_style_guide.md) — Formatting, citations, and document structure for the markdown that wraps your diagrams - [Mermaid Docs](https://mermaid.js.org/) · [Live Editor](https://mermaid.live/) · [Accessibility](h - Network accessline 458
- [Mermaid Docs](https://mermaid.js.org/) · [Live Editor](https://mermaid.live/) · [Accessibility](https://mermaid.js.org/config/accessibility.html) · [GitHub Support](https://github.blog/2022-02-14-include-diagrams-markdown-files-mermaid/)
skills/03-学术演示与可视化/markdown-mermaid-writing/templates/decision_record.md
- Network accessline 1
<!-- Source: https://github.com/SuperiorByteWorks-LLC/agent-project | License: Apache-2.0 | Author: Clayton Young / Superior Byte Works, LLC (Boreal Bytes) --> - Network accessline 197
- [Related ADR or RFC](../adr/ADR-001-agent-optimized-documentation-system.md) - [External documentation or benchmark](https://example.com) - Network accessline 198
- [External documentation or benchmark](https://example.com)
skills/03-学术演示与可视化/markdown-mermaid-writing/templates/how_to_guide.md
- Network accessline 1
<!-- Source: https://github.com/SuperiorByteWorks-LLC/agent-project | License: Apache-2.0 | Author: Clayton Young / Superior Byte Works, LLC (Boreal Bytes) --> - Network accessline 71
| Requirement | Version | Verify with | Install link | | ---------------- | ----------- | --------------------- | ------------------------------------ | | [Tool/Runtime] | ≥ [version] | `[command - Network accessline 72
| ---------------- | ----------- | --------------------- | ------------------------------------ | | [Tool/Runtime] | ≥ [version] | `[command] --version` | [Install guide](https://example.com) | | [Dependency] | ≥ [version] | `[command - Network accessline 73
| [Tool/Runtime] | ≥ [version] | `[command] --version` | [Install guide](https://example.com) | - Network accessline 74
| [Dependency] | ≥ [version] | `[command] --version` | [Install guide](https://example.com) | - Network accessline 75
| [Account/Access] | — | [How to verify] | [Sign up](https://example.com) | - Network accessline 270
- [Official documentation](https://example.com) — [Which section is most relevant] - Network accessline 271
- [Source repository](https://github.com/SuperiorByteWorks-LLC) — [For bug reports and contributions]
skills/03-学术演示与可视化/markdown-mermaid-writing/templates/issue.md
- Network accessline 1
<!-- Source: https://github.com/SuperiorByteWorks-LLC/agent-project | License: Apache-2.0 | Author: Clayton Young / Superior Byte Works, LLC (Boreal Bytes) --> - Network accessline 180
- [Related issues](../../docs/project/issues/issue-00000001-agentic-documentation-system.md) - [Relevant documentation](https://example.com) - Network accessline 181
- [Relevant documentation](https://example.com) - Network accessline 182
- [Monitoring dashboard or alert](https://example.com) - Network accessline 296
- [User feedback or support tickets](https://example.com) - [Competitive analysis](https://example.com) - [Related feature requests](../../docs/project/issues/issue-00000001-agentic-documentation-system.md) - Network accessline 297
- [Competitive analysis](https://example.com) - [Related feature requests](../../docs/project/issues/issue-00000001-agentic-documentation-system.md) - [Design document or ADR](../adr/ADR-001-agent-optimized-documentation-system.md)
skills/03-学术演示与可视化/markdown-mermaid-writing/templates/kanban.md
- Network accessline 1
<!-- Source: https://github.com/SuperiorByteWorks-LLC/agent-project | License: Apache-2.0 | Author: Clayton Young / Superior Byte Works, LLC (Boreal Bytes) -->
skills/03-学术演示与可视化/markdown-mermaid-writing/templates/presentation.md
- Network accessline 1
<!-- Source: https://github.com/SuperiorByteWorks-LLC/agent-project | License: Apache-2.0 | Author: Clayton Young / Superior Byte Works, LLC (Boreal Bytes) --> - Network accessline 290
[^1]: [Author/Org]. ([Year]). "[Title]." _[Publication]_. <https://example.com> - Network accessline 294
- [Resource title](https://example.com) — Why this is useful - Network accessline 295
- [Resource title](https://example.com) — What it provides - Network accessline 299
- [Tool name](https://example.com) — Purpose and how to access
skills/03-学术演示与可视化/markdown-mermaid-writing/templates/project_documentation.md
- Network accessline 1
<!-- Source: https://github.com/SuperiorByteWorks-LLC/agent-project | License: Apache-2.0 | Author: Clayton Young / Superior Byte Works, LLC (Boreal Bytes) --> - Network accessline 37
[]() []() - Network accessline 67
# Clone the repository git clone https://github.com/[org]/[repo].git - Network accessline 68
git clone https://github.com/[org]/[repo].git - Access to secretsline 75
[Credential-related source omitted] - Access to secretsline 76
[Credential-related source omitted] - Network accessline 85
curl http://localhost:[port]/health - Network accessline 374
# Fork and clone git clone https://github.com/[your-fork]/[repo].git - Network accessline 375
git clone https://github.com/[your-fork]/[repo].git - Network accessline 406
- [Official framework docs](https://example.com) — [What version and which sections are most relevant] - Network accessline 407
- [API specification](https://example.com) — [OpenAPI/Swagger link if applicable]
skills/03-学术演示与可视化/markdown-mermaid-writing/templates/pull_request.md
- Network accessline 1
<!-- Source: https://github.com/SuperiorByteWorks-LLC/agent-project | License: Apache-2.0 | Author: Clayton Young / Superior Byte Works, LLC (Boreal Bytes) --> - Network accessline 28
- **Naming:** `pr-` + PR number zero-padded to 8 digits + `-` + short lowercase hyphenated description - **Cross-reference:** Link to the live PR in the metadata table - **GitHub PR body:** Use only the full branch URL to this file (for exa - Network accessline 30
- **GitHub PR body:** Use only the full branch URL to this file (for example, `https://github.com/<org>/<repo>/blob/<branch>/docs/project/pr/pr-00000123-fix-auth-timeout.md`) - Network accessline 313
- [Design document or ADR](../adr/ADR-001-agent-optimized-documentation-system.md) - [Related issue](../../docs/project/issues/issue-00000001-agentic-documentation-system.md) - [Relevant documentation](https://example.com) - Network accessline 315
- [Relevant documentation](https://example.com)
skills/03-学术演示与可视化/markdown-mermaid-writing/templates/research_paper.md
- Network accessline 1
<!-- Source: https://github.com/SuperiorByteWorks-LLC/agent-project | License: Apache-2.0 | Author: Clayton Young / Superior Byte Works, LLC (Boreal Bytes) --> - Network accessline 288
[^1]: [Author/Org]. ([Year]). "[Title]." _[Publication]_. <https://example.com> - Network accessline 290
[^2]: [Author/Org]. ([Year]). "[Title]." _[Publication]_. <https://example.com> - Network accessline 292
[^3]: [Author/Org]. ([Year]). "[Title]." _[Publication]_. <https://example.com> - Network accessline 294
[^4]: [Author/Org]. ([Year]). "[Title]." _[Publication]_. <https://example.com> - Network accessline 296
[^5]: [Author/Org]. ([Year]). "[Title]." _[Publication]_. <https://example.com> - Network accessline 298
[^6]: [Author/Org]. ([Year]). "[Title]." _[Publication]_. <https://example.com> - Network accessline 300
[^7]: [Author/Org]. ([Year]). "[Title]." _[Publication]_. <https://example.com>
skills/03-学术演示与可视化/markdown-mermaid-writing/templates/status_report.md
skills/03-学术演示与可视化/paper-slide-deck/SKILL.md
skills/03-学术演示与可视化/paper-slide-deck/scripts/apply-template.ts
- Executable scriptline 1
Executable file
skills/03-学术演示与可视化/paper-slide-deck/scripts/detect-figures.ts
- Executable scriptline 1
Executable file
skills/03-学术演示与可视化/paper-slide-deck/scripts/extract-figure.ts
- Executable scriptline 1
Executable file
skills/03-学术演示与可视化/paper-slide-deck/scripts/generate-slides.py
- Executable scriptline 1
Executable file - Access to secretsline 14
[Credential-related source omitted] - Access to secretsline 125
[Credential-related source omitted] - Access to secretsline 126
[Credential-related source omitted] - Access to secretsline 127
[Credential-related source omitted] - Access to secretsline 131
[Credential-related source omitted]
skills/03-学术演示与可视化/paper-slide-deck/scripts/merge-to-pdf.ts
- Executable scriptline 1
Executable file
skills/03-学术演示与可视化/paper-slide-deck/scripts/merge-to-pptx.ts
- Executable scriptline 1
Executable file
skills/03-学术演示与可视化/paper-slide-deck/scripts/package-lock.json
- Network accessline 14
"node_modules/@napi-rs/canvas": { "version": "0.1.88", "resolved": "https://registry.npmjs.org/@napi-rs/canvas/-/canvas-0.1.88.tgz", - Network accessline 15
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"url": "https://github.com/sponsors/feross" - Network accessline 800
{ "type": "patreon", "url": "https://www.patreon.com/feross" - Network accessline 801
"type": "patreon", "url": "https://www.patreon.com/feross" }, - Network accessline 802
"url": "https://www.patreon.com/feross" - Network accessline 804
{ "type": "consulting", "url": "https://feross.org/support" - Network accessline 805
"type": "consulting", "url": "https://feross.org/support" } - Network accessline 806
"url": "https://feross.org/support" - Network accessline 816
"node_modules/string_decoder": { "version": "1.3.0", "resolved": "https://registry.npmjs.org/string_decoder/-/string_decoder-1.3.0.tgz", - Network accessline 817
"version": "1.3.0", "resolved": "https://registry.npmjs.org/string_decoder/-/string_decoder-1.3.0.tgz", "integrity": "sha512-hkRX8U1WjJFd8LsDJ2yQ/wWWxaopEsABU1XfkM8A+j0+85JAGppt16cr1Whg6KIbb4okU6Mql6BOj+uup/wKeA==", - Network accessline 818
"resolved": "https://registry.npmjs.org/string_decoder/-/string_decoder-1.3.0.tgz", - Network accessline 825
"node_modules/strip-json-comments": { "version": "2.0.1", "resolved": "https://registry.npmjs.org/strip-json-comments/-/strip-json-comments-2.0.1.tgz", - Network accessline 826
"version": "2.0.1", "resolved": "https://registry.npmjs.org/strip-json-comments/-/strip-json-comments-2.0.1.tgz", "integrity": "sha512-4gB8na07fecVVkOI6Rs4e7T6NOTki5EmL7TUduTs6bu3EdnSycntVJ4re8kgZA+wx9IueI2Y11bfbgwtzuE0KQ==", - Network accessline 827
"resolved": "https://registry.npmjs.org/strip-json-comments/-/strip-json-comments-2.0.1.tgz", - Network accessline 834
"node_modules/tar-fs": { "version": "2.1.4", "resolved": "https://registry.npmjs.org/tar-fs/-/tar-fs-2.1.4.tgz", - Network accessline 835
"version": "2.1.4", "resolved": "https://registry.npmjs.org/tar-fs/-/tar-fs-2.1.4.tgz", "integrity": "sha512-mDAjwmZdh7LTT6pNleZ05Yt65HC3E+NiQzl672vQG38jIrehtJk/J3mNwIg+vShQPcLF/LV7CMnDW6vjj6sfYQ==", - Network accessline 836
"resolved": "https://registry.npmjs.org/tar-fs/-/tar-fs-2.1.4.tgz", - Network accessline 846
"node_modules/tar-stream": { "version": "2.2.0", "resolved": "https://registry.npmjs.org/tar-stream/-/tar-stream-2.2.0.tgz", - Network accessline 847
"version": "2.2.0", "resolved": "https://registry.npmjs.org/tar-stream/-/tar-stream-2.2.0.tgz", "integrity": "sha512-ujeqbceABgwMZxEJnk2HDY2DlnUZ+9oEcb1KzTVfYHio0UE6dG71n60d8D2I4qNvleWrrXpmjpt7vZeF1LnMZQ==", - Network accessline 848
"resolved": "https://registry.npmjs.org/tar-stream/-/tar-stream-2.2.0.tgz", - Network accessline 862
"node_modules/tslib": { "version": "1.14.1", "resolved": "https://registry.npmjs.org/tslib/-/tslib-1.14.1.tgz", - Network accessline 863
"version": "1.14.1", "resolved": "https://registry.npmjs.org/tslib/-/tslib-1.14.1.tgz", "integrity": "sha512-Xni35NKzjgMrwevysHTCArtLDpPvye8zV/0E4EyYn43P7/7qvQwPh9BGkHewbMulVntbigmcT7rdX3BNo9wRJg==", - Network accessline 864
"resolved": "https://registry.npmjs.org/tslib/-/tslib-1.14.1.tgz", - Network accessline 868
"node_modules/tunnel-agent": { "version": "0.6.0", "resolved": "https://registry.npmjs.org/tunnel-agent/-/tunnel-agent-0.6.0.tgz", - Network accessline 869
"version": "0.6.0", "resolved": "https://registry.npmjs.org/tunnel-agent/-/tunnel-agent-0.6.0.tgz", "integrity": "sha512-McnNiV1l8RYeY8tBgEpuodCC1mLUdbSN+CYBL7kJsJNInOP8UjDDEwdk6Mw60vdLLrr5NHKZhMAOSrR2NZuQ+w==", - Network accessline 870
"resolved": "https://registry.npmjs.org/tunnel-agent/-/tunnel-agent-0.6.0.tgz", - Network accessline 880
"node_modules/undici-types": { "version": "6.21.0", "resolved": "https://registry.npmjs.org/undici-types/-/undici-types-6.21.0.tgz", - Network accessline 881
"version": "6.21.0", "resolved": "https://registry.npmjs.org/undici-types/-/undici-types-6.21.0.tgz", "integrity": "sha512-iwDZqg0QAGrg9Rav5H4n0M64c3mkR59cJ6wQp+7C4nI0gsmExaedaYLNO44eT4AtBBwjbTiGPMlt2Md0T9H9JQ==", - Network accessline 882
"resolved": "https://registry.npmjs.org/undici-types/-/undici-types-6.21.0.tgz", - Network accessline 886
"node_modules/util-deprecate": { "version": "1.0.2", "resolved": "https://registry.npmjs.org/util-deprecate/-/util-deprecate-1.0.2.tgz", - Network accessline 887
"version": "1.0.2", "resolved": "https://registry.npmjs.org/util-deprecate/-/util-deprecate-1.0.2.tgz", "integrity": "sha512-EPD5q1uXyFxJpCrLnCc1nHnq3gOa6DZBocAIiI2TaSCA7VCJ1UJDMagCzIkXNsUYfD1daK//LTEQ8xiIbrHtcw==", - Network accessline 888
"resolved": "https://registry.npmjs.org/util-deprecate/-/util-deprecate-1.0.2.tgz", - Network accessline 892
"node_modules/wrappy": { "version": "1.0.2", "resolved": "https://registry.npmjs.org/wrappy/-/wrappy-1.0.2.tgz", - Network accessline 893
"version": "1.0.2", "resolved": "https://registry.npmjs.org/wrappy/-/wrappy-1.0.2.tgz", "integrity": "sha512-l4Sp/DRseor9wL6EvV2+TuQn63dMkPjZ/sp9XkghTEbV9KlPS1xUsZ3u7/IQO4wxtcFB4bgpQPRcR3QCvezPcQ==", - Network accessline 894
"resolved": "https://registry.npmjs.org/wrappy/-/wrappy-1.0.2.tgz",
skills/03-学术演示与可视化/paper-slide-deck/scripts/package.json
- Executable scriptline 1
Executable file
skills/03-学术演示与可视化/pptx-posters/SKILL.md
- Broad permissionsline 1
Read Write Edit Bash
skills/03-学术演示与可视化/pptx-posters/assets/poster_quality_checklist.md
- Network accessline 202
### Color Contrast Test at: https://webaim.org/resources/contrastchecker/ - Network accessline 203
Test at: https://webaim.org/resources/contrastchecker/ - Network accessline 209
### Color Blindness Test with simulator: https://www.color-blindness.com/coblis-color-blindness-simulator/ - Network accessline 210
Test with simulator: https://www.color-blindness.com/coblis-color-blindness-simulator/
skills/03-学术演示与可视化/pptx-posters/references/poster_design_principles.md
skills/03-学术演示与可视化/pptx-posters/references/poster_layout_design.md
- Network accessline 508
\begin{column}{0.25\linewidth} \centering \qrcode[height=3cm]{https://doi.org/10.1234/paper}\\ - Network accessline 509
\centering \qrcode[height=3cm]{https://doi.org/10.1234/paper}\\ \tiny Scan for full paper - Network accessline 510
\qrcode[height=3cm]{https://doi.org/10.1234/paper}\\
skills/03-学术演示与可视化/scientific-schematics/SKILL.md
- Broad permissionsline 1
Read Write Edit Bash - Access to secretsline 76
[Credential-related source omitted] - Network accessline 79
Get an API key at: https://openrouter.ai/keys - Access to secretsline 261
[Credential-related source omitted] - Network accessline 525
**Python Libraries** - Schemdraw Documentation: https://schemdraw.readthedocs.io/ - Network accessline 526
- Schemdraw Documentation: https://schemdraw.readthedocs.io/ - Network accessline 527
- NetworkX Documentation: https://networkx.org/documentation/ - Network accessline 528
- Matplotlib Documentation: https://matplotlib.org/ - Network accessline 530
**Publication Standards** - Nature Figure Guidelines: https://www.nature.com/nature/for-authors/final-submission - Network accessline 531
- Nature Figure Guidelines: https://www.nature.com/nature/for-authors/final-submission - Network accessline 532
- Science Figure Guidelines: https://www.science.org/content/page/instructions-preparing-initial-manuscript - Network accessline 533
- CONSORT Diagram: http://www.consort-statement.org/consort-statement/flow-diagram - Access to secretsline 603
[Credential-related source omitted] - Network accessline 605
# Get key at: https://openrouter.ai/keys
skills/03-学术演示与可视化/scientific-schematics/references/QUICK_REFERENCE.md
- Network accessline 8
# Get API key from https://openrouter.ai/keys - Access to secretsline 9
[Credential-related source omitted] - Access to secretsline 12
[Credential-related source omitted] - Access to secretsline 118
[Credential-related source omitted] - Access to secretsline 136
[Credential-related source omitted] - Access to secretsline 139
[Credential-related source omitted] - Access to secretsline 193
[Credential-related source omitted] - Network accessline 204
- Implementation details: `IMPLEMENTATION_SUMMARY.md` - Example script: `example_usage.sh` - Get API key: https://openrouter.ai/keys - Network accessline 206
- Get API key: https://openrouter.ai/keys
skills/03-学术演示与可视化/scientific-schematics/references/README.md
- Access to secretsline 13
[Credential-related source omitted] - Network accessline 58
# Get OpenRouter API key # Visit: https://openrouter.ai/keys - Network accessline 59
# Visit: https://openrouter.ai/keys - Access to secretsline 62
[Credential-related source omitted] - Access to secretsline 64
[Credential-related source omitted] - Access to secretsline 65
[Credential-related source omitted] - Access to secretsline 143
[Credential-related source omitted] - Access to secretsline 244
[Credential-related source omitted] - Access to secretsline 247
[Credential-related source omitted] - Access to secretsline 250
[Credential-related source omitted] - Network accessline 269
# Check API status curl https://openrouter.ai/api/v1/models - Network accessline 270
curl https://openrouter.ai/api/v1/models
skills/03-学术演示与可视化/scientific-schematics/references/best_practices.md
- Network accessline 534
**Contrast Checking** - WebAIM Contrast Checker: https://webaim.org/resources/contrastchecker/ - Network accessline 535
- WebAIM Contrast Checker: https://webaim.org/resources/contrastchecker/ - Network accessline 536
- Colorable: https://colorable.jxnblk.com/ - Network accessline 553
- **Nature Figure Preparation**: https://www.nature.com/nature/for-authors/final-submission - Network accessline 554
- **Science Figure Guidelines**: https://www.science.org/content/page/instructions-preparing-initial-manuscript - Network accessline 555
- **WCAG Accessibility Standards**: https://www.w3.org/WAI/WCAG21/quickref/ - Network accessline 556
- **Color Universal Design (CUD)**: https://jfly.uni-koeln.de/color/ - Network accessline 557
- **ColorBrewer**: https://colorbrewer2.org/
skills/03-学术演示与可视化/scientific-schematics/scripts/example_usage.sh
- Executable scriptline 1
#!/bin/bash - Access to secretsline 5
[Credential-related source omitted] - Access to secretsline 18
[Credential-related source omitted] - Network accessline 19
[Credential-related source omitted] - Access to secretsline 19
[Credential-related source omitted] - Network accessline 20
[Credential-related source omitted] - Network accessline 21
echo "Get an API key at: https://openrouter.ai/keys" - Access to secretsline 22
[Credential-related source omitted] - Access to secretsline 26
[Credential-related source omitted]
skills/03-学术演示与可视化/scientific-schematics/scripts/generate_schematic.py
- Executable scriptline 1
#!/usr/bin/env python3 - Access to secretsline 71
[Credential-related source omitted] - Access to secretsline 86
[Credential-related source omitted] - Access to secretsline 93
[Credential-related source omitted] - Access to secretsline 94
[Credential-related source omitted] - Network accessline 95
[Credential-related source omitted] - Access to secretsline 95
[Credential-related source omitted] - Network accessline 96
print("\nFor AI generation, you need an OpenRouter API key.") print("Get one at: https://openrouter.ai/keys") print("\nSet it with:") - Network accessline 97
print("Get one at: https://openrouter.ai/keys") - Access to secretsline 99
[Credential-related source omitted] - Access to secretsline 122
[Credential-related source omitted] - Access to secretsline 123
[Credential-related source omitted]
skills/03-学术演示与可视化/scientific-schematics/scripts/generate_schematic_ai.py
- Executable scriptline 1
#!/usr/bin/env python3 - Access to secretsline 12
[Credential-related source omitted] - Access to secretsline 36
[Credential-related source omitted] - Access to secretsline 38
[Credential-related source omitted] - Access to secretsline 40
[Credential-related source omitted] - Access to secretsline 49
[Credential-related source omitted] - Access to secretsline 57
[Credential-related source omitted] - Access to secretsline 68
[Credential-related source omitted] - Access to secretsline 146
[Credential-related source omitted] - Access to secretsline 151
[Credential-related source omitted] - Access to secretsline 154
[Credential-related source omitted] - Access to secretsline 155
[Credential-related source omitted] - Access to secretsline 157
[Credential-related source omitted] - Access to secretsline 158
[Credential-related source omitted] - Access to secretsline 160
[Credential-related source omitted] - Access to secretsline 162
[Credential-related source omitted] - Access to secretsline 164
[Credential-related source omitted] - Access to secretsline 165
[Credential-related source omitted] - Network accessline 166
[Credential-related source omitted] - Access to secretsline 166
[Credential-related source omitted] - Network accessline 167
[Credential-related source omitted] - Access to secretsline 167
[Credential-related source omitted] - Network accessline 168
"Get your API key from: https://openrouter.ai/keys" - Network accessline 171
self.verbose = verbose self._last_error = None # Track last error for better reporting self.base_url = "https://openrouter.ai/api/v1" - Network accessline 173
self.base_url = "https://openrouter.ai/api/v1" - Network accessline 174
# Nano Banana 2 - Google's advanced image generation model # https://openrouter.ai/google/gemini-3-pro-image-preview - Network accessline 175
# https://openrouter.ai/google/gemini-3-pro-image-preview - Network accessline 199
[Credential-related source omitted] - Access to secretsline 199
[Credential-related source omitted] - Network accessline 200
"Content-Type": "application/json", "HTTP-Referer": "https://github.com/scientific-writer", "X-Title": "Scientific Schematic Generator" - Network accessline 201
"HTTP-Referer": "https://github.com/scientific-writer", - Network accessline 215
try: response = requests.post( - Network accessline 216
response = requests.post( - Possible obfuscationline 283
return base64.b64decode(base64_str) - Possible obfuscationline 298
return base64.b64decode(base64_str) - Possible obfuscationline 310
return base64.b64decode(base64_str) - Access to secretsline 787
[Credential-related source omitted] - Access to secretsline 800
[Credential-related source omitted] - Access to secretsline 807
[Credential-related source omitted] - Access to secretsline 808
[Credential-related source omitted] - Access to secretsline 809
[Credential-related source omitted] - Access to secretsline 811
[Credential-related source omitted] - Access to secretsline 821
[Credential-related source omitted]
skills/03-学术演示与可视化/scientific-slides/SKILL.md
skills/03-学术演示与可视化/scientific-slides/assets/beamer_template_conference.tex
skills/03-学术演示与可视化/scientific-slides/assets/beamer_template_seminar.tex
skills/03-学术演示与可视化/scientific-slides/references/beamer_guide.md
- Network accessline 749
% External links \begin{frame}{Resources} Visit \url{https://example.com} for more information. - Network accessline 751
Visit \url{https://example.com} for more information. - Network accessline 753
\href{https://github.com/user/repo}{GitHub Repository} - Network accessline 762
\begin{frame}{Scan for Paper} \begin{center} \qrcode[height=3cm]{https://doi.org/10.1234/paper} - Network accessline 764
\qrcode[height=3cm]{https://doi.org/10.1234/paper} - Network accessline 996
- Beamer User Guide: `texdoc beamer` - Theme Gallery: https://deic.uab.cat/~iblanes/beamer_gallery/ - Network accessline 997
- Theme Gallery: https://deic.uab.cat/~iblanes/beamer_gallery/ - Network accessline 998
- TikZ Examples: https://texample.net/tikz/
skills/03-学术演示与可视化/scientific-slides/references/visual_review_workflow.md
skills/03-学术演示与可视化/scientific-slides/scripts/generate_slide_image.py
- Executable scriptline 1
#!/usr/bin/env python3 - Access to secretsline 72
[Credential-related source omitted] - Access to secretsline 84
[Credential-related source omitted] - Access to secretsline 90
[Credential-related source omitted] - Access to secretsline 91
[Credential-related source omitted] - Network accessline 92
[Credential-related source omitted] - Access to secretsline 92
[Credential-related source omitted] - Network accessline 93
print("\nFor AI generation, you need an OpenRouter API key.") print("Get one at: https://openrouter.ai/keys") print("\nSet it with:") - Network accessline 94
print("Get one at: https://openrouter.ai/keys") - Access to secretsline 96
[Credential-related source omitted] - Access to secretsline 124
[Credential-related source omitted] - Access to secretsline 125
[Credential-related source omitted]
skills/03-学术演示与可视化/scientific-slides/scripts/generate_slide_image_ai.py
- Executable scriptline 1
#!/usr/bin/env python3 - Access to secretsline 18
[Credential-related source omitted] - Access to secretsline 50
[Credential-related source omitted] - Access to secretsline 57
[Credential-related source omitted] - Access to secretsline 65
[Credential-related source omitted] - Access to secretsline 76
[Credential-related source omitted] - Access to secretsline 170
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[Credential-related source omitted] - Access to secretsline 182
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[Credential-related source omitted] - Network accessline 188
[Credential-related source omitted] - Access to secretsline 188
[Credential-related source omitted] - Network accessline 189
[Credential-related source omitted] - Access to secretsline 189
[Credential-related source omitted] - Network accessline 190
"Get your API key from: https://openrouter.ai/keys" - Network accessline 193
self.verbose = verbose self._last_error = None self.base_url = "https://openrouter.ai/api/v1" - Network accessline 195
self.base_url = "https://openrouter.ai/api/v1" - Network accessline 210
[Credential-related source omitted] - Access to secretsline 210
[Credential-related source omitted] - Network accessline 211
"Content-Type": "application/json", "HTTP-Referer": "https://github.com/scientific-writer", "X-Title": "Scientific Slide Generator" - Network accessline 212
"HTTP-Referer": "https://github.com/scientific-writer", - Network accessline 226
try: response = requests.post( - Network accessline 227
response = requests.post( - Possible obfuscationline 277
return base64.b64decode(base64_str) - Possible obfuscationline 288
return base64.b64decode(base64_str) - Possible obfuscationline 299
return base64.b64decode(base64_str) - Access to secretsline 706
[Credential-related source omitted] - Access to secretsline 718
[Credential-related source omitted] - Access to secretsline 723
[Credential-related source omitted] - Access to secretsline 724
[Credential-related source omitted] - Access to secretsline 725
[Credential-related source omitted] - Access to secretsline 727
[Credential-related source omitted] - Access to secretsline 742
[Credential-related source omitted]
skills/03-学术演示与可视化/scientific-slides/scripts/pdf_to_images.py
- Executable scriptline 1
#!/usr/bin/env python3
skills/03-学术演示与可视化/scientific-slides/scripts/slides_to_pdf.py
- Executable scriptline 1
#!/usr/bin/env python3
skills/03-学术演示与可视化/scientific-slides/scripts/validate_presentation.py
- Executable scriptline 1
#!/usr/bin/env python3
skills/03-学术演示与可视化/scientific-visualization/assets/color_palettes.py
skills/03-学术演示与可视化/scientific-visualization/references/color_palettes.md
- Network accessline 233
### Online Simulators - **Coblis**: https://www.color-blindness.com/coblis-color-blindness-simulator/ - Network accessline 234
- **Coblis**: https://www.color-blindness.com/coblis-color-blindness-simulator/ - Network accessline 344
- **ColorBrewer**: http://colorbrewer2.org/ - Choose palettes by colorblind-safe option - Network accessline 345
- **Paul Tol's palettes**: https://personal.sron.nl/~pault/ - Network accessline 346
- **Okabe-Ito palette origin**: "Color Universal Design" (Okabe & Ito, 2008) - **Matplotlib colormaps**: https://matplotlib.org/stable/tutorials/colors/colormaps.html - **Seaborn palettes**: https://seaborn.pydata.org/tutorial/color_palette - Network accessline 347
- **Matplotlib colormaps**: https://matplotlib.org/stable/tutorials/colors/colormaps.html - Network accessline 348
- **Seaborn palettes**: https://seaborn.pydata.org/tutorial/color_palettes.html
skills/03-学术演示与可视化/scientific-visualization/references/matplotlib_examples.md
skills/03-学术演示与可视化/scientific-visualization/scripts/figure_export.py
- Executable scriptline 1
#!/usr/bin/env python3
skills/03-学术演示与可视化/scientific-visualization/scripts/style_presets.py
- Executable scriptline 1
#!/usr/bin/env python3
skills/04-研究方法与科学思维/consciousness-council/SKILL.md
skills/04-研究方法与科学思维/dhdna-profiler/SKILL.md
- Network accessline 14
Published research: [DHDNA Pre-print (DOI: 10.5281/zenodo.18736629)](https://doi.org/10.5281/zenodo.18736629) | [IDNA Consolidation v2 (DOI: 10.5281/zenodo.18807387)](https://doi.org/10.5281/zenodo.18807387) - Network accessline 160
[AHK Strategies](https://ahkstrategies.net) — AI Horizon Knowledge - Network accessline 161
Full platform: [themindbook.app](https://themindbook.app) - Network accessline 162
Research: [DHDNA Paper (DOI: 10.5281/zenodo.18736629)](https://doi.org/10.5281/zenodo.18736629)
skills/04-研究方法与科学思维/dhdna-profiler/references/advanced-profiling.md
- Network accessline 57
Reference: [IDNA Consolidation v2, Section 3: 4D-DHDNA (DOI: 10.5281/zenodo.18807387)](https://doi.org/10.5281/zenodo.18807387)
skills/04-研究方法与科学思维/hypothesis-generation/SKILL.md
- Broad permissionsline 1
Read Write Edit Bash
skills/04-研究方法与科学思维/hypothesis-generation/references/literature_search_strategies.md
- Network accessline 45
**Example searches:** - `https://pubmed.ncbi.nlm.nih.gov/?term=wound+healing+diabetes+review` - Network accessline 46
- `https://pubmed.ncbi.nlm.nih.gov/?term=wound+healing+diabetes+review` - Network accessline 47
- `https://pubmed.ncbi.nlm.nih.gov/?term=gut+microbiome+cognition+systematic+review` - Network accessline 104
https://pubmed.ncbi.nlm.nih.gov/?term=(mechanism+OR+pathway)+AND+[phenomenon]+AND+(molecular+OR+cellular) - Network accessline 109
https://pubmed.ncbi.nlm.nih.gov/?term=[exposure]+AND+[outcome]+AND+(randomized+controlled+trial+OR+cohort+study) - Network accessline 114
https://pubmed.ncbi.nlm.nih.gov/?term=[biomarker]+AND+[disease]+AND+(association+OR+correlation+OR+prediction) - Network accessline 119
https://pubmed.ncbi.nlm.nih.gov/?term=[intervention]+AND+[condition]+AND+(efficacy+OR+effectiveness+OR+clinical+trial)
skills/04-研究方法与科学思维/nsfc-proposal/SKILL.md
- Dangerous shell operationline 154
curl -fsSL https://raw.githubusercontent.com/LeonChaoX/qinyan-academic-skills/main/install.sh | bash -s -- -s nsfc-proposal --tool <claude|cursor|codex|gemini|openclaw> - Network accessline 154
curl -fsSL https://raw.githubusercontent.com/LeonChaoX/qinyan-academic-skills/main/install.sh | \
skills/04-研究方法与科学思维/nsfc-proposal/references/LITERATURE_SEARCH.md
- Access to secretsline 9
[Credential-related source omitted]
skills/04-研究方法与科学思维/nssfc-proposal/SKILL.md
- Dangerous shell operationline 179
curl -fsSL https://raw.githubusercontent.com/LeonChaoX/qinyan-academic-skills/main/install.sh | bash -s -- -s nssfc-proposal --tool <claude|cursor|codex|gemini|openclaw> - Network accessline 179
curl -fsSL https://raw.githubusercontent.com/LeonChaoX/qinyan-academic-skills/main/install.sh | \
skills/04-研究方法与科学思维/nssfc-proposal/references/LITERATURE_SEARCH.md
skills/04-研究方法与科学思维/research-grants/SKILL.md
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Read Write Edit Bash
skills/04-研究方法与科学思维/research-grants/references/README.md
- Network accessline 247
### Official Resources - NSF: https://www.nsf.gov/funding/ - Network accessline 248
- NSF: https://www.nsf.gov/funding/ - Network accessline 249
- NIH: https://grants.nih.gov/ - Network accessline 250
- DOE: https://science.osti.gov/grants/ - Network accessline 251
- DARPA: https://www.darpa.mil/work-with-us/opportunities
skills/04-研究方法与科学思维/research-grants/references/broader_impacts.md
- Network accessline 372
### NSF Resources - **NSF Broader Impacts Website**: https://www.nsf.gov/od/oia/special/broaderimpacts/ - Network accessline 373
- **NSF Broader Impacts Website**: https://www.nsf.gov/od/oia/special/broaderimpacts/ - Network accessline 374
- **BI Examples Repository**: https://www.cmu.edu/uro/resources for undergraduate research/best practices/broader-impacts.html
skills/04-研究方法与科学思维/research-grants/references/darpa_guidelines.md
- Network accessline 11
**Website**: https://www.darpa.mil - Network accessline 620
- **DARPA Website**: https://www.darpa.mil - Network accessline 621
- **DARPA Opportunities**: https://www.darpa.mil/work-with-us/opportunities - Network accessline 622
- **BAA Listings**: https://beta.sam.gov (search "DARPA") - Network accessline 623
- **DARPA Social Media**: Twitter @DARPA (PMs often announce programs) - **SBIR/STTR**: https://www.darpa.mil/work-with-us/for-small-businesses - **Heilmeier Catechism**: https://www.darpa.mil/about-us/timeline/heilmeier-catechism - Network accessline 624
- **SBIR/STTR**: https://www.darpa.mil/work-with-us/for-small-businesses - Network accessline 625
- **Heilmeier Catechism**: https://www.darpa.mil/about-us/timeline/heilmeier-catechism
skills/04-研究方法与科学思维/research-grants/references/doe_guidelines.md
- Network accessline 9
**Website**: https://www.energy.gov - Network accessline 435
**Sources**: - **EERE Exchange**: https://eere-exchange.energy.gov - Network accessline 436
- **EERE Exchange**: https://eere-exchange.energy.gov - Network accessline 437
- **ARPA-E OPEN**: https://arpa-e.energy.gov - Network accessline 438
- **Office of Science FOAs**: https://science.osti.gov/grants/Funding-Opportunities - Network accessline 575
- **DOE Office of Science**: https://science.osti.gov - Network accessline 576
- **ARPA-E**: https://arpa-e.energy.gov - Network accessline 577
- **EERE**: https://www.energy.gov/eere - Network accessline 578
- **DOE National Laboratories**: https://www.energy.gov/national-laboratories - Network accessline 579
- **EERE Exchange**: https://eere-exchange.energy.gov - Network accessline 580
- **Grants.gov**: https://www.grants.gov - Network accessline 581
- **SAM.gov**: https://sam.gov
skills/04-研究方法与科学思维/research-grants/references/nih_guidelines.md
- Network accessline 9
**Website**: https://www.nih.gov - Network accessline 839
- **NIH Homepage**: https://www.nih.gov - Network accessline 840
- **NIH RePORTER (funded grants)**: https://reporter.nih.gov - Network accessline 841
- **Grants & Funding**: https://grants.nih.gov - Network accessline 842
- **eRA Commons**: https://commons.era.nih.gov - Network accessline 843
- **ASSIST**: https://public.era.nih.gov/assist - Network accessline 844
- **Application Forms and Instructions**: https://grants.nih.gov/grants/how-to-apply-application-guide.html - Network accessline 845
- **NIH Data Sharing Policy**: https://sharing.nih.gov - Network accessline 846
- **Rigor and Reproducibility**: https://grants.nih.gov/reproducibility/index.htm
skills/04-研究方法与科学思维/research-grants/references/nsf_guidelines.md
- Network accessline 9
**Website**: https://www.nsf.gov - Network accessline 559
- **NSF Homepage**: https://www.nsf.gov - Network accessline 560
- **Award Search**: https://www.nsf.gov/awardsearch/ - Network accessline 561
- **Proposal & Award Policies & Procedures Guide (PAPPG)**: https://www.nsf.gov/publications/pub_summ.jsp?ods_key=pappg - Network accessline 562
- **FastLane**: https://www.fastlane.nsf.gov/ - Network accessline 563
- **Research.gov**: https://www.research.gov/ - Network accessline 564
- **Broader Impacts Resources**: https://www.nsf.gov/od/oia/special/broaderimpacts/ - Network accessline 565
- **NSF Funding Statistics**: https://www.nsf.gov/statistics/
skills/04-研究方法与科学思维/research-grants/references/nstc_guidelines.md
- Network accessline 7
**Official Name**: 國家科學及技術委員會 (National Science and Technology Council, NSTC) **Former Name**: 科技部 (Ministry of Science and Technology, MOST) **Official Website**: https://www.nstc.gov.tw/ - Network accessline 9
**Official Website**: https://www.nstc.gov.tw/ - Network accessline 152
**NSTC Website**: https://www.nstc.gov.tw/ - Network accessline 168
**nstc-proposal** - Professional LaTeX classes for NSTC proposals: - **GitHub**: https://github.com/L-TChen/nstc-proposal - Network accessline 169
- **GitHub**: https://github.com/L-TChen/nstc-proposal - Network accessline 184
# Or manual installation from GitHub git clone https://github.com/L-TChen/nstc-proposal.git - Network accessline 185
git clone https://github.com/L-TChen/nstc-proposal.git - Network accessline 213
**Engineering Division Template**: - GitHub: https://github.com/mcps5601/NSTC-proposal-LaTeX - Network accessline 214
- GitHub: https://github.com/mcps5601/NSTC-proposal-LaTeX - Network accessline 220
1. **audachang's CM03 Template** (Recommended for Overleaf users): - GitHub: https://github.com/audachang/taiwan-nstc-cm03-template - Network accessline 221
- GitHub: https://github.com/audachang/taiwan-nstc-cm03-template - Network accessline 733
For the most authoritative information, visit: **https://www.nstc.gov.tw/**
skills/04-研究方法与科学思维/scholar-evaluation/SKILL.md
- Network accessline 295
**Moussa, H. N., Da Silva, P. Q., Adu-Ampratwum, D., East, A., Lu, Z., Puccetti, N., Xue, M., Sun, H., Majumder, B. P., & Kumar, S. (2025).** _ScholarEval: Research Idea Evaluation Grounded in Literature_. arXiv preprint arXiv:2510.16234. [
skills/04-研究方法与科学思维/scholar-evaluation/scripts/calculate_scores.py
- Executable scriptline 1
#!/usr/bin/env python3
skills/04-研究方法与科学思维/scientific-critical-thinking/SKILL.md
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Read Write Edit Bash
skills/04-研究方法与科学思维/what-if-oracle/SKILL.md
- Network accessline 16
Published research: [The What-If Statement (DOI: 10.5281/zenodo.18736841)](https://doi.org/10.5281/zenodo.18736841) | [IDNA Consolidation v2 (DOI: 10.5281/zenodo.18807387)](https://doi.org/10.5281/zenodo.18807387) - Network accessline 164
[AHK Strategies](https://ahkstrategies.net) — AI Horizon Knowledge - Network accessline 165
Full platform: [themindbook.app](https://themindbook.app) - Network accessline 166
Research: [The What-If Statement (DOI: 10.5281/zenodo.18736841)](https://doi.org/10.5281/zenodo.18736841)
skills/05-生物信息与基因组学/anndata/SKILL.md
- Network accessline 394
- **Official documentation**: https://anndata.readthedocs.io/ - Network accessline 395
- **Scanpy tutorials**: https://scanpy.readthedocs.io/ - Network accessline 396
- **Scverse ecosystem**: https://scverse.org/ - Network accessline 397
- **GitHub repository**: https://github.com/scverse/anndata
skills/05-生物信息与基因组学/anndata/references/io_operations.md
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# Access Zarr from URL store = fsspec.get_mapper('https://example.com/data.zarr') - Network accessline 83
store = fsspec.get_mapper('https://example.com/data.zarr') - Network accessline 287
# Read directly from URL (for h5ad files) url = 'https://example.com/data.h5ad' - Network accessline 288
url = 'https://example.com/data.h5ad' - Network accessline 291
# For other formats, download first import urllib.request urllib.request.urlretrieve(url, 'local_file.h5ad') - Network accessline 292
import urllib.request urllib.request.urlretrieve(url, 'local_file.h5ad') adata = ad.read_h5ad('local_file.h5ad') - Network accessline 293
urllib.request.urlretrieve(url, 'local_file.h5ad')
skills/05-生物信息与基因组学/arboreto/references/distributed_computing.md
skills/05-生物信息与基因组学/arboreto/scripts/basic_grn_inference.py
- Executable scriptline 1
#!/usr/bin/env python3
skills/05-生物信息与基因组学/biopython/SKILL.md
- Access to secretsline 59
[Credential-related source omitted] - Network accessline 409
- **Official Documentation**: https://biopython.org/docs/latest/ - Network accessline 410
- **Tutorial**: https://biopython.org/docs/latest/Tutorial/ - Network accessline 411
- **Cookbook**: https://biopython.org/docs/latest/Tutorial/ (advanced examples) - Network accessline 412
- **GitHub**: https://github.com/biopython/biopython
skills/05-生物信息与基因组学/biopython/references/databases.md
skills/05-生物信息与基因组学/bioservices/SKILL.md
- Network accessline 355
For detailed API documentation and advanced features, refer to: - Official documentation: https://bioservices.readthedocs.io/ - Network accessline 356
- Official documentation: https://bioservices.readthedocs.io/ - Network accessline 357
- Source code: https://github.com/cokelaer/bioservices
skills/05-生物信息与基因组学/bioservices/references/services_reference.md
- Network accessline 127
**Key Methods:** - `search(query)`: Search gene symbols/names - `fetch(format, query)`: Retrieve gene information - Network accessline 129
- `fetch(format, query)`: Retrieve gene information - Network accessline 633
For detailed API documentation: - Official docs: https://bioservices.readthedocs.io/ - Network accessline 634
- Official docs: https://bioservices.readthedocs.io/ - Network accessline 636
- Source code: https://github.com/cokelaer/bioservices
skills/05-生物信息与基因组学/bioservices/scripts/batch_id_converter.py
- Executable scriptline 1
#!/usr/bin/env python3
skills/05-生物信息与基因组学/bioservices/scripts/compound_cross_reference.py
- Executable scriptline 1
#!/usr/bin/env python3
skills/05-生物信息与基因组学/bioservices/scripts/pathway_analysis.py
- Executable scriptline 1
#!/usr/bin/env python3
skills/05-生物信息与基因组学/bioservices/scripts/protein_analysis_workflow.py
- Executable scriptline 1
#!/usr/bin/env python3
skills/05-生物信息与基因组学/deeptools/references/effective_genome_sizes.md
- Network accessline 114
For the most up-to-date effective genome sizes and detailed calculation methods, see: - deepTools documentation: https://deeptools.readthedocs.io/en/latest/content/feature/effectiveGenomeSize.html - Network accessline 115
- deepTools documentation: https://deeptools.readthedocs.io/en/latest/content/feature/effectiveGenomeSize.html
skills/05-生物信息与基因组学/deeptools/references/normalization_methods.md
skills/05-生物信息与基因组学/deeptools/scripts/validate_files.py
- Executable scriptline 1
#!/usr/bin/env python3
skills/05-生物信息与基因组学/deeptools/scripts/workflow_generator.py
- Executable scriptline 1
#!/usr/bin/env python3
skills/05-生物信息与基因组学/etetoolkit/SKILL.md
skills/05-生物信息与基因组学/etetoolkit/scripts/quick_visualize.py
- Executable scriptline 1
#!/usr/bin/env python3
skills/05-生物信息与基因组学/etetoolkit/scripts/tree_operations.py
- Executable scriptline 1
#!/usr/bin/env python3
skills/05-生物信息与基因组学/geniml/SKILL.md
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uv uv pip install git+https://github.com/databio/geniml.git - Network accessline 289
- **Documentation**: https://docs.bedbase.org/geniml/ - Network accessline 290
- **GitHub**: https://github.com/databio/geniml - Network accessline 311
**"StarSpace not found" (BEDspace):** - Install StarSpace separately: https://github.com/facebookresearch/StarSpace - Network accessline 312
- Install StarSpace separately: https://github.com/facebookresearch/StarSpace
skills/05-生物信息与基因组学/geniml/references/bedspace.md
- Network accessline 127
BEDspace requires StarSpace to be installed separately. Download from: https://github.com/facebookresearch/StarSpace
skills/05-生物信息与基因组学/gget/SKILL.md
- Access to secretsline 652
[Credential-related source omitted] - Access to secretsline 658
[Credential-related source omitted] - Access to secretsline 663
[Credential-related source omitted] - Network accessline 865
For additional help: - Official documentation: https://pachterlab.github.io/gget/ - Network accessline 866
- Official documentation: https://pachterlab.github.io/gget/ - Network accessline 867
- GitHub issues: https://github.com/pachterlab/gget/issues - Network accessline 868
- Citation: Luebbert, L. & Pachter, L. (2023). Efficient querying of genomic reference databases with gget. Bioinformatics. https://doi.org/10.1093/bioinformatics/btac836
skills/05-生物信息与基因组学/gget/references/database_info.md
- Network accessline 20
- **Update frequency:** Regular releases (numbered); new releases approximately every 3 months - **Access:** FTP downloads, REST API - **Website:** https://www.ensembl.org/ - Network accessline 21
- **Access:** FTP downloads, REST API - **Website:** https://www.ensembl.org/ - **Notes:** - Network accessline 22
- **Website:** https://www.ensembl.org/ - Network accessline 31
- **Update frequency:** Regular updates with new assemblies - **Access:** Web service API - **Website:** https://genome.ucsc.edu/ - Network accessline 32
- **Access:** Web service API - **Website:** https://genome.ucsc.edu/ - **Notes:** - Network accessline 33
- **Website:** https://genome.ucsc.edu/ - Network accessline 43
- **Update frequency:** Regular releases (weekly for Swiss-Prot, monthly for TrEMBL) - **Access:** REST API - **Website:** https://www.uniprot.org/ - Network accessline 44
- **Access:** REST API - **Website:** https://www.uniprot.org/ - **Notes:** - Network accessline 45
- **Website:** https://www.uniprot.org/ - Network accessline 53
- **Update frequency:** Continuous updates - **Access:** E-utilities API - **Website:** https://www.ncbi.nlm.nih.gov/ - Network accessline 54
- **Access:** E-utilities API - **Website:** https://www.ncbi.nlm.nih.gov/ - **Databases:** Gene, Protein, RefSeq - Network accessline 55
- **Website:** https://www.ncbi.nlm.nih.gov/ - Network accessline 61
- **Update frequency:** Weekly updates - **Access:** REST API - **Website:** https://www.rcsb.org/ - Network accessline 62
- **Access:** REST API - **Website:** https://www.rcsb.org/ - **Notes:** - Network accessline 63
- **Website:** https://www.rcsb.org/ - Network accessline 71
- **Update frequency:** Periodic updates - **Access:** Downloaded database (via gget setup elm) - **Website:** http://elm.eu.org/ - Network accessline 72
- **Access:** Downloaded database (via gget setup elm) - **Website:** http://elm.eu.org/ - **Notes:** - Network accessline 73
- **Website:** http://elm.eu.org/ - **Notes:** - Requires local download before first use - Network accessline 97
- **Update frequency:** Periodic updates with new samples - **Access:** HTTP API - **Website:** https://maayanlab.cloud/archs4/ - Network accessline 98
- **Access:** HTTP API - **Website:** https://maayanlab.cloud/archs4/ - **Data:** - Network accessline 99
- **Website:** https://maayanlab.cloud/archs4/ - Network accessline 109
- **Update frequency:** Continuous additions of new datasets - **Access:** Census API (via cellxgene-census package) - **Website:** https://cellxgene.cziscience.com/ - Network accessline 110
- **Access:** Census API (via cellxgene-census package) - **Website:** https://cellxgene.cziscience.com/ - **Data:** - Network accessline 111
- **Website:** https://cellxgene.cziscience.com/ - Network accessline 124
- **Update frequency:** Regular releases - **Access:** REST API - **Website:** https://www.bgee.org/ - Network accessline 125
- **Access:** REST API - **Website:** https://www.bgee.org/ - **Data:** - Network accessline 126
- **Website:** https://www.bgee.org/ - Network accessline 137
- **Update frequency:** Regular updates to underlying databases - **Access:** REST API - **Website:** https://maayanlab.cloud/Enrichr/ - Network accessline 138
- **Access:** REST API - **Website:** https://maayanlab.cloud/Enrichr/ - **Databases included:** - Network accessline 139
- **Website:** https://maayanlab.cloud/Enrichr/ - Network accessline 155
- **Update frequency:** Regular releases (quarterly) - **Access:** GraphQL API - **Website:** https://www.opentargets.org/ - Network accessline 156
- **Access:** GraphQL API - **Website:** https://www.opentargets.org/ - **Data:** - Network accessline 157
- **Website:** https://www.opentargets.org/ - Network accessline 170
- **Update frequency:** Continuous addition of new studies - **Access:** Web API, downloadable datasets - **Website:** https://www.cbioportal.org/ - Network accessline 171
- **Access:** Web API, downloadable datasets - **Website:** https://www.cbioportal.org/ - **Data:** - Network accessline 172
- **Website:** https://www.cbioportal.org/ - Network accessline 184
- **Update frequency:** Regular releases - **Access:** Download (requires account and license for commercial use) - **Website:** https://cancer.sanger.ac.uk/cosmic - Network accessline 185
- **Access:** Download (requires account and license for commercial use) - **Website:** https://cancer.sanger.ac.uk/cosmic - **Data:** - Network accessline 186
- **Website:** https://cancer.sanger.ac.uk/cosmic - Network accessline 203
- **Model version:** Simplified version for local execution - **Access:** Local computation (requires model download via gget setup) - **Website:** https://alphafold.ebi.ac.uk/ - Network accessline 204
- **Access:** Local computation (requires model download via gget setup) - **Website:** https://alphafold.ebi.ac.uk/ - **Notes:** - Network accessline 205
- **Website:** https://alphafold.ebi.ac.uk/ - **Notes:** - Requires ~4GB model parameters download - Network accessline 215
- **Update frequency:** New models released periodically - **Access:** REST API (requires API key) - **Website:** https://openai.com/ - Network accessline 216
- **Access:** REST API (requires API key) - **Website:** https://openai.com/ - **Notes:** - Network accessline 217
- **Website:** https://openai.com/ - Network accessline 256
2. **Error handling:** - Database structure changes may cause temporary failures - Check GitHub issues: https://github.com/pachterlab/gget/issues - Network accessline 257
- Database structure changes may cause temporary failures - Check GitHub issues: https://github.com/pachterlab/gget/issues - Update gget if errors occur - Network accessline 258
- Check GitHub issues: https://github.com/pachterlab/gget/issues - Network accessline 297
**gget:** Luebbert, L. & Pachter, L. (2023). Efficient querying of genomic reference databases with gget. Bioinformatics. https://doi.org/10.1093/bioinformatics/btac836 - Network accessline 298
Luebbert, L. & Pachter, L. (2023). Efficient querying of genomic reference databases with gget. Bioinformatics. https://doi.org/10.1093/bioinformatics/btac836
skills/05-生物信息与基因组学/gget/references/module_reference.md
- Access to secretsline 437
[Credential-related source omitted]
skills/05-生物信息与基因组学/gget/scripts/batch_sequence_analysis.py
- Executable scriptline 1
#!/usr/bin/env python3
skills/05-生物信息与基因组学/gget/scripts/enrichment_pipeline.py
- Executable scriptline 1
#!/usr/bin/env python3
skills/05-生物信息与基因组学/gget/scripts/gene_analysis.py
- Executable scriptline 1
#!/usr/bin/env python3
skills/05-生物信息与基因组学/lamindb/SKILL.md
- Network accessline 383
- **Official Documentation**: https://docs.lamin.ai - Network accessline 384
- **API Reference**: https://docs.lamin.ai/api - Network accessline 385
- **GitHub Repository**: https://github.com/laminlabs/lamindb - Network accessline 386
- **Tutorial**: https://docs.lamin.ai/tutorial - Network accessline 387
- **FAQ**: https://docs.lamin.ai/faq
skills/05-生物信息与基因组学/lamindb/references/integrations.md
- Network accessline 45
# Initialize with custom S3 endpoint lamin init --storage 's3://bucket?endpoint_url=http://minio.example.com:9000' - Network accessline 46
lamin init --storage 's3://bucket?endpoint_url=http://minio.example.com:9000' - Access to secretsline 50
[Credential-related source omitted] - Network accessline 70
# Access remote files without copying artifact = ln.Artifact( "https://example.com/data.csv", - Network accessline 71
artifact = ln.Artifact( "https://example.com/data.csv", key="remote/data.csv" - Network accessline 72
"https://example.com/data.csv", - Access to secretsline 111
[Credential-related source omitted] - Access to secretsline 120
[Credential-related source omitted] - Access to secretsline 140
[Credential-related source omitted] - Network accessline 490
# Configure git sync export LAMINDB_SYNC_GIT_REPO=https://github.com/user/repo.git - Network accessline 491
export LAMINDB_SYNC_GIT_REPO=https://github.com/user/repo.git - Network accessline 493
# Or programmatically ln.settings.sync_git_repo = "https://github.com/user/repo.git" - Network accessline 494
ln.settings.sync_git_repo = "https://github.com/user/repo.git" - Network accessline 534
# Fetch from API response = requests.get("https://api.example.com/data") - Network accessline 535
response = requests.get("https://api.example.com/data") - Access to secretsline 613
[Credential-related source omitted]
skills/05-生物信息与基因组学/lamindb/references/setup-deployment.md
- Network accessline 66
1. Visit https://lamin.ai - Network accessline 117
# S3-compatible (MinIO, Cloudflare R2) lamin init --storage 's3://bucket?endpoint_url=http://endpoint:9000' - Network accessline 118
lamin init --storage 's3://bucket?endpoint_url=http://endpoint:9000' - Access to secretsline 206
[Credential-related source omitted] - Access to secretsline 258
[Credential-related source omitted] - Network accessline 260
lamin init --storage 's3://my-bucket?endpoint_url=http://minio.example.com:9000' - Access to secretsline 264
[Credential-related source omitted] - Network accessline 266
lamin init --storage 's3://bucket?endpoint_url=https://account-id.r2.cloudflarestorage.com' - Dangerous shell operationline 351
sudo mkdir -p /system/settings - Dangerous shell operationline 352
sudo nano /system/settings/system.env - Dangerous shell operationline 362
sudo chmod 755 /shared/cache/lamindb - Dangerous shell operationline 363
sudo chown -R shared-user:shared-group /shared/cache/lamindb - Network accessline 406
# Configure git sync lamin settings set sync-git-repo https://github.com/user/repo.git - Network accessline 407
lamin settings set sync-git-repo https://github.com/user/repo.git - Network accessline 422
# Git sync export LAMINDB_SYNC_GIT_REPO=https://github.com/user/repo.git - Network accessline 423
export LAMINDB_SYNC_GIT_REPO=https://github.com/user/repo.git
skills/05-生物信息与基因组学/phylogenetics/SKILL.md
- Network accessline 397
- **MAFFT**: https://mafft.cbrc.jp/alignment/software/ - Network accessline 398
- **IQ-TREE 2**: http://www.iqtree.org/ | Tutorial: https://www.iqtree.org/workshop/molevol2022 - Network accessline 399
- **FastTree**: http://www.microbesonline.org/fasttree/ - Network accessline 400
- **ETE3**: http://etetoolkit.org/ - Network accessline 401
- **FigTree** (GUI visualization): https://tree.bio.ed.ac.uk/software/figtree/ - Network accessline 402
- **iTOL** (web visualization): https://itol.embl.de/ - Network accessline 403
- **MUSCLE** (alternative aligner): https://www.drive5.com/muscle/ - Network accessline 404
- **TrimAl** (alignment trimming): https://vicfero.github.io/trimal/
skills/05-生物信息与基因组学/phylogenetics/scripts/phylogenetic_analysis.py
- Executable scriptline 1
Executable file
skills/05-生物信息与基因组学/pydeseq2/SKILL.md
skills/05-生物信息与基因组学/pydeseq2/scripts/run_deseq2_analysis.py
- Executable scriptline 1
#!/usr/bin/env python3
skills/05-生物信息与基因组学/pysam/SKILL.md
- Network accessline 40
# Open BAM file and fetch reads in region samfile = pysam.AlignmentFile("example.bam", "rb") for read in samfile.fetch("chr1", 1000, 2000): - Network accessline 41
samfile = pysam.AlignmentFile("example.bam", "rb") for read in samfile.fetch("chr1", 1000, 2000): print(f"{read.query_name}: {read.reference_start}") - Network accessline 42
for read in samfile.fetch("chr1", 1000, 2000): - Network accessline 58
# Open FASTA and extract sequence fasta = pysam.FastaFile("reference.fasta") sequence = fasta.fetch("chr1", 1000, 2000) - Network accessline 59
fasta = pysam.FastaFile("reference.fasta") sequence = fasta.fetch("chr1", 1000, 2000) print(sequence) - Network accessline 60
sequence = fasta.fetch("chr1", 1000, 2000) - Network accessline 81
- Opening and reading alignment files - AlignedSegment attributes and methods - Region-based fetching with `fetch()` - Network accessline 82
- AlignedSegment attributes and methods - Region-based fetching with `fetch()` - Pileup analysis for coverage - Network accessline 83
- Region-based fetching with `fetch()` - Network accessline 164
**Exception:** Region strings in `fetch()` follow samtools convention (1-based): - Network accessline 166
samfile.fetch("chr1", 999, 2000) # 0-based: positions 999-1999 - Network accessline 167
samfile.fetch("chr1:1000-2000") # 1-based string: positions 1000-2000 - Network accessline 181
Without an index, use `fetch(until_eof=True)` for sequential reading. - Network accessline 205
1. **Coordinate confusion:** Remember 0-based vs 1-based systems in different contexts 2. **Missing indices:** Many operations require index files—create them first 3. **Partial overlaps:** `fetch()` returns reads overlapping region boundar - Network accessline 206
2. **Missing indices:** Many operations require index files—create them first 3. **Partial overlaps:** `fetch()` returns reads overlapping region boundaries, not just those fully contained 4. **Iterator scope:** Keep pileup iterator referen - Network accessline 207
3. **Partial overlaps:** `fetch()` returns reads overlapping region boundaries, not just those fully contained - Network accessline 262
Official documentation: https://pysam.readthedocs.io/
skills/05-生物信息与基因组学/pysam/references/alignment_files.md
- Network accessline 56
### fetch() - Region-Based Retrieval - Network accessline 61
# Fetch specific region for read in samfile.fetch("chr1", 1000, 2000): - Network accessline 62
for read in samfile.fetch("chr1", 1000, 2000): - Network accessline 65
# Fetch entire contig for read in samfile.fetch("chr1"): - Network accessline 66
for read in samfile.fetch("chr1"): - Network accessline 69
# Fetch without index (sequential read) for read in samfile.fetch(until_eof=True): - Network accessline 70
for read in samfile.fetch(until_eof=True): - Network accessline 77
- Use `until_eof=True` for non-indexed files or sequential reading - By default, only returns mapped reads - For unmapped reads, use `fetch("*")` or `until_eof=True` - Network accessline 79
- For unmapped reads, use `fetch("*")` or `until_eof=True` - Network accessline 86
samfile = pysam.AlignmentFile("example.bam", "rb", multiple_iterators=True) iter1 = samfile.fetch("chr1", 1000, 2000) - Network accessline 87
iter1 = samfile.fetch("chr1", 1000, 2000) - Network accessline 88
iter2 = samfile.fetch("chr2", 5000, 6000) - Network accessline 91
Without `multiple_iterators=True`, a new fetch() call repositions the file pointer and breaks existing iterators. - Network accessline 163
for read in samfile.fetch("chr1", 1000, 2000): - Network accessline 247
**Exception:** Region strings in `fetch()` and `pileup()` follow samtools conventions (1-based): - Network accessline 249
# These are equivalent: samfile.fetch("chr1", 999, 2000) # Python style: 0-based - Network accessline 250
samfile.fetch("chr1", 999, 2000) # Python style: 0-based - Network accessline 251
samfile.fetch("chr1:1000-2000") # samtools style: 1-based - Network accessline 268
1. **Use indexed access** when querying specific regions repeatedly 2. **Use `pileup()` for column-wise analysis** instead of repeated fetch operations 3. **Use `fetch(until_eof=True)` for sequential reading** of non-indexed files - Network accessline 269
2. **Use `pileup()` for column-wise analysis** instead of repeated fetch operations 3. **Use `fetch(until_eof=True)` for sequential reading** of non-indexed files 4. **Avoid multiple iterators** unless necessary (performance cost) - Network accessline 270
3. **Use `fetch(until_eof=True)` for sequential reading** of non-indexed files - Network accessline 276
1. **Partial overlaps:** `fetch()` returns reads that overlap region boundaries—implement explicit filtering if exact boundaries are needed - Network accessline 277
2. **Quality score editing:** Cannot edit `query_qualities` in place after modifying `query_sequence`. Create a copy first: `quals = read.query_qualities` 3. **Missing index:** `fetch()` without `until_eof=True` requires an index file 4. ** - Network accessline 278
3. **Missing index:** `fetch()` without `until_eof=True` requires an index file
skills/05-生物信息与基因组学/pysam/references/common_workflows.md
- Network accessline 29
for read in samfile.fetch(until_eof=True): - Network accessline 68
for read in samfile.fetch(): - Network accessline 72
# Get reference sequence for aligned region ref_seq = fasta.fetch( - Network accessline 73
ref_seq = fasta.fetch( - Network accessline 204
for gene in bed.fetch(parser=pysam.asBed()): - Network accessline 208
# Find variants in gene region for variant in vcf.fetch(gene.contig, gene.start, gene.end): - Network accessline 209
for variant in vcf.fetch(gene.contig, gene.start, gene.end): - Network accessline 317
context = fasta.fetch(variant.chrom, start, end) - Network accessline 340
with open(output_fasta, 'w') as out: for gene in bed.fetch(parser=pysam.asBed()): - Network accessline 341
for gene in bed.fetch(parser=pysam.asBed()): - Network accessline 342
sequence = fasta.fetch(gene.contig, gene.start, gene.end) - Network accessline 370
for read in infile.fetch(chrom, start, end): - Network accessline 397
for read in samfile.fetch(variant.chrom, start, end): - Network accessline 404
for read in samfile.fetch(until_eof=True):
skills/05-生物信息与基因组学/pysam/references/sequence_files.md
- Network accessline 56
# Fetch specific region sequence = fasta.fetch("chr1", 1000, 2000) - Network accessline 57
sequence = fasta.fetch("chr1", 1000, 2000) - Network accessline 60
# Fetch entire chromosome chr1_seq = fasta.fetch("chr1") - Network accessline 61
chr1_seq = fasta.fetch("chr1") - Network accessline 63
# Fetch using region string (1-based) sequence = fasta.fetch(region="chr1:1001-2000") - Network accessline 64
sequence = fasta.fetch(region="chr1:1001-2000") - Network accessline 75
start = max(0, pos - window - 1) # Convert to 0-based end = pos + window return fasta.fetch(chrom, start, end) - Network accessline 77
return fasta.fetch(chrom, start, end) - Network accessline 80
def get_gene_sequence(fasta, chrom, start, end, strand): """Get gene sequence with strand awareness.""" seq = fasta.fetch(chrom, start, end) - Network accessline 82
seq = fasta.fetch(chrom, start, end) - Network accessline 92
def check_ref_allele(fasta, chrom, pos, expected_ref): """Verify reference allele at position (1-based pos).""" actual = fasta.fetch(chrom, pos-1, pos) # Convert to 0-based - Network accessline 93
"""Verify reference allele at position (1-based pos).""" actual = fasta.fetch(chrom, pos-1, pos) # Convert to 0-based return actual.upper() == expected_ref.upper() - Network accessline 94
actual = fasta.fetch(chrom, pos-1, pos) # Convert to 0-based - Network accessline 109
for chrom, start, end in regions: seq_id = f"{chrom}:{start}-{end}" sequences[seq_id] = fasta.fetch(chrom, start, end) - Network accessline 111
sequences[seq_id] = fasta.fetch(chrom, start, end) - Network accessline 137
def has_quality_sequence(fasta, chrom, start, end, max_n_frac=0.1): """Check if region has acceptable N content.""" seq = fasta.fetch(chrom, start, end) - Network accessline 138
"""Check if region has acceptable N content.""" seq = fasta.fetch(chrom, start, end) n_count = seq.upper().count('N') - Network accessline 139
seq = fasta.fetch(chrom, start, end) - Network accessline 342
# Fetch region for row in tabix.fetch("chr1", 1000000, 2000000): - Network accessline 343
for row in tabix.fetch("chr1", 1000000, 2000000): - Network accessline 346
# Parse with specific parser for row in tabix.fetch("chr1", 1000000, 2000000, parser=pysam.asBed()): - Network accessline 347
for row in tabix.fetch("chr1", 1000000, 2000000, parser=pysam.asBed()): - Network accessline 358
# Access BED fields by name for interval in bed.fetch("chr1", 1000000, 2000000, parser=pysam.asBed()): - Network accessline 359
for interval in bed.fetch("chr1", 1000000, 2000000, parser=pysam.asBed()): - Network accessline 371
# Access GTF fields for feature in gtf.fetch("chr1", 1000000, 2000000, parser=pysam.asGTF()): - Network accessline 372
for feature in gtf.fetch("chr1", 1000000, 2000000, parser=pysam.asGTF()): - Network accessline 401
1. **FASTA coordinate system:** fetch() uses 0-based coordinates, region strings use 1-based
skills/05-生物信息与基因组学/pysam/references/variant_files.md
- Network accessline 64
# Fetch variants in region (1-based coordinates for region string) for variant in vcf.fetch("chr1", 1000000, 2000000): - Network accessline 65
for variant in vcf.fetch("chr1", 1000000, 2000000): - Network accessline 68
# Using region string (1-based) for variant in vcf.fetch("chr1:1000000-2000000"): - Network accessline 69
for variant in vcf.fetch("chr1:1000000-2000000"): - Network accessline 73
**Note:** Uses **1-based coordinates** in `fetch()` calls to match VCF specification. - Network accessline 362
3. **Genotype tuples:** Genotypes are tuples, not lists—handle None values for missing data 4. **Allele indexing:** In genotype (0, 1), 0=REF, 1=first ALT, 2=second ALT, etc. 5. **Index requirement:** Region-based `fetch()` requires tabix i - Network accessline 363
4. **Allele indexing:** In genotype (0, 1), 0=REF, 1=first ALT, 2=second ALT, etc. 5. **Index requirement:** Region-based `fetch()` requires tabix index for VCF.gz 6. **Header modification:** When subsetting samples, properly update header - Network accessline 364
5. **Index requirement:** Region-based `fetch()` requires tabix index for VCF.gz
skills/05-生物信息与基因组学/scanpy/SKILL.md
skills/05-生物信息与基因组学/scanpy/assets/analysis_template.py
- Executable scriptline 1
#!/usr/bin/env python3
skills/05-生物信息与基因组学/scanpy/scripts/qc_analysis.py
- Executable scriptline 1
#!/usr/bin/env python3
skills/05-生物信息与基因组学/scikit-bio/SKILL.md
skills/05-生物信息与基因组学/scvelo/SKILL.md
- Network accessline 17
**Key resources:** - Documentation: https://scvelo.readthedocs.io/ - Network accessline 18
- Documentation: https://scvelo.readthedocs.io/ - Network accessline 19
- GitHub: https://github.com/theislab/scvelo - Network accessline 315
- **scVelo documentation**: https://scvelo.readthedocs.io/ - Network accessline 316
- **Tutorial notebooks**: https://scvelo.readthedocs.io/tutorials/ - Network accessline 317
- **GitHub**: https://github.com/theislab/scvelo - Network accessline 318
- **Paper**: Bergen V et al. (2020) Nature Biotechnology. PMID: 32747759 - **velocyto** (preprocessing): http://velocyto.org/ - **CellRank** (fate prediction, extends scVelo): https://cellrank.readthedocs.io/ - Network accessline 319
- **velocyto** (preprocessing): http://velocyto.org/ - Network accessline 320
- **CellRank** (fate prediction, extends scVelo): https://cellrank.readthedocs.io/ - Network accessline 321
- **dynamo** (metabolic labeling alternative): https://dynamo-release.readthedocs.io/
skills/05-生物信息与基因组学/scvelo/scripts/rna_velocity_workflow.py
- Executable scriptline 1
Executable file
skills/05-生物信息与基因组学/scvi-tools/SKILL.md
- Network accessline 165
- **Workflows**: `references/workflows.md` contains common workflows, best practices, hyperparameter tuning, and GPU optimization - **Model References**: Detailed documentation for each model category in the `references/` directory - **Offi - Network accessline 166
- **Model References**: Detailed documentation for each model category in the `references/` directory - **Official Documentation**: https://docs.scvi-tools.org/en/stable/ - **Tutorials**: https://docs.scvi-tools.org/en/stable/tutorials/inde - Network accessline 167
- **Official Documentation**: https://docs.scvi-tools.org/en/stable/ - Network accessline 168
- **Tutorials**: https://docs.scvi-tools.org/en/stable/tutorials/index.html - Network accessline 169
- **API Reference**: https://docs.scvi-tools.org/en/stable/api/index.html
skills/05-生物信息与基因组学/tiledbvcf/SKILL.md
- Network accessline 310
**Open Source Documentation:** - TileDB Academy: https://cloud.tiledb.com/academy/ - Network accessline 311
- TileDB Academy: https://cloud.tiledb.com/academy/ - Network accessline 312
- Population Genomics Guide: https://cloud.tiledb.com/academy/structure/life-sciences/population-genomics/ - Network accessline 313
- TileDB-VCF GitHub: https://github.com/TileDB-Inc/TileDB-VCF - Network accessline 317
**For Large-Scale/Production Genomics:** - TileDB-Cloud Platform: https://cloud.tiledb.com - Network accessline 318
- TileDB-Cloud Platform: https://cloud.tiledb.com - Network accessline 319
- TileDB Academy (All Documentation): https://cloud.tiledb.com/academy/ - Network accessline 321
**Getting Started:** - Free account signup: https://cloud.tiledb.com - Network accessline 322
- Free account signup: https://cloud.tiledb.com - Network accessline 335
# Sign up at https://cloud.tiledb.com - Access to secretsline 351
[Credential-related source omitted] - Network accessline 452
**Next Steps:** - Visit https://cloud.tiledb.com to create account - Network accessline 453
- Visit https://cloud.tiledb.com to create account - Network accessline 454
- Review documentation at https://cloud.tiledb.com/academy/
skills/05-生物信息与基因组学/zarr-python/SKILL.md
- Network accessline 768
- **Official Documentation**: https://zarr.readthedocs.io/ - Network accessline 769
- **Zarr Specifications**: https://zarr-specs.readthedocs.io/ - Network accessline 770
- **GitHub Repository**: https://github.com/zarr-developers/zarr-python - Network accessline 771
- **Community Chat**: https://gitter.im/zarr-developers/community - Network accessline 773
**Related Libraries**: - **Xarray**: https://docs.xarray.dev/ (labeled arrays) - Network accessline 774
- **Xarray**: https://docs.xarray.dev/ (labeled arrays) - Network accessline 775
- **Dask**: https://docs.dask.org/ (parallel computing) - Network accessline 776
- **NumCodecs**: https://numcodecs.readthedocs.io/ (compression codecs)
skills/06-化学信息与药物发现/datamol/SKILL.md
- Network accessline 130
# Read from cloud storage df = dm.read_sdf("s3://bucket/compounds.sdf") df = dm.read_csv("https://example.com/data.csv") - Network accessline 132
df = dm.read_csv("https://example.com/data.csv") - Network accessline 701
- **Datamol Documentation**: https://docs.datamol.io/ - Network accessline 702
- **RDKit Documentation**: https://www.rdkit.org/docs/ - Network accessline 703
- **GitHub Repository**: https://github.com/datamol-io/datamol
skills/06-化学信息与药物发现/datamol/references/io_module.md
skills/06-化学信息与药物发现/deepchem/SKILL.md
skills/06-化学信息与药物发现/deepchem/scripts/graph_neural_network.py
- Executable scriptline 1
#!/usr/bin/env python3
skills/06-化学信息与药物发现/deepchem/scripts/predict_solubility.py
- Executable scriptline 1
#!/usr/bin/env python3
skills/06-化学信息与药物发现/deepchem/scripts/transfer_learning.py
- Executable scriptline 1
#!/usr/bin/env python3
skills/06-化学信息与药物发现/diffdock/SKILL.md
- Network accessline 53
git clone https://github.com/gcorso/DiffDock.git - Network accessline 366
python app/main.py # Navigate to http://localhost:7860 - Network accessline 367
# Navigate to http://localhost:7860 - Network accessline 370
Or use the online demo without installation: - https://huggingface.co/spaces/reginabarzilaygroup/DiffDock-Web - Network accessline 371
- https://huggingface.co/spaces/reginabarzilaygroup/DiffDock-Web - Network accessline 477
- **GitHub Repository**: https://github.com/gcorso/DiffDock - Network accessline 478
- **Online Demo**: https://huggingface.co/spaces/reginabarzilaygroup/DiffDock-Web - Network accessline 479
- **DiffDock-L Paper**: https://arxiv.org/abs/2402.18396 - Network accessline 480
- **Original Paper**: https://arxiv.org/abs/2210.01776
skills/06-化学信息与药物发现/diffdock/references/workflows_examples.md
- Network accessline 10
# Clone repository git clone https://github.com/gcorso/DiffDock.git - Network accessline 11
git clone https://github.com/gcorso/DiffDock.git - Possible obfuscationline 74
python -m inference --config default_inference_args.yaml --protein_sequence "MSKGEELFTGVVPILVELDGDVNGHKFSVSGEGEGDATYGKLTLKFICTTGKLPVPWPTLVTTFSYGVQCFSRYPDHMKQHDFFKSAMPEGYVQERTIFFKDDGNYKTRAEVKFEGDTLVNRIELKGIDFKEDGNILGHKLEYNYNSHNVYIMADKQ - Possible obfuscationline 76
--protein_sequence "MSKGEELFTGVVPILVELDGDVNGHKFSVSGEGEGDATYGKLTLKFICTTGKLPVPWPTLVTTFSYGVQCFSRYPDHMKQHDFFKSAMPEGYVQERTIFFKDDGNYKTRAEVKFEGDTLVNRIELKGIDFKEDGNILGHKLEYNYNSHNVYIMADKQKNGIKVNFKIRHNIEDGSVQLADHYQQNTPIGDGPVLLPDNHYLSTQSALSKDPNEKRDHMVL - Network accessline 301
### Access Interface Navigate to `http://localhost:7860` in web browser - Network accessline 302
Navigate to `http://localhost:7860` in web browser - Network accessline 311
### Online Alternative Use the Hugging Face Spaces demo without local installation: - URL: https://huggingface.co/spaces/reginabarzilaygroup/DiffDock-Web - Network accessline 313
- URL: https://huggingface.co/spaces/reginabarzilaygroup/DiffDock-Web
skills/06-化学信息与药物发现/diffdock/scripts/analyze_results.py
- Executable scriptline 1
#!/usr/bin/env python3
skills/06-化学信息与药物发现/diffdock/scripts/prepare_batch_csv.py
- Executable scriptline 1
#!/usr/bin/env python3 - Possible obfuscationline 172
'MSKGEELFTGVVPILVELDGDVNGHKFSVSGEGEGDATYGKLTLKFICTTGKLPVPWPTLVTTFSYGVQCFSRYPDHMKQHDFFKSAMPEGYVQERTIFFKDDGNYKTRAEVKFEGDTLVNRIELKGIDFKEDGNILGHKLEYNYNSHNVYIMADKQKNGIKVNFKIRHNIEDGSVQLADHYQQNTPIGDGPVLLPDNHYLSTQSALSKDPNEKRDHMVLLEFVTAAGITHGMDELYK'
skills/06-化学信息与药物发现/diffdock/scripts/setup_check.py
skills/06-化学信息与药物发现/matchms/references/importing_exporting.md
- Network accessline 406
For detailed format specifications: - **MGF**: http://www.matrixscience.com/help/data_file_help.html - Network accessline 407
- **MGF**: http://www.matrixscience.com/help/data_file_help.html - Network accessline 408
- **MSP**: https://chemdata.nist.gov/mass-spc/ms-search/ - Network accessline 409
- **mzML**: http://www.psidev.info/mzML - Network accessline 410
- **GNPS JSON**: https://gnps.ucsd.edu/ - Network accessline 414
For complete API documentation: https://matchms.readthedocs.io/en/latest/api/matchms.importing.html - Network accessline 415
https://matchms.readthedocs.io/en/latest/api/matchms.importing.html - Network accessline 416
https://matchms.readthedocs.io/en/latest/api/matchms.exporting.html
skills/06-化学信息与药物发现/matchms/references/similarity.md
skills/06-化学信息与药物发现/matchms/references/workflows.md
skills/06-化学信息与药物发现/medchem/SKILL.md
skills/06-化学信息与药物发现/medchem/scripts/filter_molecules.py
- Executable scriptline 1
#!/usr/bin/env python3
skills/06-化学信息与药物发现/molecular-dynamics/SKILL.md
- Network accessline 15
- **OpenMM** (https://openmm.org/): High-performance MD simulation engine with GPU support, Python API, and flexible force field support - Network accessline 16
- **MDAnalysis** (https://mdanalysis.org/): Python library for reading, writing, and analyzing MD trajectories from all major simulation packages - Network accessline 450
- **OpenMM documentation**: https://openmm.org/documentation.html - Network accessline 451
- **MDAnalysis user guide**: https://docs.mdanalysis.org/ - Network accessline 452
- **GROMACS** (alternative MD engine): https://manual.gromacs.org/ - Network accessline 453
- **NAMD** (alternative): https://www.ks.uiuc.edu/Research/namd/ - Network accessline 454
- **CHARMM-GUI** (web-based system builder): https://charmm-gui.org/ - Network accessline 455
- **AmberTools** (free Amber tools): https://ambermd.org/AmberTools.php
skills/06-化学信息与药物发现/molfeat/SKILL.md
- Network accessline 505
- **Official Documentation**: https://molfeat-docs.datamol.io/ - Network accessline 506
- **GitHub Repository**: https://github.com/datamol-io/molfeat - Network accessline 507
- **PyPI Package**: https://pypi.org/project/molfeat/ - Network accessline 508
- **Tutorial**: https://portal.valencelabs.com/datamol/post/types-of-featurizers-b1e8HHrbFMkbun6
skills/06-化学信息与药物发现/pyopenms/SKILL.md
skills/06-化学信息与药物发现/pyopenms/references/identification.md
- Possible obfuscationline 231
protein_seq = "MKTAYIAKQRQISFVKSHFSRQLEERLGLIEVQAPILSRVGDGTQDNLSGAEKAVQVKVKALPDAQFEVVHSLAKWKRQTLGQHDFSAGEGLYTHMKALRPDEDRLSPLHSVYVDQWDWERVMGDGERQFSTLKSTVEAIWAGIKATEAAVSEEFGLAPFLPDQIHFVHSQELLSRYPDLDAKGRERAIAKDLGAVFLVGIGGKLSDGHRHDVRAPDYDDWSTPS
skills/06-化学信息与药物发现/pytdc/SKILL.md
skills/06-化学信息与药物发现/pytdc/references/datasets.md
- Network accessline 244
- New datasets are regularly added to TDC - Some datasets may require additional dependencies - Check the official TDC website for the most up-to-date dataset list: https://tdcommons.ai/overview/ - Network accessline 246
- Check the official TDC website for the most up-to-date dataset list: https://tdcommons.ai/overview/
skills/06-化学信息与药物发现/pytdc/references/oracles.md
- Network accessline 372
- Different oracles may have different score ranges and interpretations - Some oracles require additional dependencies or API access - Check oracle documentation for specific details: https://tdcommons.ai/functions/oracles/ - Network accessline 374
- Check oracle documentation for specific details: https://tdcommons.ai/functions/oracles/ - Network accessline 398
- TDC Oracles Documentation: https://tdcommons.ai/functions/oracles/
skills/06-化学信息与药物发现/pytdc/references/utilities.md
- Network accessline 681
- TDC Documentation: https://tdc.readthedocs.io - Network accessline 682
- Data Functions: https://tdcommons.ai/fct_overview/ - Network accessline 683
- Evaluation Metrics: https://tdcommons.ai/functions/model_eval/ - Network accessline 684
- Data Splits: https://tdcommons.ai/functions/data_split/
skills/06-化学信息与药物发现/pytdc/scripts/benchmark_evaluation.py
skills/06-化学信息与药物发现/pytdc/scripts/load_and_split_data.py
- Executable scriptline 1
#!/usr/bin/env python3
skills/06-化学信息与药物发现/pytdc/scripts/molecular_generation.py
- Executable scriptline 1
#!/usr/bin/env python3
skills/06-化学信息与药物发现/rdkit/scripts/molecular_properties.py
- Executable scriptline 1
#!/usr/bin/env python3
skills/06-化学信息与药物发现/rdkit/scripts/similarity_search.py
- Executable scriptline 1
#!/usr/bin/env python3
skills/06-化学信息与药物发现/rdkit/scripts/substructure_filter.py
- Executable scriptline 1
#!/usr/bin/env python3
skills/06-化学信息与药物发现/rowan/SKILL.md
- Network accessline 40
Generate an API key at [labs.rowansci.com/account/api-keys](https://labs.rowansci.com/account/api-keys). - Access to secretsline 45
[Credential-related source omitted] - Access to secretsline 50
[Credential-related source omitted] - Access to secretsline 53
[Credential-related source omitted] - Possible obfuscationline 184
protein_seq = "MENFQKVEKIGEGTYGVVYKARNKLTGEVVALKKIRLDTETEGVPSTAIREISLLKELNHPNIVKLLDVIHTENKLYLVFEFLHQDLKKFMDASALTGIPLPLIKSYLFQLLQGLAFCHSHRVLHRDLKPQNLLINTEGAIKLADFGLARAFGVPVRTYTHEVVTLWYRAPEILLGCKYYSTAVDIWSLGCIFAEMVTRRALFPGDSEIDQLFRIFRTLGTPDEV - Network accessline 420
- **Web Interface**: https://labs.rowansci.com - Network accessline 421
- **Documentation**: https://docs.rowansci.com - Network accessline 422
- **Python API Docs**: https://docs.rowansci.com/api/python/v2/ - Network accessline 423
- **Tutorials**: https://docs.rowansci.com/tutorials
skills/06-化学信息与药物发现/rowan/references/api_reference.md
- Access to secretsline 325
[Credential-related source omitted]
skills/06-化学信息与药物发现/torchdrug/SKILL.md
skills/07-临床医学与精准医疗/cbioportal-database/SKILL.md
- Network accessline 13
cBioPortal for Cancer Genomics (https://www.cbioportal.org/) is an open-access resource for exploring, visualizing, and analyzing multidimensional cancer genomics data. It hosts data from The Cancer Genome Atlas (TCGA), AACR Project GENIE, - Network accessline 15
**Key resources:** - cBioPortal website: https://www.cbioportal.org/ - Network accessline 16
- cBioPortal website: https://www.cbioportal.org/ - Network accessline 17
- REST API: https://www.cbioportal.org/api/ - API docs (Swagger): https://www.cbioportal.org/api/swagger-ui/index.html - Python client: `bravado` or `requests` - Network accessline 18
- API docs (Swagger): https://www.cbioportal.org/api/swagger-ui/index.html - Python client: `bravado` or `requests` - GitHub: https://github.com/cBioPortal/cbioportal - Network accessline 20
- GitHub: https://github.com/cBioPortal/cbioportal - Network accessline 39
Base URL: `https://www.cbioportal.org/api` - Network accessline 46
BASE_URL = "https://www.cbioportal.org/api" - Network accessline 49
def cbioportal_get(endpoint, params=None): url = f"{BASE_URL}/{endpoint}" response = requests.get(url, params=params, headers=HEADERS) - Network accessline 50
url = f"{BASE_URL}/{endpoint}" response = requests.get(url, params=params, headers=HEADERS) response.raise_for_status() - Network accessline 51
response = requests.get(url, params=params, headers=HEADERS) - Network accessline 55
def cbioportal_post(endpoint, body): url = f"{BASE_URL}/{endpoint}" response = requests.post(url, json=body, headers=HEADERS) - Network accessline 56
url = f"{BASE_URL}/{endpoint}" response = requests.post(url, json=body, headers=HEADERS) response.raise_for_status() - Network accessline 57
response = requests.post(url, json=body, headers=HEADERS) - Network accessline 175
# Get sample list samples = requests.get( - Network accessline 176
samples = requests.get( - Network accessline 181
(s["sampleListId"] for s in samples if s["category"] == "all_cases_in_study"), None ) total_samples = len(requests.get( - Network accessline 182
) total_samples = len(requests.get( f"{BASE_URL}/sample-lists/{all_samples_id}/sample-ids", - Network accessline 183
total_samples = len(requests.get( - Network accessline 188
# Get gene Entrez IDs gene_data = requests.post( - Network accessline 189
gene_data = requests.post( - Network accessline 245
# 1. Get gene Entrez ID gene_info = requests.post( - Network accessline 246
gene_info = requests.post( - Network accessline 308
gene_info = requests.post( - Network accessline 355
# Download TCGA BRCA data wget https://cbioportal-datahub.s3.amazonaws.com/brca_tcga.tar.gz - Network accessline 356
wget https://cbioportal-datahub.s3.amazonaws.com/brca_tcga.tar.gz - Network accessline 361
- **cBioPortal website**: https://www.cbioportal.org/ - Network accessline 362
- **API Swagger UI**: https://www.cbioportal.org/api/swagger-ui/index.html - Network accessline 363
- **Documentation**: https://docs.cbioportal.org/ - Network accessline 364
- **GitHub**: https://github.com/cBioPortal/cbioportal - Network accessline 365
- **Data hub**: https://datahub.cbioportal.org/ - Network accessline 367
- **API clients**: https://docs.cbioportal.org/web-api-and-clients/
skills/07-临床医学与精准医疗/clinical-decision-support/SKILL.md
- Broad permissionsline 1
Read Write Edit Bash
skills/07-临床医学与精准医疗/clinical-decision-support/scripts/biomarker_classifier.py
- Executable scriptline 1
#!/usr/bin/env python3
skills/07-临床医学与精准医疗/clinical-decision-support/scripts/build_decision_tree.py
- Executable scriptline 1
#!/usr/bin/env python3
skills/07-临床医学与精准医疗/clinical-decision-support/scripts/create_cohort_tables.py
- Executable scriptline 1
#!/usr/bin/env python3
skills/07-临床医学与精准医疗/clinical-decision-support/scripts/generate_survival_analysis.py
- Executable scriptline 1
#!/usr/bin/env python3
skills/07-临床医学与精准医疗/clinical-decision-support/scripts/validate_cds_document.py
- Executable scriptline 1
#!/usr/bin/env python3
skills/07-临床医学与精准医疗/clinical-reports/SKILL.md
- Broad permissionsline 1
Read Write Edit Bash
skills/07-临床医学与精准医疗/clinical-reports/references/README.md
- Network accessline 210
### External Standards - CARE Guidelines: https://www.care-statement.org/ - Network accessline 211
- CARE Guidelines: https://www.care-statement.org/ - Network accessline 212
- ICH-E3 Guideline: https://database.ich.org/sites/default/files/E3_Guideline.pdf - Network accessline 213
- CONSORT Statement: http://www.consort-statement.org/ - Network accessline 214
- HIPAA: https://www.hhs.gov/hipaa/ - Network accessline 215
- ACR Practice Parameters: https://www.acr.org/Clinical-Resources/Practice-Parameters-and-Technical-Standards - Network accessline 216
- CAP Cancer Protocols: https://www.cap.org/protocols-and-guidelines
skills/07-临床医学与精准医疗/clinical-reports/references/case_report_guidelines.md
- Network accessline 562
- CARE website: https://www.care-statement.org/
skills/07-临床医学与精准医疗/clinical-reports/references/patient_documentation.md
- Network accessline 461
Vitals: T 98.2°F, BP 142/88, HR 105, RR 24, SpO2 88% on room air → 95% on 2L NC Height: 5'10", Weight: 195 lbs (baseline 187 lbs), BMI 28
skills/07-临床医学与精准医疗/clinical-reports/scripts/check_deidentification.py
- Executable scriptline 1
#!/usr/bin/env python3
skills/07-临床医学与精准医疗/clinical-reports/scripts/compliance_checker.py
- Executable scriptline 1
#!/usr/bin/env python3
skills/07-临床医学与精准医疗/clinical-reports/scripts/extract_clinical_data.py
- Executable scriptline 1
#!/usr/bin/env python3
skills/07-临床医学与精准医疗/clinical-reports/scripts/format_adverse_events.py
- Executable scriptline 1
#!/usr/bin/env python3
skills/07-临床医学与精准医疗/clinical-reports/scripts/generate_report_template.py
- Executable scriptline 1
#!/usr/bin/env python3
skills/07-临床医学与精准医疗/clinical-reports/scripts/terminology_validator.py
- Executable scriptline 1
#!/usr/bin/env python3
skills/07-临床医学与精准医疗/clinical-reports/scripts/validate_case_report.py
- Executable scriptline 1
#!/usr/bin/env python3
skills/07-临床医学与精准医疗/clinical-reports/scripts/validate_trial_report.py
- Executable scriptline 1
#!/usr/bin/env python3
skills/07-临床医学与精准医疗/clinicaltrials-database/SKILL.md
- Network accessline 44
url = "https://clinicaltrials.gov/api/v2/studies" - Network accessline 51
response = requests.get(url, params=params) - Network accessline 64
nct_id = "NCT04852770" url = f"https://clinicaltrials.gov/api/v2/studies/{nct_id}" - Network accessline 65
url = f"https://clinicaltrials.gov/api/v2/studies/{nct_id}" - Network accessline 67
response = requests.get(url) - Network accessline 433
def search_with_rate_limit(params): try: response = requests.get("https://clinicaltrials.gov/api/v2/studies", params=params) - Network accessline 434
try: response = requests.get("https://clinicaltrials.gov/api/v2/studies", params=params) response.raise_for_status() - Network accessline 435
response = requests.get("https://clinicaltrials.gov/api/v2/studies", params=params) - Network accessline 465
try: response = requests.get(url, params=params, timeout=30) - Network accessline 466
response = requests.get(url, params=params, timeout=30) - Network accessline 494
- **Base URL**: `https://clinicaltrials.gov/api/v2` - **Authentication**: Not required (public API) - **Rate Limit**: ~50 requests/minute per IP
skills/07-临床医学与精准医疗/clinicaltrials-database/references/api_reference.md
- Network accessline 7
**Base URL:** `https://clinicaltrials.gov/api/v2` - Network accessline 69
curl "https://clinicaltrials.gov/api/v2/studies?query.cond=lung+cancer&filter.overallStatus=RECRUITING&pageSize=10&format=json" - Network accessline 107
curl "https://clinicaltrials.gov/api/v2/studies/NCT04852770?format=json" - Network accessline 160
url = "https://clinicaltrials.gov/api/v2/studies" - Network accessline 168
response = requests.get(url, params=params) - Network accessline 189
response = requests.get("https://clinicaltrials.gov/api/v2/studies", params=params) - Network accessline 203
response = requests.get("https://clinicaltrials.gov/api/v2/studies", params=params) - Network accessline 211
nct_id = "NCT04852770" url = f"https://clinicaltrials.gov/api/v2/studies/{nct_id}" - Network accessline 212
url = f"https://clinicaltrials.gov/api/v2/studies/{nct_id}" - Network accessline 214
response = requests.get(url) - Network accessline 238
response = requests.get("https://clinicaltrials.gov/api/v2/studies", params=params) - Network accessline 262
response = requests.get("https://clinicaltrials.gov/api/v2/studies", params=params) - Network accessline 298
for attempt in range(max_retries): try: response = requests.get( - Network accessline 299
try: response = requests.get( "https://clinicaltrials.gov/api/v2/studies", - Network accessline 300
response = requests.get( - Network accessline 301
"https://clinicaltrials.gov/api/v2/studies", - Network accessline 358
For detailed migration guidance, see: https://clinicaltrials.gov/data-api/about-api/api-migration
skills/07-临床医学与精准医疗/clinicaltrials-database/scripts/query_clinicaltrials.py
- Executable scriptline 1
#!/usr/bin/env python3 - Network accessline 9
API Documentation: https://clinicaltrials.gov/data-api/api - Network accessline 19
BASE_URL = "https://clinicaltrials.gov/api/v2" - Network accessline 86
url = f"{BASE_URL}/studies" response = requests.get(url, params=params) - Network accessline 87
response = requests.get(url, params=params) - Network accessline 110
response = requests.get(url, params=params)
skills/07-临床医学与精准医疗/clinpgx-database/SKILL.md
- Network accessline 43
BASE_URL = "https://api.clinpgx.org/v1/" - Network accessline 65
# Get gene details response = requests.get("https://api.clinpgx.org/v1/gene/CYP2D6") - Network accessline 66
response = requests.get("https://api.clinpgx.org/v1/gene/CYP2D6") - Network accessline 69
# Search for genes by name response = requests.get("https://api.clinpgx.org/v1/gene", - Network accessline 70
response = requests.get("https://api.clinpgx.org/v1/gene", - Network accessline 87
# Get drug details response = requests.get("https://api.clinpgx.org/v1/chemical/PA448515") # Warfarin - Network accessline 88
response = requests.get("https://api.clinpgx.org/v1/chemical/PA448515") # Warfarin - Network accessline 91
# Search drugs by name response = requests.get("https://api.clinpgx.org/v1/chemical", - Network accessline 92
response = requests.get("https://api.clinpgx.org/v1/chemical", - Network accessline 111
# Get gene-drug pair information response = requests.get("https://api.clinpgx.org/v1/geneDrugPair", - Network accessline 112
response = requests.get("https://api.clinpgx.org/v1/geneDrugPair", - Network accessline 116
# Get all pairs for a gene response = requests.get("https://api.clinpgx.org/v1/geneDrugPair", - Network accessline 117
response = requests.get("https://api.clinpgx.org/v1/geneDrugPair", - Network accessline 133
# Get CPIC guideline response = requests.get("https://api.clinpgx.org/v1/guideline/PA166104939") - Network accessline 134
response = requests.get("https://api.clinpgx.org/v1/guideline/PA166104939") - Network accessline 137
# List all CPIC guidelines response = requests.get("https://api.clinpgx.org/v1/guideline", - Network accessline 138
response = requests.get("https://api.clinpgx.org/v1/guideline", - Network accessline 163
# Get allele information response = requests.get("https://api.clinpgx.org/v1/allele/CYP2D6*4") - Network accessline 164
response = requests.get("https://api.clinpgx.org/v1/allele/CYP2D6*4") - Network accessline 167
# Get all alleles for a gene response = requests.get("https://api.clinpgx.org/v1/allele", - Network accessline 168
response = requests.get("https://api.clinpgx.org/v1/allele", - Network accessline 191
# Get variant information response = requests.get("https://api.clinpgx.org/v1/variant/rs4244285") - Network accessline 192
response = requests.get("https://api.clinpgx.org/v1/variant/rs4244285") - Network accessline 195
# Search variants by position (if supported) response = requests.get("https://api.clinpgx.org/v1/variant", - Network accessline 196
response = requests.get("https://api.clinpgx.org/v1/variant", - Network accessline 214
# Get clinical annotations response = requests.get("https://api.clinpgx.org/v1/clinicalAnnotation", - Network accessline 215
response = requests.get("https://api.clinpgx.org/v1/clinicalAnnotation", - Network accessline 219
# Filter by evidence level response = requests.get("https://api.clinpgx.org/v1/clinicalAnnotation", - Network accessline 220
response = requests.get("https://api.clinpgx.org/v1/clinicalAnnotation", - Network accessline 238
# Get drug labels with PGx information response = requests.get("https://api.clinpgx.org/v1/drugLabel", - Network accessline 239
response = requests.get("https://api.clinpgx.org/v1/drugLabel", - Network accessline 243
# Filter by regulatory source response = requests.get("https://api.clinpgx.org/v1/drugLabel", - Network accessline 244
response = requests.get("https://api.clinpgx.org/v1/drugLabel", - Network accessline 261
# Get pathway information response = requests.get("https://api.clinpgx.org/v1/pathway/PA146123006") # Warfarin pathway - Network accessline 262
response = requests.get("https://api.clinpgx.org/v1/pathway/PA146123006") # Warfarin pathway - Network accessline 265
# Search pathways by drug response = requests.get("https://api.clinpgx.org/v1/pathway", - Network accessline 266
response = requests.get("https://api.clinpgx.org/v1/pathway", - Network accessline 284
# Example: Patient is CYP2C19 *1/*2 (intermediate metabolizer) response = requests.get("https://api.clinpgx.org/v1/allele/CYP2C19*2") - Network accessline 285
response = requests.get("https://api.clinpgx.org/v1/allele/CYP2C19*2") - Network accessline 291
response = requests.get("https://api.clinpgx.org/v1/geneDrugPair", - Network accessline 298
response = requests.get("https://api.clinpgx.org/v1/guideline", - Network accessline 306
response = requests.get("https://api.clinpgx.org/v1/drugLabel", - Network accessline 320
all_interactions = {} for gene in pgx_panel: response = requests.get("https://api.clinpgx.org/v1/geneDrugPair", - Network accessline 321
for gene in pgx_panel: response = requests.get("https://api.clinpgx.org/v1/geneDrugPair", params={"gene": gene}) - Network accessline 322
response = requests.get("https://api.clinpgx.org/v1/geneDrugPair", - Network accessline 341
response = requests.get("https://api.clinpgx.org/v1/chemical", - Network accessline 348
response = requests.get("https://api.clinpgx.org/v1/clinicalAnnotation", - Network accessline 367
response = requests.get("https://api.clinpgx.org/v1/allele", - Network accessline 396
response = requests.get("https://api.clinpgx.org/v1/geneDrugPair", - Network accessline 403
response = requests.get("https://api.clinpgx.org/v1/clinicalAnnotation", - Network accessline 427
def rate_limited_request(url, params=None, delay=0.5): """Make API request with rate limiting (2 req/sec max)""" response = requests.get(url, params=params) - Network accessline 428
"""Make API request with rate limiting (2 req/sec max)""" response = requests.get(url, params=params) time.sleep(delay) # Wait 0.5 seconds between requests - Network accessline 429
response = requests.get(url, params=params) - Network accessline 435
for gene in genes: response = rate_limited_request( "https://api.clinpgx.org/v1/gene/" + gene - Network accessline 436
response = rate_limited_request( "https://api.clinpgx.org/v1/gene/" + gene ) - Network accessline 437
"https://api.clinpgx.org/v1/gene/" + gene - Network accessline 447
for attempt in range(max_retries): try: response = requests.get(url, params=params, timeout=10) - Network accessline 449
response = requests.get(url, params=params, timeout=10) - Network accessline 491
'cyp2d6_cache.json', rate_limited_request, "https://api.clinpgx.org/v1/gene/CYP2D6" - Network accessline 492
rate_limited_request, "https://api.clinpgx.org/v1/gene/CYP2D6" ) - Network accessline 493
"https://api.clinpgx.org/v1/gene/CYP2D6" - Network accessline 508
**Access**: Available at https://www.clinpgx.org/pharmacogenomic-decision-support - Network accessline 573
- ClinPGx continuously updates with new evidence and guidelines - Check publication dates for clinical annotations - Monitor ClinPGx Blog (https://blog.clinpgx.org/) for announcements - Network accessline 574
- Check publication dates for clinical annotations - Monitor ClinPGx Blog (https://blog.clinpgx.org/) for announcements - CPIC guidelines updated as new evidence emerges - Network accessline 575
- Monitor ClinPGx Blog (https://blog.clinpgx.org/) for announcements - Network accessline 594
# Get all CPIC guideline pairs response = requests.get("https://api.clinpgx.org/v1/geneDrugPair", - Network accessline 595
response = requests.get("https://api.clinpgx.org/v1/geneDrugPair", - Network accessline 608
for med in medications: for gene in patient_genes: response = requests.get("https://api.clinpgx.org/v1/geneDrugPair", - Network accessline 609
for gene in patient_genes: response = requests.get("https://api.clinpgx.org/v1/geneDrugPair", params={"gene": gene, "drug": med}) - Network accessline 610
response = requests.get("https://api.clinpgx.org/v1/geneDrugPair", - Network accessline 620
# Check for HLA-B*57:01 before abacavir trial response = requests.get("https://api.clinpgx.org/v1/geneDrugPair", - Network accessline 621
response = requests.get("https://api.clinpgx.org/v1/geneDrugPair", - Network accessline 629
- **ClinPGx website**: https://www.clinpgx.org/ - Network accessline 630
- **ClinPGx Blog**: https://blog.clinpgx.org/ - Network accessline 631
- **API documentation**: https://api.clinpgx.org/ - Network accessline 632
- **CPIC website**: https://cpicpgx.org/ - Network accessline 633
- **PharmCAT**: https://pharmcat.clinpgx.org/ - Network accessline 634
- **ClinGen**: https://clinicalgenome.org/
skills/07-临床医学与精准医疗/clinpgx-database/references/api_reference.md
- Network accessline 8
https://api.clinpgx.org/v1/ - Network accessline 53
curl "https://api.clinpgx.org/v1/gene/CYP2D6" - Network accessline 82
curl "https://api.clinpgx.org/v1/gene?q=CYP" - Network accessline 100
curl "https://api.clinpgx.org/v1/chemical/PA448515" - Network accessline 114
curl "https://api.clinpgx.org/v1/chemical?name=warfarin" - Network accessline 149
# Get all pairs for a gene curl "https://api.clinpgx.org/v1/geneDrugPair?gene=CYP2D6" - Network accessline 150
curl "https://api.clinpgx.org/v1/geneDrugPair?gene=CYP2D6" - Network accessline 152
# Get specific gene-drug pair curl "https://api.clinpgx.org/v1/geneDrugPair?gene=CYP2D6&drug=codeine" - Network accessline 153
curl "https://api.clinpgx.org/v1/geneDrugPair?gene=CYP2D6&drug=codeine" - Network accessline 155
# Get all CPIC Level A pairs curl "https://api.clinpgx.org/v1/geneDrugPair?cpicLevel=A" - Network accessline 156
curl "https://api.clinpgx.org/v1/geneDrugPair?cpicLevel=A" - Network accessline 168
"clinicalAnnotationCount": 45, "hasGuideline": true, "guidelineUrl": "https://www.clinpgx.org/guideline/..." - Network accessline 169
"hasGuideline": true, "guidelineUrl": "https://www.clinpgx.org/guideline/..." } - Network accessline 170
"guidelineUrl": "https://www.clinpgx.org/guideline/..." - Network accessline 192
# Get all CPIC guidelines curl "https://api.clinpgx.org/v1/guideline?source=CPIC" - Network accessline 193
curl "https://api.clinpgx.org/v1/guideline?source=CPIC" - Network accessline 195
# Get guideline for specific gene-drug curl "https://api.clinpgx.org/v1/guideline?gene=CYP2C19&drug=clopidogrel" - Network accessline 196
curl "https://api.clinpgx.org/v1/guideline?gene=CYP2C19&drug=clopidogrel" - Network accessline 207
curl "https://api.clinpgx.org/v1/guideline/PA166104939" - Network accessline 220
"summary": "Alternative antiplatelet therapy recommended for...", "recommendations": [...], "pdfUrl": "https://www.clinpgx.org/...", - Network accessline 221
"recommendations": [...], "pdfUrl": "https://www.clinpgx.org/...", "pmid": "23400754" - Network accessline 222
"pdfUrl": "https://www.clinpgx.org/...", - Network accessline 242
curl "https://api.clinpgx.org/v1/allele?gene=CYP2D6" - Network accessline 290
curl "https://api.clinpgx.org/v1/allele/CYP2D6*4" - Network accessline 308
curl "https://api.clinpgx.org/v1/variant/rs4244285" - Network accessline 343
curl "https://api.clinpgx.org/v1/variant?chromosome=10&position=94781859" - Network accessline 364
# Get all annotations for a gene curl "https://api.clinpgx.org/v1/clinicalAnnotation?gene=CYP2D6" - Network accessline 365
curl "https://api.clinpgx.org/v1/clinicalAnnotation?gene=CYP2D6" - Network accessline 367
# Get high-quality evidence only curl "https://api.clinpgx.org/v1/clinicalAnnotation?evidenceLevel=1A" - Network accessline 368
curl "https://api.clinpgx.org/v1/clinicalAnnotation?evidenceLevel=1A" - Network accessline 370
# Get annotations for specific gene-drug pair curl "https://api.clinpgx.org/v1/clinicalAnnotation?gene=TPMT&drug=azathioprine" - Network accessline 371
curl "https://api.clinpgx.org/v1/clinicalAnnotation?gene=TPMT&drug=azathioprine" - Network accessline 416
# Get all labels for warfarin curl "https://api.clinpgx.org/v1/drugLabel?drug=warfarin" - Network accessline 417
curl "https://api.clinpgx.org/v1/drugLabel?drug=warfarin" - Network accessline 419
# Get only FDA labels curl "https://api.clinpgx.org/v1/drugLabel?drug=warfarin&source=FDA" - Network accessline 420
curl "https://api.clinpgx.org/v1/drugLabel?drug=warfarin&source=FDA" - Network accessline 435
"biomarkers": ["CYP2C9", "VKORC1"], "testingRecommended": true, "labelUrl": "https://dailymed.nlm.nih.gov/...", - Network accessline 436
"testingRecommended": true, "labelUrl": "https://dailymed.nlm.nih.gov/...", "lastUpdated": "2024-01-15" - Network accessline 437
"labelUrl": "https://dailymed.nlm.nih.gov/...", - Network accessline 458
curl "https://api.clinpgx.org/v1/pathway/PA146123006" - Network accessline 473
curl "https://api.clinpgx.org/v1/pathway?drug=warfarin" - Network accessline 482
"genes": ["CYP2C9", "VKORC1", "CYP4F2", "GGCX"], "description": "Warfarin is metabolized primarily by CYP2C9...", "diagramUrl": "https://www.clinpgx.org/pathway/...", - Network accessline 483
"description": "Warfarin is metabolized primarily by CYP2C9...", "diagramUrl": "https://www.clinpgx.org/pathway/...", "steps": [ - Network accessline 484
"diagramUrl": "https://www.clinpgx.org/pathway/...", - Network accessline 519
for med in patient_meds: for gene in patient_genes: response = requests.get( - Network accessline 520
for gene in patient_genes: response = requests.get( "https://api.clinpgx.org/v1/geneDrugPair", - Network accessline 521
response = requests.get( - Network accessline 522
"https://api.clinpgx.org/v1/geneDrugPair", - Network accessline 534
response = requests.get( - Network accessline 535
"https://api.clinpgx.org/v1/geneDrugPair", - Network accessline 549
alleles = requests.get( - Network accessline 550
"https://api.clinpgx.org/v1/allele", - Network accessline 567
# Screen for HLA-B*57:01 before abacavir response = requests.get( - Network accessline 568
response = requests.get( - Network accessline 569
"https://api.clinpgx.org/v1/geneDrugPair", - Network accessline 605
for attempt in range(max_retries): try: response = requests.get(url, params=params, timeout=10) - Network accessline 607
response = requests.get(url, params=params, timeout=10) - Network accessline 664
results = {} for gene in genes: response = requests.get(f"https://api.clinpgx.org/v1/gene/{gene}") - Network accessline 665
for gene in genes: response = requests.get(f"https://api.clinpgx.org/v1/gene/{gene}") if response.status_code == 200: - Network accessline 666
response = requests.get(f"https://api.clinpgx.org/v1/gene/{gene}") - Network accessline 729
- API version: v1 - Stability: Beta - endpoints stable, parameters may change - Monitor: https://blog.clinpgx.org/ for updates - Network accessline 731
- Monitor: https://blog.clinpgx.org/ for updates - Network accessline 733
### Migration from PharmGKB As of July 2025, PharmGKB URLs redirect to ClinPGx. Update references: - Old: `https://api.pharmgkb.org/` - Network accessline 734
As of July 2025, PharmGKB URLs redirect to ClinPGx. Update references: - Old: `https://api.pharmgkb.org/` - New: `https://api.clinpgx.org/` - Network accessline 735
- Old: `https://api.pharmgkb.org/` - Network accessline 736
- New: `https://api.clinpgx.org/` - Network accessline 745
- **API Issues**: api@clinpgx.org - **Documentation**: https://api.clinpgx.org/ - Network accessline 746
- **Documentation**: https://api.clinpgx.org/ - Network accessline 747
- **General Questions**: https://www.clinpgx.org/page/faqs - Network accessline 748
- **Blog**: https://blog.clinpgx.org/ - Network accessline 749
- **CPIC Guidelines**: https://cpicpgx.org/
skills/07-临床医学与精准医疗/clinpgx-database/scripts/query_clinpgx.py
- Executable scriptline 1
#!/usr/bin/env python3 - Network accessline 8
ClinPGx API: https://api.clinpgx.org/ - Network accessline 20
BASE_URL = "https://api.clinpgx.org/v1/" RATE_LIMIT_DELAY = 0.5 # 500ms delay = 2 requests/second - Network accessline 36
response = requests.get(url, params=params) - Network accessline 53
for attempt in range(max_retries): try: response = requests.get(url, params=params, timeout=10) - Network accessline 55
response = requests.get(url, params=params, timeout=10)
skills/07-临床医学与精准医疗/clinvar-database/SKILL.md
- Network accessline 33
Search ClinVar using the web interface at https://www.ncbi.nlm.nih.gov/clinvar/ - Network accessline 53
# Search for pathogenic BRCA1 variants curl "https://eutils.ncbi.nlm.nih.gov/entrez/eutils/esearch.fcgi?db=clinvar&term=BRCA1[gene]+AND+pathogenic[CLNSIG]&retmode=json" - Network accessline 54
curl "https://eutils.ncbi.nlm.nih.gov/entrez/eutils/esearch.fcgi?db=clinvar&term=BRCA1[gene]+AND+pathogenic[CLNSIG]&retmode=json" - Network accessline 120
# Download latest monthly XML release wget ftp://ftp.ncbi.nlm.nih.gov/pub/clinvar/xml/clinvar_variation/ClinVarVariationRelease_00-latest.xml.gz - Network accessline 121
wget ftp://ftp.ncbi.nlm.nih.gov/pub/clinvar/xml/clinvar_variation/ClinVarVariationRelease_00-latest.xml.gz - Network accessline 123
# Download VCF for GRCh38 wget ftp://ftp.ncbi.nlm.nih.gov/pub/clinvar/vcf_GRCh38/clinvar.vcf.gz - Network accessline 124
wget ftp://ftp.ncbi.nlm.nih.gov/pub/clinvar/vcf_GRCh38/clinvar.vcf.gz - Network accessline 240
**Submission methods:** - Web submission portal: https://submit.ncbi.nlm.nih.gov/subs/clinvar/ - Network accessline 241
- Web submission portal: https://submit.ncbi.nlm.nih.gov/subs/clinvar/ - Network accessline 274
wget ftp://ftp.ncbi.nlm.nih.gov/pub/clinvar/vcf_GRCh38/clinvar.vcf.gz - Network accessline 275
wget ftp://ftp.ncbi.nlm.nih.gov/pub/clinvar/vcf_GRCh38/clinvar.vcf.gz.tbi - Network accessline 310
wget ftp://ftp.ncbi.nlm.nih.gov/pub/clinvar/xml/clinvar_variation/ClinVarVariationRelease_YYYY-MM.xml.gz - Network accessline 351
- ClinVar home: https://www.ncbi.nlm.nih.gov/clinvar/ - Network accessline 352
- ClinVar documentation: https://www.ncbi.nlm.nih.gov/clinvar/docs/ - Network accessline 353
- E-utilities documentation: https://www.ncbi.nlm.nih.gov/books/NBK25501/ - Network accessline 355
- ClinGen expert panels: https://clinicalgenome.org/
skills/07-临床医学与精准医疗/clinvar-database/references/api_reference.md
- Network accessline 15
https://eutils.ncbi.nlm.nih.gov/entrez/eutils/ - Network accessline 25
https://eutils.ncbi.nlm.nih.gov/entrez/eutils/esearch.fcgi - Network accessline 37
# Search for BRCA1 pathogenic variants curl "https://eutils.ncbi.nlm.nih.gov/entrez/eutils/esearch.fcgi?db=clinvar&term=BRCA1[gene]+AND+pathogenic[CLNSIG]&retmode=json&retmax=100" - Network accessline 38
curl "https://eutils.ncbi.nlm.nih.gov/entrez/eutils/esearch.fcgi?db=clinvar&term=BRCA1[gene]+AND+pathogenic[CLNSIG]&retmode=json&retmax=100" - Network accessline 54
https://eutils.ncbi.nlm.nih.gov/entrez/eutils/esummary.fcgi - Network accessline 65
# Get summary for specific variant curl "https://eutils.ncbi.nlm.nih.gov/entrez/eutils/esummary.fcgi?db=clinvar&id=12345&retmode=json&version=2.0" - Network accessline 66
curl "https://eutils.ncbi.nlm.nih.gov/entrez/eutils/esummary.fcgi?db=clinvar&id=12345&retmode=json&version=2.0" - Network accessline 84
https://eutils.ncbi.nlm.nih.gov/entrez/eutils/efetch.fcgi - Network accessline 94
# Fetch full VCV record curl "https://eutils.ncbi.nlm.nih.gov/entrez/eutils/efetch.fcgi?db=clinvar&id=12345&rettype=vcv" - Network accessline 95
curl "https://eutils.ncbi.nlm.nih.gov/entrez/eutils/efetch.fcgi?db=clinvar&id=12345&rettype=vcv" - Network accessline 103
https://eutils.ncbi.nlm.nih.gov/entrez/eutils/elink.fcgi - Network accessline 114
# Find PubMed articles for a variant curl "https://eutils.ncbi.nlm.nih.gov/entrez/eutils/elink.fcgi?dbfrom=clinvar&db=pubmed&id=12345" - Network accessline 115
curl "https://eutils.ncbi.nlm.nih.gov/entrez/eutils/elink.fcgi?dbfrom=clinvar&db=pubmed&id=12345" - Network accessline 121
# Step 1: Search for variants SEARCH_URL="https://eutils.ncbi.nlm.nih.gov/entrez/eutils/esearch.fcgi?db=clinvar&term=CFTR[gene]+AND+pathogenic[CLNSIG]&retmode=json&retmax=10" - Network accessline 122
SEARCH_URL="https://eutils.ncbi.nlm.nih.gov/entrez/eutils/esearch.fcgi?db=clinvar&term=CFTR[gene]+AND+pathogenic[CLNSIG]&retmode=json&retmax=10" - Network accessline 127
# Step 3: Retrieve summaries SUMMARY_URL="https://eutils.ncbi.nlm.nih.gov/entrez/eutils/esummary.fcgi?db=clinvar&id=<ids>&retmode=json&version=2.0" - Network accessline 128
SUMMARY_URL="https://eutils.ncbi.nlm.nih.gov/entrez/eutils/esummary.fcgi?db=clinvar&id=<ids>&retmode=json&version=2.0" - Network accessline 130
# Step 4: Fetch full records if needed FETCH_URL="https://eutils.ncbi.nlm.nih.gov/entrez/eutils/efetch.fcgi?db=clinvar&id=<ids>&rettype=vcv" - Network accessline 131
FETCH_URL="https://eutils.ncbi.nlm.nih.gov/entrez/eutils/efetch.fcgi?db=clinvar&id=<ids>&rettype=vcv" - Dangerous shell operationline 138
sh -c "$(curl -fsSL ftp://ftp.ncbi.nlm.nih.gov/entrez/entrezdirect/install-edirect.sh)" - Network accessline 138
sh -c "$(curl -fsSL ftp://ftp.ncbi.nlm.nih.gov/entrez/entrezdirect/install-edirect.sh)" - Network accessline 169
### API Key Setup 1. Register for NCBI account at https://www.ncbi.nlm.nih.gov/account/ - Network accessline 170
[Credential-related source omitted] - Access to secretsline 172
[Credential-related source omitted] - Network accessline 225
- NCBI E-utilities documentation: https://www.ncbi.nlm.nih.gov/books/NBK25501/ - Network accessline 226
- ClinVar web services: https://www.ncbi.nlm.nih.gov/clinvar/docs/maintenance_use/ - Network accessline 227
- Entrez Direct cookbook: https://www.ncbi.nlm.nih.gov/books/NBK179288/
skills/07-临床医学与精准医疗/clinvar-database/references/clinical_significance.md
- Network accessline 215
- ACMG/AMP Variant Interpretation Guidelines: Richards et al., 2015 - ClinGen Sequence Variant Interpretation Working Group: https://clinicalgenome.org/ - Network accessline 216
- ClinGen Sequence Variant Interpretation Working Group: https://clinicalgenome.org/ - Network accessline 217
- ClinVar Clinical Significance Documentation: https://www.ncbi.nlm.nih.gov/clinvar/docs/clinsig/ - Network accessline 218
- Review Status Documentation: https://www.ncbi.nlm.nih.gov/clinvar/docs/review_status/
skills/07-临床医学与精准医疗/clinvar-database/references/data_formats.md
- Network accessline 324
# Get latest file LATEST=$(curl -s ${BASE_URL}/ | \ - Network accessline 325
LATEST=$(curl -s ${BASE_URL}/ | \ - Network accessline 329
# Download wget ${BASE_URL}/${LATEST} - Network accessline 330
wget ${BASE_URL}/${LATEST} - Network accessline 341
# XML wget ${FTP_BASE}/xml/clinvar_variation/ClinVarVariationRelease_00-latest.xml.gz - Network accessline 342
wget ${FTP_BASE}/xml/clinvar_variation/ClinVarVariationRelease_00-latest.xml.gz - Network accessline 344
# VCF (both assemblies) wget ${FTP_BASE}/vcf_GRCh37/clinvar.vcf.gz - Network accessline 345
wget ${FTP_BASE}/vcf_GRCh37/clinvar.vcf.gz - Network accessline 346
wget ${FTP_BASE}/vcf_GRCh38/clinvar.vcf.gz - Network accessline 348
# Tab-delimited wget ${FTP_BASE}/tab_delimited/variant_summary.txt.gz - Network accessline 349
wget ${FTP_BASE}/tab_delimited/variant_summary.txt.gz - Network accessline 350
wget ${FTP_BASE}/tab_delimited/var_citations.txt.gz - Network accessline 355
- ClinVar FTP Primer: https://www.ncbi.nlm.nih.gov/clinvar/docs/ftp_primer/ - Network accessline 356
- XML Schema Documentation: https://www.ncbi.nlm.nih.gov/clinvar/docs/xml_schemas/ - Network accessline 357
- VCF Specification: https://samtools.github.io/hts-specs/VCFv4.3.pdf - Network accessline 358
- Release Notes: https://ftp.ncbi.nlm.nih.gov/pub/clinvar/xml/README.txt
skills/07-临床医学与精准医疗/cosmic-database/SKILL.md
- Network accessline 29
### Account Registration COSMIC requires authentication for data downloads: - **Academic users**: Free access with registration at https://cancer.sanger.ac.uk/cosmic/register - Network accessline 30
COSMIC requires authentication for data downloads: - **Academic users**: Free access with registration at https://cancer.sanger.ac.uk/cosmic/register - **Commercial users**: License required (contact QIAGEN) - Network accessline 31
- **Academic users**: Free access with registration at https://cancer.sanger.ac.uk/cosmic/register - Network accessline 239
vcf = pysam.VariantFile('CosmicCodingMuts.vcf.gz') for record in vcf.fetch('17', 7577000, 7579000): # TP53 region - Network accessline 240
for record in vcf.fetch('17', 7577000, 7579000): # TP53 region - Network accessline 325
- **COSMIC Website**: https://cancer.sanger.ac.uk/cosmic - Network accessline 326
- **Documentation**: https://cancer.sanger.ac.uk/cosmic/help - Network accessline 327
- **Release Notes**: https://cancer.sanger.ac.uk/cosmic/release_notes
skills/07-临床医学与精准医疗/cosmic-database/references/cosmic_data_reference.md
- Network accessline 7
**Website**: https://cancer.sanger.ac.uk/cosmic - Network accessline 14
- **Academic users**: Free access (registration required) - **Commercial users**: License required (contact QIAGEN) - **Registration**: https://cancer.sanger.ac.uk/cosmic/register - Network accessline 16
- **Registration**: https://cancer.sanger.ac.uk/cosmic/register - Network accessline 18
### Download Methods 1. **Web Browser**: Interactive search at https://cancer.sanger.ac.uk/cosmic - Network accessline 19
1. **Web Browser**: Interactive search at https://cancer.sanger.ac.uk/cosmic 2. **File Downloads**: Programmatic access via download API 3. **Data Files**: TSV, CSV, and VCF formats - Network accessline 217
- **Documentation**: https://cancer.sanger.ac.uk/cosmic/help - Network accessline 218
- **Release Notes**: https://cancer.sanger.ac.uk/cosmic/release_notes
skills/07-临床医学与精准医疗/cosmic-database/scripts/download_cosmic.py
- Executable scriptline 1
#!/usr/bin/env python3 - Network accessline 57
base_url = "https://cancer.sanger.ac.uk/cosmic/file_download/" - Network accessline 65
print(f"Requesting download URL for: {filepath}") r = requests.get( - Network accessline 66
r = requests.get( - Network accessline 92
print(f"Downloading file from: {download_url}") file_response = requests.get(download_url, stream=True, timeout=300) - Network accessline 93
file_response = requests.get(download_url, stream=True, timeout=300)
skills/07-临床医学与精准医疗/depmap/SKILL.md
- Network accessline 19
**Key resources:** - DepMap Portal: https://depmap.org/portal/ - DepMap data downloads: https://depmap.org/portal/download/all/ - Network accessline 20
- DepMap Portal: https://depmap.org/portal/ - DepMap data downloads: https://depmap.org/portal/download/all/ - Python package: `depmap` (or access via API/downloads) - Network accessline 21
- DepMap data downloads: https://depmap.org/portal/download/all/ - Network accessline 23
- API: https://depmap.org/portal/api/ - Network accessline 67
BASE_URL = "https://depmap.org/portal/api" - Network accessline 69
def depmap_get(endpoint, params=None): url = f"{BASE_URL}/{endpoint}" response = requests.get(url, params=params) - Network accessline 70
url = f"{BASE_URL}/{endpoint}" response = requests.get(url, params=params) response.raise_for_status() - Network accessline 71
response = requests.get(url, params=params) - Network accessline 84
"dataset": dataset } response = requests.get(url, params=params) - Network accessline 85
} response = requests.get(url, params=params) return response.json() - Network accessline 86
response = requests.get(url, params=params) - Network accessline 92
url = f"{BASE_URL}/data/gene_dependency" params = {"gene_name": gene_symbol, "dataset_name": dataset_name} response = requests.get(url, params=params) - Network accessline 93
params = {"gene_name": gene_symbol, "dataset_name": dataset_name} response = requests.get(url, params=params) data = response.json() - Network accessline 94
response = requests.get(url, params=params) - Network accessline 107
def download_depmap_data(url, output_path): """Download a DepMap data file.""" response = requests.get(url, stream=True) - Network accessline 108
"""Download a DepMap data file.""" response = requests.get(url, stream=True) with open(output_path, 'wb') as f: - Network accessline 109
response = requests.get(url, stream=True) - Network accessline 114
# DepMap 24Q4 data files (update version as needed) FILES = { "crispr_gene_effect": "https://figshare.com/ndownloader/files/...", - Network accessline 115
FILES = { "crispr_gene_effect": "https://figshare.com/ndownloader/files/...", # OR download from: https://depmap.org/portal/download/all/ - Network accessline 116
"crispr_gene_effect": "https://figshare.com/ndownloader/files/...", # OR download from: https://depmap.org/portal/download/all/ # Files available: - Network accessline 117
# OR download from: https://depmap.org/portal/download/all/ - Network accessline 282
Download all files from: https://depmap.org/portal/download/all/ - Network accessline 295
- **DepMap Portal**: https://depmap.org/portal/ - **Data downloads**: https://depmap.org/portal/download/all/ - Network accessline 296
- **Data downloads**: https://depmap.org/portal/download/all/ - Network accessline 297
- **DepMap paper**: Behan FM et al. (2019) Nature. PMID: 30971826 - **Chronos paper**: Dempster JM et al. (2021) Nature Methods. PMID: 34349281 - **GitHub**: https://github.com/broadinstitute/depmap-portal - Network accessline 298
- **Chronos paper**: Dempster JM et al. (2021) Nature Methods. PMID: 34349281 - **GitHub**: https://github.com/broadinstitute/depmap-portal - **Figshare**: https://figshare.com/articles/dataset/DepMap_24Q4_Public/27993966 - Network accessline 299
- **GitHub**: https://github.com/broadinstitute/depmap-portal - Network accessline 300
- **Figshare**: https://figshare.com/articles/dataset/DepMap_24Q4_Public/27993966
skills/07-临床医学与精准医疗/fda-database/SKILL.md
- Access to secretsline 44
[Credential-related source omitted] - Network accessline 64
Register at: https://open.fda.gov/apis/authentication/ - Access to secretsline 68
[Credential-related source omitted] - Access to secretsline 426
[Credential-related source omitted] - Network accessline 490
- **openFDA Homepage**: https://open.fda.gov/ - Network accessline 491
- **API Documentation**: https://open.fda.gov/apis/ - Network accessline 492
- **Interactive API Explorer**: https://open.fda.gov/apis/try-the-api/ - Network accessline 493
- **GitHub Repository**: https://github.com/FDA/openfda - Network accessline 494
- **Terms of Service**: https://open.fda.gov/terms/ - Network accessline 514
- **GitHub Issues**: https://github.com/FDA/openfda/issues
skills/07-临床医学与精准医疗/fda-database/references/animal_veterinary.md
- Network accessline 13
**Endpoint**: `https://api.fda.gov/animalandveterinary/event.json` - Network accessline 93
[Credential-related source omitted] - Access to secretsline 93
[Credential-related source omitted] - Network accessline 94
url = "https://api.fda.gov/animalandveterinary/event.json" - Access to secretsline 98
[Credential-related source omitted] - Network accessline 103
response = requests.get(url, params=params) - Access to secretsline 110
[Credential-related source omitted] - Access to secretsline 119
[Credential-related source omitted] - Access to secretsline 128
[Credential-related source omitted] - Access to secretsline 138
[Credential-related source omitted] - Access to secretsline 147
[Credential-related source omitted] - Access to secretsline 156
[Credential-related source omitted] - Access to secretsline 182
[Credential-related source omitted] - Access to secretsline 189
[Credential-related source omitted] - Network accessline 197
url = "https://api.fda.gov/animalandveterinary/event.json" - Access to secretsline 199
[Credential-related source omitted] - Network accessline 204
response = requests.get(url, params=params) - Access to secretsline 246
[Credential-related source omitted] - Access to secretsline 252
[Credential-related source omitted] - Network accessline 261
url = "https://api.fda.gov/animalandveterinary/event.json" - Access to secretsline 263
[Credential-related source omitted] - Network accessline 268
response = requests.get(url, params=params) - Access to secretsline 299
[Credential-related source omitted] - Access to secretsline 306
[Credential-related source omitted] - Network accessline 313
url = "https://api.fda.gov/animalandveterinary/event.json" - Access to secretsline 318
[Credential-related source omitted] - Network accessline 323
response = requests.get(url, params=params) - Network accessline 373
- OpenFDA Animal & Veterinary API: https://open.fda.gov/apis/animalandveterinary/ - Network accessline 374
- FDA Center for Veterinary Medicine: https://www.fda.gov/animal-veterinary - Network accessline 375
- VeDDRA: https://www.veddra.org/
skills/07-临床医学与精准医疗/fda-database/references/api_basics.md
- Network accessline 11
https://api.fda.gov/{category}/{endpoint}.json - Network accessline 14
Examples: - `https://api.fda.gov/drug/event.json` - Network accessline 15
- `https://api.fda.gov/drug/event.json` - Network accessline 16
- `https://api.fda.gov/device/510k.json` - Network accessline 17
- `https://api.fda.gov/food/enforcement.json` - Network accessline 29
**Registration**: Visit https://open.fda.gov/apis/authentication/ to sign up - Network accessline 44
[Credential-related source omitted] - Access to secretsline 44
[Credential-related source omitted] - Network accessline 45
url = "https://api.fda.gov/drug/event.json" - Access to secretsline 48
[Credential-related source omitted] - Network accessline 53
response = requests.get(url, params=params) - Network accessline 60
[Credential-related source omitted] - Access to secretsline 60
[Credential-related source omitted] - Network accessline 61
url = "https://api.fda.gov/drug/event.json" - Network accessline 68
[Credential-related source omitted] - Access to secretsline 68
[Credential-related source omitted] - Network accessline 84
response = requests.get(url, params=params) - Network accessline 104
for attempt in range(max_retries): try: response = requests.get(url, params=params) - Network accessline 105
try: response = requests.get(url, params=params) response.raise_for_status() - Network accessline 106
response = requests.get(url, params=params) - Access to secretsline 130
[Credential-related source omitted] - Access to secretsline 147
[Credential-related source omitted] - Access to secretsline 269
[Credential-related source omitted] - Access to secretsline 277
[Credential-related source omitted] - Network accessline 283
response = requests.get(url, params=params) - Network accessline 487
"""Safely call FDA API with comprehensive error handling.""" try: response = requests.get(url, params=params, timeout=30) - Network accessline 488
try: response = requests.get(url, params=params, timeout=30) response.raise_for_status() - Network accessline 489
response = requests.get(url, params=params, timeout=30) - Network accessline 582
# Make request response = requests.get(url, params=params) - Network accessline 583
response = requests.get(url, params=params) - Network accessline 610
# Remove requests older than 1 minute while self.requests and now - self.requests[0] > 60: self.requests.popleft() - Network accessline 612
self.requests.popleft() - Network accessline 617
if sleep_time > 0: time.sleep(sleep_time) self.requests.popleft() - Network accessline 619
self.requests.popleft() - Network accessline 621
self.requests.append(time.time()) - Network accessline 627
"""Make request with rate limiting.""" rate_limiter.wait_if_needed() return requests.get(url, params=params) - Network accessline 629
return requests.get(url, params=params) - Network accessline 668
"limit": 10 } results[drug] = requests.get(url, params=params).json() - Network accessline 670
results[drug] = requests.get(url, params=params).json() - Network accessline 675
- **openFDA Homepage**: https://open.fda.gov/ - Network accessline 676
- **API Documentation**: https://open.fda.gov/apis/ - Network accessline 677
- **Interactive API Explorer**: https://open.fda.gov/apis/try-the-api/ - Network accessline 678
- **Terms of Service**: https://open.fda.gov/terms/ - Network accessline 679
- **GitHub**: https://github.com/FDA/openfda - Network accessline 684
For questions or issues: - **GitHub Issues**: https://github.com/FDA/openfda/issues - Network accessline 685
- **GitHub Issues**: https://github.com/FDA/openfda/issues
skills/07-临床医学与精准医疗/fda-database/references/devices.md
- Network accessline 21
**Endpoint**: `https://api.fda.gov/device/event.json` - Network accessline 53
[Credential-related source omitted] - Access to secretsline 53
[Credential-related source omitted] - Network accessline 54
url = "https://api.fda.gov/device/event.json" - Access to secretsline 58
[Credential-related source omitted] - Network accessline 63
response = requests.get(url, params=params) - Access to secretsline 70
[Credential-related source omitted] - Access to secretsline 79
[Credential-related source omitted] - Network accessline 88
**Endpoint**: `https://api.fda.gov/device/510k.json` - Access to secretsline 122
[Credential-related source omitted] - Network accessline 128
response = requests.get("https://api.fda.gov/device/510k.json", params=params) - Access to secretsline 134
[Credential-related source omitted] - Access to secretsline 143
[Credential-related source omitted] - Network accessline 151
**Endpoint**: `https://api.fda.gov/device/classification.json` - Access to secretsline 184
[Credential-related source omitted] - Network accessline 189
response = requests.get("https://api.fda.gov/device/classification.json", params=params) - Access to secretsline 195
[Credential-related source omitted] - Access to secretsline 204
[Credential-related source omitted] - Network accessline 212
**Endpoint**: `https://api.fda.gov/device/enforcement.json` - Access to secretsline 243
[Credential-related source omitted] - Network accessline 249
response = requests.get("https://api.fda.gov/device/enforcement.json", params=params) - Access to secretsline 255
[Credential-related source omitted] - Network accessline 263
**Endpoint**: `https://api.fda.gov/device/recall.json` - Access to secretsline 290
[Credential-related source omitted] - Network accessline 295
response = requests.get("https://api.fda.gov/device/recall.json", params=params) - Network accessline 300
**Endpoint**: `https://api.fda.gov/device/pma.json` - Access to secretsline 332
[Credential-related source omitted] - Network accessline 337
response = requests.get("https://api.fda.gov/device/pma.json", params=params) - Access to secretsline 343
[Credential-related source omitted] - Network accessline 351
**Endpoint**: `https://api.fda.gov/device/registrationlisting.json` - Access to secretsline 385
[Credential-related source omitted] - Network accessline 390
response = requests.get("https://api.fda.gov/device/registrationlisting.json", params=params) - Access to secretsline 396
[Credential-related source omitted] - Network accessline 404
**Endpoint**: `https://api.fda.gov/device/udi.json` - Access to secretsline 446
[Credential-related source omitted] - Network accessline 451
response = requests.get("https://api.fda.gov/device/udi.json", params=params) - Access to secretsline 457
[Credential-related source omitted] - Access to secretsline 466
[Credential-related source omitted] - Network accessline 474
**Endpoint**: `https://api.fda.gov/device/covid19serology.json` - Access to secretsline 500
[Credential-related source omitted] - Network accessline 505
response = requests.get("https://api.fda.gov/device/covid19serology.json", params=params) - Access to secretsline 511
[Credential-related source omitted] - Access to secretsline 522
[Credential-related source omitted] - Access to secretsline 528
[Credential-related source omitted] - Network accessline 535
# Search adverse events events_url = "https://api.fda.gov/device/event.json" - Network accessline 536
events_url = "https://api.fda.gov/device/event.json" - Access to secretsline 538
[Credential-related source omitted] - Network accessline 540
"limit": 10 } results["adverse_events"] = requests.get(events_url, params=events_params).json() - Network accessline 542
results["adverse_events"] = requests.get(events_url, params=events_params).json() - Network accessline 544
# Search 510(k) clearances fiveten_url = "https://api.fda.gov/device/510k.json" - Network accessline 545
fiveten_url = "https://api.fda.gov/device/510k.json" - Access to secretsline 547
[Credential-related source omitted] - Network accessline 549
"limit": 10 } results["510k_clearances"] = requests.get(fiveten_url, params=fiveten_params).json() - Network accessline 551
results["510k_clearances"] = requests.get(fiveten_url, params=fiveten_params).json() - Network accessline 553
# Search recalls recall_url = "https://api.fda.gov/device/enforcement.json" - Network accessline 554
recall_url = "https://api.fda.gov/device/enforcement.json" - Access to secretsline 556
[Credential-related source omitted] - Network accessline 558
"limit": 10 } results["recalls"] = requests.get(recall_url, params=recall_params).json() - Network accessline 560
results["recalls"] = requests.get(recall_url, params=recall_params).json() - Network accessline 562
# Search UDI udi_url = "https://api.fda.gov/device/udi.json" - Network accessline 563
udi_url = "https://api.fda.gov/device/udi.json" - Access to secretsline 565
[Credential-related source omitted] - Network accessline 567
"limit": 10 } results["udi"] = requests.get(udi_url, params=udi_params).json() - Network accessline 569
results["udi"] = requests.get(udi_url, params=udi_params).json() - Access to secretsline 577
[Credential-related source omitted] - Access to secretsline 583
[Credential-related source omitted] - Network accessline 586
Classification details dictionary """ url = "https://api.fda.gov/device/classification.json" - Network accessline 587
""" url = "https://api.fda.gov/device/classification.json" params = { - Network accessline 588
url = "https://api.fda.gov/device/classification.json" - Access to secretsline 590
[Credential-related source omitted] - Network accessline 595
response = requests.get(url, params=params) - Network accessline 628
- OpenFDA Device API Documentation: https://open.fda.gov/apis/device/ - Network accessline 629
- Device Classification Database: https://www.accessdata.fda.gov/scripts/cdrh/cfdocs/cfpcd/classification.cfm - Network accessline 630
- GUDID: https://accessgudid.nlm.nih.gov/
skills/07-临床医学与精准医疗/fda-database/references/drugs.md
- Network accessline 13
**Endpoint**: `https://api.fda.gov/drug/event.json` - Network accessline 39
[Credential-related source omitted] - Access to secretsline 39
[Credential-related source omitted] - Network accessline 40
url = "https://api.fda.gov/drug/event.json" - Access to secretsline 44
[Credential-related source omitted] - Network accessline 49
response = requests.get(url, params=params) - Access to secretsline 56
[Credential-related source omitted] - Network accessline 64
**Endpoint**: `https://api.fda.gov/drug/label.json` - Access to secretsline 95
[Credential-related source omitted] - Network accessline 100
response = requests.get("https://api.fda.gov/drug/label.json", params=params) - Access to secretsline 113
[Credential-related source omitted] - Network accessline 121
**Endpoint**: `https://api.fda.gov/drug/ndc.json` - Access to secretsline 152
[Credential-related source omitted] - Network accessline 157
response = requests.get("https://api.fda.gov/drug/ndc.json", params=params) - Access to secretsline 163
[Credential-related source omitted] - Access to secretsline 172
[Credential-related source omitted] - Network accessline 180
**Endpoint**: `https://api.fda.gov/drug/enforcement.json` - Access to secretsline 215
[Credential-related source omitted] - Network accessline 221
response = requests.get("https://api.fda.gov/drug/enforcement.json", params=params) - Access to secretsline 227
[Credential-related source omitted] - Access to secretsline 236
[Credential-related source omitted] - Network accessline 244
**Endpoint**: `https://api.fda.gov/drug/drugsfda.json` - Access to secretsline 277
[Credential-related source omitted] - Network accessline 282
response = requests.get("https://api.fda.gov/drug/drugsfda.json", params=params) - Access to secretsline 288
[Credential-related source omitted] - Access to secretsline 297
[Credential-related source omitted] - Network accessline 305
**Endpoint**: `https://api.fda.gov/drug/drugshortages.json` - Access to secretsline 333
[Credential-related source omitted] - Network accessline 338
response = requests.get("https://api.fda.gov/drug/drugshortages.json", params=params) - Access to secretsline 344
[Credential-related source omitted] - Access to secretsline 353
[Credential-related source omitted] - Network accessline 371
Args: endpoint: Full URL endpoint (e.g., "https://api.fda.gov/drug/event.json") - Network accessline 372
endpoint: Full URL endpoint (e.g., "https://api.fda.gov/drug/event.json") - Network accessline 379
for attempt in range(max_retries): try: response = requests.get(endpoint, params=params, timeout=30) - Network accessline 380
try: response = requests.get(endpoint, params=params, timeout=30) response.raise_for_status() - Network accessline 381
response = requests.get(endpoint, params=params, timeout=30) - Access to secretsline 408
[Credential-related source omitted] - Access to secretsline 415
[Credential-related source omitted] - Access to secretsline 427
[Credential-related source omitted] - Network accessline 465
- OpenFDA Drug API Documentation: https://open.fda.gov/apis/drug/ - Network accessline 466
- API Basics: See `api_basics.md` in this references directory - Python examples: See `scripts/fda_drug_query.py` - Field reference guides: Available at https://open.fda.gov/apis/drug/[endpoint]/searchable-fields/ - Network accessline 468
- Field reference guides: Available at https://open.fda.gov/apis/drug/[endpoint]/searchable-fields/
skills/07-临床医学与精准医疗/fda-database/references/foods.md
- Network accessline 13
**Endpoint**: `https://api.fda.gov/food/event.json` - Network accessline 54
[Credential-related source omitted] - Access to secretsline 54
[Credential-related source omitted] - Network accessline 55
url = "https://api.fda.gov/food/event.json" - Access to secretsline 59
[Credential-related source omitted] - Network accessline 64
response = requests.get(url, params=params) - Access to secretsline 71
[Credential-related source omitted] - Access to secretsline 80
[Credential-related source omitted] - Access to secretsline 90
[Credential-related source omitted] - Network accessline 98
**Endpoint**: `https://api.fda.gov/food/enforcement.json` - Access to secretsline 146
[Credential-related source omitted] - Network accessline 152
response = requests.get("https://api.fda.gov/food/enforcement.json", params=params) - Access to secretsline 158
[Credential-related source omitted] - Access to secretsline 167
[Credential-related source omitted] - Access to secretsline 177
[Credential-related source omitted] - Access to secretsline 186
[Credential-related source omitted] - Access to secretsline 195
[Credential-related source omitted] - Access to secretsline 206
[Credential-related source omitted] - Access to secretsline 212
[Credential-related source omitted] - Network accessline 226
url = "https://api.fda.gov/food/enforcement.json" - Access to secretsline 231
[Credential-related source omitted] - Network accessline 236
response = requests.get(url, params=params) - Access to secretsline 250
[Credential-related source omitted] - Access to secretsline 256
[Credential-related source omitted] - Network accessline 264
url = "https://api.fda.gov/food/event.json" - Access to secretsline 266
[Credential-related source omitted] - Network accessline 271
response = requests.get(url, params=params) - Access to secretsline 304
[Credential-related source omitted] - Access to secretsline 310
[Credential-related source omitted] - Network accessline 319
url = "https://api.fda.gov/food/enforcement.json" - Access to secretsline 327
[Credential-related source omitted] - Network accessline 333
response = requests.get(url, params=params) - Network accessline 370
- OpenFDA Food API Documentation: https://open.fda.gov/apis/food/ - Network accessline 371
- CFSAN Adverse Event Reporting: https://www.fda.gov/food/compliance-enforcement-food/cfsan-adverse-event-reporting-system-caers - Network accessline 372
- Food Recalls: https://www.fda.gov/safety/recalls-market-withdrawals-safety-alerts
skills/07-临床医学与精准医疗/fda-database/references/other.md
- Network accessline 13
**Endpoint**: `https://api.fda.gov/other/substance.json` - Network accessline 81
[Credential-related source omitted] - Access to secretsline 81
[Credential-related source omitted] - Network accessline 82
url = "https://api.fda.gov/other/substance.json" - Access to secretsline 86
[Credential-related source omitted] - Network accessline 91
response = requests.get(url, params=params) - Access to secretsline 98
[Credential-related source omitted] - Access to secretsline 107
[Credential-related source omitted] - Access to secretsline 116
[Credential-related source omitted] - Access to secretsline 125
[Credential-related source omitted] - Access to secretsline 134
[Credential-related source omitted] - Network accessline 142
**Endpoint**: `https://api.fda.gov/other/nsde.json` - Access to secretsline 170
[Credential-related source omitted] - Network accessline 175
response = requests.get("https://api.fda.gov/other/nsde.json", params=params) - Access to secretsline 181
[Credential-related source omitted] - Access to secretsline 192
[Credential-related source omitted] - Access to secretsline 198
[Credential-related source omitted] - Network accessline 205
url = "https://api.fda.gov/other/substance.json" - Access to secretsline 207
[Credential-related source omitted] - Network accessline 212
response = requests.get(url, params=params) - Access to secretsline 256
[Credential-related source omitted] - Access to secretsline 262
[Credential-related source omitted] - Network accessline 269
url = "https://api.fda.gov/other/substance.json" - Access to secretsline 271
[Credential-related source omitted] - Network accessline 276
response = requests.get(url, params=params) - Access to secretsline 302
[Credential-related source omitted] - Access to secretsline 308
[Credential-related source omitted] - Network accessline 315
url = "https://api.fda.gov/other/substance.json" - Access to secretsline 317
[Credential-related source omitted] - Network accessline 322
response = requests.get(url, params=params) - Access to secretsline 351
[Credential-related source omitted] - Access to secretsline 357
[Credential-related source omitted] - Network accessline 364
# First search drug label database label_url = "https://api.fda.gov/drug/label.json" - Network accessline 365
label_url = "https://api.fda.gov/drug/label.json" - Access to secretsline 367
[Credential-related source omitted] - Network accessline 372
response = requests.get(label_url, params=label_params) - Access to secretsline 396
[Credential-related source omitted] - Network accessline 468
- FDA Substance Registration System: https://fdasis.nlm.nih.gov/srs/ - Network accessline 469
- UNII Search: https://precision.fda.gov/uniisearch - Network accessline 470
- OpenFDA Other APIs: https://open.fda.gov/apis/other/
skills/07-临床医学与精准医疗/fda-database/scripts/fda_examples.py
- Executable scriptline 1
#!/usr/bin/env python3 - Access to secretsline 299
[Credential-related source omitted] - Access to secretsline 301
[Credential-related source omitted] - Access to secretsline 302
[Credential-related source omitted] - Access to secretsline 304
[Credential-related source omitted] - Access to secretsline 307
[Credential-related source omitted]
skills/07-临床医学与精准医疗/fda-database/scripts/fda_query.py
- Executable scriptline 1
#!/usr/bin/env python3 - Access to secretsline 11
[Credential-related source omitted] - Network accessline 37
while self.requests and now - self.requests[0] > 60: self.requests.popleft() - Network accessline 38
self.requests.popleft() - Network accessline 44
print(f"Rate limit approaching. Waiting {sleep_time:.1f} seconds...") time.sleep(sleep_time) self.requests.popleft() - Network accessline 46
self.requests.popleft() - Network accessline 48
self.requests.append(time.time()) - Network accessline 87
BASE_URL = "https://api.fda.gov" - Access to secretsline 89
[Credential-related source omitted] - Access to secretsline 95
[Credential-related source omitted] - Access to secretsline 100
[Credential-related source omitted] - Access to secretsline 121
[Credential-related source omitted] - Access to secretsline 122
[Credential-related source omitted] - Network accessline 134
try: response = requests.get(url, params=params, timeout=30) - Network accessline 135
response = requests.get(url, params=params, timeout=30) - Access to secretsline 408
[Credential-related source omitted] - Access to secretsline 411
[Credential-related source omitted]
skills/07-临床医学与精准医疗/imaging-data-commons/SKILL.md
- Network accessline 8
idc-index: "0.11.10" idc-data-version: "v23" repository: https://github.com/ImagingDataCommons/idc-claude-skill - Network accessline 9
idc-data-version: "v23" repository: https://github.com/ImagingDataCommons/idc-claude-skill --- - Network accessline 10
repository: https://github.com/ImagingDataCommons/idc-claude-skill - Network accessline 21
**Primary tool:** `idc-index` ([GitHub](https://github.com/imagingdatacommons/idc-index)) - Network accessline 122
**Complete index table documentation:** Use https://idc-index.readthedocs.io/en/latest/indices_reference.html for quick check of available tables and columns without executing any code. - Network accessline 543
**License types in IDC:** - **CC BY 4.0** / **CC BY 3.0** (~97% of data) - Allows commercial use with attribution - **CC BY-NC 4.0** / **CC BY-NC 3.0** (~3% of data) - Non-commercial use only - Network accessline 544
- **CC BY 4.0** / **CC BY 3.0** (~97% of data) - Allows commercial use with attribution - **CC BY-NC 4.0** / **CC BY-NC 3.0** (~3% of data) - Non-commercial use only - **Custom licenses** (rare) - Some collections have specific terms (e.g., - Network accessline 545
- **CC BY-NC 4.0** / **CC BY-NC 3.0** (~3% of data) - Non-commercial use only - Network accessline 645
**Before using BigQuery**, always check if a specialized index table already has the metadata you need: 1. Use `client.indices_overview` or the [idc-index indices reference](https://idc-index.readthedocs.io/en/latest/indices_reference.html) - Network accessline 646
1. Use `client.indices_overview` or the [idc-index indices reference](https://idc-index.readthedocs.io/en/latest/indices_reference.html) to discover all available tables and their columns 2. Fetch the relevant index: `client.fetch_index("ta - Network accessline 655
|------|------|-----------| | Programmatic queries & downloads | `idc-index` | This document | | Interactive exploration | IDC Portal | https://portal.imaging.datacommons.cancer.gov/ | - Network accessline 656
| Programmatic queries & downloads | `idc-index` | This document | | Interactive exploration | IDC Portal | https://portal.imaging.datacommons.cancer.gov/ | | Complex metadata queries | BigQuery | `references/bigquery_guide.md` | - Network accessline 657
| Interactive exploration | IDC Portal | https://portal.imaging.datacommons.cancer.gov/ | - Network accessline 659
| 3D visualization & analysis | SlicerIDCBrowser | https://github.com/ImagingDataCommons/SlicerIDCBrowser | - Network accessline 744
- **Estimate size first** - Check collection size before downloading - some collection sizes are in terabytes! - **Save manifests** - Always save query results with Series UIDs for reproducibility and data provenance - **Read documentation* - Network accessline 745
- **Save manifests** - Always save query results with Series UIDs for reproducibility and data provenance - **Read documentation** - IDC data structure and metadata fields are documented at https://learn.canceridc.dev/ - **Use IDC forum** - - Network accessline 746
- **Read documentation** - IDC data structure and metadata fields are documented at https://learn.canceridc.dev/ - Network accessline 747
- **Use IDC forum** - Search for questons/answers and ask your questions to the IDC maintainers and users at https://discourse.canceridc.dev/ - Network accessline 828
- **[indices_reference](https://idc-index.readthedocs.io/en/latest/indices_reference.html)** - External documentation for index tables (may be ahead of the installed version) - Network accessline 832
- **IDC Portal**: https://portal.imaging.datacommons.cancer.gov/explore/ - Network accessline 833
- **Documentation**: https://learn.canceridc.dev/ - Network accessline 834
- **Tutorials**: https://github.com/ImagingDataCommons/IDC-Tutorials - Network accessline 835
- **User Forum**: https://discourse.canceridc.dev/ - Network accessline 836
- **idc-index GitHub**: https://github.com/ImagingDataCommons/idc-index - Network accessline 837
- **Citation**: Fedorov, A., et al. "National Cancer Institute Imaging Data Commons: Toward Transparency, Reproducibility, and Scalability in Imaging Artificial Intelligence." RadioGraphics 43.12 (2023). https://doi.org/10.1148/rg.230180 - Network accessline 841
This skill version is available in skill metadata. To check for updates: - Visit the [releases page](https://github.com/ImagingDataCommons/idc-claude-skill/releases) - Network accessline 842
- Visit the [releases page](https://github.com/ImagingDataCommons/idc-claude-skill/releases)
skills/07-临床医学与精准医疗/imaging-data-commons/references/bigquery_guide.md
- Network accessline 48
### dicom_all Primary table joining complete DICOM metadata with IDC-specific columns (collection_id, gcs_url, license). Contains all DICOM tags from `dicom_metadata` plus collection and administrative metadata. See [dicom_all.sql](https:// - Network accessline 49
Primary table joining complete DICOM metadata with IDC-specific columns (collection_id, gcs_url, license). Contains all DICOM tags from `dicom_metadata` plus collection and administrative metadata. See [dicom_all.sql](https://github.com/Ima - Network accessline 333
- IDC data is de-identified; private tags containing PHI may have been removed or modified - The same tag may have different meanings across software versions - Always verify query results visually using the [IDC Viewer](https://viewer.imag - Network accessline 335
- Always verify query results visually using the [IDC Viewer](https://viewer.imaging.datacommons.cancer.gov/) before large-scale analysis - Network accessline 339
**Manufacturer DICOM Conformance Statements:** - [GE Healthcare MR](https://www.gehealthcare.com/products/interoperability/dicom/magnetic-resonance-imaging-dicom-conformance-statements) - Network accessline 340
- [GE Healthcare MR](https://www.gehealthcare.com/products/interoperability/dicom/magnetic-resonance-imaging-dicom-conformance-statements) - Network accessline 341
- [Siemens MR](https://www.siemens-healthineers.com/services/it-standards/dicom-conformance-statements-magnetic-resonance) - Network accessline 342
- [Siemens CT](https://www.siemens-healthineers.com/services/it-standards/dicom-conformance-statements-computed-tomography) - Network accessline 344
**DICOM Standard:** - [Part 5 Section 7.8 - Private Data Elements](https://dicom.nema.org/medical/dicom/current/output/chtml/part05/sect_7.8.html) - Network accessline 345
- [Part 5 Section 7.8 - Private Data Elements](https://dicom.nema.org/medical/dicom/current/output/chtml/part05/sect_7.8.html) - Network accessline 346
- [Part 15 Appendix E - De-identification Profiles](https://dicom.nema.org/medical/dicom/current/output/chtml/part15/chapter_e.html) - Network accessline 348
**Community Resources:** - [NAMIC Wiki: DWI/DTI DICOM](https://www.na-mic.org/wiki/NAMIC_Wiki:DTI:DICOM_for_DWI_and_DTI) - comprehensive vendor comparison for diffusion imaging - Network accessline 349
- [NAMIC Wiki: DWI/DTI DICOM](https://www.na-mic.org/wiki/NAMIC_Wiki:DTI:DICOM_for_DWI_and_DTI) - comprehensive vendor comparison for diffusion imaging - Network accessline 350
- [StandardizeBValue](https://github.com/nslay/StandardizeBValue) - tool to extract vendor b-values to standard tags - Network accessline 553
- [Understanding the BigQuery DICOM schema](https://docs.cloud.google.com/healthcare-api/docs/how-tos/dicom-bigquery-schema) - Network accessline 554
- [BigQuery Query Syntax](https://docs.cloud.google.com/bigquery/docs/reference/standard-sql/query-syntax) - Network accessline 555
- [Kaggle Intro to SQL](https://www.kaggle.com/learn/intro-to-sql) - Network accessline 556
- [Sample BigQuery queries of IDC data](https://github.com/ImagingDataCommons/idc-bigquery-cookbook)
skills/07-临床医学与精准医疗/imaging-data-commons/references/cli_guide.md
skills/07-临床医学与精准医疗/imaging-data-commons/references/clinical_data_guide.md
- Network accessline 312
**IDC Documentation:** - [Clinical data organization](https://learn.canceridc.dev/data/organization-of-data/clinical) - How clinical data is organized in IDC - Network accessline 313
- [Clinical data organization](https://learn.canceridc.dev/data/organization-of-data/clinical) - How clinical data is organized in IDC - Network accessline 314
- [Clinical data dashboard](https://datastudio.google.com/u/0/reporting/04cf5976-4ea0-4fee-a749-8bfd162f2e87/page/p_s7mk6eybqc) - Visual summary of available clinical data - Network accessline 315
- [idc-index clinical_index documentation](https://idc-index.readthedocs.io/en/latest/column_descriptions.html#clinical-index) - Network accessline 321
**IDC Tutorials:** - [clinical_data_intro.ipynb](https://github.com/ImagingDataCommons/IDC-Tutorials/blob/master/notebooks/advanced_topics/clinical_data_intro.ipynb) - Network accessline 322
- [clinical_data_intro.ipynb](https://github.com/ImagingDataCommons/IDC-Tutorials/blob/master/notebooks/advanced_topics/clinical_data_intro.ipynb) - Network accessline 323
- [exploring_clinical_data.ipynb](https://github.com/ImagingDataCommons/IDC-Tutorials/blob/master/notebooks/getting_started/exploring_clinical_data.ipynb) - Network accessline 324
- [nlst_clinical_data.ipynb](https://github.com/ImagingDataCommons/IDC-Tutorials/blob/master/notebooks/collections_demos/nlst_clinical_data.ipynb)
skills/07-临床医学与精准医疗/imaging-data-commons/references/cloud_storage_guide.md
- Network accessline 134
# Install s5cmd # macOS: brew install s5cmd # Linux: download from https://github.com/peak/s5cmd/releases - Network accessline 136
# Linux: download from https://github.com/peak/s5cmd/releases - Network accessline 289
- **Download defaults to AWS buckets**: request GCS if needed - **Save manifests**: Store the `series_aws_url` or `crdc_series_uuid` values for reproducibility - **Check licenses**: Query `license_short_name` before commercial use; CC-NC da - Network accessline 290
- **Save manifests**: Store the `series_aws_url` or `crdc_series_uuid` values for reproducibility - **Check licenses**: Query `license_short_name` before commercial use; CC-NC data requires non-commercial use - **Use current version unless - Network accessline 291
- **Check licenses**: Query `license_short_name` before commercial use; CC-NC data requires non-commercial use - Network accessline 314
**IDC Documentation:** - [Files and metadata](https://learn.canceridc.dev/data/organization-of-data/files-and-metadata) - Bucket organization details - Network accessline 315
- [Files and metadata](https://learn.canceridc.dev/data/organization-of-data/files-and-metadata) - Bucket organization details - Network accessline 316
- [Data versioning](https://learn.canceridc.dev/data/data-versioning) - Versioning scheme explanation - Network accessline 317
- [Resolving GUIDs and UUIDs](https://learn.canceridc.dev/data/organization-of-data/guids-and-uuids) - CRDC UUID documentation - Network accessline 318
- [Direct loading from cloud](https://learn.canceridc.dev/data/downloading-data/direct-loading) - Python examples for cloud access - Network accessline 320
**AWS Resources:** - [NCI IDC on AWS Open Data Registry](https://registry.opendata.aws/nci-imaging-data-commons/) - Bucket ARNs and access info - Network accessline 321
- [NCI IDC on AWS Open Data Registry](https://registry.opendata.aws/nci-imaging-data-commons/) - Bucket ARNs and access info - Network accessline 322
- [s5cmd](https://github.com/peak/s5cmd) - High-performance S3 client (used internally by idc-index) - Network accessline 323
- [AWS CLI S3 commands](https://docs.aws.amazon.com/cli/latest/reference/s3/) - Standard AWS command-line interface - Network accessline 324
- [Boto3 S3 documentation](https://boto3.amazonaws.com/v1/documentation/api/latest/reference/services/s3.html) - AWS SDK for Python - Network accessline 326
**Google Cloud Resources:** - [gsutil tool](https://cloud.google.com/storage/docs/gsutil) - Google Cloud Storage command-line tool - Network accessline 327
- [gsutil tool](https://cloud.google.com/storage/docs/gsutil) - Google Cloud Storage command-line tool - Network accessline 328
- [gcloud storage commands](https://cloud.google.com/sdk/gcloud/reference/storage) - Modern GCS CLI (recommended over gsutil) - Network accessline 329
- [Google Cloud Storage Python client](https://cloud.google.com/python/docs/reference/storage/latest) - GCS SDK for Python
skills/07-临床医学与精准医疗/imaging-data-commons/references/dicomweb_guide.md
- Network accessline 20
https://proxy.imaging.datacommons.cancer.gov/current/viewer-only-no-downloads-see-tinyurl-dot-com-slash-3j3d9jyp/dicomWeb - Network accessline 34
https://healthcare.googleapis.com/v1/projects/nci-idc-data/locations/us-central1/datasets/idc/dicomStores/idc-store-v{VERSION}/dicomWeb - Network accessline 123
IDC DICOMweb is provided through Google Cloud Healthcare API DICOM stores. The implementation follows DICOM PS3.18 Web Services with specific characteristics documented in the [Google Healthcare DICOM conformance statement](https://docs.clo - Network accessline 188
base_url = "https://proxy.imaging.datacommons.cancer.gov/current/viewer-only-no-downloads-see-tinyurl-dot-com-slash-3j3d9jyp/dicomWeb" - Network accessline 190
# Search for a specific study study_uid = "1.3.6.1.4.1.14519.5.2.1.6450.9002.307623500513044641407722230440" response = requests.get( - Network accessline 191
study_uid = "1.3.6.1.4.1.14519.5.2.1.6450.9002.307623500513044641407722230440" response = requests.get( f"{base_url}/studies", - Network accessline 192
response = requests.get( - Network accessline 208
base_url = "https://proxy.imaging.datacommons.cancer.gov/current/viewer-only-no-downloads-see-tinyurl-dot-com-slash-3j3d9jyp/dicomWeb" - Network accessline 211
response = requests.get( - Network accessline 231
base_url = "https://proxy.imaging.datacommons.cancer.gov/current/viewer-only-no-downloads-see-tinyurl-dot-com-slash-3j3d9jyp/dicomWeb" - Network accessline 235
response = requests.get( - Network accessline 254
base_url = "https://proxy.imaging.datacommons.cancer.gov/current/viewer-only-no-downloads-see-tinyurl-dot-com-slash-3j3d9jyp/dicomWeb" - Network accessline 258
response = requests.get( - Network accessline 294
# Use DICOMweb to stream metadata without downloading files base_url = "https://proxy.imaging.datacommons.cancer.gov/current/viewer-only-no-downloads-see-tinyurl-dot-com-slash-3j3d9jyp/dicomWeb" - Network accessline 295
base_url = "https://proxy.imaging.datacommons.cancer.gov/current/viewer-only-no-downloads-see-tinyurl-dot-com-slash-3j3d9jyp/dicomWeb" - Network accessline 297
response = requests.get( - Network accessline 336
# Build authenticated request base_url = "https://healthcare.googleapis.com/v1/projects/nci-idc-data/locations/us-central1/datasets/idc/dicomStores/idc-store-v23/dicomWeb" - Network accessline 337
base_url = "https://healthcare.googleapis.com/v1/projects/nci-idc-data/locations/us-central1/datasets/idc/dicomStores/idc-store-v23/dicomWeb" - Network accessline 339
response = requests.get( - Network accessline 386
**IDC Documentation:** - [IDC DICOM Stores](https://learn.canceridc.dev/data/organization-of-data/dicom-stores) - Data coverage and bucket details - Network accessline 387
- [IDC DICOM Stores](https://learn.canceridc.dev/data/organization-of-data/dicom-stores) - Data coverage and bucket details - Network accessline 388
- [IDC DICOMweb Access](https://learn.canceridc.dev/data/downloading-data/dicomweb-access) - Endpoint usage and differences - Network accessline 389
- [IDC Proxy Policy](https://learn.canceridc.dev/portal/proxy-policy) - Quota policies and usage restrictions - Network accessline 390
- [IDC User Guide](https://learn.canceridc.dev/) - Complete documentation - Network accessline 392
**DICOMweb Standards and Tools:** - [Google Healthcare DICOM Conformance Statement](https://docs.cloud.google.com/healthcare-api/docs/dicom) - Network accessline 393
- [Google Healthcare DICOM Conformance Statement](https://docs.cloud.google.com/healthcare-api/docs/dicom) - Network accessline 394
- [DICOMweb Standard](https://www.dicomstandard.org/using/dicomweb) - Network accessline 395
- [dicomweb-client Python library](https://dicomweb-client.readthedocs.io/)
skills/07-临床医学与精准医疗/imaging-data-commons/references/digital_pathology_guide.md
- Network accessline 100
|--------|----------| | `primaryAnatomicStructureModifier_CodeMeaning` | Structured tissue type from DICOM specimen metadata (e.g., `Neoplasm, Primary`, `Normal`, `Tumor`, `Neoplasm, Metastatic`). Works across all collections with SM data. - Network accessline 102
| `ContainerIdentifier` | Slide/container identifier. For TCGA collections, contains the [TCGA barcode](https://docs.gdc.cancer.gov/Encyclopedia/pages/TCGA_Barcode/) where the [sample type code](https://gdc.cancer.gov/resources-tcga-users/t - Network accessline 284
Annotation objects can also contain per-annotation **measurements** (e.g., nucleus area, eccentricity) stored within the DICOM file. These are not in the index tables — extract them after download using [highdicom](https://github.com/Imagin - Network accessline 392
**Python Libraries:** - [highdicom](https://github.com/ImagingDataCommons/highdicom) - High-level DICOM abstractions for Python. Create and read DICOM Segmentations (SEG), Structured Reports (SR), and parametric maps for pathology and radio - Network accessline 393
- [highdicom](https://github.com/ImagingDataCommons/highdicom) - High-level DICOM abstractions for Python. Create and read DICOM Segmentations (SEG), Structured Reports (SR), and parametric maps for pathology and radiology. Developed by IDC - Network accessline 394
- [wsidicom](https://github.com/imi-bigpicture/wsidicom) - Python package for reading DICOM WSI datasets. Parses metadata into easy-to-use dataclasses for whole slide image analysis. - Network accessline 395
- [TIA-Toolbox](https://github.com/TissueImageAnalytics/tiatoolbox) - End-to-end computational pathology library with DICOM support via `DICOMWSIReader`. Provides tile extraction, feature extraction, and pretrained deep learning models. - Network accessline 396
- [EZ-WSI-DICOMweb](https://github.com/GoogleCloudPlatform/EZ-WSI-DICOMweb) - Extract image patches from DICOM whole slide images via DICOMweb. Designed for AI/ML workflows with cloud DICOM stores. - Network accessline 398
**Viewers:** - [Slim](https://github.com/ImagingDataCommons/slim) - Web-based DICOM slide microscopy viewer and annotation tool. Supports brightfield and multiplexed immunofluorescence imaging via DICOMweb. Developed by IDC. - Network accessline 399
- [Slim](https://github.com/ImagingDataCommons/slim) - Web-based DICOM slide microscopy viewer and annotation tool. Supports brightfield and multiplexed immunofluorescence imaging via DICOMweb. Developed by IDC. - Network accessline 400
- [QuPath](https://qupath.github.io/) - Cross-platform open source software for whole slide image analysis. Supports DICOM WSI via Bio-Formats and OpenSlide (v0.4.0+). - Network accessline 402
**Conversion:** - [dicom_wsi](https://github.com/Steven-N-Hart/dicom_wsi) - Python implementation for converting proprietary WSI formats to DICOM-compliant files. - Network accessline 403
- [dicom_wsi](https://github.com/Steven-N-Hart/dicom_wsi) - Python implementation for converting proprietary WSI formats to DICOM-compliant files.
skills/07-临床医学与精准医疗/imaging-data-commons/references/index_tables_guide.md
- Network accessline 7
**Complete index table documentation:** https://idc-index.readthedocs.io/en/latest/indices_reference.html - Network accessline 107
| `PatientSex` | STRING | Yes | Patient sex | | `PatientAge` | STRING | Yes | Patient age at time of study | | `license_short_name` | STRING | No | License type (CC BY 4.0, CC BY-NC 4.0, etc.) | - Network accessline 108
| `PatientAge` | STRING | Yes | Patient age at time of study | | `license_short_name` | STRING | No | License type (CC BY 4.0, CC BY-NC 4.0, etc.) | | `series_size_MB` | FLOAT | No | Size of series in megabytes | - Network accessline 109
| `license_short_name` | STRING | No | License type (CC BY 4.0, CC BY-NC 4.0, etc.) | - Network accessline 113
**DICOM = Yes**: Column value extracted from the DICOM attribute with the same name. Refer to the [DICOM standard](https://dicom.nema.org/medical/dicom/current/output/chtml/part06/chapter_6.html) for numeric tag mappings. Use standard DICOM - Network accessline 143
- Complete index table documentation: https://idc-index.readthedocs.io/en/latest/indices_reference.html
skills/07-临床医学与精准医疗/imaging-data-commons/references/use_cases.md
- Network accessline 138
For additional visualization options, see the [IDC Portal getting started guide](https://learn.canceridc.dev/portal/getting-started) or [SlicerIDCBrowser](https://github.com/ImagingDataCommons/SlicerIDCBrowser) for 3D Slicer integration.
skills/07-临床医学与精准医疗/monarch-database/SKILL.md
- Network accessline 13
The Monarch Initiative (https://monarchinitiative.org/) is a multi-species integrated knowledgebase that links genes, diseases, and phenotypes across humans and model organisms. It integrates data from over 40 sources including OMIM, ORPHAN - Network accessline 21
**Key resources:** - Monarch portal: https://monarchinitiative.org/ - Network accessline 22
- Monarch portal: https://monarchinitiative.org/ - Network accessline 23
- Monarch API v3: https://api-v3.monarchinitiative.org/v3/ - Network accessline 24
- API docs: https://api-v3.monarchinitiative.org/v3/docs - Network accessline 25
- HPO browser: https://hpo.jax.org/ - Network accessline 43
Base URL: `https://api-v3.monarchinitiative.org/v3/` - Network accessline 48
BASE_URL = "https://api-v3.monarchinitiative.org/v3" - Network accessline 51
"""Make a GET request to the Monarch API.""" url = f"{BASE_URL}/{endpoint}" response = requests.get(url, params=params, headers={"Accept": "application/json"}) - Network accessline 52
url = f"{BASE_URL}/{endpoint}" response = requests.get(url, params=params, headers={"Accept": "application/json"}) response.raise_for_status() - Network accessline 53
response = requests.get(url, params=params, headers={"Accept": "application/json"}) - Network accessline 264
top_n: Number of top candidates to return """ BASE_URL = "https://api-v3.monarchinitiative.org/v3" - Network accessline 266
BASE_URL = "https://api-v3.monarchinitiative.org/v3" - Network accessline 271
for hpo_id in patient_hpo_terms: data = requests.get( - Network accessline 272
data = requests.get( - Network accessline 367
- **Monarch portal**: https://monarchinitiative.org/ - Network accessline 368
- **API v3 docs**: https://api-v3.monarchinitiative.org/v3/docs - Network accessline 369
- **HPO browser**: https://hpo.jax.org/ - Network accessline 370
- **MONDO ontology**: https://mondo.monarchinitiative.org/ - Network accessline 372
- **Phenomizer** (HPO-based diagnosis): https://compbio.charite.de/phenomizer/
skills/07-临床医学与精准医疗/monarch-database/references/phenotype_ontology.md
- Network accessline 110
"""Convert OMIM ID to MONDO ID via Monarch API.""" search_id = f"OMIM:{omim_id}" if not omim_id.startswith("OMIM:") else omim_id data = requests.get( - Network accessline 111
search_id = f"OMIM:{omim_id}" if not omim_id.startswith("OMIM:") else omim_id data = requests.get( f"https://api-v3.monarchinitiative.org/v3/entity/{search_id}" - Network accessline 112
data = requests.get( - Network accessline 113
f"https://api-v3.monarchinitiative.org/v3/entity/{search_id}" - Network accessline 150
"""Compute semantic similarity between two sets of disease phenotypes.""" # Get phenotype sets for each disease url = "https://api-v3.monarchinitiative.org/v3/semsim/compare" - Network accessline 151
# Get phenotype sets for each disease url = "https://api-v3.monarchinitiative.org/v3/semsim/compare" params = { - Network accessline 152
url = "https://api-v3.monarchinitiative.org/v3/semsim/compare" - Network accessline 156
"metric": metric } response = requests.get(url, params=params) - Network accessline 157
} response = requests.get(url, params=params) return response.json() - Network accessline 158
response = requests.get(url, params=params)
skills/07-临床医学与精准医疗/neurokit2/SKILL.md
- Network accessline 279
uv pip install https://github.com/neuropsychology/NeuroKit/zipball/dev - Network accessline 351
- Official Documentation: https://neuropsychology.github.io/NeuroKit/ - Network accessline 352
- GitHub Repository: https://github.com/neuropsychology/NeuroKit - Network accessline 353
- Publication: Makowski et al. (2021). NeuroKit2: A Python toolbox for neurophysiological signal processing. Behavior Research Methods. https://doi.org/10.3758/s13428-020-01516-y
skills/07-临床医学与精准医疗/neuropixels-analysis/SKILL.md
- Network accessline 341
- **SpikeInterface Docs**: https://spikeinterface.readthedocs.io/ - Network accessline 342
- **Neuropixels Tutorial**: https://spikeinterface.readthedocs.io/en/stable/how_to/analyze_neuropixels.html - Network accessline 343
- **Kilosort4 GitHub**: https://github.com/MouseLand/Kilosort - Network accessline 344
- **IBL Neuropixel Tools**: https://github.com/int-brain-lab/ibl-neuropixel - Network accessline 345
- **Allen Institute ecephys**: https://github.com/AllenInstitute/ecephys_spike_sorting - Network accessline 346
- **Bombcell (Automated QC)**: https://github.com/Julie-Fabre/bombcell - Network accessline 347
- **SpikeAgent (AI Curation)**: https://github.com/SpikeAgent/SpikeAgent
skills/07-临床医学与精准医疗/neuropixels-analysis/assets/analysis_template.py
- Executable scriptline 1
#!/usr/bin/env python
skills/07-临床医学与精准医疗/neuropixels-analysis/references/AI_CURATION.md
- Access to secretsline 299
[Credential-related source omitted] - Access to secretsline 308
[Credential-related source omitted] - Network accessline 343
- [SpikeAgent](https://github.com/SpikeAgent/SpikeAgent) - AI-powered spike sorting assistant - Network accessline 344
- [Anthropic Vision API](https://docs.anthropic.com/en/docs/vision) - Network accessline 345
- [GPT-4 Vision](https://platform.openai.com/docs/guides/vision)
skills/07-临床医学与精准医疗/neuropixels-analysis/references/AUTOMATED_CURATION.md
- Network accessline 39
# Or development version git clone https://github.com/Julie-Fabre/bombcell.git - Network accessline 40
git clone https://github.com/Julie-Fabre/bombcell.git - Network accessline 192
pip install unitmatch # Or from source git clone https://github.com/EnnyvanBeest/UnitMatch.git - Network accessline 194
git clone https://github.com/EnnyvanBeest/UnitMatch.git - Network accessline 354
- [Bombcell GitHub](https://github.com/Julie-Fabre/bombcell) - Network accessline 355
- [UnitMatch GitHub](https://github.com/EnnyvanBeest/UnitMatch) - Network accessline 356
- [SpikeInterface Curation](https://spikeinterface.readthedocs.io/en/stable/modules/curation.html)
skills/07-临床医学与精准医疗/neuropixels-analysis/references/MOTION_CORRECTION.md
- Network accessline 320
- [SpikeInterface Motion Correction Docs](https://spikeinterface.readthedocs.io/en/stable/modules/motion_correction.html) - Network accessline 321
- [Handle Drift Tutorial](https://spikeinterface.readthedocs.io/en/stable/how_to/handle_drift.html) - Network accessline 322
- [DREDge GitHub](https://github.com/evarol/DREDge)
skills/07-临床医学与精准医疗/neuropixels-analysis/references/QUALITY_METRICS.md
skills/07-临床医学与精准医疗/neuropixels-analysis/scripts/compute_metrics.py
- Executable scriptline 1
#!/usr/bin/env python
skills/07-临床医学与精准医疗/neuropixels-analysis/scripts/explore_recording.py
- Executable scriptline 1
#!/usr/bin/env python3
skills/07-临床医学与精准医疗/neuropixels-analysis/scripts/export_to_phy.py
- Executable scriptline 1
#!/usr/bin/env python
skills/07-临床医学与精准医疗/neuropixels-analysis/scripts/neuropixels_pipeline.py
- Executable scriptline 1
#!/usr/bin/env python3 - Network accessline 10
References: - https://spikeinterface.readthedocs.io/en/stable/how_to/analyze_neuropixels.html - Network accessline 11
- https://spikeinterface.readthedocs.io/en/stable/how_to/analyze_neuropixels.html - Network accessline 12
- https://github.com/AllenInstitute/ecephys_spike_sorting
skills/07-临床医学与精准医疗/neuropixels-analysis/scripts/preprocess_recording.py
- Executable scriptline 1
#!/usr/bin/env python
skills/07-临床医学与精准医疗/neuropixels-analysis/scripts/run_sorting.py
- Executable scriptline 1
#!/usr/bin/env python
skills/07-临床医学与精准医疗/pathml/references/data_management.md
- Network accessline 738
- **HDF5 Documentation:** https://www.hdfgroup.org/solutions/hdf5/ - Network accessline 739
- **h5py:** https://docs.h5py.org/ - Network accessline 740
- **DVC (Data Version Control):** https://dvc.org/ - Network accessline 741
- **Dask:** https://docs.dask.org/ - Network accessline 742
- **PathML Data Management API:** https://pathml.readthedocs.io/en/latest/api_data_reference.html
skills/07-临床医学与精准医疗/pathml/references/graphs.md
skills/07-临床医学与精准医疗/pathml/references/image_loading.md
- Network accessline 445
- **PathML Documentation:** https://pathml.readthedocs.io/ - Network accessline 446
- **OpenSlide:** https://openslide.org/ (underlying library for WSI formats) - Network accessline 447
- **Bio-Formats:** https://www.openmicroscopy.org/bio-formats/ (alternative backend) - Network accessline 448
- **DICOM Standard:** https://www.dicomstandard.org/
skills/07-临床医学与精准医疗/pathml/references/machine_learning.md
- Network accessline 721
- **PathML ML API:** https://pathml.readthedocs.io/en/latest/api_ml_reference.html - Network accessline 722
- **HoVer-Net Paper:** Graham et al., "HoVer-Net: Simultaneous Segmentation and Classification of Nuclei in Multi-Tissue Histology Images," Medical Image Analysis, 2019 - **PanNuke Dataset:** https://warwick.ac.uk/fac/cross_fac/tia/data/pan - Network accessline 723
- **PanNuke Dataset:** https://warwick.ac.uk/fac/cross_fac/tia/data/pannuke - Network accessline 724
- **PyTorch Lightning:** https://www.pytorchlightning.ai/ - Network accessline 725
- **ONNX Runtime:** https://onnxruntime.ai/
skills/07-临床医学与精准医疗/pathml/references/multiparametric.md
- Network accessline 173
cytoplasm_channel='CD45', model='mesmer', api_url='https://deepcell.org/api/predict', - Network accessline 174
model='mesmer', api_url='https://deepcell.org/api/predict', timeout=300 # Timeout in seconds - Network accessline 175
api_url='https://deepcell.org/api/predict', - Network accessline 681
- **PathML Multiparametric API:** https://pathml.readthedocs.io/en/latest/api_multiparametric_reference.html - Network accessline 682
- **CODEX:** https://www.akoyabio.com/codex/ - Network accessline 683
- **Vectra:** https://www.akoyabio.com/vectra/ - Network accessline 684
- **DeepCell Mesmer:** https://www.deepcell.org/ - Network accessline 685
- **Scanpy:** https://scanpy.readthedocs.io/ (single-cell analysis) - Network accessline 686
- **Squidpy:** https://squidpy.readthedocs.io/ (spatial omics analysis)
skills/07-临床医学与精准医疗/pathml/references/preprocessing.md
- Network accessline 353
cytoplasm_channel='CD45', model='mesmer', api_url='https://deepcell.org/api' - Network accessline 354
model='mesmer', api_url='https://deepcell.org/api' ) - Network accessline 355
api_url='https://deepcell.org/api' - Network accessline 718
- **PathML Preprocessing API:** https://pathml.readthedocs.io/en/latest/api_preprocessing_reference.html - Network accessline 720
- Macenko et al. 2009: "A method for normalizing histology slides for quantitative analysis" - Vahadane et al. 2016: "Structure-Preserving Color Normalization and Sparse Stain Separation" - **DeepCell Mesmer:** https://www.deepcell.org/ ( - Network accessline 722
- **DeepCell Mesmer:** https://www.deepcell.org/ (cell segmentation model)
skills/07-临床医学与精准医疗/pydicom/SKILL.md
- Network accessline 2
name: pydicom description: Python library for working with DICOM (Digital Imaging and Communications in Medicine) files. Use this skill when reading, writing, or modifying medical imaging data in DICOM format, extracting pixel data from med - Network accessline 3
description: Python library for working with DICOM (Digital Imaging and Communications in Medicine) files. Use this skill when reading, writing, or modifying medical imaging data in DICOM format, extracting pixel data from medical images (C - Network accessline 4
license: https://github.com/pydicom/pydicom/blob/main/LICENSE - Network accessline 427
Official pydicom documentation: https://pydicom.github.io/pydicom/dev/ - Network accessline 428
- User Guide: https://pydicom.github.io/pydicom/dev/guides/user/index.html - Network accessline 429
- Tutorials: https://pydicom.github.io/pydicom/dev/tutorials/index.html - Network accessline 430
- API Reference: https://pydicom.github.io/pydicom/dev/reference/index.html - Network accessline 431
- Examples: https://pydicom.github.io/pydicom/dev/auto_examples/index.html
skills/07-临床医学与精准医疗/pydicom/references/common_tags.md
skills/07-临床医学与精准医疗/pydicom/references/transfer_syntaxes.md
- Network accessline 350
- DICOM Standard Part 5 (Data Structures and Encoding): https://dicom.nema.org/medical/dicom/current/output/chtml/part05/PS3.5.html - Network accessline 351
- Pydicom Transfer Syntax Documentation: https://pydicom.github.io/pydicom/stable/guides/user/transfer_syntaxes.html - Network accessline 352
- Pydicom Compression Guide: https://pydicom.github.io/pydicom/stable/old/image_data_compression.html
skills/07-临床医学与精准医疗/pydicom/scripts/anonymize_dicom.py
- Executable scriptline 1
#!/usr/bin/env python3
skills/07-临床医学与精准医疗/pydicom/scripts/dicom_to_image.py
- Executable scriptline 1
#!/usr/bin/env python3
skills/07-临床医学与精准医疗/pydicom/scripts/extract_metadata.py
- Executable scriptline 1
#!/usr/bin/env python3
skills/07-临床医学与精准医疗/pyhealth/SKILL.md
skills/07-临床医学与精准医疗/treatment-plans/SKILL.md
- Broad permissionsline 1
Read Write Edit Bash - Dangerous shell operationline 1390
sudo cp assets/medical_treatment_plan.sty /usr/local/texlive/texmf-local/tex/latex/ - Dangerous shell operationline 1391
sudo texhash - Network accessline 1413
**Download from CTAN:** - Visit: https://ctan.org/ - Network accessline 1414
- Visit: https://ctan.org/ - Search for medical document classes - Download and install per package instructions - Dangerous shell operationline 1423
sudo tlmgr update --self - Dangerous shell operationline 1424
sudo tlmgr install tcolorbox tikz pgf - Dangerous shell operationline 1439
sudo tlmgr install tcolorbox tikz pgf
skills/07-临床医学与精准医疗/treatment-plans/assets/STYLING_QUICK_REFERENCE.md
- Dangerous shell operationline 171
sudo tlmgr install tcolorbox tikz pgf
skills/07-临床医学与精准医疗/treatment-plans/scripts/check_completeness.py
- Executable scriptline 1
#!/usr/bin/env python3
skills/07-临床医学与精准医疗/treatment-plans/scripts/generate_template.py
- Executable scriptline 1
#!/usr/bin/env python3
skills/07-临床医学与精准医疗/treatment-plans/scripts/timeline_generator.py
- Executable scriptline 1
#!/usr/bin/env python3
skills/07-临床医学与精准医疗/treatment-plans/scripts/validate_treatment_plan.py
- Executable scriptline 1
#!/usr/bin/env python3
skills/08-蛋白质工程与结构生物学/adaptyv/SKILL.md
- Access to secretsline 24
[Credential-related source omitted] - Access to secretsline 30
[Credential-related source omitted] - Network accessline 52
[Credential-related source omitted] - Access to secretsline 52
[Credential-related source omitted] - Network accessline 53
base_url = "https://kq5jp7qj7wdqklhsxmovkzn4l40obksv.lambda-url.eu-central-1.on.aws" - Access to secretsline 56
[Credential-related source omitted] - Network accessline 60
# Submit experiment response = requests.post( - Network accessline 61
response = requests.post( - Network accessline 65
"sequences": ">protein1\nMKVLWALLGLLGAA...", "experiment_type": "binding", "webhook_url": "https://your-webhook.com/callback" - Network accessline 66
"experiment_type": "binding", "webhook_url": "https://your-webhook.com/callback" } - Network accessline 67
"webhook_url": "https://your-webhook.com/callback"
skills/08-蛋白质工程与结构生物学/adaptyv/reference/api_reference.md
- Network accessline 6
https://kq5jp7qj7wdqklhsxmovkzn4l40obksv.lambda-url.eu-central-1.on.aws - Access to secretsline 14
[Credential-related source omitted] - Access to secretsline 23
[Credential-related source omitted] - Network accessline 41
"experiment_type": "binding|expression|thermostability|enzyme_activity", "target_id": "optional_target_identifier", "webhook_url": "https://your-webhook.com/callback", - Network accessline 42
"target_id": "optional_target_identifier", "webhook_url": "https://your-webhook.com/callback", "metadata": { - Network accessline 43
"webhook_url": "https://your-webhook.com/callback", - Network accessline 145
], "download_urls": { "raw_data": "https://...", - Network accessline 146
"download_urls": { "raw_data": "https://...", "analysis_package": "https://...", - Network accessline 147
"raw_data": "https://...", - Network accessline 148
"analysis_package": "https://...", - Network accessline 149
"report": "https://..." - Network accessline 305
For API issues or questions: - Email: support@adaptyvbio.com - Documentation updates: https://docs.adaptyvbio.com - Network accessline 306
- Email: support@adaptyvbio.com - Documentation updates: https://docs.adaptyvbio.com - Report bugs with experiment IDs and request details - Network accessline 307
- Documentation updates: https://docs.adaptyvbio.com
skills/08-蛋白质工程与结构生物学/adaptyv/reference/examples.md
- Network accessline 15
[Credential-related source omitted] - Access to secretsline 16
[Credential-related source omitted] - Network accessline 17
BASE_URL = "https://kq5jp7qj7wdqklhsxmovkzn4l40obksv.lambda-url.eu-central-1.on.aws" - Access to secretsline 21
[Credential-related source omitted] - Network accessline 26
"""Verify API connection and credentials""" try: response = requests.get(f"{BASE_URL}/organization/credits", headers=HEADERS) - Network accessline 27
try: response = requests.get(f"{BASE_URL}/organization/credits", headers=HEADERS) response.raise_for_status() - Network accessline 28
response = requests.get(f"{BASE_URL}/organization/credits", headers=HEADERS) - Access to secretsline 42
[Credential-related source omitted] - Network accessline 79
response = requests.post( - Network accessline 129
response = requests.post( - Network accessline 185
response = requests.post( - Network accessline 200
# Example webhook_url = "https://your-server.com/adaptyv-webhook" - Network accessline 201
webhook_url = "https://your-server.com/adaptyv-webhook" - Network accessline 221
response = requests.get( - Network accessline 263
response = requests.get( - Network accessline 337
# Get results response = requests.get( - Network accessline 338
response = requests.get( - Network accessline 479
response = requests.get( - Network accessline 528
response = requests.post( - Network accessline 775
for attempt in range(max_retries): try: response = requests.request(method, url, **kwargs) - Network accessline 776
try: response = requests.request(method, url, **kwargs) response.raise_for_status() - Network accessline 777
response = requests.request(method, url, **kwargs)
skills/08-蛋白质工程与结构生物学/adaptyv/reference/experiments.md
skills/08-蛋白质工程与结构生物学/adaptyv/reference/protein_optimization.md
- Network accessline 75
def predict_solubility_netsolp(sequence): """Predict protein solubility using NetSolP web service""" url = "https://services.healthtech.dtu.dk/services/NetSolP-1.0/api/predict" - Network accessline 77
url = "https://services.healthtech.dtu.dk/services/NetSolP-1.0/api/predict" - Network accessline 84
response = requests.post(url, data=data) - Network accessline 597
# Submit to Adaptyv import requests response = requests.post( - Network accessline 598
import requests response = requests.post( "https://kq5jp7qj7wdqklhsxmovkzn4l40obksv.lambda-url.eu-central-1.on.aws/experiments", - Network accessline 599
response = requests.post( - Network accessline 600
"https://kq5jp7qj7wdqklhsxmovkzn4l40obksv.lambda-url.eu-central-1.on.aws/experiments", - Access to secretsline 601
[Credential-related source omitted]
skills/08-蛋白质工程与结构生物学/alphafold-database/SKILL.md
- Network accessline 59
# Get prediction metadata for a UniProt accession uniprot_id = "P00520" api_url = f"https://alphafold.ebi.ac.uk/api/prediction/{uniprot_id}" - Network accessline 60
uniprot_id = "P00520" api_url = f"https://alphafold.ebi.ac.uk/api/prediction/{uniprot_id}" response = requests.get(api_url) - Network accessline 61
api_url = f"https://alphafold.ebi.ac.uk/api/prediction/{uniprot_id}" response = requests.get(api_url) prediction_data = response.json() - Network accessline 62
response = requests.get(api_url) - Network accessline 77
def get_uniprot_ids(query, query_type='PDB_ID'): """Query UniProt to get accession IDs""" url = 'https://www.uniprot.org/uploadlists/' - Network accessline 78
"""Query UniProt to get accession IDs""" url = 'https://www.uniprot.org/uploadlists/' params = { - Network accessline 79
url = 'https://www.uniprot.org/uploadlists/' - Network accessline 85
} data = urllib.parse.urlencode(params).encode('ascii') with urllib.request.urlopen(urllib.request.Request(url, data)) as response: - Network accessline 86
data = urllib.parse.urlencode(params).encode('ascii') with urllib.request.urlopen(urllib.request.Request(url, data)) as response: return response.read().decode('utf-8').splitlines() - Network accessline 87
with urllib.request.urlopen(urllib.request.Request(url, data)) as response: - Network accessline 112
# Model coordinates (mmCIF) model_url = f"https://alphafold.ebi.ac.uk/files/{alphafold_id}-model_{version}.cif" response = requests.get(model_url) - Network accessline 113
model_url = f"https://alphafold.ebi.ac.uk/files/{alphafold_id}-model_{version}.cif" response = requests.get(model_url) with open(f"{alphafold_id}.cif", "w") as f: - Network accessline 114
response = requests.get(model_url) - Network accessline 118
# Confidence scores (JSON) confidence_url = f"https://alphafold.ebi.ac.uk/files/{alphafold_id}-confidence_{version}.json" response = requests.get(confidence_url) - Network accessline 119
confidence_url = f"https://alphafold.ebi.ac.uk/files/{alphafold_id}-confidence_{version}.json" response = requests.get(confidence_url) confidence_data = response.json() - Network accessline 120
response = requests.get(confidence_url) - Network accessline 123
# Predicted Aligned Error (JSON) pae_url = f"https://alphafold.ebi.ac.uk/files/{alphafold_id}-predicted_aligned_error_{version}.json" response = requests.get(pae_url) - Network accessline 124
pae_url = f"https://alphafold.ebi.ac.uk/files/{alphafold_id}-predicted_aligned_error_{version}.json" response = requests.get(pae_url) pae_data = response.json() - Network accessline 125
response = requests.get(pae_url) - Network accessline 132
# Download as PDB format instead of mmCIF pdb_url = f"https://alphafold.ebi.ac.uk/files/{alphafold_id}-model_{version}.pdb" - Network accessline 133
pdb_url = f"https://alphafold.ebi.ac.uk/files/{alphafold_id}-model_{version}.pdb" response = requests.get(pdb_url) with open(f"{alphafold_id}.pdb", "wb") as f: - Network accessline 134
response = requests.get(pdb_url) - Network accessline 149
# Load confidence scores alphafold_id = "AF-P00520-F1" confidence_url = f"https://alphafold.ebi.ac.uk/files/{alphafold_id}-confidence_v4.json" - Network accessline 150
alphafold_id = "AF-P00520-F1" confidence_url = f"https://alphafold.ebi.ac.uk/files/{alphafold_id}-confidence_v4.json" confidence = requests.get(confidence_url).json() - Network accessline 151
confidence_url = f"https://alphafold.ebi.ac.uk/files/{alphafold_id}-confidence_v4.json" - Network accessline 152
confidence = requests.get(confidence_url).json() - Network accessline 175
# Load PAE matrix pae_url = f"https://alphafold.ebi.ac.uk/files/{alphafold_id}-predicted_aligned_error_v4.json" pae = requests.get(pae_url).json() - Network accessline 176
pae_url = f"https://alphafold.ebi.ac.uk/files/{alphafold_id}-predicted_aligned_error_v4.json" - Network accessline 177
pae = requests.get(pae_url).json() - Network accessline 241
> ⚠<U+FE0F> **Security Note**: The example below uses `shell=True` for simplicity. In production environments, prefer using `subprocess.run()` with a list of arguments to prevent command injection vulnerabilities. See [Python subprocess security]( - Network accessline 341
# Get confidence data alphafold_id = pred['entryId'] conf_url = f"https://alphafold.ebi.ac.uk/files/{alphafold_id}-confidence_v4.json" - Network accessline 342
alphafold_id = pred['entryId'] conf_url = f"https://alphafold.ebi.ac.uk/files/{alphafold_id}-confidence_v4.json" conf_data = requests.get(conf_url).json() - Network accessline 343
conf_url = f"https://alphafold.ebi.ac.uk/files/{alphafold_id}-confidence_v4.json" - Network accessline 344
conf_data = requests.get(conf_url).json() - Network accessline 391
# Query via 3D-Beacons uniprot_id = "P00520" url = f"https://www.ebi.ac.uk/pdbe/pdbe-kb/3dbeacons/api/uniprot/summary/{uniprot_id}.json" - Network accessline 392
uniprot_id = "P00520" url = f"https://www.ebi.ac.uk/pdbe/pdbe-kb/3dbeacons/api/uniprot/summary/{uniprot_id}.json" response = requests.get(url) - Network accessline 393
url = f"https://www.ebi.ac.uk/pdbe/pdbe-kb/3dbeacons/api/uniprot/summary/{uniprot_id}.json" response = requests.get(url) data = response.json() - Network accessline 394
response = requests.get(url) - Network accessline 502
- **AlphaFold DB Website:** https://alphafold.ebi.ac.uk/ - Network accessline 503
- **API Documentation:** https://alphafold.ebi.ac.uk/api-docs - Network accessline 504
- **Google Cloud Dataset:** https://cloud.google.com/blog/products/ai-machine-learning/alphafold-protein-structure-database - Network accessline 505
- **3D-Beacons API:** https://www.ebi.ac.uk/pdbe/pdbe-kb/3dbeacons/ - Network accessline 506
- **AlphaFold Papers:** - Nature (2021): https://doi.org/10.1038/s41586-021-03819-2 - Nucleic Acids Research (2024): https://doi.org/10.1093/nar/gkad1011 - Network accessline 507
- Nature (2021): https://doi.org/10.1038/s41586-021-03819-2 - Network accessline 508
- Nucleic Acids Research (2024): https://doi.org/10.1093/nar/gkad1011 - Network accessline 509
- **Biopython Documentation:** https://biopython.org/docs/dev/api/Bio.PDB.alphafold_db.html - Network accessline 510
- **GitHub Repository:** https://github.com/google-deepmind/alphafold
skills/08-蛋白质工程与结构生物学/alphafold-database/references/api_reference.md
- Network accessline 23
https://alphafold.ebi.ac.uk/api/ - Network accessline 39
curl https://alphafold.ebi.ac.uk/api/prediction/P00520 - Network accessline 57
"latestVersion": 4, "allVersions": [1, 2, 3, 4], "cifUrl": "https://alphafold.ebi.ac.uk/files/AF-P00520-F1-model_v4.cif", - Network accessline 58
"allVersions": [1, 2, 3, 4], "cifUrl": "https://alphafold.ebi.ac.uk/files/AF-P00520-F1-model_v4.cif", "bcifUrl": "https://alphafold.ebi.ac.uk/files/AF-P00520-F1-model_v4.bcif", - Network accessline 59
"cifUrl": "https://alphafold.ebi.ac.uk/files/AF-P00520-F1-model_v4.cif", - Network accessline 60
"bcifUrl": "https://alphafold.ebi.ac.uk/files/AF-P00520-F1-model_v4.bcif", - Network accessline 61
"pdbUrl": "https://alphafold.ebi.ac.uk/files/AF-P00520-F1-model_v4.pdb", - Network accessline 62
"paeImageUrl": "https://alphafold.ebi.ac.uk/files/AF-P00520-F1-predicted_aligned_error_v4.png", - Network accessline 63
"paeDocUrl": "https://alphafold.ebi.ac.uk/files/AF-P00520-F1-predicted_aligned_error_v4.json" - Network accessline 89
**Endpoint:** `https://www.ebi.ac.uk/pdbe/pdbe-kb/3dbeacons/api/uniprot/summary/{uniprot_id}.json` - Network accessline 95
uniprot_id = "P00520" url = f"https://www.ebi.ac.uk/pdbe/pdbe-kb/3dbeacons/api/uniprot/summary/{uniprot_id}.json" response = requests.get(url) - Network accessline 96
url = f"https://www.ebi.ac.uk/pdbe/pdbe-kb/3dbeacons/api/uniprot/summary/{uniprot_id}.json" response = requests.get(url) data = response.json() - Network accessline 97
response = requests.get(url) - Network accessline 117
https://alphafold.ebi.ac.uk/files/{alphafold_id}-{file_type}_{version}.{extension} - Network accessline 132
https://alphafold.ebi.ac.uk/files/AF-P00520-F1-model_v4.cif - Network accessline 141
https://alphafold.ebi.ac.uk/files/AF-P00520-F1-model_v4.bcif - Network accessline 150
https://alphafold.ebi.ac.uk/files/AF-P00520-F1-model_v4.pdb - Network accessline 161
https://alphafold.ebi.ac.uk/files/AF-P00520-F1-confidence_v4.json - Network accessline 179
https://alphafold.ebi.ac.uk/files/AF-P00520-F1-predicted_aligned_error_v4.json - Network accessline 197
https://alphafold.ebi.ac.uk/files/AF-P00520-F1-predicted_aligned_error_v4.png - Network accessline 303
# Or install Google Cloud SDK curl https://sdk.cloud.google.com | bash - Dangerous shell operationline 304
curl https://sdk.cloud.google.com | bash - Network accessline 304
curl https://sdk.cloud.google.com | bash - Network accessline 406
- **AlphaFold GitHub:** https://github.com/google-deepmind/alphafold - Network accessline 407
- **Google Cloud Documentation:** https://cloud.google.com/datasets/alphafold - Network accessline 408
- **3D-Beacons Documentation:** https://www.ebi.ac.uk/pdbe/pdbe-kb/3dbeacons/docs - Network accessline 409
- **Biopython Tutorial:** https://biopython.org/wiki/AlphaFold
skills/08-蛋白质工程与结构生物学/esm/SKILL.md
- Network accessline 50
[Credential-related source omitted] - Network accessline 51
[Credential-related source omitted] - Network accessline 248
No additional dependencies needed. Obtain Forge API token at https://forge.evolutionaryscale.ai - Network accessline 292
- **GitHub Repository:** https://github.com/evolutionaryscale/esm - Network accessline 293
- **Forge Platform:** https://forge.evolutionaryscale.ai - Network accessline 294
- **Scientific Paper:** Hayes et al., Science (2025) - https://www.science.org/doi/10.1126/science.ads0018 - Network accessline 295
- **Blog Posts:** - ESM3 Release: https://www.evolutionaryscale.ai/blog/esm3-release - ESM C Launch: https://www.evolutionaryscale.ai/blog/esm-cambrian - Network accessline 296
- ESM3 Release: https://www.evolutionaryscale.ai/blog/esm3-release - Network accessline 297
- ESM C Launch: https://www.evolutionaryscale.ai/blog/esm-cambrian - Network accessline 298
- **Community:** Slack community at https://bit.ly/3FKwcWd - Network accessline 303
ESM is designed for beneficial applications in protein engineering, drug discovery, and scientific research. Follow the Responsible Biodesign Framework (https://responsiblebiodesign.ai/) when designing novel proteins. Consider biosafety and
skills/08-蛋白质工程与结构生物学/esm/references/esm-c-api.md
skills/08-蛋白质工程与结构生物学/esm/references/forge-api.md
- Network accessline 19
Sign up and get your API token at: https://forge.evolutionaryscale.ai - Network accessline 36
client = ESM3ForgeInferenceClient( model="esm3-medium-2024-08", url="https://forge.evolutionaryscale.ai", - Network accessline 37
[Credential-related source omitted] - Network accessline 38
url="https://forge.evolutionaryscale.ai", - Network accessline 78
client = ESM3ForgeInferenceClient( model="esm3-medium-2024-08", url="https://custom.forge.instance.com", - Network accessline 79
[Credential-related source omitted] - Network accessline 80
url="https://custom.forge.instance.com", - Network accessline 602
**More Information:** - AWS Marketplace: https://aws.amazon.com/marketplace/seller-profile?id=seller-iw2nbscescndm - Network accessline 603
- AWS Marketplace: https://aws.amazon.com/marketplace/seller-profile?id=seller-iw2nbscescndm - Network accessline 654
- **Forge Platform**: https://forge.evolutionaryscale.ai - Network accessline 655
- **API Documentation**: Check Forge dashboard for latest API specs - **Community Support**: Slack community at https://bit.ly/3FKwcWd - **Enterprise Contact**: Contact EvolutionaryScale for custom deployments - Network accessline 656
- **Community Support**: Slack community at https://bit.ly/3FKwcWd
skills/08-蛋白质工程与结构生物学/glycoengineering/SKILL.md
- Possible obfuscationline 89
fc_sequence = "APELLGGPSVFLFPPKPKDTLMISRTPEVTCVVVDVSHEDPEVKFNWYVDGVEVHNAKTKPREEQYNSTYRVVSVLTVLHQDWLNGKEYKCKVSNKALPAPIEKTISKAKGQPREPQVYTLPPSREEMTKNQVSLTCLVKGFYPSDIAVEWESNGQPENNYKTTPPVLDSDGSFFLYSKLTVDKSRWQQGNVFSCSVMHEALHNHYTQKSLSLSPGK" - Network accessline 198
Web service for high-accuracy O-GalNAc site prediction: - **URL**: https://services.healthtech.dtu.dk/services/NetOGlyc-4.0/ - Network accessline 199
- **URL**: https://services.healthtech.dtu.dk/services/NetOGlyc-4.0/ - Network accessline 212
Note: This uses the DTU Health Tech web service. Results take ~1-5 min. """ url = "https://services.healthtech.dtu.dk/cgi-bin/webface2.cgi" - Network accessline 213
""" url = "https://services.healthtech.dtu.dk/cgi-bin/webface2.cgi" # NetOGlyc submission (parameters may vary with web service version) - Network accessline 214
url = "https://services.healthtech.dtu.dk/cgi-bin/webface2.cgi" - Network accessline 215
# NetOGlyc submission (parameters may vary with web service version) # Recommend using the web interface directly for most use cases print("Submit sequence at: https://services.healthtech.dtu.dk/services/NetOGlyc-4.0/") - Network accessline 216
# Recommend using the web interface directly for most use cases print("Submit sequence at: https://services.healthtech.dtu.dk/services/NetOGlyc-4.0/") return url - Network accessline 217
print("Submit sequence at: https://services.healthtech.dtu.dk/services/NetOGlyc-4.0/") - Network accessline 220
# Also: NetNGlyc for N-glycosylation prediction # URL: https://services.healthtech.dtu.dk/services/NetNGlyc-1.0/ - Network accessline 221
# URL: https://services.healthtech.dtu.dk/services/NetNGlyc-1.0/ - Network accessline 226
GlycoShield-MD analyzes how glycans shield protein surfaces during MD simulations: - **URL**: https://gitlab.mpcdf.mpg.de/dioscuri-biophysics/glycoshield-md/ - Network accessline 227
- **URL**: https://gitlab.mpcdf.mpg.de/dioscuri-biophysics/glycoshield-md/ - Network accessline 245
- **URL**: https://www.eurocarbdb.org/project/glycoworkbench - Network accessline 251
- **URL**: https://glyconnect.expasy.org/ - Network accessline 258
def query_glyconnect(uniprot_id: str) -> dict: """Query GlyConnect for glycosylation data for a protein.""" url = f"https://glyconnect.expasy.org/api/proteins/uniprot/{uniprot_id}" - Network accessline 259
"""Query GlyConnect for glycosylation data for a protein.""" url = f"https://glyconnect.expasy.org/api/proteins/uniprot/{uniprot_id}" response = requests.get(url, headers={"Accept": "application/json"}) - Network accessline 260
url = f"https://glyconnect.expasy.org/api/proteins/uniprot/{uniprot_id}" response = requests.get(url, headers={"Accept": "application/json"}) if response.status_code == 200: - Network accessline 261
response = requests.get(url, headers={"Accept": "application/json"}) - Network accessline 272
- **URL**: https://unicarbkb.org/ - Network accessline 332
- **GlyTouCan** (glycan structure repository): https://glytoucan.org/ - Network accessline 333
- **GlyConnect**: https://glyconnect.expasy.org/ - Network accessline 334
- **CFG Functional Glycomics**: http://www.functionalglycomics.org/ - Network accessline 335
- **DTU Health Tech servers** (NetNGlyc, NetOGlyc): https://services.healthtech.dtu.dk/ - Network accessline 336
- **GlycoWorkbench**: https://glycoworkbench.software.informer.com/
skills/08-蛋白质工程与结构生物学/glycoengineering/references/glycan_databases.md
- Network accessline 5
### GlyTouCan - **URL**: https://glytoucan.org/ - Network accessline 6
- **URL**: https://glytoucan.org/ - Network accessline 14
def lookup_glytoucan(glytoucan_id: str) -> dict: """Fetch glycan details from GlyTouCan.""" url = f"https://api.glytoucan.org/glycan/{glytoucan_id}" - Network accessline 15
"""Fetch glycan details from GlyTouCan.""" url = f"https://api.glytoucan.org/glycan/{glytoucan_id}" response = requests.get(url, headers={"Accept": "application/json"}) - Network accessline 16
url = f"https://api.glytoucan.org/glycan/{glytoucan_id}" response = requests.get(url, headers={"Accept": "application/json"}) return response.json() if response.ok else {} - Network accessline 17
response = requests.get(url, headers={"Accept": "application/json"}) - Network accessline 21
### GlyConnect - **URL**: https://glyconnect.expasy.org/ - Network accessline 22
- **URL**: https://glyconnect.expasy.org/ - Network accessline 30
def get_glycoprotein_info(uniprot_id: str) -> dict: """Get glycosylation data for a protein from GlyConnect.""" base_url = "https://glyconnect.expasy.org/api" - Network accessline 31
"""Get glycosylation data for a protein from GlyConnect.""" base_url = "https://glyconnect.expasy.org/api" response = requests.get(f"{base_url}/proteins/uniprot/{uniprot_id}") - Network accessline 32
base_url = "https://glyconnect.expasy.org/api" response = requests.get(f"{base_url}/proteins/uniprot/{uniprot_id}") return response.json() if response.ok else {} - Network accessline 33
response = requests.get(f"{base_url}/proteins/uniprot/{uniprot_id}") - Network accessline 36
def get_glycan_compositions(glyconnect_protein_id: int) -> list: """Get all glycan compositions for a GlyConnect protein entry.""" base_url = "https://glyconnect.expasy.org/api" - Network accessline 37
"""Get all glycan compositions for a GlyConnect protein entry.""" base_url = "https://glyconnect.expasy.org/api" response = requests.get(f"{base_url}/compositions/protein/{glyconnect_protein_id}") - Network accessline 38
base_url = "https://glyconnect.expasy.org/api" response = requests.get(f"{base_url}/compositions/protein/{glyconnect_protein_id}") return response.json().get("data", []) if response.ok else [] - Network accessline 39
response = requests.get(f"{base_url}/compositions/protein/{glyconnect_protein_id}") - Network accessline 43
### UniCarbKB - **URL**: https://unicarbkb.org/ - Network accessline 44
- **URL**: https://unicarbkb.org/ - Network accessline 48
### KEGG Glycan - **URL**: https://www.genome.jp/kegg/glycan/ - Network accessline 49
- **URL**: https://www.genome.jp/kegg/glycan/ - Network accessline 53
### CAZy (Carbohydrate-Active Enzymes) - **URL**: http://www.cazy.org/ - Network accessline 54
- **URL**: http://www.cazy.org/ - Network accessline 60
### NetNGlyc 1.0 - **URL**: https://services.healthtech.dtu.dk/services/NetNGlyc-1.0/ - Network accessline 61
- **URL**: https://services.healthtech.dtu.dk/services/NetNGlyc-1.0/ - Network accessline 66
### NetOGlyc 4.0 - **URL**: https://services.healthtech.dtu.dk/services/NetOGlyc-4.0/ - Network accessline 67
- **URL**: https://services.healthtech.dtu.dk/services/NetOGlyc-4.0/ - Network accessline 91
### GlycoWorkbench - **URL**: https://www.eurocarbdb.org/project/glycoworkbench - Network accessline 92
- **URL**: https://www.eurocarbdb.org/project/glycoworkbench
skills/08-蛋白质工程与结构生物学/interpro-database/SKILL.md
- Network accessline 13
InterPro (https://www.ebi.ac.uk/interpro/) is a comprehensive resource for protein family and domain classification maintained by EMBL-EBI. It integrates signatures from 13 member databases including Pfam, PANTHER, PRINTS, ProSite, SMART, T - Network accessline 22
**Key resources:** - InterPro website: https://www.ebi.ac.uk/interpro/ - Network accessline 23
- InterPro website: https://www.ebi.ac.uk/interpro/ - Network accessline 24
- REST API: https://www.ebi.ac.uk/interpro/api/ - API documentation: https://github.com/ProteinsWebTeam/interpro7-api/blob/master/docs/ - Python client: via `requests` - Network accessline 25
- API documentation: https://github.com/ProteinsWebTeam/interpro7-api/blob/master/docs/ - Network accessline 44
Base URL: `https://www.ebi.ac.uk/interpro/api/` - Network accessline 49
BASE_URL = "https://www.ebi.ac.uk/interpro/api" - Network accessline 52
url = f"{BASE_URL}/{endpoint}" headers = {"Accept": "application/json"} response = requests.get(url, params=params, headers=headers) - Network accessline 53
headers = {"Accept": "application/json"} response = requests.get(url, params=params, headers=headers) response.raise_for_status() - Network accessline 54
response = requests.get(url, params=params, headers=headers) - Network accessline 213
1. **Run InterProScan** locally or via the web (https://www.ebi.ac.uk/interpro/search/sequence/) to scan a protein sequence - Network accessline 301
- **InterPro website**: https://www.ebi.ac.uk/interpro/ - Network accessline 302
- **InterProScan** (run locally): https://github.com/ebi-pf-team/interproscan - Network accessline 303
- **API documentation**: https://github.com/ProteinsWebTeam/interpro7-api/blob/master/docs/ - Network accessline 304
- **Pfam**: https://www.ebi.ac.uk/interpro/entry/pfam/
skills/08-蛋白质工程与结构生物学/interpro-database/references/domain_analysis.md
skills/08-蛋白质工程与结构生物学/pdb-database/SKILL.md
- Possible obfuscationline 57
value="MTEYKLVVVGAGGVGKSALTIQLIQNHFVDEYDPTIEDSYRKQVVIDGETCLLDILDTAGQEEYSAMRDQYMRTGEGFLCVFAINNTKSFEDIHHYREQIKRVKDSEDVPMVLVGNKCDLPSRTVDTKQAQDLARSYGIPFIETSAKTRQGVDDAFYTLVREIRKHKEKMSKDGKKKKKKSKTKCVIM", - Network accessline 103
# Get entry-level data entry_data = fetch("4HHB", schema=Schema.ENTRY) - Network accessline 104
entry_data = fetch("4HHB", schema=Schema.ENTRY) - Network accessline 111
# Get protein/nucleic acid information entity_data = fetch("4HHB_1", schema=Schema.POLYMER_ENTITY) - Network accessline 112
entity_data = fetch("4HHB_1", schema=Schema.POLYMER_ENTITY) - Network accessline 135
} """ data = fetch(query_type="graphql", query=query) - Network accessline 137
data = fetch(query_type="graphql", query=query) - Network accessline 144
**Download Methods:** - **PDB format** (legacy text format): `https://files.rcsb.org/download/{PDB_ID}.pdb` - Network accessline 145
- **PDB format** (legacy text format): `https://files.rcsb.org/download/{PDB_ID}.pdb` - Network accessline 146
- **mmCIF format** (modern standard): `https://files.rcsb.org/download/{PDB_ID}.cif` - Network accessline 148
- **Biological assembly**: `https://files.rcsb.org/download/{PDB_ID}.pdb1` (for assembly 1) - Network accessline 156
# Download PDB format pdb_url = f"https://files.rcsb.org/download/{pdb_id}.pdb" - Network accessline 157
pdb_url = f"https://files.rcsb.org/download/{pdb_id}.pdb" - Network accessline 158
response = requests.get(pdb_url) - Network accessline 162
# Download mmCIF format cif_url = f"https://files.rcsb.org/download/{pdb_id}.cif" - Network accessline 163
cif_url = f"https://files.rcsb.org/download/{pdb_id}.cif" - Network accessline 164
response = requests.get(cif_url) - Network accessline 193
# Get experimental details data = fetch("4HHB", schema=Schema.ENTRY) - Network accessline 194
data = fetch("4HHB", schema=Schema.ENTRY) - Network accessline 215
for pdb_id in pdb_ids: try: data = fetch(pdb_id, schema=Schema.ENTRY) - Network accessline 216
try: data = fetch(pdb_id, schema=Schema.ENTRY) results[pdb_id] = { - Network accessline 217
data = fetch(pdb_id, schema=Schema.ENTRY) - Network accessline 301
- **RCSB PDB Website:** https://www.rcsb.org - Network accessline 302
- **PDB-101 Educational Portal:** https://pdb101.rcsb.org - Network accessline 303
- **API Documentation:** https://www.rcsb.org/docs/programmatic-access/web-apis-overview - Network accessline 304
- **Python Package Docs:** https://rcsbapi.readthedocs.io/ - Network accessline 305
- **Data API Documentation:** https://data.rcsb.org/ - Network accessline 306
- **GitHub Repository:** https://github.com/rcsb/py-rcsb-api
skills/08-蛋白质工程与结构生物学/pdb-database/references/api_reference.md
- Network accessline 32
Base URL: `https://data.rcsb.org/rest/v1/` - Network accessline 36
GET https://data.rcsb.org/rest/v1/core/entry/{entry_id} - Network accessline 41
GET https://data.rcsb.org/rest/v1/core/polymer_entity/{entry_id}_{entity_id} - Network accessline 46
GET https://data.rcsb.org/rest/v1/core/assembly/{entry_id}/{assembly_id} - Network accessline 51
# Get entry data for hemoglobin curl https://data.rcsb.org/rest/v1/core/entry/4HHB - Network accessline 52
curl https://data.rcsb.org/rest/v1/core/entry/4HHB - Network accessline 54
# Get first polymer entity curl https://data.rcsb.org/rest/v1/core/polymer_entity/4HHB_1 - Network accessline 55
curl https://data.rcsb.org/rest/v1/core/polymer_entity/4HHB_1 - Network accessline 57
# Get biological assembly 1 curl https://data.rcsb.org/rest/v1/core/assembly/4HHB/1 - Network accessline 58
curl https://data.rcsb.org/rest/v1/core/assembly/4HHB/1 - Network accessline 63
Endpoint: `https://data.rcsb.org/graphql` - Network accessline 113
response = requests.post( - Network accessline 114
"https://data.rcsb.org/graphql", - Possible obfuscationline 239
value="MTEYKLVVVGAGGVGKSALTIQLIQNHFVDEYDPTIEDSYRKQVVIDGETCLLDILDTAGQEEYSAMRDQYMRTGEGFLCVFAINNTKSFEDIHHYREQIKRVKDSEDVPMVLVGNKCDLPSRTVDTKQAQDLARSYGIPFIETSAKTRQGVDDAFYTLVREIRKHKEKMSKDGKKKKKKSKTKCVIM", - Network accessline 363
https://files.rcsb.org/download/{PDB_ID}.pdb - Network accessline 368
https://files.rcsb.org/download/{PDB_ID}.cif - Network accessline 373
https://files.rcsb.org/download/{PDB_ID}-sf.cif - Network accessline 378
https://files.rcsb.org/download/{PDB_ID}.pdb1 # Assembly 1 - Network accessline 379
https://files.rcsb.org/download/{PDB_ID}.pdb2 # Assembly 2 - Network accessline 384
https://www.rcsb.org/fasta/entry/{PDB_ID} - Network accessline 399
output_dir: Directory to save file """ base_url = "https://files.rcsb.org/download" - Network accessline 400
""" base_url = "https://files.rcsb.org/download" url = f"{base_url}/{pdb_id}.{format}" - Network accessline 401
base_url = "https://files.rcsb.org/download" - Network accessline 404
response = requests.get(url) - Network accessline 446
for attempt in range(max_retries): response = requests.get(url) - Network accessline 447
response = requests.get(url) - Network accessline 488
for i, pdb_id in enumerate(pdb_ids): try: data = fetch(pdb_id, schema=Schema.ENTRY) - Network accessline 489
try: data = fetch(pdb_id, schema=Schema.ENTRY) results[pdb_id] = data - Network accessline 490
data = fetch(pdb_id, schema=Schema.ENTRY) - Network accessline 600
# Test with curl import subprocess - Network accessline 601
import subprocess result = subprocess.run( ["curl", "https://data.rcsb.org/rest/v1/core/entry/4HHB"], - Network accessline 602
result = subprocess.run( ["curl", "https://data.rcsb.org/rest/v1/core/entry/4HHB"], capture_output=True, - Network accessline 603
["curl", "https://data.rcsb.org/rest/v1/core/entry/4HHB"], - Network accessline 612
- **API Documentation:** https://www.rcsb.org/docs/programmatic-access/web-apis-overview - Network accessline 613
- **Data API Redoc:** https://data.rcsb.org/redoc/index.html - Network accessline 614
- **GraphQL Schema:** https://data.rcsb.org/graphql - Network accessline 615
- **Python Package Docs:** https://rcsbapi.readthedocs.io/ - Network accessline 616
- **GitHub Issues:** https://github.com/rcsb/py-rcsb-api/issues - Network accessline 617
- **Community Forum:** https://www.rcsb.org/help
skills/08-蛋白质工程与结构生物学/uniprot-database/SKILL.md
- Network accessline 54
Use the API search endpoint: `https://rest.uniprot.org/uniprotkb/search?query={query}&format={format}` - Network accessline 66
**Retrieve endpoint:** `https://rest.uniprot.org/uniprotkb/{accession}.{format}` - Network accessline 68
Example: `https://rest.uniprot.org/uniprotkb/P12345.fasta` - Network accessline 74
**ID Mapping workflow:** 1. Submit mapping job to: `https://rest.uniprot.org/idmapping/run` - Network accessline 75
1. Submit mapping job to: `https://rest.uniprot.org/idmapping/run` - Network accessline 76
2. Check job status: `https://rest.uniprot.org/idmapping/status/{jobId}` - Network accessline 77
3. Retrieve results: `https://rest.uniprot.org/idmapping/results/{jobId}` - Network accessline 95
`https://rest.uniprot.org/uniprotkb/stream?query={query}&format={format}` - Network accessline 115
**Example:** `https://rest.uniprot.org/uniprotkb/search?query=insulin&fields=accession,gene_names,organism_name,length,sequence&format=tsv` - Network accessline 188
- **API Documentation**: https://www.uniprot.org/help/api - Network accessline 189
- **Interactive API Explorer**: https://www.uniprot.org/api-documentation - Network accessline 190
- **REST Tutorial**: https://www.uniprot.org/help/uniprot_rest_tutorial - Network accessline 191
- **Query Syntax Help**: https://www.uniprot.org/help/query-fields - Network accessline 192
- **SPARQL Endpoint**: https://sparql.uniprot.org/ (for advanced graph queries)
skills/08-蛋白质工程与结构生物学/uniprot-database/references/api_examples.md
- Network accessline 11
# Search for human insulin proteins url = "https://rest.uniprot.org/uniprotkb/search" - Network accessline 12
url = "https://rest.uniprot.org/uniprotkb/search" - Network accessline 19
response = requests.get(url, params=params) - Network accessline 30
# Get human insulin sequence in FASTA format accession = "P01308" url = f"https://rest.uniprot.org/uniprotkb/{accession}.fasta" - Network accessline 32
url = f"https://rest.uniprot.org/uniprotkb/{accession}.fasta" - Network accessline 34
response = requests.get(url) - Network accessline 42
# Get specific fields only url = "https://rest.uniprot.org/uniprotkb/search" - Network accessline 43
url = "https://rest.uniprot.org/uniprotkb/search" - Network accessline 50
response = requests.get(url, params=params) - Network accessline 59
def map_uniprot_ids(ids, from_db, to_db): # Submit job submit_url = "https://rest.uniprot.org/idmapping/run" - Network accessline 60
# Submit job submit_url = "https://rest.uniprot.org/idmapping/run" data = { - Network accessline 61
submit_url = "https://rest.uniprot.org/idmapping/run" - Network accessline 68
response = requests.post(submit_url, data=data) - Network accessline 71
# Poll for completion status_url = f"https://rest.uniprot.org/idmapping/status/{job_id}" - Network accessline 72
status_url = f"https://rest.uniprot.org/idmapping/status/{job_id}" while True: response = requests.get(status_url) - Network accessline 73
while True: response = requests.get(status_url) status = response.json() - Network accessline 74
response = requests.get(status_url) - Network accessline 80
# Get results results_url = f"https://rest.uniprot.org/idmapping/results/{job_id}" response = requests.get(results_url) - Network accessline 81
results_url = f"https://rest.uniprot.org/idmapping/results/{job_id}" response = requests.get(results_url) return response.json() - Network accessline 82
response = requests.get(results_url) - Network accessline 95
# Stream all reviewed human proteins url = "https://rest.uniprot.org/uniprotkb/stream" - Network accessline 96
url = "https://rest.uniprot.org/uniprotkb/stream" - Network accessline 102
response = requests.get(url, params=params, stream=True) - Network accessline 115
def get_all_results(query, fields=None): """Get all results with pagination""" url = "https://rest.uniprot.org/uniprotkb/search" - Network accessline 116
"""Get all results with pagination""" url = "https://rest.uniprot.org/uniprotkb/search" all_results = [] - Network accessline 117
url = "https://rest.uniprot.org/uniprotkb/search" - Network accessline 129
while True: response = requests.get(url, params=params) - Network accessline 130
response = requests.get(url, params=params) - Network accessline 150
## cURL Examples - Network accessline 154
# Search for insulin proteins curl "https://rest.uniprot.org/uniprotkb/search?query=insulin&format=json&size=5" - Network accessline 155
curl "https://rest.uniprot.org/uniprotkb/search?query=insulin&format=json&size=5" - Network accessline 160
# Get human insulin in FASTA format curl "https://rest.uniprot.org/uniprotkb/P01308.fasta" - Network accessline 161
curl "https://rest.uniprot.org/uniprotkb/P01308.fasta" - Network accessline 166
# Get specific fields in TSV format curl "https://rest.uniprot.org/uniprotkb/search?query=gene:BRCA1&format=tsv&fields=accession,gene_names,length" - Network accessline 167
curl "https://rest.uniprot.org/uniprotkb/search?query=gene:BRCA1&format=tsv&fields=accession,gene_names,length" - Network accessline 172
# Submit mapping job curl -X POST "https://rest.uniprot.org/idmapping/run" \ - Network accessline 173
curl -X POST "https://rest.uniprot.org/idmapping/run" \ - Network accessline 180
# Get mapping results (replace JOB_ID) curl "https://rest.uniprot.org/idmapping/results/JOB_ID" - Network accessline 181
curl "https://rest.uniprot.org/idmapping/results/JOB_ID" - Network accessline 186
# Download all human reviewed proteins curl "https://rest.uniprot.org/uniprotkb/stream?query=organism_id:9606+AND+reviewed:true&format=fasta" \ - Network accessline 187
curl "https://rest.uniprot.org/uniprotkb/stream?query=organism_id:9606+AND+reviewed:true&format=fasta" \ - Network accessline 198
# Search for insulin proteins url <- "https://rest.uniprot.org/uniprotkb/search" - Network accessline 199
url <- "https://rest.uniprot.org/uniprotkb/search" - Network accessline 218
# Get protein sequence accession <- "P01308" url <- paste0("https://rest.uniprot.org/uniprotkb/", accession, ".fasta") - Network accessline 220
url <- paste0("https://rest.uniprot.org/uniprotkb/", accession, ".fasta") - Network accessline 232
# Get data as TSV url <- "https://rest.uniprot.org/uniprotkb/search" - Network accessline 233
url <- "https://rest.uniprot.org/uniprotkb/search" - Network accessline 249
// Search for proteins async function searchUniProt(query) { const url = `https://rest.uniprot.org/uniprotkb/search?query=${encodeURIComponent(query)}&format=json&size=10`; - Network accessline 251
const url = `https://rest.uniprot.org/uniprotkb/search?query=${encodeURIComponent(query)}&format=json&size=10`; - Network accessline 253
const response = await fetch(url); - Network accessline 266
async function getProtein(accession, format = "json") { const url = `https://rest.uniprot.org/uniprotkb/${accession}.${format}`; - Network accessline 267
const url = `https://rest.uniprot.org/uniprotkb/${accession}.${format}`; - Network accessline 269
const response = await fetch(url); - Network accessline 285
async function mapIds(ids, fromDb, toDb) { // Submit job const submitUrl = "https://rest.uniprot.org/idmapping/run"; - Network accessline 286
// Submit job const submitUrl = "https://rest.uniprot.org/idmapping/run"; const formData = new URLSearchParams({ - Network accessline 287
const submitUrl = "https://rest.uniprot.org/idmapping/run"; - Network accessline 294
const submitResponse = await fetch(submitUrl, { - Network accessline 300
// Poll for completion const statusUrl = `https://rest.uniprot.org/idmapping/status/${jobId}`; - Network accessline 301
const statusUrl = `https://rest.uniprot.org/idmapping/status/${jobId}`; while (true) { const statusResponse = await fetch(statusUrl); - Network accessline 302
while (true) { const statusResponse = await fetch(statusUrl); const status = await statusResponse.json(); - Network accessline 303
const statusResponse = await fetch(statusUrl); - Network accessline 313
// Get results const resultsUrl = `https://rest.uniprot.org/idmapping/results/${jobId}`; const resultsResponse = await fetch(resultsUrl); - Network accessline 314
const resultsUrl = `https://rest.uniprot.org/idmapping/results/${jobId}`; const resultsResponse = await fetch(resultsUrl); return await resultsResponse.json(); - Network accessline 315
const resultsResponse = await fetch(resultsUrl); - Network accessline 332
class UniProtClient: def __init__(self, rate_limit=1.0): self.base_url = "https://rest.uniprot.org" - Network accessline 333
def __init__(self, rate_limit=1.0): self.base_url = "https://rest.uniprot.org" self.rate_limit = rate_limit - Network accessline 334
self.base_url = "https://rest.uniprot.org" - Network accessline 356
response = requests.get( - Network accessline 384
def download_with_progress(query, output_file, format="fasta"): """Download results with progress bar""" url = "https://rest.uniprot.org/uniprotkb/stream" - Network accessline 385
"""Download results with progress bar""" url = "https://rest.uniprot.org/uniprotkb/stream" params = { - Network accessline 386
url = "https://rest.uniprot.org/uniprotkb/stream" - Network accessline 392
response = requests.get(url, params=params, stream=True) - Network accessline 410
- API Documentation: https://www.uniprot.org/help/api - Network accessline 411
- Interactive API Explorer: https://www.uniprot.org/api-documentation - Network accessline 412
- Python client (Unipressed): https://github.com/multimeric/Unipressed - Network accessline 413
- Bioservices package: https://bioservices.readthedocs.io/
skills/08-蛋白质工程与结构生物学/uniprot-database/references/api_fields.md
- Network accessline 9
https://rest.uniprot.org/uniprotkb/search?query=insulin&fields=accession,gene_names,organism_name,length - Network accessline 217
curl https://rest.uniprot.org/configure/uniprotkb/result-fields - Network accessline 222
import requests response = requests.get("https://rest.uniprot.org/configure/uniprotkb/result-fields") - Network accessline 223
response = requests.get("https://rest.uniprot.org/configure/uniprotkb/result-fields") - Network accessline 273
- Interactive field explorer: https://www.uniprot.org/api-documentation - Network accessline 274
- API fields endpoint: https://rest.uniprot.org/configure/uniprotkb/result-fields - Network accessline 275
- Return fields documentation: https://www.uniprot.org/help/return_fields
skills/08-蛋白质工程与结构生物学/uniprot-database/references/id_mapping_databases.md
- Network accessline 8
import requests response = requests.get("https://rest.uniprot.org/configure/idmapping/fields") - Network accessline 9
response = requests.get("https://rest.uniprot.org/configure/idmapping/fields") - Network accessline 260
GET https://rest.uniprot.org/configure/idmapping/fields - Network accessline 265
POST https://rest.uniprot.org/idmapping/run - Network accessline 273
GET https://rest.uniprot.org/idmapping/status/{jobId} - Network accessline 278
GET https://rest.uniprot.org/idmapping/results/{jobId} - Network accessline 283
- ID Mapping tool: https://www.uniprot.org/id-mapping - Network accessline 284
- API documentation: https://www.uniprot.org/help/id_mapping - Network accessline 285
- Programmatic access: https://www.uniprot.org/help/api_idmapping
skills/08-蛋白质工程与结构生物学/uniprot-database/references/query_syntax.md
- Network accessline 220
Test queries using: - **Web interface**: https://www.uniprot.org/uniprotkb - Network accessline 221
- **Web interface**: https://www.uniprot.org/uniprotkb - Network accessline 222
- **API**: https://rest.uniprot.org/uniprotkb/search?query=YOUR_QUERY - Network accessline 223
- **API documentation**: https://www.uniprot.org/help/query-fields - Network accessline 254
- Full query field reference: https://www.uniprot.org/help/query-fields - Network accessline 255
- API query documentation: https://www.uniprot.org/help/api_queries - Network accessline 256
- Text search documentation: https://www.uniprot.org/help/text-search
skills/08-蛋白质工程与结构生物学/uniprot-database/scripts/uniprot_client.py
- Executable scriptline 1
#!/usr/bin/env python3 - Network accessline 31
BASE_URL = "https://rest.uniprot.org" - Network accessline 61
response = requests.get(endpoint, params=params) - Network accessline 83
response = requests.get(endpoint) - Network accessline 134
response = requests.get(endpoint, params=params, stream=True) - Network accessline 173
response = requests.post(submit_endpoint, data=data) - Network accessline 180
while True: response = requests.get(status_endpoint) - Network accessline 181
response = requests.get(status_endpoint) - Network accessline 193
params = {"format": format} response = requests.get(results_endpoint, params=params) - Network accessline 194
response = requests.get(results_endpoint, params=params) - Network accessline 212
response = requests.get(endpoint) - Network accessline 227
response = requests.get(endpoint)
skills/09-机器学习与人工智能/aeon/SKILL.md
- Network accessline 368
- Documentation: https://www.aeon-toolkit.org/ - Network accessline 369
- GitHub: https://github.com/aeon-toolkit/aeon - Network accessline 370
- Examples: https://www.aeon-toolkit.org/en/stable/examples.html - Network accessline 371
- API Reference: https://www.aeon-toolkit.org/en/stable/api_reference.html
skills/09-机器学习与人工智能/aeon/references/networks.md
- Access to secretsline 182
[Credential-related source omitted]
skills/09-机器学习与人工智能/hypogenic/SKILL.md
- Network accessline 23
# Clone example datasets git clone https://github.com/ChicagoHAI/HypoGeniC-datasets.git ./data - Network accessline 24
git clone https://github.com/ChicagoHAI/HypoGeniC-datasets.git ./data - Network accessline 134
# For HypoGeniC examples git clone https://github.com/ChicagoHAI/HypoGeniC-datasets.git ./data - Network accessline 135
git clone https://github.com/ChicagoHAI/HypoGeniC-datasets.git ./data - Network accessline 137
# For HypoRefine/Union examples git clone https://github.com/ChicagoHAI/Hypothesis-agent-datasets.git ./data - Network accessline 138
git clone https://github.com/ChicagoHAI/Hypothesis-agent-datasets.git ./data - Network accessline 289
# Clone example datasets first # git clone https://github.com/ChicagoHAI/HypoGeniC-datasets.git ./data - Network accessline 290
# git clone https://github.com/ChicagoHAI/HypoGeniC-datasets.git ./data - Network accessline 315
# For literature-integrated approaches # git clone https://github.com/ChicagoHAI/Hypothesis-agent-datasets.git ./data - Network accessline 316
# git clone https://github.com/ChicagoHAI/Hypothesis-agent-datasets.git ./data - Network accessline 544
- **Paper:** https://arxiv.org/abs/2504.11524 - Network accessline 554
archivePrefix={arXiv}, primaryClass={cs.AI}, url={https://arxiv.org/abs/2504.11524}, - Network accessline 555
primaryClass={cs.AI}, url={https://arxiv.org/abs/2504.11524}, } - Network accessline 556
url={https://arxiv.org/abs/2504.11524}, - Network accessline 564
- **Paper:** https://arxiv.org/abs/2410.17309 - Network accessline 565
- **Code:** https://github.com/ChicagoHAI/hypothesis-generation - Network accessline 575
archivePrefix={arXiv}, primaryClass={cs.AI}, url={https://arxiv.org/abs/2410.17309}, - Network accessline 576
primaryClass={cs.AI}, url={https://arxiv.org/abs/2410.17309}, } - Network accessline 577
url={https://arxiv.org/abs/2410.17309}, - Network accessline 585
- **Paper:** https://aclanthology.org/2024.nlp4science-1.10/ - Network accessline 593
booktitle = {Proceedings of EMNLP Workshop of NLP for Science}, year={2024}, url={https://aclanthology.org/2024.nlp4science-1.10/}, - Network accessline 594
year={2024}, url={https://aclanthology.org/2024.nlp4science-1.10/}, } - Network accessline 595
url={https://aclanthology.org/2024.nlp4science-1.10/}, - Network accessline 603
- **GitHub Repository:** https://github.com/ChicagoHAI/hypothesis-generation - Network accessline 604
- **PyPI Package:** https://pypi.org/project/hypogenic/ - Network accessline 606
- **Issues & Support:** https://github.com/ChicagoHAI/hypothesis-generation/issues - Network accessline 613
# HypoGeniC examples (data-driven only) git clone https://github.com/ChicagoHAI/HypoGeniC-datasets.git ./data - Network accessline 614
git clone https://github.com/ChicagoHAI/HypoGeniC-datasets.git ./data - Network accessline 616
# HypoRefine/Union examples (literature + data) git clone https://github.com/ChicagoHAI/Hypothesis-agent-datasets.git ./data - Network accessline 617
git clone https://github.com/ChicagoHAI/Hypothesis-agent-datasets.git ./data
skills/09-机器学习与人工智能/hypogenic/references/config_template.yaml
- Access to secretsline 17
[Credential-related source omitted]
skills/09-机器学习与人工智能/pufferlib/SKILL.md
skills/09-机器学习与人工智能/pufferlib/scripts/env_template.py
- Executable scriptline 1
#!/usr/bin/env python3
skills/09-机器学习与人工智能/pufferlib/scripts/train_template.py
- Executable scriptline 1
#!/usr/bin/env python3
skills/09-机器学习与人工智能/pymc/assets/hierarchical_model_template.py
- Executable scriptline 1
Executable file
skills/09-机器学习与人工智能/pymc/assets/linear_regression_template.py
- Executable scriptline 1
Executable file
skills/09-机器学习与人工智能/pymc/scripts/model_comparison.py
- Executable scriptline 1
Executable file
skills/09-机器学习与人工智能/pymc/scripts/model_diagnostics.py
- Executable scriptline 1
Executable file
skills/09-机器学习与人工智能/pymoo/SKILL.md
- Network accessline 559
**Documentation:** https://pymoo.org/
skills/09-机器学习与人工智能/pymoo/scripts/custom_problem_example.py
- Executable scriptline 1
Executable file
skills/09-机器学习与人工智能/pymoo/scripts/decision_making_example.py
- Executable scriptline 1
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skills/09-机器学习与人工智能/pymoo/scripts/many_objective_example.py
- Executable scriptline 1
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skills/09-机器学习与人工智能/pymoo/scripts/multi_objective_example.py
- Executable scriptline 1
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skills/09-机器学习与人工智能/pymoo/scripts/single_objective_example.py
- Executable scriptline 1
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skills/09-机器学习与人工智能/pytorch-lightning/references/data_module.md
skills/09-机器学习与人工智能/pytorch-lightning/references/distributed_training.md
- Access to secretsline 522
[Credential-related source omitted]
skills/09-机器学习与人工智能/pytorch-lightning/references/logging.md
- Network accessline 80
mlflow_logger = pl_loggers.MLFlowLogger( experiment_name="my_experiment", tracking_uri="http://localhost:5000", - Network accessline 81
experiment_name="my_experiment", tracking_uri="http://localhost:5000", run_name="run_1" - Network accessline 82
tracking_uri="http://localhost:5000", - Access to secretsline 103
[Credential-related source omitted] - Access to secretsline 125
[Credential-related source omitted]
skills/09-机器学习与人工智能/pytorch-lightning/scripts/quick_trainer_setup.py
- Executable scriptline 1
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skills/09-机器学习与人工智能/pytorch-lightning/scripts/template_datamodule.py
- Executable scriptline 1
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skills/09-机器学习与人工智能/pytorch-lightning/scripts/template_lightning_module.py
- Executable scriptline 1
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skills/09-机器学习与人工智能/scikit-learn/SKILL.md
- Network accessline 515
- Official Documentation: https://scikit-learn.org/stable/ - Network accessline 516
- User Guide: https://scikit-learn.org/stable/user_guide.html - Network accessline 517
- API Reference: https://scikit-learn.org/stable/api/index.html - Network accessline 518
- Examples Gallery: https://scikit-learn.org/stable/auto_examples/index.html
skills/09-机器学习与人工智能/scikit-learn/references/quick_reference.md
- Network accessline 429
- Official Documentation: https://scikit-learn.org/stable/ - Network accessline 430
- User Guide: https://scikit-learn.org/stable/user_guide.html - Network accessline 431
- API Reference: https://scikit-learn.org/stable/api/index.html - Network accessline 432
- Examples: https://scikit-learn.org/stable/auto_examples/index.html - Network accessline 433
- Tutorials: https://scikit-learn.org/stable/tutorial/index.html
skills/09-机器学习与人工智能/scikit-learn/scripts/classification_pipeline.py
- Executable scriptline 1
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skills/09-机器学习与人工智能/scikit-learn/scripts/clustering_analysis.py
- Executable scriptline 1
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skills/09-机器学习与人工智能/scikit-survival/SKILL.md
- Network accessline 357
- **Official Documentation**: https://scikit-survival.readthedocs.io/ - Network accessline 358
- **GitHub Repository**: https://github.com/sebp/scikit-survival - Network accessline 360
- **API Reference**: Complete list of classes and functions at https://scikit-survival.readthedocs.io/en/stable/api/index.html
skills/09-机器学习与人工智能/shap/SKILL.md
skills/09-机器学习与人工智能/stable-baselines3/references/algorithms.md
- Network accessline 330
- **RL Baselines3 Zoo**: Collection of pre-trained agents and hyperparameters: https://github.com/DLR-RM/rl-baselines3-zoo
skills/09-机器学习与人工智能/stable-baselines3/references/callbacks.md
- Network accessline 554
- Official SB3 Callbacks Guide: https://stable-baselines3.readthedocs.io/en/master/guide/callbacks.html - Network accessline 555
- Callback API Reference: https://stable-baselines3.readthedocs.io/en/master/common/callbacks.html - Network accessline 556
- TensorBoard Documentation: https://www.tensorflow.org/tensorboard
skills/09-机器学习与人工智能/stable-baselines3/references/custom_environments.md
- Network accessline 524
- Template: See `scripts/custom_env_template.py` - Gymnasium Documentation: https://gymnasium.farama.org/ - Network accessline 525
- Gymnasium Documentation: https://gymnasium.farama.org/ - Network accessline 526
- SB3 Custom Env Guide: https://stable-baselines3.readthedocs.io/en/master/guide/custom_env.html
skills/09-机器学习与人工智能/stable-baselines3/references/vectorized_envs.md
- Network accessline 566
- Official SB3 VecEnv Guide: https://stable-baselines3.readthedocs.io/en/master/guide/vec_envs.html - Network accessline 567
- VecEnv API Reference: https://stable-baselines3.readthedocs.io/en/master/common/vec_env.html - Network accessline 568
- Multiprocessing Best Practices: https://docs.python.org/3/library/multiprocessing.html
skills/09-机器学习与人工智能/stable-baselines3/scripts/custom_env_template.py
- Executable scriptline 1
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skills/09-机器学习与人工智能/stable-baselines3/scripts/evaluate_agent.py
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skills/09-机器学习与人工智能/stable-baselines3/scripts/train_rl_agent.py
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skills/09-机器学习与人工智能/timesfm-forecasting/SKILL.md
- Broad permissionsline 1
Read Write Edit Bash - Network accessline 135
# CUDA 12.1 (NVIDIA GPU) pip install torch>=2.0.0 --index-url https://download.pytorch.org/whl/cu121 - Network accessline 136
pip install torch>=2.0.0 --index-url https://download.pytorch.org/whl/cu121 - Network accessline 138
# CPU only pip install torch>=2.0.0 --index-url https://download.pytorch.org/whl/cpu - Network accessline 139
pip install torch>=2.0.0 --index-url https://download.pytorch.org/whl/cpu - Network accessline 681
- **Paper**: [A Decoder-Only Foundation Model for Time-Series Forecasting](https://arxiv.org/abs/2310.10688) (ICML 2024) - Network accessline 682
- **Repository**: https://github.com/google-research/timesfm - Network accessline 683
- **Hugging Face**: https://huggingface.co/collections/google/timesfm-release-66e4be5fdb56e960c1e482a6 - Network accessline 684
- **Google Blog**: https://research.google/blog/a-decoder-only-foundation-model-for-time-series-forecasting/ - Network accessline 685
- **BigQuery Integration**: https://cloud.google.com/bigquery/docs/timesfm-model
skills/09-机器学习与人工智能/timesfm-forecasting/examples/anomaly-detection/detect_anomalies.py
- Executable scriptline 1
#!/usr/bin/env python3
skills/09-机器学习与人工智能/timesfm-forecasting/examples/anomaly-detection/output/anomaly_detection.png
- Network accessline 1
version https://git-lfs.github.com/spec/v1
skills/09-机器学习与人工智能/timesfm-forecasting/examples/covariates-forecasting/demo_covariates.py
- Executable scriptline 1
#!/usr/bin/env python3 - Network accessline 20
import tempfile, urllib.request tmp = tempfile.mkdtemp(prefix="timesfm_retail_") # urllib.request.urlretrieve("https://...store_sales.csv", f"{tmp}/store_sales.csv") - Network accessline 21
tmp = tempfile.mkdtemp(prefix="timesfm_retail_") # urllib.request.urlretrieve("https://...store_sales.csv", f"{tmp}/store_sales.csv") # df = pd.read_csv(f"{tmp}/store_sales.csv") - Network accessline 22
# urllib.request.urlretrieve("https://...store_sales.csv", f"{tmp}/store_sales.csv")
skills/09-机器学习与人工智能/timesfm-forecasting/examples/covariates-forecasting/output/covariates_data.png
- Network accessline 1
version https://git-lfs.github.com/spec/v1
skills/09-机器学习与人工智能/timesfm-forecasting/examples/global-temperature/generate_animation_data.py
- Executable scriptline 1
#!/usr/bin/env python3
skills/09-机器学习与人工智能/timesfm-forecasting/examples/global-temperature/generate_gif.py
- Executable scriptline 1
#!/usr/bin/env python3
skills/09-机器学习与人工智能/timesfm-forecasting/examples/global-temperature/generate_html.py
- Executable scriptline 1
#!/usr/bin/env python3 - Network accessline 23
<meta name="viewport" content="width=device-width, initial-scale=1.0"> <title>TimesFM Interactive Forecast Animation</title> <script src="https://cdn.jsdelivr.net/npm/chart.js"></script> - Network accessline 24
<title>TimesFM Interactive Forecast Animation</title> <script src="https://cdn.jsdelivr.net/npm/chart.js"></script> <style> - Network accessline 25
<script src="https://cdn.jsdelivr.net/npm/chart.js"></script> - Network accessline 218
<footer> <p>TimesFM 1.0 (200M) PyTorch • <a href="https://github.com/google-research/timesfm">Google Research</a></p> - Network accessline 219
<p>TimesFM 1.0 (200M) PyTorch • <a href="https://github.com/google-research/timesfm">Google Research</a></p>
skills/09-机器学习与人工智能/timesfm-forecasting/examples/global-temperature/output/forecast_animation.gif
- Network accessline 1
version https://git-lfs.github.com/spec/v1
skills/09-机器学习与人工智能/timesfm-forecasting/examples/global-temperature/output/forecast_visualization.png
- Network accessline 1
version https://git-lfs.github.com/spec/v1
skills/09-机器学习与人工智能/timesfm-forecasting/examples/global-temperature/output/interactive_forecast.html
- Network accessline 5
<meta name="viewport" content="width=device-width, initial-scale=1.0"> <title>TimesFM Interactive Forecast Animation</title> <script src="https://cdn.jsdelivr.net/npm/chart.js"></script> - Network accessline 6
<title>TimesFM Interactive Forecast Animation</title> <script src="https://cdn.jsdelivr.net/npm/chart.js"></script> <style> - Network accessline 7
<script src="https://cdn.jsdelivr.net/npm/chart.js"></script> - Network accessline 200
<footer> <p>TimesFM 1.0 (200M) PyTorch • <a href="https://github.com/google-research/timesfm">Google Research</a></p> - Network accessline 201
<p>TimesFM 1.0 (200M) PyTorch • <a href="https://github.com/google-research/timesfm">Google Research</a></p>
skills/09-机器学习与人工智能/timesfm-forecasting/examples/global-temperature/run_example.sh
- Executable scriptline 1
#!/bin/bash
skills/09-机器学习与人工智能/timesfm-forecasting/examples/global-temperature/run_forecast.py
- Executable scriptline 1
#!/usr/bin/env python3
skills/09-机器学习与人工智能/timesfm-forecasting/examples/global-temperature/visualize_forecast.py
- Executable scriptline 1
#!/usr/bin/env python3
skills/09-机器学习与人工智能/timesfm-forecasting/scripts/check_system.py
skills/09-机器学习与人工智能/timesfm-forecasting/scripts/forecast_csv.py
- Executable scriptline 1
#!/usr/bin/env python3
skills/09-机器学习与人工智能/torch-geometric/SKILL.md
- Network accessline 36
uv pip install pyg_lib torch_scatter torch_sparse torch_cluster torch_spline_conv -f https://data.pyg.org/whl/torch-${TORCH}+${CUDA}.html - Network accessline 670
- **Documentation**: https://pytorch-geometric.readthedocs.io/ - Network accessline 671
- **GitHub**: https://github.com/pyg-team/pytorch_geometric - Network accessline 672
- **Tutorials**: https://pytorch-geometric.readthedocs.io/en/latest/get_started/introduction.html - Network accessline 673
- **Examples**: https://github.com/pyg-team/pytorch_geometric/tree/master/examples
skills/09-机器学习与人工智能/torch-geometric/scripts/benchmark_model.py
- Executable scriptline 1
#!/usr/bin/env python3
skills/09-机器学习与人工智能/torch-geometric/scripts/create_gnn_template.py
- Executable scriptline 1
#!/usr/bin/env python3
skills/09-机器学习与人工智能/torch-geometric/scripts/visualize_graph.py
- Executable scriptline 1
#!/usr/bin/env python3
skills/09-机器学习与人工智能/transformers/SKILL.md
skills/09-机器学习与人工智能/transformers/references/pipelines.md
- Network accessline 298
pipe("https://example.com/image.jpg")
skills/10-材料科学与物理计算/astropy/SKILL.md
skills/10-材料科学与物理计算/astropy/references/wcs_and_other_modules.md
skills/10-材料科学与物理计算/cirq/SKILL.md
- Network accessline 319
- **Official Documentation**: https://quantumai.google/cirq - Network accessline 320
- **API Reference**: https://quantumai.google/reference/python/cirq - Network accessline 321
- **Tutorials**: https://quantumai.google/cirq/tutorials - Network accessline 322
- **Examples**: https://github.com/quantumlib/Cirq/tree/master/examples - Network accessline 323
- **ReCirq**: https://github.com/quantumlib/ReCirq
skills/10-材料科学与物理计算/cirq/references/hardware.md
- Access to secretsline 163
[Credential-related source omitted] - Access to secretsline 166
[Credential-related source omitted] - Access to secretsline 175
[Credential-related source omitted] - Access to secretsline 294
[Credential-related source omitted] - Network accessline 296
# Create service service = cirq_aqt.AQTSampler( remote_host='https://gateway.aqt.eu', - Network accessline 297
[Credential-related source omitted] - Network accessline 298
remote_host='https://gateway.aqt.eu', - Access to secretsline 299
[Credential-related source omitted] - Network accessline 343
# Create sampler sampler = cirq_pasqal.PasqalSampler( remote_host='https://api.pasqal.cloud', - Network accessline 344
[Credential-related source omitted] - Network accessline 345
remote_host='https://api.pasqal.cloud', - Access to secretsline 346
[Credential-related source omitted] - Network accessline 470
# Install gcloud CLI # Visit: https://cloud.google.com/sdk/docs/install - Network accessline 471
# Visit: https://cloud.google.com/sdk/docs/install - Access to secretsline 483
[Credential-related source omitted] - Network accessline 488
# Use Azure CLI or workspace connection string # See: https://docs.microsoft.com/azure/quantum/ - Network accessline 489
# See: https://docs.microsoft.com/azure/quantum/ - Access to secretsline 495
[Credential-related source omitted] - Access to secretsline 501
[Credential-related source omitted]
skills/10-材料科学与物理计算/cobrapy/SKILL.md
- Network accessline 460
Official documentation: https://cobrapy.readthedocs.io/en/latest/
skills/10-材料科学与物理计算/cobrapy/references/api_quick_reference.md
- Network accessline 655
This quick reference covers the most commonly used COBRApy functions and patterns. For complete API documentation, see https://cobrapy.readthedocs.io/
skills/10-材料科学与物理计算/fluidsim/SKILL.md
- Network accessline 338
**Documentation**: https://fluidsim.readthedocs.io/
skills/10-材料科学与物理计算/fluidsim/references/advanced_features.md
skills/10-材料科学与物理计算/matlab/SKILL.md
- Network accessline 2
name: matlab description: MATLAB and GNU Octave numerical computing for matrix operations, data analysis, visualization, and scientific computing. Use when writing MATLAB/Octave scripts for linear algebra, signal processing, image processin - Network accessline 3
description: MATLAB and GNU Octave numerical computing for matrix operations, data analysis, visualization, and scientific computing. Use when writing MATLAB/Octave scripts for linear algebra, signal processing, image processing, differenti - Network accessline 4
license: For MATLAB (https://www.mathworks.com/pricing-licensing.html) and for Octave (GNU General Public License version 3) - Dangerous shell operationline 31
sudo apt install octave - Network accessline 33
# Windows - download from https://octave.org/download - Network accessline 369
- MATLAB Documentation: https://www.mathworks.com/help/matlab/ - Network accessline 370
- GNU Octave Manual: https://docs.octave.org/latest/ - Network accessline 371
- MATLAB Onramp (free course): https://www.mathworks.com/learn/tutorials/matlab-onramp.html - Network accessline 372
- File Exchange: https://www.mathworks.com/matlabcentral/fileexchange/
skills/10-材料科学与物理计算/matlab/references/octave-compatibility.md
- Dangerous shell operationline 24
sudo apt install octave - Dangerous shell operationline 27
sudo dnf install octave - Network accessline 29
# Windows # Download installer from https://octave.org/download - Network accessline 30
# Download installer from https://octave.org/download - Network accessline 493
% Install from URL pkg install 'http://example.com/package.tar.gz' - Network accessline 494
pkg install 'http://example.com/package.tar.gz'
skills/10-材料科学与物理计算/matlab/references/python-integration.md
skills/10-材料科学与物理计算/pennylane/SKILL.md
- Network accessline 219
- Official documentation: https://docs.pennylane.ai - Network accessline 220
- Codebook (tutorials): https://pennylane.ai/codebook - Network accessline 221
- QML demonstrations: https://pennylane.ai/qml/demonstrations - Network accessline 222
- Community forum: https://discuss.pennylane.ai - Network accessline 223
- GitHub: https://github.com/PennyLaneAI/pennylane
skills/10-材料科学与物理计算/pennylane/references/devices_backends.md
- Access to secretsline 255
[Credential-related source omitted]
skills/10-材料科学与物理计算/pennylane/references/getting_started.md
skills/10-材料科学与物理计算/pymatgen/SKILL.md
- Access to secretsline 70
[Credential-related source omitted] - Network accessline 369
**Setup:** 1. Get API key from https://next-gen.materialsproject.org/ - Network accessline 370
1. Get API key from https://next-gen.materialsproject.org/ - Access to secretsline 371
[Credential-related source omitted] - Access to secretsline 655
[Credential-related source omitted] - Access to secretsline 657
[Credential-related source omitted] - Network accessline 675
- **Documentation**: https://pymatgen.org/ - Network accessline 676
- **Materials Project**: https://materialsproject.org/ - Network accessline 677
- **GitHub**: https://github.com/materialsproject/pymatgen - Network accessline 678
- **Forum**: https://matsci.org/ - Network accessline 679
- **Example notebooks**: https://matgenb.materialsvirtuallab.org/
skills/10-材料科学与物理计算/pymatgen/references/materials_project_api.md
- Network accessline 19
1. Visit https://next-gen.materialsproject.org/ - Access to secretsline 26
[Credential-related source omitted] - Network accessline 504
- **API Documentation**: https://docs.materialsproject.org/ - Network accessline 505
- **Materials Project Website**: https://next-gen.materialsproject.org/ - Network accessline 506
- **GitHub**: https://github.com/materialsproject/api - Network accessline 507
- **Forum**: https://matsci.org/
skills/10-材料科学与物理计算/pymatgen/scripts/phase_diagram_generator.py
- Executable scriptline 1
#!/usr/bin/env python3 - Access to secretsline 37
[Credential-related source omitted] - Access to secretsline 39
[Credential-related source omitted] - Network accessline 40
[Credential-related source omitted] - Access to secretsline 40
[Credential-related source omitted] - Network accessline 41
[Credential-related source omitted] - Access to secretsline 41
[Credential-related source omitted] - Network accessline 42
print("Get your API key from https://next-gen.materialsproject.org/") - Access to secretsline 43
[Credential-related source omitted] - Access to secretsline 45
[Credential-related source omitted] - Access to secretsline 56
[Credential-related source omitted] - Access to secretsline 64
[Credential-related source omitted] - Access to secretsline 175
[Credential-related source omitted]
skills/10-材料科学与物理计算/pymatgen/scripts/structure_analyzer.py
- Executable scriptline 1
#!/usr/bin/env python3
skills/10-材料科学与物理计算/pymatgen/scripts/structure_converter.py
- Executable scriptline 1
#!/usr/bin/env python3
skills/10-材料科学与物理计算/qiskit/SKILL.md
- Network accessline 269
- **Official Docs**: https://quantum.ibm.com/docs - Network accessline 270
- **Qiskit Textbook**: https://qiskit.org/learn - Network accessline 271
- **API Reference**: https://docs.quantum.ibm.com/api/qiskit - Network accessline 272
- **Patterns Guide**: https://quantum.cloud.ibm.com/docs/en/guides/intro-to-patterns
skills/10-材料科学与物理计算/qiskit/references/algorithms.md
- Network accessline 598
**Official Documentation:** - [Qiskit Textbook](https://qiskit.org/learn) - Network accessline 599
- [Qiskit Textbook](https://qiskit.org/learn) - Network accessline 600
- [Qiskit Nature Documentation](https://qiskit.org/ecosystem/nature) - Network accessline 601
- [Qiskit Machine Learning Documentation](https://qiskit.org/ecosystem/machine-learning) - Network accessline 602
- [Qiskit Optimization Documentation](https://qiskit.org/ecosystem/optimization)
skills/10-材料科学与物理计算/qiskit/references/setup.md
- Network accessline 33
Check the [Qiskit PyPI page](https://pypi.org/project/qiskit/) for currently supported Python versions. As of 2025, Qiskit typically supports Python 3.8+. - Network accessline 41
1. Visit [IBM Quantum Platform](https://quantum.ibm.com/) - Access to secretsline 67
[Credential-related source omitted]
skills/10-材料科学与物理计算/qutip/SKILL.md
skills/10-材料科学与物理计算/simpy/scripts/basic_simulation_template.py
- Executable scriptline 1
#!/usr/bin/env python3
skills/10-材料科学与物理计算/simpy/scripts/resource_monitor.py
- Executable scriptline 1
#!/usr/bin/env python3
skills/11-数据分析与统计建模/exploratory-data-analysis/references/chemistry_molecular_formats.md
- Network accessline 290
### .nc / .netcdf - Amber NetCDF Trajectory
skills/11-数据分析与统计建模/exploratory-data-analysis/references/general_scientific_formats.md
- Network accessline 376
### .nc / .netcdf - Network Common Data Form
skills/11-数据分析与统计建模/exploratory-data-analysis/scripts/eda_analyzer.py
- Executable scriptline 1
#!/usr/bin/env python3
skills/11-数据分析与统计建模/matplotlib/SKILL.md
- Network accessline 2
name: matplotlib description: Low-level plotting library for full customization. Use when you need fine-grained control over every plot element, creating novel plot types, or integrating with specific scientific workflows. Export to PNG/PDF - Network accessline 3
description: Low-level plotting library for full customization. Use when you need fine-grained control over every plot element, creating novel plot types, or integrating with specific scientific workflows. Export to PNG/PDF/SVG for publicat - Network accessline 4
license: https://github.com/matplotlib/matplotlib/tree/main/LICENSE - Network accessline 355
- Official documentation: https://matplotlib.org/ - Network accessline 356
- Gallery: https://matplotlib.org/stable/gallery/index.html - Network accessline 357
- Cheatsheets: https://matplotlib.org/cheatsheets/ - Network accessline 358
- Tutorials: https://matplotlib.org/stable/tutorials/index.html
skills/11-数据分析与统计建模/matplotlib/references/api_reference.md
skills/11-数据分析与统计建模/matplotlib/scripts/plot_template.py
- Executable scriptline 1
#!/usr/bin/env python3
skills/11-数据分析与统计建模/matplotlib/scripts/style_configurator.py
- Executable scriptline 1
#!/usr/bin/env python3
skills/11-数据分析与统计建模/networkx/SKILL.md
- Network accessline 431
- **Official Documentation**: https://networkx.org/documentation/latest/ - Network accessline 432
- **Tutorial**: https://networkx.org/documentation/latest/tutorial.html - Network accessline 433
- **Gallery**: https://networkx.org/documentation/latest/auto_examples/index.html - Network accessline 434
- **GitHub**: https://github.com/networkx/networkx
skills/11-数据分析与统计建模/plotly/SKILL.md
skills/11-数据分析与统计建模/polars/SKILL.md
- Network accessline 2
name: polars description: Fast in-memory DataFrame library for datasets that fit in RAM. Use when pandas is too slow but data still fits in memory. Lazy evaluation, parallel execution, Apache Arrow backend. Best for 1-100GB datasets, ETL pi - Network accessline 3
description: Fast in-memory DataFrame library for datasets that fit in RAM. Use when pandas is too slow but data still fits in memory. Lazy evaluation, parallel execution, Apache Arrow backend. Best for 1-100GB datasets, ETL pipelines, fast - Network accessline 4
license: https://github.com/pola-rs/polars/blob/main/LICENSE
skills/11-数据分析与统计建模/polars/references/io_guide.md
- Access to secretsline 270
[Credential-related source omitted] - Access to secretsline 271
[Credential-related source omitted] - Access to secretsline 272
[Credential-related source omitted] - Access to secretsline 287
[Credential-related source omitted] - Access to secretsline 288
[Credential-related source omitted] - Access to secretsline 301
[Credential-related source omitted]
skills/11-数据分析与统计建模/statistical-analysis/SKILL.md
skills/11-数据分析与统计建模/statistical-analysis/references/reporting_standards.md
skills/11-数据分析与统计建模/statistical-analysis/scripts/assumption_checks.py
- Executable scriptline 1
Executable file
skills/11-数据分析与统计建模/statsmodels/SKILL.md
- Network accessline 607
For detailed documentation and examples: - Official docs: https://www.statsmodels.org/stable/ - Network accessline 608
- Official docs: https://www.statsmodels.org/stable/ - Network accessline 609
- User guide: https://www.statsmodels.org/stable/user-guide.html - Network accessline 610
- Examples: https://www.statsmodels.org/stable/examples/index.html - Network accessline 611
- API reference: https://www.statsmodels.org/stable/api.html
skills/11-数据分析与统计建模/sympy/SKILL.md
- Network accessline 2
name: sympy description: Use this skill when working with symbolic mathematics in Python. This skill should be used for symbolic computation tasks including solving equations algebraically, performing calculus operations (derivatives, integ - Network accessline 3
description: Use this skill when working with symbolic mathematics in Python. This skill should be used for symbolic computation tasks including solving equations algebraically, performing calculus operations (derivatives, integrals, limits - Network accessline 4
license: https://github.com/sympy/sympy/blob/master/LICENSE - Network accessline 494
- Official Documentation: https://docs.sympy.org/ - Network accessline 495
- Tutorial: https://docs.sympy.org/latest/tutorials/intro-tutorial/index.html - Network accessline 496
- API Reference: https://docs.sympy.org/latest/reference/index.html - Network accessline 497
- Examples: https://github.com/sympy/sympy/tree/master/examples
skills/11-数据分析与统计建模/sympy/references/code-generation-printing.md
- Dangerous shell operationline 205
# This can be eval()'ed to recreate the expression
skills/12-科学数据库/bindingdb-database/SKILL.md
- Network accessline 13
BindingDB (https://www.bindingdb.org/) is the primary public database of measured drug-protein binding affinities. It contains over 3 million binding data records for ~1.4 million compounds tested against ~9,200 protein targets, curated fro - Network accessline 15
**Key resources:** - BindingDB website: https://www.bindingdb.org/ - Network accessline 16
- BindingDB website: https://www.bindingdb.org/ - REST API: https://www.bindingdb.org/axis2/services/BDBService - Downloads: https://www.bindingdb.org/bind/chemsearch/marvin/Download.jsp - Network accessline 17
- REST API: https://www.bindingdb.org/axis2/services/BDBService - Downloads: https://www.bindingdb.org/bind/chemsearch/marvin/Download.jsp - GitHub: https://github.com/drugilsberg/bindingdb - Network accessline 18
- Downloads: https://www.bindingdb.org/bind/chemsearch/marvin/Download.jsp - Network accessline 19
- GitHub: https://github.com/drugilsberg/bindingdb - Network accessline 37
Base URL: `https://www.bindingdb.org/axis2/services/BDBService` - Network accessline 42
BASE_URL = "https://www.bindingdb.org/axis2/services/BDBService" - Network accessline 45
"""Query the BindingDB REST API.""" url = f"{BASE_URL}/{method}" response = requests.get(url, params=params, headers={"Accept": "application/json"}) - Network accessline 46
url = f"{BASE_URL}/{method}" response = requests.get(url, params=params, headers={"Accept": "application/json"}) response.raise_for_status() - Network accessline 47
response = requests.get(url, params=params, headers={"Accept": "application/json"}) - Network accessline 117
""" Load BindingDB TSV file. Download from: https://www.bindingdb.org/bind/chemsearch/marvin/Download.jsp - Network accessline 118
Load BindingDB TSV file. Download from: https://www.bindingdb.org/bind/chemsearch/marvin/Download.jsp """ - Network accessline 119
Download from: https://www.bindingdb.org/bind/chemsearch/marvin/Download.jsp - Network accessline 328
- **BindingDB website**: https://www.bindingdb.org/ - **Data downloads**: https://www.bindingdb.org/bind/chemsearch/marvin/Download.jsp - Network accessline 329
- **Data downloads**: https://www.bindingdb.org/bind/chemsearch/marvin/Download.jsp - Network accessline 330
- **API documentation**: https://www.bindingdb.org/bind/BindingDBRESTfulAPI.jsp - Network accessline 332
- **Related resources**: ChEMBL (https://www.ebi.ac.uk/chembl/), PubChem BioAssay
skills/12-科学数据库/bindingdb-database/references/affinity_queries.md
- Network accessline 42
REST API query for BindingDB affinities by UniProt target ID. """ url = "https://www.bindingdb.org/axis2/services/BDBService/getLigandsByUniprotID" - Network accessline 43
""" url = "https://www.bindingdb.org/axis2/services/BDBService/getLigandsByUniprotID" params = { - Network accessline 44
url = "https://www.bindingdb.org/axis2/services/BDBService/getLigandsByUniprotID" - Network accessline 49
"response": "json" } response = requests.get(url, params=params) - Network accessline 50
} response = requests.get(url, params=params) return response.json() - Network accessline 51
response = requests.get(url, params=params) - Network accessline 72
def query_by_pubchem_cid(pubchem_cid): """Get all binding data for a specific compound by PubChem CID.""" url = "https://www.bindingdb.org/axis2/services/BDBService/getAffinitiesByCID" - Network accessline 73
"""Get all binding data for a specific compound by PubChem CID.""" url = "https://www.bindingdb.org/axis2/services/BDBService/getAffinitiesByCID" params = {"cid": pubchem_cid, "response": "json"} - Network accessline 74
url = "https://www.bindingdb.org/axis2/services/BDBService/getAffinitiesByCID" params = {"cid": pubchem_cid, "response": "json"} response = requests.get(url, params=params) - Network accessline 75
params = {"cid": pubchem_cid, "response": "json"} response = requests.get(url, params=params) return response.json() - Network accessline 76
response = requests.get(url, params=params) - Network accessline 86
def query_by_target_name(target_name, affinity_cutoff=100): """Query BindingDB by target name.""" url = "https://www.bindingdb.org/axis2/services/BDBService/getAffinitiesByTarget" - Network accessline 87
"""Query BindingDB by target name.""" url = "https://www.bindingdb.org/axis2/services/BDBService/getAffinitiesByTarget" params = { - Network accessline 88
url = "https://www.bindingdb.org/axis2/services/BDBService/getAffinitiesByTarget" - Network accessline 92
"response": "json" } response = requests.get(url, params=params) - Network accessline 93
} response = requests.get(url, params=params) return response.json() - Network accessline 94
response = requests.get(url, params=params)
skills/12-科学数据库/brenda-database/SKILL.md
- Access to secretsline 305
[Credential-related source omitted] - Network accessline 317
3. **Register for BRENDA access**: - Visit https://www.brenda-enzymes.org/ - Network accessline 318
- Visit https://www.brenda-enzymes.org/ - Access to secretsline 677
[Credential-related source omitted] - Network accessline 713
- BRENDA Home: https://www.brenda-enzymes.org/ - Network accessline 714
- BRENDA SOAP API Documentation: https://www.brenda-enzymes.org/soap.php - Network accessline 715
- Enzyme Commission (EC) Numbers: https://www.qmul.ac.uk/sbcs/iubmb/enzyme/ - Network accessline 716
- Zeep SOAP Client: https://python-zeep.readthedocs.io/ - Network accessline 717
- Enzyme Nomenclature: https://www.iubmb.org/enzyme/
skills/12-科学数据库/brenda-database/references/api_reference.md
- Network accessline 11
https://www.brenda-enzymes.org/soap/brenda_zeep.wsdl - Access to secretsline 214
[Credential-related source omitted] - Access to secretsline 215
[Credential-related source omitted] - Access to secretsline 216
[Credential-related source omitted] - Access to secretsline 217
[Credential-related source omitted] - Network accessline 509
- **BRENDA Website**: https://www.brenda-enzymes.org/ - Network accessline 510
- **SOAP API Documentation**: https://www.brenda-enzymes.org/soap.php - Network accessline 511
- **Enzyme Nomenclature**: https://www.iubmb.org/enzyme/ - Network accessline 512
- **EC Number Database**: https://www.qmul.ac.uk/sbcs/iubmb/enzyme/ - Network accessline 516
- **Zeep (SOAP Client)**: https://python-zeep.readthedocs.io/ - Network accessline 517
- **PubChemPy**: https://pubchempy.readthedocs.io/ - Network accessline 518
- **BioPython**: https://biopython.org/ - Network accessline 519
- **RDKit**: https://www.rdkit.org/
skills/12-科学数据库/brenda-database/scripts/brenda_queries.py
- Executable scriptline 1
Executable file
skills/12-科学数据库/brenda-database/scripts/brenda_visualization.py
- Executable scriptline 1
Executable file
skills/12-科学数据库/brenda-database/scripts/enzyme_pathway_builder.py
- Executable scriptline 1
Executable file
skills/12-科学数据库/chembl-database/SKILL.md
- Network accessline 382
- ChEMBL website: https://www.ebi.ac.uk/chembl/ - Network accessline 383
- API documentation: https://www.ebi.ac.uk/chembl/api/data/docs - Network accessline 384
- Python client GitHub: https://github.com/chembl/chembl_webresource_client - Network accessline 385
- Interface documentation: https://chembl.gitbook.io/chembl-interface-documentation/ - Network accessline 386
- Example notebooks: https://github.com/chembl/notebooks
skills/12-科学数据库/chembl-database/references/api_reference.md
- Network accessline 269
- Official API documentation: https://www.ebi.ac.uk/chembl/api/data/docs - Network accessline 270
- Python client GitHub: https://github.com/chembl/chembl_webresource_client - Network accessline 271
- ChEMBL interface docs: https://chembl.gitbook.io/chembl-interface-documentation/ - Network accessline 272
- Example notebooks: https://github.com/chembl/notebooks
skills/12-科学数据库/chembl-database/scripts/example_queries.py
- Executable scriptline 1
#!/usr/bin/env python3
skills/12-科学数据库/datacommons-client/SKILL.md
- Network accessline 49
# Get latest population data response = client.observation.fetch( - Network accessline 50
response = client.observation.fetch( - Network accessline 56
# Get time series response = client.observation.fetch( - Network accessline 57
response = client.observation.fetch( - Network accessline 63
# Query by hierarchy response = client.observation.fetch( - Network accessline 64
response = client.observation.fetch( - Network accessline 154
response = client.observation.fetch( - Network accessline 189
# Or explore via the web interface # https://datacommons.org/tools/statvar - Network accessline 190
# https://datacommons.org/tools/statvar - Network accessline 198
response = client.observation.fetch( - Network accessline 220
[Credential-related source omitted] - Access to secretsline 220
[Credential-related source omitted] - Access to secretsline 221
[Credential-related source omitted] - Network accessline 222
- Request keys at: https://apikeys.datacommons.org/ - Network accessline 224
**For custom Data Commons instances:** - No API key required - Specify custom endpoint: `client = DataCommonsClient(url="https://custom.datacommons.org")` - Network accessline 226
- Specify custom endpoint: `client = DataCommonsClient(url="https://custom.datacommons.org")` - Network accessline 239
- **Official Documentation**: https://docs.datacommons.org/api/python/v2/ - Network accessline 240
- **Statistical Variable Explorer**: https://datacommons.org/tools/statvar - Network accessline 241
- **Data Commons Browser**: https://datacommons.org/browser/ - Network accessline 242
- **GitHub Repository**: https://github.com/datacommonsorg/api-python
skills/12-科学数据库/datacommons-client/references/getting_started.md
- Access to secretsline 14
[Credential-related source omitted] - Network accessline 17
Request an API key at: https://apikeys.datacommons.org/ - Network accessline 43
# Step 2: Query population data response = client.observation.fetch( - Network accessline 44
response = client.observation.fetch( - Network accessline 68
# Query unemployment rate over time response = client.observation.fetch( - Network accessline 69
response = client.observation.fetch( - Network accessline 96
# Query median income for all California counties response = client.observation.fetch( - Network accessline 97
response = client.observation.fetch( - Network accessline 149
response = client.observation.fetch( - Network accessline 200
# Step 4: Query specific variables response = client.observation.fetch( - Network accessline 201
response = client.observation.fetch( - Network accessline 221
# Query with facet filtering response = client.observation.fetch( - Network accessline 222
response = client.observation.fetch( - Network accessline 232
# Compare with all sources response_all = client.observation.fetch( - Network accessline 233
response_all = client.observation.fetch( - Network accessline 334
response = client.observation.fetch( - Network accessline 414
1. Explore available statistical variables: https://datacommons.org/tools/statvar - Network accessline 415
2. Browse the knowledge graph: https://datacommons.org/browser/ - Network accessline 417
4. Check official documentation: https://docs.datacommons.org/api/python/v2/
skills/12-科学数据库/datacommons-client/references/node.md
- Network accessline 16
### 1. fetch() - Network accessline 37
# Get outgoing properties from California response = client.node.fetch( - Network accessline 38
response = client.node.fetch( - Network accessline 43
# Get incoming properties (what points to this node) response = client.node.fetch( - Network accessline 44
response = client.node.fetch( - Network accessline 193
# First page response = client.node.fetch( - Network accessline 194
response = client.node.fetch( - Network accessline 200
# Get next page if available if response.nextToken: next_response = client.node.fetch( - Network accessline 201
if response.nextToken: next_response = client.node.fetch( node_dcids=["country/USA"], - Network accessline 202
next_response = client.node.fetch( - Network accessline 238
# Find all entities that reference a specific node references = client.node.fetch( - Network accessline 239
references = client.node.fetch(
skills/12-科学数据库/datacommons-client/references/observation.md
- Network accessline 12
### 1. fetch() - Network accessline 42
# Get latest population for multiple entities response = client.observation.fetch( - Network accessline 43
response = client.observation.fetch( - Network accessline 49
# Get complete time series response = client.observation.fetch( - Network accessline 50
response = client.observation.fetch( - Network accessline 56
# Use relation expressions to query hierarchies response = client.observation.fetch( - Network accessline 57
response = client.observation.fetch( - Network accessline 81
Explicit method targeting specific entities by DCID (functionally equivalent to `fetch()` with entity_dcids). - Network accessline 119
response = client.observation.fetch( - Network accessline 130
response = client.observation.fetch( - Network accessline 141
response = client.observation.fetch( - Network accessline 152
# Get data for all counties within California response = client.observation.fetch( - Network accessline 153
response = client.observation.fetch( - Network accessline 169
response = client.observation.fetch( - Network accessline 183
- **orderedFacets** are sorted by reliability/recency - Use relation expressions for complex graph queries - The `fetch()` method is the most flexible—use it for most queries - Network accessline 185
- The `fetch()` method is the most flexible—use it for most queries
skills/12-科学数据库/datacommons-client/references/resolve.md
- Network accessline 17
### 1. fetch() - Network accessline 31
# Resolve by name response = client.resolve.fetch( - Network accessline 32
response = client.resolve.fetch( - Network accessline 145
# Step 3: Query data using DCIDs data_response = client.observation.fetch( - Network accessline 146
data_response = client.observation.fetch( - Network accessline 202
# Now query data for that place response = client.observation.fetch( - Network accessline 203
response = client.observation.fetch(
skills/12-科学数据库/drugbank-database/references/chemical-analysis.md
- Network accessline 21
"""Extract chemical structure representations""" root = get_drugbank_root() ns = {'db': 'http://www.drugbank.ca'} - Network accessline 23
ns = {'db': 'http://www.drugbank.ca'} - Network accessline 73
"""Extract all calculated and experimental properties""" root = get_drugbank_root() ns = {'db': 'http://www.drugbank.ca'} - Network accessline 75
ns = {'db': 'http://www.drugbank.ca'} - Network accessline 218
"""Find structurally similar drugs in DrugBank""" root = get_drugbank_root() ns = {'db': 'http://www.drugbank.ca'} - Network accessline 220
ns = {'db': 'http://www.drugbank.ca'} - Network accessline 337
"""Find drugs containing a specific substructure""" root = get_drugbank_root() ns = {'db': 'http://www.drugbank.ca'} - Network accessline 339
ns = {'db': 'http://www.drugbank.ca'} - Network accessline 544
"""Export all chemical properties to CSV""" root = get_drugbank_root() ns = {'db': 'http://www.drugbank.ca'} - Network accessline 546
ns = {'db': 'http://www.drugbank.ca'}
skills/12-科学数据库/drugbank-database/references/data-access.md
- Network accessline 146
# Query by DrugBank ID drug_id = "DB00001" url = f"https://go.drugbank.com/drugs/{drug_id}.json" - Network accessline 147
[Credential-related source omitted] - Network accessline 148
url = f"https://go.drugbank.com/drugs/{drug_id}.json" - Access to secretsline 149
[Credential-related source omitted] - Network accessline 151
response = requests.get(url, headers=headers)
skills/12-科学数据库/drugbank-database/references/drug-queries.md
- Network accessline 62
# Define namespace ns = {'db': 'http://www.drugbank.ca'} - Network accessline 63
ns = {'db': 'http://www.drugbank.ca'} - Network accessline 77
"""Retrieve drug entry by DrugBank ID (e.g., 'DB00001')""" root = get_drugbank_root() ns = {'db': 'http://www.drugbank.ca'} - Network accessline 79
ns = {'db': 'http://www.drugbank.ca'} - Network accessline 97
"""Find drug by name (case-insensitive)""" root = get_drugbank_root() ns = {'db': 'http://www.drugbank.ca'} - Network accessline 99
ns = {'db': 'http://www.drugbank.ca'} - Network accessline 121
"""Find drug by CAS registry number""" root = get_drugbank_root() ns = {'db': 'http://www.drugbank.ca'} - Network accessline 123
ns = {'db': 'http://www.drugbank.ca'} - Network accessline 136
def extract_basic_info(drug): """Extract essential drug information""" ns = {'db': 'http://www.drugbank.ca'} - Network accessline 138
ns = {'db': 'http://www.drugbank.ca'} - Network accessline 157
def extract_chemical_properties(drug): """Extract chemical structure and properties""" ns = {'db': 'http://www.drugbank.ca'} - Network accessline 159
ns = {'db': 'http://www.drugbank.ca'} - Network accessline 195
def extract_pharmacology(drug): """Extract pharmacological information""" ns = {'db': 'http://www.drugbank.ca'} - Network accessline 197
ns = {'db': 'http://www.drugbank.ca'} - Network accessline 217
def extract_external_identifiers(drug): """Extract cross-references to other databases""" ns = {'db': 'http://www.drugbank.ca'} - Network accessline 219
ns = {'db': 'http://www.drugbank.ca'} - Network accessline 249
"""Build searchable dictionary of all drugs""" root = get_drugbank_root() ns = {'db': 'http://www.drugbank.ca'} - Network accessline 251
ns = {'db': 'http://www.drugbank.ca'} - Network accessline 281
"""Create pandas DataFrame of drug information""" root = get_drugbank_root() ns = {'db': 'http://www.drugbank.ca'} - Network accessline 283
ns = {'db': 'http://www.drugbank.ca'} - Network accessline 309
"""Get drugs of specific type""" root = get_drugbank_root() ns = {'db': 'http://www.drugbank.ca'} - Network accessline 311
ns = {'db': 'http://www.drugbank.ca'} - Network accessline 330
"""Search drugs by keyword in specific field""" root = get_drugbank_root() ns = {'db': 'http://www.drugbank.ca'} - Network accessline 332
ns = {'db': 'http://www.drugbank.ca'} - Network accessline 360
"""Build indexes for faster lookups""" root = get_drugbank_root() ns = {'db': 'http://www.drugbank.ca'} - Network accessline 362
ns = {'db': 'http://www.drugbank.ca'}
skills/12-科学数据库/drugbank-database/references/interactions.md
- Network accessline 36
"""Get all interactions for a specific drug""" root = get_drugbank_root() ns = {'db': 'http://www.drugbank.ca'} - Network accessline 38
ns = {'db': 'http://www.drugbank.ca'} - Network accessline 68
"""Build complete interaction network (all drug pairs)""" root = get_drugbank_root() ns = {'db': 'http://www.drugbank.ca'} - Network accessline 70
ns = {'db': 'http://www.drugbank.ca'} - Network accessline 97
"""Rank drugs by number of known interactions""" root = get_drugbank_root() ns = {'db': 'http://www.drugbank.ca'} - Network accessline 99
ns = {'db': 'http://www.drugbank.ca'} - Network accessline 273
"""Export all interactions as edge list (CSV)""" root = get_drugbank_root() ns = {'db': 'http://www.drugbank.ca'} - Network accessline 275
ns = {'db': 'http://www.drugbank.ca'}
skills/12-科学数据库/drugbank-database/references/targets-pathways.md
- Network accessline 74
"""Extract all targets for a drug""" root = get_drugbank_root() ns = {'db': 'http://www.drugbank.ca'} - Network accessline 76
ns = {'db': 'http://www.drugbank.ca'} - Network accessline 124
"""Get targets, enzymes, transporters, and carriers for a drug""" root = get_drugbank_root() ns = {'db': 'http://www.drugbank.ca'} - Network accessline 126
ns = {'db': 'http://www.drugbank.ca'} - Network accessline 168
"""Build matrix of drugs vs targets""" root = get_drugbank_root() ns = {'db': 'http://www.drugbank.ca'} - Network accessline 170
ns = {'db': 'http://www.drugbank.ca'} - Network accessline 207
"""Find all drugs that target a specific protein""" root = get_drugbank_root() ns = {'db': 'http://www.drugbank.ca'} - Network accessline 209
ns = {'db': 'http://www.drugbank.ca'} - Network accessline 272
"""Extract pathway information for a drug""" root = get_drugbank_root() ns = {'db': 'http://www.drugbank.ca'} - Network accessline 274
ns = {'db': 'http://www.drugbank.ca'} - Network accessline 315
"""Build network of pathways and drugs""" root = get_drugbank_root() ns = {'db': 'http://www.drugbank.ca'} - Network accessline 317
ns = {'db': 'http://www.drugbank.ca'} - Network accessline 350
root = get_drugbank_root() ns = {'db': 'http://www.drugbank.ca'} - Network accessline 351
ns = {'db': 'http://www.drugbank.ca'} - Network accessline 482
"""Extract Gene Ontology terms for drug targets""" root = get_drugbank_root() ns = {'db': 'http://www.drugbank.ca'} - Network accessline 484
ns = {'db': 'http://www.drugbank.ca'}
skills/12-科学数据库/drugbank-database/scripts/drugbank_helper.py
skills/12-科学数据库/ena-database/SKILL.md
- Network accessline 56
**ENA Portal API** - Advanced search functionality across all ENA data types - Documentation: https://www.ebi.ac.uk/ena/portal/api/doc - Network accessline 57
- Documentation: https://www.ebi.ac.uk/ena/portal/api/doc - Network accessline 60
**ENA Browser API** - Direct retrieval of records and metadata - Documentation: https://www.ebi.ac.uk/ena/browser/api/doc - Network accessline 61
- Documentation: https://www.ebi.ac.uk/ena/browser/api/doc - Network accessline 68
**ENA Cross Reference Service** - Access related records from external databases - Endpoint: https://www.ebi.ac.uk/ena/xref/rest/ - Network accessline 69
- Endpoint: https://www.ebi.ac.uk/ena/xref/rest/ - Network accessline 71
**CRAM Reference Registry** - Retrieve reference sequences - Endpoint: https://www.ebi.ac.uk/ena/cram/ - Network accessline 72
- Endpoint: https://www.ebi.ac.uk/ena/cram/ - Network accessline 94
# Search for samples from a specific study base_url = "https://www.ebi.ac.uk/ena/portal/api/search" - Network accessline 95
base_url = "https://www.ebi.ac.uk/ena/portal/api/search" - Network accessline 103
response = requests.get(base_url, params=params) - Network accessline 130
# Download run files using Browser API accession = "ERR123456" url = f"https://www.ebi.ac.uk/ena/browser/api/xml/{accession}" - Network accessline 132
url = f"https://www.ebi.ac.uk/ena/browser/api/xml/{accession}" - Network accessline 137
# Use Portal API to list samples study_id = "PRJNA123456" url = f"https://www.ebi.ac.uk/ena/portal/api/search?result=sample&query=study_accession={study_id}&format=tsv" - Network accessline 139
url = f"https://www.ebi.ac.uk/ena/portal/api/search?result=sample&query=study_accession={study_id}&format=tsv" - Network accessline 144
# Search assemblies by taxonomy organism = "Escherichia coli" url = f"https://www.ebi.ac.uk/ena/portal/api/search?result=assembly&query=tax_tree({organism})&format=json" - Network accessline 146
url = f"https://www.ebi.ac.uk/ena/portal/api/search?result=assembly&query=tax_tree({organism})&format=json" - Network accessline 151
# Query taxonomy API taxon_id = "562" # E. coli url = f"https://www.ebi.ac.uk/ena/taxonomy/rest/tax-id/{taxon_id}" - Network accessline 153
url = f"https://www.ebi.ac.uk/ena/taxonomy/rest/tax-id/{taxon_id}"
skills/12-科学数据库/ena-database/references/api_reference.md
- Network accessline 7
**Base URL:** `https://www.ebi.ac.uk/ena/portal/api` - Network accessline 9
**Official Documentation:** https://www.ebi.ac.uk/ena/portal/api/doc - Network accessline 66
# Search for human samples url = "https://www.ebi.ac.uk/ena/portal/api/search" - Network accessline 67
url = "https://www.ebi.ac.uk/ena/portal/api/search" - Network accessline 73
"limit": 100 } response = requests.get(url, params=params) - Network accessline 75
response = requests.get(url, params=params) - Network accessline 81
"format": "tsv" } response = requests.get(url, params=params) - Network accessline 83
response = requests.get(url, params=params) - Network accessline 89
"format": "json" } response = requests.get(url, params=params) - Network accessline 91
response = requests.get(url, params=params) - Network accessline 112
# Get all available fields for samples url = "https://www.ebi.ac.uk/ena/portal/api/returnFields" - Network accessline 113
url = "https://www.ebi.ac.uk/ena/portal/api/returnFields" params = {"result": "sample"} response = requests.get(url, params=params) - Network accessline 114
params = {"result": "sample"} response = requests.get(url, params=params) fields = response.json() - Network accessline 115
response = requests.get(url, params=params) - Network accessline 130
url = "https://www.ebi.ac.uk/ena/portal/api/results" response = requests.get(url) - Network accessline 131
response = requests.get(url) - Network accessline 164
# Get FASTQ download URLs for a run url = "https://www.ebi.ac.uk/ena/portal/api/filereport" - Network accessline 165
url = "https://www.ebi.ac.uk/ena/portal/api/filereport" - Network accessline 170
"fields": "run_accession,fastq_ftp,fastq_md5,fastq_bytes" } response = requests.get(url, params=params) - Network accessline 171
} response = requests.get(url, params=params) file_info = response.json() - Network accessline 172
response = requests.get(url, params=params) - Network accessline 183
**Base URL:** `https://www.ebi.ac.uk/ena/browser/api` - Network accessline 185
**Official Documentation:** https://www.ebi.ac.uk/ena/browser/api/doc - Network accessline 206
# Get sample metadata in XML accession = "SAMEA123456" url = f"https://www.ebi.ac.uk/ena/browser/api/xml/{accession}" - Network accessline 207
accession = "SAMEA123456" url = f"https://www.ebi.ac.uk/ena/browser/api/xml/{accession}" response = requests.get(url) - Network accessline 208
url = f"https://www.ebi.ac.uk/ena/browser/api/xml/{accession}" response = requests.get(url) xml_data = response.text - Network accessline 209
response = requests.get(url) - Network accessline 212
# Get study with cross-references url = f"https://www.ebi.ac.uk/ena/browser/api/xml/PRJNA123456" - Network accessline 213
url = f"https://www.ebi.ac.uk/ena/browser/api/xml/PRJNA123456" params = {"includeLinks": "true"} response = requests.get(url, params=params) - Network accessline 215
response = requests.get(url, params=params) - Network accessline 238
# Get sequence in EMBL format url = "https://www.ebi.ac.uk/ena/browser/api/text/LN847353" response = requests.get(url) - Network accessline 239
url = "https://www.ebi.ac.uk/ena/browser/api/text/LN847353" response = requests.get(url) embl_format = response.text - Network accessline 240
response = requests.get(url) - Network accessline 264
# Get full sequence url = "https://www.ebi.ac.uk/ena/browser/api/fasta/LN847353" response = requests.get(url) - Network accessline 265
url = "https://www.ebi.ac.uk/ena/browser/api/fasta/LN847353" response = requests.get(url) fasta_data = response.text - Network accessline 266
response = requests.get(url) - Network accessline 269
# Get subsequence url = "https://www.ebi.ac.uk/ena/browser/api/fasta/LN847353" - Network accessline 270
url = "https://www.ebi.ac.uk/ena/browser/api/fasta/LN847353" params = {"range": "1000-2000"} response = requests.get(url, params=params) - Network accessline 272
response = requests.get(url, params=params) - Network accessline 294
# Get all links for a sample url = "https://www.ebi.ac.uk/ena/browser/api/links/sample/SAMEA123456" response = requests.get(url) - Network accessline 295
url = "https://www.ebi.ac.uk/ena/browser/api/links/sample/SAMEA123456" - Network accessline 296
response = requests.get(url) - Network accessline 301
**Base URL:** `https://www.ebi.ac.uk/ena/taxonomy/rest` - Network accessline 316
# Get E. coli taxonomy taxon_id = "562" url = f"https://www.ebi.ac.uk/ena/taxonomy/rest/tax-id/{taxon_id}" - Network accessline 317
taxon_id = "562" url = f"https://www.ebi.ac.uk/ena/taxonomy/rest/tax-id/{taxon_id}" response = requests.get(url) - Network accessline 318
url = f"https://www.ebi.ac.uk/ena/taxonomy/rest/tax-id/{taxon_id}" response = requests.get(url) taxonomy = response.json() - Network accessline 319
response = requests.get(url) - Network accessline 335
# Search by scientific name name = "Escherichia coli" url = f"https://www.ebi.ac.uk/ena/taxonomy/rest/scientific-name/{name}" - Network accessline 336
name = "Escherichia coli" url = f"https://www.ebi.ac.uk/ena/taxonomy/rest/scientific-name/{name}" response = requests.get(url) - Network accessline 337
url = f"https://www.ebi.ac.uk/ena/taxonomy/rest/scientific-name/{name}" - Network accessline 338
response = requests.get(url) - Network accessline 352
# Get suggestions partial = "Escheri" url = f"https://www.ebi.ac.uk/ena/taxonomy/rest/suggest-for-submission/{partial}" - Network accessline 353
partial = "Escheri" url = f"https://www.ebi.ac.uk/ena/taxonomy/rest/suggest-for-submission/{partial}" response = requests.get(url) - Network accessline 354
url = f"https://www.ebi.ac.uk/ena/taxonomy/rest/suggest-for-submission/{partial}" - Network accessline 355
response = requests.get(url) - Network accessline 360
**Base URL:** `https://www.ebi.ac.uk/ena/xref/rest` - Network accessline 382
# Get cross-references for an SRA accession url = "https://www.ebi.ac.uk/ena/xref/rest/json/sra/SRR000001" response = requests.get(url) - Network accessline 383
url = "https://www.ebi.ac.uk/ena/xref/rest/json/sra/SRR000001" response = requests.get(url) xrefs = response.json() - Network accessline 384
response = requests.get(url) - Network accessline 390
**Base URL:** `https://www.ebi.ac.uk/ena/cram` - Network accessline 405
# Get reference by MD5 md5 = "7c3f69f0c5f0f0de6d7c34e7c2e25f5c" url = f"https://www.ebi.ac.uk/ena/cram/md5/{md5}" - Network accessline 406
md5 = "7c3f69f0c5f0f0de6d7c34e7c2e25f5c" url = f"https://www.ebi.ac.uk/ena/cram/md5/{md5}" response = requests.get(url) - Network accessline 407
url = f"https://www.ebi.ac.uk/ena/cram/md5/{md5}" response = requests.get(url) reference_fasta = response.text - Network accessline 408
response = requests.get(url) - Network accessline 436
def create_session_with_retries(): """Create requests session with retry logic""" session = requests.Session() - Network accessline 437
"""Create requests session with retry logic""" session = requests.Session() retries = Retry( - Network accessline 438
session = requests.Session() - Network accessline 475
accessions = ["ERR001", "ERR002", "ERR003"] for acc in accessions: response = requests.get(f"{base_url}/xml/{acc}") - Network accessline 476
for acc in accessions: response = requests.get(f"{base_url}/xml/{acc}") # Process response - Network accessline 477
response = requests.get(f"{base_url}/xml/{acc}")
skills/12-科学数据库/ensembl-database/SKILL.md
- Network accessline 63
server = "https://rest.ensembl.org" - Network accessline 65
# Symbol lookup response = requests.get( - Network accessline 66
response = requests.get( - Network accessline 172
**Important:** Use `https://grch37.rest.ensembl.org` for GRCh37/hg19 queries and `https://rest.ensembl.org` for current assemblies. - Network accessline 207
def query_ensembl(endpoint, params=None, max_retries=3): server = "https://rest.ensembl.org" - Network accessline 208
server = "https://rest.ensembl.org" - Network accessline 211
for attempt in range(max_retries): response = requests.get( - Network accessline 212
response = requests.get( - Network accessline 304
- **Official Documentation:** https://rest.ensembl.org/documentation - Network accessline 305
- **Python Package Docs:** https://ensemblrest.readthedocs.io - Network accessline 306
- **EBI Training:** https://www.ebi.ac.uk/training/online/courses/ensembl-rest-api/ - Network accessline 307
- **Ensembl Browser:** https://useast.ensembl.org - Network accessline 308
- **GitHub Examples:** https://github.com/Ensembl/ensembl-rest/wiki
skills/12-科学数据库/ensembl-database/references/api_endpoints.md
skills/12-科学数据库/ensembl-database/scripts/ensembl_query.py
- Executable scriptline 1
#!/usr/bin/env python3 - Network accessline 22
def __init__(self, server: str = "https://rest.ensembl.org", rate_limit: int = 15): - Network accessline 81
try: if method == "POST": response = requests.post(url, headers=headers, json=data) - Network accessline 82
if method == "POST": response = requests.post(url, headers=headers, json=data) else: - Network accessline 83
response = requests.post(url, headers=headers, json=data) - Network accessline 85
response = requests.get(url, headers=headers, params=params) - Network accessline 157
headers = {"Content-Type": "text/x-fasta"} url = f"{self.server}{endpoint}" response = requests.get(url, headers=headers, params=params) - Network accessline 158
url = f"{self.server}{endpoint}" response = requests.get(url, headers=headers, params=params) return response.text - Network accessline 159
response = requests.get(url, headers=headers, params=params) - Network accessline 184
headers = {"Content-Type": "text/x-fasta"} url = f"{self.server}{endpoint}" response = requests.get(url, headers=headers) - Network accessline 185
url = f"{self.server}{endpoint}" response = requests.get(url, headers=headers) return response.text - Network accessline 186
response = requests.get(url, headers=headers) - Network accessline 356
server = "https://rest.ensembl.org" - Network accessline 358
server = "https://grch37.rest.ensembl.org"
skills/12-科学数据库/gene-database/SKILL.md
skills/12-科学数据库/gene-database/references/api_reference.md
- Network accessline 21
https://eutils.ncbi.nlm.nih.gov/entrez/eutils/ - Access to secretsline 27
[Credential-related source omitted] - Network accessline 55
curl "https://eutils.ncbi.nlm.nih.gov/entrez/eutils/esearch.fcgi?db=gene&term=BRCA1[gene]+AND+human[organism]&retmode=json" - Network accessline 87
curl "https://eutils.ncbi.nlm.nih.gov/entrez/eutils/esummary.fcgi?db=gene&id=672&retmode=json" - Network accessline 129
curl "https://eutils.ncbi.nlm.nih.gov/entrez/eutils/efetch.fcgi?db=gene&id=672&retmode=xml" - Network accessline 155
# Get related PubMed articles curl "https://eutils.ncbi.nlm.nih.gov/entrez/eutils/elink.fcgi?dbfrom=gene&db=pubmed&id=672&retmode=json" - Network accessline 156
curl "https://eutils.ncbi.nlm.nih.gov/entrez/eutils/elink.fcgi?dbfrom=gene&db=pubmed&id=672&retmode=json" - Network accessline 171
curl "https://eutils.ncbi.nlm.nih.gov/entrez/eutils/einfo.fcgi?db=gene&retmode=json" - Network accessline 183
https://api.ncbi.nlm.nih.gov/datasets/v2alpha/gene - Access to secretsline 191
[Credential-related source omitted] - Network accessline 203
curl "https://api.ncbi.nlm.nih.gov/datasets/v2alpha/gene/id/672" - Network accessline 262
curl "https://api.ncbi.nlm.nih.gov/datasets/v2alpha/gene/symbol/BRCA1/taxon/9606" - Network accessline 282
curl -X POST "https://api.ncbi.nlm.nih.gov/datasets/v2alpha/gene/id" \ - Network accessline 293
1. Create an NCBI account at https://www.ncbi.nlm.nih.gov/account/ - Network accessline 401
- **E-utilities Documentation:** https://www.ncbi.nlm.nih.gov/books/NBK25501/ - Network accessline 402
- **Datasets API Documentation:** https://www.ncbi.nlm.nih.gov/datasets/docs/v2/ - Network accessline 403
- **Gene Database Help:** https://www.ncbi.nlm.nih.gov/gene/ - Network accessline 404
- **API Key Registration:** https://www.ncbi.nlm.nih.gov/account/
skills/12-科学数据库/gene-database/references/common_workflows.md
- Access to secretsline 47
[Credential-related source omitted] - Access to secretsline 50
[Credential-related source omitted] - Network accessline 166
# Get HomoloGene links for a gene curl "https://eutils.ncbi.nlm.nih.gov/entrez/eutils/elink.fcgi?dbfrom=gene&db=homologene&id=7157&retmode=json" - Network accessline 167
curl "https://eutils.ncbi.nlm.nih.gov/entrez/eutils/elink.fcgi?dbfrom=gene&db=homologene&id=7157&retmode=json" - Network accessline 208
# Search for pathway genes query = "MAPK signaling pathway[pathway] AND human[organism]" url = f"https://eutils.ncbi.nlm.nih.gov/entrez/eutils/esearch.fcgi?db=gene&term={query}&retmode=json&retmax=200" - Network accessline 210
url = f"https://eutils.ncbi.nlm.nih.gov/entrez/eutils/esearch.fcgi?db=gene&term={query}&retmode=json&retmax=200" - Network accessline 212
with urllib.request.urlopen(url) as response: - Network accessline 253
# Get ClinVar records for a gene curl "https://eutils.ncbi.nlm.nih.gov/entrez/eutils/elink.fcgi?dbfrom=gene&db=clinvar&id=672&retmode=json" - Network accessline 254
curl "https://eutils.ncbi.nlm.nih.gov/entrez/eutils/elink.fcgi?dbfrom=gene&db=clinvar&id=672&retmode=json" - Network accessline 307
# Get all publications for BRCA1 curl "https://eutils.ncbi.nlm.nih.gov/entrez/eutils/elink.fcgi?dbfrom=gene&db=pubmed&id=672&retmode=json" - Network accessline 308
curl "https://eutils.ncbi.nlm.nih.gov/entrez/eutils/elink.fcgi?dbfrom=gene&db=pubmed&id=672&retmode=json" - Network accessline 329
# Get publications for gene url = f"https://eutils.ncbi.nlm.nih.gov/entrez/eutils/elink.fcgi?dbfrom=gene&db=pubmed&id={gene_id}&retmode=json" - Network accessline 330
url = f"https://eutils.ncbi.nlm.nih.gov/entrez/eutils/elink.fcgi?dbfrom=gene&db=pubmed&id={gene_id}&retmode=json" - Network accessline 332
with urllib.request.urlopen(url) as response:
skills/12-科学数据库/gene-database/scripts/batch_gene_lookup.py
- Executable scriptline 1
#!/usr/bin/env python3 - Access to secretsline 41
[Credential-related source omitted] - Access to secretsline 48
[Credential-related source omitted] - Network accessline 53
base_url = "https://eutils.ncbi.nlm.nih.gov/entrez/eutils/" - Access to secretsline 57
[Credential-related source omitted] - Access to secretsline 72
[Credential-related source omitted] - Access to secretsline 73
[Credential-related source omitted] - Network accessline 77
try: with urllib.request.urlopen(url) as response: - Network accessline 78
with urllib.request.urlopen(url) as response: - Access to secretsline 96
[Credential-related source omitted] - Access to secretsline 103
[Credential-related source omitted] - Network accessline 109
base_url = "https://eutils.ncbi.nlm.nih.gov/entrez/eutils/" - Access to secretsline 113
[Credential-related source omitted] - Access to secretsline 125
[Credential-related source omitted] - Access to secretsline 126
[Credential-related source omitted] - Network accessline 130
try: with urllib.request.urlopen(url) as response: - Network accessline 131
with urllib.request.urlopen(url) as response: - Access to secretsline 147
[Credential-related source omitted] - Access to secretsline 153
[Credential-related source omitted] - Access to secretsline 158
[Credential-related source omitted] - Access to secretsline 183
[Credential-related source omitted] - Access to secretsline 190
[Credential-related source omitted] - Access to secretsline 197
[Credential-related source omitted] - Access to secretsline 209
[Credential-related source omitted] - Access to secretsline 276
[Credential-related source omitted] - Access to secretsline 279
[Credential-related source omitted]
skills/12-科学数据库/gene-database/scripts/fetch_gene_data.py
- Executable scriptline 1
#!/usr/bin/env python3 - Network accessline 17
DATASETS_API_BASE = "https://api.ncbi.nlm.nih.gov/datasets/v2alpha/gene" - Access to secretsline 56
[Credential-related source omitted] - Access to secretsline 62
[Credential-related source omitted] - Access to secretsline 70
[Credential-related source omitted] - Access to secretsline 71
[Credential-related source omitted] - Network accessline 73
try: req = urllib.request.Request(url, headers=headers) - Network accessline 74
req = urllib.request.Request(url, headers=headers) - Network accessline 75
with urllib.request.urlopen(req) as response: - Access to secretsline 87
[Credential-related source omitted] - Access to secretsline 94
[Credential-related source omitted] - Access to secretsline 108
[Credential-related source omitted] - Access to secretsline 109
[Credential-related source omitted] - Network accessline 111
try: req = urllib.request.Request(url, headers=headers) - Network accessline 112
req = urllib.request.Request(url, headers=headers) - Network accessline 113
with urllib.request.urlopen(req) as response: - Access to secretsline 125
[Credential-related source omitted] - Access to secretsline 131
[Credential-related source omitted] - Access to secretsline 142
[Credential-related source omitted] - Access to secretsline 143
[Credential-related source omitted] - Network accessline 145
try: req = urllib.request.Request(url, data=data, headers=headers, method='POST') - Network accessline 146
req = urllib.request.Request(url, data=data, headers=headers, method='POST') - Network accessline 147
with urllib.request.urlopen(req) as response: - Access to secretsline 260
[Credential-related source omitted] - Access to secretsline 262
[Credential-related source omitted] - Access to secretsline 264
[Credential-related source omitted]
skills/12-科学数据库/gene-database/scripts/query_gene.py
- Executable scriptline 1
#!/usr/bin/env python3 - Network accessline 19
BASE_URL = "https://eutils.ncbi.nlm.nih.gov/entrez/eutils/" - Access to secretsline 23
[Credential-related source omitted] - Access to secretsline 30
[Credential-related source omitted] - Access to secretsline 42
[Credential-related source omitted] - Access to secretsline 43
[Credential-related source omitted] - Network accessline 47
try: with urllib.request.urlopen(url) as response: - Network accessline 48
with urllib.request.urlopen(url) as response: - Access to secretsline 65
[Credential-related source omitted] - Access to secretsline 71
[Credential-related source omitted] - Access to secretsline 82
[Credential-related source omitted] - Access to secretsline 83
[Credential-related source omitted] - Network accessline 87
try: with urllib.request.urlopen(url) as response: - Network accessline 88
with urllib.request.urlopen(url) as response: - Access to secretsline 99
[Credential-related source omitted] - Access to secretsline 106
[Credential-related source omitted] - Access to secretsline 117
[Credential-related source omitted] - Access to secretsline 118
[Credential-related source omitted] - Network accessline 122
try: with urllib.request.urlopen(url) as response: - Network accessline 123
with urllib.request.urlopen(url) as response: - Access to secretsline 134
[Credential-related source omitted] - Access to secretsline 142
[Credential-related source omitted] - Access to secretsline 153
[Credential-related source omitted] - Access to secretsline 163
[Credential-related source omitted] - Access to secretsline 183
[Credential-related source omitted] - Access to secretsline 190
[Credential-related source omitted] - Access to secretsline 194
[Credential-related source omitted] - Access to secretsline 198
[Credential-related source omitted] - Access to secretsline 243
[Credential-related source omitted] - Access to secretsline 247
[Credential-related source omitted]
skills/12-科学数据库/geo-database/SKILL.md
- Network accessline 398
**Using wget or curl for Downloads:** - Network accessline 401
# Download series matrix file wget ftp://ftp.ncbi.nlm.nih.gov/geo/series/GSE123nnn/GSE123456/matrix/GSE123456_series_matrix.txt.gz - Network accessline 402
wget ftp://ftp.ncbi.nlm.nih.gov/geo/series/GSE123nnn/GSE123456/matrix/GSE123456_series_matrix.txt.gz - Network accessline 404
# Download all supplementary files for a series wget -r -np -nd ftp://ftp.ncbi.nlm.nih.gov/geo/series/GSE123nnn/GSE123456/suppl/ - Network accessline 405
wget -r -np -nd ftp://ftp.ncbi.nlm.nih.gov/geo/series/GSE123nnn/GSE123456/suppl/ - Network accessline 407
# Download SOFT format family file wget ftp://ftp.ncbi.nlm.nih.gov/geo/series/GSE123nnn/GSE123456/soft/GSE123456_family.soft.gz - Network accessline 408
wget ftp://ftp.ncbi.nlm.nih.gov/geo/series/GSE123nnn/GSE123456/soft/GSE123456_family.soft.gz - Network accessline 673
# Optional: Set API key for increased rate limits # Get your API key from: https://www.ncbi.nlm.nih.gov/account/ - Network accessline 674
# Get your API key from: https://www.ncbi.nlm.nih.gov/account/ - Access to secretsline 675
[Credential-related source omitted] - Network accessline 725
- Web-based statistical analysis tool integrated into GEO - Accessible at: https://www.ncbi.nlm.nih.gov/geo/geo2r/?acc=GSExxxxx - Network accessline 726
- Accessible at: https://www.ncbi.nlm.nih.gov/geo/geo2r/?acc=GSExxxxx - Network accessline 739
- No rate limits for FTP downloads - Preferred method for bulk downloads - Can download entire directories with wget -r - Network accessline 741
- Can download entire directories with wget -r - Network accessline 805
- **GEO Website:** https://www.ncbi.nlm.nih.gov/geo/ - Network accessline 806
- **GEO Submission Guidelines:** https://www.ncbi.nlm.nih.gov/geo/info/submission.html - Network accessline 807
- **GEOparse Documentation:** https://geoparse.readthedocs.io/ - Network accessline 808
- **E-utilities Documentation:** https://www.ncbi.nlm.nih.gov/books/NBK25501/ - Network accessline 809
- **GEO FTP Site:** ftp://ftp.ncbi.nlm.nih.gov/geo/ - **GEO2R Tool:** https://www.ncbi.nlm.nih.gov/geo/geo2r/ - **NCBI API Keys:** https://ncbiinsights.ncbi.nlm.nih.gov/2017/11/02/new-api-keys-for-the-e-utilities/ - Network accessline 810
- **GEO2R Tool:** https://www.ncbi.nlm.nih.gov/geo/geo2r/ - Network accessline 811
- **NCBI API Keys:** https://ncbiinsights.ncbi.nlm.nih.gov/2017/11/02/new-api-keys-for-the-e-utilities/ - Network accessline 812
- **Biopython Tutorial:** https://biopython.org/DIST/docs/tutorial/Tutorial.html
skills/12-科学数据库/geo-database/references/geo_reference.md
- Network accessline 12
https://eutils.ncbi.nlm.nih.gov/entrez/eutils/ - Network accessline 23
https://eutils.ncbi.nlm.nih.gov/entrez/eutils/esearch.fcgi - Network accessline 68
https://eutils.ncbi.nlm.nih.gov/entrez/eutils/esummary.fcgi - Network accessline 108
https://eutils.ncbi.nlm.nih.gov/entrez/eutils/efetch.fcgi - Network accessline 138
https://eutils.ncbi.nlm.nih.gov/entrez/eutils/elink.fcgi - Network accessline 173
https://eutils.ncbi.nlm.nih.gov/entrez/eutils/epost.fcgi - Network accessline 202
https://eutils.ncbi.nlm.nih.gov/entrez/eutils/einfo.fcgi - Network accessline 293
!Database_name = Gene Expression Omnibus (GEO) !Database_institute = NCBI NLM NIH !Database_web_link = http://www.ncbi.nlm.nih.gov/geo - Network accessline 294
!Database_institute = NCBI NLM NIH !Database_web_link = http://www.ncbi.nlm.nih.gov/geo !Database_email = geo@ncbi.nlm.nih.gov - Network accessline 295
!Database_web_link = http://www.ncbi.nlm.nih.gov/geo - Network accessline 433
<?xml version="1.0" encoding="UTF-8"?> <MINiML xmlns="http://www.ncbi.nlm.nih.gov/geo/info/MINiML" - Network accessline 434
<MINiML xmlns="http://www.ncbi.nlm.nih.gov/geo/info/MINiML" - Network accessline 435
xmlns:xsi="http://www.w3.org/2001/XMLSchema-instance">
skills/12-科学数据库/gnomad-database/SKILL.md
- Network accessline 15
**Key resources:** - gnomAD browser: https://gnomad.broadinstitute.org/ - Network accessline 16
- gnomAD browser: https://gnomad.broadinstitute.org/ - Network accessline 17
- GraphQL API: https://gnomad.broadinstitute.org/api - Network accessline 18
- Data downloads: https://gnomad.broadinstitute.org/downloads - Network accessline 19
- Documentation: https://gnomad.broadinstitute.org/help - Network accessline 36
gnomAD uses a GraphQL API accessible at `https://gnomad.broadinstitute.org/api`. Most queries fetch variants by gene or specific genomic position. - Network accessline 53
def query_gnomad_gene(gene_symbol, dataset="gnomad_r4", reference_genome="GRCh38"): """Fetch variants in a gene from gnomAD.""" url = "https://gnomad.broadinstitute.org/api" - Network accessline 55
url = "https://gnomad.broadinstitute.org/api" - Network accessline 100
response = requests.post(url, json={"query": query, "variables": variables}) - Network accessline 122
def query_gnomad_variant(variant_id, dataset="gnomad_r4"): """Fetch details for a specific variant (e.g., '1-55516888-G-GA').""" url = "https://gnomad.broadinstitute.org/api" - Network accessline 124
url = "https://gnomad.broadinstitute.org/api" - Network accessline 171
response = requests.post( - Network accessline 198
def query_gnomad_constraint(gene_symbol, reference_genome="GRCh38"): """Fetch constraint scores for a gene.""" url = "https://gnomad.broadinstitute.org/api" - Network accessline 200
url = "https://gnomad.broadinstitute.org/api" - Network accessline 228
response = requests.post( - Network accessline 261
def get_population_frequencies(variant_id, dataset="gnomad_r4"): """Extract per-population allele frequencies for a variant.""" url = "https://gnomad.broadinstitute.org/api" - Network accessline 263
url = "https://gnomad.broadinstitute.org/api" - Network accessline 282
response = requests.post( - Network accessline 315
def query_gnomad_sv(gene_symbol): """Query structural variants overlapping a gene.""" url = "https://gnomad.broadinstitute.org/api" - Network accessline 317
url = "https://gnomad.broadinstitute.org/api" - Network accessline 336
response = requests.post(url, json={"query": query, "variables": {"gene_symbol": gene_symbol}}) - Network accessline 382
- **Browser**: https://gnomad.broadinstitute.org/ — interactive variant and gene browsing - Network accessline 383
- **GraphQL API**: https://gnomad.broadinstitute.org/api — programmatic access - Network accessline 384
- **Downloads**: https://gnomad.broadinstitute.org/downloads — VCF, Hail tables, constraint tables - Network accessline 389
- **gnomAD website**: https://gnomad.broadinstitute.org/ - Network accessline 390
- **gnomAD blog**: https://gnomad.broadinstitute.org/news - Network accessline 391
- **Downloads**: https://gnomad.broadinstitute.org/downloads - Network accessline 392
- **API explorer**: https://gnomad.broadinstitute.org/api (interactive GraphiQL) - Network accessline 393
- **Constraint documentation**: https://gnomad.broadinstitute.org/help/constraint - Network accessline 395
- **GitHub**: https://github.com/broadinstitute/gnomad-browser
skills/12-科学数据库/gnomad-database/references/graphql_queries.md
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POST https://gnomad.broadinstitute.org/api - Network accessline 191
def gnomad_query(query: str, variables: dict, retries: int = 3) -> dict: """Execute a gnomAD GraphQL query with retry logic.""" url = "https://gnomad.broadinstitute.org/api" - Network accessline 192
"""Execute a gnomAD GraphQL query with retry logic.""" url = "https://gnomad.broadinstitute.org/api" headers = {"Content-Type": "application/json"} - Network accessline 193
url = "https://gnomad.broadinstitute.org/api" - Network accessline 196
for attempt in range(retries): try: response = requests.post( - Network accessline 197
try: response = requests.post( url, - Network accessline 198
response = requests.post(
skills/12-科学数据库/gnomad-database/references/variant_interpretation.md
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Always check that gnomAD has adequate coverage at the variant position before concluding absence is meaningful. The gnomAD browser shows coverage tracks, and coverage data can be downloaded from: - https://gnomad.broadinstitute.org/download - Network accessline 68
- https://gnomad.broadinstitute.org/downloads#v4-coverage
skills/12-科学数据库/gtex-database/SKILL.md
- Network accessline 15
**Key resources:** - GTEx Portal: https://gtexportal.org/ - Network accessline 16
- GTEx Portal: https://gtexportal.org/ - Network accessline 17
- GTEx API v2: https://gtexportal.org/api/v2/ - Network accessline 18
- Data downloads: https://gtexportal.org/home/downloads/adult-gtex/ - Network accessline 19
- Documentation: https://gtexportal.org/home/documentationPage - Network accessline 37
Base URL: `https://gtexportal.org/api/v2/` - Network accessline 44
BASE_URL = "https://gtexportal.org/api/v2" - Network accessline 47
"""Make a GET request to the GTEx API.""" url = f"{BASE_URL}/{endpoint}" response = requests.get(url, params=params, headers={"Accept": "application/json"}) - Network accessline 48
url = f"{BASE_URL}/{endpoint}" response = requests.get(url, params=params, headers={"Accept": "application/json"}) response.raise_for_status() - Network accessline 49
response = requests.get(url, params=params, headers={"Accept": "application/json"}) - Network accessline 60
def get_gene_expression_by_tissue(gene_id_or_symbol, dataset_id="gtex_v10"): """Get median gene expression across all tissues.""" url = "https://gtexportal.org/api/v2/expression/medianGeneExpression" - Network accessline 61
"""Get median gene expression across all tissues.""" url = "https://gtexportal.org/api/v2/expression/medianGeneExpression" params = { - Network accessline 62
url = "https://gtexportal.org/api/v2/expression/medianGeneExpression" - Network accessline 66
"itemsPerPage": 100 } response = requests.get(url, params=params) - Network accessline 67
} response = requests.get(url, params=params) data = response.json() - Network accessline 68
response = requests.get(url, params=params) - Network accessline 92
def query_eqtl(gene_id, tissue_id=None, dataset_id="gtex_v10"): """Query significant eQTLs for a gene, optionally filtered by tissue.""" url = "https://gtexportal.org/api/v2/association/singleTissueEqtl" - Network accessline 93
"""Query significant eQTLs for a gene, optionally filtered by tissue.""" url = "https://gtexportal.org/api/v2/association/singleTissueEqtl" params = { - Network accessline 94
url = "https://gtexportal.org/api/v2/association/singleTissueEqtl" - Network accessline 105
while True: params["page"] = page response = requests.get(url, params=params) - Network accessline 106
params["page"] = page response = requests.get(url, params=params) data = response.json() - Network accessline 107
response = requests.get(url, params=params) - Network accessline 132
def query_variant_eqtl(variant_id, tissue_id=None, dataset_id="gtex_v10"): """Get all eQTL associations for a specific variant.""" url = "https://gtexportal.org/api/v2/association/singleTissueEqtl" - Network accessline 133
"""Get all eQTL associations for a specific variant.""" url = "https://gtexportal.org/api/v2/association/singleTissueEqtl" params = { - Network accessline 134
url = "https://gtexportal.org/api/v2/association/singleTissueEqtl" - Network accessline 143
response = requests.get(url, params=params) - Network accessline 155
def get_egenes(tissue_id, dataset_id="gtex_v10"): """Get all eGenes (genes with at least one significant eQTL) in a tissue.""" url = "https://gtexportal.org/api/v2/association/egene" - Network accessline 156
"""Get all eGenes (genes with at least one significant eQTL) in a tissue.""" url = "https://gtexportal.org/api/v2/association/egene" params = { - Network accessline 157
url = "https://gtexportal.org/api/v2/association/egene" - Network accessline 166
while True: params["page"] = page response = requests.get(url, params=params) - Network accessline 167
params["page"] = page response = requests.get(url, params=params) data = response.json() - Network accessline 168
response = requests.get(url, params=params) - Network accessline 189
def get_tissues(dataset_id="gtex_v10"): """Get all available tissues with metadata.""" url = "https://gtexportal.org/api/v2/dataset/tissueSiteDetail" - Network accessline 190
"""Get all available tissues with metadata.""" url = "https://gtexportal.org/api/v2/dataset/tissueSiteDetail" params = {"datasetId": dataset_id, "itemsPerPage": 100} - Network accessline 191
url = "https://gtexportal.org/api/v2/dataset/tissueSiteDetail" params = {"datasetId": dataset_id, "itemsPerPage": 100} response = requests.get(url, params=params) - Network accessline 192
params = {"datasetId": dataset_id, "itemsPerPage": 100} response = requests.get(url, params=params) return response.json()["data"] - Network accessline 193
response = requests.get(url, params=params) - Network accessline 208
def query_sqtl(gene_id, tissue_id=None, dataset_id="gtex_v10"): """Query significant sQTLs for a gene.""" url = "https://gtexportal.org/api/v2/association/singleTissueSqtl" - Network accessline 209
"""Query significant sQTLs for a gene.""" url = "https://gtexportal.org/api/v2/association/singleTissueSqtl" params = { - Network accessline 210
url = "https://gtexportal.org/api/v2/association/singleTissueSqtl" - Network accessline 219
response = requests.get(url, params=params) - Network accessline 237
def interpret_gwas_variant(variant_id, dataset_id="gtex_v10"): """Find all genes regulated by a GWAS variant.""" url = "https://gtexportal.org/api/v2/association/singleTissueEqtl" - Network accessline 238
"""Find all genes regulated by a GWAS variant.""" url = "https://gtexportal.org/api/v2/association/singleTissueEqtl" params = {"variantId": variant_id, "datasetId": dataset_id, "itemsPerPage": 500} - Network accessline 239
url = "https://gtexportal.org/api/v2/association/singleTissueEqtl" params = {"variantId": variant_id, "datasetId": dataset_id, "itemsPerPage": 500} response = requests.get(url, params=params) - Network accessline 240
params = {"variantId": variant_id, "datasetId": dataset_id, "itemsPerPage": 500} response = requests.get(url, params=params) data = response.json() - Network accessline 241
response = requests.get(url, params=params) - Network accessline 302
# All significant eQTLs (v10) wget https://storage.googleapis.com/adult-gtex/bulk-qtl/v10/single-tissue-cis-qtl/GTEx_Analysis_v10_eQTL.tar - Network accessline 303
wget https://storage.googleapis.com/adult-gtex/bulk-qtl/v10/single-tissue-cis-qtl/GTEx_Analysis_v10_eQTL.tar - Network accessline 305
# Normalized expression matrices wget https://storage.googleapis.com/adult-gtex/bulk-gex/v10/rna-seq/GTEx_Analysis_v10_RNASeQCv2.4.2_gene_reads.gct.gz - Network accessline 306
wget https://storage.googleapis.com/adult-gtex/bulk-gex/v10/rna-seq/GTEx_Analysis_v10_RNASeQCv2.4.2_gene_reads.gct.gz - Network accessline 311
- **GTEx Portal**: https://gtexportal.org/ - Network accessline 312
- **API documentation**: https://gtexportal.org/api/v2/ - Network accessline 313
- **Data downloads**: https://gtexportal.org/home/downloads/adult-gtex/ - Network accessline 314
- **GitHub**: https://github.com/broadinstitute/gtex-pipeline
skills/12-科学数据库/gtex-database/references/api_reference.md
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https://gtexportal.org/api/v2/ - Network accessline 171
def safe_gtex_query(endpoint, params): url = f"https://gtexportal.org/api/v2/{endpoint}" - Network accessline 172
url = f"https://gtexportal.org/api/v2/{endpoint}" try: response = requests.get(url, params=params, timeout=30) - Network accessline 173
try: response = requests.get(url, params=params, timeout=30) response.raise_for_status() - Network accessline 174
response = requests.get(url, params=params, timeout=30)
skills/12-科学数据库/gwas-database/SKILL.md
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The web interface at https://www.ebi.ac.uk/gwas/ supports multiple search modes: - Network accessline 90
**Base URLs:** - GWAS Catalog API: `https://www.ebi.ac.uk/gwas/rest/api` - Network accessline 91
- GWAS Catalog API: `https://www.ebi.ac.uk/gwas/rest/api` - Network accessline 92
- Summary Statistics API: `https://www.ebi.ac.uk/gwas/summary-statistics/api` - Network accessline 94
**API Documentation:** - Main API docs: https://www.ebi.ac.uk/gwas/rest/docs/api - Network accessline 95
- Main API docs: https://www.ebi.ac.uk/gwas/rest/docs/api - Network accessline 96
- Summary stats docs: https://www.ebi.ac.uk/gwas/summary-statistics/docs/ - Network accessline 104
# Get a specific study url = "https://www.ebi.ac.uk/gwas/rest/api/studies/GCST001795" response = requests.get(url, headers={"Content-Type": "application/json"}) - Network accessline 105
url = "https://www.ebi.ac.uk/gwas/rest/api/studies/GCST001795" response = requests.get(url, headers={"Content-Type": "application/json"}) study = response.json() - Network accessline 106
response = requests.get(url, headers={"Content-Type": "application/json"}) - Network accessline 112
# Find associations for a variant variant = "rs7903146" url = f"https://www.ebi.ac.uk/gwas/rest/api/singleNucleotidePolymorphisms/{variant}/associations" - Network accessline 113
variant = "rs7903146" url = f"https://www.ebi.ac.uk/gwas/rest/api/singleNucleotidePolymorphisms/{variant}/associations" params = {"projection": "associationBySnp"} - Network accessline 114
url = f"https://www.ebi.ac.uk/gwas/rest/api/singleNucleotidePolymorphisms/{variant}/associations" params = {"projection": "associationBySnp"} response = requests.get(url, params=params, headers={"Content-Type": "application/json"}) - Network accessline 115
params = {"projection": "associationBySnp"} response = requests.get(url, params=params, headers={"Content-Type": "application/json"}) associations = response.json() - Network accessline 116
response = requests.get(url, params=params, headers={"Content-Type": "application/json"}) - Network accessline 122
# Get variant details url = "https://www.ebi.ac.uk/gwas/rest/api/singleNucleotidePolymorphisms/rs7903146" response = requests.get(url, headers={"Content-Type": "application/json"}) - Network accessline 123
url = "https://www.ebi.ac.uk/gwas/rest/api/singleNucleotidePolymorphisms/rs7903146" response = requests.get(url, headers={"Content-Type": "application/json"}) variant_info = response.json() - Network accessline 124
response = requests.get(url, headers={"Content-Type": "application/json"}) - Network accessline 130
# Get trait information url = "https://www.ebi.ac.uk/gwas/rest/api/efoTraits/EFO_0001360" response = requests.get(url, headers={"Content-Type": "application/json"}) - Network accessline 131
url = "https://www.ebi.ac.uk/gwas/rest/api/efoTraits/EFO_0001360" response = requests.get(url, headers={"Content-Type": "application/json"}) trait_info = response.json() - Network accessline 132
response = requests.get(url, headers={"Content-Type": "application/json"}) - Network accessline 142
trait = "EFO_0001360" # Type 2 diabetes base_url = "https://www.ebi.ac.uk/gwas/rest/api" - Network accessline 143
base_url = "https://www.ebi.ac.uk/gwas/rest/api" - Network accessline 145
# Query associations for this trait url = f"{base_url}/efoTraits/{trait}/associations" response = requests.get(url, headers={"Content-Type": "application/json"}) - Network accessline 146
url = f"{base_url}/efoTraits/{trait}/associations" response = requests.get(url, headers={"Content-Type": "application/json"}) associations = response.json() - Network accessline 147
response = requests.get(url, headers={"Content-Type": "application/json"}) - Network accessline 162
variant = "rs7903146" base_url = "https://www.ebi.ac.uk/gwas/rest/api" - Network accessline 163
base_url = "https://www.ebi.ac.uk/gwas/rest/api" - Network accessline 165
# Get variant details url = f"{base_url}/singleNucleotidePolymorphisms/{variant}" response = requests.get(url, headers={"Content-Type": "application/json"}) - Network accessline 166
url = f"{base_url}/singleNucleotidePolymorphisms/{variant}" response = requests.get(url, headers={"Content-Type": "application/json"}) variant_data = response.json() - Network accessline 167
response = requests.get(url, headers={"Content-Type": "application/json"}) - Network accessline 171
url = f"{base_url}/singleNucleotidePolymorphisms/{variant}/associations" params = {"projection": "associationBySnp"} response = requests.get(url, params=params, headers={"Content-Type": "application/json"}) - Network accessline 172
params = {"projection": "associationBySnp"} response = requests.get(url, params=params, headers={"Content-Type": "application/json"}) associations = response.json() - Network accessline 173
response = requests.get(url, params=params, headers={"Content-Type": "application/json"}) - Network accessline 187
# Query summary statistics API base_url = "https://www.ebi.ac.uk/gwas/summary-statistics/api" - Network accessline 188
base_url = "https://www.ebi.ac.uk/gwas/summary-statistics/api" - Network accessline 196
"size": 100 # Number of results } response = requests.get(url, params=params) - Network accessline 197
} response = requests.get(url, params=params) results = response.json() - Network accessline 198
response = requests.get(url, params=params) - Network accessline 219
base_url = "https://www.ebi.ac.uk/gwas/rest/api" - Network accessline 224
"bpEnd": end_pos } response = requests.get(url, params=params, headers={"Content-Type": "application/json"}) - Network accessline 225
} response = requests.get(url, params=params, headers={"Content-Type": "application/json"}) variants_in_region = response.json() - Network accessline 226
response = requests.get(url, params=params, headers={"Content-Type": "application/json"}) - Network accessline 234
**Access Methods:** 1. **FTP download**: http://ftp.ebi.ac.uk/pub/databases/gwas/summary_statistics/ - Network accessline 235
1. **FTP download**: http://ftp.ebi.ac.uk/pub/databases/gwas/summary_statistics/ - Network accessline 250
# Get available summary statistics base_url = "https://www.ebi.ac.uk/gwas/summary-statistics/api" - Network accessline 251
base_url = "https://www.ebi.ac.uk/gwas/summary-statistics/api" url = f"{base_url}/studies/GCST001234" response = requests.get(url) - Network accessline 252
url = f"{base_url}/studies/GCST001234" response = requests.get(url) study_info = response.json() - Network accessline 253
response = requests.get(url) - Network accessline 284
# API responses include _links for related resources response = requests.get("https://www.ebi.ac.uk/gwas/rest/api/studies/GCST001234") - Network accessline 285
response = requests.get("https://www.ebi.ac.uk/gwas/rest/api/studies/GCST001234") - Network accessline 288
# Follow link to associations associations_url = study['_links']['associations']['href'] associations_response = requests.get(associations_url) - Network accessline 290
associations_response = requests.get(associations_url) - Network accessline 303
url = f"https://www.ebi.ac.uk/gwas/rest/api/efoTraits/{efo_id}/associations" - Network accessline 326
url = f"https://www.ebi.ac.uk/gwas/rest/api/singleNucleotidePolymorphisms/{rs_id}" - Network accessline 331
url = f"https://www.ebi.ac.uk/gwas/rest/api/singleNucleotidePolymorphisms/{rs_id}/associations" - Network accessline 348
url = f"https://www.ebi.ac.uk/gwas/rest/api/singleNucleotidePolymorphisms/search/findByGene" - Network accessline 406
# Via FTP wget ftp://ftp.ebi.ac.uk/pub/databases/gwas/summary_statistics/GCSTXXXXXX/harmonised/GCSTXXXXXX-harmonised.tsv.gz - Network accessline 407
wget ftp://ftp.ebi.ac.uk/pub/databases/gwas/summary_statistics/GCSTXXXXXX/harmonised/GCSTXXXXXX-harmonised.tsv.gz - Network accessline 412
url = f"https://www.ebi.ac.uk/gwas/summary-statistics/api/chromosomes/{chrom}/associations" - Network accessline 497
pandas DataFrame with association results """ base_url = "https://www.ebi.ac.uk/gwas/rest/api" - Network accessline 498
""" base_url = "https://www.ebi.ac.uk/gwas/rest/api" url = f"{base_url}/efoTraits/{trait_id}/associations" - Network accessline 499
base_url = "https://www.ebi.ac.uk/gwas/rest/api" - Network accessline 506
while True: params = {"page": page, "size": 100} response = requests.get(url, params=params, headers=headers) - Network accessline 508
response = requests.get(url, params=params, headers=headers) - Network accessline 564
The GWAS Catalog team provides workshop materials: - GitHub repository: https://github.com/EBISPOT/GWAS_Catalog-workshop - Network accessline 565
- GitHub repository: https://github.com/EBISPOT/GWAS_Catalog-workshop - Network accessline 598
- **GWAS Catalog website**: https://www.ebi.ac.uk/gwas/ - Network accessline 599
- **Documentation**: https://www.ebi.ac.uk/gwas/docs - Network accessline 600
- **API documentation**: https://www.ebi.ac.uk/gwas/rest/docs/api - Network accessline 601
- **Summary Statistics API**: https://www.ebi.ac.uk/gwas/summary-statistics/docs/ - Network accessline 602
- **FTP site**: http://ftp.ebi.ac.uk/pub/databases/gwas/ - Network accessline 603
- **Training materials**: https://github.com/EBISPOT/GWAS_Catalog-workshop - Network accessline 604
- **PGS Catalog** (polygenic scores): https://www.pgscatalog.org/
skills/12-科学数据库/gwas-database/references/api_reference.md
- Network accessline 28
GWAS Catalog API: https://www.ebi.ac.uk/gwas/rest/api - Network accessline 29
Summary Statistics API: https://www.ebi.ac.uk/gwas/summary-statistics/api - Network accessline 62
def query_with_rate_limit(url, delay=0.1): response = requests.get(url) - Network accessline 63
response = requests.get(url) - Network accessline 101
# Get a specific study url = "https://www.ebi.ac.uk/gwas/rest/api/studies/GCST001795" response = requests.get(url, headers={"Content-Type": "application/json"}) - Network accessline 102
url = "https://www.ebi.ac.uk/gwas/rest/api/studies/GCST001795" response = requests.get(url, headers={"Content-Type": "application/json"}) study = response.json() - Network accessline 103
response = requests.get(url, headers={"Content-Type": "application/json"}) - Network accessline 151
# Find all associations for type 2 diabetes trait_id = "EFO_0001360" url = f"https://www.ebi.ac.uk/gwas/rest/api/efoTraits/{trait_id}/associations" - Network accessline 152
trait_id = "EFO_0001360" url = f"https://www.ebi.ac.uk/gwas/rest/api/efoTraits/{trait_id}/associations" params = {"size": 100, "page": 0} - Network accessline 153
url = f"https://www.ebi.ac.uk/gwas/rest/api/efoTraits/{trait_id}/associations" params = {"size": 100, "page": 0} response = requests.get(url, params=params, headers={"Content-Type": "application/json"}) - Network accessline 154
params = {"size": 100, "page": 0} response = requests.get(url, params=params, headers={"Content-Type": "application/json"}) data = response.json() - Network accessline 155
response = requests.get(url, params=params, headers={"Content-Type": "application/json"}) - Network accessline 196
# Get variant information rs_id = "rs7903146" url = f"https://www.ebi.ac.uk/gwas/rest/api/singleNucleotidePolymorphisms/{rs_id}" - Network accessline 197
rs_id = "rs7903146" url = f"https://www.ebi.ac.uk/gwas/rest/api/singleNucleotidePolymorphisms/{rs_id}" response = requests.get(url, headers={"Content-Type": "application/json"}) - Network accessline 198
url = f"https://www.ebi.ac.uk/gwas/rest/api/singleNucleotidePolymorphisms/{rs_id}" response = requests.get(url, headers={"Content-Type": "application/json"}) variant = response.json() - Network accessline 199
response = requests.get(url, headers={"Content-Type": "application/json"}) - Network accessline 234
# Get trait details trait_id = "EFO_0001360" url = f"https://www.ebi.ac.uk/gwas/rest/api/efoTraits/{trait_id}" - Network accessline 235
trait_id = "EFO_0001360" url = f"https://www.ebi.ac.uk/gwas/rest/api/efoTraits/{trait_id}" response = requests.get(url, headers={"Content-Type": "application/json"}) - Network accessline 236
url = f"https://www.ebi.ac.uk/gwas/rest/api/efoTraits/{trait_id}" response = requests.get(url, headers={"Content-Type": "application/json"}) trait = response.json() - Network accessline 237
response = requests.get(url, headers={"Content-Type": "application/json"}) - Network accessline 269
def get_all_associations(trait_id): """Retrieve all associations for a trait with pagination""" base_url = "https://www.ebi.ac.uk/gwas/rest/api" - Network accessline 270
"""Retrieve all associations for a trait with pagination""" base_url = "https://www.ebi.ac.uk/gwas/rest/api" url = f"{base_url}/efoTraits/{trait_id}/associations" - Network accessline 271
base_url = "https://www.ebi.ac.uk/gwas/rest/api" - Network accessline 276
while True: params = {"page": page, "size": 100} response = requests.get(url, params=params, headers={"Content-Type": "application/json"}) - Network accessline 278
response = requests.get(url, params=params, headers={"Content-Type": "application/json"}) - Network accessline 302
# Get study and follow links to associations response = requests.get("https://www.ebi.ac.uk/gwas/rest/api/studies/GCST001795") - Network accessline 303
response = requests.get("https://www.ebi.ac.uk/gwas/rest/api/studies/GCST001795") - Network accessline 306
# Follow link to associations associations_url = study['_links']['associations']['href'] associations_response = requests.get(associations_url) - Network accessline 307
associations_url = study['_links']['associations']['href'] associations_response = requests.get(associations_url) associations = associations_response.json() - Network accessline 308
associations_response = requests.get(associations_url) - Network accessline 318
https://www.ebi.ac.uk/gwas/summary-statistics/api - Network accessline 357
# Find highly significant associations for a trait trait_id = "EFO_0001360" base_url = "https://www.ebi.ac.uk/gwas/summary-statistics/api" - Network accessline 358
trait_id = "EFO_0001360" base_url = "https://www.ebi.ac.uk/gwas/summary-statistics/api" url = f"{base_url}/traits/{trait_id}/associations" - Network accessline 359
base_url = "https://www.ebi.ac.uk/gwas/summary-statistics/api" - Network accessline 363
"size": 100 } response = requests.get(url, params=params) - Network accessline 364
} response = requests.get(url, params=params) results = response.json() - Network accessline 365
response = requests.get(url, params=params) - Network accessline 390
base_url = "https://www.ebi.ac.uk/gwas/summary-statistics/api" - Network accessline 395
"size": 1000 } response = requests.get(url, params=params) - Network accessline 396
} response = requests.get(url, params=params) variants = response.json() - Network accessline 397
response = requests.get(url, params=params) - Network accessline 451
"_links": { "self": { "href": "https://www.ebi.ac.uk/gwas/rest/api/associations/12345" - Network accessline 452
"self": { "href": "https://www.ebi.ac.uk/gwas/rest/api/associations/12345" } - Network accessline 453
"href": "https://www.ebi.ac.uk/gwas/rest/api/associations/12345" - Network accessline 459
"_links": { "self": { "href": "https://www.ebi.ac.uk/gwas/rest/api/efoTraits/EFO_0001360/associations?page=0" - Network accessline 460
"self": { "href": "https://www.ebi.ac.uk/gwas/rest/api/efoTraits/EFO_0001360/associations?page=0" }, - Network accessline 461
"href": "https://www.ebi.ac.uk/gwas/rest/api/efoTraits/EFO_0001360/associations?page=0" - Network accessline 462
}, "next": { "href": "https://www.ebi.ac.uk/gwas/rest/api/efoTraits/EFO_0001360/associations?page=1" - Network accessline 463
"next": { "href": "https://www.ebi.ac.uk/gwas/rest/api/efoTraits/EFO_0001360/associations?page=1" } - Network accessline 464
"href": "https://www.ebi.ac.uk/gwas/rest/api/efoTraits/EFO_0001360/associations?page=1" - Network accessline 511
"""Make API request with error handling""" try: response = requests.get(url, params=params, timeout=30) - Network accessline 512
try: response = requests.get(url, params=params, timeout=30) response.raise_for_status() - Network accessline 513
response = requests.get(url, params=params, timeout=30) - Network accessline 538
def get_variant_pleiotropy(rs_id): """Get all traits associated with a variant""" base_url = "https://www.ebi.ac.uk/gwas/rest/api" - Network accessline 539
"""Get all traits associated with a variant""" base_url = "https://www.ebi.ac.uk/gwas/rest/api" url = f"{base_url}/singleNucleotidePolymorphisms/{rs_id}/associations" - Network accessline 540
base_url = "https://www.ebi.ac.uk/gwas/rest/api" - Network accessline 544
response = requests.get(url, params=params, headers={"Content-Type": "application/json"}) - Network accessline 568
def get_significant_associations(trait_id, p_threshold=5e-8): """Get genome-wide significant associations""" base_url = "https://www.ebi.ac.uk/gwas/rest/api" - Network accessline 569
"""Get genome-wide significant associations""" base_url = "https://www.ebi.ac.uk/gwas/rest/api" url = f"{base_url}/efoTraits/{trait_id}/associations" - Network accessline 570
base_url = "https://www.ebi.ac.uk/gwas/rest/api" - Network accessline 576
while True: params = {"page": page, "size": 100} response = requests.get(url, params=params, headers={"Content-Type": "application/json"}) - Network accessline 578
response = requests.get(url, params=params, headers={"Content-Type": "application/json"}) - Network accessline 604
def get_complete_variant_data(rs_id): """Get variant data from both APIs""" main_url = f"https://www.ebi.ac.uk/gwas/rest/api/singleNucleotidePolymorphisms/{rs_id}" - Network accessline 606
main_url = f"https://www.ebi.ac.uk/gwas/rest/api/singleNucleotidePolymorphisms/{rs_id}" - Network accessline 608
# Get basic variant info response = requests.get(main_url, headers={"Content-Type": "application/json"}) - Network accessline 609
response = requests.get(main_url, headers={"Content-Type": "application/json"}) - Network accessline 612
# Get associations assoc_url = f"{main_url}/associations" response = requests.get(assoc_url, headers={"Content-Type": "application/json"}) - Network accessline 613
assoc_url = f"{main_url}/associations" response = requests.get(assoc_url, headers={"Content-Type": "application/json"}) associations = response.json() - Network accessline 614
response = requests.get(assoc_url, headers={"Content-Type": "application/json"}) - Network accessline 632
"""Query variants in genomic region""" # From main API base_url = "https://www.ebi.ac.uk/gwas/rest/api" - Network accessline 633
# From main API base_url = "https://www.ebi.ac.uk/gwas/rest/api" url = f"{base_url}/singleNucleotidePolymorphisms/search/findByChromBpLocationRange" - Network accessline 634
base_url = "https://www.ebi.ac.uk/gwas/rest/api" - Network accessline 643
response = requests.get(url, params=params, headers={"Content-Type": "application/json"}) - Network accessline 646
# Can also query summary statistics API sumstats_url = f"https://www.ebi.ac.uk/gwas/summary-statistics/api/chromosomes/{chromosome}/associations" - Network accessline 647
sumstats_url = f"https://www.ebi.ac.uk/gwas/summary-statistics/api/chromosomes/{chromosome}/associations" - Network accessline 652
sumstats_response = requests.get(sumstats_url, params=sumstats_params) - Network accessline 670
class GWASCatalogQuery: def __init__(self): self.base_url = "https://www.ebi.ac.uk/gwas/rest/api" - Network accessline 671
def __init__(self): self.base_url = "https://www.ebi.ac.uk/gwas/rest/api" self.headers = {"Content-Type": "application/json"} - Network accessline 672
self.base_url = "https://www.ebi.ac.uk/gwas/rest/api" - Network accessline 681
while True: params = {"page": page, "size": 100} response = requests.get(url, params=params, headers=self.headers) - Network accessline 683
response = requests.get(url, params=params, headers=self.headers) - Network accessline 712
"""Get detailed variant information""" url = f"{self.base_url}/singleNucleotidePolymorphisms/{rs_id}" response = requests.get(url, headers=self.headers) - Network accessline 714
response = requests.get(url, headers=self.headers) - Network accessline 722
url = f"{self.base_url}/singleNucleotidePolymorphisms/search/findByGene" params = {"geneName": gene_name} response = requests.get(url, params=params, headers=self.headers) - Network accessline 724
response = requests.get(url, params=params, headers=self.headers) - Network accessline 759
"""Download summary statistics from FTP""" # FTP URL pattern ftp_base = "http://ftp.ebi.ac.uk/pub/databases/gwas/summary_statistics" - Network accessline 761
ftp_base = "http://ftp.ebi.ac.uk/pub/databases/gwas/summary_statistics" - Network accessline 768
try: response = requests.get(harmonised_url, stream=True) - Network accessline 769
response = requests.get(harmonised_url, stream=True) - Network accessline 789
- **Interactive API Documentation**: https://www.ebi.ac.uk/gwas/rest/docs/api - Network accessline 790
- **Summary Statistics API Docs**: https://www.ebi.ac.uk/gwas/summary-statistics/docs/ - Network accessline 791
- **Workshop Materials**: https://github.com/EBISPOT/GWAS_Catalog-workshop - Network accessline 792
- **Blog Post on API v2**: https://ebispot.github.io/gwas-blog/rest-api-v2-release/ - Network accessline 793
- **R Package (gwasrapidd)**: https://cran.r-project.org/package=gwasrapidd
skills/12-科学数据库/hmdb-database/SKILL.md
skills/12-科学数据库/jaspar-database/SKILL.md
- Network accessline 13
JASPAR (https://jaspar.elixir.no/) is the gold-standard open-access database of curated, non-redundant transcription factor (TF) binding profiles stored as position frequency matrices (PFMs). JASPAR 2024 contains 1,210 non-redundant TF bind - Network accessline 15
**Key resources:** - JASPAR portal: https://jaspar.elixir.no/ - Network accessline 16
- JASPAR portal: https://jaspar.elixir.no/ - Network accessline 17
- REST API: https://jaspar.elixir.no/api/v1/ - Network accessline 18
- API docs: https://jaspar.elixir.no/api/v1/docs/ - Network accessline 37
Base URL: `https://jaspar.elixir.no/api/v1/` - Network accessline 42
BASE_URL = "https://jaspar.elixir.no/api/v1" - Network accessline 44
def jaspar_get(endpoint, params=None): url = f"{BASE_URL}/{endpoint}" response = requests.get(url, params=params, headers={"Accept": "application/json"}) - Network accessline 45
url = f"{BASE_URL}/{endpoint}" response = requests.get(url, params=params, headers={"Accept": "application/json"}) response.raise_for_status() - Network accessline 46
response = requests.get(url, params=params, headers={"Accept": "application/json"}) - Network accessline 278
# 1. Get relevant TF matrices (e.g., all human TFs in CORE collection) response = requests.get( - Network accessline 279
response = requests.get( - Network accessline 280
"https://jaspar.elixir.no/api/v1/matrix/", - Network accessline 288
all_hits = [] for m in matrices[:10]: # Limit for demo pwm_data = requests.get(f"https://jaspar.elixir.no/api/v1/matrix/{m['matrix_id']}/").json() - Network accessline 289
for m in matrices[:10]: # Limit for demo pwm_data = requests.get(f"https://jaspar.elixir.no/api/v1/matrix/{m['matrix_id']}/").json() pfm = pfm_data["pfm"] - Network accessline 290
pwm_data = requests.get(f"https://jaspar.elixir.no/api/v1/matrix/{m['matrix_id']}/").json() - Network accessline 345
- **JASPAR portal**: https://jaspar.elixir.no/ - Network accessline 346
- **API documentation**: https://jaspar.elixir.no/api/v1/docs/ - Network accessline 347
- **JASPAR 2024 paper**: Castro-Mondragon et al. (2022) Nucleic Acids Research. PMID: 34875674 - **Biopython motifs**: https://biopython.org/docs/latest/Tutorial/chapter_motifs.html - **FIMO tool** (for large-scale scanning): https://meme-s - Network accessline 348
- **Biopython motifs**: https://biopython.org/docs/latest/Tutorial/chapter_motifs.html - Network accessline 349
- **FIMO tool** (for large-scale scanning): https://meme-suite.org/meme/tools/fimo - Network accessline 350
- **HOMER** (motif enrichment): http://homer.ucsd.edu/homer/ - Network accessline 351
- **GitHub**: https://github.com/wassermanlab/JASPAR-UCSC-tracks
skills/12-科学数据库/jaspar-database/references/api_reference.md
- Network accessline 6
https://jaspar.elixir.no/api/v1/ - Network accessline 35
{ "count": 1210, "next": "https://jaspar.elixir.no/api/v1/matrix/?page=2", - Network accessline 36
"count": 1210, "next": "https://jaspar.elixir.no/api/v1/matrix/?page=2", "previous": null, - Network accessline 37
"next": "https://jaspar.elixir.no/api/v1/matrix/?page=2", - Network accessline 148
def download_all_human_profiles(output_file="jaspar_human_profiles.json"): """Download all human TF profiles from JASPAR CORE collection.""" url = "https://jaspar.elixir.no/api/v1/matrix/" - Network accessline 149
"""Download all human TF profiles from JASPAR CORE collection.""" url = "https://jaspar.elixir.no/api/v1/matrix/" params = { - Network accessline 150
url = "https://jaspar.elixir.no/api/v1/matrix/" - Network accessline 159
profiles = [] while True: response = requests.get(url, params=params) - Network accessline 160
while True: response = requests.get(url, params=params) data = response.json() - Network accessline 161
response = requests.get(url, params=params) - Network accessline 171
full_profiles = [] for p in profiles: detail_url = f"https://jaspar.elixir.no/api/v1/matrix/{p['matrix_id']}/" - Network accessline 172
for p in profiles: detail_url = f"https://jaspar.elixir.no/api/v1/matrix/{p['matrix_id']}/" detail = requests.get(detail_url).json() - Network accessline 173
detail_url = f"https://jaspar.elixir.no/api/v1/matrix/{p['matrix_id']}/" detail = requests.get(detail_url).json() full_profiles.append(detail) - Network accessline 174
detail = requests.get(detail_url).json()
skills/12-科学数据库/kegg-database/SKILL.md
skills/12-科学数据库/kegg-database/references/kegg_reference.md
- Network accessline 7
**Base URL**: https://rest.kegg.jp - Network accessline 8
**Official Documentation**: https://www.kegg.jp/kegg/rest/keggapi.html - Network accessline 322
- **KEGG Mapper**: https://www.kegg.jp/kegg/mapper/ - Interactive pathway mapping - Network accessline 323
- **BlastKOALA**: Automated annotation for sequenced genomes - **GhostKOALA**: Annotation for metagenomes and metatranscriptomes - **KEGG Modules**: https://www.kegg.jp/kegg/module.html - Network accessline 324
- **GhostKOALA**: Annotation for metagenomes and metatranscriptomes - **KEGG Modules**: https://www.kegg.jp/kegg/module.html - **KEGG Brite**: https://www.kegg.jp/kegg/brite.html - Network accessline 325
- **KEGG Modules**: https://www.kegg.jp/kegg/module.html - Network accessline 326
- **KEGG Brite**: https://www.kegg.jp/kegg/brite.html
skills/12-科学数据库/kegg-database/scripts/kegg_api.py
- Executable scriptline 1
Executable file - Network accessline 7
API Base URL: https://rest.kegg.jp - Network accessline 8
Documentation: https://www.kegg.jp/kegg/rest/keggapi.html - Network accessline 19
KEGG_BASE_URL = "https://rest.kegg.jp" - Network accessline 35
url = f"{KEGG_BASE_URL}/info/{database}" try: with urllib.request.urlopen(url) as response: - Network accessline 36
try: with urllib.request.urlopen(url) as response: return response.read().decode('utf-8') - Network accessline 37
with urllib.request.urlopen(url) as response: - Network accessline 64
try: with urllib.request.urlopen(url) as response: - Network accessline 65
with urllib.request.urlopen(url) as response: - Network accessline 103
try: with urllib.request.urlopen(url) as response: - Network accessline 104
with urllib.request.urlopen(url) as response: - Network accessline 150
try: with urllib.request.urlopen(url) as response: - Network accessline 151
with urllib.request.urlopen(url) as response: - Network accessline 178
url = f"{KEGG_BASE_URL}/conv/{target_db}/{source_db}" try: with urllib.request.urlopen(url) as response: - Network accessline 179
try: with urllib.request.urlopen(url) as response: return response.read().decode('utf-8') - Network accessline 180
with urllib.request.urlopen(url) as response: - Network accessline 207
url = f"{KEGG_BASE_URL}/link/{target_db}/{source_db}" try: with urllib.request.urlopen(url) as response: - Network accessline 208
try: with urllib.request.urlopen(url) as response: return response.read().decode('utf-8') - Network accessline 209
with urllib.request.urlopen(url) as response: - Network accessline 233
url = f"{KEGG_BASE_URL}/ddi/{entries_str}" try: with urllib.request.urlopen(url) as response: - Network accessline 234
try: with urllib.request.urlopen(url) as response: return response.read().decode('utf-8') - Network accessline 235
with urllib.request.urlopen(url) as response:
skills/12-科学数据库/metabolomics-workbench-database/SKILL.md
- Network accessline 35
# Get compound information by PubChem CID response = requests.get('https://www.metabolomicsworkbench.org/rest/compound/pubchem_cid/5281365/all/json') - Network accessline 36
response = requests.get('https://www.metabolomicsworkbench.org/rest/compound/pubchem_cid/5281365/all/json') - Network accessline 38
# Download molecular structure as PNG response = requests.get('https://www.metabolomicsworkbench.org/rest/compound/regno/11/png') - Network accessline 39
response = requests.get('https://www.metabolomicsworkbench.org/rest/compound/regno/11/png') - Network accessline 41
# Get compound name by registry number response = requests.get('https://www.metabolomicsworkbench.org/rest/compound/regno/11/name/json') - Network accessline 42
response = requests.get('https://www.metabolomicsworkbench.org/rest/compound/regno/11/name/json') - Network accessline 58
# List all available public studies response = requests.get('https://www.metabolomicsworkbench.org/rest/study/study_id/ST/available/json') - Network accessline 59
response = requests.get('https://www.metabolomicsworkbench.org/rest/study/study_id/ST/available/json') - Network accessline 61
# Get study summary response = requests.get('https://www.metabolomicsworkbench.org/rest/study/study_id/ST000001/summary/json') - Network accessline 62
response = requests.get('https://www.metabolomicsworkbench.org/rest/study/study_id/ST000001/summary/json') - Network accessline 64
# Retrieve experimental data response = requests.get('https://www.metabolomicsworkbench.org/rest/study/study_id/ST000001/data/json') - Network accessline 65
response = requests.get('https://www.metabolomicsworkbench.org/rest/study/study_id/ST000001/data/json') - Network accessline 67
# Find studies containing a specific metabolite response = requests.get('https://www.metabolomicsworkbench.org/rest/study/refmet_name/Tyrosine/summary/json') - Network accessline 68
response = requests.get('https://www.metabolomicsworkbench.org/rest/study/refmet_name/Tyrosine/summary/json') - Network accessline 83
# Standardize a metabolite name response = requests.get('https://www.metabolomicsworkbench.org/rest/refmet/match/citrate/name/json') - Network accessline 84
response = requests.get('https://www.metabolomicsworkbench.org/rest/refmet/match/citrate/name/json') - Network accessline 86
# Query by molecular formula response = requests.get('https://www.metabolomicsworkbench.org/rest/refmet/formula/C12H24O2/all/json') - Network accessline 87
response = requests.get('https://www.metabolomicsworkbench.org/rest/refmet/formula/C12H24O2/all/json') - Network accessline 89
# Get all metabolites in a specific class response = requests.get('https://www.metabolomicsworkbench.org/rest/refmet/main_class/Fatty%20Acids/all/json') - Network accessline 90
response = requests.get('https://www.metabolomicsworkbench.org/rest/refmet/main_class/Fatty%20Acids/all/json') - Network accessline 92
# Retrieve complete RefMet database response = requests.get('https://www.metabolomicsworkbench.org/rest/refmet/all/json') - Network accessline 93
response = requests.get('https://www.metabolomicsworkbench.org/rest/refmet/all/json') - Network accessline 108
# Search by m/z value with M+H adduct response = requests.get('https://www.metabolomicsworkbench.org/rest/moverz/MB/635.52/M+H/0.5/json') - Network accessline 109
response = requests.get('https://www.metabolomicsworkbench.org/rest/moverz/MB/635.52/M+H/0.5/json') - Network accessline 111
# Calculate exact mass for a metabolite with specific adduct response = requests.get('https://www.metabolomicsworkbench.org/rest/moverz/exactmass/PC(34:1)/M+H/json') - Network accessline 112
response = requests.get('https://www.metabolomicsworkbench.org/rest/moverz/exactmass/PC(34:1)/M+H/json') - Network accessline 114
# Search across RefMet database response = requests.get('https://www.metabolomicsworkbench.org/rest/moverz/REFMET/200.15/M-H/0.3/json') - Network accessline 115
response = requests.get('https://www.metabolomicsworkbench.org/rest/moverz/REFMET/200.15/M-H/0.3/json') - Network accessline 131
# Find human blood studies on diabetes using LC-MS response = requests.get('https://www.metabolomicsworkbench.org/rest/metstat/LCMS;POSITIVE;HILIC;Human;Blood;Diabetes/json') - Network accessline 132
response = requests.get('https://www.metabolomicsworkbench.org/rest/metstat/LCMS;POSITIVE;HILIC;Human;Blood;Diabetes/json') - Network accessline 134
# Find all human blood studies containing tyrosine response = requests.get('https://www.metabolomicsworkbench.org/rest/metstat/;;;Human;Blood;;;Tyrosine/json') - Network accessline 135
response = requests.get('https://www.metabolomicsworkbench.org/rest/metstat/;;;Human;Blood;;;Tyrosine/json') - Network accessline 137
# Filter by analytical method only response = requests.get('https://www.metabolomicsworkbench.org/rest/metstat/GCMS;;;;;;/json') - Network accessline 138
response = requests.get('https://www.metabolomicsworkbench.org/rest/metstat/GCMS;;;;;;/json') - Network accessline 153
# Get gene information by symbol response = requests.get('https://www.metabolomicsworkbench.org/rest/gene/gene_symbol/ACACA/all/json') - Network accessline 154
response = requests.get('https://www.metabolomicsworkbench.org/rest/gene/gene_symbol/ACACA/all/json') - Network accessline 156
# Retrieve protein data by UniProt ID response = requests.get('https://www.metabolomicsworkbench.org/rest/protein/uniprot_id/Q13085/all/json') - Network accessline 157
response = requests.get('https://www.metabolomicsworkbench.org/rest/protein/uniprot_id/Q13085/all/json') - Network accessline 168
response = requests.get('https://www.metabolomicsworkbench.org/rest/refmet/match/glucose/name/json') - Network accessline 173
response = requests.get('https://www.metabolomicsworkbench.org/rest/study/refmet_name/Glucose/summary/json') - Network accessline 178
response = requests.get('https://www.metabolomicsworkbench.org/rest/study/study_id/ST000001/data/json') - Network accessline 187
response = requests.get('https://www.metabolomicsworkbench.org/rest/moverz/MB/180.06/M+H/0.5/json') - Network accessline 194
response = requests.get('https://www.metabolomicsworkbench.org/rest/compound/regno/{regno}/all/json') - Network accessline 199
response = requests.get('https://www.metabolomicsworkbench.org/rest/compound/regno/{regno}/png') - Network accessline 208
response = requests.get('https://www.metabolomicsworkbench.org/rest/metstat/LCMS;POSITIVE;;Human;;Cancer/json') - Network accessline 215
response = requests.get('https://www.metabolomicsworkbench.org/rest/study/study_id/ST{ID}/summary/json') - Network accessline 220
response = requests.get('https://www.metabolomicsworkbench.org/rest/study/study_id/ST{ID}/data/json')
skills/12-科学数据库/metabolomics-workbench-database/references/api_reference.md
- Network accessline 7
https://www.metabolomicsworkbench.org/rest/ - Network accessline 69
# Get all compound data by PubChem CID curl "https://www.metabolomicsworkbench.org/rest/compound/pubchem_cid/5281365/all/json" - Network accessline 70
curl "https://www.metabolomicsworkbench.org/rest/compound/pubchem_cid/5281365/all/json" - Network accessline 72
# Get compound name by registry number curl "https://www.metabolomicsworkbench.org/rest/compound/regno/11/name/json" - Network accessline 73
curl "https://www.metabolomicsworkbench.org/rest/compound/regno/11/name/json" - Network accessline 75
# Download structure as PNG curl "https://www.metabolomicsworkbench.org/rest/compound/regno/11/png" -o structure.png - Network accessline 76
curl "https://www.metabolomicsworkbench.org/rest/compound/regno/11/png" -o structure.png - Network accessline 78
# Get compound by KEGG ID curl "https://www.metabolomicsworkbench.org/rest/compound/kegg_id/C00031/all/json" - Network accessline 79
curl "https://www.metabolomicsworkbench.org/rest/compound/kegg_id/C00031/all/json" - Network accessline 81
# Get compound by molecular formula curl "https://www.metabolomicsworkbench.org/rest/compound/formula/C6H12O6/all/json" - Network accessline 82
curl "https://www.metabolomicsworkbench.org/rest/compound/formula/C6H12O6/all/json" - Network accessline 125
# List all publicly available studies curl "https://www.metabolomicsworkbench.org/rest/study/study_id/ST/available/json" - Network accessline 126
curl "https://www.metabolomicsworkbench.org/rest/study/study_id/ST/available/json" - Network accessline 128
# Get study summary curl "https://www.metabolomicsworkbench.org/rest/study/study_id/ST000001/summary/json" - Network accessline 129
curl "https://www.metabolomicsworkbench.org/rest/study/study_id/ST000001/summary/json" - Network accessline 131
# Get experimental data curl "https://www.metabolomicsworkbench.org/rest/study/study_id/ST000001/data/json" - Network accessline 132
curl "https://www.metabolomicsworkbench.org/rest/study/study_id/ST000001/data/json" - Network accessline 134
# Get study factors curl "https://www.metabolomicsworkbench.org/rest/study/study_id/ST000001/factors/json" - Network accessline 135
curl "https://www.metabolomicsworkbench.org/rest/study/study_id/ST000001/factors/json" - Network accessline 137
# Find studies containing a specific metabolite curl "https://www.metabolomicsworkbench.org/rest/study/refmet_name/Tyrosine/summary/json" - Network accessline 138
curl "https://www.metabolomicsworkbench.org/rest/study/refmet_name/Tyrosine/summary/json" - Network accessline 140
# Search studies by investigator curl "https://www.metabolomicsworkbench.org/rest/study/last_name/Smith/summary/json" - Network accessline 141
curl "https://www.metabolomicsworkbench.org/rest/study/last_name/Smith/summary/json" - Network accessline 143
# Download complete study in mwTab format curl "https://www.metabolomicsworkbench.org/rest/study/study_id/ST000001/mwtab/txt" - Network accessline 144
curl "https://www.metabolomicsworkbench.org/rest/study/study_id/ST000001/mwtab/txt" - Network accessline 186
# Standardize a metabolite name curl "https://www.metabolomicsworkbench.org/rest/refmet/match/citrate/name/json" - Network accessline 187
curl "https://www.metabolomicsworkbench.org/rest/refmet/match/citrate/name/json" - Network accessline 189
# Get all RefMet data for a metabolite curl "https://www.metabolomicsworkbench.org/rest/refmet/name/Glucose/all/json" - Network accessline 190
curl "https://www.metabolomicsworkbench.org/rest/refmet/name/Glucose/all/json" - Network accessline 192
# Query by molecular formula curl "https://www.metabolomicsworkbench.org/rest/refmet/formula/C6H12O6/all/json" - Network accessline 193
curl "https://www.metabolomicsworkbench.org/rest/refmet/formula/C6H12O6/all/json" - Network accessline 195
# Get all metabolites in a main class curl "https://www.metabolomicsworkbench.org/rest/refmet/main_class/Fatty%20Acids/all/json" - Network accessline 196
curl "https://www.metabolomicsworkbench.org/rest/refmet/main_class/Fatty%20Acids/all/json" - Network accessline 198
# Query by exact mass curl "https://www.metabolomicsworkbench.org/rest/refmet/exactmass/180.0634/all/json" - Network accessline 199
curl "https://www.metabolomicsworkbench.org/rest/refmet/exactmass/180.0634/all/json" - Network accessline 201
# Download complete RefMet database curl "https://www.metabolomicsworkbench.org/rest/refmet/all/json" - Network accessline 202
curl "https://www.metabolomicsworkbench.org/rest/refmet/all/json" - Network accessline 242
# Human blood diabetes studies with LC-MS HILIC positive mode curl "https://www.metabolomicsworkbench.org/rest/metstat/LCMS;POSITIVE;HILIC;Human;Blood;Diabetes/json" - Network accessline 243
curl "https://www.metabolomicsworkbench.org/rest/metstat/LCMS;POSITIVE;HILIC;Human;Blood;Diabetes/json" - Network accessline 245
# All human blood studies containing tyrosine curl "https://www.metabolomicsworkbench.org/rest/metstat/;;;Human;Blood;;;Tyrosine/json" - Network accessline 246
curl "https://www.metabolomicsworkbench.org/rest/metstat/;;;Human;Blood;;;Tyrosine/json" - Network accessline 248
# All GC-MS studies regardless of other parameters curl "https://www.metabolomicsworkbench.org/rest/metstat/GCMS;;;;;;/json" - Network accessline 249
curl "https://www.metabolomicsworkbench.org/rest/metstat/GCMS;;;;;;/json" - Network accessline 251
# Mouse liver studies curl "https://www.metabolomicsworkbench.org/rest/metstat/;;;Mouse;Liver;;/json" - Network accessline 252
curl "https://www.metabolomicsworkbench.org/rest/metstat/;;;Mouse;Liver;;/json" - Network accessline 254
# All studies measuring glucose curl "https://www.metabolomicsworkbench.org/rest/metstat/;;;;;;;Glucose/json" - Network accessline 255
curl "https://www.metabolomicsworkbench.org/rest/metstat/;;;;;;;Glucose/json" - Network accessline 318
# Search for compounds with m/z 635.52 (M+H) in MB database curl "https://www.metabolomicsworkbench.org/rest/moverz/MB/635.52/M+H/0.5/json" - Network accessline 319
curl "https://www.metabolomicsworkbench.org/rest/moverz/MB/635.52/M+H/0.5/json" - Network accessline 321
# Search in RefMet with negative mode curl "https://www.metabolomicsworkbench.org/rest/moverz/REFMET/200.15/M-H/0.3/json" - Network accessline 322
curl "https://www.metabolomicsworkbench.org/rest/moverz/REFMET/200.15/M-H/0.3/json" - Network accessline 324
# Search lipids database curl "https://www.metabolomicsworkbench.org/rest/moverz/LIPIDS/760.59/M+Na/0.5/json" - Network accessline 325
curl "https://www.metabolomicsworkbench.org/rest/moverz/LIPIDS/760.59/M+Na/0.5/json" - Network accessline 327
# Calculate exact mass for known metabolite curl "https://www.metabolomicsworkbench.org/rest/moverz/exactmass/PC(34:1)/M+H/json" - Network accessline 328
curl "https://www.metabolomicsworkbench.org/rest/moverz/exactmass/PC(34:1)/M+H/json" - Network accessline 330
# High-resolution MS search (tight tolerance) curl "https://www.metabolomicsworkbench.org/rest/moverz/MB/180.0634/M+H/0.01/json" - Network accessline 331
curl "https://www.metabolomicsworkbench.org/rest/moverz/MB/180.0634/M+H/0.01/json" - Network accessline 369
# Get gene information by symbol curl "https://www.metabolomicsworkbench.org/rest/gene/gene_symbol/ACACA/all/json" - Network accessline 370
curl "https://www.metabolomicsworkbench.org/rest/gene/gene_symbol/ACACA/all/json" - Network accessline 372
# Get gene by NCBI Gene ID curl "https://www.metabolomicsworkbench.org/rest/gene/gene_id/31/all/json" - Network accessline 373
curl "https://www.metabolomicsworkbench.org/rest/gene/gene_id/31/all/json" - Network accessline 375
# Search by gene name curl "https://www.metabolomicsworkbench.org/rest/gene/gene_name/carboxylase/summary/json" - Network accessline 376
curl "https://www.metabolomicsworkbench.org/rest/gene/gene_name/carboxylase/summary/json" - Network accessline 424
# Get protein information by UniProt ID curl "https://www.metabolomicsworkbench.org/rest/protein/uniprot_id/Q13085/all/json" - Network accessline 425
curl "https://www.metabolomicsworkbench.org/rest/protein/uniprot_id/Q13085/all/json" - Network accessline 427
# Get protein by gene symbol curl "https://www.metabolomicsworkbench.org/rest/protein/gene_symbol/ACACA/all/json" - Network accessline 428
curl "https://www.metabolomicsworkbench.org/rest/protein/gene_symbol/ACACA/all/json" - Network accessline 430
# Get protein sequence curl "https://www.metabolomicsworkbench.org/rest/protein/uniprot_id/Q13085/seq/json" - Network accessline 431
curl "https://www.metabolomicsworkbench.org/rest/protein/uniprot_id/Q13085/seq/json" - Network accessline 433
# Search by RefSeq ID curl "https://www.metabolomicsworkbench.org/rest/protein/refseq_id/NP_001084/all/json" - Network accessline 434
curl "https://www.metabolomicsworkbench.org/rest/protein/refseq_id/NP_001084/all/json" - Network accessline 458
- **Interactive REST URL Creator**: https://www.metabolomicsworkbench.org/tools/mw_rest.php - Network accessline 459
- **Official API Specification**: https://www.metabolomicsworkbench.org/tools/MWRestAPIv1.1.pdf - Network accessline 470
# 1. Standardize metabolite name using RefMet metabolite = "citrate" response = requests.get(f'https://www.metabolomicsworkbench.org/rest/refmet/match/{metabolite}/name/json') - Network accessline 471
metabolite = "citrate" response = requests.get(f'https://www.metabolomicsworkbench.org/rest/refmet/match/{metabolite}/name/json') standardized_name = response.json()['name'] - Network accessline 472
response = requests.get(f'https://www.metabolomicsworkbench.org/rest/refmet/match/{metabolite}/name/json') - Network accessline 475
# 2. Search for studies containing this metabolite response = requests.get(f'https://www.metabolomicsworkbench.org/rest/study/refmet_name/{standardized_name}/summary/json') - Network accessline 476
response = requests.get(f'https://www.metabolomicsworkbench.org/rest/study/refmet_name/{standardized_name}/summary/json') - Network accessline 479
# 3. Get detailed data from a specific study study_id = studies[0]['study_id'] response = requests.get(f'https://www.metabolomicsworkbench.org/rest/study/study_id/{study_id}/data/json') - Network accessline 480
study_id = studies[0]['study_id'] response = requests.get(f'https://www.metabolomicsworkbench.org/rest/study/study_id/{study_id}/data/json') data = response.json() - Network accessline 481
response = requests.get(f'https://www.metabolomicsworkbench.org/rest/study/study_id/{study_id}/data/json') - Network accessline 484
# 4. Perform m/z search for compound identification mz_value = 180.06 response = requests.get(f'https://www.metabolomicsworkbench.org/rest/moverz/MB/{mz_value}/M+H/0.5/json') - Network accessline 485
mz_value = 180.06 response = requests.get(f'https://www.metabolomicsworkbench.org/rest/moverz/MB/{mz_value}/M+H/0.5/json') matches = response.json() - Network accessline 486
response = requests.get(f'https://www.metabolomicsworkbench.org/rest/moverz/MB/{mz_value}/M+H/0.5/json') - Network accessline 489
# 5. Get compound structure regno = matches[0]['regno'] response = requests.get(f'https://www.metabolomicsworkbench.org/rest/compound/regno/{regno}/png') - Network accessline 490
regno = matches[0]['regno'] response = requests.get(f'https://www.metabolomicsworkbench.org/rest/compound/regno/{regno}/png') with open('structure.png', 'wb') as f: - Network accessline 491
response = requests.get(f'https://www.metabolomicsworkbench.org/rest/compound/regno/{regno}/png')
skills/12-科学数据库/opentargets-database/SKILL.md
- Network accessline 227
Key information: - **Endpoint:** `https://api.platform.opentargets.org/api/v4/graphql` - Network accessline 228
- **Endpoint:** `https://api.platform.opentargets.org/api/v4/graphql` - Network accessline 229
- **Interactive browser:** `https://api.platform.opentargets.org/api/v4/graphql/browser` - Network accessline 358
**Release information:** Check https://platform-docs.opentargets.org/release-notes for the latest updates.
skills/12-科学数据库/opentargets-database/references/api_reference.md
- Network accessline 6
https://api.platform.opentargets.org/api/v4/graphql - Network accessline 11
https://api.platform.opentargets.org/api/v4/graphql/browser - Network accessline 18
1. **GraphQL API** - Best for single entity queries and flexible data retrieval 2. **Web Interface** - Interactive platform at https://platform.opentargets.org - Network accessline 19
2. **Web Interface** - Interactive platform at https://platform.opentargets.org - Network accessline 20
3. **Data Downloads** - FTP at https://ftp.ebi.ac.uk/pub/databases/opentargets/platform/ - Network accessline 64
# Make the request base_url = "https://api.platform.opentargets.org/api/v4/graphql" response = requests.post(base_url, json={"query": query_string, "variables": variables}) - Network accessline 65
base_url = "https://api.platform.opentargets.org/api/v4/graphql" response = requests.post(base_url, json={"query": query_string, "variables": variables}) data = json.loads(response.text) - Network accessline 66
response = requests.post(base_url, json={"query": query_string, "variables": variables})
skills/12-科学数据库/opentargets-database/scripts/query_opentargets.py
skills/12-科学数据库/pubchem-database/SKILL.md
- Network accessline 199
cid = 2244 # Aspirin url = f"https://pubchem.ncbi.nlm.nih.gov/rest/pug/compound/cid/{cid}/PNG?image_size=large" response = requests.get(url) - Network accessline 200
url = f"https://pubchem.ncbi.nlm.nih.gov/rest/pug/compound/cid/{cid}/PNG?image_size=large" - Network accessline 201
response = requests.get(url) - Network accessline 233
# Get bioassay summary for a compound cid = 2244 # Aspirin url = f"https://pubchem.ncbi.nlm.nih.gov/rest/pug/compound/cid/{cid}/assaysummary/JSON" - Network accessline 235
url = f"https://pubchem.ncbi.nlm.nih.gov/rest/pug/compound/cid/{cid}/assaysummary/JSON" - Network accessline 237
response = requests.get(url) - Network accessline 259
cid = 2244 url = f"https://pubchem.ncbi.nlm.nih.gov/rest/pug_view/data/compound/{cid}/JSON" - Network accessline 260
url = f"https://pubchem.ncbi.nlm.nih.gov/rest/pug_view/data/compound/{cid}/JSON" - Network accessline 262
response = requests.get(url) - Network accessline 277
# Get only drug information url = f"https://pubchem.ncbi.nlm.nih.gov/rest/pug_view/data/compound/{cid}/JSON?heading=Drug and Medication Information" - Network accessline 278
url = f"https://pubchem.ncbi.nlm.nih.gov/rest/pug_view/data/compound/{cid}/JSON?heading=Drug and Medication Information" - Network accessline 567
- PubChem Home: https://pubchem.ncbi.nlm.nih.gov/ - Network accessline 568
- PUG-REST Documentation: https://pubchem.ncbi.nlm.nih.gov/docs/pug-rest - Network accessline 569
- PUG-REST Tutorial: https://pubchem.ncbi.nlm.nih.gov/docs/pug-rest-tutorial - Network accessline 570
- PubChemPy Documentation: https://pubchempy.readthedocs.io/ - Network accessline 571
- PubChemPy GitHub: https://github.com/mcs07/PubChemPy
skills/12-科学数据库/pubchem-database/references/api_reference.md
- Network accessline 20
https://pubchem.ncbi.nlm.nih.gov/rest/pug/<input>/<operation>/<output> - Network accessline 154
# Step 1: Submit similarity search response = requests.post( - Network accessline 155
response = requests.post( - Network accessline 156
"https://pubchem.ncbi.nlm.nih.gov/rest/pug/compound/similarity/smiles/{smiles}/cids/JSON", - Network accessline 161
# Step 2: Check status status_url = f"https://pubchem.ncbi.nlm.nih.gov/rest/pug/compound/listkey/{listkey}/cids/JSON" - Network accessline 162
status_url = f"https://pubchem.ncbi.nlm.nih.gov/rest/pug/compound/listkey/{listkey}/cids/JSON" - Network accessline 411
cid = 2244 # Aspirin url = f"https://pubchem.ncbi.nlm.nih.gov/rest/pug/compound/cid/{cid}/assaysummary/JSON" - Network accessline 412
url = f"https://pubchem.ncbi.nlm.nih.gov/rest/pug/compound/cid/{cid}/assaysummary/JSON" - Network accessline 414
response = requests.get(url) - Network accessline 429
8. **Use asynchronous pattern**: For large similarity/substructure searches 9. **Specify output format**: Choose JSON for programmatic access, SDF for cheminformatics tools 10. **Read documentation**: Full PUG-REST documentation available a - Network accessline 431
10. **Read documentation**: Full PUG-REST documentation available at https://pubchem.ncbi.nlm.nih.gov/docs/pug-rest - Network accessline 435
- PubChem Home: https://pubchem.ncbi.nlm.nih.gov/ - Network accessline 436
- PUG-REST Documentation: https://pubchem.ncbi.nlm.nih.gov/docs/pug-rest - Network accessline 437
- PUG-REST Tutorial: https://pubchem.ncbi.nlm.nih.gov/docs/pug-rest-tutorial - Network accessline 438
- PubChemPy Documentation: https://pubchempy.readthedocs.io/ - Network accessline 439
- PubChemPy GitHub: https://github.com/mcs07/PubChemPy - Network accessline 440
- IUPAC Tutorial: https://iupac.github.io/WFChemCookbook/datasources/pubchem_pugrest.html
skills/12-科学数据库/pubchem-database/scripts/bioactivity_query.py
- Executable scriptline 1
#!/usr/bin/env python3 - Network accessline 21
BASE_URL = "https://pubchem.ncbi.nlm.nih.gov/rest/pug" - Network accessline 22
PUG_VIEW_URL = "https://pubchem.ncbi.nlm.nih.gov/rest/pug_view" - Network accessline 42
try: if method.upper() == 'GET': response = requests.get(url, **kwargs) - Network accessline 43
if method.upper() == 'GET': response = requests.get(url, **kwargs) else: - Network accessline 44
response = requests.get(url, **kwargs) - Network accessline 46
response = requests.post(url, **kwargs)
skills/12-科学数据库/pubchem-database/scripts/compound_search.py
- Executable scriptline 1
#!/usr/bin/env python3
skills/12-科学数据库/reactome-database/SKILL.md
- Network accessline 41
**API Base URL:** `https://reactome.org/ContentService` - Network accessline 52
**API Base URL:** `https://reactome.org/AnalysisService` - Network accessline 75
response = requests.get("https://reactome.org/ContentService/data/database/version") - Network accessline 84
entity_id = "R-HSA-69278" # Example pathway ID response = requests.get(f"https://reactome.org/ContentService/data/query/{entity_id}") - Network accessline 85
response = requests.get(f"https://reactome.org/ContentService/data/query/{entity_id}") - Network accessline 93
event_id = "R-HSA-69278" response = requests.get( - Network accessline 94
response = requests.get( - Network accessline 95
f"https://reactome.org/ContentService/data/event/{event_id}/participatingPhysicalEntities" - Network accessline 129
# Submit analysis response = requests.post( - Network accessline 130
response = requests.post( - Network accessline 131
"https://reactome.org/AnalysisService/identifiers/", - Network accessline 146
# Token is valid for 7 days response = requests.get(f"https://reactome.org/AnalysisService/token/{token}") - Network accessline 147
response = requests.get(f"https://reactome.org/AnalysisService/token/{token}") - Network accessline 171
response = requests.post( - Network accessline 172
"https://reactome.org/AnalysisService/identifiers/", - Network accessline 185
response = requests.post( - Network accessline 186
"https://reactome.org/AnalysisService/identifiers/projection/", - Network accessline 197
[Credential-related source omitted] - Network accessline 198
pathway_id = "R-HSA-69278" url = f"https://reactome.org/PathwayBrowser/#{pathway_id}&DTAB=AN&ANALYSIS={token}" print(f"View results: {url}") - Network accessline 199
url = f"https://reactome.org/PathwayBrowser/#{pathway_id}&DTAB=AN&ANALYSIS={token}" - Network accessline 260
- **API Documentation**: https://reactome.org/dev - Network accessline 261
- **User Guide**: https://reactome.org/userguide - **Documentation Portal**: https://reactome.org/documentation - **Data Downloads**: https://reactome.org/download-data - Network accessline 262
- **Documentation Portal**: https://reactome.org/documentation - **Data Downloads**: https://reactome.org/download-data - **reactome2py Docs**: https://reactome.github.io/reactome2py/ - Network accessline 263
- **Data Downloads**: https://reactome.org/download-data - Network accessline 264
- **reactome2py Docs**: https://reactome.github.io/reactome2py/
skills/12-科学数据库/reactome-database/references/api_reference.md
- Network accessline 7
- **Content Service**: `https://reactome.org/ContentService` - Network accessline 8
- **Analysis Service**: `https://reactome.org/AnalysisService` - Network accessline 25
import requests response = requests.get("https://reactome.org/ContentService/data/database/version") - Network accessline 26
response = requests.get("https://reactome.org/ContentService/data/database/version") - Network accessline 56
import requests response = requests.get("https://reactome.org/ContentService/data/query/R-HSA-69278") - Network accessline 57
response = requests.get("https://reactome.org/ContentService/data/query/R-HSA-69278") - Network accessline 76
response = requests.get("https://reactome.org/ContentService/data/query/R-HSA-69278/displayName") - Network accessline 94
response = requests.get( - Network accessline 95
"https://reactome.org/ContentService/data/event/R-HSA-69278/participatingPhysicalEntities" - Network accessline 126
response = requests.get( - Network accessline 127
"https://reactome.org/ContentService/data/query", - Network accessline 207
response = requests.post( - Network accessline 208
"https://reactome.org/AnalysisService/identifiers/", - Network accessline 257
[Credential-related source omitted] - Network accessline 258
response = requests.get(f"https://reactome.org/AnalysisService/token/{token}") - Network accessline 371
# Retrieve results later result = requests.get(f"https://reactome.org/AnalysisService/token/{token}") - Network accessline 372
result = requests.get(f"https://reactome.org/AnalysisService/token/{token}") - Network accessline 390
# For mouse genes, project to human pathways response = requests.post( - Network accessline 391
response = requests.post( - Network accessline 392
"https://reactome.org/AnalysisService/identifiers/projection/", - Network accessline 418
# Submit analysis data = "\n".join(genes) response = requests.post( - Network accessline 419
data = "\n".join(genes) response = requests.post( "https://reactome.org/AnalysisService/identifiers/", - Network accessline 420
response = requests.post( - Network accessline 421
"https://reactome.org/AnalysisService/identifiers/", - Network accessline 448
if significant: top_pathway = significant[0] url = f"https://reactome.org/PathwayBrowser/#{top_pathway['stId']}&DTAB=AN&ANALYSIS={token}" - Network accessline 449
top_pathway = significant[0] url = f"https://reactome.org/PathwayBrowser/#{top_pathway['stId']}&DTAB=AN&ANALYSIS={token}" print(f"View top result: {url}") - Network accessline 450
url = f"https://reactome.org/PathwayBrowser/#{top_pathway['stId']}&DTAB=AN&ANALYSIS={token}" - Network accessline 462
- **Interactive API Documentation**: https://reactome.org/dev/content-service - Network accessline 463
- **Analysis Service Docs**: https://reactome.org/dev/analysis - **User Guide**: https://reactome.org/userguide - **Data Downloads**: https://reactome.org/download-data - Network accessline 464
- **User Guide**: https://reactome.org/userguide - Network accessline 465
- **Data Downloads**: https://reactome.org/download-data
skills/12-科学数据库/reactome-database/scripts/reactome_query.py
- Executable scriptline 1
#!/usr/bin/env python3 - Network accessline 31
CONTENT_BASE = "https://reactome.org/ContentService" - Network accessline 32
ANALYSIS_BASE = "https://reactome.org/AnalysisService" - Network accessline 36
response = requests.get(f"{self.CONTENT_BASE}/data/database/version") - Network accessline 42
response = requests.get(f"{self.CONTENT_BASE}/data/query/{pathway_id}") - Network accessline 48
response = requests.get( - Network accessline 56
response = requests.get( - Network accessline 65
data = "\n".join(gene_list) response = requests.post( - Network accessline 66
response = requests.post( - Network accessline 76
response = requests.get(f"{self.ANALYSIS_BASE}/token/{token}") - Network accessline 219
[Credential-related source omitted] - Network accessline 220
top_pathway = pathways[0]['stId'] url = f"https://reactome.org/PathwayBrowser/#{top_pathway}&DTAB=AN&ANALYSIS={token}" print(f"\nView top result in browser:") - Network accessline 221
url = f"https://reactome.org/PathwayBrowser/#{top_pathway}&DTAB=AN&ANALYSIS={token}"
skills/12-科学数据库/string-database/SKILL.md
- Network accessline 413
Full list available at: https://string-db.org/cgi/input?input_page_active_form=organisms - Network accessline 513
For proteome-scale analysis or complete species network upload: - Visit https://string-db.org - Network accessline 514
- Visit https://string-db.org - Network accessline 518
For bulk downloads of complete datasets: - Download page: https://string-db.org/cgi/download - Network accessline 519
- Download page: https://string-db.org/cgi/download - Network accessline 531
When using STRING in publications, cite the most recent publication from: https://string-db.org/cgi/about
skills/12-科学数据库/string-database/references/string_reference.md
- Network accessline 17
- **Current version**: https://string-db.org/api - Network accessline 18
- **Version-specific**: https://version-12-0.string-db.org/api (for reproducibility) - Network accessline 19
- **API documentation**: https://string-db.org/help/api/ - Network accessline 265
Full list: https://string-db.org/cgi/input?input_page_active_form=organisms - Network accessline 337
For complete proteome analysis: 1. Navigate to https://string-db.org/ - Network accessline 338
1. Navigate to https://string-db.org/ - Access to secretsline 349
[Credential-related source omitted] - Network accessline 386
import requests url = "https://string-db.org/api/tsv/network" - Network accessline 387
url = "https://string-db.org/api/tsv/network" params = {"identifiers": "TP53", "species": 9606} response = requests.get(url, params=params) - Network accessline 389
response = requests.get(url, params=params) - Network accessline 394
import urllib.request url = "https://string-db.org/api/tsv/network?identifiers=TP53&species=9606" - Network accessline 395
url = "https://string-db.org/api/tsv/network?identifiers=TP53&species=9606" - Network accessline 396
response = urllib.request.urlopen(url) - Network accessline 426
- **Rate limiting**: No strict limit, but avoid rapid-fire requests - **Recommendation**: Wait 1 second between calls - **Large datasets**: Use bulk download from https://string-db.org/cgi/download - Network accessline 427
- **Recommendation**: Wait 1 second between calls - **Large datasets**: Use bulk download from https://string-db.org/cgi/download - **Proteome-scale**: Use web upload feature instead of API - Network accessline 428
- **Large datasets**: Use bulk download from https://string-db.org/cgi/download - Network accessline 433
- **STRING website**: https://string-db.org - **Download page**: https://string-db.org/cgi/download - Network accessline 434
- **Download page**: https://string-db.org/cgi/download - Network accessline 435
- **Help center**: https://string-db.org/help/ - Network accessline 436
- **API documentation**: https://string-db.org/help/api/ - Network accessline 437
- **Publications**: https://string-db.org/cgi/about
skills/12-科学数据库/string-database/scripts/string_api.py
- Executable scriptline 1
Executable file - Network accessline 7
API Base URL: https://string-db.org/api - Network accessline 8
Documentation: https://string-db.org/help/api/ - Network accessline 21
STRING_BASE_URL = "https://string-db.org/api" - Network accessline 65
try: with urllib.request.urlopen(url, data=data) as response: - Network accessline 66
with urllib.request.urlopen(url, data=data) as response: - Network accessline 118
try: with urllib.request.urlopen(url) as response: - Network accessline 119
with urllib.request.urlopen(url) as response: - Network accessline 167
try: with urllib.request.urlopen(url) as response: - Network accessline 168
with urllib.request.urlopen(url) as response: - Network accessline 211
try: with urllib.request.urlopen(url) as response: - Network accessline 212
with urllib.request.urlopen(url) as response: - Network accessline 250
try: with urllib.request.urlopen(url) as response: - Network accessline 251
with urllib.request.urlopen(url) as response: - Network accessline 292
try: with urllib.request.urlopen(url) as response: - Network accessline 293
with urllib.request.urlopen(url) as response: - Network accessline 330
try: with urllib.request.urlopen(url) as response: - Network accessline 331
with urllib.request.urlopen(url) as response: - Network accessline 349
try: with urllib.request.urlopen(url) as response: - Network accessline 350
with urllib.request.urlopen(url) as response:
skills/12-科学数据库/uspto-database/SKILL.md
- Network accessline 38
- Access to patent data through June 30, 2025 - 45 requests/minute rate limit - **Base URL**: `https://search.patentsview.org/api/v1/` - Network accessline 40
- **Base URL**: `https://search.patentsview.org/api/v1/` - Network accessline 49
- Trademark status, ownership, prosecution history - Search by serial or registration number - **Base URL**: `https://tsdrapi.uspto.gov/ts/cd/` - Network accessline 51
- **Base URL**: `https://tsdrapi.uspto.gov/ts/cd/` - Network accessline 69
USPTO APIs require an API key. Register at: **https://account.uspto.gov/api-manager/** - Network accessline 70
**https://account.uspto.gov/api-manager/** - Network accessline 72
API key for **PatentSearch API** is provided by PatentsView. Register at: **https://patentsview.org/api-v01-information-page** - Network accessline 73
**https://patentsview.org/api-v01-information-page** - Access to secretsline 77
[Credential-related source omitted] - Access to secretsline 78
[Credential-related source omitted] - Network accessline 153
url = "https://search.patentsview.org/api/v1/patent" - Access to secretsline 155
[Credential-related source omitted] - Network accessline 172
response = requests.post(url, headers=headers, json=query) - Access to secretsline 354
[Credential-related source omitted] - Access to secretsline 356
[Credential-related source omitted] - Network accessline 404
**Base URL**: `https://assignment-api.uspto.gov/patent/v1.4/` - Network accessline 411
[Credential-related source omitted] - Access to secretsline 411
[Credential-related source omitted] - Network accessline 412
url = f"https://assignment-api.uspto.gov/patent/v1.4/assignment/patent/{patent_number}" - Access to secretsline 413
[Credential-related source omitted] - Network accessline 415
response = requests.get(url, headers=headers) - Access to secretsline 419
[Credential-related source omitted] - Network accessline 434
[Credential-related source omitted] - Access to secretsline 434
[Credential-related source omitted] - Network accessline 435
url = "https://assignment-api.uspto.gov/patent/v1.4/assignment/search" - Access to secretsline 436
[Credential-related source omitted] - Network accessline 439
response = requests.post(url, headers=headers, json=data) - Access to secretsline 501
[Credential-related source omitted] - Access to secretsline 511
[Credential-related source omitted] - Network accessline 520
# 3. Get assignment history import requests assign_url = f"https://assignment-api.uspto.gov/patent/v1.4/assignment/patent/{patent_number}" - Network accessline 521
[Credential-related source omitted] - Network accessline 522
[Credential-related source omitted] - Network accessline 523
[Credential-related source omitted] - Access to secretsline 523
[Credential-related source omitted] - Network accessline 585
### API Documentation - **PatentSearch API**: https://search.patentsview.org/docs/ - Network accessline 586
- **PatentSearch API**: https://search.patentsview.org/docs/ - Network accessline 587
- **USPTO Developer Portal**: https://developer.uspto.gov/ - Network accessline 588
- **USPTO Open Data Portal**: https://data.uspto.gov/ - Network accessline 589
- **API Key Registration**: https://account.uspto.gov/api-manager/ - Network accessline 591
### Python Libraries - **uspto-opendata-python**: https://pypi.org/project/uspto-opendata-python/ - Network accessline 592
- **uspto-opendata-python**: https://pypi.org/project/uspto-opendata-python/ - Network accessline 593
- **USPTO Docs**: https://docs.ip-tools.org/uspto-opendata-python/
skills/12-科学数据库/uspto-database/references/additional_apis.md
- Access to secretsline 56
[Credential-related source omitted] - Network accessline 124
**Base URL:** `https://assignment-api.uspto.gov/patent/` - Network accessline 168
[Credential-related source omitted] - Access to secretsline 168
[Credential-related source omitted] - Network accessline 169
[Credential-related source omitted] - Network accessline 170
url = f"https://assignment-api.uspto.gov/patent/v1.4/assignment/patent/{patent_number}" - Access to secretsline 171
[Credential-related source omitted] - Network accessline 173
response = requests.get(url, headers=headers) - Network accessline 179
[Credential-related source omitted] - Access to secretsline 179
[Credential-related source omitted] - Network accessline 180
[Credential-related source omitted] - Network accessline 181
url = "https://assignment-api.uspto.gov/patent/v1.4/assignment/search" - Access to secretsline 182
[Credential-related source omitted] - Network accessline 185
response = requests.post(url, headers=headers, json=data) - Access to secretsline 295
[Credential-related source omitted] - Access to secretsline 300
[Credential-related source omitted] - Access to secretsline 303
[Credential-related source omitted] - Access to secretsline 312
[Credential-related source omitted] - Access to secretsline 319
[Credential-related source omitted] - Access to secretsline 322
[Credential-related source omitted] - Access to secretsline 325
[Credential-related source omitted] - Access to secretsline 328
[Credential-related source omitted] - Access to secretsline 331
[Credential-related source omitted] - Access to secretsline 334
[Credential-related source omitted] - Access to secretsline 342
[Credential-related source omitted] - Access to secretsline 347
[Credential-related source omitted] - Access to secretsline 355
[Credential-related source omitted] - Access to secretsline 356
[Credential-related source omitted] - Access to secretsline 357
[Credential-related source omitted] - Network accessline 384
All APIs require registration at: **https://account.uspto.gov/api-manager/** - Network accessline 385
**https://account.uspto.gov/api-manager/** - Network accessline 391
- **Developer Portal**: https://developer.uspto.gov/ - Network accessline 392
- **Open Data Portal**: https://data.uspto.gov/ - Network accessline 393
- **API Catalog**: https://developer.uspto.gov/api-catalog
skills/12-科学数据库/uspto-database/references/patentsearch_api.md
- Network accessline 7
**Base URL:** `https://search.patentsview.org/api/v1/` - Access to secretsline 14
[Credential-related source omitted] - Network accessline 17
Register for an API key at: https://account.uspto.gov/api-manager/ - Network accessline 176
[Credential-related source omitted] - Access to secretsline 176
[Credential-related source omitted] - Access to secretsline 177
[Credential-related source omitted] - Network accessline 197
url = "https://search.patentsview.org/api/v1/patent" - Access to secretsline 199
[Credential-related source omitted] - Network accessline 214
response = requests.post(url, headers=headers, json=data) - Network accessline 234
Refer to the full field dictionary at: https://search.patentsview.org/docs/ - Network accessline 264
- **Official Documentation**: https://search.patentsview.org/docs/ - Network accessline 265
- **API Key Registration**: https://account.uspto.gov/api-manager/
skills/12-科学数据库/uspto-database/references/peds_api.md
- Network accessline 209
- **Library Documentation**: https://docs.ip-tools.org/uspto-opendata-python/ - Network accessline 210
- **PyPI Package**: https://pypi.org/project/uspto-opendata-python/ - Network accessline 211
- **GitHub Repository**: https://github.com/ip-tools/uspto-opendata-python - Network accessline 212
- **USPTO PEDS Portal**: https://ped.uspto.gov/
skills/12-科学数据库/uspto-database/references/trademark_api.md
- Network accessline 18
**Base URL:** `https://tsdrapi.uspto.gov/ts/cd/` - Network accessline 22
Requires API key registration at: https://account.uspto.gov/api-manager/ - Access to secretsline 26
[Credential-related source omitted] - Network accessline 39
[Credential-related source omitted] - Network accessline 40
"https://tsdrapi.uspto.gov/ts/cd/casedocs/sn87654321/info.json" - Network accessline 111
[Credential-related source omitted] - Access to secretsline 111
[Credential-related source omitted] - Network accessline 112
[Credential-related source omitted] - Network accessline 113
url = f"https://tsdrapi.uspto.gov/ts/cd/casedocs/sn{serial_number}/info.json" - Access to secretsline 114
[Credential-related source omitted] - Network accessline 116
response = requests.get(url, headers=headers) - Access to secretsline 123
[Credential-related source omitted] - Network accessline 141
**Base URL:** `https://assignment-api.uspto.gov/trademark/` - Access to secretsline 147
[Credential-related source omitted] - Network accessline 228
[Credential-related source omitted] - Access to secretsline 228
[Credential-related source omitted] - Network accessline 229
[Credential-related source omitted] - Network accessline 230
url = f"https://assignment-api.uspto.gov/trademark/v1.4/assignment/application/{registration_number}" - Access to secretsline 231
[Credential-related source omitted] - Network accessline 233
response = requests.get(url, headers=headers) - Access to secretsline 240
[Credential-related source omitted] - Access to secretsline 263
[Credential-related source omitted] - Access to secretsline 265
[Credential-related source omitted] - Access to secretsline 288
[Credential-related source omitted] - Access to secretsline 290
[Credential-related source omitted] - Access to secretsline 311
[Credential-related source omitted] - Access to secretsline 321
[Credential-related source omitted] - Network accessline 354
- **TSDR API**: https://developer.uspto.gov/api-catalog/tsdr-data-api - Network accessline 355
- **Assignment API**: https://developer.uspto.gov/api-catalog/trademark-assignment-search-data-api - Network accessline 356
- **API Key Registration**: https://account.uspto.gov/api-manager/ - Network accessline 357
- **Trademark Search**: https://tmsearch.uspto.gov/ - Network accessline 358
- **Swagger Documentation**: https://developer.uspto.gov/swagger/tsdr-api-v1
skills/12-科学数据库/uspto-database/scripts/patent_search.py
- Executable scriptline 1
#!/usr/bin/env python3 - Network accessline 8
Requires: - requests library: pip install requests - USPTO API key from https://account.uspto.gov/api-manager/ - Network accessline 10
- USPTO API key from https://account.uspto.gov/api-manager/ - Access to secretsline 13
[Credential-related source omitted] - Network accessline 27
BASE_URL = "https://search.patentsview.org/api/v1" - Access to secretsline 29
[Credential-related source omitted] - Access to secretsline 34
[Credential-related source omitted] - Access to secretsline 36
[Credential-related source omitted] - Access to secretsline 37
[Credential-related source omitted] - Access to secretsline 38
[Credential-related source omitted] - Access to secretsline 41
[Credential-related source omitted] - Network accessline 70
response = requests.post(url, headers=self.headers, json=data)
skills/12-科学数据库/uspto-database/scripts/peds_client.py
- Executable scriptline 1
#!/usr/bin/env python3
skills/12-科学数据库/uspto-database/scripts/trademark_client.py
- Executable scriptline 1
#!/usr/bin/env python3 - Network accessline 8
Requires: - requests library: pip install requests - USPTO API key from https://account.uspto.gov/api-manager/ - Network accessline 10
- USPTO API key from https://account.uspto.gov/api-manager/ - Access to secretsline 13
[Credential-related source omitted] - Network accessline 26
TSDR_BASE_URL = "https://tsdrapi.uspto.gov/ts/cd" - Network accessline 27
ASSIGNMENT_BASE_URL = "https://assignment-api.uspto.gov/trademark" - Access to secretsline 29
[Credential-related source omitted] - Access to secretsline 34
[Credential-related source omitted] - Access to secretsline 36
[Credential-related source omitted] - Access to secretsline 37
[Credential-related source omitted] - Access to secretsline 38
[Credential-related source omitted] - Access to secretsline 40
[Credential-related source omitted] - Network accessline 54
try: response = requests.get(url, headers=self.headers) - Network accessline 55
response = requests.get(url, headers=self.headers) - Network accessline 75
try: response = requests.get(url, headers=self.headers) - Network accessline 76
response = requests.get(url, headers=self.headers) - Network accessline 256
Environment: Set USPTO_API_KEY environment variable with your API key from: https://account.uspto.gov/api-manager/ - Network accessline 257
[Credential-related source omitted] - Access to secretsline 257
[Credential-related source omitted] - Network accessline 258
https://account.uspto.gov/api-manager/ - Access to secretsline 273
[Credential-related source omitted] - Access to secretsline 278
[Credential-related source omitted]
skills/12-科学数据库/zinc-database/SKILL.md
- Network accessline 43
Primary access point: https://zinc.docking.org/ - Network accessline 44
Interactive searching: https://cartblanche22.docking.org/ - Network accessline 50
**Base URL**: `https://cartblanche22.docking.org/` - Network accessline 60
**Web interface**: https://cartblanche22.docking.org/search/zincid - Network accessline 64
curl "https://cartblanche22.docking.org/[email protected]_fields=smiles,zinc_id" - Network accessline 69
curl "https://cartblanche22.docking.org/substances.txt:zinc_id=ZINC000000000001,ZINC000000000002&output_fields=smiles,zinc_id,tranche" - Network accessline 78
**Web interface**: https://cartblanche22.docking.org/search/smiles - Network accessline 82
curl "https://cartblanche22.docking.org/[email protected]=4-Fadist=4" - Network accessline 93
curl "https://cartblanche22.docking.org/smiles.txt:smiles=c1ccccc1" - Network accessline 98
curl "https://cartblanche22.docking.org/smiles.txt:smiles=c1ccccc1&dist=3&output_fields=zinc_id,smiles,tranche" - Network accessline 105
**Web interface**: https://cartblanche22.docking.org/search/catitems - Network accessline 109
curl "https://cartblanche22.docking.org/catitems.txt:catitem_id=SUPPLIER-CODE-123" - Network accessline 121
**Web interface**: https://cartblanche22.docking.org/search/random - Network accessline 125
curl "https://cartblanche22.docking.org/substance/random.txt:count=100" - Network accessline 135
curl "https://cartblanche22.docking.org/substance/random.txt:count=1000&subset=lead-like&output_fields=zinc_id,smiles,tranche" - Network accessline 146
# Example: Get drug-like compounds with specific LogP and MW curl "https://cartblanche22.docking.org/substance/random.txt:count=10000&subset=drug-like&output_fields=zinc_id,smiles,tranche" > docking_library.txt - Network accessline 147
curl "https://cartblanche22.docking.org/substance/random.txt:count=10000&subset=drug-like&output_fields=zinc_id,smiles,tranche" > docking_library.txt - Network accessline 173
curl "https://cartblanche22.docking.org/smiles.txt:smiles=CC(C)Cc1ccc(cc1)C(C)C(=O)O&dist=5&output_fields=zinc_id,smiles,catalogs" > analogs.txt - Network accessline 201
curl "https://cartblanche22.docking.org/substances.txt:zinc_id=ZINC000000000001,ZINC000000000002&output_fields=zinc_id,smiles,supplier_code,catalogs" - Network accessline 215
curl "https://cartblanche22.docking.org/substance/random.txt:count=5000&subset=lead-like&output_fields=zinc_id,smiles,tranche" > chemical_space_sample.txt - Network accessline 234
curl "https://cartblanche22.docking.org/substances.txt:zinc_id=ZINC000000000001&output_fields=zinc_id,smiles,catalogs,tranche" - Network accessline 260
**File repository**: https://files.docking.org/zinc22/ - Network accessline 267
Refer to ZINC documentation at https://wiki.docking.org for downloading protocols and batch access methods. - Network accessline 271
### Using curl with Python - Network accessline 277
def query_zinc_by_id(zinc_id, output_fields="zinc_id,smiles,catalogs"): """Query ZINC22 by ZINC ID.""" url = f"https://cartblanche22.docking.org/[email protected]_id={zinc_id}&output_fields={output_fields}" - Network accessline 278
"""Query ZINC22 by ZINC ID.""" url = f"https://cartblanche22.docking.org/[email protected]_id={zinc_id}&output_fields={output_fields}" result = subprocess.run(['curl', url], capture_output=True, text=True) - Network accessline 279
url = f"https://cartblanche22.docking.org/[email protected]_id={zinc_id}&output_fields={output_fields}" result = subprocess.run(['curl', url], capture_output=True, text=True) return result.stdout - Network accessline 280
result = subprocess.run(['curl', url], capture_output=True, text=True) - Network accessline 283
def search_by_smiles(smiles, dist=0, adist=0, output_fields="zinc_id,smiles"): """Search ZINC22 by SMILES with optional distance parameters.""" url = f"https://cartblanche22.docking.org/smiles.txt:smiles={smiles}&dist={dist}&adist={ - Network accessline 284
"""Search ZINC22 by SMILES with optional distance parameters.""" url = f"https://cartblanche22.docking.org/smiles.txt:smiles={smiles}&dist={dist}&adist={adist}&output_fields={output_fields}" result = subprocess.run(['curl', url], ca - Network accessline 285
url = f"https://cartblanche22.docking.org/smiles.txt:smiles={smiles}&dist={dist}&adist={adist}&output_fields={output_fields}" result = subprocess.run(['curl', url], capture_output=True, text=True) return result.stdout - Network accessline 286
result = subprocess.run(['curl', url], capture_output=True, text=True) - Network accessline 289
def get_random_compounds(count=100, subset=None, output_fields="zinc_id,smiles,tranche"): """Get random compounds from ZINC22.""" url = f"https://cartblanche22.docking.org/substance/random.txt:count={count}&output_fields={output_fie - Network accessline 290
"""Get random compounds from ZINC22.""" url = f"https://cartblanche22.docking.org/substance/random.txt:count={count}&output_fields={output_fields}" if subset: - Network accessline 291
url = f"https://cartblanche22.docking.org/substance/random.txt:count={count}&output_fields={output_fields}" - Network accessline 292
if subset: url += f"&subset={subset}" result = subprocess.run(['curl', url], capture_output=True, text=True) - Network accessline 293
url += f"&subset={subset}" result = subprocess.run(['curl', url], capture_output=True, text=True) return result.stdout - Network accessline 294
result = subprocess.run(['curl', url], capture_output=True, text=True) - Network accessline 337
- **Rate limiting**: Respect server resources; avoid rapid consecutive requests - **Caching**: Store frequently accessed compounds locally - **Parallel downloads**: When downloading 3D structures, use parallel wget or aria2c for file reposi - Network accessline 338
- **Caching**: Store frequently accessed compounds locally - **Parallel downloads**: When downloading 3D structures, use parallel wget or aria2c for file repositories - **Subset filtering**: Use lead-like, drug-like, or fragment subsets to - Network accessline 339
- **Parallel downloads**: When downloading 3D structures, use parallel wget or aria2c for file repositories - Network accessline 385
- **ZINC Website**: https://zinc.docking.org/ - Network accessline 386
- **CartBlanche22 Interface**: https://cartblanche22.docking.org/ - Network accessline 387
- **ZINC Wiki**: https://wiki.docking.org/ - Network accessline 388
- **File Repository**: https://files.docking.org/zinc22/ - Network accessline 389
- **GitHub**: https://github.com/docking-org/
skills/12-科学数据库/zinc-database/references/api_reference.md
- Network accessline 9
### ZINC22 (Current) - **CartBlanche22 API**: `https://cartblanche22.docking.org/` - Network accessline 10
- **CartBlanche22 API**: `https://cartblanche22.docking.org/` - Network accessline 11
- **File Repository**: `https://files.docking.org/zinc22/` - Network accessline 12
- **Main Website**: `https://zinc.docking.org/` - Network accessline 14
### ZINC20 (Maintained) - **API**: `https://zinc20.docking.org/` - Network accessline 15
- **API**: `https://zinc20.docking.org/` - Network accessline 16
- **File Repository**: `https://files.docking.org/zinc20/` - Network accessline 18
### Documentation - **Wiki**: `https://wiki.docking.org/` - Network accessline 19
- **Wiki**: `https://wiki.docking.org/` - Network accessline 20
- **GitHub**: `https://github.com/docking-org/` - Network accessline 36
https://cartblanche22.docking.org/substances.txt:zinc_id={ZINC_ID}&output_fields={FIELDS} - Network accessline 43
curl "https://cartblanche22.docking.org/[email protected]_fields=zinc_id,smiles,catalogs" - Network accessline 48
curl "https://cartblanche22.docking.org/substances.txt:zinc_id=ZINC000000000001,ZINC000000000002,ZINC000000000003&output_fields=zinc_id,smiles,tranche" - Network accessline 53
# Create file with ZINC IDs (one per line or comma-separated) curl -X POST "https://cartblanche22.docking.org/substances.txt?output_fields=zinc_id,smiles" \ - Network accessline 54
curl -X POST "https://cartblanche22.docking.org/substances.txt?output_fields=zinc_id,smiles" \ - Network accessline 79
https://cartblanche22.docking.org/smiles.txt:smiles={SMILES}&dist={DIST}&adist={ADIST}&output_fields={FIELDS} - Network accessline 86
curl "https://cartblanche22.docking.org/smiles.txt:smiles=c1ccccc1&output_fields=zinc_id,smiles" - Network accessline 91
curl "https://cartblanche22.docking.org/smiles.txt:smiles=CC(C)Cc1ccc(cc1)C(C)C(=O)O&dist=3&output_fields=zinc_id,smiles,catalogs" - Network accessline 96
curl "https://cartblanche22.docking.org/smiles.txt:smiles=c1ccccc1&dist=5&adist=5&output_fields=zinc_id,smiles,tranche" - Network accessline 101
# Original: CC(=O)Oc1ccccc1C(=O)O # Encoded: CC%28%3DO%29Oc1ccccc1C%28%3DO%29O curl "https://cartblanche22.docking.org/smiles.txt:smiles=CC%28%3DO%29Oc1ccccc1C%28%3DO%29O&dist=2" - Network accessline 103
curl "https://cartblanche22.docking.org/smiles.txt:smiles=CC%28%3DO%29Oc1ccccc1C%28%3DO%29O&dist=2" - Network accessline 124
https://cartblanche22.docking.org/catitems.txt:catitem_id={SUPPLIER_CODE}&output_fields={FIELDS} - Network accessline 129
curl "https://cartblanche22.docking.org/catitems.txt:catitem_id=SUPPLIER-12345&output_fields=zinc_id,smiles,supplier_code,catalogs" - Network accessline 145
https://cartblanche22.docking.org/substance/random.txt:count={COUNT}&subset={SUBSET}&output_fields={FIELDS} - Network accessline 152
curl "https://cartblanche22.docking.org/substance/random.txt" - Network accessline 157
curl "https://cartblanche22.docking.org/substance/random.txt:count=1000&subset=lead-like&output_fields=zinc_id,smiles,tranche" - Network accessline 162
curl "https://cartblanche22.docking.org/substance/random.txt:count=5000&subset=drug-like&output_fields=zinc_id,smiles" - Network accessline 167
curl "https://cartblanche22.docking.org/substance/random.txt:count=500&subset=fragment&output_fields=zinc_id,smiles,tranche" - Network accessline 206
curl "https://cartblanche22.docking.org/[email protected]_fields=zinc_id,smiles" - Network accessline 211
curl "https://cartblanche22.docking.org/[email protected]_fields=zinc_id,smiles,tranche,catalogs" - Network accessline 282
# Download all compounds in a specific tranche wget https://files.docking.org/zinc22/H05/H05P035M400-0.db2.gz - Network accessline 283
wget https://files.docking.org/zinc22/H05/H05P035M400-0.db2.gz - Network accessline 293
https://files.docking.org/zinc22/ - Network accessline 316
wget https://files.docking.org/zinc22/H05/H05P035M400-0.db2.gz - Network accessline 321
# Create URL list cat > tranche_urls.txt <<EOF https://files.docking.org/zinc22/H05/H05P035M400-0.db2.gz - Network accessline 322
cat > tranche_urls.txt <<EOF https://files.docking.org/zinc22/H05/H05P035M400-0.db2.gz https://files.docking.org/zinc22/H05/H05P035M400-0.db2.gz - Network accessline 323
https://files.docking.org/zinc22/H05/H05P035M400-0.db2.gz - Network accessline 324
https://files.docking.org/zinc22/H05/H05P035M400-0.db2.gz - Network accessline 325
https://files.docking.org/zinc22/H05/H05P040M400-0.db2.gz - Network accessline 334
wget -r -np -nH --cut-dirs=1 -A "*.db2.gz" \ - Network accessline 335
https://files.docking.org/zinc22/H05/ - Network accessline 381
# Structure search if smiles: url = f"https://cartblanche22.docking.org/smiles.txt:smiles={smiles}&dist={dist}&output_fields={fields_str}" - Network accessline 383
url = f"https://cartblanche22.docking.org/smiles.txt:smiles={smiles}&dist={dist}&output_fields={fields_str}" - Network accessline 386
elif zinc_ids: zinc_ids_str = ','.join(zinc_ids) url = f"https://cartblanche22.docking.org/substances.txt:zinc_id={zinc_ids_str}&output_fields={fields_str}" - Network accessline 388
url = f"https://cartblanche22.docking.org/substances.txt:zinc_id={zinc_ids_str}&output_fields={fields_str}" - Network accessline 390
# Random sampling elif count: url = f"https://cartblanche22.docking.org/substance/random.txt:count={count}&output_fields={fields_str}" - Network accessline 391
elif count: url = f"https://cartblanche22.docking.org/substance/random.txt:count={count}&output_fields={fields_str}" if subset: - Network accessline 392
url = f"https://cartblanche22.docking.org/substance/random.txt:count={count}&output_fields={fields_str}" - Network accessline 399
# Execute query result = subprocess.run(['curl', '-s', url], - Network accessline 400
result = subprocess.run(['curl', '-s', url], - Network accessline 528
try: result = subprocess.run( ['curl', '-s', '--max-time', str(timeout), url], - Network accessline 529
result = subprocess.run( ['curl', '-s', '--max-time', str(timeout), url], capture_output=True, - Network accessline 557
# 1. Download tranche files wget https://files.docking.org/zinc22/H05/H05P035M400-0.db2.gz - Network accessline 558
wget https://files.docking.org/zinc22/H05/H05P035M400-0.db2.gz - Network accessline 570
# 1. Download MOL2 format wget https://files.docking.org/zinc22/H05/H05P035M400-0.mol2.gz - Network accessline 571
wget https://files.docking.org/zinc22/H05/H05P035M400-0.mol2.gz - Network accessline 653
result = subprocess.run(['curl', '-v', url], - Network accessline 685
- **ZINC Wiki**: https://wiki.docking.org/ - Network accessline 686
- **ZINC22 Documentation**: https://wiki.docking.org/index.php/Category:ZINC22 - Network accessline 687
- **ZINC API Guide**: https://wiki.docking.org/index.php/ZINC_api - Network accessline 688
- **File Access Guide**: https://wiki.docking.org/index.php/ZINC22:Getting_started
skills/13-实验室自动化与集成/benchling-integration/SKILL.md
- Network accessline 47
benchling = Benchling( url="https://your-tenant.benchling.com", - Network accessline 48
url="https://your-tenant.benchling.com", - Access to secretsline 49
[Credential-related source omitted] - Access to secretsline 59
[Credential-related source omitted] - Network accessline 60
) benchling = Benchling( url="https://your-tenant.benchling.com", - Network accessline 61
benchling = Benchling( url="https://your-tenant.benchling.com", auth_method=auth_method - Network accessline 62
url="https://your-tenant.benchling.com", - Network accessline 335
benchling = Benchling( url="https://your-tenant.benchling.com", - Network accessline 336
url="https://your-tenant.benchling.com", - Access to secretsline 337
[Credential-related source omitted] - Network accessline 474
- **Official Documentation:** https://docs.benchling.com - Network accessline 475
- **Python SDK Reference:** https://benchling.com/sdk-docs/ - Network accessline 476
- **API Reference:** https://benchling.com/api/reference
skills/13-实验室自动化与集成/benchling-integration/references/api_endpoints.md
- Network accessline 7
https://{tenant}.benchling.com/api/v2 - Network accessline 24
[Credential-related source omitted] - Access to secretsline 24
[Credential-related source omitted] - Network accessline 25
https://your-tenant.benchling.com/api/v2/dna-sequences \ - Access to secretsline 26
[Credential-related source omitted] - Network accessline 31
[Credential-related source omitted] - Access to secretsline 31
[Credential-related source omitted] - Network accessline 32
https://your-tenant.benchling.com/api/v2/dna-sequences \ - Access to secretsline 33
[Credential-related source omitted] - Network accessline 79
curl -X GET \ - Network accessline 80
"https://your-tenant.benchling.com/api/v2/dna-sequences?pageSize=50&nextToken=abc123" - Network accessline 791
curl -X GET \ - Network accessline 792
"https://tenant.benchling.com/api/v2/dna-sequences?name=plasmid&folderId=fld_abc&archived=false" - Network accessline 816
async function fetchSequence(id) { try { const response = await fetch( - Network accessline 817
try { const response = await fetch( `https://tenant.benchling.com/api/v2/dna-sequences/${id}`, - Network accessline 818
const response = await fetch( - Network accessline 819
`https://tenant.benchling.com/api/v2/dna-sequences/${id}`, - Network accessline 856
do { const url = new URL('https://tenant.benchling.com/api/v2/dna-sequences'); - Network accessline 857
const url = new URL('https://tenant.benchling.com/api/v2/dna-sequences'); - Network accessline 863
const response = await fetch(url, { - Network accessline 881
- **API Documentation:** https://benchling.com/api/reference - Network accessline 882
- **Interactive API Explorer:** https://your-tenant.benchling.com/api/reference (requires authentication) - Network accessline 883
- **Changelog:** https://docs.benchling.com/changelog
skills/13-实验室自动化与集成/benchling-integration/references/authentication.md
- Network accessline 28
benchling = Benchling( url="https://your-tenant.benchling.com", - Network accessline 29
url="https://your-tenant.benchling.com", - Network accessline 36
curl -X GET \ - Network accessline 37
https://your-tenant.benchling.com/api/v2/dna-sequences \ - Access to secretsline 49
[Credential-related source omitted] - Access to secretsline 50
[Credential-related source omitted] - Access to secretsline 54
[Credential-related source omitted] - Access to secretsline 83
[Credential-related source omitted] - Network accessline 86
benchling = Benchling( url="https://your-tenant.benchling.com", - Network accessline 87
url="https://your-tenant.benchling.com", - Network accessline 96
# Get access token curl -X POST \ - Network accessline 97
[Credential-related source omitted] - Access to secretsline 97
[Credential-related source omitted] - Network accessline 98
https://your-tenant.benchling.com/api/v2/token \ - Access to secretsline 102
[Credential-related source omitted] - Access to secretsline 106
[Credential-related source omitted] - Network accessline 111
# Use access token curl -X GET \ - Network accessline 112
curl -X GET \ - Network accessline 113
https://your-tenant.benchling.com/api/v2/dna-sequences \ - Network accessline 147
benchling = Benchling( url="https://your-tenant.benchling.com", - Network accessline 148
url="https://your-tenant.benchling.com", - Network accessline 155
curl -X GET \ - Network accessline 156
https://your-tenant.benchling.com/api/v2/dna-sequences \ - Access to secretsline 181
[Credential-related source omitted] - Access to secretsline 184
[Credential-related source omitted] - Access to secretsline 185
[Credential-related source omitted] - Network accessline 290
# Test API key curl -X GET \ - Network accessline 291
[Credential-related source omitted] - Access to secretsline 291
[Credential-related source omitted] - Network accessline 292
https://your-tenant.benchling.com/api/v2/users/me \ - Access to secretsline 293
[Credential-related source omitted] - Network accessline 296
# Test OAuth token curl -X GET \ - Network accessline 297
curl -X GET \ - Network accessline 298
https://your-tenant.benchling.com/api/v2/users/me \ - Network accessline 310
try: benchling = Benchling( url="https://your-tenant.benchling.com", - Network accessline 311
benchling = Benchling( url="https://your-tenant.benchling.com", auth_method=ApiKeyAuth("your_api_key") - Network accessline 312
url="https://your-tenant.benchling.com", - Access to secretsline 313
[Credential-related source omitted] - Network accessline 330
tenants = { "production": { "url": "https://prod.benchling.com", - Network accessline 331
[Credential-related source omitted] - Network accessline 332
"url": "https://prod.benchling.com", - Access to secretsline 333
[Credential-related source omitted] - Network accessline 334
}, "staging": { "url": "https://staging.benchling.com", - Network accessline 335
[Credential-related source omitted] - Network accessline 336
"url": "https://staging.benchling.com", - Access to secretsline 337
[Credential-related source omitted] - Access to secretsline 346
[Credential-related source omitted] - Network accessline 362
# Custom httpx client with certificate verification custom_client = httpx.Client( - Network accessline 363
custom_client = httpx.Client( - Network accessline 368
benchling = Benchling( url="https://your-tenant.benchling.com", - Network accessline 369
url="https://your-tenant.benchling.com", - Access to secretsline 370
[Credential-related source omitted] - Network accessline 377
- **Official Authentication Docs:** https://docs.benchling.com/docs/authentication - Network accessline 378
- **Developer Console:** https://your-tenant.benchling.com/developer - Network accessline 379
- **SDK Documentation:** https://benchling.com/sdk-docs/
skills/13-实验室自动化与集成/benchling-integration/references/sdk_reference.md
- Network accessline 29
benchling = Benchling( url="https://your-tenant.benchling.com", - Network accessline 30
url="https://your-tenant.benchling.com", - Access to secretsline 31
[Credential-related source omitted] - Network accessline 571
benchling = Benchling( url="https://your-tenant.benchling.com", - Network accessline 572
url="https://your-tenant.benchling.com", - Access to secretsline 573
[Credential-related source omitted] - Network accessline 577
# Disable retries benchling = Benchling( url="https://your-tenant.benchling.com", - Network accessline 578
benchling = Benchling( url="https://your-tenant.benchling.com", auth_method=ApiKeyAuth("your_api_key"), - Network accessline 579
url="https://your-tenant.benchling.com", - Access to secretsline 580
[Credential-related source omitted] - Network accessline 771
- **SDK Source:** https://github.com/benchling/benchling-sdk - Network accessline 772
- **SDK Docs:** https://benchling.com/sdk-docs/ - Network accessline 773
- **API Reference:** https://benchling.com/api/reference - Network accessline 774
- **Common Examples:** https://docs.benchling.com/docs/common-sdk-interactions-and-examples
skills/13-实验室自动化与集成/dnanexus-integration/SKILL.md
skills/13-实验室自动化与集成/dnanexus-integration/references/configuration.md
- Access to secretsline 363
[Credential-related source omitted]
skills/13-实验室自动化与集成/dnanexus-integration/references/python-sdk.md
skills/13-实验室自动化与集成/ginkgo-cloud-lab/SKILL.md
- Network accessline 10
Ginkgo Cloud Lab (https://cloud.ginkgo.bio) provides remote access to Ginkgo Bioworks' autonomous lab infrastructure. Protocols are executed on Reconfigurable Automation Carts (RACs) -- modular units with robotic arms, maglev sample transpo - Network accessline 39
1. Select a protocol at https://cloud.ginkgo.bio/protocols - Network accessline 49
Access Ginkgo Cloud Lab at https://cloud.ginkgo.bio. Account creation or institutional access may be required. Contact Ginkgo at cloud@ginkgo.bio for access questions.
skills/13-实验室自动化与集成/ginkgo-cloud-lab/references/cell-free-protein-expression-optimization.md
- Network accessline 3
**URL:** https://cloud.ginkgo.bio/protocols/cell-free-protein-expression-optimization
skills/13-实验室自动化与集成/ginkgo-cloud-lab/references/cell-free-protein-expression-validation.md
- Network accessline 3
**URL:** https://cloud.ginkgo.bio/protocols/cell-free-protein-expression-validation
skills/13-实验室自动化与集成/ginkgo-cloud-lab/references/fluorescent-pixel-art-generation.md
- Network accessline 3
**URL:** https://cloud.ginkgo.bio/protocols/fluorescent-pixel-art-generation
skills/13-实验室自动化与集成/labarchive-integration/SKILL.md
- Network accessline 48
api_url: https://api.labarchives.com/api # or regional endpoint - Network accessline 53
**Regional API endpoints:** - US/International: `https://api.labarchives.com/api` - Network accessline 54
- US/International: `https://api.labarchives.com/api` - Network accessline 55
- Australia: `https://auapi.labarchives.com/api` - Network accessline 56
- UK: `https://ukapi.labarchives.com/api` - Access to secretsline 79
[Credential-related source omitted] - Access to secretsline 200
[Credential-related source omitted] - Network accessline 223
git clone https://github.com/mcmero/labarchives-py
skills/13-实验室自动化与集成/labarchive-integration/references/api_reference.md
- Network accessline 13
| Region | Base URL | |--------|----------| | US/International | `https://api.labarchives.com/api` | - Network accessline 14
|--------|----------| | US/International | `https://api.labarchives.com/api` | | Australia | `https://auapi.labarchives.com/api` | - Network accessline 15
| US/International | `https://api.labarchives.com/api` | - Network accessline 16
| Australia | `https://auapi.labarchives.com/api` | - Network accessline 17
| UK | `https://ukapi.labarchives.com/api` | - Network accessline 216
[Credential-related source omitted] - Network accessline 218
response = requests.post(url, files=files, data=params)
skills/13-实验室自动化与集成/labarchive-integration/references/authentication_guide.md
- Network accessline 45
# Regional API endpoint api_url: https://api.labarchives.com/api - Network accessline 46
api_url: https://api.labarchives.com/api - Network accessline 62
export LABARCHIVES_API_URL="https://api.labarchives.com/api" - Network accessline 73
| Region | Endpoint | Use if your LabArchives URL is | |--------|----------|--------------------------------| | US/International | `https://api.labarchives.com/api` | `mynotebook.labarchives.com` | - Network accessline 74
|--------|----------|--------------------------------| | US/International | `https://api.labarchives.com/api` | `mynotebook.labarchives.com` | | Australia | `https://auapi.labarchives.com/api` | `aunotebook.labarchives.com` | - Network accessline 75
| US/International | `https://api.labarchives.com/api` | `mynotebook.labarchives.com` | - Network accessline 76
| Australia | `https://auapi.labarchives.com/api` | `aunotebook.labarchives.com` | - Network accessline 77
| UK | `https://ukapi.labarchives.com/api` | `uknotebook.labarchives.com` | - Network accessline 132
# Make authenticated request response = requests.get(url, params=params) - Network accessline 133
response = requests.get(url, params=params) - Network accessline 149
# Configuration api_url <- "https://api.labarchives.com/api" - Network accessline 150
api_url <- "https://api.labarchives.com/api" - Network accessline 234
# Configure proxy proxies = { 'http': 'http://proxy.university.edu:8080', - Network accessline 235
proxies = { 'http': 'http://proxy.university.edu:8080', 'https': 'http://proxy.university.edu:8080' - Network accessline 236
'http': 'http://proxy.university.edu:8080', - Network accessline 237
'https': 'http://proxy.university.edu:8080' - Network accessline 240
# Make request with proxy response = requests.get(url, params=params, proxies=proxies) - Network accessline 241
response = requests.get(url, params=params, proxies=proxies) - Network accessline 249
# Disable SSL verification (use only for testing) response = requests.get(url, params=params, verify=False) - Network accessline 250
response = requests.get(url, params=params, verify=False) - Access to secretsline 273
[Credential-related source omitted]
skills/13-实验室自动化与集成/labarchive-integration/references/integrations.md
- Network accessline 94
response = requests.post(url, files=files, data=params) - Access to secretsline 286
[Credential-related source omitted] - Network accessline 290
# Step 1: Get authorization code auth_url = "https://mynotebook.labarchives.com/oauth/authorize" - Network accessline 291
auth_url = "https://mynotebook.labarchives.com/oauth/authorize" - Network accessline 300
# Step 2: Exchange code for access token token_url = "https://mynotebook.labarchives.com/oauth/token" - Network accessline 301
token_url = "https://mynotebook.labarchives.com/oauth/token" - Access to secretsline 304
[Credential-related source omitted] - Network accessline 310
response = requests.post(token_url, data=token_params) - Access to secretsline 313
[Credential-related source omitted]
skills/13-实验室自动化与集成/labarchive-integration/scripts/entry_operations.py
- Executable scriptline 1
#!/usr/bin/env python3 - Network accessline 39
except ImportError: print("❌ labarchives-py package not installed") print(" Install with: pip install git+https://github.com/mcmero/labarchives-py") - Network accessline 40
print("❌ labarchives-py package not installed") print(" Install with: pip install git+https://github.com/mcmero/labarchives-py") sys.exit(1) - Network accessline 41
print(" Install with: pip install git+https://github.com/mcmero/labarchives-py") - Network accessline 172
response = requests.post(url, files=files, data=data)
skills/13-实验室自动化与集成/labarchive-integration/scripts/notebook_operations.py
- Executable scriptline 1
#!/usr/bin/env python3 - Network accessline 38
except ImportError: print("❌ labarchives-py package not installed") print(" Install with: pip install git+https://github.com/mcmero/labarchives-py") - Network accessline 39
print("❌ labarchives-py package not installed") print(" Install with: pip install git+https://github.com/mcmero/labarchives-py") sys.exit(1) - Network accessline 40
print(" Install with: pip install git+https://github.com/mcmero/labarchives-py")
skills/13-实验室自动化与集成/labarchive-integration/scripts/setup_config.py
- Executable scriptline 1
#!/usr/bin/env python3 - Network accessline 24
endpoints = { '1': 'https://api.labarchives.com/api', - Network accessline 25
'1': 'https://api.labarchives.com/api', - Network accessline 26
'2': 'https://auapi.labarchives.com/api', - Network accessline 27
'3': 'https://ukapi.labarchives.com/api' - Network accessline 153
except ImportError: print("\n⚠<U+FE0F> labarchives-py package not installed") print(" Install with: pip install git+https://github.com/mcmero/labarchives-py") - Network accessline 155
print(" Install with: pip install git+https://github.com/mcmero/labarchives-py")
skills/13-实验室自动化与集成/latchbio-integration/SKILL.md
- Network accessline 338
- **Official Documentation**: https://docs.latch.bio - Network accessline 339
- **GitHub Repository**: https://github.com/latchbio/latch - Network accessline 340
- **Slack Community**: Join Latch SDK workspace - **API Reference**: https://docs.latch.bio/api/latch.html - **Blog**: https://blog.latch.bio - Network accessline 341
- **API Reference**: https://docs.latch.bio/api/latch.html - Network accessline 342
- **Blog**: https://blog.latch.bio
skills/13-实验室自动化与集成/latchbio-integration/references/data-management.md
- Access to secretsline 380
[Credential-related source omitted]
skills/13-实验室自动化与集成/latchbio-integration/references/verified-workflows.md
- Network accessline 439
- **Documentation**: https://docs.latch.bio
skills/13-实验室自动化与集成/latchbio-integration/references/workflow-creation.md
skills/13-实验室自动化与集成/omero-integration/SKILL.md
- Network accessline 236
- **Official Documentation**: https://omero.readthedocs.io/en/stable/developers/Python.html - Network accessline 237
- **BlitzGateway API**: https://omero.readthedocs.io/en/stable/developers/Python.html#omero-blitzgateway - Network accessline 238
- **OMERO Model**: https://omero.readthedocs.io/en/stable/developers/Model.html - Network accessline 239
- **Community Forum**: https://forum.image.sc/tag/omero
skills/13-实验室自动化与集成/omero-integration/references/connection.md
skills/13-实验室自动化与集成/opentrons-integration/scripts/basic_protocol_template.py
- Executable scriptline 1
#!/usr/bin/env python3
skills/13-实验室自动化与集成/opentrons-integration/scripts/pcr_setup_template.py
- Executable scriptline 1
#!/usr/bin/env python3
skills/13-实验室自动化与集成/opentrons-integration/scripts/serial_dilution_template.py
- Executable scriptline 1
#!/usr/bin/env python3
skills/13-实验室自动化与集成/protocolsio-integration/SKILL.md
- Access to secretsline 124
[Credential-related source omitted] - Network accessline 152
https://protocols.io/api/v3 - Access to secretsline 157
[Credential-related source omitted] - Access to secretsline 254
[Credential-related source omitted] - Network accessline 257
# Search for CRISPR protocols response = requests.get( - Network accessline 258
response = requests.get( - Network accessline 259
"https://protocols.io/api/v3/protocols", - Access to secretsline 279
[Credential-related source omitted] - Network accessline 292
response = requests.post( - Network accessline 293
"https://protocols.io/api/v3/protocols", - Access to secretsline 307
[Credential-related source omitted] - Network accessline 319
response = requests.post( - Network accessline 320
"https://protocols.io/api/v3/workspaces/12345/files/upload", - Network accessline 339
for attempt in range(max_retries): try: response = requests.get(url, headers=headers) - Network accessline 341
response = requests.get(url, headers=headers) - Network accessline 391
- **Official API Documentation**: https://apidoc.protocols.io/ - Network accessline 392
- **Protocols.io Platform**: https://www.protocols.io/
skills/13-实验室自动化与集成/protocolsio-integration/references/additional_features.md
- Network accessline 9
All endpoints use the base URL: `https://protocols.io/api/v3` - Network accessline 31
[Credential-related source omitted] - Network accessline 32
"https://protocols.io/api/v3/profile" - Network accessline 54
curl -X PATCH \ - Network accessline 60
"orcid": "0000-0001-2345-6789" }' \ "https://protocols.io/api/v3/profile" - Network accessline 61
}' "https://protocols.io/api/v3/profile" - Network accessline 62
"https://protocols.io/api/v3/profile" - Network accessline 103
[Credential-related source omitted] - Network accessline 104
"https://protocols.io/api/v3/publications?category=molecular-biology&date_from=2025-01-01&order_field=published_on&order_dir=desc" - Network accessline 142
curl -X POST \ - Network accessline 151
"results": "Flow cytometry confirmed 87% GFP+ cells after 72h. Western blot showed complete knockout in positive population." }' \ "https://protocols.io/api/v3/protocols/12345/runs" - Network accessline 152
}' "https://protocols.io/api/v3/protocols/12345/runs" - Network accessline 153
"https://protocols.io/api/v3/protocols/12345/runs" - Network accessline 217
[Credential-related source omitted] - Network accessline 218
"https://protocols.io/api/v3/notifications?read=false&type=comment" - Network accessline 266
[Credential-related source omitted] - Network accessline 267
"https://protocols.io/api/v3/organizations/12345/export?format=json&include_files=true&include_comments=true" - Network accessline 277
# Search for relevant protocols response = requests.get( - Network accessline 278
response = requests.get( - Network accessline 279
'https://protocols.io/api/v3/publications', - Network accessline 285
for protocol in response.json()['items']: # Get full details details = requests.get( - Network accessline 286
# Get full details details = requests.get( f'https://protocols.io/api/v3/protocols/{protocol["id"]}', - Network accessline 287
details = requests.get( - Network accessline 288
f'https://protocols.io/api/v3/protocols/{protocol["id"]}',
skills/13-实验室自动化与集成/protocolsio-integration/references/authentication.md
- Access to secretsline 9
[Credential-related source omitted] - Access to secretsline 15
[Credential-related source omitted] - Access to secretsline 26
[Credential-related source omitted] - Network accessline 36
GET https://protocols.io/api/v3/oauth/authorize - Network accessline 47
https://protocols.io/api/v3/oauth/authorize?client_id=YOUR_CLIENT_ID&redirect_uri=YOUR_REDIRECT_URI&response_type=code&state=RANDOM_STRING - Network accessline 55
POST https://protocols.io/api/v3/oauth/token - Access to secretsline 62
[Credential-related source omitted] - Access to secretsline 66
[Credential-related source omitted] - Network accessline 76
POST https://protocols.io/api/v3/oauth/token - Access to secretsline 83
[Credential-related source omitted]
skills/13-实验室自动化与集成/protocolsio-integration/references/discussions.md
- Network accessline 9
All discussion endpoints use the base URL: `https://protocols.io/api/v3` - Network accessline 44
curl -X POST \ - Network accessline 49
}' \ "https://protocols.io/api/v3/protocols/12345/comments" - Network accessline 50
"https://protocols.io/api/v3/protocols/12345/comments" - Network accessline 58
curl -X POST \ - Network accessline 63
"parent_comment_id": 67890 }' \ "https://protocols.io/api/v3/protocols/12345/comments" - Network accessline 64
}' "https://protocols.io/api/v3/protocols/12345/comments" - Network accessline 65
"https://protocols.io/api/v3/protocols/12345/comments" - Network accessline 117
curl -X POST \ - Network accessline 122
}' \ "https://protocols.io/api/v3/protocols/12345/steps/67890/comments" - Network accessline 123
"https://protocols.io/api/v3/protocols/12345/steps/67890/comments" - Network accessline 187
{ "body": "## Important Note\n\nWe achieved better results with:\n\n- Increasing temperature to 37°C\n- Extending incubation to 2 hours\n- Using freshly prepared reagents\n\nSee our publication: [doi:10.xxxx/xxxxx](https://doi.org/...)" - Network accessline 188
"body": "## Important Note\n\nWe achieved better results with:\n\n- Increasing temperature to 37°C\n- Extending incubation to 2 hours\n- Using freshly prepared reagents\n\nSee our publication: [doi:10.xxxx/xxxxx](https://doi.org/...)"
skills/13-实验室自动化与集成/protocolsio-integration/references/file_manager.md
- Network accessline 9
All file manager endpoints use the base URL: `https://protocols.io/api/v3` - Network accessline 41
[Credential-related source omitted] - Network accessline 42
"https://protocols.io/api/v3/workspaces/12345/files/search?query=microscopy&type=file" - Network accessline 63
[Credential-related source omitted] - Network accessline 64
"https://protocols.io/api/v3/workspaces/12345/folders/root?order_by=modified&order_dir=desc" - Network accessline 86
curl -X POST \ - Network accessline 90
-F "description=Experimental results from trial #3" \ -F "tags=experiment,data,2025" \ "https://protocols.io/api/v3/workspaces/12345/files/upload" - Network accessline 92
"https://protocols.io/api/v3/workspaces/12345/files/upload" - Network accessline 122
[Credential-related source omitted] - Network accessline 124
"https://protocols.io/api/v3/workspaces/12345/files/67890/download" - Network accessline 155
curl -X PATCH \ - Network accessline 160
"tags": "experiment,data,2025,revised" }' \ "https://protocols.io/api/v3/workspaces/12345/files/67890" - Network accessline 161
}' "https://protocols.io/api/v3/workspaces/12345/files/67890" - Network accessline 162
"https://protocols.io/api/v3/workspaces/12345/files/67890" - Network accessline 194
curl -X POST \ - Network accessline 200
"description": "All experimental data from 2025" }' \ "https://protocols.io/api/v3/workspaces/12345/folders" - Network accessline 201
}' "https://protocols.io/api/v3/workspaces/12345/folders" - Network accessline 202
"https://protocols.io/api/v3/workspaces/12345/folders" - Network accessline 237
# Upload the data file curl -X POST \ - Network accessline 238
curl -X POST \ - Network accessline 240
-F "file=@results.csv" \ -F "description=Results from protocol execution" \ "https://protocols.io/api/v3/workspaces/12345/files/upload" - Network accessline 242
"https://protocols.io/api/v3/workspaces/12345/files/upload"
skills/13-实验室自动化与集成/protocolsio-integration/references/protocols_api.md
- Network accessline 9
All protocol endpoints use the base URL: `https://protocols.io/api/v3` - Network accessline 45
[Credential-related source omitted] - Network accessline 46
"https://protocols.io/api/v3/protocols?filter=public&key=CRISPR&page_size=20&content_format=html" - Network accessline 97
curl -X POST \ - Network accessline 103
"tags": ["CRISPR", "gene editing", "molecular biology"] }' \ "https://protocols.io/api/v3/protocols" - Network accessline 104
}' "https://protocols.io/api/v3/protocols" - Network accessline 105
"https://protocols.io/api/v3/protocols" - Network accessline 135
curl -X POST \ - Network accessline 142
"temperature": 25 }' \ "https://protocols.io/api/v3/protocols/12345/steps" - Network accessline 143
}' "https://protocols.io/api/v3/protocols/12345/steps" - Network accessline 144
"https://protocols.io/api/v3/protocols/12345/steps" - Network accessline 200
curl -X POST \ - Network accessline 205
"publish_type": "new" }' \ "https://protocols.io/api/v3/protocols/12345/publish" - Network accessline 206
}' "https://protocols.io/api/v3/protocols/12345/publish" - Network accessline 207
"https://protocols.io/api/v3/protocols/12345/publish" - Network accessline 243
https://protocols.io/api/v3/view/crispr-protocol-abc123.pdf?compact=1
skills/13-实验室自动化与集成/protocolsio-integration/references/workspaces.md
- Network accessline 9
All workspace endpoints use the base URL: `https://protocols.io/api/v3` - Network accessline 32
[Credential-related source omitted] - Network accessline 33
"https://protocols.io/api/v3/workspaces" - Network accessline 80
curl -X POST \ - Network accessline 85
}' \ "https://protocols.io/api/v3/workspaces/12345/join-request" - Network accessline 86
"https://protocols.io/api/v3/workspaces/12345/join-request" - Network accessline 119
[Credential-related source omitted] - Network accessline 120
"https://protocols.io/api/v3/workspaces/12345/protocols?filter=all&order_field=modified_on&order_dir=desc"
skills/13-实验室自动化与集成/pylabrobot/SKILL.md
- Network accessline 129
6. **Temperature Control**: Set temperatures early as heating/cooling takes time 7. **Modular Protocols**: Break complex workflows into reusable functions 8. **Documentation**: Reference official docs at https://docs.pylabrobot.org for late - Network accessline 131
8. **Documentation**: Reference official docs at https://docs.pylabrobot.org for latest features - Network accessline 177
- **Official Documentation**: https://docs.pylabrobot.org - Network accessline 178
- **GitHub Repository**: https://github.com/PyLabRobot/pylabrobot - Network accessline 179
- **Community Forum**: https://discuss.pylabrobot.org - Network accessline 180
- **PyPI Package**: https://pypi.org/project/PyLabRobot/
skills/13-实验室自动化与集成/pylabrobot/references/analytical-equipment.md
- Network accessline 461
- Plate Reading Documentation: https://docs.pylabrobot.org/user_guide/02_analytical/ - Network accessline 462
- BMG CLARIOstar Guide: https://docs.pylabrobot.org/user_guide/02_analytical/plate-reading/bmg-clariostar.html - Network accessline 463
- API Reference: https://docs.pylabrobot.org/api/pylabrobot.plate_reading.html - Network accessline 464
- Supported Equipment: https://docs.pylabrobot.org/user_guide/machines.html
skills/13-实验室自动化与集成/pylabrobot/references/hardware-backends.md
- Network accessline 477
- Backend Documentation: https://docs.pylabrobot.org/user_guide/backends.html - Network accessline 478
- Supported Machines: https://docs.pylabrobot.org/user_guide/machines.html - Network accessline 479
- API Reference: https://docs.pylabrobot.org/api/pylabrobot.liquid_handling.backends.html - Network accessline 480
- GitHub Examples: https://github.com/PyLabRobot/pylabrobot/tree/main/examples
skills/13-实验室自动化与集成/pylabrobot/references/liquid-handling.md
- Network accessline 401
- Official Liquid Handling Guide: https://docs.pylabrobot.org/user_guide/basic.html - Network accessline 402
- API Reference: https://docs.pylabrobot.org/api/pylabrobot.liquid_handling.html - Network accessline 403
- Example Protocols: https://github.com/PyLabRobot/pylabrobot/tree/main/examples
skills/13-实验室自动化与集成/pylabrobot/references/material-handling.md
- Network accessline 617
- Material Handling Documentation: https://docs.pylabrobot.org/user_guide/01_material-handling/ - Network accessline 618
- Heater Shakers: https://docs.pylabrobot.org/user_guide/01_material-handling/heating_shaking/ - Network accessline 619
- API Reference: https://docs.pylabrobot.org/api/ - Network accessline 620
- Supported Equipment: https://docs.pylabrobot.org/user_guide/machines.html
skills/13-实验室自动化与集成/pylabrobot/references/resources.md
- Network accessline 486
- Resource Documentation: https://docs.pylabrobot.org/resources/introduction.html - Network accessline 487
- Custom Resources Guide: https://docs.pylabrobot.org/resources/custom-resources.html - Network accessline 488
- API Reference: https://docs.pylabrobot.org/api/pylabrobot.resources.html - Network accessline 489
- Deck Layouts: https://github.com/PyLabRobot/pylabrobot/tree/main/pylabrobot/resources/deck
skills/13-实验室自动化与集成/pylabrobot/references/visualization.md
- Network accessline 35
**Default Settings:** - Port: 1234 (http://localhost:1234) - Network accessline 36
- Port: 1234 (http://localhost:1234) - Network accessline 529
- Visualizer Documentation: https://docs.pylabrobot.org/user_guide/using-the-visualizer.html (if available) - Network accessline 530
- Simulation Guide: https://docs.pylabrobot.org/user_guide/simulation.html (if available) - Network accessline 531
- API Reference: https://docs.pylabrobot.org/api/pylabrobot.visualizer.html - Network accessline 532
- GitHub Examples: https://github.com/PyLabRobot/pylabrobot/tree/main/examples
skills/14-文档处理与数据工具/denario/references/installation.md
- Network accessline 42
# Clone the repository git clone https://github.com/AstroPilot-AI/Denario.git - Network accessline 43
git clone https://github.com/AstroPilot-AI/Denario.git - Access to secretsline 66
[Credential-related source omitted] - Network accessline 69
Access the GUI at `http://localhost:8501` after the container starts. - Dangerous shell operationline 126
sudo apt-get install texlive-full - Network accessline 134
### Windows Download and install [MiKTeX](https://miktex.org/download) or [TeX Live](https://tug.org/texlive/). - Network accessline 135
Download and install [MiKTeX](https://miktex.org/download) or [TeX Live](https://tug.org/texlive/).
skills/14-文档处理与数据工具/denario/references/llm_configuration.md
- Network accessline 28
1. **Create Google Cloud Project** - Navigate to [Google Cloud Console](https://console.cloud.google.com/) - Network accessline 29
- Navigate to [Google Cloud Console](https://console.cloud.google.com/) - Network accessline 49
1. **Create OpenAI Account** - Visit [platform.openai.com](https://platform.openai.com/) - Network accessline 50
- Visit [platform.openai.com](https://platform.openai.com/) - Access to secretsline 60
[Credential-related source omitted] - Access to secretsline 69
[Credential-related source omitted] - Access to secretsline 77
[Credential-related source omitted] - Access to secretsline 82
[Credential-related source omitted] - Access to secretsline 88
[Credential-related source omitted] - Access to secretsline 116
[Credential-related source omitted] - Access to secretsline 119
[Credential-related source omitted] - Access to secretsline 120
[Credential-related source omitted] - Network accessline 136
# Linux/macOS curl https://sdk.cloud.google.com | bash - Dangerous shell operationline 137
curl https://sdk.cloud.google.com | bash - Network accessline 137
curl https://sdk.cloud.google.com | bash - Access to secretsline 174
[Credential-related source omitted] - Access to secretsline 175
[Credential-related source omitted] - Network accessline 202
- **OpenAI**: Track usage at [platform.openai.com/usage](https://platform.openai.com/usage) - Access to secretsline 227
[Credential-related source omitted] - Access to secretsline 248
[Credential-related source omitted]
skills/14-文档处理与数据工具/docx/LICENSE.txt
- Network accessline 6
exists, use is governed by Anthropic's Consumer Terms of Service or Commercial Terms of Service, as applicable: https://www.anthropic.com/legal/consumer-terms - Network accessline 7
Commercial Terms of Service, as applicable: https://www.anthropic.com/legal/consumer-terms https://www.anthropic.com/legal/commercial-terms - Network accessline 8
https://www.anthropic.com/legal/consumer-terms - Network accessline 9
https://www.anthropic.com/legal/commercial-terms
skills/14-文档处理与数据工具/docx/SKILL.md
- Network accessline 252
children: [new ExternalHyperlink({ children: [new TextRun({ text: "Click here", style: "Hyperlink" })], link: "https://example.com", - Network accessline 253
children: [new TextRun({ text: "Click here", style: "Hyperlink" })], link: "https://example.com", })] - Network accessline 254
link: "https://example.com",
skills/14-文档处理与数据工具/docx/scripts/__init__.py
- Executable scriptline 1
Executable file
skills/14-文档处理与数据工具/docx/scripts/accept_changes.py
- Executable scriptline 1
Executable file - Network accessline 19
ACCEPT_CHANGES_MACRO = """<?xml version="1.0" encoding="UTF-8"?> <!DOCTYPE script:module PUBLIC "-//OpenOffice.org//DTD OfficeDocument 1.0//EN" "module.dtd"> <script:module xmlns:script="http://openoffice.org/2000/script" script:name="Modul - Network accessline 20
<!DOCTYPE script:module PUBLIC "-//OpenOffice.org//DTD OfficeDocument 1.0//EN" "module.dtd"> <script:module xmlns:script="http://openoffice.org/2000/script" script:name="Module1" script:language="StarBasic"> Sub AcceptAllTrackedChanges( - Network accessline 21
<script:module xmlns:script="http://openoffice.org/2000/script" script:name="Module1" script:language="StarBasic">
skills/14-文档处理与数据工具/docx/scripts/comment.py
- Executable scriptline 1
Executable file - Network accessline 25
TEMPLATE_DIR = Path(__file__).parent / "templates" NS = { "w": "http://schemas.openxmlformats.org/wordprocessingml/2006/main", - Network accessline 26
NS = { "w": "http://schemas.openxmlformats.org/wordprocessingml/2006/main", "w14": "http://schemas.microsoft.com/office/word/2010/wordml", - Network accessline 27
"w": "http://schemas.openxmlformats.org/wordprocessingml/2006/main", - Network accessline 28
"w14": "http://schemas.microsoft.com/office/word/2010/wordml", - Network accessline 29
"w15": "http://schemas.microsoft.com/office/word/2012/wordml", - Network accessline 30
"w16cid": "http://schemas.microsoft.com/office/word/2016/wordml/cid", - Network accessline 31
"w16cex": "http://schemas.microsoft.com/office/word/2018/wordml/cex", - Network accessline 149
rels = [ ( "http://schemas.openxmlformats.org/officeDocument/2006/relationships/comments", - Network accessline 150
( "http://schemas.openxmlformats.org/officeDocument/2006/relationships/comments", "comments.xml", - Network accessline 151
"http://schemas.openxmlformats.org/officeDocument/2006/relationships/comments", - Network accessline 153
), ( "http://schemas.microsoft.com/office/2011/relationships/commentsExtended", - Network accessline 154
( "http://schemas.microsoft.com/office/2011/relationships/commentsExtended", "commentsExtended.xml", - Network accessline 155
"http://schemas.microsoft.com/office/2011/relationships/commentsExtended", - Network accessline 157
), ( "http://schemas.microsoft.com/office/2016/09/relationships/commentsIds", - Network accessline 158
( "http://schemas.microsoft.com/office/2016/09/relationships/commentsIds", "commentsIds.xml", - Network accessline 159
"http://schemas.microsoft.com/office/2016/09/relationships/commentsIds", - Network accessline 161
), ( "http://schemas.microsoft.com/office/2018/08/relationships/commentsExtensible", - Network accessline 162
( "http://schemas.microsoft.com/office/2018/08/relationships/commentsExtensible", "commentsExtensible.xml", - Network accessline 163
"http://schemas.microsoft.com/office/2018/08/relationships/commentsExtensible",
skills/14-文档处理与数据工具/docx/scripts/office/helpers/__init__.py
- Executable scriptline 1
Executable file
skills/14-文档处理与数据工具/docx/scripts/office/helpers/merge_runs.py
- Executable scriptline 1
Executable file
skills/14-文档处理与数据工具/docx/scripts/office/helpers/simplify_redlines.py
skills/14-文档处理与数据工具/docx/scripts/office/pack.py
- Executable scriptline 1
Executable file
skills/14-文档处理与数据工具/docx/scripts/office/schemas/ISO-IEC29500-4_2016/dml-chart.xsd
- Network accessline 1
<?xml version="1.0" encoding="utf-8"?> <xsd:schema xmlns:xsd="http://www.w3.org/2001/XMLSchema" - Network accessline 2
<xsd:schema xmlns:xsd="http://www.w3.org/2001/XMLSchema" - Network accessline 3
xmlns:a="http://schemas.openxmlformats.org/drawingml/2006/main" - Network accessline 4
xmlns:r="http://schemas.openxmlformats.org/officeDocument/2006/relationships" - Network accessline 5
xmlns="http://schemas.openxmlformats.org/drawingml/2006/chart" - Network accessline 6
xmlns:cdr="http://schemas.openxmlformats.org/drawingml/2006/chartDrawing" - Network accessline 7
xmlns:s="http://schemas.openxmlformats.org/officeDocument/2006/sharedTypes" - Network accessline 8
targetNamespace="http://schemas.openxmlformats.org/drawingml/2006/chart" - Network accessline 9
elementFormDefault="qualified" attributeFormDefault="unqualified" blockDefault="#all"> <xsd:import namespace="http://schemas.openxmlformats.org/officeDocument/2006/relationships" schemaLocation="shared-relationshipReference.xsd"/> - Network accessline 10
<xsd:import namespace="http://schemas.openxmlformats.org/officeDocument/2006/relationships" - Network accessline 11
schemaLocation="shared-relationshipReference.xsd"/> <xsd:import namespace="http://schemas.openxmlformats.org/drawingml/2006/main" schemaLocation="dml-main.xsd"/> - Network accessline 12
<xsd:import namespace="http://schemas.openxmlformats.org/drawingml/2006/main" - Network accessline 13
schemaLocation="dml-main.xsd"/> <xsd:import namespace="http://schemas.openxmlformats.org/drawingml/2006/chartDrawing" schemaLocation="dml-chartDrawing.xsd"/> - Network accessline 14
<xsd:import namespace="http://schemas.openxmlformats.org/drawingml/2006/chartDrawing" - Network accessline 15
schemaLocation="dml-chartDrawing.xsd"/> <xsd:import namespace="http://schemas.openxmlformats.org/officeDocument/2006/sharedTypes" schemaLocation="shared-commonSimpleTypes.xsd"/> - Network accessline 16
<xsd:import namespace="http://schemas.openxmlformats.org/officeDocument/2006/sharedTypes"
skills/14-文档处理与数据工具/docx/scripts/office/schemas/ISO-IEC29500-4_2016/dml-chartDrawing.xsd
- Network accessline 1
<?xml version="1.0" encoding="utf-8"?> <xsd:schema xmlns:xsd="http://www.w3.org/2001/XMLSchema" - Network accessline 2
<xsd:schema xmlns:xsd="http://www.w3.org/2001/XMLSchema" - Network accessline 3
xmlns:a="http://schemas.openxmlformats.org/drawingml/2006/main" - Network accessline 4
xmlns="http://schemas.openxmlformats.org/drawingml/2006/chartDrawing" - Network accessline 5
targetNamespace="http://schemas.openxmlformats.org/drawingml/2006/chartDrawing" - Network accessline 6
elementFormDefault="qualified"> <xsd:import namespace="http://schemas.openxmlformats.org/drawingml/2006/main" schemaLocation="dml-main.xsd"/> - Network accessline 7
<xsd:import namespace="http://schemas.openxmlformats.org/drawingml/2006/main"
skills/14-文档处理与数据工具/docx/scripts/office/schemas/ISO-IEC29500-4_2016/dml-diagram.xsd
- Network accessline 1
<?xml version="1.0" encoding="utf-8"?> <xsd:schema xmlns:xsd="http://www.w3.org/2001/XMLSchema" - Network accessline 2
<xsd:schema xmlns:xsd="http://www.w3.org/2001/XMLSchema" - Network accessline 3
xmlns="http://schemas.openxmlformats.org/drawingml/2006/diagram" - Network accessline 4
xmlns:a="http://schemas.openxmlformats.org/drawingml/2006/main" - Network accessline 5
xmlns:r="http://schemas.openxmlformats.org/officeDocument/2006/relationships" - Network accessline 6
xmlns:s="http://schemas.openxmlformats.org/officeDocument/2006/sharedTypes" - Network accessline 7
targetNamespace="http://schemas.openxmlformats.org/drawingml/2006/diagram" - Network accessline 8
elementFormDefault="qualified" attributeFormDefault="unqualified"> <xsd:import namespace="http://schemas.openxmlformats.org/officeDocument/2006/relationships" schemaLocation="shared-relationshipReference.xsd"/> - Network accessline 9
<xsd:import namespace="http://schemas.openxmlformats.org/officeDocument/2006/relationships" - Network accessline 10
schemaLocation="shared-relationshipReference.xsd"/> <xsd:import namespace="http://schemas.openxmlformats.org/drawingml/2006/main" schemaLocation="dml-main.xsd"/> - Network accessline 11
<xsd:import namespace="http://schemas.openxmlformats.org/drawingml/2006/main" - Network accessline 12
schemaLocation="dml-main.xsd"/> <xsd:import namespace="http://schemas.openxmlformats.org/officeDocument/2006/sharedTypes" schemaLocation="shared-commonSimpleTypes.xsd"/> - Network accessline 13
<xsd:import namespace="http://schemas.openxmlformats.org/officeDocument/2006/sharedTypes"
skills/14-文档处理与数据工具/docx/scripts/office/schemas/ISO-IEC29500-4_2016/dml-lockedCanvas.xsd
- Network accessline 1
<?xml version="1.0" encoding="utf-8"?> <xsd:schema xmlns:xsd="http://www.w3.org/2001/XMLSchema" - Network accessline 2
<xsd:schema xmlns:xsd="http://www.w3.org/2001/XMLSchema" - Network accessline 3
xmlns="http://schemas.openxmlformats.org/drawingml/2006/lockedCanvas" - Network accessline 4
xmlns:a="http://schemas.openxmlformats.org/drawingml/2006/main" - Network accessline 5
xmlns:r="http://schemas.openxmlformats.org/officeDocument/2006/relationships" - Network accessline 6
elementFormDefault="qualified" targetNamespace="http://schemas.openxmlformats.org/drawingml/2006/lockedCanvas"> <xsd:import namespace="http://schemas.openxmlformats.org/drawingml/2006/main" - Network accessline 7
targetNamespace="http://schemas.openxmlformats.org/drawingml/2006/lockedCanvas"> - Network accessline 8
<xsd:import namespace="http://schemas.openxmlformats.org/drawingml/2006/main"
skills/14-文档处理与数据工具/docx/scripts/office/schemas/ISO-IEC29500-4_2016/dml-main.xsd
- Network accessline 1
<?xml version="1.0" encoding="utf-8"?> <xsd:schema xmlns:xsd="http://www.w3.org/2001/XMLSchema" - Network accessline 2
<xsd:schema xmlns:xsd="http://www.w3.org/2001/XMLSchema" - Network accessline 3
xmlns:r="http://schemas.openxmlformats.org/officeDocument/2006/relationships" - Network accessline 4
xmlns:s="http://schemas.openxmlformats.org/officeDocument/2006/sharedTypes" - Network accessline 5
xmlns="http://schemas.openxmlformats.org/drawingml/2006/main" - Network accessline 6
targetNamespace="http://schemas.openxmlformats.org/drawingml/2006/main" - Network accessline 7
elementFormDefault="qualified"> <xsd:import namespace="http://schemas.openxmlformats.org/officeDocument/2006/relationships" schemaLocation="shared-relationshipReference.xsd"/> - Network accessline 8
<xsd:import namespace="http://schemas.openxmlformats.org/officeDocument/2006/relationships" - Network accessline 9
schemaLocation="shared-relationshipReference.xsd"/> <xsd:import namespace="http://schemas.openxmlformats.org/officeDocument/2006/sharedTypes" schemaLocation="shared-commonSimpleTypes.xsd"/> - Network accessline 10
<xsd:import namespace="http://schemas.openxmlformats.org/officeDocument/2006/sharedTypes" - Network accessline 11
schemaLocation="shared-commonSimpleTypes.xsd"/> <xsd:import namespace="http://schemas.openxmlformats.org/drawingml/2006/diagram" schemaLocation="dml-diagram.xsd"/> - Network accessline 12
<xsd:import namespace="http://schemas.openxmlformats.org/drawingml/2006/diagram" - Network accessline 13
schemaLocation="dml-diagram.xsd"/> <xsd:import namespace="http://schemas.openxmlformats.org/drawingml/2006/chart" schemaLocation="dml-chart.xsd"/> - Network accessline 14
<xsd:import namespace="http://schemas.openxmlformats.org/drawingml/2006/chart" - Network accessline 15
schemaLocation="dml-chart.xsd"/> <xsd:import namespace="http://schemas.openxmlformats.org/drawingml/2006/picture" schemaLocation="dml-picture.xsd"/> - Network accessline 16
<xsd:import namespace="http://schemas.openxmlformats.org/drawingml/2006/picture" - Network accessline 17
schemaLocation="dml-picture.xsd"/> <xsd:import namespace="http://schemas.openxmlformats.org/drawingml/2006/lockedCanvas" schemaLocation="dml-lockedCanvas.xsd"/> - Network accessline 18
<xsd:import namespace="http://schemas.openxmlformats.org/drawingml/2006/lockedCanvas"
skills/14-文档处理与数据工具/docx/scripts/office/schemas/ISO-IEC29500-4_2016/dml-picture.xsd
- Network accessline 1
<?xml version="1.0" encoding="utf-8"?> <xsd:schema xmlns:xsd="http://www.w3.org/2001/XMLSchema" - Network accessline 2
<xsd:schema xmlns:xsd="http://www.w3.org/2001/XMLSchema" - Network accessline 3
xmlns="http://schemas.openxmlformats.org/drawingml/2006/picture" - Network accessline 4
xmlns:a="http://schemas.openxmlformats.org/drawingml/2006/main" elementFormDefault="qualified" - Network accessline 5
targetNamespace="http://schemas.openxmlformats.org/drawingml/2006/picture"> - Network accessline 6
<xsd:import namespace="http://schemas.openxmlformats.org/drawingml/2006/main"
skills/14-文档处理与数据工具/docx/scripts/office/schemas/ISO-IEC29500-4_2016/dml-spreadsheetDrawing.xsd
- Network accessline 1
<?xml version="1.0" encoding="utf-8"?> <xsd:schema xmlns:xsd="http://www.w3.org/2001/XMLSchema" - Network accessline 2
<xsd:schema xmlns:xsd="http://www.w3.org/2001/XMLSchema" - Network accessline 3
xmlns:a="http://schemas.openxmlformats.org/drawingml/2006/main" - Network accessline 4
xmlns="http://schemas.openxmlformats.org/drawingml/2006/spreadsheetDrawing" - Network accessline 5
xmlns:r="http://schemas.openxmlformats.org/officeDocument/2006/relationships" - Network accessline 6
targetNamespace="http://schemas.openxmlformats.org/drawingml/2006/spreadsheetDrawing" - Network accessline 7
elementFormDefault="qualified"> <xsd:import namespace="http://schemas.openxmlformats.org/drawingml/2006/main" schemaLocation="dml-main.xsd"/> - Network accessline 8
<xsd:import namespace="http://schemas.openxmlformats.org/drawingml/2006/main" - Network accessline 9
schemaLocation="dml-main.xsd"/> <xsd:import schemaLocation="shared-relationshipReference.xsd" namespace="http://schemas.openxmlformats.org/officeDocument/2006/relationships"/> - Network accessline 10
<xsd:import schemaLocation="shared-relationshipReference.xsd" namespace="http://schemas.openxmlformats.org/officeDocument/2006/relationships"/> <xsd:element name="from" type="CT_Marker"/> - Network accessline 11
namespace="http://schemas.openxmlformats.org/officeDocument/2006/relationships"/>
skills/14-文档处理与数据工具/docx/scripts/office/schemas/ISO-IEC29500-4_2016/dml-wordprocessingDrawing.xsd
- Network accessline 1
<?xml version="1.0" encoding="utf-8"?> <xsd:schema xmlns:xsd="http://www.w3.org/2001/XMLSchema" - Network accessline 2
<xsd:schema xmlns:xsd="http://www.w3.org/2001/XMLSchema" - Network accessline 3
xmlns:a="http://schemas.openxmlformats.org/drawingml/2006/main" - Network accessline 4
xmlns:w="http://schemas.openxmlformats.org/wordprocessingml/2006/main" - Network accessline 5
xmlns:dpct="http://schemas.openxmlformats.org/drawingml/2006/picture" - Network accessline 6
xmlns:r="http://schemas.openxmlformats.org/officeDocument/2006/relationships" - Network accessline 7
xmlns="http://schemas.openxmlformats.org/drawingml/2006/wordprocessingDrawing" - Network accessline 8
targetNamespace="http://schemas.openxmlformats.org/drawingml/2006/wordprocessingDrawing" - Network accessline 9
elementFormDefault="qualified"> <xsd:import namespace="http://schemas.openxmlformats.org/drawingml/2006/main" schemaLocation="dml-main.xsd"/> - Network accessline 10
<xsd:import namespace="http://schemas.openxmlformats.org/drawingml/2006/main" - Network accessline 11
schemaLocation="dml-main.xsd"/> <xsd:import schemaLocation="wml.xsd" namespace="http://schemas.openxmlformats.org/wordprocessingml/2006/main"/> - Network accessline 12
<xsd:import schemaLocation="wml.xsd" namespace="http://schemas.openxmlformats.org/wordprocessingml/2006/main"/> <xsd:import namespace="http://schemas.openxmlformats.org/drawingml/2006/picture" - Network accessline 13
namespace="http://schemas.openxmlformats.org/wordprocessingml/2006/main"/> - Network accessline 14
<xsd:import namespace="http://schemas.openxmlformats.org/drawingml/2006/picture" - Network accessline 15
schemaLocation="dml-picture.xsd"/> <xsd:import namespace="http://schemas.openxmlformats.org/officeDocument/2006/relationships" schemaLocation="shared-relationshipReference.xsd"/> - Network accessline 16
<xsd:import namespace="http://schemas.openxmlformats.org/officeDocument/2006/relationships"
skills/14-文档处理与数据工具/docx/scripts/office/schemas/ISO-IEC29500-4_2016/pml.xsd
- Network accessline 1
<?xml version="1.0" encoding="utf-8"?> <xsd:schema xmlns:xsd="http://www.w3.org/2001/XMLSchema" - Network accessline 2
<xsd:schema xmlns:xsd="http://www.w3.org/2001/XMLSchema" - Network accessline 3
xmlns="http://schemas.openxmlformats.org/presentationml/2006/main" - Network accessline 4
xmlns:p="http://schemas.openxmlformats.org/presentationml/2006/main" - Network accessline 5
xmlns:a="http://schemas.openxmlformats.org/drawingml/2006/main" - Network accessline 6
xmlns:r="http://schemas.openxmlformats.org/officeDocument/2006/relationships" - Network accessline 7
xmlns:s="http://schemas.openxmlformats.org/officeDocument/2006/sharedTypes" - Network accessline 8
elementFormDefault="qualified" targetNamespace="http://schemas.openxmlformats.org/presentationml/2006/main"> <xsd:import namespace="http://schemas.openxmlformats.org/officeDocument/2006/relationships" - Network accessline 9
targetNamespace="http://schemas.openxmlformats.org/presentationml/2006/main"> - Network accessline 10
<xsd:import namespace="http://schemas.openxmlformats.org/officeDocument/2006/relationships" - Network accessline 11
schemaLocation="shared-relationshipReference.xsd"/> <xsd:import namespace="http://schemas.openxmlformats.org/drawingml/2006/main" schemaLocation="dml-main.xsd"/> - Network accessline 12
<xsd:import namespace="http://schemas.openxmlformats.org/drawingml/2006/main" - Network accessline 13
schemaLocation="dml-main.xsd"/> <xsd:import namespace="http://schemas.openxmlformats.org/officeDocument/2006/sharedTypes" schemaLocation="shared-commonSimpleTypes.xsd"/> - Network accessline 14
<xsd:import namespace="http://schemas.openxmlformats.org/officeDocument/2006/sharedTypes"
skills/14-文档处理与数据工具/docx/scripts/office/schemas/ISO-IEC29500-4_2016/shared-additionalCharacteristics.xsd
- Network accessline 1
<?xml version="1.0" encoding="utf-8"?> <xsd:schema xmlns:xsd="http://www.w3.org/2001/XMLSchema" - Network accessline 2
<xsd:schema xmlns:xsd="http://www.w3.org/2001/XMLSchema" - Network accessline 3
xmlns="http://schemas.openxmlformats.org/officeDocument/2006/characteristics" - Network accessline 4
targetNamespace="http://schemas.openxmlformats.org/officeDocument/2006/characteristics"
skills/14-文档处理与数据工具/docx/scripts/office/schemas/ISO-IEC29500-4_2016/shared-bibliography.xsd
- Network accessline 1
<?xml version="1.0" encoding="utf-8"?> <xsd:schema xmlns:xsd="http://www.w3.org/2001/XMLSchema" - Network accessline 2
<xsd:schema xmlns:xsd="http://www.w3.org/2001/XMLSchema" - Network accessline 3
xmlns="http://schemas.openxmlformats.org/officeDocument/2006/bibliography" - Network accessline 4
xmlns:s="http://schemas.openxmlformats.org/officeDocument/2006/sharedTypes" - Network accessline 5
targetNamespace="http://schemas.openxmlformats.org/officeDocument/2006/bibliography" - Network accessline 6
elementFormDefault="qualified"> <xsd:import namespace="http://schemas.openxmlformats.org/officeDocument/2006/sharedTypes" schemaLocation="shared-commonSimpleTypes.xsd"/> - Network accessline 7
<xsd:import namespace="http://schemas.openxmlformats.org/officeDocument/2006/sharedTypes"
skills/14-文档处理与数据工具/docx/scripts/office/schemas/ISO-IEC29500-4_2016/shared-commonSimpleTypes.xsd
- Network accessline 1
<?xml version="1.0" encoding="utf-8"?> <xsd:schema xmlns:xsd="http://www.w3.org/2001/XMLSchema" - Network accessline 2
<xsd:schema xmlns:xsd="http://www.w3.org/2001/XMLSchema" - Network accessline 3
xmlns="http://schemas.openxmlformats.org/officeDocument/2006/sharedTypes" - Network accessline 4
targetNamespace="http://schemas.openxmlformats.org/officeDocument/2006/sharedTypes"
skills/14-文档处理与数据工具/docx/scripts/office/schemas/ISO-IEC29500-4_2016/shared-customXmlDataProperties.xsd
- Network accessline 1
<?xml version="1.0" encoding="utf-8"?> <xsd:schema xmlns:xsd="http://www.w3.org/2001/XMLSchema" - Network accessline 2
<xsd:schema xmlns:xsd="http://www.w3.org/2001/XMLSchema" - Network accessline 3
xmlns="http://schemas.openxmlformats.org/officeDocument/2006/customXml" - Network accessline 4
xmlns:s="http://schemas.openxmlformats.org/officeDocument/2006/sharedTypes" - Network accessline 5
targetNamespace="http://schemas.openxmlformats.org/officeDocument/2006/customXml" - Network accessline 6
elementFormDefault="qualified" attributeFormDefault="qualified" blockDefault="#all"> <xsd:import namespace="http://schemas.openxmlformats.org/officeDocument/2006/sharedTypes" schemaLocation="shared-commonSimpleTypes.xsd"/> - Network accessline 7
<xsd:import namespace="http://schemas.openxmlformats.org/officeDocument/2006/sharedTypes"
skills/14-文档处理与数据工具/docx/scripts/office/schemas/ISO-IEC29500-4_2016/shared-customXmlSchemaProperties.xsd
- Network accessline 1
<?xml version="1.0" encoding="utf-8"?> <xsd:schema xmlns:xsd="http://www.w3.org/2001/XMLSchema" - Network accessline 2
<xsd:schema xmlns:xsd="http://www.w3.org/2001/XMLSchema" - Network accessline 3
xmlns="http://schemas.openxmlformats.org/schemaLibrary/2006/main" - Network accessline 4
targetNamespace="http://schemas.openxmlformats.org/schemaLibrary/2006/main"
skills/14-文档处理与数据工具/docx/scripts/office/schemas/ISO-IEC29500-4_2016/shared-documentPropertiesCustom.xsd
- Network accessline 1
<?xml version="1.0" encoding="utf-8"?> <xsd:schema xmlns:xsd="http://www.w3.org/2001/XMLSchema" - Network accessline 2
<xsd:schema xmlns:xsd="http://www.w3.org/2001/XMLSchema" - Network accessline 3
xmlns="http://schemas.openxmlformats.org/officeDocument/2006/custom-properties" - Network accessline 4
xmlns:vt="http://schemas.openxmlformats.org/officeDocument/2006/docPropsVTypes" - Network accessline 5
xmlns:s="http://schemas.openxmlformats.org/officeDocument/2006/sharedTypes" - Network accessline 6
targetNamespace="http://schemas.openxmlformats.org/officeDocument/2006/custom-properties" - Network accessline 7
blockDefault="#all" elementFormDefault="qualified"> <xsd:import namespace="http://schemas.openxmlformats.org/officeDocument/2006/docPropsVTypes" schemaLocation="shared-documentPropertiesVariantTypes.xsd"/> - Network accessline 8
<xsd:import namespace="http://schemas.openxmlformats.org/officeDocument/2006/docPropsVTypes" - Network accessline 9
schemaLocation="shared-documentPropertiesVariantTypes.xsd"/> <xsd:import namespace="http://schemas.openxmlformats.org/officeDocument/2006/sharedTypes" schemaLocation="shared-commonSimpleTypes.xsd"/> - Network accessline 10
<xsd:import namespace="http://schemas.openxmlformats.org/officeDocument/2006/sharedTypes"
skills/14-文档处理与数据工具/docx/scripts/office/schemas/ISO-IEC29500-4_2016/shared-documentPropertiesExtended.xsd
- Network accessline 1
<?xml version="1.0" encoding="utf-8"?> <xsd:schema xmlns:xsd="http://www.w3.org/2001/XMLSchema" - Network accessline 2
<xsd:schema xmlns:xsd="http://www.w3.org/2001/XMLSchema" - Network accessline 3
xmlns="http://schemas.openxmlformats.org/officeDocument/2006/extended-properties" - Network accessline 4
xmlns:vt="http://schemas.openxmlformats.org/officeDocument/2006/docPropsVTypes" - Network accessline 5
targetNamespace="http://schemas.openxmlformats.org/officeDocument/2006/extended-properties" - Network accessline 6
elementFormDefault="qualified" blockDefault="#all"> <xsd:import namespace="http://schemas.openxmlformats.org/officeDocument/2006/docPropsVTypes" schemaLocation="shared-documentPropertiesVariantTypes.xsd"/> - Network accessline 7
<xsd:import namespace="http://schemas.openxmlformats.org/officeDocument/2006/docPropsVTypes"
skills/14-文档处理与数据工具/docx/scripts/office/schemas/ISO-IEC29500-4_2016/shared-documentPropertiesVariantTypes.xsd
- Network accessline 1
<?xml version="1.0" encoding="utf-8"?> <xsd:schema xmlns:xsd="http://www.w3.org/2001/XMLSchema" - Network accessline 2
<xsd:schema xmlns:xsd="http://www.w3.org/2001/XMLSchema" - Network accessline 3
xmlns="http://schemas.openxmlformats.org/officeDocument/2006/docPropsVTypes" - Network accessline 4
xmlns:s="http://schemas.openxmlformats.org/officeDocument/2006/sharedTypes" - Network accessline 5
targetNamespace="http://schemas.openxmlformats.org/officeDocument/2006/docPropsVTypes" - Network accessline 6
blockDefault="#all" elementFormDefault="qualified"> <xsd:import namespace="http://schemas.openxmlformats.org/officeDocument/2006/sharedTypes" schemaLocation="shared-commonSimpleTypes.xsd"/> - Network accessline 7
<xsd:import namespace="http://schemas.openxmlformats.org/officeDocument/2006/sharedTypes"
skills/14-文档处理与数据工具/docx/scripts/office/schemas/ISO-IEC29500-4_2016/shared-math.xsd
- Network accessline 1
<?xml version="1.0" encoding="utf-8"?> <xsd:schema xmlns:xsd="http://www.w3.org/2001/XMLSchema" - Network accessline 2
<xsd:schema xmlns:xsd="http://www.w3.org/2001/XMLSchema" - Network accessline 3
xmlns="http://schemas.openxmlformats.org/officeDocument/2006/math" - Network accessline 4
xmlns:m="http://schemas.openxmlformats.org/officeDocument/2006/math" - Network accessline 5
xmlns:w="http://schemas.openxmlformats.org/wordprocessingml/2006/main" - Network accessline 6
xmlns:s="http://schemas.openxmlformats.org/officeDocument/2006/sharedTypes" - Network accessline 7
elementFormDefault="qualified" attributeFormDefault="qualified" blockDefault="#all" targetNamespace="http://schemas.openxmlformats.org/officeDocument/2006/math"> <xsd:import namespace="http://schemas.openxmlformats.org/wordprocessingml/ - Network accessline 8
targetNamespace="http://schemas.openxmlformats.org/officeDocument/2006/math"> - Network accessline 9
<xsd:import namespace="http://schemas.openxmlformats.org/wordprocessingml/2006/main" - Network accessline 10
schemaLocation="wml.xsd"/> <xsd:import namespace="http://schemas.openxmlformats.org/officeDocument/2006/sharedTypes" schemaLocation="shared-commonSimpleTypes.xsd"/> - Network accessline 11
<xsd:import namespace="http://schemas.openxmlformats.org/officeDocument/2006/sharedTypes" - Network accessline 12
schemaLocation="shared-commonSimpleTypes.xsd"/> <xsd:import namespace="http://www.w3.org/XML/1998/namespace" schemaLocation="xml.xsd"/> <xsd:simpleType name="ST_Integer255"> - Network accessline 13
<xsd:import namespace="http://www.w3.org/XML/1998/namespace" schemaLocation="xml.xsd"/>
skills/14-文档处理与数据工具/docx/scripts/office/schemas/ISO-IEC29500-4_2016/shared-relationshipReference.xsd
- Network accessline 1
<?xml version="1.0" encoding="utf-8"?> <xsd:schema xmlns:xsd="http://www.w3.org/2001/XMLSchema" - Network accessline 2
<xsd:schema xmlns:xsd="http://www.w3.org/2001/XMLSchema" - Network accessline 3
xmlns="http://schemas.openxmlformats.org/officeDocument/2006/relationships" - Network accessline 4
xmlns:r="http://schemas.openxmlformats.org/officeDocument/2006/relationships" - Network accessline 5
elementFormDefault="qualified" targetNamespace="http://schemas.openxmlformats.org/officeDocument/2006/relationships" blockDefault="#all"> - Network accessline 6
targetNamespace="http://schemas.openxmlformats.org/officeDocument/2006/relationships"
skills/14-文档处理与数据工具/docx/scripts/office/schemas/ISO-IEC29500-4_2016/sml.xsd
- Network accessline 1
<?xml version="1.0" encoding="utf-8"?> <xsd:schema xmlns:xsd="http://www.w3.org/2001/XMLSchema" - Network accessline 2
<xsd:schema xmlns:xsd="http://www.w3.org/2001/XMLSchema" - Network accessline 3
xmlns="http://schemas.openxmlformats.org/spreadsheetml/2006/main" - Network accessline 4
xmlns:r="http://schemas.openxmlformats.org/officeDocument/2006/relationships" - Network accessline 5
xmlns:xdr="http://schemas.openxmlformats.org/drawingml/2006/spreadsheetDrawing" - Network accessline 6
xmlns:s="http://schemas.openxmlformats.org/officeDocument/2006/sharedTypes" - Network accessline 7
targetNamespace="http://schemas.openxmlformats.org/spreadsheetml/2006/main" - Network accessline 8
elementFormDefault="qualified"> <xsd:import namespace="http://schemas.openxmlformats.org/officeDocument/2006/relationships" schemaLocation="shared-relationshipReference.xsd"/> - Network accessline 9
<xsd:import namespace="http://schemas.openxmlformats.org/officeDocument/2006/relationships" - Network accessline 10
schemaLocation="shared-relationshipReference.xsd"/> <xsd:import namespace="http://schemas.openxmlformats.org/officeDocument/2006/sharedTypes" schemaLocation="shared-commonSimpleTypes.xsd"/> - Network accessline 11
<xsd:import namespace="http://schemas.openxmlformats.org/officeDocument/2006/sharedTypes" - Network accessline 12
schemaLocation="shared-commonSimpleTypes.xsd"/> <xsd:import namespace="http://schemas.openxmlformats.org/drawingml/2006/spreadsheetDrawing" - Network accessline 13
<xsd:import namespace="http://schemas.openxmlformats.org/drawingml/2006/spreadsheetDrawing" schemaLocation="dml-spreadsheetDrawing.xsd"/> - Network accessline 14
namespace="http://schemas.openxmlformats.org/drawingml/2006/spreadsheetDrawing"
skills/14-文档处理与数据工具/docx/scripts/office/schemas/ISO-IEC29500-4_2016/vml-main.xsd
- Network accessline 1
<?xml version="1.0" encoding="utf-8"?> <xsd:schema xmlns:xsd="http://www.w3.org/2001/XMLSchema" xmlns="urn:schemas-microsoft-com:vml" - Network accessline 2
<xsd:schema xmlns:xsd="http://www.w3.org/2001/XMLSchema" xmlns="urn:schemas-microsoft-com:vml" - Network accessline 3
xmlns:pvml="urn:schemas-microsoft-com:office:powerpoint" xmlns:o="urn:schemas-microsoft-com:office:office" xmlns:w="http://schemas.openxmlformats.org/wordprocessingml/2006/main" - Network accessline 4
xmlns:o="urn:schemas-microsoft-com:office:office" xmlns:w="http://schemas.openxmlformats.org/wordprocessingml/2006/main" xmlns:w10="urn:schemas-microsoft-com:office:word" - Network accessline 5
xmlns:w="http://schemas.openxmlformats.org/wordprocessingml/2006/main" - Network accessline 6
xmlns:w10="urn:schemas-microsoft-com:office:word" xmlns:r="http://schemas.openxmlformats.org/officeDocument/2006/relationships" xmlns:x="urn:schemas-microsoft-com:office:excel" - Network accessline 7
xmlns:r="http://schemas.openxmlformats.org/officeDocument/2006/relationships" - Network accessline 8
xmlns:x="urn:schemas-microsoft-com:office:excel" xmlns:s="http://schemas.openxmlformats.org/officeDocument/2006/sharedTypes" targetNamespace="urn:schemas-microsoft-com:vml" elementFormDefault="qualified" - Network accessline 9
xmlns:s="http://schemas.openxmlformats.org/officeDocument/2006/sharedTypes" - Network accessline 12
<xsd:import namespace="urn:schemas-microsoft-com:office:office" schemaLocation="vml-officeDrawing.xsd"/> <xsd:import namespace="http://schemas.openxmlformats.org/wordprocessingml/2006/main" - Network accessline 13
schemaLocation="vml-officeDrawing.xsd"/> <xsd:import namespace="http://schemas.openxmlformats.org/wordprocessingml/2006/main" schemaLocation="wml.xsd"/> - Network accessline 14
<xsd:import namespace="http://schemas.openxmlformats.org/wordprocessingml/2006/main" - Network accessline 16
<xsd:import namespace="urn:schemas-microsoft-com:office:word" schemaLocation="vml-wordprocessingDrawing.xsd"/> <xsd:import namespace="http://schemas.openxmlformats.org/officeDocument/2006/relationships" - Network accessline 17
schemaLocation="vml-wordprocessingDrawing.xsd"/> <xsd:import namespace="http://schemas.openxmlformats.org/officeDocument/2006/relationships" schemaLocation="shared-relationshipReference.xsd"/> - Network accessline 18
<xsd:import namespace="http://schemas.openxmlformats.org/officeDocument/2006/relationships" - Network accessline 22
<xsd:import namespace="urn:schemas-microsoft-com:office:powerpoint" schemaLocation="vml-presentationDrawing.xsd"/> <xsd:import namespace="http://schemas.openxmlformats.org/officeDocument/2006/sharedTypes" - Network accessline 23
schemaLocation="vml-presentationDrawing.xsd"/> <xsd:import namespace="http://schemas.openxmlformats.org/officeDocument/2006/sharedTypes" schemaLocation="shared-commonSimpleTypes.xsd"/> - Network accessline 24
<xsd:import namespace="http://schemas.openxmlformats.org/officeDocument/2006/sharedTypes"
skills/14-文档处理与数据工具/docx/scripts/office/schemas/ISO-IEC29500-4_2016/vml-officeDrawing.xsd
- Network accessline 1
<?xml version="1.0" encoding="utf-8"?> <xsd:schema xmlns:xsd="http://www.w3.org/2001/XMLSchema" - Network accessline 2
<xsd:schema xmlns:xsd="http://www.w3.org/2001/XMLSchema" - Network accessline 3
xmlns="urn:schemas-microsoft-com:office:office" xmlns:v="urn:schemas-microsoft-com:vml" xmlns:r="http://schemas.openxmlformats.org/officeDocument/2006/relationships" xmlns:s="http://schemas.openxmlformats.org/officeDocument/2006/sharedT - Network accessline 4
xmlns:r="http://schemas.openxmlformats.org/officeDocument/2006/relationships" - Network accessline 5
xmlns:s="http://schemas.openxmlformats.org/officeDocument/2006/sharedTypes" - Network accessline 7
attributeFormDefault="unqualified"> <xsd:import namespace="urn:schemas-microsoft-com:vml" schemaLocation="vml-main.xsd"/> <xsd:import namespace="http://schemas.openxmlformats.org/officeDocument/2006/relationships" - Network accessline 8
<xsd:import namespace="urn:schemas-microsoft-com:vml" schemaLocation="vml-main.xsd"/> <xsd:import namespace="http://schemas.openxmlformats.org/officeDocument/2006/relationships" schemaLocation="shared-relationshipReference.xsd"/> - Network accessline 9
<xsd:import namespace="http://schemas.openxmlformats.org/officeDocument/2006/relationships" - Network accessline 10
schemaLocation="shared-relationshipReference.xsd"/> <xsd:import namespace="http://schemas.openxmlformats.org/officeDocument/2006/sharedTypes" schemaLocation="shared-commonSimpleTypes.xsd"/> - Network accessline 11
<xsd:import namespace="http://schemas.openxmlformats.org/officeDocument/2006/sharedTypes"
skills/14-文档处理与数据工具/docx/scripts/office/schemas/ISO-IEC29500-4_2016/vml-presentationDrawing.xsd
skills/14-文档处理与数据工具/docx/scripts/office/schemas/ISO-IEC29500-4_2016/vml-spreadsheetDrawing.xsd
- Network accessline 1
<?xml version="1.0" encoding="utf-8"?> <xsd:schema xmlns:xsd="http://www.w3.org/2001/XMLSchema" - Network accessline 2
<xsd:schema xmlns:xsd="http://www.w3.org/2001/XMLSchema" - Network accessline 3
xmlns="urn:schemas-microsoft-com:office:excel" xmlns:s="http://schemas.openxmlformats.org/officeDocument/2006/sharedTypes" targetNamespace="urn:schemas-microsoft-com:office:excel" elementFormDefault="qualified" - Network accessline 4
xmlns:s="http://schemas.openxmlformats.org/officeDocument/2006/sharedTypes" - Network accessline 5
targetNamespace="urn:schemas-microsoft-com:office:excel" elementFormDefault="qualified" attributeFormDefault="unqualified"> <xsd:import namespace="http://schemas.openxmlformats.org/officeDocument/2006/sharedTypes" - Network accessline 6
attributeFormDefault="unqualified"> <xsd:import namespace="http://schemas.openxmlformats.org/officeDocument/2006/sharedTypes" schemaLocation="shared-commonSimpleTypes.xsd"/> - Network accessline 7
<xsd:import namespace="http://schemas.openxmlformats.org/officeDocument/2006/sharedTypes"
skills/14-文档处理与数据工具/docx/scripts/office/schemas/ISO-IEC29500-4_2016/vml-wordprocessingDrawing.xsd
skills/14-文档处理与数据工具/docx/scripts/office/schemas/ISO-IEC29500-4_2016/wml.xsd
- Network accessline 1
<?xml version="1.0" encoding="utf-8"?> <xsd:schema xmlns:xsd="http://www.w3.org/2001/XMLSchema" - Network accessline 2
<xsd:schema xmlns:xsd="http://www.w3.org/2001/XMLSchema" - Network accessline 3
xmlns:m="http://schemas.openxmlformats.org/officeDocument/2006/math" - Network accessline 4
xmlns:r="http://schemas.openxmlformats.org/officeDocument/2006/relationships" - Network accessline 5
xmlns:sl="http://schemas.openxmlformats.org/schemaLibrary/2006/main" - Network accessline 6
xmlns:wp="http://schemas.openxmlformats.org/drawingml/2006/wordprocessingDrawing" - Network accessline 7
xmlns="http://schemas.openxmlformats.org/wordprocessingml/2006/main" - Network accessline 8
xmlns:s="http://schemas.openxmlformats.org/officeDocument/2006/sharedTypes" - Network accessline 9
xmlns:mc="http://schemas.openxmlformats.org/markup-compatibility/2006" - Network accessline 10
elementFormDefault="qualified" attributeFormDefault="qualified" blockDefault="#all" targetNamespace="http://schemas.openxmlformats.org/wordprocessingml/2006/main"> <xsd:import namespace="http://schemas.openxmlformats.org/markup-compatib - Network accessline 11
targetNamespace="http://schemas.openxmlformats.org/wordprocessingml/2006/main"> - Network accessline 12
<xsd:import namespace="http://schemas.openxmlformats.org/markup-compatibility/2006" schemaLocation="../mce/mc.xsd"/> - Network accessline 13
<xsd:import namespace="http://schemas.openxmlformats.org/drawingml/2006/wordprocessingDrawing" - Network accessline 14
schemaLocation="dml-wordprocessingDrawing.xsd"/> <xsd:import namespace="http://schemas.openxmlformats.org/officeDocument/2006/math" schemaLocation="shared-math.xsd"/> - Network accessline 15
<xsd:import namespace="http://schemas.openxmlformats.org/officeDocument/2006/math" - Network accessline 16
schemaLocation="shared-math.xsd"/> <xsd:import namespace="http://schemas.openxmlformats.org/officeDocument/2006/relationships" schemaLocation="shared-relationshipReference.xsd"/> - Network accessline 17
<xsd:import namespace="http://schemas.openxmlformats.org/officeDocument/2006/relationships" - Network accessline 18
schemaLocation="shared-relationshipReference.xsd"/> <xsd:import namespace="http://schemas.openxmlformats.org/officeDocument/2006/sharedTypes" schemaLocation="shared-commonSimpleTypes.xsd"/> - Network accessline 19
<xsd:import namespace="http://schemas.openxmlformats.org/officeDocument/2006/sharedTypes" - Network accessline 20
schemaLocation="shared-commonSimpleTypes.xsd"/> <xsd:import namespace="http://schemas.openxmlformats.org/schemaLibrary/2006/main" schemaLocation="shared-customXmlSchemaProperties.xsd"/> - Network accessline 21
<xsd:import namespace="http://schemas.openxmlformats.org/schemaLibrary/2006/main" - Network accessline 22
schemaLocation="shared-customXmlSchemaProperties.xsd"/> <xsd:import namespace="http://www.w3.org/XML/1998/namespace"/> <xsd:complexType name="CT_Empty"/> - Network accessline 23
<xsd:import namespace="http://www.w3.org/XML/1998/namespace"/>
skills/14-文档处理与数据工具/docx/scripts/office/schemas/ISO-IEC29500-4_2016/xml.xsd
- Network accessline 1
<?xml version='1.0'?> <xs:schema targetNamespace="http://www.w3.org/XML/1998/namespace" xmlns:xs="http://www.w3.org/2001/XMLSchema" xml:lang="en"> - Network accessline 2
<xs:schema targetNamespace="http://www.w3.org/XML/1998/namespace" xmlns:xs="http://www.w3.org/2001/XMLSchema" xml:lang="en"> - Network accessline 4
<xs:annotation> <xs:documentation> See http://www.w3.org/XML/1998/namespace.html and - Network accessline 5
<xs:documentation> See http://www.w3.org/XML/1998/namespace.html and http://www.w3.org/TR/REC-xml for information about this namespace. - Network accessline 6
See http://www.w3.org/XML/1998/namespace.html and - Network accessline 7
http://www.w3.org/TR/REC-xml for information about this namespace. - Network accessline 55
<schema . . .> . . . <import namespace="http://www.w3.org/XML/1998/namespace" - Network accessline 56
. . . <import namespace="http://www.w3.org/XML/1998/namespace" schemaLocation="http://www.w3.org/2001/03/xml.xsd"/> - Network accessline 57
<import namespace="http://www.w3.org/XML/1998/namespace" - Network accessline 58
schemaLocation="http://www.w3.org/2001/03/xml.xsd"/> - Network accessline 72
<xs:documentation>In keeping with the XML Schema WG's standard versioning policy, this schema document will persist at http://www.w3.org/2001/03/xml.xsd. - Network accessline 73
policy, this schema document will persist at http://www.w3.org/2001/03/xml.xsd. At the date of issue it can also be found at - Network accessline 74
http://www.w3.org/2001/03/xml.xsd. - Network accessline 75
At the date of issue it can also be found at http://www.w3.org/2001/xml.xsd. The schema document at that URI may however change in the future, - Network accessline 76
http://www.w3.org/2001/xml.xsd. - Network accessline 79
itself. In other words, if the XML Schema namespace changes, the version of this document at http://www.w3.org/2001/xml.xsd will change - Network accessline 80
of this document at http://www.w3.org/2001/xml.xsd will change accordingly; the version at - Network accessline 81
http://www.w3.org/2001/xml.xsd will change - Network accessline 82
accordingly; the version at http://www.w3.org/2001/03/xml.xsd will not change. </xs:documentation> - Network accessline 83
http://www.w3.org/2001/03/xml.xsd will not change. - Network accessline 103
<xs:attribute name="base" type="xs:anyURI"> <xs:annotation> <xs:documentation>See http://www.w3.org/TR/xmlbase/ for - Network accessline 104
<xs:annotation> <xs:documentation>See http://www.w3.org/TR/xmlbase/ for information about this attribute.</xs:documentation> - Network accessline 105
<xs:documentation>See http://www.w3.org/TR/xmlbase/ for
skills/14-文档处理与数据工具/docx/scripts/office/schemas/ecma/fouth-edition/opc-contentTypes.xsd
- Network accessline 1
<U+FEFF><?xml version="1.0" encoding="UTF-8" standalone="no"?> <xs:schema xmlns="http://schemas.openxmlformats.org/package/2006/content-types" - Network accessline 2
<xs:schema xmlns="http://schemas.openxmlformats.org/package/2006/content-types" - Network accessline 3
xmlns:xs="http://www.w3.org/2001/XMLSchema" - Network accessline 4
targetNamespace="http://schemas.openxmlformats.org/package/2006/content-types"
skills/14-文档处理与数据工具/docx/scripts/office/schemas/ecma/fouth-edition/opc-coreProperties.xsd
- Network accessline 1
<U+FEFF><?xml version="1.0" encoding="UTF-8"?> <xs:schema targetNamespace="http://schemas.openxmlformats.org/package/2006/metadata/core-properties" - Network accessline 2
<xs:schema targetNamespace="http://schemas.openxmlformats.org/package/2006/metadata/core-properties" - Network accessline 3
xmlns="http://schemas.openxmlformats.org/package/2006/metadata/core-properties" - Network accessline 4
xmlns:xs="http://www.w3.org/2001/XMLSchema" xmlns:dc="http://purl.org/dc/elements/1.1/" - Network accessline 5
xmlns:dcterms="http://purl.org/dc/terms/" elementFormDefault="qualified" blockDefault="#all"> - Network accessline 7
<xs:import namespace="http://purl.org/dc/elements/1.1/" - Network accessline 8
schemaLocation="http://dublincore.org/schemas/xmls/qdc/2003/04/02/dc.xsd"/> - Network accessline 9
<xs:import namespace="http://purl.org/dc/terms/" - Network accessline 10
schemaLocation="http://dublincore.org/schemas/xmls/qdc/2003/04/02/dcterms.xsd"/> - Network accessline 11
<xs:import id="xml" namespace="http://www.w3.org/XML/1998/namespace"/>
skills/14-文档处理与数据工具/docx/scripts/office/schemas/ecma/fouth-edition/opc-digSig.xsd
- Network accessline 1
<?xml version="1.0" encoding="UTF-8"?> <xsd:schema xmlns="http://schemas.openxmlformats.org/package/2006/digital-signature" - Network accessline 2
<xsd:schema xmlns="http://schemas.openxmlformats.org/package/2006/digital-signature" - Network accessline 3
xmlns:xsd="http://www.w3.org/2001/XMLSchema" - Network accessline 4
targetNamespace="http://schemas.openxmlformats.org/package/2006/digital-signature"
skills/14-文档处理与数据工具/docx/scripts/office/schemas/ecma/fouth-edition/opc-relationships.xsd
- Network accessline 1
<U+FEFF><?xml version="1.0" encoding="UTF-8" standalone="no"?> <xsd:schema xmlns="http://schemas.openxmlformats.org/package/2006/relationships" - Network accessline 2
<xsd:schema xmlns="http://schemas.openxmlformats.org/package/2006/relationships" - Network accessline 3
xmlns:xsd="http://www.w3.org/2001/XMLSchema" - Network accessline 4
targetNamespace="http://schemas.openxmlformats.org/package/2006/relationships"
skills/14-文档处理与数据工具/docx/scripts/office/schemas/mce/mc.xsd
- Network accessline 1
<?xml version="1.0" encoding="utf-8"?> <xsd:schema xmlns:mc="http://schemas.openxmlformats.org/markup-compatibility/2006" - Network accessline 2
<xsd:schema xmlns:mc="http://schemas.openxmlformats.org/markup-compatibility/2006" - Network accessline 3
attributeFormDefault="unqualified" elementFormDefault="qualified" targetNamespace="http://schemas.openxmlformats.org/markup-compatibility/2006" xmlns:xsd="http://www.w3.org/2001/XMLSchema"> - Network accessline 4
targetNamespace="http://schemas.openxmlformats.org/markup-compatibility/2006" - Network accessline 5
xmlns:xsd="http://www.w3.org/2001/XMLSchema"> - Network accessline 7
<!-- This XSD is a modified version of the one found at: https://github.com/plutext/docx4j/blob/master/xsd/mce/markup-compatibility-2006-MINIMAL.xsd - Network accessline 9
https://github.com/plutext/docx4j/blob/master/xsd/mce/markup-compatibility-2006-MINIMAL.xsd - Network accessline 15
<w:document xmlns:mc="http://schemas.openxmlformats.org/markup-compatibility/2006" - Network accessline 16
xmlns:mc="http://schemas.openxmlformats.org/markup-compatibility/2006" - Network accessline 17
xmlns:w="http://schemas.openxmlformats.org/wordprocessingml/2006/main"
skills/14-文档处理与数据工具/docx/scripts/office/schemas/microsoft/wml-2010.xsd
- Network accessline 1
<xsd:schema xmlns:xsd="http://www.w3.org/2001/XMLSchema" xmlns:w12="http://schemas.openxmlformats.org/wordprocessingml/2006/main" elementFormDefault="qualified" attributeFormDefault="qualified" blockDefault="#all" xmlns:r="http://schemas.op - Network accessline 2
<!-- <xsd:import id="rel" namespace="http://schemas.openxmlformats.org/officeDocument/2006/relationships" schemaLocation="orel.xsd"/> --> - Network accessline 3
<xsd:import id="w" namespace="http://schemas.openxmlformats.org/wordprocessingml/2006/main" schemaLocation="../ISO-IEC29500-4_2016/wml.xsd"/> - Network accessline 4
<!-- <xsd:import namespace="http://schemas.openxmlformats.org/drawingml/2006/main" schemaLocation="oartbasetypes.xsd"/> - Network accessline 5
<xsd:import namespace="http://schemas.openxmlformats.org/drawingml/2006/main" schemaLocation="oartsplineproperties.xsd"/> -->
skills/14-文档处理与数据工具/docx/scripts/office/schemas/microsoft/wml-2012.xsd
- Network accessline 1
<xsd:schema xmlns:xsd="http://www.w3.org/2001/XMLSchema" xmlns:w12="http://schemas.openxmlformats.org/wordprocessingml/2006/main" xmlns:s="http://schemas.openxmlformats.org/officeDocument/2006/sharedTypes" elementFormDefault="qualified" att - Network accessline 2
<xsd:import id="w12" namespace="http://schemas.openxmlformats.org/wordprocessingml/2006/main" schemaLocation="../ISO-IEC29500-4_2016/wml.xsd"/> - Network accessline 3
<xsd:import namespace="http://schemas.openxmlformats.org/officeDocument/2006/sharedTypes" schemaLocation="../ISO-IEC29500-4_2016/shared-commonSimpleTypes.xsd"/>
skills/14-文档处理与数据工具/docx/scripts/office/schemas/microsoft/wml-2018.xsd
- Network accessline 1
<xsd:schema xmlns:xsd="http://www.w3.org/2001/XMLSchema" xmlns:w12="http://schemas.openxmlformats.org/wordprocessingml/2006/main" elementFormDefault="qualified" attributeFormDefault="qualified" blockDefault="#all" xmlns="http://schemas.micr - Network accessline 2
<xsd:import id="w12" namespace="http://schemas.openxmlformats.org/wordprocessingml/2006/main" schemaLocation="../ISO-IEC29500-4_2016/wml.xsd"/>
skills/14-文档处理与数据工具/docx/scripts/office/schemas/microsoft/wml-cex-2018.xsd
- Network accessline 1
<xsd:schema xmlns:xsd="http://www.w3.org/2001/XMLSchema" xmlns:w="http://schemas.openxmlformats.org/wordprocessingml/2006/main" xmlns:s="http://schemas.openxmlformats.org/officeDocument/2006/sharedTypes" xmlns:w16="http://schemas.microsoft. - Network accessline 2
<xsd:import id="w16" namespace="http://schemas.microsoft.com/office/word/2018/wordml" schemaLocation="wml-2018.xsd"/> - Network accessline 3
<xsd:import id="w" namespace="http://schemas.openxmlformats.org/wordprocessingml/2006/main" schemaLocation="../ISO-IEC29500-4_2016/wml.xsd"/> - Network accessline 4
<xsd:import id="s" namespace="http://schemas.openxmlformats.org/officeDocument/2006/sharedTypes" schemaLocation="../ISO-IEC29500-4_2016/shared-commonSimpleTypes.xsd"/>
skills/14-文档处理与数据工具/docx/scripts/office/schemas/microsoft/wml-cid-2016.xsd
- Network accessline 1
<xsd:schema xmlns:xsd="http://www.w3.org/2001/XMLSchema" xmlns:w12="http://schemas.openxmlformats.org/wordprocessingml/2006/main" elementFormDefault="qualified" attributeFormDefault="qualified" blockDefault="#all" xmlns="http://schemas.micr - Network accessline 2
<xsd:import id="w12" namespace="http://schemas.openxmlformats.org/wordprocessingml/2006/main" schemaLocation="../ISO-IEC29500-4_2016/wml.xsd"/>
skills/14-文档处理与数据工具/docx/scripts/office/schemas/microsoft/wml-sdtdatahash-2020.xsd
- Network accessline 1
<xsd:schema xmlns:xsd="http://www.w3.org/2001/XMLSchema" xmlns:w12="http://schemas.openxmlformats.org/wordprocessingml/2006/main" elementFormDefault="qualified" attributeFormDefault="qualified" blockDefault="#all" xmlns="http://schemas.micr - Network accessline 2
<xsd:import id="w12" namespace="http://schemas.openxmlformats.org/wordprocessingml/2006/main" schemaLocation="../ISO-IEC29500-4_2016/wml.xsd"/>
skills/14-文档处理与数据工具/docx/scripts/office/schemas/microsoft/wml-symex-2015.xsd
- Network accessline 1
<xsd:schema xmlns:xsd="http://www.w3.org/2001/XMLSchema" xmlns:w12="http://schemas.openxmlformats.org/wordprocessingml/2006/main" elementFormDefault="qualified" attributeFormDefault="qualified" blockDefault="#all" xmlns="http://schemas.micr - Network accessline 2
<xsd:import id="w12" namespace="http://schemas.openxmlformats.org/wordprocessingml/2006/main" schemaLocation="../ISO-IEC29500-4_2016/wml.xsd"/>
skills/14-文档处理与数据工具/docx/scripts/office/soffice.py
- Executable scriptline 1
Executable file - Access to secretsline 25
[Credential-related source omitted] - Network accessline 44
def _needs_shim() -> bool: try: s = socket.socket(socket.AF_UNIX, socket.SOCK_STREAM) - Network accessline 45
try: s = socket.socket(socket.AF_UNIX, socket.SOCK_STREAM) s.close() - Network accessline 46
s = socket.socket(socket.AF_UNIX, socket.SOCK_STREAM)
skills/14-文档处理与数据工具/docx/scripts/office/unpack.py
- Executable scriptline 1
Executable file
skills/14-文档处理与数据工具/docx/scripts/office/validate.py
- Executable scriptline 1
Executable file
skills/14-文档处理与数据工具/docx/scripts/office/validators/__init__.py
- Executable scriptline 1
Executable file
skills/14-文档处理与数据工具/docx/scripts/office/validators/base.py
- Executable scriptline 1
Executable file - Network accessline 61
MC_NAMESPACE = "http://schemas.openxmlformats.org/markup-compatibility/2006" - Network accessline 62
XML_NAMESPACE = "http://www.w3.org/XML/1998/namespace" - Network accessline 64
PACKAGE_RELATIONSHIPS_NAMESPACE = ( "http://schemas.openxmlformats.org/package/2006/relationships" - Network accessline 65
"http://schemas.openxmlformats.org/package/2006/relationships" - Network accessline 66
) OFFICE_RELATIONSHIPS_NAMESPACE = ( "http://schemas.openxmlformats.org/officeDocument/2006/relationships" - Network accessline 67
OFFICE_RELATIONSHIPS_NAMESPACE = ( "http://schemas.openxmlformats.org/officeDocument/2006/relationships" ) - Network accessline 68
"http://schemas.openxmlformats.org/officeDocument/2006/relationships" - Network accessline 69
) CONTENT_TYPES_NAMESPACE = ( "http://schemas.openxmlformats.org/package/2006/content-types" - Network accessline 70
CONTENT_TYPES_NAMESPACE = ( "http://schemas.openxmlformats.org/package/2006/content-types" ) - Network accessline 71
"http://schemas.openxmlformats.org/package/2006/content-types" - Network accessline 76
OOXML_NAMESPACES = { "http://schemas.openxmlformats.org/officeDocument/2006/math", - Network accessline 77
"http://schemas.openxmlformats.org/officeDocument/2006/math", - Network accessline 78
"http://schemas.openxmlformats.org/officeDocument/2006/relationships", - Network accessline 79
"http://schemas.openxmlformats.org/schemaLibrary/2006/main", - Network accessline 80
"http://schemas.openxmlformats.org/drawingml/2006/main", - Network accessline 81
"http://schemas.openxmlformats.org/drawingml/2006/chart", - Network accessline 82
"http://schemas.openxmlformats.org/drawingml/2006/chartDrawing", - Network accessline 83
"http://schemas.openxmlformats.org/drawingml/2006/diagram", - Network accessline 84
"http://schemas.openxmlformats.org/drawingml/2006/picture", - Network accessline 85
"http://schemas.openxmlformats.org/drawingml/2006/spreadsheetDrawing", - Network accessline 86
"http://schemas.openxmlformats.org/drawingml/2006/wordprocessingDrawing", - Network accessline 87
"http://schemas.openxmlformats.org/wordprocessingml/2006/main", - Network accessline 88
"http://schemas.openxmlformats.org/presentationml/2006/main", - Network accessline 89
"http://schemas.openxmlformats.org/spreadsheetml/2006/main", - Network accessline 90
"http://schemas.openxmlformats.org/officeDocument/2006/sharedTypes", - Network accessline 91
"http://www.w3.org/XML/1998/namespace",
skills/14-文档处理与数据工具/docx/scripts/office/validators/docx.py
- Executable scriptline 1
Executable file - Network accessline 18
WORD_2006_NAMESPACE = "http://schemas.openxmlformats.org/wordprocessingml/2006/main" - Network accessline 19
W14_NAMESPACE = "http://schemas.microsoft.com/office/word/2010/wordml" - Network accessline 20
W16CID_NAMESPACE = "http://schemas.microsoft.com/office/word/2016/wordml/cid"
skills/14-文档处理与数据工具/docx/scripts/office/validators/pptx.py
skills/14-文档处理与数据工具/docx/scripts/office/validators/redlining.py
- Executable scriptline 1
Executable file - Network accessline 17
self.author = author self.namespaces = { "w": "http://schemas.openxmlformats.org/wordprocessingml/2006/main" - Network accessline 18
self.namespaces = { "w": "http://schemas.openxmlformats.org/wordprocessingml/2006/main" } - Network accessline 19
"w": "http://schemas.openxmlformats.org/wordprocessingml/2006/main"
skills/14-文档处理与数据工具/docx/scripts/templates/comments.xml
- Network accessline 1
<?xml version="1.0" ?> <w:comments xmlns:wpc="http://schemas.microsoft.com/office/word/2010/wordprocessingCanvas" xmlns:cx="http://schemas.microsoft.com/office/drawing/2014/chartex" xmlns:cx1="http://schemas.microsoft.com/office/drawing/201 - Network accessline 2
<w:comments xmlns:wpc="http://schemas.microsoft.com/office/word/2010/wordprocessingCanvas" xmlns:cx="http://schemas.microsoft.com/office/drawing/2014/chartex" xmlns:cx1="http://schemas.microsoft.com/office/drawing/2015/9/8/chartex" xmlns:cx
skills/14-文档处理与数据工具/docx/scripts/templates/commentsExtended.xml
- Network accessline 1
<?xml version="1.0" ?> <w15:commentsEx xmlns:wpc="http://schemas.microsoft.com/office/word/2010/wordprocessingCanvas" xmlns:cx="http://schemas.microsoft.com/office/drawing/2014/chartex" xmlns:cx1="http://schemas.microsoft.com/office/drawing - Network accessline 2
<w15:commentsEx xmlns:wpc="http://schemas.microsoft.com/office/word/2010/wordprocessingCanvas" xmlns:cx="http://schemas.microsoft.com/office/drawing/2014/chartex" xmlns:cx1="http://schemas.microsoft.com/office/drawing/2015/9/8/chartex" xmln
skills/14-文档处理与数据工具/docx/scripts/templates/commentsExtensible.xml
- Network accessline 1
<?xml version="1.0" ?> <w16cex:commentsExtensible xmlns:wpc="http://schemas.microsoft.com/office/word/2010/wordprocessingCanvas" xmlns:cx="http://schemas.microsoft.com/office/drawing/2014/chartex" xmlns:cx1="http://schemas.microsoft.com/off - Network accessline 2
<w16cex:commentsExtensible xmlns:wpc="http://schemas.microsoft.com/office/word/2010/wordprocessingCanvas" xmlns:cx="http://schemas.microsoft.com/office/drawing/2014/chartex" xmlns:cx1="http://schemas.microsoft.com/office/drawing/2015/9/8/ch
skills/14-文档处理与数据工具/docx/scripts/templates/commentsIds.xml
- Network accessline 1
<?xml version="1.0" ?> <w16cid:commentsIds xmlns:wpc="http://schemas.microsoft.com/office/word/2010/wordprocessingCanvas" xmlns:cx="http://schemas.microsoft.com/office/drawing/2014/chartex" xmlns:cx1="http://schemas.microsoft.com/office/dra - Network accessline 2
<w16cid:commentsIds xmlns:wpc="http://schemas.microsoft.com/office/word/2010/wordprocessingCanvas" xmlns:cx="http://schemas.microsoft.com/office/drawing/2014/chartex" xmlns:cx1="http://schemas.microsoft.com/office/drawing/2015/9/8/chartex"
skills/14-文档处理与数据工具/docx/scripts/templates/people.xml
skills/14-文档处理与数据工具/markitdown/SKILL.md
- Broad permissionsline 1
Read Write Edit Bash - Network accessline 85
# Or from source git clone https://github.com/microsoft/markitdown.git - Network accessline 86
git clone https://github.com/microsoft/markitdown.git - Network accessline 135
[Credential-related source omitted] - Network accessline 136
[Credential-related source omitted] - Access to secretsline 136
[Credential-related source omitted] - Network accessline 137
base_url="https://openrouter.ai/api/v1" - Network accessline 258
[Credential-related source omitted] - Network accessline 259
[Credential-related source omitted] - Access to secretsline 259
[Credential-related source omitted] - Network accessline 260
base_url="https://openrouter.ai/api/v1" - Network accessline 308
# Convert YouTube video to transcript result = md.convert("https://www.youtube.com/watch?v=VIDEO_ID") - Network accessline 309
result = md.convert("https://www.youtube.com/watch?v=VIDEO_ID") - Network accessline 415
[Credential-related source omitted] - Network accessline 416
[Credential-related source omitted] - Access to secretsline 416
[Credential-related source omitted] - Network accessline 417
base_url="https://openrouter.ai/api/v1" - Dangerous shell operationline 461
sudo apt-get install tesseract-ocr - Network accessline 480
- **MarkItDown GitHub**: https://github.com/microsoft/markitdown - Network accessline 481
- **PyPI**: https://pypi.org/project/markitdown/ - Network accessline 482
- **OpenRouter**: https://openrouter.ai (for AI-enhanced conversions) - Network accessline 483
- **OpenRouter API Keys**: https://openrouter.ai/keys - Network accessline 484
- **OpenRouter Models**: https://openrouter.ai/models
skills/14-文档处理与数据工具/markitdown/assets/example_usage.md
- Network accessline 129
[Credential-related source omitted] - Network accessline 130
[Credential-related source omitted] - Access to secretsline 130
[Credential-related source omitted] - Network accessline 131
base_url="https://openrouter.ai/api/v1" - Network accessline 162
[Credential-related source omitted] - Network accessline 163
[Credential-related source omitted] - Access to secretsline 163
[Credential-related source omitted] - Network accessline 164
base_url="https://openrouter.ai/api/v1" - Network accessline 320
# Get transcript video_url = "https://www.youtube.com/watch?v=VIDEO_ID" - Network accessline 321
video_url = "https://www.youtube.com/watch?v=VIDEO_ID"
skills/14-文档处理与数据工具/markitdown/references/api_reference.md
- Network accessline 230
[Credential-related source omitted] - Network accessline 231
[Credential-related source omitted] - Access to secretsline 231
[Credential-related source omitted] - Network accessline 232
base_url="https://openrouter.ai/api/v1" - Network accessline 252
See https://openrouter.ai/models for the complete list. - Network accessline 287
md = MarkItDown( docintel_endpoint="https://YOUR-RESOURCE.cognitiveservices.azure.com/" - Network accessline 288
docintel_endpoint="https://YOUR-RESOURCE.cognitiveservices.azure.com/" - Access to secretsline 298
[Credential-related source omitted] - Network accessline 393
[Credential-related source omitted] - Access to secretsline 393
[Credential-related source omitted] - Network accessline 395
| `AZURE_DOCUMENT_INTELLIGENCE_ENDPOINT` | Azure DI endpoint | `https://...` |
skills/14-文档处理与数据工具/markitdown/references/file_formats.md
skills/14-文档处理与数据工具/markitdown/scripts/batch_convert.py
- Executable scriptline 1
#!/usr/bin/env python3
skills/14-文档处理与数据工具/markitdown/scripts/convert_literature.py
- Executable scriptline 1
#!/usr/bin/env python3
skills/14-文档处理与数据工具/markitdown/scripts/convert_with_ai.py
- Executable scriptline 1
#!/usr/bin/env python3 - Access to secretsline 73
[Credential-related source omitted] - Access to secretsline 84
[Credential-related source omitted] - Network accessline 94
[Credential-related source omitted] - Network accessline 95
[Credential-related source omitted] - Access to secretsline 95
[Credential-related source omitted] - Network accessline 96
base_url="https://openrouter.ai/api/v1" - Access to secretsline 163
[Credential-related source omitted] - Access to secretsline 167
[Credential-related source omitted] - Access to secretsline 179
[Credential-related source omitted] - Access to secretsline 214
[Credential-related source omitted] - Network accessline 215
[Credential-related source omitted] - Access to secretsline 215
[Credential-related source omitted] - Network accessline 216
[Credential-related source omitted] - Access to secretsline 216
[Credential-related source omitted] - Network accessline 217
print("Get your API key at: https://openrouter.ai/keys") - Access to secretsline 229
[Credential-related source omitted]
skills/14-文档处理与数据工具/open-notebook/SKILL.md
- Network accessline 24
**Repository:** https://github.com/lfnovo/open-notebook - Network accessline 38
# Download the docker-compose file curl -o docker-compose.yml https://raw.githubusercontent.com/lfnovo/open-notebook/main/docker-compose.yml - Network accessline 39
curl -o docker-compose.yml https://raw.githubusercontent.com/lfnovo/open-notebook/main/docker-compose.yml - Access to secretsline 42
[Credential-related source omitted] - Network accessline 48
Access the application: - **Frontend UI:** http://localhost:8502 - Network accessline 49
- **Frontend UI:** http://localhost:8502 - Network accessline 50
- **REST API:** http://localhost:5055 - Network accessline 51
- **API Documentation:** http://localhost:5055/docs - Network accessline 67
BASE_URL = "http://localhost:5055/api" - Network accessline 69
# Add a credential for an AI provider response = requests.post(f"{BASE_URL}/credentials", json={ - Network accessline 70
response = requests.post(f"{BASE_URL}/credentials", json={ - Access to secretsline 73
[Credential-related source omitted] - Network accessline 77
# Discover available models response = requests.post( - Network accessline 78
response = requests.post( - Network accessline 83
# Register discovered models requests.post( - Network accessline 84
requests.post( - Network accessline 98
BASE_URL = "http://localhost:5055/api" - Network accessline 100
# Create a notebook response = requests.post(f"{BASE_URL}/notebooks", json={ - Network accessline 101
response = requests.post(f"{BASE_URL}/notebooks", json={ - Network accessline 113
# Add a web URL source response = requests.post(f"{BASE_URL}/sources", data={ - Network accessline 114
response = requests.post(f"{BASE_URL}/sources", data={ - Network accessline 115
"url": "https://arxiv.org/abs/2301.00001", - Network accessline 121
# Upload a PDF file with open("paper.pdf", "rb") as f: response = requests.post( - Network accessline 122
with open("paper.pdf", "rb") as f: response = requests.post( f"{BASE_URL}/sources", - Network accessline 123
response = requests.post( - Network accessline 134
# Create a human note response = requests.post(f"{BASE_URL}/notes", json={ - Network accessline 135
response = requests.post(f"{BASE_URL}/notes", json={ - Network accessline 147
# Create a chat session session = requests.post(f"{BASE_URL}/chat/sessions", json={ - Network accessline 148
session = requests.post(f"{BASE_URL}/chat/sessions", json={ - Network accessline 153
# Send a message with context from sources response = requests.post(f"{BASE_URL}/chat/execute", json={ - Network accessline 154
response = requests.post(f"{BASE_URL}/chat/execute", json={ - Network accessline 165
# Vector search across the knowledge base results = requests.post(f"{BASE_URL}/search", json={ - Network accessline 166
results = requests.post(f"{BASE_URL}/search", json={ - Network accessline 172
# Ask a question with AI-powered answer answer = requests.post(f"{BASE_URL}/search/ask/simple", json={ - Network accessline 173
answer = requests.post(f"{BASE_URL}/search/ask/simple", json={ - Network accessline 182
# Generate a podcast episode job = requests.post(f"{BASE_URL}/podcasts/generate", json={ - Network accessline 183
job = requests.post(f"{BASE_URL}/podcasts/generate", json={ - Network accessline 189
# Check generation status status = requests.get(f"{BASE_URL}/podcasts/jobs/{job['job_id']}").json() - Network accessline 190
status = requests.get(f"{BASE_URL}/podcasts/jobs/{job['job_id']}").json() - Network accessline 192
# Download audio when ready audio = requests.get( - Network accessline 193
audio = requests.get( - Network accessline 202
# Create a custom transformation transform = requests.post(f"{BASE_URL}/transformations", json={ - Network accessline 203
transform = requests.post(f"{BASE_URL}/transformations", json={ - Network accessline 211
# Execute transformation on text result = requests.post(f"{BASE_URL}/transformations/execute", json={ - Network accessline 212
result = requests.post(f"{BASE_URL}/transformations/execute", json={ - Network accessline 254
The REST API is available at `http://localhost:5055/api` with interactive documentation at `/docs`.
skills/14-文档处理与数据工具/open-notebook/references/api_reference.md
skills/14-文档处理与数据工具/open-notebook/references/configuration.md
- Access to secretsline 26
[Credential-related source omitted] - Access to secretsline 45
[Credential-related source omitted] - Access to secretsline 91
[Credential-related source omitted] - Access to secretsline 92
[Credential-related source omitted] - Access to secretsline 93
[Credential-related source omitted] - Access to secretsline 94
[Credential-related source omitted] - Access to secretsline 95
[Credential-related source omitted] - Access to secretsline 96
[Credential-related source omitted] - Network accessline 114
BASE_URL = "http://localhost:5055/api" - Network accessline 116
# 1. Create credential cred = requests.post(f"{BASE_URL}/credentials", json={ - Network accessline 117
cred = requests.post(f"{BASE_URL}/credentials", json={ - Access to secretsline 120
[Credential-related source omitted] - Network accessline 123
# 2. Test connection test = requests.post(f"{BASE_URL}/credentials/{cred['id']}/test").json() - Network accessline 124
test = requests.post(f"{BASE_URL}/credentials/{cred['id']}/test").json() - Network accessline 127
# 3. Discover and register models discovered = requests.post( - Network accessline 128
discovered = requests.post( - Network accessline 132
requests.post( - Network accessline 137
# 4. Auto-assign defaults requests.post(f"{BASE_URL}/models/auto-assign") - Network accessline 138
requests.post(f"{BASE_URL}/models/auto-assign") - Network accessline 156
Then configure Ollama as a provider with base URL `http://ollama:11434`. - Network accessline 178
location / { proxy_pass http://localhost:8502; - Network accessline 179
proxy_pass http://localhost:8502; - Network accessline 186
location /api/ { proxy_pass http://localhost:5055/api/; - Network accessline 187
proxy_pass http://localhost:5055/api/;
skills/14-文档处理与数据工具/open-notebook/references/examples.md
- Network accessline 11
BASE_URL = "http://localhost:5055/api" - Network accessline 17
# 1. Create a research notebook notebook = requests.post(f"{BASE_URL}/notebooks", json={ - Network accessline 18
notebook = requests.post(f"{BASE_URL}/notebooks", json={ - Network accessline 25
# 2. Add sources from URLs urls = [ "https://www.nature.com/articles/s41568-020-0281-y", - Network accessline 26
urls = [ "https://www.nature.com/articles/s41568-020-0281-y", "https://www.cell.com/cancer-cell/fulltext/S1535-6108(20)30211-8", - Network accessline 27
"https://www.nature.com/articles/s41568-020-0281-y", - Network accessline 28
"https://www.cell.com/cancer-cell/fulltext/S1535-6108(20)30211-8", - Network accessline 31
source_ids = [] for url in urls: source = requests.post(f"{BASE_URL}/sources", data={ - Network accessline 32
for url in urls: source = requests.post(f"{BASE_URL}/sources", data={ "url": url, - Network accessline 33
source = requests.post(f"{BASE_URL}/sources", data={ - Network accessline 42
for source_id in source_ids: while True: status = requests.get( - Network accessline 43
while True: status = requests.get( f"{BASE_URL}/sources/{source_id}/status" - Network accessline 44
status = requests.get( - Network accessline 52
# 4. Create a chat session and ask questions session = requests.post(f"{BASE_URL}/chat/sessions", json={ - Network accessline 53
session = requests.post(f"{BASE_URL}/chat/sessions", json={ - Network accessline 58
answer = requests.post(f"{BASE_URL}/chat/execute", json={ - Network accessline 65
# 5. Search across materials results = requests.post(f"{BASE_URL}/search", json={ - Network accessline 66
results = requests.post(f"{BASE_URL}/search", json={ - Network accessline 73
# 6. Create a human note summarizing findings note = requests.post(f"{BASE_URL}/notes", json={ - Network accessline 74
note = requests.post(f"{BASE_URL}/notes", json={ - Network accessline 92
BASE_URL = "http://localhost:5055/api" - Network accessline 97
for path in file_paths: with open(path, "rb") as f: response = requests.post( - Network accessline 98
with open(path, "rb") as f: response = requests.post( f"{BASE_URL}/sources", - Network accessline 99
response = requests.post( - Network accessline 127
BASE_URL = "http://localhost:5055/api" - Network accessline 136
# Submit podcast generation job job = requests.post(f"{BASE_URL}/podcasts/generate", json={ - Network accessline 137
job = requests.post(f"{BASE_URL}/podcasts/generate", json={ - Network accessline 148
# Poll for completion while True: status = requests.get(f"{BASE_URL}/podcasts/jobs/{job_id}").json() - Network accessline 149
while True: status = requests.get(f"{BASE_URL}/podcasts/jobs/{job_id}").json() print(f"Status: {status.get('status', 'processing')}") - Network accessline 150
status = requests.get(f"{BASE_URL}/podcasts/jobs/{job_id}").json() - Network accessline 157
# Download the audio episode_id = status["episode_id"] audio = requests.get( - Network accessline 158
episode_id = status["episode_id"] audio = requests.get( f"{BASE_URL}/podcasts/episodes/{episode_id}/audio" - Network accessline 159
audio = requests.get( - Network accessline 176
BASE_URL = "http://localhost:5055/api" - Network accessline 182
# Create a methodology extraction transformation transform = requests.post(f"{BASE_URL}/transformations", json={ - Network accessline 183
transform = requests.post(f"{BASE_URL}/transformations", json={ - Network accessline 195
# Get models to find a suitable one models = requests.get(f"{BASE_URL}/models", params={ - Network accessline 196
models = requests.get(f"{BASE_URL}/models", params={ - Network accessline 201
# Execute the transformation result = requests.post(f"{BASE_URL}/transformations/execute", json={ - Network accessline 202
result = requests.post(f"{BASE_URL}/transformations/execute", json={ - Network accessline 219
BASE_URL = "http://localhost:5055/api" - Network accessline 225
# Get sources from a specific notebook sources = requests.get(f"{BASE_URL}/sources", params={ - Network accessline 226
sources = requests.get(f"{BASE_URL}/sources", params={ - Network accessline 231
# Vector search restricted to notebook sources results = requests.post(f"{BASE_URL}/search", json={ - Network accessline 232
results = requests.post(f"{BASE_URL}/search", json={ - Network accessline 245
# Get an AI-powered answer answer = requests.post(f"{BASE_URL}/search/ask/simple", json={ - Network accessline 246
answer = requests.post(f"{BASE_URL}/search/ask/simple", json={ - Network accessline 261
BASE_URL = "http://localhost:5055/api" - Network accessline 267
# Check available providers providers = requests.get(f"{BASE_URL}/models/providers").json() - Network accessline 268
providers = requests.get(f"{BASE_URL}/models/providers").json() - Network accessline 271
# Discover models from a provider discovered = requests.get( - Network accessline 272
discovered = requests.get( - Network accessline 277
# Sync models to make them available requests.post(f"{BASE_URL}/models/sync/openai") - Network accessline 278
requests.post(f"{BASE_URL}/models/sync/openai") - Network accessline 280
# Auto-assign default models requests.post(f"{BASE_URL}/models/auto-assign") - Network accessline 281
requests.post(f"{BASE_URL}/models/auto-assign") - Network accessline 283
# Check current defaults defaults = requests.get(f"{BASE_URL}/models/defaults").json() - Network accessline 284
defaults = requests.get(f"{BASE_URL}/models/defaults").json()
skills/14-文档处理与数据工具/open-notebook/scripts/chat_interaction.py
- Executable scriptline 1
Executable file - Network accessline 10
Usage: export OPEN_NOTEBOOK_URL="http://localhost:5055" - Network accessline 11
export OPEN_NOTEBOOK_URL="http://localhost:5055" - Network accessline 18
BASE_URL = os.getenv("OPEN_NOTEBOOK_URL", "http://localhost:5055") + "/api" - Network accessline 27
if model_override: payload["model_override"] = model_override response = requests.post(f"{BASE_URL}/chat/sessions", json=payload) - Network accessline 28
payload["model_override"] = model_override response = requests.post(f"{BASE_URL}/chat/sessions", json=payload) response.raise_for_status() - Network accessline 29
response = requests.post(f"{BASE_URL}/chat/sessions", json=payload) - Network accessline 38
response = requests.get( - Network accessline 62
if model_override: payload["model_override"] = model_override response = requests.post(f"{BASE_URL}/chat/execute", json=payload) - Network accessline 63
payload["model_override"] = model_override response = requests.post(f"{BASE_URL}/chat/execute", json=payload) response.raise_for_status() - Network accessline 64
response = requests.post(f"{BASE_URL}/chat/execute", json=payload) - Network accessline 74
response = requests.get(f"{BASE_URL}/chat/sessions/{session_id}") - Network accessline 91
if note_ids: payload["note_ids"] = note_ids response = requests.post(f"{BASE_URL}/chat/context", json=payload) - Network accessline 92
payload["note_ids"] = note_ids response = requests.post(f"{BASE_URL}/chat/context", json=payload) response.raise_for_status() - Network accessline 93
response = requests.post(f"{BASE_URL}/chat/context", json=payload) - Network accessline 103
response = requests.post(f"{BASE_URL}/search", json={ - Network accessline 120
response = requests.post(f"{BASE_URL}/search/ask/simple", json={ - Network accessline 132
response = requests.delete(f"{BASE_URL}/chat/sessions/{session_id}") - Network accessline 141
notebook = requests.post(f"{BASE_URL}/notebooks", json={ - Network accessline 148
requests.post(f"{BASE_URL}/sources", data={ - Network accessline 187
print() delete_chat_session(session["id"]) requests.delete(f"{BASE_URL}/notebooks/{notebook_id}") - Network accessline 188
delete_chat_session(session["id"]) requests.delete(f"{BASE_URL}/notebooks/{notebook_id}") print("Cleanup complete") - Network accessline 189
requests.delete(f"{BASE_URL}/notebooks/{notebook_id}")
skills/14-文档处理与数据工具/open-notebook/scripts/notebook_management.py
- Executable scriptline 1
Executable file - Network accessline 10
Usage: export OPEN_NOTEBOOK_URL="http://localhost:5055" - Network accessline 11
export OPEN_NOTEBOOK_URL="http://localhost:5055" - Network accessline 18
BASE_URL = os.getenv("OPEN_NOTEBOOK_URL", "http://localhost:5055") + "/api" - Network accessline 23
response = requests.post(f"{BASE_URL}/notebooks", json={ - Network accessline 35
response = requests.get(f"{BASE_URL}/notebooks", params={ - Network accessline 50
response = requests.get(f"{BASE_URL}/notebooks/{notebook_id}") - Network accessline 62
if archived is not None: payload["archived"] = archived response = requests.put( - Network accessline 63
payload["archived"] = archived response = requests.put( f"{BASE_URL}/notebooks/{notebook_id}", json=payload - Network accessline 64
response = requests.put( - Network accessline 76
preview = requests.get( - Network accessline 82
response = requests.delete( - Network accessline 92
response = requests.post( - Network accessline 101
response = requests.delete(
skills/14-文档处理与数据工具/open-notebook/scripts/source_ingestion.py
- Executable scriptline 1
Executable file - Network accessline 10
Usage: export OPEN_NOTEBOOK_URL="http://localhost:5055" - Network accessline 11
export OPEN_NOTEBOOK_URL="http://localhost:5055" - Network accessline 19
BASE_URL = os.getenv("OPEN_NOTEBOOK_URL", "http://localhost:5055") + "/api" - Network accessline 24
response = requests.post(f"{BASE_URL}/sources", data={ - Network accessline 37
response = requests.post(f"{BASE_URL}/sources", data={ - Network accessline 50
filename = os.path.basename(file_path) with open(file_path, "rb") as f: response = requests.post( - Network accessline 51
with open(file_path, "rb") as f: response = requests.post( f"{BASE_URL}/sources", - Network accessline 52
response = requests.post( - Network accessline 68
elapsed = 0 while elapsed < timeout: response = requests.get(f"{BASE_URL}/sources/{source_id}/status") - Network accessline 69
while elapsed < timeout: response = requests.get(f"{BASE_URL}/sources/{source_id}/status") response.raise_for_status() - Network accessline 70
response = requests.get(f"{BASE_URL}/sources/{source_id}/status") - Network accessline 88
if notebook_id: params["notebook_id"] = notebook_id response = requests.get(f"{BASE_URL}/sources", params=params) - Network accessline 89
params["notebook_id"] = notebook_id response = requests.get(f"{BASE_URL}/sources", params=params) response.raise_for_status() - Network accessline 90
response = requests.get(f"{BASE_URL}/sources", params=params) - Network accessline 101
response = requests.get(f"{BASE_URL}/sources/{source_id}/insights") - Network accessline 108
response = requests.post(f"{BASE_URL}/sources/{source_id}/retry") - Network accessline 116
response = requests.delete(f"{BASE_URL}/sources/{source_id}") - Network accessline 125
notebook = requests.post(f"{BASE_URL}/notebooks", json={ - Network accessline 133
url_source = add_url_source( notebook_id, "https://en.wikipedia.org/wiki/CRISPR_gene_editing", - Network accessline 134
notebook_id, "https://en.wikipedia.org/wiki/CRISPR_gene_editing", ) - Network accessline 135
"https://en.wikipedia.org/wiki/CRISPR_gene_editing", - Network accessline 157
delete_source(url_source["id"]) delete_source(text_source["id"]) requests.delete(f"{BASE_URL}/notebooks/{notebook_id}") - Network accessline 158
delete_source(text_source["id"]) requests.delete(f"{BASE_URL}/notebooks/{notebook_id}") print("Cleanup complete") - Network accessline 159
requests.delete(f"{BASE_URL}/notebooks/{notebook_id}")
skills/14-文档处理与数据工具/open-notebook/scripts/test_open_notebook_skill.py
- Executable scriptline 1
Executable file
skills/14-文档处理与数据工具/pdf/LICENSE.txt
- Network accessline 6
exists, use is governed by Anthropic's Consumer Terms of Service or Commercial Terms of Service, as applicable: https://www.anthropic.com/legal/consumer-terms - Network accessline 7
Commercial Terms of Service, as applicable: https://www.anthropic.com/legal/consumer-terms https://www.anthropic.com/legal/commercial-terms - Network accessline 8
https://www.anthropic.com/legal/consumer-terms - Network accessline 9
https://www.anthropic.com/legal/commercial-terms
skills/14-文档处理与数据工具/pdf/scripts/check_bounding_boxes.py
- Executable scriptline 1
Executable file
skills/14-文档处理与数据工具/pdf/scripts/check_fillable_fields.py
- Executable scriptline 1
Executable file
skills/14-文档处理与数据工具/pdf/scripts/convert_pdf_to_images.py
- Executable scriptline 1
Executable file
skills/14-文档处理与数据工具/pdf/scripts/create_validation_image.py
- Executable scriptline 1
Executable file
skills/14-文档处理与数据工具/pdf/scripts/extract_form_field_info.py
- Executable scriptline 1
Executable file
skills/14-文档处理与数据工具/pdf/scripts/extract_form_structure.py
- Executable scriptline 1
Executable file
skills/14-文档处理与数据工具/pdf/scripts/fill_fillable_fields.py
- Executable scriptline 1
Executable file
skills/14-文档处理与数据工具/pdf/scripts/fill_pdf_form_with_annotations.py
- Executable scriptline 1
Executable file
skills/14-文档处理与数据工具/pptx/LICENSE.txt
- Network accessline 6
exists, use is governed by Anthropic's Consumer Terms of Service or Commercial Terms of Service, as applicable: https://www.anthropic.com/legal/consumer-terms - Network accessline 7
Commercial Terms of Service, as applicable: https://www.anthropic.com/legal/consumer-terms https://www.anthropic.com/legal/commercial-terms - Network accessline 8
https://www.anthropic.com/legal/consumer-terms - Network accessline 9
https://www.anthropic.com/legal/commercial-terms
skills/14-文档处理与数据工具/pptx/pptxgenjs.md
- Network accessline 145
// From URL slide.addImage({ path: "https://example.com/image.jpg", x: 1, y: 1, w: 5, h: 3 }); - Network accessline 146
slide.addImage({ path: "https://example.com/image.jpg", x: 1, y: 1, w: 5, h: 3 }); - Network accessline 162
flipV: false, // Vertical flip altText: "Description", // Accessibility hyperlink: { url: "https://example.com" } - Network accessline 163
altText: "Description", // Accessibility hyperlink: { url: "https://example.com" } }); - Network accessline 164
hyperlink: { url: "https://example.com" } - Network accessline 257
// Image from URL slide.background = { path: "https://example.com/bg.jpg" }; - Network accessline 258
slide.background = { path: "https://example.com/bg.jpg" };
skills/14-文档处理与数据工具/pptx/scripts/__init__.py
- Executable scriptline 1
Executable file
skills/14-文档处理与数据工具/pptx/scripts/add_slide.py
- Executable scriptline 1
Executable file - Network accessline 48
slide_xml = '''<?xml version="1.0" encoding="UTF-8" standalone="yes"?> <p:sld xmlns:a="http://schemas.openxmlformats.org/drawingml/2006/main" xmlns:r="http://schemas.openxmlformats.org/officeDocument/2006/relationships" xmlns:p="http://sche - Network accessline 49
<p:sld xmlns:a="http://schemas.openxmlformats.org/drawingml/2006/main" xmlns:r="http://schemas.openxmlformats.org/officeDocument/2006/relationships" xmlns:p="http://schemas.openxmlformats.org/presentationml/2006/main"> - Network accessline 73
rels_dir.mkdir(exist_ok=True) rels_xml = f'''<?xml version="1.0" encoding="UTF-8" standalone="yes"?> <Relationships xmlns="http://schemas.openxmlformats.org/package/2006/relationships"> - Network accessline 74
rels_xml = f'''<?xml version="1.0" encoding="UTF-8" standalone="yes"?> <Relationships xmlns="http://schemas.openxmlformats.org/package/2006/relationships"> <Relationship Id="rId1" Type="http://schemas.openxmlformats.org/officeDocument/200 - Network accessline 75
<Relationships xmlns="http://schemas.openxmlformats.org/package/2006/relationships"> - Network accessline 76
<Relationship Id="rId1" Type="http://schemas.openxmlformats.org/officeDocument/2006/relationships/slideLayout" Target="../slideLayouts/{layout_file}"/> - Network accessline 149
new_rel = f'<Relationship Id="{rid}" Type="http://schemas.openxmlformats.org/officeDocument/2006/relationships/slide" Target="slides/{dest}"/>'
skills/14-文档处理与数据工具/pptx/scripts/clean.py
- Executable scriptline 1
Executable file
skills/14-文档处理与数据工具/pptx/scripts/office/helpers/__init__.py
- Executable scriptline 1
Executable file
skills/14-文档处理与数据工具/pptx/scripts/office/helpers/merge_runs.py
- Executable scriptline 1
Executable file
skills/14-文档处理与数据工具/pptx/scripts/office/helpers/simplify_redlines.py
skills/14-文档处理与数据工具/pptx/scripts/office/pack.py
- Executable scriptline 1
Executable file
skills/14-文档处理与数据工具/pptx/scripts/office/schemas/ISO-IEC29500-4_2016/dml-chart.xsd
- Network accessline 1
<?xml version="1.0" encoding="utf-8"?> <xsd:schema xmlns:xsd="http://www.w3.org/2001/XMLSchema" - Network accessline 2
<xsd:schema xmlns:xsd="http://www.w3.org/2001/XMLSchema" - Network accessline 3
xmlns:a="http://schemas.openxmlformats.org/drawingml/2006/main" - Network accessline 4
xmlns:r="http://schemas.openxmlformats.org/officeDocument/2006/relationships" - Network accessline 5
xmlns="http://schemas.openxmlformats.org/drawingml/2006/chart" - Network accessline 6
xmlns:cdr="http://schemas.openxmlformats.org/drawingml/2006/chartDrawing" - Network accessline 7
xmlns:s="http://schemas.openxmlformats.org/officeDocument/2006/sharedTypes" - Network accessline 8
targetNamespace="http://schemas.openxmlformats.org/drawingml/2006/chart" - Network accessline 9
elementFormDefault="qualified" attributeFormDefault="unqualified" blockDefault="#all"> <xsd:import namespace="http://schemas.openxmlformats.org/officeDocument/2006/relationships" schemaLocation="shared-relationshipReference.xsd"/> - Network accessline 10
<xsd:import namespace="http://schemas.openxmlformats.org/officeDocument/2006/relationships" - Network accessline 11
schemaLocation="shared-relationshipReference.xsd"/> <xsd:import namespace="http://schemas.openxmlformats.org/drawingml/2006/main" schemaLocation="dml-main.xsd"/> - Network accessline 12
<xsd:import namespace="http://schemas.openxmlformats.org/drawingml/2006/main" - Network accessline 13
schemaLocation="dml-main.xsd"/> <xsd:import namespace="http://schemas.openxmlformats.org/drawingml/2006/chartDrawing" schemaLocation="dml-chartDrawing.xsd"/> - Network accessline 14
<xsd:import namespace="http://schemas.openxmlformats.org/drawingml/2006/chartDrawing" - Network accessline 15
schemaLocation="dml-chartDrawing.xsd"/> <xsd:import namespace="http://schemas.openxmlformats.org/officeDocument/2006/sharedTypes" schemaLocation="shared-commonSimpleTypes.xsd"/> - Network accessline 16
<xsd:import namespace="http://schemas.openxmlformats.org/officeDocument/2006/sharedTypes"
skills/14-文档处理与数据工具/pptx/scripts/office/schemas/ISO-IEC29500-4_2016/dml-chartDrawing.xsd
- Network accessline 1
<?xml version="1.0" encoding="utf-8"?> <xsd:schema xmlns:xsd="http://www.w3.org/2001/XMLSchema" - Network accessline 2
<xsd:schema xmlns:xsd="http://www.w3.org/2001/XMLSchema" - Network accessline 3
xmlns:a="http://schemas.openxmlformats.org/drawingml/2006/main" - Network accessline 4
xmlns="http://schemas.openxmlformats.org/drawingml/2006/chartDrawing" - Network accessline 5
targetNamespace="http://schemas.openxmlformats.org/drawingml/2006/chartDrawing" - Network accessline 6
elementFormDefault="qualified"> <xsd:import namespace="http://schemas.openxmlformats.org/drawingml/2006/main" schemaLocation="dml-main.xsd"/> - Network accessline 7
<xsd:import namespace="http://schemas.openxmlformats.org/drawingml/2006/main"
skills/14-文档处理与数据工具/pptx/scripts/office/schemas/ISO-IEC29500-4_2016/dml-diagram.xsd
- Network accessline 1
<?xml version="1.0" encoding="utf-8"?> <xsd:schema xmlns:xsd="http://www.w3.org/2001/XMLSchema" - Network accessline 2
<xsd:schema xmlns:xsd="http://www.w3.org/2001/XMLSchema" - Network accessline 3
xmlns="http://schemas.openxmlformats.org/drawingml/2006/diagram" - Network accessline 4
xmlns:a="http://schemas.openxmlformats.org/drawingml/2006/main" - Network accessline 5
xmlns:r="http://schemas.openxmlformats.org/officeDocument/2006/relationships" - Network accessline 6
xmlns:s="http://schemas.openxmlformats.org/officeDocument/2006/sharedTypes" - Network accessline 7
targetNamespace="http://schemas.openxmlformats.org/drawingml/2006/diagram" - Network accessline 8
elementFormDefault="qualified" attributeFormDefault="unqualified"> <xsd:import namespace="http://schemas.openxmlformats.org/officeDocument/2006/relationships" schemaLocation="shared-relationshipReference.xsd"/> - Network accessline 9
<xsd:import namespace="http://schemas.openxmlformats.org/officeDocument/2006/relationships" - Network accessline 10
schemaLocation="shared-relationshipReference.xsd"/> <xsd:import namespace="http://schemas.openxmlformats.org/drawingml/2006/main" schemaLocation="dml-main.xsd"/> - Network accessline 11
<xsd:import namespace="http://schemas.openxmlformats.org/drawingml/2006/main" - Network accessline 12
schemaLocation="dml-main.xsd"/> <xsd:import namespace="http://schemas.openxmlformats.org/officeDocument/2006/sharedTypes" schemaLocation="shared-commonSimpleTypes.xsd"/> - Network accessline 13
<xsd:import namespace="http://schemas.openxmlformats.org/officeDocument/2006/sharedTypes"
skills/14-文档处理与数据工具/pptx/scripts/office/schemas/ISO-IEC29500-4_2016/dml-lockedCanvas.xsd
- Network accessline 1
<?xml version="1.0" encoding="utf-8"?> <xsd:schema xmlns:xsd="http://www.w3.org/2001/XMLSchema" - Network accessline 2
<xsd:schema xmlns:xsd="http://www.w3.org/2001/XMLSchema" - Network accessline 3
xmlns="http://schemas.openxmlformats.org/drawingml/2006/lockedCanvas" - Network accessline 4
xmlns:a="http://schemas.openxmlformats.org/drawingml/2006/main" - Network accessline 5
xmlns:r="http://schemas.openxmlformats.org/officeDocument/2006/relationships" - Network accessline 6
elementFormDefault="qualified" targetNamespace="http://schemas.openxmlformats.org/drawingml/2006/lockedCanvas"> <xsd:import namespace="http://schemas.openxmlformats.org/drawingml/2006/main" - Network accessline 7
targetNamespace="http://schemas.openxmlformats.org/drawingml/2006/lockedCanvas"> - Network accessline 8
<xsd:import namespace="http://schemas.openxmlformats.org/drawingml/2006/main"
skills/14-文档处理与数据工具/pptx/scripts/office/schemas/ISO-IEC29500-4_2016/dml-main.xsd
- Network accessline 1
<?xml version="1.0" encoding="utf-8"?> <xsd:schema xmlns:xsd="http://www.w3.org/2001/XMLSchema" - Network accessline 2
<xsd:schema xmlns:xsd="http://www.w3.org/2001/XMLSchema" - Network accessline 3
xmlns:r="http://schemas.openxmlformats.org/officeDocument/2006/relationships" - Network accessline 4
xmlns:s="http://schemas.openxmlformats.org/officeDocument/2006/sharedTypes" - Network accessline 5
xmlns="http://schemas.openxmlformats.org/drawingml/2006/main" - Network accessline 6
targetNamespace="http://schemas.openxmlformats.org/drawingml/2006/main" - Network accessline 7
elementFormDefault="qualified"> <xsd:import namespace="http://schemas.openxmlformats.org/officeDocument/2006/relationships" schemaLocation="shared-relationshipReference.xsd"/> - Network accessline 8
<xsd:import namespace="http://schemas.openxmlformats.org/officeDocument/2006/relationships" - Network accessline 9
schemaLocation="shared-relationshipReference.xsd"/> <xsd:import namespace="http://schemas.openxmlformats.org/officeDocument/2006/sharedTypes" schemaLocation="shared-commonSimpleTypes.xsd"/> - Network accessline 10
<xsd:import namespace="http://schemas.openxmlformats.org/officeDocument/2006/sharedTypes" - Network accessline 11
schemaLocation="shared-commonSimpleTypes.xsd"/> <xsd:import namespace="http://schemas.openxmlformats.org/drawingml/2006/diagram" schemaLocation="dml-diagram.xsd"/> - Network accessline 12
<xsd:import namespace="http://schemas.openxmlformats.org/drawingml/2006/diagram" - Network accessline 13
schemaLocation="dml-diagram.xsd"/> <xsd:import namespace="http://schemas.openxmlformats.org/drawingml/2006/chart" schemaLocation="dml-chart.xsd"/> - Network accessline 14
<xsd:import namespace="http://schemas.openxmlformats.org/drawingml/2006/chart" - Network accessline 15
schemaLocation="dml-chart.xsd"/> <xsd:import namespace="http://schemas.openxmlformats.org/drawingml/2006/picture" schemaLocation="dml-picture.xsd"/> - Network accessline 16
<xsd:import namespace="http://schemas.openxmlformats.org/drawingml/2006/picture" - Network accessline 17
schemaLocation="dml-picture.xsd"/> <xsd:import namespace="http://schemas.openxmlformats.org/drawingml/2006/lockedCanvas" schemaLocation="dml-lockedCanvas.xsd"/> - Network accessline 18
<xsd:import namespace="http://schemas.openxmlformats.org/drawingml/2006/lockedCanvas"
skills/14-文档处理与数据工具/pptx/scripts/office/schemas/ISO-IEC29500-4_2016/dml-picture.xsd
- Network accessline 1
<?xml version="1.0" encoding="utf-8"?> <xsd:schema xmlns:xsd="http://www.w3.org/2001/XMLSchema" - Network accessline 2
<xsd:schema xmlns:xsd="http://www.w3.org/2001/XMLSchema" - Network accessline 3
xmlns="http://schemas.openxmlformats.org/drawingml/2006/picture" - Network accessline 4
xmlns:a="http://schemas.openxmlformats.org/drawingml/2006/main" elementFormDefault="qualified" - Network accessline 5
targetNamespace="http://schemas.openxmlformats.org/drawingml/2006/picture"> - Network accessline 6
<xsd:import namespace="http://schemas.openxmlformats.org/drawingml/2006/main"
skills/14-文档处理与数据工具/pptx/scripts/office/schemas/ISO-IEC29500-4_2016/dml-spreadsheetDrawing.xsd
- Network accessline 1
<?xml version="1.0" encoding="utf-8"?> <xsd:schema xmlns:xsd="http://www.w3.org/2001/XMLSchema" - Network accessline 2
<xsd:schema xmlns:xsd="http://www.w3.org/2001/XMLSchema" - Network accessline 3
xmlns:a="http://schemas.openxmlformats.org/drawingml/2006/main" - Network accessline 4
xmlns="http://schemas.openxmlformats.org/drawingml/2006/spreadsheetDrawing" - Network accessline 5
xmlns:r="http://schemas.openxmlformats.org/officeDocument/2006/relationships" - Network accessline 6
targetNamespace="http://schemas.openxmlformats.org/drawingml/2006/spreadsheetDrawing" - Network accessline 7
elementFormDefault="qualified"> <xsd:import namespace="http://schemas.openxmlformats.org/drawingml/2006/main" schemaLocation="dml-main.xsd"/> - Network accessline 8
<xsd:import namespace="http://schemas.openxmlformats.org/drawingml/2006/main" - Network accessline 9
schemaLocation="dml-main.xsd"/> <xsd:import schemaLocation="shared-relationshipReference.xsd" namespace="http://schemas.openxmlformats.org/officeDocument/2006/relationships"/> - Network accessline 10
<xsd:import schemaLocation="shared-relationshipReference.xsd" namespace="http://schemas.openxmlformats.org/officeDocument/2006/relationships"/> <xsd:element name="from" type="CT_Marker"/> - Network accessline 11
namespace="http://schemas.openxmlformats.org/officeDocument/2006/relationships"/>
skills/14-文档处理与数据工具/pptx/scripts/office/schemas/ISO-IEC29500-4_2016/dml-wordprocessingDrawing.xsd
- Network accessline 1
<?xml version="1.0" encoding="utf-8"?> <xsd:schema xmlns:xsd="http://www.w3.org/2001/XMLSchema" - Network accessline 2
<xsd:schema xmlns:xsd="http://www.w3.org/2001/XMLSchema" - Network accessline 3
xmlns:a="http://schemas.openxmlformats.org/drawingml/2006/main" - Network accessline 4
xmlns:w="http://schemas.openxmlformats.org/wordprocessingml/2006/main" - Network accessline 5
xmlns:dpct="http://schemas.openxmlformats.org/drawingml/2006/picture" - Network accessline 6
xmlns:r="http://schemas.openxmlformats.org/officeDocument/2006/relationships" - Network accessline 7
xmlns="http://schemas.openxmlformats.org/drawingml/2006/wordprocessingDrawing" - Network accessline 8
targetNamespace="http://schemas.openxmlformats.org/drawingml/2006/wordprocessingDrawing" - Network accessline 9
elementFormDefault="qualified"> <xsd:import namespace="http://schemas.openxmlformats.org/drawingml/2006/main" schemaLocation="dml-main.xsd"/> - Network accessline 10
<xsd:import namespace="http://schemas.openxmlformats.org/drawingml/2006/main" - Network accessline 11
schemaLocation="dml-main.xsd"/> <xsd:import schemaLocation="wml.xsd" namespace="http://schemas.openxmlformats.org/wordprocessingml/2006/main"/> - Network accessline 12
<xsd:import schemaLocation="wml.xsd" namespace="http://schemas.openxmlformats.org/wordprocessingml/2006/main"/> <xsd:import namespace="http://schemas.openxmlformats.org/drawingml/2006/picture" - Network accessline 13
namespace="http://schemas.openxmlformats.org/wordprocessingml/2006/main"/> - Network accessline 14
<xsd:import namespace="http://schemas.openxmlformats.org/drawingml/2006/picture" - Network accessline 15
schemaLocation="dml-picture.xsd"/> <xsd:import namespace="http://schemas.openxmlformats.org/officeDocument/2006/relationships" schemaLocation="shared-relationshipReference.xsd"/> - Network accessline 16
<xsd:import namespace="http://schemas.openxmlformats.org/officeDocument/2006/relationships"
skills/14-文档处理与数据工具/pptx/scripts/office/schemas/ISO-IEC29500-4_2016/pml.xsd
- Network accessline 1
<?xml version="1.0" encoding="utf-8"?> <xsd:schema xmlns:xsd="http://www.w3.org/2001/XMLSchema" - Network accessline 2
<xsd:schema xmlns:xsd="http://www.w3.org/2001/XMLSchema" - Network accessline 3
xmlns="http://schemas.openxmlformats.org/presentationml/2006/main" - Network accessline 4
xmlns:p="http://schemas.openxmlformats.org/presentationml/2006/main" - Network accessline 5
xmlns:a="http://schemas.openxmlformats.org/drawingml/2006/main" - Network accessline 6
xmlns:r="http://schemas.openxmlformats.org/officeDocument/2006/relationships" - Network accessline 7
xmlns:s="http://schemas.openxmlformats.org/officeDocument/2006/sharedTypes" - Network accessline 8
elementFormDefault="qualified" targetNamespace="http://schemas.openxmlformats.org/presentationml/2006/main"> <xsd:import namespace="http://schemas.openxmlformats.org/officeDocument/2006/relationships" - Network accessline 9
targetNamespace="http://schemas.openxmlformats.org/presentationml/2006/main"> - Network accessline 10
<xsd:import namespace="http://schemas.openxmlformats.org/officeDocument/2006/relationships" - Network accessline 11
schemaLocation="shared-relationshipReference.xsd"/> <xsd:import namespace="http://schemas.openxmlformats.org/drawingml/2006/main" schemaLocation="dml-main.xsd"/> - Network accessline 12
<xsd:import namespace="http://schemas.openxmlformats.org/drawingml/2006/main" - Network accessline 13
schemaLocation="dml-main.xsd"/> <xsd:import namespace="http://schemas.openxmlformats.org/officeDocument/2006/sharedTypes" schemaLocation="shared-commonSimpleTypes.xsd"/> - Network accessline 14
<xsd:import namespace="http://schemas.openxmlformats.org/officeDocument/2006/sharedTypes"
skills/14-文档处理与数据工具/pptx/scripts/office/schemas/ISO-IEC29500-4_2016/shared-additionalCharacteristics.xsd
- Network accessline 1
<?xml version="1.0" encoding="utf-8"?> <xsd:schema xmlns:xsd="http://www.w3.org/2001/XMLSchema" - Network accessline 2
<xsd:schema xmlns:xsd="http://www.w3.org/2001/XMLSchema" - Network accessline 3
xmlns="http://schemas.openxmlformats.org/officeDocument/2006/characteristics" - Network accessline 4
targetNamespace="http://schemas.openxmlformats.org/officeDocument/2006/characteristics"
skills/14-文档处理与数据工具/pptx/scripts/office/schemas/ISO-IEC29500-4_2016/shared-bibliography.xsd
- Network accessline 1
<?xml version="1.0" encoding="utf-8"?> <xsd:schema xmlns:xsd="http://www.w3.org/2001/XMLSchema" - Network accessline 2
<xsd:schema xmlns:xsd="http://www.w3.org/2001/XMLSchema" - Network accessline 3
xmlns="http://schemas.openxmlformats.org/officeDocument/2006/bibliography" - Network accessline 4
xmlns:s="http://schemas.openxmlformats.org/officeDocument/2006/sharedTypes" - Network accessline 5
targetNamespace="http://schemas.openxmlformats.org/officeDocument/2006/bibliography" - Network accessline 6
elementFormDefault="qualified"> <xsd:import namespace="http://schemas.openxmlformats.org/officeDocument/2006/sharedTypes" schemaLocation="shared-commonSimpleTypes.xsd"/> - Network accessline 7
<xsd:import namespace="http://schemas.openxmlformats.org/officeDocument/2006/sharedTypes"
skills/14-文档处理与数据工具/pptx/scripts/office/schemas/ISO-IEC29500-4_2016/shared-commonSimpleTypes.xsd
- Network accessline 1
<?xml version="1.0" encoding="utf-8"?> <xsd:schema xmlns:xsd="http://www.w3.org/2001/XMLSchema" - Network accessline 2
<xsd:schema xmlns:xsd="http://www.w3.org/2001/XMLSchema" - Network accessline 3
xmlns="http://schemas.openxmlformats.org/officeDocument/2006/sharedTypes" - Network accessline 4
targetNamespace="http://schemas.openxmlformats.org/officeDocument/2006/sharedTypes"
skills/14-文档处理与数据工具/pptx/scripts/office/schemas/ISO-IEC29500-4_2016/shared-customXmlDataProperties.xsd
- Network accessline 1
<?xml version="1.0" encoding="utf-8"?> <xsd:schema xmlns:xsd="http://www.w3.org/2001/XMLSchema" - Network accessline 2
<xsd:schema xmlns:xsd="http://www.w3.org/2001/XMLSchema" - Network accessline 3
xmlns="http://schemas.openxmlformats.org/officeDocument/2006/customXml" - Network accessline 4
xmlns:s="http://schemas.openxmlformats.org/officeDocument/2006/sharedTypes" - Network accessline 5
targetNamespace="http://schemas.openxmlformats.org/officeDocument/2006/customXml" - Network accessline 6
elementFormDefault="qualified" attributeFormDefault="qualified" blockDefault="#all"> <xsd:import namespace="http://schemas.openxmlformats.org/officeDocument/2006/sharedTypes" schemaLocation="shared-commonSimpleTypes.xsd"/> - Network accessline 7
<xsd:import namespace="http://schemas.openxmlformats.org/officeDocument/2006/sharedTypes"
skills/14-文档处理与数据工具/pptx/scripts/office/schemas/ISO-IEC29500-4_2016/shared-customXmlSchemaProperties.xsd
- Network accessline 1
<?xml version="1.0" encoding="utf-8"?> <xsd:schema xmlns:xsd="http://www.w3.org/2001/XMLSchema" - Network accessline 2
<xsd:schema xmlns:xsd="http://www.w3.org/2001/XMLSchema" - Network accessline 3
xmlns="http://schemas.openxmlformats.org/schemaLibrary/2006/main" - Network accessline 4
targetNamespace="http://schemas.openxmlformats.org/schemaLibrary/2006/main"
skills/14-文档处理与数据工具/pptx/scripts/office/schemas/ISO-IEC29500-4_2016/shared-documentPropertiesCustom.xsd
- Network accessline 1
<?xml version="1.0" encoding="utf-8"?> <xsd:schema xmlns:xsd="http://www.w3.org/2001/XMLSchema" - Network accessline 2
<xsd:schema xmlns:xsd="http://www.w3.org/2001/XMLSchema" - Network accessline 3
xmlns="http://schemas.openxmlformats.org/officeDocument/2006/custom-properties" - Network accessline 4
xmlns:vt="http://schemas.openxmlformats.org/officeDocument/2006/docPropsVTypes" - Network accessline 5
xmlns:s="http://schemas.openxmlformats.org/officeDocument/2006/sharedTypes" - Network accessline 6
targetNamespace="http://schemas.openxmlformats.org/officeDocument/2006/custom-properties" - Network accessline 7
blockDefault="#all" elementFormDefault="qualified"> <xsd:import namespace="http://schemas.openxmlformats.org/officeDocument/2006/docPropsVTypes" schemaLocation="shared-documentPropertiesVariantTypes.xsd"/> - Network accessline 8
<xsd:import namespace="http://schemas.openxmlformats.org/officeDocument/2006/docPropsVTypes" - Network accessline 9
schemaLocation="shared-documentPropertiesVariantTypes.xsd"/> <xsd:import namespace="http://schemas.openxmlformats.org/officeDocument/2006/sharedTypes" schemaLocation="shared-commonSimpleTypes.xsd"/> - Network accessline 10
<xsd:import namespace="http://schemas.openxmlformats.org/officeDocument/2006/sharedTypes"
skills/14-文档处理与数据工具/pptx/scripts/office/schemas/ISO-IEC29500-4_2016/shared-documentPropertiesExtended.xsd
- Network accessline 1
<?xml version="1.0" encoding="utf-8"?> <xsd:schema xmlns:xsd="http://www.w3.org/2001/XMLSchema" - Network accessline 2
<xsd:schema xmlns:xsd="http://www.w3.org/2001/XMLSchema" - Network accessline 3
xmlns="http://schemas.openxmlformats.org/officeDocument/2006/extended-properties" - Network accessline 4
xmlns:vt="http://schemas.openxmlformats.org/officeDocument/2006/docPropsVTypes" - Network accessline 5
targetNamespace="http://schemas.openxmlformats.org/officeDocument/2006/extended-properties" - Network accessline 6
elementFormDefault="qualified" blockDefault="#all"> <xsd:import namespace="http://schemas.openxmlformats.org/officeDocument/2006/docPropsVTypes" schemaLocation="shared-documentPropertiesVariantTypes.xsd"/> - Network accessline 7
<xsd:import namespace="http://schemas.openxmlformats.org/officeDocument/2006/docPropsVTypes"
skills/14-文档处理与数据工具/pptx/scripts/office/schemas/ISO-IEC29500-4_2016/shared-documentPropertiesVariantTypes.xsd
- Network accessline 1
<?xml version="1.0" encoding="utf-8"?> <xsd:schema xmlns:xsd="http://www.w3.org/2001/XMLSchema" - Network accessline 2
<xsd:schema xmlns:xsd="http://www.w3.org/2001/XMLSchema" - Network accessline 3
xmlns="http://schemas.openxmlformats.org/officeDocument/2006/docPropsVTypes" - Network accessline 4
xmlns:s="http://schemas.openxmlformats.org/officeDocument/2006/sharedTypes" - Network accessline 5
targetNamespace="http://schemas.openxmlformats.org/officeDocument/2006/docPropsVTypes" - Network accessline 6
blockDefault="#all" elementFormDefault="qualified"> <xsd:import namespace="http://schemas.openxmlformats.org/officeDocument/2006/sharedTypes" schemaLocation="shared-commonSimpleTypes.xsd"/> - Network accessline 7
<xsd:import namespace="http://schemas.openxmlformats.org/officeDocument/2006/sharedTypes"
skills/14-文档处理与数据工具/pptx/scripts/office/schemas/ISO-IEC29500-4_2016/shared-math.xsd
- Network accessline 1
<?xml version="1.0" encoding="utf-8"?> <xsd:schema xmlns:xsd="http://www.w3.org/2001/XMLSchema" - Network accessline 2
<xsd:schema xmlns:xsd="http://www.w3.org/2001/XMLSchema" - Network accessline 3
xmlns="http://schemas.openxmlformats.org/officeDocument/2006/math" - Network accessline 4
xmlns:m="http://schemas.openxmlformats.org/officeDocument/2006/math" - Network accessline 5
xmlns:w="http://schemas.openxmlformats.org/wordprocessingml/2006/main" - Network accessline 6
xmlns:s="http://schemas.openxmlformats.org/officeDocument/2006/sharedTypes" - Network accessline 7
elementFormDefault="qualified" attributeFormDefault="qualified" blockDefault="#all" targetNamespace="http://schemas.openxmlformats.org/officeDocument/2006/math"> <xsd:import namespace="http://schemas.openxmlformats.org/wordprocessingml/ - Network accessline 8
targetNamespace="http://schemas.openxmlformats.org/officeDocument/2006/math"> - Network accessline 9
<xsd:import namespace="http://schemas.openxmlformats.org/wordprocessingml/2006/main" - Network accessline 10
schemaLocation="wml.xsd"/> <xsd:import namespace="http://schemas.openxmlformats.org/officeDocument/2006/sharedTypes" schemaLocation="shared-commonSimpleTypes.xsd"/> - Network accessline 11
<xsd:import namespace="http://schemas.openxmlformats.org/officeDocument/2006/sharedTypes" - Network accessline 12
schemaLocation="shared-commonSimpleTypes.xsd"/> <xsd:import namespace="http://www.w3.org/XML/1998/namespace" schemaLocation="xml.xsd"/> <xsd:simpleType name="ST_Integer255"> - Network accessline 13
<xsd:import namespace="http://www.w3.org/XML/1998/namespace" schemaLocation="xml.xsd"/>
skills/14-文档处理与数据工具/pptx/scripts/office/schemas/ISO-IEC29500-4_2016/shared-relationshipReference.xsd
- Network accessline 1
<?xml version="1.0" encoding="utf-8"?> <xsd:schema xmlns:xsd="http://www.w3.org/2001/XMLSchema" - Network accessline 2
<xsd:schema xmlns:xsd="http://www.w3.org/2001/XMLSchema" - Network accessline 3
xmlns="http://schemas.openxmlformats.org/officeDocument/2006/relationships" - Network accessline 4
xmlns:r="http://schemas.openxmlformats.org/officeDocument/2006/relationships" - Network accessline 5
elementFormDefault="qualified" targetNamespace="http://schemas.openxmlformats.org/officeDocument/2006/relationships" blockDefault="#all"> - Network accessline 6
targetNamespace="http://schemas.openxmlformats.org/officeDocument/2006/relationships"
skills/14-文档处理与数据工具/pptx/scripts/office/schemas/ISO-IEC29500-4_2016/sml.xsd
- Network accessline 1
<?xml version="1.0" encoding="utf-8"?> <xsd:schema xmlns:xsd="http://www.w3.org/2001/XMLSchema" - Network accessline 2
<xsd:schema xmlns:xsd="http://www.w3.org/2001/XMLSchema" - Network accessline 3
xmlns="http://schemas.openxmlformats.org/spreadsheetml/2006/main" - Network accessline 4
xmlns:r="http://schemas.openxmlformats.org/officeDocument/2006/relationships" - Network accessline 5
xmlns:xdr="http://schemas.openxmlformats.org/drawingml/2006/spreadsheetDrawing" - Network accessline 6
xmlns:s="http://schemas.openxmlformats.org/officeDocument/2006/sharedTypes" - Network accessline 7
targetNamespace="http://schemas.openxmlformats.org/spreadsheetml/2006/main" - Network accessline 8
elementFormDefault="qualified"> <xsd:import namespace="http://schemas.openxmlformats.org/officeDocument/2006/relationships" schemaLocation="shared-relationshipReference.xsd"/> - Network accessline 9
<xsd:import namespace="http://schemas.openxmlformats.org/officeDocument/2006/relationships" - Network accessline 10
schemaLocation="shared-relationshipReference.xsd"/> <xsd:import namespace="http://schemas.openxmlformats.org/officeDocument/2006/sharedTypes" schemaLocation="shared-commonSimpleTypes.xsd"/> - Network accessline 11
<xsd:import namespace="http://schemas.openxmlformats.org/officeDocument/2006/sharedTypes" - Network accessline 12
schemaLocation="shared-commonSimpleTypes.xsd"/> <xsd:import namespace="http://schemas.openxmlformats.org/drawingml/2006/spreadsheetDrawing" - Network accessline 13
<xsd:import namespace="http://schemas.openxmlformats.org/drawingml/2006/spreadsheetDrawing" schemaLocation="dml-spreadsheetDrawing.xsd"/> - Network accessline 14
namespace="http://schemas.openxmlformats.org/drawingml/2006/spreadsheetDrawing"
skills/14-文档处理与数据工具/pptx/scripts/office/schemas/ISO-IEC29500-4_2016/vml-main.xsd
- Network accessline 1
<?xml version="1.0" encoding="utf-8"?> <xsd:schema xmlns:xsd="http://www.w3.org/2001/XMLSchema" xmlns="urn:schemas-microsoft-com:vml" - Network accessline 2
<xsd:schema xmlns:xsd="http://www.w3.org/2001/XMLSchema" xmlns="urn:schemas-microsoft-com:vml" - Network accessline 3
xmlns:pvml="urn:schemas-microsoft-com:office:powerpoint" xmlns:o="urn:schemas-microsoft-com:office:office" xmlns:w="http://schemas.openxmlformats.org/wordprocessingml/2006/main" - Network accessline 4
xmlns:o="urn:schemas-microsoft-com:office:office" xmlns:w="http://schemas.openxmlformats.org/wordprocessingml/2006/main" xmlns:w10="urn:schemas-microsoft-com:office:word" - Network accessline 5
xmlns:w="http://schemas.openxmlformats.org/wordprocessingml/2006/main" - Network accessline 6
xmlns:w10="urn:schemas-microsoft-com:office:word" xmlns:r="http://schemas.openxmlformats.org/officeDocument/2006/relationships" xmlns:x="urn:schemas-microsoft-com:office:excel" - Network accessline 7
xmlns:r="http://schemas.openxmlformats.org/officeDocument/2006/relationships" - Network accessline 8
xmlns:x="urn:schemas-microsoft-com:office:excel" xmlns:s="http://schemas.openxmlformats.org/officeDocument/2006/sharedTypes" targetNamespace="urn:schemas-microsoft-com:vml" elementFormDefault="qualified" - Network accessline 9
xmlns:s="http://schemas.openxmlformats.org/officeDocument/2006/sharedTypes" - Network accessline 12
<xsd:import namespace="urn:schemas-microsoft-com:office:office" schemaLocation="vml-officeDrawing.xsd"/> <xsd:import namespace="http://schemas.openxmlformats.org/wordprocessingml/2006/main" - Network accessline 13
schemaLocation="vml-officeDrawing.xsd"/> <xsd:import namespace="http://schemas.openxmlformats.org/wordprocessingml/2006/main" schemaLocation="wml.xsd"/> - Network accessline 14
<xsd:import namespace="http://schemas.openxmlformats.org/wordprocessingml/2006/main" - Network accessline 16
<xsd:import namespace="urn:schemas-microsoft-com:office:word" schemaLocation="vml-wordprocessingDrawing.xsd"/> <xsd:import namespace="http://schemas.openxmlformats.org/officeDocument/2006/relationships" - Network accessline 17
schemaLocation="vml-wordprocessingDrawing.xsd"/> <xsd:import namespace="http://schemas.openxmlformats.org/officeDocument/2006/relationships" schemaLocation="shared-relationshipReference.xsd"/> - Network accessline 18
<xsd:import namespace="http://schemas.openxmlformats.org/officeDocument/2006/relationships" - Network accessline 22
<xsd:import namespace="urn:schemas-microsoft-com:office:powerpoint" schemaLocation="vml-presentationDrawing.xsd"/> <xsd:import namespace="http://schemas.openxmlformats.org/officeDocument/2006/sharedTypes" - Network accessline 23
schemaLocation="vml-presentationDrawing.xsd"/> <xsd:import namespace="http://schemas.openxmlformats.org/officeDocument/2006/sharedTypes" schemaLocation="shared-commonSimpleTypes.xsd"/> - Network accessline 24
<xsd:import namespace="http://schemas.openxmlformats.org/officeDocument/2006/sharedTypes"
skills/14-文档处理与数据工具/pptx/scripts/office/schemas/ISO-IEC29500-4_2016/vml-officeDrawing.xsd
- Network accessline 1
<?xml version="1.0" encoding="utf-8"?> <xsd:schema xmlns:xsd="http://www.w3.org/2001/XMLSchema" - Network accessline 2
<xsd:schema xmlns:xsd="http://www.w3.org/2001/XMLSchema" - Network accessline 3
xmlns="urn:schemas-microsoft-com:office:office" xmlns:v="urn:schemas-microsoft-com:vml" xmlns:r="http://schemas.openxmlformats.org/officeDocument/2006/relationships" xmlns:s="http://schemas.openxmlformats.org/officeDocument/2006/sharedT - Network accessline 4
xmlns:r="http://schemas.openxmlformats.org/officeDocument/2006/relationships" - Network accessline 5
xmlns:s="http://schemas.openxmlformats.org/officeDocument/2006/sharedTypes" - Network accessline 7
attributeFormDefault="unqualified"> <xsd:import namespace="urn:schemas-microsoft-com:vml" schemaLocation="vml-main.xsd"/> <xsd:import namespace="http://schemas.openxmlformats.org/officeDocument/2006/relationships" - Network accessline 8
<xsd:import namespace="urn:schemas-microsoft-com:vml" schemaLocation="vml-main.xsd"/> <xsd:import namespace="http://schemas.openxmlformats.org/officeDocument/2006/relationships" schemaLocation="shared-relationshipReference.xsd"/> - Network accessline 9
<xsd:import namespace="http://schemas.openxmlformats.org/officeDocument/2006/relationships" - Network accessline 10
schemaLocation="shared-relationshipReference.xsd"/> <xsd:import namespace="http://schemas.openxmlformats.org/officeDocument/2006/sharedTypes" schemaLocation="shared-commonSimpleTypes.xsd"/> - Network accessline 11
<xsd:import namespace="http://schemas.openxmlformats.org/officeDocument/2006/sharedTypes"
skills/14-文档处理与数据工具/pptx/scripts/office/schemas/ISO-IEC29500-4_2016/vml-presentationDrawing.xsd
skills/14-文档处理与数据工具/pptx/scripts/office/schemas/ISO-IEC29500-4_2016/vml-spreadsheetDrawing.xsd
- Network accessline 1
<?xml version="1.0" encoding="utf-8"?> <xsd:schema xmlns:xsd="http://www.w3.org/2001/XMLSchema" - Network accessline 2
<xsd:schema xmlns:xsd="http://www.w3.org/2001/XMLSchema" - Network accessline 3
xmlns="urn:schemas-microsoft-com:office:excel" xmlns:s="http://schemas.openxmlformats.org/officeDocument/2006/sharedTypes" targetNamespace="urn:schemas-microsoft-com:office:excel" elementFormDefault="qualified" - Network accessline 4
xmlns:s="http://schemas.openxmlformats.org/officeDocument/2006/sharedTypes" - Network accessline 5
targetNamespace="urn:schemas-microsoft-com:office:excel" elementFormDefault="qualified" attributeFormDefault="unqualified"> <xsd:import namespace="http://schemas.openxmlformats.org/officeDocument/2006/sharedTypes" - Network accessline 6
attributeFormDefault="unqualified"> <xsd:import namespace="http://schemas.openxmlformats.org/officeDocument/2006/sharedTypes" schemaLocation="shared-commonSimpleTypes.xsd"/> - Network accessline 7
<xsd:import namespace="http://schemas.openxmlformats.org/officeDocument/2006/sharedTypes"
skills/14-文档处理与数据工具/pptx/scripts/office/schemas/ISO-IEC29500-4_2016/vml-wordprocessingDrawing.xsd
skills/14-文档处理与数据工具/pptx/scripts/office/schemas/ISO-IEC29500-4_2016/wml.xsd
- Network accessline 1
<?xml version="1.0" encoding="utf-8"?> <xsd:schema xmlns:xsd="http://www.w3.org/2001/XMLSchema" - Network accessline 2
<xsd:schema xmlns:xsd="http://www.w3.org/2001/XMLSchema" - Network accessline 3
xmlns:m="http://schemas.openxmlformats.org/officeDocument/2006/math" - Network accessline 4
xmlns:r="http://schemas.openxmlformats.org/officeDocument/2006/relationships" - Network accessline 5
xmlns:sl="http://schemas.openxmlformats.org/schemaLibrary/2006/main" - Network accessline 6
xmlns:wp="http://schemas.openxmlformats.org/drawingml/2006/wordprocessingDrawing" - Network accessline 7
xmlns="http://schemas.openxmlformats.org/wordprocessingml/2006/main" - Network accessline 8
xmlns:s="http://schemas.openxmlformats.org/officeDocument/2006/sharedTypes" - Network accessline 9
xmlns:mc="http://schemas.openxmlformats.org/markup-compatibility/2006" - Network accessline 10
elementFormDefault="qualified" attributeFormDefault="qualified" blockDefault="#all" targetNamespace="http://schemas.openxmlformats.org/wordprocessingml/2006/main"> <xsd:import namespace="http://schemas.openxmlformats.org/markup-compatib - Network accessline 11
targetNamespace="http://schemas.openxmlformats.org/wordprocessingml/2006/main"> - Network accessline 12
<xsd:import namespace="http://schemas.openxmlformats.org/markup-compatibility/2006" schemaLocation="../mce/mc.xsd"/> - Network accessline 13
<xsd:import namespace="http://schemas.openxmlformats.org/drawingml/2006/wordprocessingDrawing" - Network accessline 14
schemaLocation="dml-wordprocessingDrawing.xsd"/> <xsd:import namespace="http://schemas.openxmlformats.org/officeDocument/2006/math" schemaLocation="shared-math.xsd"/> - Network accessline 15
<xsd:import namespace="http://schemas.openxmlformats.org/officeDocument/2006/math" - Network accessline 16
schemaLocation="shared-math.xsd"/> <xsd:import namespace="http://schemas.openxmlformats.org/officeDocument/2006/relationships" schemaLocation="shared-relationshipReference.xsd"/> - Network accessline 17
<xsd:import namespace="http://schemas.openxmlformats.org/officeDocument/2006/relationships" - Network accessline 18
schemaLocation="shared-relationshipReference.xsd"/> <xsd:import namespace="http://schemas.openxmlformats.org/officeDocument/2006/sharedTypes" schemaLocation="shared-commonSimpleTypes.xsd"/> - Network accessline 19
<xsd:import namespace="http://schemas.openxmlformats.org/officeDocument/2006/sharedTypes" - Network accessline 20
schemaLocation="shared-commonSimpleTypes.xsd"/> <xsd:import namespace="http://schemas.openxmlformats.org/schemaLibrary/2006/main" schemaLocation="shared-customXmlSchemaProperties.xsd"/> - Network accessline 21
<xsd:import namespace="http://schemas.openxmlformats.org/schemaLibrary/2006/main" - Network accessline 22
schemaLocation="shared-customXmlSchemaProperties.xsd"/> <xsd:import namespace="http://www.w3.org/XML/1998/namespace"/> <xsd:complexType name="CT_Empty"/> - Network accessline 23
<xsd:import namespace="http://www.w3.org/XML/1998/namespace"/>
skills/14-文档处理与数据工具/pptx/scripts/office/schemas/ISO-IEC29500-4_2016/xml.xsd
- Network accessline 1
<?xml version='1.0'?> <xs:schema targetNamespace="http://www.w3.org/XML/1998/namespace" xmlns:xs="http://www.w3.org/2001/XMLSchema" xml:lang="en"> - Network accessline 2
<xs:schema targetNamespace="http://www.w3.org/XML/1998/namespace" xmlns:xs="http://www.w3.org/2001/XMLSchema" xml:lang="en"> - Network accessline 4
<xs:annotation> <xs:documentation> See http://www.w3.org/XML/1998/namespace.html and - Network accessline 5
<xs:documentation> See http://www.w3.org/XML/1998/namespace.html and http://www.w3.org/TR/REC-xml for information about this namespace. - Network accessline 6
See http://www.w3.org/XML/1998/namespace.html and - Network accessline 7
http://www.w3.org/TR/REC-xml for information about this namespace. - Network accessline 55
<schema . . .> . . . <import namespace="http://www.w3.org/XML/1998/namespace" - Network accessline 56
. . . <import namespace="http://www.w3.org/XML/1998/namespace" schemaLocation="http://www.w3.org/2001/03/xml.xsd"/> - Network accessline 57
<import namespace="http://www.w3.org/XML/1998/namespace" - Network accessline 58
schemaLocation="http://www.w3.org/2001/03/xml.xsd"/> - Network accessline 72
<xs:documentation>In keeping with the XML Schema WG's standard versioning policy, this schema document will persist at http://www.w3.org/2001/03/xml.xsd. - Network accessline 73
policy, this schema document will persist at http://www.w3.org/2001/03/xml.xsd. At the date of issue it can also be found at - Network accessline 74
http://www.w3.org/2001/03/xml.xsd. - Network accessline 75
At the date of issue it can also be found at http://www.w3.org/2001/xml.xsd. The schema document at that URI may however change in the future, - Network accessline 76
http://www.w3.org/2001/xml.xsd. - Network accessline 79
itself. In other words, if the XML Schema namespace changes, the version of this document at http://www.w3.org/2001/xml.xsd will change - Network accessline 80
of this document at http://www.w3.org/2001/xml.xsd will change accordingly; the version at - Network accessline 81
http://www.w3.org/2001/xml.xsd will change - Network accessline 82
accordingly; the version at http://www.w3.org/2001/03/xml.xsd will not change. </xs:documentation> - Network accessline 83
http://www.w3.org/2001/03/xml.xsd will not change. - Network accessline 103
<xs:attribute name="base" type="xs:anyURI"> <xs:annotation> <xs:documentation>See http://www.w3.org/TR/xmlbase/ for - Network accessline 104
<xs:annotation> <xs:documentation>See http://www.w3.org/TR/xmlbase/ for information about this attribute.</xs:documentation> - Network accessline 105
<xs:documentation>See http://www.w3.org/TR/xmlbase/ for
skills/14-文档处理与数据工具/pptx/scripts/office/schemas/ecma/fouth-edition/opc-contentTypes.xsd
- Network accessline 1
<U+FEFF><?xml version="1.0" encoding="UTF-8" standalone="no"?> <xs:schema xmlns="http://schemas.openxmlformats.org/package/2006/content-types" - Network accessline 2
<xs:schema xmlns="http://schemas.openxmlformats.org/package/2006/content-types" - Network accessline 3
xmlns:xs="http://www.w3.org/2001/XMLSchema" - Network accessline 4
targetNamespace="http://schemas.openxmlformats.org/package/2006/content-types"
skills/14-文档处理与数据工具/pptx/scripts/office/schemas/ecma/fouth-edition/opc-coreProperties.xsd
- Network accessline 1
<U+FEFF><?xml version="1.0" encoding="UTF-8"?> <xs:schema targetNamespace="http://schemas.openxmlformats.org/package/2006/metadata/core-properties" - Network accessline 2
<xs:schema targetNamespace="http://schemas.openxmlformats.org/package/2006/metadata/core-properties" - Network accessline 3
xmlns="http://schemas.openxmlformats.org/package/2006/metadata/core-properties" - Network accessline 4
xmlns:xs="http://www.w3.org/2001/XMLSchema" xmlns:dc="http://purl.org/dc/elements/1.1/" - Network accessline 5
xmlns:dcterms="http://purl.org/dc/terms/" elementFormDefault="qualified" blockDefault="#all"> - Network accessline 7
<xs:import namespace="http://purl.org/dc/elements/1.1/" - Network accessline 8
schemaLocation="http://dublincore.org/schemas/xmls/qdc/2003/04/02/dc.xsd"/> - Network accessline 9
<xs:import namespace="http://purl.org/dc/terms/" - Network accessline 10
schemaLocation="http://dublincore.org/schemas/xmls/qdc/2003/04/02/dcterms.xsd"/> - Network accessline 11
<xs:import id="xml" namespace="http://www.w3.org/XML/1998/namespace"/>
skills/14-文档处理与数据工具/pptx/scripts/office/schemas/ecma/fouth-edition/opc-digSig.xsd
- Network accessline 1
<?xml version="1.0" encoding="UTF-8"?> <xsd:schema xmlns="http://schemas.openxmlformats.org/package/2006/digital-signature" - Network accessline 2
<xsd:schema xmlns="http://schemas.openxmlformats.org/package/2006/digital-signature" - Network accessline 3
xmlns:xsd="http://www.w3.org/2001/XMLSchema" - Network accessline 4
targetNamespace="http://schemas.openxmlformats.org/package/2006/digital-signature"
skills/14-文档处理与数据工具/pptx/scripts/office/schemas/ecma/fouth-edition/opc-relationships.xsd
- Network accessline 1
<U+FEFF><?xml version="1.0" encoding="UTF-8" standalone="no"?> <xsd:schema xmlns="http://schemas.openxmlformats.org/package/2006/relationships" - Network accessline 2
<xsd:schema xmlns="http://schemas.openxmlformats.org/package/2006/relationships" - Network accessline 3
xmlns:xsd="http://www.w3.org/2001/XMLSchema" - Network accessline 4
targetNamespace="http://schemas.openxmlformats.org/package/2006/relationships"
skills/14-文档处理与数据工具/pptx/scripts/office/schemas/mce/mc.xsd
- Network accessline 1
<?xml version="1.0" encoding="utf-8"?> <xsd:schema xmlns:mc="http://schemas.openxmlformats.org/markup-compatibility/2006" - Network accessline 2
<xsd:schema xmlns:mc="http://schemas.openxmlformats.org/markup-compatibility/2006" - Network accessline 3
attributeFormDefault="unqualified" elementFormDefault="qualified" targetNamespace="http://schemas.openxmlformats.org/markup-compatibility/2006" xmlns:xsd="http://www.w3.org/2001/XMLSchema"> - Network accessline 4
targetNamespace="http://schemas.openxmlformats.org/markup-compatibility/2006" - Network accessline 5
xmlns:xsd="http://www.w3.org/2001/XMLSchema"> - Network accessline 7
<!-- This XSD is a modified version of the one found at: https://github.com/plutext/docx4j/blob/master/xsd/mce/markup-compatibility-2006-MINIMAL.xsd - Network accessline 9
https://github.com/plutext/docx4j/blob/master/xsd/mce/markup-compatibility-2006-MINIMAL.xsd - Network accessline 15
<w:document xmlns:mc="http://schemas.openxmlformats.org/markup-compatibility/2006" - Network accessline 16
xmlns:mc="http://schemas.openxmlformats.org/markup-compatibility/2006" - Network accessline 17
xmlns:w="http://schemas.openxmlformats.org/wordprocessingml/2006/main"
skills/14-文档处理与数据工具/pptx/scripts/office/schemas/microsoft/wml-2010.xsd
- Network accessline 1
<xsd:schema xmlns:xsd="http://www.w3.org/2001/XMLSchema" xmlns:w12="http://schemas.openxmlformats.org/wordprocessingml/2006/main" elementFormDefault="qualified" attributeFormDefault="qualified" blockDefault="#all" xmlns:r="http://schemas.op - Network accessline 2
<!-- <xsd:import id="rel" namespace="http://schemas.openxmlformats.org/officeDocument/2006/relationships" schemaLocation="orel.xsd"/> --> - Network accessline 3
<xsd:import id="w" namespace="http://schemas.openxmlformats.org/wordprocessingml/2006/main" schemaLocation="../ISO-IEC29500-4_2016/wml.xsd"/> - Network accessline 4
<!-- <xsd:import namespace="http://schemas.openxmlformats.org/drawingml/2006/main" schemaLocation="oartbasetypes.xsd"/> - Network accessline 5
<xsd:import namespace="http://schemas.openxmlformats.org/drawingml/2006/main" schemaLocation="oartsplineproperties.xsd"/> -->
skills/14-文档处理与数据工具/pptx/scripts/office/schemas/microsoft/wml-2012.xsd
- Network accessline 1
<xsd:schema xmlns:xsd="http://www.w3.org/2001/XMLSchema" xmlns:w12="http://schemas.openxmlformats.org/wordprocessingml/2006/main" xmlns:s="http://schemas.openxmlformats.org/officeDocument/2006/sharedTypes" elementFormDefault="qualified" att - Network accessline 2
<xsd:import id="w12" namespace="http://schemas.openxmlformats.org/wordprocessingml/2006/main" schemaLocation="../ISO-IEC29500-4_2016/wml.xsd"/> - Network accessline 3
<xsd:import namespace="http://schemas.openxmlformats.org/officeDocument/2006/sharedTypes" schemaLocation="../ISO-IEC29500-4_2016/shared-commonSimpleTypes.xsd"/>
skills/14-文档处理与数据工具/pptx/scripts/office/schemas/microsoft/wml-2018.xsd
- Network accessline 1
<xsd:schema xmlns:xsd="http://www.w3.org/2001/XMLSchema" xmlns:w12="http://schemas.openxmlformats.org/wordprocessingml/2006/main" elementFormDefault="qualified" attributeFormDefault="qualified" blockDefault="#all" xmlns="http://schemas.micr - Network accessline 2
<xsd:import id="w12" namespace="http://schemas.openxmlformats.org/wordprocessingml/2006/main" schemaLocation="../ISO-IEC29500-4_2016/wml.xsd"/>
skills/14-文档处理与数据工具/pptx/scripts/office/schemas/microsoft/wml-cex-2018.xsd
- Network accessline 1
<xsd:schema xmlns:xsd="http://www.w3.org/2001/XMLSchema" xmlns:w="http://schemas.openxmlformats.org/wordprocessingml/2006/main" xmlns:s="http://schemas.openxmlformats.org/officeDocument/2006/sharedTypes" xmlns:w16="http://schemas.microsoft. - Network accessline 2
<xsd:import id="w16" namespace="http://schemas.microsoft.com/office/word/2018/wordml" schemaLocation="wml-2018.xsd"/> - Network accessline 3
<xsd:import id="w" namespace="http://schemas.openxmlformats.org/wordprocessingml/2006/main" schemaLocation="../ISO-IEC29500-4_2016/wml.xsd"/> - Network accessline 4
<xsd:import id="s" namespace="http://schemas.openxmlformats.org/officeDocument/2006/sharedTypes" schemaLocation="../ISO-IEC29500-4_2016/shared-commonSimpleTypes.xsd"/>
skills/14-文档处理与数据工具/pptx/scripts/office/schemas/microsoft/wml-cid-2016.xsd
- Network accessline 1
<xsd:schema xmlns:xsd="http://www.w3.org/2001/XMLSchema" xmlns:w12="http://schemas.openxmlformats.org/wordprocessingml/2006/main" elementFormDefault="qualified" attributeFormDefault="qualified" blockDefault="#all" xmlns="http://schemas.micr - Network accessline 2
<xsd:import id="w12" namespace="http://schemas.openxmlformats.org/wordprocessingml/2006/main" schemaLocation="../ISO-IEC29500-4_2016/wml.xsd"/>
skills/14-文档处理与数据工具/pptx/scripts/office/schemas/microsoft/wml-sdtdatahash-2020.xsd
- Network accessline 1
<xsd:schema xmlns:xsd="http://www.w3.org/2001/XMLSchema" xmlns:w12="http://schemas.openxmlformats.org/wordprocessingml/2006/main" elementFormDefault="qualified" attributeFormDefault="qualified" blockDefault="#all" xmlns="http://schemas.micr - Network accessline 2
<xsd:import id="w12" namespace="http://schemas.openxmlformats.org/wordprocessingml/2006/main" schemaLocation="../ISO-IEC29500-4_2016/wml.xsd"/>
skills/14-文档处理与数据工具/pptx/scripts/office/schemas/microsoft/wml-symex-2015.xsd
- Network accessline 1
<xsd:schema xmlns:xsd="http://www.w3.org/2001/XMLSchema" xmlns:w12="http://schemas.openxmlformats.org/wordprocessingml/2006/main" elementFormDefault="qualified" attributeFormDefault="qualified" blockDefault="#all" xmlns="http://schemas.micr - Network accessline 2
<xsd:import id="w12" namespace="http://schemas.openxmlformats.org/wordprocessingml/2006/main" schemaLocation="../ISO-IEC29500-4_2016/wml.xsd"/>
skills/14-文档处理与数据工具/pptx/scripts/office/soffice.py
- Executable scriptline 1
Executable file - Access to secretsline 25
[Credential-related source omitted] - Network accessline 44
def _needs_shim() -> bool: try: s = socket.socket(socket.AF_UNIX, socket.SOCK_STREAM) - Network accessline 45
try: s = socket.socket(socket.AF_UNIX, socket.SOCK_STREAM) s.close() - Network accessline 46
s = socket.socket(socket.AF_UNIX, socket.SOCK_STREAM)
skills/14-文档处理与数据工具/pptx/scripts/office/unpack.py
- Executable scriptline 1
Executable file
skills/14-文档处理与数据工具/pptx/scripts/office/validate.py
- Executable scriptline 1
Executable file
skills/14-文档处理与数据工具/pptx/scripts/office/validators/__init__.py
- Executable scriptline 1
Executable file
skills/14-文档处理与数据工具/pptx/scripts/office/validators/base.py
- Executable scriptline 1
Executable file - Network accessline 61
MC_NAMESPACE = "http://schemas.openxmlformats.org/markup-compatibility/2006" - Network accessline 62
XML_NAMESPACE = "http://www.w3.org/XML/1998/namespace" - Network accessline 64
PACKAGE_RELATIONSHIPS_NAMESPACE = ( "http://schemas.openxmlformats.org/package/2006/relationships" - Network accessline 65
"http://schemas.openxmlformats.org/package/2006/relationships" - Network accessline 66
) OFFICE_RELATIONSHIPS_NAMESPACE = ( "http://schemas.openxmlformats.org/officeDocument/2006/relationships" - Network accessline 67
OFFICE_RELATIONSHIPS_NAMESPACE = ( "http://schemas.openxmlformats.org/officeDocument/2006/relationships" ) - Network accessline 68
"http://schemas.openxmlformats.org/officeDocument/2006/relationships" - Network accessline 69
) CONTENT_TYPES_NAMESPACE = ( "http://schemas.openxmlformats.org/package/2006/content-types" - Network accessline 70
CONTENT_TYPES_NAMESPACE = ( "http://schemas.openxmlformats.org/package/2006/content-types" ) - Network accessline 71
"http://schemas.openxmlformats.org/package/2006/content-types" - Network accessline 76
OOXML_NAMESPACES = { "http://schemas.openxmlformats.org/officeDocument/2006/math", - Network accessline 77
"http://schemas.openxmlformats.org/officeDocument/2006/math", - Network accessline 78
"http://schemas.openxmlformats.org/officeDocument/2006/relationships", - Network accessline 79
"http://schemas.openxmlformats.org/schemaLibrary/2006/main", - Network accessline 80
"http://schemas.openxmlformats.org/drawingml/2006/main", - Network accessline 81
"http://schemas.openxmlformats.org/drawingml/2006/chart", - Network accessline 82
"http://schemas.openxmlformats.org/drawingml/2006/chartDrawing", - Network accessline 83
"http://schemas.openxmlformats.org/drawingml/2006/diagram", - Network accessline 84
"http://schemas.openxmlformats.org/drawingml/2006/picture", - Network accessline 85
"http://schemas.openxmlformats.org/drawingml/2006/spreadsheetDrawing", - Network accessline 86
"http://schemas.openxmlformats.org/drawingml/2006/wordprocessingDrawing", - Network accessline 87
"http://schemas.openxmlformats.org/wordprocessingml/2006/main", - Network accessline 88
"http://schemas.openxmlformats.org/presentationml/2006/main", - Network accessline 89
"http://schemas.openxmlformats.org/spreadsheetml/2006/main", - Network accessline 90
"http://schemas.openxmlformats.org/officeDocument/2006/sharedTypes", - Network accessline 91
"http://www.w3.org/XML/1998/namespace",
skills/14-文档处理与数据工具/pptx/scripts/office/validators/docx.py
- Executable scriptline 1
Executable file - Network accessline 18
WORD_2006_NAMESPACE = "http://schemas.openxmlformats.org/wordprocessingml/2006/main" - Network accessline 19
W14_NAMESPACE = "http://schemas.microsoft.com/office/word/2010/wordml" - Network accessline 20
W16CID_NAMESPACE = "http://schemas.microsoft.com/office/word/2016/wordml/cid"
skills/14-文档处理与数据工具/pptx/scripts/office/validators/pptx.py
skills/14-文档处理与数据工具/pptx/scripts/office/validators/redlining.py
- Executable scriptline 1
Executable file - Network accessline 17
self.author = author self.namespaces = { "w": "http://schemas.openxmlformats.org/wordprocessingml/2006/main" - Network accessline 18
self.namespaces = { "w": "http://schemas.openxmlformats.org/wordprocessingml/2006/main" } - Network accessline 19
"w": "http://schemas.openxmlformats.org/wordprocessingml/2006/main"
skills/14-文档处理与数据工具/pptx/scripts/thumbnail.py
- Executable scriptline 1
Executable file
skills/14-文档处理与数据工具/xlsx/LICENSE.txt
- Network accessline 6
exists, use is governed by Anthropic's Consumer Terms of Service or Commercial Terms of Service, as applicable: https://www.anthropic.com/legal/consumer-terms - Network accessline 7
Commercial Terms of Service, as applicable: https://www.anthropic.com/legal/consumer-terms https://www.anthropic.com/legal/commercial-terms - Network accessline 8
https://www.anthropic.com/legal/consumer-terms - Network accessline 9
https://www.anthropic.com/legal/commercial-terms
skills/14-文档处理与数据工具/xlsx/scripts/office/helpers/__init__.py
- Executable scriptline 1
Executable file
skills/14-文档处理与数据工具/xlsx/scripts/office/helpers/merge_runs.py
- Executable scriptline 1
Executable file
skills/14-文档处理与数据工具/xlsx/scripts/office/helpers/simplify_redlines.py
skills/14-文档处理与数据工具/xlsx/scripts/office/pack.py
- Executable scriptline 1
Executable file
skills/14-文档处理与数据工具/xlsx/scripts/office/schemas/ISO-IEC29500-4_2016/dml-chart.xsd
- Network accessline 1
<?xml version="1.0" encoding="utf-8"?> <xsd:schema xmlns:xsd="http://www.w3.org/2001/XMLSchema" - Network accessline 2
<xsd:schema xmlns:xsd="http://www.w3.org/2001/XMLSchema" - Network accessline 3
xmlns:a="http://schemas.openxmlformats.org/drawingml/2006/main" - Network accessline 4
xmlns:r="http://schemas.openxmlformats.org/officeDocument/2006/relationships" - Network accessline 5
xmlns="http://schemas.openxmlformats.org/drawingml/2006/chart" - Network accessline 6
xmlns:cdr="http://schemas.openxmlformats.org/drawingml/2006/chartDrawing" - Network accessline 7
xmlns:s="http://schemas.openxmlformats.org/officeDocument/2006/sharedTypes" - Network accessline 8
targetNamespace="http://schemas.openxmlformats.org/drawingml/2006/chart" - Network accessline 9
elementFormDefault="qualified" attributeFormDefault="unqualified" blockDefault="#all"> <xsd:import namespace="http://schemas.openxmlformats.org/officeDocument/2006/relationships" schemaLocation="shared-relationshipReference.xsd"/> - Network accessline 10
<xsd:import namespace="http://schemas.openxmlformats.org/officeDocument/2006/relationships" - Network accessline 11
schemaLocation="shared-relationshipReference.xsd"/> <xsd:import namespace="http://schemas.openxmlformats.org/drawingml/2006/main" schemaLocation="dml-main.xsd"/> - Network accessline 12
<xsd:import namespace="http://schemas.openxmlformats.org/drawingml/2006/main" - Network accessline 13
schemaLocation="dml-main.xsd"/> <xsd:import namespace="http://schemas.openxmlformats.org/drawingml/2006/chartDrawing" schemaLocation="dml-chartDrawing.xsd"/> - Network accessline 14
<xsd:import namespace="http://schemas.openxmlformats.org/drawingml/2006/chartDrawing" - Network accessline 15
schemaLocation="dml-chartDrawing.xsd"/> <xsd:import namespace="http://schemas.openxmlformats.org/officeDocument/2006/sharedTypes" schemaLocation="shared-commonSimpleTypes.xsd"/> - Network accessline 16
<xsd:import namespace="http://schemas.openxmlformats.org/officeDocument/2006/sharedTypes"
skills/14-文档处理与数据工具/xlsx/scripts/office/schemas/ISO-IEC29500-4_2016/dml-chartDrawing.xsd
- Network accessline 1
<?xml version="1.0" encoding="utf-8"?> <xsd:schema xmlns:xsd="http://www.w3.org/2001/XMLSchema" - Network accessline 2
<xsd:schema xmlns:xsd="http://www.w3.org/2001/XMLSchema" - Network accessline 3
xmlns:a="http://schemas.openxmlformats.org/drawingml/2006/main" - Network accessline 4
xmlns="http://schemas.openxmlformats.org/drawingml/2006/chartDrawing" - Network accessline 5
targetNamespace="http://schemas.openxmlformats.org/drawingml/2006/chartDrawing" - Network accessline 6
elementFormDefault="qualified"> <xsd:import namespace="http://schemas.openxmlformats.org/drawingml/2006/main" schemaLocation="dml-main.xsd"/> - Network accessline 7
<xsd:import namespace="http://schemas.openxmlformats.org/drawingml/2006/main"
skills/14-文档处理与数据工具/xlsx/scripts/office/schemas/ISO-IEC29500-4_2016/dml-diagram.xsd
- Network accessline 1
<?xml version="1.0" encoding="utf-8"?> <xsd:schema xmlns:xsd="http://www.w3.org/2001/XMLSchema" - Network accessline 2
<xsd:schema xmlns:xsd="http://www.w3.org/2001/XMLSchema" - Network accessline 3
xmlns="http://schemas.openxmlformats.org/drawingml/2006/diagram" - Network accessline 4
xmlns:a="http://schemas.openxmlformats.org/drawingml/2006/main" - Network accessline 5
xmlns:r="http://schemas.openxmlformats.org/officeDocument/2006/relationships" - Network accessline 6
xmlns:s="http://schemas.openxmlformats.org/officeDocument/2006/sharedTypes" - Network accessline 7
targetNamespace="http://schemas.openxmlformats.org/drawingml/2006/diagram" - Network accessline 8
elementFormDefault="qualified" attributeFormDefault="unqualified"> <xsd:import namespace="http://schemas.openxmlformats.org/officeDocument/2006/relationships" schemaLocation="shared-relationshipReference.xsd"/> - Network accessline 9
<xsd:import namespace="http://schemas.openxmlformats.org/officeDocument/2006/relationships" - Network accessline 10
schemaLocation="shared-relationshipReference.xsd"/> <xsd:import namespace="http://schemas.openxmlformats.org/drawingml/2006/main" schemaLocation="dml-main.xsd"/> - Network accessline 11
<xsd:import namespace="http://schemas.openxmlformats.org/drawingml/2006/main" - Network accessline 12
schemaLocation="dml-main.xsd"/> <xsd:import namespace="http://schemas.openxmlformats.org/officeDocument/2006/sharedTypes" schemaLocation="shared-commonSimpleTypes.xsd"/> - Network accessline 13
<xsd:import namespace="http://schemas.openxmlformats.org/officeDocument/2006/sharedTypes"
skills/14-文档处理与数据工具/xlsx/scripts/office/schemas/ISO-IEC29500-4_2016/dml-lockedCanvas.xsd
- Network accessline 1
<?xml version="1.0" encoding="utf-8"?> <xsd:schema xmlns:xsd="http://www.w3.org/2001/XMLSchema" - Network accessline 2
<xsd:schema xmlns:xsd="http://www.w3.org/2001/XMLSchema" - Network accessline 3
xmlns="http://schemas.openxmlformats.org/drawingml/2006/lockedCanvas" - Network accessline 4
xmlns:a="http://schemas.openxmlformats.org/drawingml/2006/main" - Network accessline 5
xmlns:r="http://schemas.openxmlformats.org/officeDocument/2006/relationships" - Network accessline 6
elementFormDefault="qualified" targetNamespace="http://schemas.openxmlformats.org/drawingml/2006/lockedCanvas"> <xsd:import namespace="http://schemas.openxmlformats.org/drawingml/2006/main" - Network accessline 7
targetNamespace="http://schemas.openxmlformats.org/drawingml/2006/lockedCanvas"> - Network accessline 8
<xsd:import namespace="http://schemas.openxmlformats.org/drawingml/2006/main"
skills/14-文档处理与数据工具/xlsx/scripts/office/schemas/ISO-IEC29500-4_2016/dml-main.xsd
- Network accessline 1
<?xml version="1.0" encoding="utf-8"?> <xsd:schema xmlns:xsd="http://www.w3.org/2001/XMLSchema" - Network accessline 2
<xsd:schema xmlns:xsd="http://www.w3.org/2001/XMLSchema" - Network accessline 3
xmlns:r="http://schemas.openxmlformats.org/officeDocument/2006/relationships" - Network accessline 4
xmlns:s="http://schemas.openxmlformats.org/officeDocument/2006/sharedTypes" - Network accessline 5
xmlns="http://schemas.openxmlformats.org/drawingml/2006/main" - Network accessline 6
targetNamespace="http://schemas.openxmlformats.org/drawingml/2006/main" - Network accessline 7
elementFormDefault="qualified"> <xsd:import namespace="http://schemas.openxmlformats.org/officeDocument/2006/relationships" schemaLocation="shared-relationshipReference.xsd"/> - Network accessline 8
<xsd:import namespace="http://schemas.openxmlformats.org/officeDocument/2006/relationships" - Network accessline 9
schemaLocation="shared-relationshipReference.xsd"/> <xsd:import namespace="http://schemas.openxmlformats.org/officeDocument/2006/sharedTypes" schemaLocation="shared-commonSimpleTypes.xsd"/> - Network accessline 10
<xsd:import namespace="http://schemas.openxmlformats.org/officeDocument/2006/sharedTypes" - Network accessline 11
schemaLocation="shared-commonSimpleTypes.xsd"/> <xsd:import namespace="http://schemas.openxmlformats.org/drawingml/2006/diagram" schemaLocation="dml-diagram.xsd"/> - Network accessline 12
<xsd:import namespace="http://schemas.openxmlformats.org/drawingml/2006/diagram" - Network accessline 13
schemaLocation="dml-diagram.xsd"/> <xsd:import namespace="http://schemas.openxmlformats.org/drawingml/2006/chart" schemaLocation="dml-chart.xsd"/> - Network accessline 14
<xsd:import namespace="http://schemas.openxmlformats.org/drawingml/2006/chart" - Network accessline 15
schemaLocation="dml-chart.xsd"/> <xsd:import namespace="http://schemas.openxmlformats.org/drawingml/2006/picture" schemaLocation="dml-picture.xsd"/> - Network accessline 16
<xsd:import namespace="http://schemas.openxmlformats.org/drawingml/2006/picture" - Network accessline 17
schemaLocation="dml-picture.xsd"/> <xsd:import namespace="http://schemas.openxmlformats.org/drawingml/2006/lockedCanvas" schemaLocation="dml-lockedCanvas.xsd"/> - Network accessline 18
<xsd:import namespace="http://schemas.openxmlformats.org/drawingml/2006/lockedCanvas"
skills/14-文档处理与数据工具/xlsx/scripts/office/schemas/ISO-IEC29500-4_2016/dml-picture.xsd
- Network accessline 1
<?xml version="1.0" encoding="utf-8"?> <xsd:schema xmlns:xsd="http://www.w3.org/2001/XMLSchema" - Network accessline 2
<xsd:schema xmlns:xsd="http://www.w3.org/2001/XMLSchema" - Network accessline 3
xmlns="http://schemas.openxmlformats.org/drawingml/2006/picture" - Network accessline 4
xmlns:a="http://schemas.openxmlformats.org/drawingml/2006/main" elementFormDefault="qualified" - Network accessline 5
targetNamespace="http://schemas.openxmlformats.org/drawingml/2006/picture"> - Network accessline 6
<xsd:import namespace="http://schemas.openxmlformats.org/drawingml/2006/main"
skills/14-文档处理与数据工具/xlsx/scripts/office/schemas/ISO-IEC29500-4_2016/dml-spreadsheetDrawing.xsd
- Network accessline 1
<?xml version="1.0" encoding="utf-8"?> <xsd:schema xmlns:xsd="http://www.w3.org/2001/XMLSchema" - Network accessline 2
<xsd:schema xmlns:xsd="http://www.w3.org/2001/XMLSchema" - Network accessline 3
xmlns:a="http://schemas.openxmlformats.org/drawingml/2006/main" - Network accessline 4
xmlns="http://schemas.openxmlformats.org/drawingml/2006/spreadsheetDrawing" - Network accessline 5
xmlns:r="http://schemas.openxmlformats.org/officeDocument/2006/relationships" - Network accessline 6
targetNamespace="http://schemas.openxmlformats.org/drawingml/2006/spreadsheetDrawing" - Network accessline 7
elementFormDefault="qualified"> <xsd:import namespace="http://schemas.openxmlformats.org/drawingml/2006/main" schemaLocation="dml-main.xsd"/> - Network accessline 8
<xsd:import namespace="http://schemas.openxmlformats.org/drawingml/2006/main" - Network accessline 9
schemaLocation="dml-main.xsd"/> <xsd:import schemaLocation="shared-relationshipReference.xsd" namespace="http://schemas.openxmlformats.org/officeDocument/2006/relationships"/> - Network accessline 10
<xsd:import schemaLocation="shared-relationshipReference.xsd" namespace="http://schemas.openxmlformats.org/officeDocument/2006/relationships"/> <xsd:element name="from" type="CT_Marker"/> - Network accessline 11
namespace="http://schemas.openxmlformats.org/officeDocument/2006/relationships"/>
skills/14-文档处理与数据工具/xlsx/scripts/office/schemas/ISO-IEC29500-4_2016/dml-wordprocessingDrawing.xsd
- Network accessline 1
<?xml version="1.0" encoding="utf-8"?> <xsd:schema xmlns:xsd="http://www.w3.org/2001/XMLSchema" - Network accessline 2
<xsd:schema xmlns:xsd="http://www.w3.org/2001/XMLSchema" - Network accessline 3
xmlns:a="http://schemas.openxmlformats.org/drawingml/2006/main" - Network accessline 4
xmlns:w="http://schemas.openxmlformats.org/wordprocessingml/2006/main" - Network accessline 5
xmlns:dpct="http://schemas.openxmlformats.org/drawingml/2006/picture" - Network accessline 6
xmlns:r="http://schemas.openxmlformats.org/officeDocument/2006/relationships" - Network accessline 7
xmlns="http://schemas.openxmlformats.org/drawingml/2006/wordprocessingDrawing" - Network accessline 8
targetNamespace="http://schemas.openxmlformats.org/drawingml/2006/wordprocessingDrawing" - Network accessline 9
elementFormDefault="qualified"> <xsd:import namespace="http://schemas.openxmlformats.org/drawingml/2006/main" schemaLocation="dml-main.xsd"/> - Network accessline 10
<xsd:import namespace="http://schemas.openxmlformats.org/drawingml/2006/main" - Network accessline 11
schemaLocation="dml-main.xsd"/> <xsd:import schemaLocation="wml.xsd" namespace="http://schemas.openxmlformats.org/wordprocessingml/2006/main"/> - Network accessline 12
<xsd:import schemaLocation="wml.xsd" namespace="http://schemas.openxmlformats.org/wordprocessingml/2006/main"/> <xsd:import namespace="http://schemas.openxmlformats.org/drawingml/2006/picture" - Network accessline 13
namespace="http://schemas.openxmlformats.org/wordprocessingml/2006/main"/> - Network accessline 14
<xsd:import namespace="http://schemas.openxmlformats.org/drawingml/2006/picture" - Network accessline 15
schemaLocation="dml-picture.xsd"/> <xsd:import namespace="http://schemas.openxmlformats.org/officeDocument/2006/relationships" schemaLocation="shared-relationshipReference.xsd"/> - Network accessline 16
<xsd:import namespace="http://schemas.openxmlformats.org/officeDocument/2006/relationships"
skills/14-文档处理与数据工具/xlsx/scripts/office/schemas/ISO-IEC29500-4_2016/pml.xsd
- Network accessline 1
<?xml version="1.0" encoding="utf-8"?> <xsd:schema xmlns:xsd="http://www.w3.org/2001/XMLSchema" - Network accessline 2
<xsd:schema xmlns:xsd="http://www.w3.org/2001/XMLSchema" - Network accessline 3
xmlns="http://schemas.openxmlformats.org/presentationml/2006/main" - Network accessline 4
xmlns:p="http://schemas.openxmlformats.org/presentationml/2006/main" - Network accessline 5
xmlns:a="http://schemas.openxmlformats.org/drawingml/2006/main" - Network accessline 6
xmlns:r="http://schemas.openxmlformats.org/officeDocument/2006/relationships" - Network accessline 7
xmlns:s="http://schemas.openxmlformats.org/officeDocument/2006/sharedTypes" - Network accessline 8
elementFormDefault="qualified" targetNamespace="http://schemas.openxmlformats.org/presentationml/2006/main"> <xsd:import namespace="http://schemas.openxmlformats.org/officeDocument/2006/relationships" - Network accessline 9
targetNamespace="http://schemas.openxmlformats.org/presentationml/2006/main"> - Network accessline 10
<xsd:import namespace="http://schemas.openxmlformats.org/officeDocument/2006/relationships" - Network accessline 11
schemaLocation="shared-relationshipReference.xsd"/> <xsd:import namespace="http://schemas.openxmlformats.org/drawingml/2006/main" schemaLocation="dml-main.xsd"/> - Network accessline 12
<xsd:import namespace="http://schemas.openxmlformats.org/drawingml/2006/main" - Network accessline 13
schemaLocation="dml-main.xsd"/> <xsd:import namespace="http://schemas.openxmlformats.org/officeDocument/2006/sharedTypes" schemaLocation="shared-commonSimpleTypes.xsd"/> - Network accessline 14
<xsd:import namespace="http://schemas.openxmlformats.org/officeDocument/2006/sharedTypes"
skills/14-文档处理与数据工具/xlsx/scripts/office/schemas/ISO-IEC29500-4_2016/shared-additionalCharacteristics.xsd
- Network accessline 1
<?xml version="1.0" encoding="utf-8"?> <xsd:schema xmlns:xsd="http://www.w3.org/2001/XMLSchema" - Network accessline 2
<xsd:schema xmlns:xsd="http://www.w3.org/2001/XMLSchema" - Network accessline 3
xmlns="http://schemas.openxmlformats.org/officeDocument/2006/characteristics" - Network accessline 4
targetNamespace="http://schemas.openxmlformats.org/officeDocument/2006/characteristics"
skills/14-文档处理与数据工具/xlsx/scripts/office/schemas/ISO-IEC29500-4_2016/shared-bibliography.xsd
- Network accessline 1
<?xml version="1.0" encoding="utf-8"?> <xsd:schema xmlns:xsd="http://www.w3.org/2001/XMLSchema" - Network accessline 2
<xsd:schema xmlns:xsd="http://www.w3.org/2001/XMLSchema" - Network accessline 3
xmlns="http://schemas.openxmlformats.org/officeDocument/2006/bibliography" - Network accessline 4
xmlns:s="http://schemas.openxmlformats.org/officeDocument/2006/sharedTypes" - Network accessline 5
targetNamespace="http://schemas.openxmlformats.org/officeDocument/2006/bibliography" - Network accessline 6
elementFormDefault="qualified"> <xsd:import namespace="http://schemas.openxmlformats.org/officeDocument/2006/sharedTypes" schemaLocation="shared-commonSimpleTypes.xsd"/> - Network accessline 7
<xsd:import namespace="http://schemas.openxmlformats.org/officeDocument/2006/sharedTypes"
skills/14-文档处理与数据工具/xlsx/scripts/office/schemas/ISO-IEC29500-4_2016/shared-commonSimpleTypes.xsd
- Network accessline 1
<?xml version="1.0" encoding="utf-8"?> <xsd:schema xmlns:xsd="http://www.w3.org/2001/XMLSchema" - Network accessline 2
<xsd:schema xmlns:xsd="http://www.w3.org/2001/XMLSchema" - Network accessline 3
xmlns="http://schemas.openxmlformats.org/officeDocument/2006/sharedTypes" - Network accessline 4
targetNamespace="http://schemas.openxmlformats.org/officeDocument/2006/sharedTypes"
skills/14-文档处理与数据工具/xlsx/scripts/office/schemas/ISO-IEC29500-4_2016/shared-customXmlDataProperties.xsd
- Network accessline 1
<?xml version="1.0" encoding="utf-8"?> <xsd:schema xmlns:xsd="http://www.w3.org/2001/XMLSchema" - Network accessline 2
<xsd:schema xmlns:xsd="http://www.w3.org/2001/XMLSchema" - Network accessline 3
xmlns="http://schemas.openxmlformats.org/officeDocument/2006/customXml" - Network accessline 4
xmlns:s="http://schemas.openxmlformats.org/officeDocument/2006/sharedTypes" - Network accessline 5
targetNamespace="http://schemas.openxmlformats.org/officeDocument/2006/customXml" - Network accessline 6
elementFormDefault="qualified" attributeFormDefault="qualified" blockDefault="#all"> <xsd:import namespace="http://schemas.openxmlformats.org/officeDocument/2006/sharedTypes" schemaLocation="shared-commonSimpleTypes.xsd"/> - Network accessline 7
<xsd:import namespace="http://schemas.openxmlformats.org/officeDocument/2006/sharedTypes"
skills/14-文档处理与数据工具/xlsx/scripts/office/schemas/ISO-IEC29500-4_2016/shared-customXmlSchemaProperties.xsd
- Network accessline 1
<?xml version="1.0" encoding="utf-8"?> <xsd:schema xmlns:xsd="http://www.w3.org/2001/XMLSchema" - Network accessline 2
<xsd:schema xmlns:xsd="http://www.w3.org/2001/XMLSchema" - Network accessline 3
xmlns="http://schemas.openxmlformats.org/schemaLibrary/2006/main" - Network accessline 4
targetNamespace="http://schemas.openxmlformats.org/schemaLibrary/2006/main"
skills/14-文档处理与数据工具/xlsx/scripts/office/schemas/ISO-IEC29500-4_2016/shared-documentPropertiesCustom.xsd
- Network accessline 1
<?xml version="1.0" encoding="utf-8"?> <xsd:schema xmlns:xsd="http://www.w3.org/2001/XMLSchema" - Network accessline 2
<xsd:schema xmlns:xsd="http://www.w3.org/2001/XMLSchema" - Network accessline 3
xmlns="http://schemas.openxmlformats.org/officeDocument/2006/custom-properties" - Network accessline 4
xmlns:vt="http://schemas.openxmlformats.org/officeDocument/2006/docPropsVTypes" - Network accessline 5
xmlns:s="http://schemas.openxmlformats.org/officeDocument/2006/sharedTypes" - Network accessline 6
targetNamespace="http://schemas.openxmlformats.org/officeDocument/2006/custom-properties" - Network accessline 7
blockDefault="#all" elementFormDefault="qualified"> <xsd:import namespace="http://schemas.openxmlformats.org/officeDocument/2006/docPropsVTypes" schemaLocation="shared-documentPropertiesVariantTypes.xsd"/> - Network accessline 8
<xsd:import namespace="http://schemas.openxmlformats.org/officeDocument/2006/docPropsVTypes" - Network accessline 9
schemaLocation="shared-documentPropertiesVariantTypes.xsd"/> <xsd:import namespace="http://schemas.openxmlformats.org/officeDocument/2006/sharedTypes" schemaLocation="shared-commonSimpleTypes.xsd"/> - Network accessline 10
<xsd:import namespace="http://schemas.openxmlformats.org/officeDocument/2006/sharedTypes"
skills/14-文档处理与数据工具/xlsx/scripts/office/schemas/ISO-IEC29500-4_2016/shared-documentPropertiesExtended.xsd
- Network accessline 1
<?xml version="1.0" encoding="utf-8"?> <xsd:schema xmlns:xsd="http://www.w3.org/2001/XMLSchema" - Network accessline 2
<xsd:schema xmlns:xsd="http://www.w3.org/2001/XMLSchema" - Network accessline 3
xmlns="http://schemas.openxmlformats.org/officeDocument/2006/extended-properties" - Network accessline 4
xmlns:vt="http://schemas.openxmlformats.org/officeDocument/2006/docPropsVTypes" - Network accessline 5
targetNamespace="http://schemas.openxmlformats.org/officeDocument/2006/extended-properties" - Network accessline 6
elementFormDefault="qualified" blockDefault="#all"> <xsd:import namespace="http://schemas.openxmlformats.org/officeDocument/2006/docPropsVTypes" schemaLocation="shared-documentPropertiesVariantTypes.xsd"/> - Network accessline 7
<xsd:import namespace="http://schemas.openxmlformats.org/officeDocument/2006/docPropsVTypes"
skills/14-文档处理与数据工具/xlsx/scripts/office/schemas/ISO-IEC29500-4_2016/shared-documentPropertiesVariantTypes.xsd
- Network accessline 1
<?xml version="1.0" encoding="utf-8"?> <xsd:schema xmlns:xsd="http://www.w3.org/2001/XMLSchema" - Network accessline 2
<xsd:schema xmlns:xsd="http://www.w3.org/2001/XMLSchema" - Network accessline 3
xmlns="http://schemas.openxmlformats.org/officeDocument/2006/docPropsVTypes" - Network accessline 4
xmlns:s="http://schemas.openxmlformats.org/officeDocument/2006/sharedTypes" - Network accessline 5
targetNamespace="http://schemas.openxmlformats.org/officeDocument/2006/docPropsVTypes" - Network accessline 6
blockDefault="#all" elementFormDefault="qualified"> <xsd:import namespace="http://schemas.openxmlformats.org/officeDocument/2006/sharedTypes" schemaLocation="shared-commonSimpleTypes.xsd"/> - Network accessline 7
<xsd:import namespace="http://schemas.openxmlformats.org/officeDocument/2006/sharedTypes"
skills/14-文档处理与数据工具/xlsx/scripts/office/schemas/ISO-IEC29500-4_2016/shared-math.xsd
- Network accessline 1
<?xml version="1.0" encoding="utf-8"?> <xsd:schema xmlns:xsd="http://www.w3.org/2001/XMLSchema" - Network accessline 2
<xsd:schema xmlns:xsd="http://www.w3.org/2001/XMLSchema" - Network accessline 3
xmlns="http://schemas.openxmlformats.org/officeDocument/2006/math" - Network accessline 4
xmlns:m="http://schemas.openxmlformats.org/officeDocument/2006/math" - Network accessline 5
xmlns:w="http://schemas.openxmlformats.org/wordprocessingml/2006/main" - Network accessline 6
xmlns:s="http://schemas.openxmlformats.org/officeDocument/2006/sharedTypes" - Network accessline 7
elementFormDefault="qualified" attributeFormDefault="qualified" blockDefault="#all" targetNamespace="http://schemas.openxmlformats.org/officeDocument/2006/math"> <xsd:import namespace="http://schemas.openxmlformats.org/wordprocessingml/ - Network accessline 8
targetNamespace="http://schemas.openxmlformats.org/officeDocument/2006/math"> - Network accessline 9
<xsd:import namespace="http://schemas.openxmlformats.org/wordprocessingml/2006/main" - Network accessline 10
schemaLocation="wml.xsd"/> <xsd:import namespace="http://schemas.openxmlformats.org/officeDocument/2006/sharedTypes" schemaLocation="shared-commonSimpleTypes.xsd"/> - Network accessline 11
<xsd:import namespace="http://schemas.openxmlformats.org/officeDocument/2006/sharedTypes" - Network accessline 12
schemaLocation="shared-commonSimpleTypes.xsd"/> <xsd:import namespace="http://www.w3.org/XML/1998/namespace" schemaLocation="xml.xsd"/> <xsd:simpleType name="ST_Integer255"> - Network accessline 13
<xsd:import namespace="http://www.w3.org/XML/1998/namespace" schemaLocation="xml.xsd"/>
skills/14-文档处理与数据工具/xlsx/scripts/office/schemas/ISO-IEC29500-4_2016/shared-relationshipReference.xsd
- Network accessline 1
<?xml version="1.0" encoding="utf-8"?> <xsd:schema xmlns:xsd="http://www.w3.org/2001/XMLSchema" - Network accessline 2
<xsd:schema xmlns:xsd="http://www.w3.org/2001/XMLSchema" - Network accessline 3
xmlns="http://schemas.openxmlformats.org/officeDocument/2006/relationships" - Network accessline 4
xmlns:r="http://schemas.openxmlformats.org/officeDocument/2006/relationships" - Network accessline 5
elementFormDefault="qualified" targetNamespace="http://schemas.openxmlformats.org/officeDocument/2006/relationships" blockDefault="#all"> - Network accessline 6
targetNamespace="http://schemas.openxmlformats.org/officeDocument/2006/relationships"
skills/14-文档处理与数据工具/xlsx/scripts/office/schemas/ISO-IEC29500-4_2016/sml.xsd
- Network accessline 1
<?xml version="1.0" encoding="utf-8"?> <xsd:schema xmlns:xsd="http://www.w3.org/2001/XMLSchema" - Network accessline 2
<xsd:schema xmlns:xsd="http://www.w3.org/2001/XMLSchema" - Network accessline 3
xmlns="http://schemas.openxmlformats.org/spreadsheetml/2006/main" - Network accessline 4
xmlns:r="http://schemas.openxmlformats.org/officeDocument/2006/relationships" - Network accessline 5
xmlns:xdr="http://schemas.openxmlformats.org/drawingml/2006/spreadsheetDrawing" - Network accessline 6
xmlns:s="http://schemas.openxmlformats.org/officeDocument/2006/sharedTypes" - Network accessline 7
targetNamespace="http://schemas.openxmlformats.org/spreadsheetml/2006/main" - Network accessline 8
elementFormDefault="qualified"> <xsd:import namespace="http://schemas.openxmlformats.org/officeDocument/2006/relationships" schemaLocation="shared-relationshipReference.xsd"/> - Network accessline 9
<xsd:import namespace="http://schemas.openxmlformats.org/officeDocument/2006/relationships" - Network accessline 10
schemaLocation="shared-relationshipReference.xsd"/> <xsd:import namespace="http://schemas.openxmlformats.org/officeDocument/2006/sharedTypes" schemaLocation="shared-commonSimpleTypes.xsd"/> - Network accessline 11
<xsd:import namespace="http://schemas.openxmlformats.org/officeDocument/2006/sharedTypes" - Network accessline 12
schemaLocation="shared-commonSimpleTypes.xsd"/> <xsd:import namespace="http://schemas.openxmlformats.org/drawingml/2006/spreadsheetDrawing" - Network accessline 13
<xsd:import namespace="http://schemas.openxmlformats.org/drawingml/2006/spreadsheetDrawing" schemaLocation="dml-spreadsheetDrawing.xsd"/> - Network accessline 14
namespace="http://schemas.openxmlformats.org/drawingml/2006/spreadsheetDrawing"
skills/14-文档处理与数据工具/xlsx/scripts/office/schemas/ISO-IEC29500-4_2016/vml-main.xsd
- Network accessline 1
<?xml version="1.0" encoding="utf-8"?> <xsd:schema xmlns:xsd="http://www.w3.org/2001/XMLSchema" xmlns="urn:schemas-microsoft-com:vml" - Network accessline 2
<xsd:schema xmlns:xsd="http://www.w3.org/2001/XMLSchema" xmlns="urn:schemas-microsoft-com:vml" - Network accessline 3
xmlns:pvml="urn:schemas-microsoft-com:office:powerpoint" xmlns:o="urn:schemas-microsoft-com:office:office" xmlns:w="http://schemas.openxmlformats.org/wordprocessingml/2006/main" - Network accessline 4
xmlns:o="urn:schemas-microsoft-com:office:office" xmlns:w="http://schemas.openxmlformats.org/wordprocessingml/2006/main" xmlns:w10="urn:schemas-microsoft-com:office:word" - Network accessline 5
xmlns:w="http://schemas.openxmlformats.org/wordprocessingml/2006/main" - Network accessline 6
xmlns:w10="urn:schemas-microsoft-com:office:word" xmlns:r="http://schemas.openxmlformats.org/officeDocument/2006/relationships" xmlns:x="urn:schemas-microsoft-com:office:excel" - Network accessline 7
xmlns:r="http://schemas.openxmlformats.org/officeDocument/2006/relationships" - Network accessline 8
xmlns:x="urn:schemas-microsoft-com:office:excel" xmlns:s="http://schemas.openxmlformats.org/officeDocument/2006/sharedTypes" targetNamespace="urn:schemas-microsoft-com:vml" elementFormDefault="qualified" - Network accessline 9
xmlns:s="http://schemas.openxmlformats.org/officeDocument/2006/sharedTypes" - Network accessline 12
<xsd:import namespace="urn:schemas-microsoft-com:office:office" schemaLocation="vml-officeDrawing.xsd"/> <xsd:import namespace="http://schemas.openxmlformats.org/wordprocessingml/2006/main" - Network accessline 13
schemaLocation="vml-officeDrawing.xsd"/> <xsd:import namespace="http://schemas.openxmlformats.org/wordprocessingml/2006/main" schemaLocation="wml.xsd"/> - Network accessline 14
<xsd:import namespace="http://schemas.openxmlformats.org/wordprocessingml/2006/main" - Network accessline 16
<xsd:import namespace="urn:schemas-microsoft-com:office:word" schemaLocation="vml-wordprocessingDrawing.xsd"/> <xsd:import namespace="http://schemas.openxmlformats.org/officeDocument/2006/relationships" - Network accessline 17
schemaLocation="vml-wordprocessingDrawing.xsd"/> <xsd:import namespace="http://schemas.openxmlformats.org/officeDocument/2006/relationships" schemaLocation="shared-relationshipReference.xsd"/> - Network accessline 18
<xsd:import namespace="http://schemas.openxmlformats.org/officeDocument/2006/relationships" - Network accessline 22
<xsd:import namespace="urn:schemas-microsoft-com:office:powerpoint" schemaLocation="vml-presentationDrawing.xsd"/> <xsd:import namespace="http://schemas.openxmlformats.org/officeDocument/2006/sharedTypes" - Network accessline 23
schemaLocation="vml-presentationDrawing.xsd"/> <xsd:import namespace="http://schemas.openxmlformats.org/officeDocument/2006/sharedTypes" schemaLocation="shared-commonSimpleTypes.xsd"/> - Network accessline 24
<xsd:import namespace="http://schemas.openxmlformats.org/officeDocument/2006/sharedTypes"
skills/14-文档处理与数据工具/xlsx/scripts/office/schemas/ISO-IEC29500-4_2016/vml-officeDrawing.xsd
- Network accessline 1
<?xml version="1.0" encoding="utf-8"?> <xsd:schema xmlns:xsd="http://www.w3.org/2001/XMLSchema" - Network accessline 2
<xsd:schema xmlns:xsd="http://www.w3.org/2001/XMLSchema" - Network accessline 3
xmlns="urn:schemas-microsoft-com:office:office" xmlns:v="urn:schemas-microsoft-com:vml" xmlns:r="http://schemas.openxmlformats.org/officeDocument/2006/relationships" xmlns:s="http://schemas.openxmlformats.org/officeDocument/2006/sharedT - Network accessline 4
xmlns:r="http://schemas.openxmlformats.org/officeDocument/2006/relationships" - Network accessline 5
xmlns:s="http://schemas.openxmlformats.org/officeDocument/2006/sharedTypes" - Network accessline 7
attributeFormDefault="unqualified"> <xsd:import namespace="urn:schemas-microsoft-com:vml" schemaLocation="vml-main.xsd"/> <xsd:import namespace="http://schemas.openxmlformats.org/officeDocument/2006/relationships" - Network accessline 8
<xsd:import namespace="urn:schemas-microsoft-com:vml" schemaLocation="vml-main.xsd"/> <xsd:import namespace="http://schemas.openxmlformats.org/officeDocument/2006/relationships" schemaLocation="shared-relationshipReference.xsd"/> - Network accessline 9
<xsd:import namespace="http://schemas.openxmlformats.org/officeDocument/2006/relationships" - Network accessline 10
schemaLocation="shared-relationshipReference.xsd"/> <xsd:import namespace="http://schemas.openxmlformats.org/officeDocument/2006/sharedTypes" schemaLocation="shared-commonSimpleTypes.xsd"/> - Network accessline 11
<xsd:import namespace="http://schemas.openxmlformats.org/officeDocument/2006/sharedTypes"
skills/14-文档处理与数据工具/xlsx/scripts/office/schemas/ISO-IEC29500-4_2016/vml-presentationDrawing.xsd
skills/14-文档处理与数据工具/xlsx/scripts/office/schemas/ISO-IEC29500-4_2016/vml-spreadsheetDrawing.xsd
- Network accessline 1
<?xml version="1.0" encoding="utf-8"?> <xsd:schema xmlns:xsd="http://www.w3.org/2001/XMLSchema" - Network accessline 2
<xsd:schema xmlns:xsd="http://www.w3.org/2001/XMLSchema" - Network accessline 3
xmlns="urn:schemas-microsoft-com:office:excel" xmlns:s="http://schemas.openxmlformats.org/officeDocument/2006/sharedTypes" targetNamespace="urn:schemas-microsoft-com:office:excel" elementFormDefault="qualified" - Network accessline 4
xmlns:s="http://schemas.openxmlformats.org/officeDocument/2006/sharedTypes" - Network accessline 5
targetNamespace="urn:schemas-microsoft-com:office:excel" elementFormDefault="qualified" attributeFormDefault="unqualified"> <xsd:import namespace="http://schemas.openxmlformats.org/officeDocument/2006/sharedTypes" - Network accessline 6
attributeFormDefault="unqualified"> <xsd:import namespace="http://schemas.openxmlformats.org/officeDocument/2006/sharedTypes" schemaLocation="shared-commonSimpleTypes.xsd"/> - Network accessline 7
<xsd:import namespace="http://schemas.openxmlformats.org/officeDocument/2006/sharedTypes"
skills/14-文档处理与数据工具/xlsx/scripts/office/schemas/ISO-IEC29500-4_2016/vml-wordprocessingDrawing.xsd
skills/14-文档处理与数据工具/xlsx/scripts/office/schemas/ISO-IEC29500-4_2016/wml.xsd
- Network accessline 1
<?xml version="1.0" encoding="utf-8"?> <xsd:schema xmlns:xsd="http://www.w3.org/2001/XMLSchema" - Network accessline 2
<xsd:schema xmlns:xsd="http://www.w3.org/2001/XMLSchema" - Network accessline 3
xmlns:m="http://schemas.openxmlformats.org/officeDocument/2006/math" - Network accessline 4
xmlns:r="http://schemas.openxmlformats.org/officeDocument/2006/relationships" - Network accessline 5
xmlns:sl="http://schemas.openxmlformats.org/schemaLibrary/2006/main" - Network accessline 6
xmlns:wp="http://schemas.openxmlformats.org/drawingml/2006/wordprocessingDrawing" - Network accessline 7
xmlns="http://schemas.openxmlformats.org/wordprocessingml/2006/main" - Network accessline 8
xmlns:s="http://schemas.openxmlformats.org/officeDocument/2006/sharedTypes" - Network accessline 9
xmlns:mc="http://schemas.openxmlformats.org/markup-compatibility/2006" - Network accessline 10
elementFormDefault="qualified" attributeFormDefault="qualified" blockDefault="#all" targetNamespace="http://schemas.openxmlformats.org/wordprocessingml/2006/main"> <xsd:import namespace="http://schemas.openxmlformats.org/markup-compatib - Network accessline 11
targetNamespace="http://schemas.openxmlformats.org/wordprocessingml/2006/main"> - Network accessline 12
<xsd:import namespace="http://schemas.openxmlformats.org/markup-compatibility/2006" schemaLocation="../mce/mc.xsd"/> - Network accessline 13
<xsd:import namespace="http://schemas.openxmlformats.org/drawingml/2006/wordprocessingDrawing" - Network accessline 14
schemaLocation="dml-wordprocessingDrawing.xsd"/> <xsd:import namespace="http://schemas.openxmlformats.org/officeDocument/2006/math" schemaLocation="shared-math.xsd"/> - Network accessline 15
<xsd:import namespace="http://schemas.openxmlformats.org/officeDocument/2006/math" - Network accessline 16
schemaLocation="shared-math.xsd"/> <xsd:import namespace="http://schemas.openxmlformats.org/officeDocument/2006/relationships" schemaLocation="shared-relationshipReference.xsd"/> - Network accessline 17
<xsd:import namespace="http://schemas.openxmlformats.org/officeDocument/2006/relationships" - Network accessline 18
schemaLocation="shared-relationshipReference.xsd"/> <xsd:import namespace="http://schemas.openxmlformats.org/officeDocument/2006/sharedTypes" schemaLocation="shared-commonSimpleTypes.xsd"/> - Network accessline 19
<xsd:import namespace="http://schemas.openxmlformats.org/officeDocument/2006/sharedTypes" - Network accessline 20
schemaLocation="shared-commonSimpleTypes.xsd"/> <xsd:import namespace="http://schemas.openxmlformats.org/schemaLibrary/2006/main" schemaLocation="shared-customXmlSchemaProperties.xsd"/> - Network accessline 21
<xsd:import namespace="http://schemas.openxmlformats.org/schemaLibrary/2006/main" - Network accessline 22
schemaLocation="shared-customXmlSchemaProperties.xsd"/> <xsd:import namespace="http://www.w3.org/XML/1998/namespace"/> <xsd:complexType name="CT_Empty"/> - Network accessline 23
<xsd:import namespace="http://www.w3.org/XML/1998/namespace"/>
skills/14-文档处理与数据工具/xlsx/scripts/office/schemas/ISO-IEC29500-4_2016/xml.xsd
- Network accessline 1
<?xml version='1.0'?> <xs:schema targetNamespace="http://www.w3.org/XML/1998/namespace" xmlns:xs="http://www.w3.org/2001/XMLSchema" xml:lang="en"> - Network accessline 2
<xs:schema targetNamespace="http://www.w3.org/XML/1998/namespace" xmlns:xs="http://www.w3.org/2001/XMLSchema" xml:lang="en"> - Network accessline 4
<xs:annotation> <xs:documentation> See http://www.w3.org/XML/1998/namespace.html and - Network accessline 5
<xs:documentation> See http://www.w3.org/XML/1998/namespace.html and http://www.w3.org/TR/REC-xml for information about this namespace. - Network accessline 6
See http://www.w3.org/XML/1998/namespace.html and - Network accessline 7
http://www.w3.org/TR/REC-xml for information about this namespace. - Network accessline 55
<schema . . .> . . . <import namespace="http://www.w3.org/XML/1998/namespace" - Network accessline 56
. . . <import namespace="http://www.w3.org/XML/1998/namespace" schemaLocation="http://www.w3.org/2001/03/xml.xsd"/> - Network accessline 57
<import namespace="http://www.w3.org/XML/1998/namespace" - Network accessline 58
schemaLocation="http://www.w3.org/2001/03/xml.xsd"/> - Network accessline 72
<xs:documentation>In keeping with the XML Schema WG's standard versioning policy, this schema document will persist at http://www.w3.org/2001/03/xml.xsd. - Network accessline 73
policy, this schema document will persist at http://www.w3.org/2001/03/xml.xsd. At the date of issue it can also be found at - Network accessline 74
http://www.w3.org/2001/03/xml.xsd. - Network accessline 75
At the date of issue it can also be found at http://www.w3.org/2001/xml.xsd. The schema document at that URI may however change in the future, - Network accessline 76
http://www.w3.org/2001/xml.xsd. - Network accessline 79
itself. In other words, if the XML Schema namespace changes, the version of this document at http://www.w3.org/2001/xml.xsd will change - Network accessline 80
of this document at http://www.w3.org/2001/xml.xsd will change accordingly; the version at - Network accessline 81
http://www.w3.org/2001/xml.xsd will change - Network accessline 82
accordingly; the version at http://www.w3.org/2001/03/xml.xsd will not change. </xs:documentation> - Network accessline 83
http://www.w3.org/2001/03/xml.xsd will not change. - Network accessline 103
<xs:attribute name="base" type="xs:anyURI"> <xs:annotation> <xs:documentation>See http://www.w3.org/TR/xmlbase/ for - Network accessline 104
<xs:annotation> <xs:documentation>See http://www.w3.org/TR/xmlbase/ for information about this attribute.</xs:documentation> - Network accessline 105
<xs:documentation>See http://www.w3.org/TR/xmlbase/ for
skills/14-文档处理与数据工具/xlsx/scripts/office/schemas/ecma/fouth-edition/opc-contentTypes.xsd
- Network accessline 1
<U+FEFF><?xml version="1.0" encoding="UTF-8" standalone="no"?> <xs:schema xmlns="http://schemas.openxmlformats.org/package/2006/content-types" - Network accessline 2
<xs:schema xmlns="http://schemas.openxmlformats.org/package/2006/content-types" - Network accessline 3
xmlns:xs="http://www.w3.org/2001/XMLSchema" - Network accessline 4
targetNamespace="http://schemas.openxmlformats.org/package/2006/content-types"
skills/14-文档处理与数据工具/xlsx/scripts/office/schemas/ecma/fouth-edition/opc-coreProperties.xsd
- Network accessline 1
<U+FEFF><?xml version="1.0" encoding="UTF-8"?> <xs:schema targetNamespace="http://schemas.openxmlformats.org/package/2006/metadata/core-properties" - Network accessline 2
<xs:schema targetNamespace="http://schemas.openxmlformats.org/package/2006/metadata/core-properties" - Network accessline 3
xmlns="http://schemas.openxmlformats.org/package/2006/metadata/core-properties" - Network accessline 4
xmlns:xs="http://www.w3.org/2001/XMLSchema" xmlns:dc="http://purl.org/dc/elements/1.1/" - Network accessline 5
xmlns:dcterms="http://purl.org/dc/terms/" elementFormDefault="qualified" blockDefault="#all"> - Network accessline 7
<xs:import namespace="http://purl.org/dc/elements/1.1/" - Network accessline 8
schemaLocation="http://dublincore.org/schemas/xmls/qdc/2003/04/02/dc.xsd"/> - Network accessline 9
<xs:import namespace="http://purl.org/dc/terms/" - Network accessline 10
schemaLocation="http://dublincore.org/schemas/xmls/qdc/2003/04/02/dcterms.xsd"/> - Network accessline 11
<xs:import id="xml" namespace="http://www.w3.org/XML/1998/namespace"/>
skills/14-文档处理与数据工具/xlsx/scripts/office/schemas/ecma/fouth-edition/opc-digSig.xsd
- Network accessline 1
<?xml version="1.0" encoding="UTF-8"?> <xsd:schema xmlns="http://schemas.openxmlformats.org/package/2006/digital-signature" - Network accessline 2
<xsd:schema xmlns="http://schemas.openxmlformats.org/package/2006/digital-signature" - Network accessline 3
xmlns:xsd="http://www.w3.org/2001/XMLSchema" - Network accessline 4
targetNamespace="http://schemas.openxmlformats.org/package/2006/digital-signature"
skills/14-文档处理与数据工具/xlsx/scripts/office/schemas/ecma/fouth-edition/opc-relationships.xsd
- Network accessline 1
<U+FEFF><?xml version="1.0" encoding="UTF-8" standalone="no"?> <xsd:schema xmlns="http://schemas.openxmlformats.org/package/2006/relationships" - Network accessline 2
<xsd:schema xmlns="http://schemas.openxmlformats.org/package/2006/relationships" - Network accessline 3
xmlns:xsd="http://www.w3.org/2001/XMLSchema" - Network accessline 4
targetNamespace="http://schemas.openxmlformats.org/package/2006/relationships"
skills/14-文档处理与数据工具/xlsx/scripts/office/schemas/mce/mc.xsd
- Network accessline 1
<?xml version="1.0" encoding="utf-8"?> <xsd:schema xmlns:mc="http://schemas.openxmlformats.org/markup-compatibility/2006" - Network accessline 2
<xsd:schema xmlns:mc="http://schemas.openxmlformats.org/markup-compatibility/2006" - Network accessline 3
attributeFormDefault="unqualified" elementFormDefault="qualified" targetNamespace="http://schemas.openxmlformats.org/markup-compatibility/2006" xmlns:xsd="http://www.w3.org/2001/XMLSchema"> - Network accessline 4
targetNamespace="http://schemas.openxmlformats.org/markup-compatibility/2006" - Network accessline 5
xmlns:xsd="http://www.w3.org/2001/XMLSchema"> - Network accessline 7
<!-- This XSD is a modified version of the one found at: https://github.com/plutext/docx4j/blob/master/xsd/mce/markup-compatibility-2006-MINIMAL.xsd - Network accessline 9
https://github.com/plutext/docx4j/blob/master/xsd/mce/markup-compatibility-2006-MINIMAL.xsd - Network accessline 15
<w:document xmlns:mc="http://schemas.openxmlformats.org/markup-compatibility/2006" - Network accessline 16
xmlns:mc="http://schemas.openxmlformats.org/markup-compatibility/2006" - Network accessline 17
xmlns:w="http://schemas.openxmlformats.org/wordprocessingml/2006/main"
skills/14-文档处理与数据工具/xlsx/scripts/office/schemas/microsoft/wml-2010.xsd
- Network accessline 1
<xsd:schema xmlns:xsd="http://www.w3.org/2001/XMLSchema" xmlns:w12="http://schemas.openxmlformats.org/wordprocessingml/2006/main" elementFormDefault="qualified" attributeFormDefault="qualified" blockDefault="#all" xmlns:r="http://schemas.op - Network accessline 2
<!-- <xsd:import id="rel" namespace="http://schemas.openxmlformats.org/officeDocument/2006/relationships" schemaLocation="orel.xsd"/> --> - Network accessline 3
<xsd:import id="w" namespace="http://schemas.openxmlformats.org/wordprocessingml/2006/main" schemaLocation="../ISO-IEC29500-4_2016/wml.xsd"/> - Network accessline 4
<!-- <xsd:import namespace="http://schemas.openxmlformats.org/drawingml/2006/main" schemaLocation="oartbasetypes.xsd"/> - Network accessline 5
<xsd:import namespace="http://schemas.openxmlformats.org/drawingml/2006/main" schemaLocation="oartsplineproperties.xsd"/> -->
skills/14-文档处理与数据工具/xlsx/scripts/office/schemas/microsoft/wml-2012.xsd
- Network accessline 1
<xsd:schema xmlns:xsd="http://www.w3.org/2001/XMLSchema" xmlns:w12="http://schemas.openxmlformats.org/wordprocessingml/2006/main" xmlns:s="http://schemas.openxmlformats.org/officeDocument/2006/sharedTypes" elementFormDefault="qualified" att - Network accessline 2
<xsd:import id="w12" namespace="http://schemas.openxmlformats.org/wordprocessingml/2006/main" schemaLocation="../ISO-IEC29500-4_2016/wml.xsd"/> - Network accessline 3
<xsd:import namespace="http://schemas.openxmlformats.org/officeDocument/2006/sharedTypes" schemaLocation="../ISO-IEC29500-4_2016/shared-commonSimpleTypes.xsd"/>
skills/14-文档处理与数据工具/xlsx/scripts/office/schemas/microsoft/wml-2018.xsd
- Network accessline 1
<xsd:schema xmlns:xsd="http://www.w3.org/2001/XMLSchema" xmlns:w12="http://schemas.openxmlformats.org/wordprocessingml/2006/main" elementFormDefault="qualified" attributeFormDefault="qualified" blockDefault="#all" xmlns="http://schemas.micr - Network accessline 2
<xsd:import id="w12" namespace="http://schemas.openxmlformats.org/wordprocessingml/2006/main" schemaLocation="../ISO-IEC29500-4_2016/wml.xsd"/>
skills/14-文档处理与数据工具/xlsx/scripts/office/schemas/microsoft/wml-cex-2018.xsd
- Network accessline 1
<xsd:schema xmlns:xsd="http://www.w3.org/2001/XMLSchema" xmlns:w="http://schemas.openxmlformats.org/wordprocessingml/2006/main" xmlns:s="http://schemas.openxmlformats.org/officeDocument/2006/sharedTypes" xmlns:w16="http://schemas.microsoft. - Network accessline 2
<xsd:import id="w16" namespace="http://schemas.microsoft.com/office/word/2018/wordml" schemaLocation="wml-2018.xsd"/> - Network accessline 3
<xsd:import id="w" namespace="http://schemas.openxmlformats.org/wordprocessingml/2006/main" schemaLocation="../ISO-IEC29500-4_2016/wml.xsd"/> - Network accessline 4
<xsd:import id="s" namespace="http://schemas.openxmlformats.org/officeDocument/2006/sharedTypes" schemaLocation="../ISO-IEC29500-4_2016/shared-commonSimpleTypes.xsd"/>
skills/14-文档处理与数据工具/xlsx/scripts/office/schemas/microsoft/wml-cid-2016.xsd
- Network accessline 1
<xsd:schema xmlns:xsd="http://www.w3.org/2001/XMLSchema" xmlns:w12="http://schemas.openxmlformats.org/wordprocessingml/2006/main" elementFormDefault="qualified" attributeFormDefault="qualified" blockDefault="#all" xmlns="http://schemas.micr - Network accessline 2
<xsd:import id="w12" namespace="http://schemas.openxmlformats.org/wordprocessingml/2006/main" schemaLocation="../ISO-IEC29500-4_2016/wml.xsd"/>
skills/14-文档处理与数据工具/xlsx/scripts/office/schemas/microsoft/wml-sdtdatahash-2020.xsd
- Network accessline 1
<xsd:schema xmlns:xsd="http://www.w3.org/2001/XMLSchema" xmlns:w12="http://schemas.openxmlformats.org/wordprocessingml/2006/main" elementFormDefault="qualified" attributeFormDefault="qualified" blockDefault="#all" xmlns="http://schemas.micr - Network accessline 2
<xsd:import id="w12" namespace="http://schemas.openxmlformats.org/wordprocessingml/2006/main" schemaLocation="../ISO-IEC29500-4_2016/wml.xsd"/>
skills/14-文档处理与数据工具/xlsx/scripts/office/schemas/microsoft/wml-symex-2015.xsd
- Network accessline 1
<xsd:schema xmlns:xsd="http://www.w3.org/2001/XMLSchema" xmlns:w12="http://schemas.openxmlformats.org/wordprocessingml/2006/main" elementFormDefault="qualified" attributeFormDefault="qualified" blockDefault="#all" xmlns="http://schemas.micr - Network accessline 2
<xsd:import id="w12" namespace="http://schemas.openxmlformats.org/wordprocessingml/2006/main" schemaLocation="../ISO-IEC29500-4_2016/wml.xsd"/>
skills/14-文档处理与数据工具/xlsx/scripts/office/soffice.py
- Executable scriptline 1
Executable file - Access to secretsline 25
[Credential-related source omitted] - Network accessline 44
def _needs_shim() -> bool: try: s = socket.socket(socket.AF_UNIX, socket.SOCK_STREAM) - Network accessline 45
try: s = socket.socket(socket.AF_UNIX, socket.SOCK_STREAM) s.close() - Network accessline 46
s = socket.socket(socket.AF_UNIX, socket.SOCK_STREAM)
skills/14-文档处理与数据工具/xlsx/scripts/office/unpack.py
- Executable scriptline 1
Executable file
skills/14-文档处理与数据工具/xlsx/scripts/office/validate.py
- Executable scriptline 1
Executable file
skills/14-文档处理与数据工具/xlsx/scripts/office/validators/__init__.py
- Executable scriptline 1
Executable file
skills/14-文档处理与数据工具/xlsx/scripts/office/validators/base.py
- Executable scriptline 1
Executable file - Network accessline 61
MC_NAMESPACE = "http://schemas.openxmlformats.org/markup-compatibility/2006" - Network accessline 62
XML_NAMESPACE = "http://www.w3.org/XML/1998/namespace" - Network accessline 64
PACKAGE_RELATIONSHIPS_NAMESPACE = ( "http://schemas.openxmlformats.org/package/2006/relationships" - Network accessline 65
"http://schemas.openxmlformats.org/package/2006/relationships" - Network accessline 66
) OFFICE_RELATIONSHIPS_NAMESPACE = ( "http://schemas.openxmlformats.org/officeDocument/2006/relationships" - Network accessline 67
OFFICE_RELATIONSHIPS_NAMESPACE = ( "http://schemas.openxmlformats.org/officeDocument/2006/relationships" ) - Network accessline 68
"http://schemas.openxmlformats.org/officeDocument/2006/relationships" - Network accessline 69
) CONTENT_TYPES_NAMESPACE = ( "http://schemas.openxmlformats.org/package/2006/content-types" - Network accessline 70
CONTENT_TYPES_NAMESPACE = ( "http://schemas.openxmlformats.org/package/2006/content-types" ) - Network accessline 71
"http://schemas.openxmlformats.org/package/2006/content-types" - Network accessline 76
OOXML_NAMESPACES = { "http://schemas.openxmlformats.org/officeDocument/2006/math", - Network accessline 77
"http://schemas.openxmlformats.org/officeDocument/2006/math", - Network accessline 78
"http://schemas.openxmlformats.org/officeDocument/2006/relationships", - Network accessline 79
"http://schemas.openxmlformats.org/schemaLibrary/2006/main", - Network accessline 80
"http://schemas.openxmlformats.org/drawingml/2006/main", - Network accessline 81
"http://schemas.openxmlformats.org/drawingml/2006/chart", - Network accessline 82
"http://schemas.openxmlformats.org/drawingml/2006/chartDrawing", - Network accessline 83
"http://schemas.openxmlformats.org/drawingml/2006/diagram", - Network accessline 84
"http://schemas.openxmlformats.org/drawingml/2006/picture", - Network accessline 85
"http://schemas.openxmlformats.org/drawingml/2006/spreadsheetDrawing", - Network accessline 86
"http://schemas.openxmlformats.org/drawingml/2006/wordprocessingDrawing", - Network accessline 87
"http://schemas.openxmlformats.org/wordprocessingml/2006/main", - Network accessline 88
"http://schemas.openxmlformats.org/presentationml/2006/main", - Network accessline 89
"http://schemas.openxmlformats.org/spreadsheetml/2006/main", - Network accessline 90
"http://schemas.openxmlformats.org/officeDocument/2006/sharedTypes", - Network accessline 91
"http://www.w3.org/XML/1998/namespace",
skills/14-文档处理与数据工具/xlsx/scripts/office/validators/docx.py
- Executable scriptline 1
Executable file - Network accessline 18
WORD_2006_NAMESPACE = "http://schemas.openxmlformats.org/wordprocessingml/2006/main" - Network accessline 19
W14_NAMESPACE = "http://schemas.microsoft.com/office/word/2010/wordml" - Network accessline 20
W16CID_NAMESPACE = "http://schemas.microsoft.com/office/word/2016/wordml/cid"
skills/14-文档处理与数据工具/xlsx/scripts/office/validators/pptx.py
skills/14-文档处理与数据工具/xlsx/scripts/office/validators/redlining.py
- Executable scriptline 1
Executable file - Network accessline 17
self.author = author self.namespaces = { "w": "http://schemas.openxmlformats.org/wordprocessingml/2006/main" - Network accessline 18
self.namespaces = { "w": "http://schemas.openxmlformats.org/wordprocessingml/2006/main" } - Network accessline 19
"w": "http://schemas.openxmlformats.org/wordprocessingml/2006/main"
skills/14-文档处理与数据工具/xlsx/scripts/recalc.py
- Executable scriptline 1
Executable file - Network accessline 21
RECALCULATE_MACRO = """<?xml version="1.0" encoding="UTF-8"?> <!DOCTYPE script:module PUBLIC "-//OpenOffice.org//DTD OfficeDocument 1.0//EN" "module.dtd"> <script:module xmlns:script="http://openoffice.org/2000/script" script:name="Module1" - Network accessline 22
<!DOCTYPE script:module PUBLIC "-//OpenOffice.org//DTD OfficeDocument 1.0//EN" "module.dtd"> <script:module xmlns:script="http://openoffice.org/2000/script" script:name="Module1" script:language="StarBasic"> Sub RecalculateAndSave() - Network accessline 23
<script:module xmlns:script="http://openoffice.org/2000/script" script:name="Module1" script:language="StarBasic">
skills/15-金融与经济数据/alpha-vantage/SKILL.md
- Network accessline 15
1. Get a free key at https://www.alphavantage.co/support/#api-key (premium plans available for higher rate limits) - Access to secretsline 19
[Credential-related source omitted] - Network accessline 33
[Credential-related source omitted] - Network accessline 40
[Credential-related source omitted] - Access to secretsline 40
[Credential-related source omitted] - Network accessline 41
BASE_URL = "https://www.alphavantage.co/query" - Network accessline 43
[Credential-related source omitted] - Network accessline 44
[Credential-related source omitted] - Access to secretsline 44
[Credential-related source omitted]
skills/15-金融与经济数据/alpha-vantage/references/fundamentals.md
- Network accessline 186
# Returns CSV format - use requests directly import requests, csv, io, os resp = requests.get( - Network accessline 187
import requests, csv, io, os resp = requests.get( "https://www.alphavantage.co/query", - Network accessline 188
resp = requests.get( - Network accessline 189
"https://www.alphavantage.co/query", - Access to secretsline 190
[Credential-related source omitted] - Network accessline 202
# Returns CSV resp = requests.get( - Network accessline 203
resp = requests.get( - Network accessline 204
"https://www.alphavantage.co/query", - Access to secretsline 205
[Credential-related source omitted] - Network accessline 215
# Returns CSV resp = requests.get( - Network accessline 216
resp = requests.get( - Network accessline 217
"https://www.alphavantage.co/query", - Access to secretsline 218
[Credential-related source omitted]
skills/15-金融与经济数据/alpha-vantage/references/time-series.md
- Network accessline 3
Base URL: `https://www.alphavantage.co/query`
skills/15-金融与经济数据/edgartools/references/companies.md
- Network accessline 61
company.fiscal_year_end # "0630" (June 30) company.exchange # "Nasdaq" company.website # "https://www.microsoft.com" - Network accessline 62
company.exchange # "Nasdaq" company.website # "https://www.microsoft.com" company.city # "Redmond" - Network accessline 63
company.website # "https://www.microsoft.com"
skills/15-金融与经济数据/edgartools/references/filings.md
skills/15-金融与经济数据/fred-economic-data/SKILL.md
- Network accessline 27
1. Create an account at https://fredaccount.stlouisfed.org - Access to secretsline 32
[Credential-related source omitted] - Access to secretsline 38
[Credential-related source omitted] - Access to secretsline 49
[Credential-related source omitted] - Network accessline 72
[Credential-related source omitted] - Access to secretsline 72
[Credential-related source omitted] - Network accessline 73
BASE_URL = "https://api.stlouisfed.org/fred" - Network accessline 75
# Get series observations response = requests.get( - Network accessline 76
response = requests.get( - Access to secretsline 79
[Credential-related source omitted] - Network accessline 425
- **FRED Homepage**: https://fred.stlouisfed.org/ - Network accessline 426
- **API Documentation**: https://fred.stlouisfed.org/docs/api/fred/ - Network accessline 427
- **GeoFRED Maps**: https://geofred.stlouisfed.org/ - Network accessline 428
- **ALFRED (Vintage Data)**: https://alfred.stlouisfed.org/ - Network accessline 429
- **Terms of Use**: https://fred.stlouisfed.org/legal/
skills/15-金融与经济数据/fred-economic-data/references/api_basics.md
- Network accessline 6
https://api.stlouisfed.org/fred/ - Network accessline 11
https://api.stlouisfed.org/geofred/ - Access to secretsline 19
[Credential-related source omitted] - Network accessline 24
1. Create account at https://fredaccount.stlouisfed.org - Access to secretsline 53
[Credential-related source omitted] - Access to secretsline 128
[Credential-related source omitted] - Access to secretsline 135
[Credential-related source omitted] - Network accessline 181
response = requests.get( - Network accessline 182
"https://api.stlouisfed.org/fred/series/observations", - Access to secretsline 184
[Credential-related source omitted] - Access to secretsline 206
[Credential-related source omitted] - Access to secretsline 211
[Credential-related source omitted]
skills/15-金融与经济数据/fred-economic-data/references/categories.md
- Network accessline 34
**URL:** `https://api.stlouisfed.org/fred/category` - Access to secretsline 40
[Credential-related source omitted] - Network accessline 52
# Get root category response = requests.get( - Network accessline 53
response = requests.get( - Network accessline 54
"https://api.stlouisfed.org/fred/category", - Access to secretsline 56
[Credential-related source omitted] - Network accessline 62
# Get Trade Balance category response = requests.get( - Network accessline 63
response = requests.get( - Network accessline 64
"https://api.stlouisfed.org/fred/category", - Access to secretsline 66
[Credential-related source omitted] - Network accessline 93
**URL:** `https://api.stlouisfed.org/fred/category/children` - Access to secretsline 99
[Credential-related source omitted] - Network accessline 113
# Get children of International Trade category (13) response = requests.get( - Network accessline 114
response = requests.get( - Network accessline 115
"https://api.stlouisfed.org/fred/category/children", - Access to secretsline 117
[Credential-related source omitted] - Network accessline 144
**URL:** `https://api.stlouisfed.org/fred/category/related` - Access to secretsline 150
[Credential-related source omitted] - Network accessline 166
response = requests.get( - Network accessline 167
"https://api.stlouisfed.org/fred/category/related", - Access to secretsline 169
[Credential-related source omitted] - Network accessline 193
**URL:** `https://api.stlouisfed.org/fred/category/series` - Access to secretsline 199
[Credential-related source omitted] - Network accessline 236
# Get series in Trade Balance category with monthly frequency response = requests.get( - Network accessline 237
response = requests.get( - Network accessline 238
"https://api.stlouisfed.org/fred/category/series", - Access to secretsline 240
[Credential-related source omitted] - Network accessline 280
**URL:** `https://api.stlouisfed.org/fred/category/tags` - Access to secretsline 286
[Credential-related source omitted] - Network accessline 319
# Get frequency tags for Trade Balance category response = requests.get( - Network accessline 320
response = requests.get( - Network accessline 321
"https://api.stlouisfed.org/fred/category/tags", - Access to secretsline 323
[Credential-related source omitted] - Network accessline 349
**URL:** `https://api.stlouisfed.org/fred/category/related_tags` - Access to secretsline 355
[Credential-related source omitted] - Network accessline 377
# Get tags related to 'services' and 'quarterly' in Trade Balance category response = requests.get( - Network accessline 378
response = requests.get( - Network accessline 379
"https://api.stlouisfed.org/fred/category/related_tags", - Access to secretsline 381
[Credential-related source omitted] - Access to secretsline 413
[Credential-related source omitted] - Network accessline 418
# Get children response = requests.get( - Network accessline 419
response = requests.get( - Network accessline 420
"https://api.stlouisfed.org/fred/category/children", - Access to secretsline 422
[Credential-related source omitted] - Access to secretsline 434
[Credential-related source omitted] - Access to secretsline 441
[Credential-related source omitted]
skills/15-金融与经济数据/fred-economic-data/references/geofred.md
- Network accessline 15
https://api.stlouisfed.org/geofred/ - Network accessline 33
**URL:** `https://api.stlouisfed.org/geofred/shapes/file` - Access to secretsline 39
[Credential-related source omitted] - Network accessline 64
# Get US state boundaries response = requests.get( - Network accessline 65
response = requests.get( - Network accessline 66
"https://api.stlouisfed.org/geofred/shapes/file", - Access to secretsline 68
[Credential-related source omitted] - Network accessline 112
# Get shapes shapes = requests.get( - Network accessline 113
shapes = requests.get( - Network accessline 114
"https://api.stlouisfed.org/geofred/shapes/file", - Access to secretsline 115
[Credential-related source omitted] - Network accessline 118
# Get unemployment data data = requests.get( - Network accessline 119
data = requests.get( - Network accessline 120
"https://api.stlouisfed.org/geofred/regional/data", - Access to secretsline 122
[Credential-related source omitted] - Network accessline 152
**URL:** `https://api.stlouisfed.org/geofred/series/group` - Access to secretsline 158
[Credential-related source omitted] - Network accessline 170
# Get info about Texas employment series response = requests.get( - Network accessline 171
response = requests.get( - Network accessline 172
"https://api.stlouisfed.org/geofred/series/group", - Access to secretsline 174
[Credential-related source omitted] - Network accessline 219
**URL:** `https://api.stlouisfed.org/geofred/series/data` - Access to secretsline 225
[Credential-related source omitted] - Network accessline 241
# Get Wisconsin per capita income data response = requests.get( - Network accessline 242
response = requests.get( - Network accessline 243
"https://api.stlouisfed.org/geofred/series/data", - Access to secretsline 245
[Credential-related source omitted] - Network accessline 290
**URL:** `https://api.stlouisfed.org/geofred/regional/data` - Access to secretsline 296
[Credential-related source omitted] - Network accessline 339
response = requests.get( - Network accessline 340
"https://api.stlouisfed.org/geofred/regional/data", - Access to secretsline 342
[Credential-related source omitted] - Network accessline 387
response = requests.get( - Network accessline 388
"https://api.stlouisfed.org/geofred/regional/data", - Access to secretsline 390
[Credential-related source omitted] - Network accessline 405
response = requests.get( - Network accessline 406
"https://api.stlouisfed.org/geofred/regional/data", - Access to secretsline 408
[Credential-related source omitted] - Access to secretsline 438
[Credential-related source omitted] - Network accessline 449
for name, params in indicators.items(): response = requests.get( - Network accessline 450
response = requests.get( - Network accessline 451
"https://api.stlouisfed.org/geofred/regional/data", - Access to secretsline 453
[Credential-related source omitted] - Access to secretsline 478
[Credential-related source omitted] - Access to secretsline 487
[Credential-related source omitted] - Network accessline 490
# Get shapes shapes = requests.get( - Network accessline 491
shapes = requests.get( - Network accessline 492
f"https://api.stlouisfed.org/geofred/shapes/file", - Access to secretsline 493
[Credential-related source omitted] - Network accessline 496
# Get data response = requests.get( - Network accessline 497
response = requests.get( - Network accessline 498
"https://api.stlouisfed.org/geofred/regional/data", - Access to secretsline 500
[Credential-related source omitted] - Access to secretsline 533
[Credential-related source omitted] - Access to secretsline 544
[Credential-related source omitted] - Network accessline 557
response = requests.get( - Network accessline 558
"https://api.stlouisfed.org/geofred/regional/data", - Access to secretsline 560
[Credential-related source omitted] - Access to secretsline 582
[Credential-related source omitted]
skills/15-金融与经济数据/fred-economic-data/references/releases.md
- Network accessline 33
**URL:** `https://api.stlouisfed.org/fred/releases` - Access to secretsline 39
[Credential-related source omitted] - Network accessline 56
response = requests.get( - Network accessline 57
"https://api.stlouisfed.org/fred/releases", - Access to secretsline 59
[Credential-related source omitted] - Network accessline 80
"name": "Advance Monthly Sales for Retail and Food Services", "press_release": true, "link": "http://www.census.gov/retail/" - Network accessline 81
"press_release": true, "link": "http://www.census.gov/retail/" }, - Network accessline 82
"link": "http://www.census.gov/retail/" - Network accessline 88
"name": "Gross Domestic Product", "press_release": true, "link": "http://www.bea.gov/national/index.htm" - Network accessline 89
"press_release": true, "link": "http://www.bea.gov/national/index.htm" } - Network accessline 90
"link": "http://www.bea.gov/national/index.htm" - Network accessline 102
**URL:** `https://api.stlouisfed.org/fred/releases/dates` - Access to secretsline 108
[Credential-related source omitted] - Network accessline 134
response = requests.get( - Network accessline 135
"https://api.stlouisfed.org/fred/releases/dates", - Access to secretsline 137
[Credential-related source omitted] - Network accessline 176
**URL:** `https://api.stlouisfed.org/fred/release` - Access to secretsline 182
[Credential-related source omitted] - Network accessline 196
# Get GDP release info response = requests.get( - Network accessline 197
response = requests.get( - Network accessline 198
"https://api.stlouisfed.org/fred/release", - Access to secretsline 200
[Credential-related source omitted] - Network accessline 218
"name": "Gross Domestic Product", "press_release": true, "link": "http://www.bea.gov/national/index.htm" - Network accessline 219
"press_release": true, "link": "http://www.bea.gov/national/index.htm" } - Network accessline 220
"link": "http://www.bea.gov/national/index.htm" - Network accessline 232
**URL:** `https://api.stlouisfed.org/fred/release/dates` - Access to secretsline 238
[Credential-related source omitted] - Network accessline 256
# Get historical GDP release dates response = requests.get( - Network accessline 257
response = requests.get( - Network accessline 258
"https://api.stlouisfed.org/fred/release/dates", - Access to secretsline 260
[Credential-related source omitted] - Network accessline 292
**URL:** `https://api.stlouisfed.org/fred/release/series` - Access to secretsline 298
[Credential-related source omitted] - Network accessline 335
# Get quarterly series from GDP release response = requests.get( - Network accessline 336
response = requests.get( - Network accessline 337
"https://api.stlouisfed.org/fred/release/series", - Access to secretsline 339
[Credential-related source omitted] - Network accessline 389
**URL:** `https://api.stlouisfed.org/fred/release/sources` - Access to secretsline 395
[Credential-related source omitted] - Network accessline 409
response = requests.get( - Network accessline 410
"https://api.stlouisfed.org/fred/release/sources", - Access to secretsline 412
[Credential-related source omitted] - Network accessline 429
"realtime_end": "2023-08-14", "name": "U.S. Department of Commerce: Bureau of Economic Analysis", "link": "http://www.bea.gov/" - Network accessline 430
"name": "U.S. Department of Commerce: Bureau of Economic Analysis", "link": "http://www.bea.gov/" }, - Network accessline 431
"link": "http://www.bea.gov/" - Network accessline 436
"realtime_end": "2023-08-14", "name": "U.S. Department of Commerce: Census Bureau", "link": "http://www.census.gov/" - Network accessline 437
"name": "U.S. Department of Commerce: Census Bureau", "link": "http://www.census.gov/" } - Network accessline 438
"link": "http://www.census.gov/" - Network accessline 450
**URL:** `https://api.stlouisfed.org/fred/release/tags` - Access to secretsline 456
[Credential-related source omitted] - Network accessline 477
response = requests.get( - Network accessline 478
"https://api.stlouisfed.org/fred/release/tags", - Access to secretsline 480
[Credential-related source omitted] - Network accessline 494
**URL:** `https://api.stlouisfed.org/fred/release/related_tags` - Access to secretsline 500
[Credential-related source omitted] - Network accessline 523
**URL:** `https://api.stlouisfed.org/fred/release/tables` - Access to secretsline 529
[Credential-related source omitted] - Network accessline 555
# Get GDP release table structure response = requests.get( - Network accessline 556
response = requests.get( - Network accessline 557
"https://api.stlouisfed.org/fred/release/tables", - Access to secretsline 559
[Credential-related source omitted] - Access to secretsline 611
[Credential-related source omitted] - Network accessline 616
response = requests.get( - Network accessline 617
"https://api.stlouisfed.org/fred/releases/dates", - Access to secretsline 619
[Credential-related source omitted]
skills/15-金融与经济数据/fred-economic-data/references/series.md
- Network accessline 24
**URL:** `https://api.stlouisfed.org/fred/series` - Access to secretsline 30
[Credential-related source omitted] - Network accessline 59
response = requests.get( - Network accessline 60
"https://api.stlouisfed.org/fred/series", - Access to secretsline 62
[Credential-related source omitted] - Network accessline 96
**URL:** `https://api.stlouisfed.org/fred/series/observations` - Access to secretsline 102
[Credential-related source omitted] - Network accessline 161
# Get quarterly GDP with percent change transformation response = requests.get( - Network accessline 162
response = requests.get( - Network accessline 163
"https://api.stlouisfed.org/fred/series/observations", - Access to secretsline 165
[Credential-related source omitted] - Network accessline 213
**URL:** `https://api.stlouisfed.org/fred/series/categories` - Access to secretsline 219
[Credential-related source omitted] - Network accessline 225
response = requests.get( - Network accessline 226
"https://api.stlouisfed.org/fred/series/categories", - Access to secretsline 228
[Credential-related source omitted] - Network accessline 252
**URL:** `https://api.stlouisfed.org/fred/series/release` - Access to secretsline 258
[Credential-related source omitted] - Network accessline 264
response = requests.get( - Network accessline 265
"https://api.stlouisfed.org/fred/series/release", - Access to secretsline 267
[Credential-related source omitted] - Network accessline 281
"name": "Gross Domestic Product", "press_release": true, "link": "http://www.bea.gov/national/index.htm" - Network accessline 282
"press_release": true, "link": "http://www.bea.gov/national/index.htm" } - Network accessline 283
"link": "http://www.bea.gov/national/index.htm" - Network accessline 295
**URL:** `https://api.stlouisfed.org/fred/series/search` - Access to secretsline 301
[Credential-related source omitted] - Network accessline 338
# Search for inflation-related series response = requests.get( - Network accessline 339
response = requests.get( - Network accessline 340
"https://api.stlouisfed.org/fred/series/search", - Access to secretsline 342
[Credential-related source omitted] - Network accessline 380
**URL:** `https://api.stlouisfed.org/fred/series/tags` - Access to secretsline 386
[Credential-related source omitted] - Network accessline 400
response = requests.get( - Network accessline 401
"https://api.stlouisfed.org/fred/series/tags", - Access to secretsline 403
[Credential-related source omitted] - Network accessline 428
**URL:** `https://api.stlouisfed.org/fred/series/updates` - Access to secretsline 434
[Credential-related source omitted] - Network accessline 452
response = requests.get( - Network accessline 453
"https://api.stlouisfed.org/fred/series/updates", - Access to secretsline 455
[Credential-related source omitted] - Network accessline 469
**URL:** `https://api.stlouisfed.org/fred/series/vintagedates` - Access to secretsline 475
[Credential-related source omitted] - Network accessline 492
response = requests.get( - Network accessline 493
"https://api.stlouisfed.org/fred/series/vintagedates", - Access to secretsline 495
[Credential-related source omitted] - Network accessline 521
**URL:** `https://api.stlouisfed.org/fred/series/search/tags` - Access to secretsline 527
[Credential-related source omitted] - Network accessline 549
**URL:** `https://api.stlouisfed.org/fred/series/search/related_tags` - Access to secretsline 555
[Credential-related source omitted] - Network accessline 575
response = requests.get( - Network accessline 576
"https://api.stlouisfed.org/fred/series/search/related_tags", - Access to secretsline 578
[Credential-related source omitted]
skills/15-金融与经济数据/fred-economic-data/references/sources.md
- Network accessline 27
**URL:** `https://api.stlouisfed.org/fred/sources` - Access to secretsline 33
[Credential-related source omitted] - Network accessline 50
response = requests.get( - Network accessline 51
"https://api.stlouisfed.org/fred/sources", - Access to secretsline 53
[Credential-related source omitted] - Network accessline 73
"realtime_end": "2023-08-14", "name": "Board of Governors of the Federal Reserve System (US)", "link": "http://www.federalreserve.gov/" - Network accessline 74
"name": "Board of Governors of the Federal Reserve System (US)", "link": "http://www.federalreserve.gov/" }, - Network accessline 75
"link": "http://www.federalreserve.gov/" - Network accessline 80
"realtime_end": "2023-08-14", "name": "Federal Reserve Bank of Philadelphia", "link": "http://www.philadelphiafed.org/" - Network accessline 81
"name": "Federal Reserve Bank of Philadelphia", "link": "http://www.philadelphiafed.org/" }, - Network accessline 82
"link": "http://www.philadelphiafed.org/" - Network accessline 87
"realtime_end": "2023-08-14", "name": "U.S. Department of Commerce: Bureau of Economic Analysis", "link": "http://www.bea.gov/" - Network accessline 88
"name": "U.S. Department of Commerce: Bureau of Economic Analysis", "link": "http://www.bea.gov/" }, - Network accessline 89
"link": "http://www.bea.gov/" - Network accessline 94
"realtime_end": "2023-08-14", "name": "U.S. Bureau of Labor Statistics", "link": "http://www.bls.gov/" - Network accessline 95
"name": "U.S. Bureau of Labor Statistics", "link": "http://www.bls.gov/" } - Network accessline 96
"link": "http://www.bls.gov/" - Network accessline 108
**URL:** `https://api.stlouisfed.org/fred/source` - Access to secretsline 114
[Credential-related source omitted] - Network accessline 128
# Get Federal Reserve Board info response = requests.get( - Network accessline 129
response = requests.get( - Network accessline 130
"https://api.stlouisfed.org/fred/source", - Access to secretsline 132
[Credential-related source omitted] - Network accessline 149
"realtime_end": "2023-08-14", "name": "Board of Governors of the Federal Reserve System (US)", "link": "http://www.federalreserve.gov/" - Network accessline 150
"name": "Board of Governors of the Federal Reserve System (US)", "link": "http://www.federalreserve.gov/" } - Network accessline 151
"link": "http://www.federalreserve.gov/" - Network accessline 163
**URL:** `https://api.stlouisfed.org/fred/source/releases` - Access to secretsline 169
[Credential-related source omitted] - Network accessline 187
# Get releases from the Federal Reserve Board response = requests.get( - Network accessline 188
response = requests.get( - Network accessline 189
"https://api.stlouisfed.org/fred/source/releases", - Access to secretsline 191
[Credential-related source omitted] - Network accessline 213
"name": "G.17 Industrial Production and Capacity Utilization", "press_release": true, "link": "http://www.federalreserve.gov/releases/g17/" - Network accessline 214
"press_release": true, "link": "http://www.federalreserve.gov/releases/g17/" }, - Network accessline 215
"link": "http://www.federalreserve.gov/releases/g17/" - Network accessline 221
"name": "G.19 Consumer Credit", "press_release": true, "link": "http://www.federalreserve.gov/releases/g19/" - Network accessline 222
"press_release": true, "link": "http://www.federalreserve.gov/releases/g19/" }, - Network accessline 223
"link": "http://www.federalreserve.gov/releases/g19/" - Network accessline 229
"name": "H.3 Aggregate Reserves of Depository Institutions", "press_release": true, "link": "http://www.federalreserve.gov/releases/h3/" - Network accessline 230
"press_release": true, "link": "http://www.federalreserve.gov/releases/h3/" }, - Network accessline 231
"link": "http://www.federalreserve.gov/releases/h3/" - Network accessline 237
"name": "H.6 Money Stock Measures", "press_release": true, "link": "http://www.federalreserve.gov/releases/h6/" - Network accessline 238
"press_release": true, "link": "http://www.federalreserve.gov/releases/h6/" } - Network accessline 239
"link": "http://www.federalreserve.gov/releases/h6/" - Access to secretsline 272
[Credential-related source omitted] - Network accessline 275
# Get source info source_info = requests.get( - Network accessline 276
source_info = requests.get( - Network accessline 277
"https://api.stlouisfed.org/fred/source", - Access to secretsline 279
[Credential-related source omitted] - Network accessline 285
# Get all releases from this source releases = requests.get( - Network accessline 286
releases = requests.get( - Network accessline 287
"https://api.stlouisfed.org/fred/source/releases", - Access to secretsline 289
[Credential-related source omitted] - Access to secretsline 301
[Credential-related source omitted] - Access to secretsline 307
[Credential-related source omitted] - Network accessline 311
for sid in source_ids: response = requests.get( - Network accessline 312
response = requests.get( - Network accessline 313
"https://api.stlouisfed.org/fred/source/releases", - Access to secretsline 315
[Credential-related source omitted] - Network accessline 322
# Get source name source_resp = requests.get( - Network accessline 323
source_resp = requests.get( - Network accessline 324
"https://api.stlouisfed.org/fred/source", - Access to secretsline 326
[Credential-related source omitted] - Access to secretsline 342
[Credential-related source omitted] - Access to secretsline 348
[Credential-related source omitted] - Network accessline 351
response = requests.get( - Network accessline 352
"https://api.stlouisfed.org/fred/sources", - Access to secretsline 354
[Credential-related source omitted] - Network accessline 362
for source in sources: # Get releases for each source releases_resp = requests.get( - Network accessline 363
# Get releases for each source releases_resp = requests.get( "https://api.stlouisfed.org/fred/source/releases", - Network accessline 364
releases_resp = requests.get( - Network accessline 365
"https://api.stlouisfed.org/fred/source/releases", - Access to secretsline 367
[Credential-related source omitted]
skills/15-金融与经济数据/fred-economic-data/references/tags.md
- Network accessline 32
**URL:** `https://api.stlouisfed.org/fred/tags` - Access to secretsline 38
[Credential-related source omitted] - Network accessline 58
response = requests.get( - Network accessline 59
"https://api.stlouisfed.org/fred/tags", - Access to secretsline 61
[Credential-related source omitted] - Network accessline 111
response = requests.get( - Network accessline 112
"https://api.stlouisfed.org/fred/tags", - Access to secretsline 114
[Credential-related source omitted] - Network accessline 126
# Find tags related to inflation response = requests.get( - Network accessline 127
response = requests.get( - Network accessline 128
"https://api.stlouisfed.org/fred/tags", - Access to secretsline 130
[Credential-related source omitted] - Network accessline 145
**URL:** `https://api.stlouisfed.org/fred/related_tags` - Access to secretsline 151
[Credential-related source omitted] - Network accessline 172
response = requests.get( - Network accessline 173
"https://api.stlouisfed.org/fred/related_tags", - Access to secretsline 175
[Credential-related source omitted] - Network accessline 220
response = requests.get( - Network accessline 221
"https://api.stlouisfed.org/fred/related_tags", - Access to secretsline 223
[Credential-related source omitted] - Network accessline 239
**URL:** `https://api.stlouisfed.org/fred/tags/series` - Access to secretsline 245
[Credential-related source omitted] - Network accessline 279
response = requests.get( - Network accessline 280
"https://api.stlouisfed.org/fred/tags/series", - Access to secretsline 282
[Credential-related source omitted] - Network accessline 328
response = requests.get( - Network accessline 329
"https://api.stlouisfed.org/fred/tags/series", - Access to secretsline 331
[Credential-related source omitted] - Network accessline 344
response = requests.get( - Network accessline 345
"https://api.stlouisfed.org/fred/tags/series", - Access to secretsline 347
[Credential-related source omitted] - Access to secretsline 408
[Credential-related source omitted] - Network accessline 411
# Step 1: Find tags matching the topic response = requests.get( - Network accessline 412
response = requests.get( - Network accessline 413
"https://api.stlouisfed.org/fred/tags", - Access to secretsline 415
[Credential-related source omitted] - Network accessline 428
# Step 2: Find related tags top_tag = initial_tags[0]["name"] response = requests.get( - Network accessline 429
top_tag = initial_tags[0]["name"] response = requests.get( "https://api.stlouisfed.org/fred/related_tags", - Network accessline 430
response = requests.get( - Network accessline 431
"https://api.stlouisfed.org/fred/related_tags", - Access to secretsline 433
[Credential-related source omitted] - Access to secretsline 449
[Credential-related source omitted] - Access to secretsline 455
[Credential-related source omitted] - Network accessline 464
response = requests.get( - Network accessline 465
"https://api.stlouisfed.org/fred/tags/series", - Access to secretsline 467
[Credential-related source omitted] - Access to secretsline 480
[Credential-related source omitted]
skills/15-金融与经济数据/fred-economic-data/scripts/fred_examples.py
- Executable scriptline 1
Executable file - Access to secretsline 321
[Credential-related source omitted] - Access to secretsline 322
[Credential-related source omitted] - Network accessline 323
[Credential-related source omitted] - Access to secretsline 323
[Credential-related source omitted] - Network accessline 324
print("\nTo get an API key:") print(" 1. Create account at https://fredaccount.stlouisfed.org") print(" 2. Request API key from your account dashboard") - Network accessline 325
print(" 1. Create account at https://fredaccount.stlouisfed.org") - Access to secretsline 328
[Credential-related source omitted]
skills/15-金融与经济数据/fred-economic-data/scripts/fred_query.py
- Executable scriptline 1
Executable file - Access to secretsline 19
[Credential-related source omitted] - Network accessline 24
BASE_URL = "https://api.stlouisfed.org/fred" - Network accessline 25
GEOFRED_URL = "https://api.stlouisfed.org/geofred" - Access to secretsline 29
[Credential-related source omitted] - Access to secretsline 38
[Credential-related source omitted] - Access to secretsline 43
[Credential-related source omitted] - Access to secretsline 44
[Credential-related source omitted] - Access to secretsline 46
[Credential-related source omitted] - Access to secretsline 62
[Credential-related source omitted] - Network accessline 73
for attempt in range(self.max_retries): try: response = requests.get(url, params=params, timeout=30) - Network accessline 75
response = requests.get(url, params=params, timeout=30) - Access to secretsline 561
[Credential-related source omitted] - Access to secretsline 567
[Credential-related source omitted] - Access to secretsline 573
[Credential-related source omitted] - Access to secretsline 581
[Credential-related source omitted] - Access to secretsline 582
[Credential-related source omitted] - Access to secretsline 583
[Credential-related source omitted] - Access to secretsline 590
[Credential-related source omitted]
skills/15-金融与经济数据/hedgefundmonitor/SKILL.md
- Network accessline 13
**Base URL:** `https://data.financialresearch.gov/hf/v1` - Network accessline 21
BASE = "https://data.financialresearch.gov/hf/v1" - Network accessline 23
# List all available datasets resp = requests.get(f"{BASE}/series/dataset") - Network accessline 24
resp = requests.get(f"{BASE}/series/dataset") - Network accessline 28
# Search for series by keyword resp = requests.get(f"{BASE}/metadata/search", params={"query": "*leverage*"}) - Network accessline 29
resp = requests.get(f"{BASE}/metadata/search", params={"query": "*leverage*"}) - Network accessline 33
# Fetch a single time series resp = requests.get(f"{BASE}/series/timeseries", params={ - Network accessline 34
resp = requests.get(f"{BASE}/series/timeseries", params={
skills/15-金融与经济数据/hedgefundmonitor/references/api-overview.md
- Network accessline 6
https://data.financialresearch.gov/hf/v1
skills/15-金融与经济数据/hedgefundmonitor/references/endpoints-combined.md
- Network accessline 5
**URL:** `GET https://data.financialresearch.gov/hf/v1/series/full` - Network accessline 41
BASE = "https://data.financialresearch.gov/hf/v1" - Network accessline 43
resp = requests.get(f"{BASE}/series/full", params={ - Network accessline 64
**URL:** `GET https://data.financialresearch.gov/hf/v1/series/multifull` - Network accessline 83
# Fetch multiple leverage series at once resp = requests.get(f"{BASE}/series/multifull", params={ - Network accessline 84
resp = requests.get(f"{BASE}/series/multifull", params={ - Network accessline 107
**URL:** `GET https://data.financialresearch.gov/hf/v1/series/dataset` - Network accessline 130
# List all available datasets resp = requests.get(f"{BASE}/series/dataset") - Network accessline 131
resp = requests.get(f"{BASE}/series/dataset") - Network accessline 135
# Download full FPF dataset (recent data only) resp = requests.get(f"{BASE}/series/dataset", params={ - Network accessline 136
resp = requests.get(f"{BASE}/series/dataset", params={ - Network accessline 144
# Annual data with custom periodicity resp = requests.get(f"{BASE}/series/dataset", params={ - Network accessline 145
resp = requests.get(f"{BASE}/series/dataset", params={ - Network accessline 153
# Only final vintage resp = requests.get(f"{BASE}/series/dataset", params={ - Network accessline 154
resp = requests.get(f"{BASE}/series/dataset", params={ - Network accessline 164
**URL:** `GET https://data.financialresearch.gov/hf/v1/categories` - Network accessline 188
# Download leverage category as CSV resp = requests.get(f"{BASE}/categories", params={"category": "leverage"}) - Network accessline 189
resp = requests.get(f"{BASE}/categories", params={"category": "leverage"}) - Network accessline 194
# Also accessible via direct URL: # https://data.financialresearch.gov/hf/v1/categories?category=leverage - Network accessline 195
# https://data.financialresearch.gov/hf/v1/categories?category=leverage
skills/15-金融与经济数据/hedgefundmonitor/references/endpoints-metadata.md
- Network accessline 5
**URL:** `GET https://data.financialresearch.gov/hf/v1/metadata/mnemonics` - Network accessline 19
import requests BASE = "https://data.financialresearch.gov/hf/v1" - Network accessline 20
BASE = "https://data.financialresearch.gov/hf/v1" - Network accessline 22
# All mnemonics (flat list) resp = requests.get(f"{BASE}/metadata/mnemonics") - Network accessline 23
resp = requests.get(f"{BASE}/metadata/mnemonics") - Network accessline 27
# Mnemonics for a single dataset with names resp = requests.get(f"{BASE}/metadata/mnemonics", params={"dataset": "fpf"}) - Network accessline 28
resp = requests.get(f"{BASE}/metadata/mnemonics", params={"dataset": "fpf"}) - Network accessline 31
# All mnemonics grouped by dataset resp = requests.get(f"{BASE}/metadata/mnemonics", params={"output": "by_dataset"}) - Network accessline 32
resp = requests.get(f"{BASE}/metadata/mnemonics", params={"output": "by_dataset"}) - Network accessline 41
**URL:** `GET https://data.financialresearch.gov/hf/v1/metadata/query` - Network accessline 66
# Full metadata resp = requests.get(f"{BASE}/metadata/query", params={ - Network accessline 67
resp = requests.get(f"{BASE}/metadata/query", params={ - Network accessline 75
# Specific subfield only resp = requests.get(f"{BASE}/metadata/query", params={ - Network accessline 76
resp = requests.get(f"{BASE}/metadata/query", params={ - Network accessline 82
# Multiple fields resp = requests.get(f"{BASE}/metadata/query", params={ - Network accessline 83
resp = requests.get(f"{BASE}/metadata/query", params={ - Network accessline 93
**URL:** `GET https://data.financialresearch.gov/hf/v1/metadata/search` - Network accessline 118
# Find series containing "leverage" anywhere resp = requests.get(f"{BASE}/metadata/search", params={"query": "*leverage*"}) - Network accessline 119
resp = requests.get(f"{BASE}/metadata/search", params={"query": "*leverage*"}) - Network accessline 124
# Find series starting with "Fund" resp = requests.get(f"{BASE}/metadata/search", params={"query": "Fund*"}) - Network accessline 125
resp = requests.get(f"{BASE}/metadata/search", params={"query": "Fund*"}) - Network accessline 127
# Find by exact dataset name resp = requests.get(f"{BASE}/metadata/search", params={"query": "FICC*"}) - Network accessline 128
resp = requests.get(f"{BASE}/metadata/search", params={"query": "FICC*"}) - Network accessline 130
# Search for stress test series resp = requests.get(f"{BASE}/metadata/search", params={"query": "*stress*"}) - Network accessline 131
resp = requests.get(f"{BASE}/metadata/search", params={"query": "*stress*"})
skills/15-金融与经济数据/hedgefundmonitor/references/endpoints-series-data.md
- Network accessline 5
**URL:** `GET https://data.financialresearch.gov/hf/v1/series/timeseries` - Network accessline 41
BASE = "https://data.financialresearch.gov/hf/v1" - Network accessline 43
# Full history for a series resp = requests.get(f"{BASE}/series/timeseries", params={ - Network accessline 44
resp = requests.get(f"{BASE}/series/timeseries", params={ - Network accessline 51
# Filtered date range with null removal resp = requests.get(f"{BASE}/series/timeseries", params={ - Network accessline 52
resp = requests.get(f"{BASE}/series/timeseries", params={ - Network accessline 59
# Annual frequency (calendar year end) resp = requests.get(f"{BASE}/series/timeseries", params={ - Network accessline 60
resp = requests.get(f"{BASE}/series/timeseries", params={ - Network accessline 66
# Epoch milliseconds for charting libraries resp = requests.get(f"{BASE}/series/timeseries", params={ - Network accessline 67
resp = requests.get(f"{BASE}/series/timeseries", params={ - Network accessline 77
**URL:** `GET https://data.financialresearch.gov/hf/v1/calc/spread` - Network accessline 109
# Spread between two repo rates resp = requests.get(f"{BASE}/calc/spread", params={ - Network accessline 110
resp = requests.get(f"{BASE}/calc/spread", params={ - Network accessline 119
# Annual spread with mean aggregation resp = requests.get(f"{BASE}/calc/spread", params={ - Network accessline 120
resp = requests.get(f"{BASE}/calc/spread", params={
skills/15-金融与经济数据/hedgefundmonitor/references/examples.md
- Network accessline 15
BASE = "https://data.financialresearch.gov/hf/v1" - Network accessline 17
# List all datasets resp = requests.get(f"{BASE}/series/dataset") - Network accessline 18
resp = requests.get(f"{BASE}/series/dataset") - Network accessline 22
# List all mnemonics for FPF with names resp = requests.get(f"{BASE}/metadata/mnemonics", params={"dataset": "fpf"}) - Network accessline 23
resp = requests.get(f"{BASE}/metadata/mnemonics", params={"dataset": "fpf"}) - Network accessline 27
# Search for leverage-related series resp = requests.get(f"{BASE}/metadata/search", params={"query": "*leverage*"}) - Network accessline 28
resp = requests.get(f"{BASE}/metadata/search", params={"query": "*leverage*"}) - Network accessline 42
BASE = "https://data.financialresearch.gov/hf/v1" - Network accessline 44
# Fetch overall leverage ratio resp = requests.get(f"{BASE}/series/timeseries", params={ - Network accessline 45
resp = requests.get(f"{BASE}/series/timeseries", params={ - Network accessline 52
# Get metadata meta_resp = requests.get(f"{BASE}/metadata/query", params={ - Network accessline 53
meta_resp = requests.get(f"{BASE}/metadata/query", params={ - Network accessline 76
BASE = "https://data.financialresearch.gov/hf/v1" - Network accessline 85
resp = requests.get(f"{BASE}/series/multifull", params={ - Network accessline 112
BASE = "https://data.financialresearch.gov/hf/v1" - Network accessline 114
# Download entire FPF dataset, recent data only resp = requests.get(f"{BASE}/series/dataset", params={ - Network accessline 115
resp = requests.get(f"{BASE}/series/dataset", params={ - Network accessline 146
BASE = "https://data.financialresearch.gov/hf/v1" - Network accessline 156
resp = requests.get(f"{BASE}/series/multifull", params={ - Network accessline 184
BASE = "https://data.financialresearch.gov/hf/v1" - Network accessline 186
resp = requests.get(f"{BASE}/series/multifull", params={ - Network accessline 219
BASE = "https://data.financialresearch.gov/hf/v1" - Network accessline 221
# Download the leverage category as a DataFrame resp = requests.get(f"{BASE}/categories", params={"category": "leverage"}) - Network accessline 222
resp = requests.get(f"{BASE}/categories", params={"category": "leverage"}) - Network accessline 235
BASE = "https://data.financialresearch.gov/hf/v1" - Network accessline 240
resp = requests.get(f"{BASE}/series/multifull", params={ - Network accessline 267
BASE = "https://data.financialresearch.gov/hf/v1" - Network accessline 270
"""Fetch only the most recent FPF observations (for periodic refreshes).""" start = (datetime.today() - timedelta(days=days_back)).strftime("%Y-%m-%d") resp = requests.get(f"{BASE}/series/dataset", params={ - Network accessline 271
start = (datetime.today() - timedelta(days=days_back)).strftime("%Y-%m-%d") resp = requests.get(f"{BASE}/series/dataset", params={ "dataset": "fpf", - Network accessline 272
resp = requests.get(f"{BASE}/series/dataset", params={
skills/15-金融与经济数据/market-research-reports/SKILL.md
- Broad permissionsline 1
Read Write Edit Bash
skills/15-金融与经济数据/market-research-reports/scripts/generate_market_visuals.py
- Executable scriptline 1
#!/usr/bin/env python3
skills/15-金融与经济数据/usfiscaldata/SKILL.md
- Network accessline 13
**Base URL:** `https://api.fiscaldata.treasury.gov/services/api/fiscal_service` - Network accessline 21
BASE_URL = "https://api.fiscaldata.treasury.gov/services/api/fiscal_service" - Network accessline 23
# Get the current national debt (Debt to the Penny) resp = requests.get(f"{BASE_URL}/v2/accounting/od/debt_to_penny", params={ - Network accessline 24
resp = requests.get(f"{BASE_URL}/v2/accounting/od/debt_to_penny", params={ - Network accessline 33
# Get Treasury exchange rates for recent quarters resp = requests.get(f"{BASE_URL}/v1/accounting/od/rates_of_exchange", params={ - Network accessline 34
resp = requests.get(f"{BASE_URL}/v1/accounting/od/rates_of_exchange", params={ - Network accessline 131
params = params or {} params["page[size]"] = 10000 # max size to minimize requests resp = requests.get(f"{BASE_URL}{endpoint}", params=params) - Network accessline 132
params["page[size]"] = 10000 # max size to minimize requests resp = requests.get(f"{BASE_URL}{endpoint}", params=params) result = resp.json() - Network accessline 133
resp = requests.get(f"{BASE_URL}{endpoint}", params=params) - Network accessline 144
# Sum all deposits/withdrawals by record_date and transaction type resp = requests.get(f"{BASE_URL}/v1/accounting/dts/deposits_withdrawals_operating_cash", params={ - Network accessline 145
resp = requests.get(f"{BASE_URL}/v1/accounting/dts/deposits_withdrawals_operating_cash", params={
skills/15-金融与经济数据/usfiscaldata/references/api-basics.md
- Network accessline 16
Base URL: https://api.fiscaldata.treasury.gov/services/api/fiscal_service - Network accessline 20
Full URL: https://api.fiscaldata.treasury.gov/services/api/fiscal_service/v2/accounting/od/debt_to_penny?fields=record_date,tot_pub_debt_out_amt&sort=-record_date&page[size]=5 - Network accessline 21
https://api.fiscaldata.treasury.gov/services/api/fiscal_service/v2/accounting/od/debt_to_penny?fields=record_date,tot_pub_debt_out_amt&sort=-record_date&page[size]=5 - Network accessline 68
def get_with_retry(url, params, retries=3): for attempt in range(retries): resp = requests.get(url, params=params) - Network accessline 69
for attempt in range(retries): resp = requests.get(url, params=params) if resp.status_code == 429: - Network accessline 70
resp = requests.get(url, params=params) - Network accessline 86
The [Fiscal Service Data Registry](https://fiscal.treasury.gov/data-registry/index.html) contains field definitions, authoritative sources, data types, and formats across federal government data.
skills/15-金融与经济数据/usfiscaldata/references/datasets-debt.md
- Network accessline 20
# Current national debt resp = requests.get( - Network accessline 21
resp = requests.get( - Network accessline 22
"https://api.fiscaldata.treasury.gov/services/api/fiscal_service/v2/accounting/od/debt_to_penny", - Network accessline 28
# Debt over the last year resp = requests.get( - Network accessline 29
resp = requests.get( - Network accessline 30
"https://api.fiscaldata.treasury.gov/services/api/fiscal_service/v2/accounting/od/debt_to_penny", - Network accessline 56
# Full historical debt series resp = requests.get( - Network accessline 57
resp = requests.get( - Network accessline 58
"https://api.fiscaldata.treasury.gov/services/api/fiscal_service/v2/accounting/od/historical_debt_outstanding", - Network accessline 105
# TROR data, sorted by funding type resp = requests.get( - Network accessline 106
resp = requests.get( - Network accessline 107
"https://api.fiscaldata.treasury.gov/services/api/fiscal_service/v2/debt/tror", - Network accessline 137
# Get total interest expense by month resp = requests.get( - Network accessline 138
resp = requests.get( - Network accessline 139
"https://api.fiscaldata.treasury.gov/services/api/fiscal_service/v2/accounting/od/interest_expense",
skills/15-金融与经济数据/usfiscaldata/references/datasets-fiscal.md
- Network accessline 38
# Get current Treasury General Account (TGA) balance resp = requests.get( - Network accessline 39
resp = requests.get( - Network accessline 40
"https://api.fiscaldata.treasury.gov/services/api/fiscal_service/v1/accounting/dts/operating_cash_balance", - Network accessline 46
# Get deposits and withdrawals for a specific period resp = requests.get( - Network accessline 47
resp = requests.get( - Network accessline 48
"https://api.fiscaldata.treasury.gov/services/api/fiscal_service/v1/accounting/dts/deposits_withdrawals_operating_cash", - Network accessline 60
# Get sum of today's transaction amounts by transaction type resp = requests.get( - Network accessline 61
resp = requests.get( - Network accessline 62
"https://api.fiscaldata.treasury.gov/services/api/fiscal_service/v1/accounting/dts/deposits_withdrawals_operating_cash", - Network accessline 114
# MTS Table 1: Summary of receipts and outlays resp = requests.get( - Network accessline 115
resp = requests.get( - Network accessline 116
"https://api.fiscaldata.treasury.gov/services/api/fiscal_service/v1/accounting/mts/mts_table_1", - Network accessline 124
# MTS Table 9: Get line 120 (Total Receipts) for most recent period resp = requests.get( - Network accessline 125
resp = requests.get( - Network accessline 126
"https://api.fiscaldata.treasury.gov/services/api/fiscal_service/v1/accounting/mts/mts_table_9",
skills/15-金融与经济数据/usfiscaldata/references/datasets-interest-rates.md
- Network accessline 20
# Get average rates for all marketable securities, most recent month resp = requests.get( - Network accessline 21
resp = requests.get( - Network accessline 22
"https://api.fiscaldata.treasury.gov/services/api/fiscal_service/v2/accounting/od/avg_interest_rates", - Network accessline 33
# Historical rate for a specific security type resp = requests.get( - Network accessline 34
resp = requests.get( - Network accessline 35
"https://api.fiscaldata.treasury.gov/services/api/fiscal_service/v2/accounting/od/avg_interest_rates", - Network accessline 78
# Get all current exchange rates (latest quarter) resp = requests.get( - Network accessline 79
resp = requests.get( - Network accessline 80
"https://api.fiscaldata.treasury.gov/services/api/fiscal_service/v1/accounting/od/rates_of_exchange", - Network accessline 89
# Euro rate history resp = requests.get( - Network accessline 90
resp = requests.get( - Network accessline 91
"https://api.fiscaldata.treasury.gov/services/api/fiscal_service/v1/accounting/od/rates_of_exchange",
skills/15-金融与经济数据/usfiscaldata/references/datasets-securities.md
- Network accessline 27
# Get recent 10-year Treasury note auctions resp = requests.get( - Network accessline 28
resp = requests.get( - Network accessline 29
"https://api.fiscaldata.treasury.gov/services/api/fiscal_service/v1/accounting/od/auctions_query", - Network accessline 38
# Get all auctions in 2024 resp = requests.get( - Network accessline 39
resp = requests.get( - Network accessline 40
"https://api.fiscaldata.treasury.gov/services/api/fiscal_service/v1/accounting/od/auctions_query", - Network accessline 66
# Get upcoming auctions resp = requests.get( - Network accessline 67
resp = requests.get( - Network accessline 68
"https://api.fiscaldata.treasury.gov/services/api/fiscal_service/v1/accounting/od/upcoming_auctions", - Network accessline 108
# Current I Bond rates resp = requests.get( - Network accessline 109
resp = requests.get( - Network accessline 110
"https://api.fiscaldata.treasury.gov/services/api/fiscal_service/v2/accounting/od/i_bond_interest_rates",
skills/15-金融与经济数据/usfiscaldata/references/examples.md
- Network accessline 11
BASE_URL = "https://api.fiscaldata.treasury.gov/services/api/fiscal_service" - Network accessline 13
def fetch(endpoint, **params): - Network accessline 14
resp = requests.get(f"{BASE_URL}{endpoint}", params=params) - Network accessline 22
# Current total public debt result = fetch("/v2/accounting/od/debt_to_penny", - Network accessline 23
result = fetch("/v2/accounting/od/debt_to_penny", - Network accessline 29
# Debt trend over last 5 years result = fetch("/v2/accounting/od/debt_to_penny", - Network accessline 30
result = fetch("/v2/accounting/od/debt_to_penny", - Network accessline 44
# All current Treasury exchange rates result = fetch("/v1/accounting/od/rates_of_exchange", - Network accessline 45
result = fetch("/v1/accounting/od/rates_of_exchange", - Network accessline 67
# Recent 10-year note auctions result = fetch("/v1/accounting/od/auctions_query", - Network accessline 68
result = fetch("/v1/accounting/od/auctions_query", - Network accessline 80
# Auction yield trend: 2-year vs 10-year def get_auction_yields(term, n=24): result = fetch("/v1/accounting/od/auctions_query", - Network accessline 81
def get_auction_yields(term, n=24): result = fetch("/v1/accounting/od/auctions_query", fields="record_date,security_term,accepted_comp_bid_rate_amt", - Network accessline 82
result = fetch("/v1/accounting/od/auctions_query", - Network accessline 102
# Recent Treasury General Account (TGA) balance result = fetch("/v1/accounting/dts/operating_cash_balance", - Network accessline 103
result = fetch("/v1/accounting/dts/operating_cash_balance", - Network accessline 111
# Monthly total receipts and withdrawals result = fetch("/v1/accounting/dts/deposits_withdrawals_operating_cash", - Network accessline 112
result = fetch("/v1/accounting/dts/deposits_withdrawals_operating_cash", - Network accessline 125
# Federal budget receipts and outlays (MTS Table 1) result = fetch("/v1/accounting/mts/mts_table_1", - Network accessline 126
result = fetch("/v1/accounting/mts/mts_table_1", - Network accessline 141
# Average interest rates on all marketable Treasury securities result = fetch("/v2/accounting/od/avg_interest_rates", - Network accessline 142
result = fetch("/v2/accounting/od/avg_interest_rates", - Network accessline 153
# I Bond rates history result = fetch("/v2/accounting/od/i_bond_interest_rates", - Network accessline 154
result = fetch("/v2/accounting/od/i_bond_interest_rates", - Network accessline 169
# Total debt at end of FY fy_end = f"{fy}-09-30" result = fetch("/v2/accounting/od/debt_to_penny", - Network accessline 170
fy_end = f"{fy}-09-30" result = fetch("/v2/accounting/od/debt_to_penny", filter=f"record_date:lte:{fy_end}", - Network accessline 171
result = fetch("/v2/accounting/od/debt_to_penny", - Network accessline 176
# Interest expense for FY result = fetch("/v2/accounting/od/interest_expense", - Network accessline 177
result = fetch("/v2/accounting/od/interest_expense", - Network accessline 207
BASE_URL <- "https://api.fiscaldata.treasury.gov/services/api/fiscal_service" - Network accessline 246
result = fetch("/v2/accounting/od/debt_to_penny", **{"page[size]": 1}) - Network accessline 256
Browse the full list of 54 datasets and 182 endpoints at: - `https://fiscaldata.treasury.gov/datasets/` — searchable dataset catalog - Network accessline 257
- `https://fiscaldata.treasury.gov/datasets/` — searchable dataset catalog - Network accessline 258
- `https://fiscaldata.treasury.gov/api-documentation/#list-of-endpoints-table` — full endpoint table
skills/15-金融与经济数据/usfiscaldata/references/parameters.md
- Network accessline 124
base = "https://api.fiscaldata.treasury.gov/services/api/fiscal_service" - Network accessline 127
while True: resp = requests.get(f"{base}{endpoint}", params=params) - Network accessline 128
resp = requests.get(f"{base}{endpoint}", params=params) - Network accessline 149
"page[number]": 1 } resp = requests.get( - Network accessline 150
} resp = requests.get( "https://api.fiscaldata.treasury.gov/services/api/fiscal_service/v1/accounting/od/rates_of_exchange", - Network accessline 151
resp = requests.get( - Network accessline 152
"https://api.fiscaldata.treasury.gov/services/api/fiscal_service/v1/accounting/od/rates_of_exchange",
skills/15-金融与经济数据/usfiscaldata/references/response-format.md
- Network accessline 92
resp = requests.get(url, params=params) - Network accessline 118
def api_to_dataframe(endpoint, params=None): """Fetch API data and return a typed DataFrame.""" base = "https://api.fiscaldata.treasury.gov/services/api/fiscal_service" - Network accessline 119
"""Fetch API data and return a typed DataFrame.""" base = "https://api.fiscaldata.treasury.gov/services/api/fiscal_service" resp = requests.get(f"{base}{endpoint}", params=params) - Network accessline 120
base = "https://api.fiscaldata.treasury.gov/services/api/fiscal_service" resp = requests.get(f"{base}{endpoint}", params=params) resp.raise_for_status() - Network accessline 121
resp = requests.get(f"{base}{endpoint}", params=params) - Network accessline 157
resp = requests.get( - Network accessline 158
"https://api.fiscaldata.treasury.gov/services/api/fiscal_service/v2/accounting/od/debt_to_penny", - Network accessline 171
resp = requests.get( - Network accessline 172
"https://api.fiscaldata.treasury.gov/services/api/fiscal_service/v2/accounting/od/debt_to_penny",
skills/16-地理空间与遥感/geomaster/SKILL.md
- Network accessline 261
# Search Sentinel-2 via STAC catalog = pystac_client.Client.open( "https://planetarycomputer.microsoft.com/api/stac/v1", - Network accessline 262
catalog = pystac_client.Client.open( "https://planetarycomputer.microsoft.com/api/stac/v1", modifier=planetary_computer.sign_inplace, - Network accessline 263
"https://planetarycomputer.microsoft.com/api/stac/v1",
skills/16-地理空间与遥感/geomaster/references/big-data.md
- Network accessline 141
# Search catalog catalog = pystac_client.Client.open( "https://planetarycomputer.microsoft.com/api/stac/v1", - Network accessline 142
catalog = pystac_client.Client.open( "https://planetarycomputer.microsoft.com/api/stac/v1", modifier=planetary_computer.sign_inplace, - Network accessline 143
"https://planetarycomputer.microsoft.com/api/stac/v1",
skills/16-地理空间与遥感/geomaster/references/coordinate-systems.md
skills/16-地理空间与遥感/geomaster/references/data-sources.md
- Network accessline 9
| Platform | Resolution | Coverage | Access | |----------|------------|----------|--------| | **Sentinel-2** | 10-60m | Global | https://scihub.copernicus.eu/ | - Network accessline 10
|----------|------------|----------|--------| | **Sentinel-2** | 10-60m | Global | https://scihub.copernicus.eu/ | | **Sentinel-1** | 5-40m (SAR) | Global | https://scihub.copernicus.eu/ | - Network accessline 11
| **Sentinel-2** | 10-60m | Global | https://scihub.copernicus.eu/ | - Network accessline 12
| **Sentinel-1** | 5-40m (SAR) | Global | https://scihub.copernicus.eu/ | - Network accessline 13
| **Sentinel-3** | 300m-1km | Global | https://scihub.copernicus.eu/ | - Network accessline 14
| **Sentinel-5P** | Various | Global | https://scihub.copernicus.eu/ | - Network accessline 20
api = SentinelAPI('user', 'password', 'https://scihub.copernicus.eu/dhus') - Network accessline 34
| Platform | Resolution | Coverage | Access | |----------|------------|----------|--------| | **Landsat 9** | 30m | Global | https://earthexplorer.usgs.gov/ | - Network accessline 35
|----------|------------|----------|--------| | **Landsat 9** | 30m | Global | https://earthexplorer.usgs.gov/ | | **Landsat 8** | 30m | Global | https://earthexplorer.usgs.gov/ | - Network accessline 36
| **Landsat 9** | 30m | Global | https://earthexplorer.usgs.gov/ | - Network accessline 37
| **Landsat 8** | 30m | Global | https://earthexplorer.usgs.gov/ | - Network accessline 38
| **Landsat 7** | 15-60m | Global | https://earthexplorer.usgs.gov/ | - Network accessline 39
| **Landsat 5-7** | 30-60m | Global | https://earthexplorer.usgs.gov/ | - Network accessline 52
| Dataset | Resolution | Coverage | Source | |---------|------------|----------|--------| | **AW3D30** | 30m | Global | https://www.eorc.jaxa.jp/ALOS/en/aw3d30/ | - Network accessline 53
|---------|------------|----------|--------| | **AW3D30** | 30m | Global | https://www.eorc.jaxa.jp/ALOS/en/aw3d30/ | | **SRTM** | 30m | 56°S-60°N | https://www.usgs.gov/ | - Network accessline 54
| **AW3D30** | 30m | Global | https://www.eorc.jaxa.jp/ALOS/en/aw3d30/ | - Network accessline 55
| **SRTM** | 30m | 56°S-60°N | https://www.usgs.gov/ | - Network accessline 56
| **ASTER GDEM** | 30m | 83°S-83°N | https://asterweb.jpl.nasa.gov/ | - Network accessline 57
| **Copernicus DEM** | 30m | Global | https://copernicus.eu/ | - Network accessline 58
| **ArcticDEM** | 2-10m | Arctic | https://www.pgc.umn.edu/ | - Network accessline 73
| Dataset | Resolution | Classes | Source | |---------|------------|---------|--------| | **ESA WorldCover** | 10m | 11 classes | https://worldcover2021.esa.int/ | - Network accessline 74
|---------|------------|---------|--------| | **ESA WorldCover** | 10m | 11 classes | https://worldcover2021.esa.int/ | | **ESRI Land Cover** | 10m | 10 classes | https://www.esri.com/ | - Network accessline 75
| **ESA WorldCover** | 10m | 11 classes | https://worldcover2021.esa.int/ | - Network accessline 76
| **ESRI Land Cover** | 10m | 10 classes | https://www.esri.com/ | - Network accessline 77
| **Copernicus Global** | 100m | 23 classes | https://land.copernicus.eu/ | - Network accessline 78
| **MODIS MCD12Q1** | 500m | 17 classes | https://lpdaac.usgs.gov/ | - Network accessline 79
| **NLCD (US)** | 30m | 20 classes | https://www.mrlc.gov/ | - Network accessline 85
| Dataset | Resolution | Temporal | Access | |---------|------------|----------|--------| | **ERA5** | 31km | Hourly (1979+) | https://cds.climate.copernicus.eu/ | - Network accessline 86
|---------|------------|----------|--------| | **ERA5** | 31km | Hourly (1979+) | https://cds.climate.copernicus.eu/ | | **MERRA-2** | 50km | Hourly (1980+) | https://gmao.gsfc.nasa.gov/ | - Network accessline 87
| **ERA5** | 31km | Hourly (1979+) | https://cds.climate.copernicus.eu/ | - Network accessline 88
| **MERRA-2** | 50km | Hourly (1980+) | https://gmao.gsfc.nasa.gov/ | - Network accessline 89
| **JRA-55** | 55km | 3-hourly (1958+) | https://jra.kishou.go.jp/ | - Network accessline 133
overpass_url = "http://overpass-api.de/api/interpreter" - Network accessline 140
response = requests.get(overpass_url, params={'data': query}) - Network accessline 151
# Admin boundaries (scale: 10m, 50m, 110m) countries = gpd.read_file('https://naturalearth.s3.amazonaws.com/10m_cultural/ne_10m_admin_0_countries.zip') - Network accessline 152
countries = gpd.read_file('https://naturalearth.s3.amazonaws.com/10m_cultural/ne_10m_admin_0_countries.zip') - Network accessline 153
urban_areas = gpd.read_file('https://naturalearth.s3.amazonaws.com/10m_cultural/ne_10m_urban_areas.zip') - Network accessline 154
ports = gpd.read_file('https://naturalearth.s3.amazonaws.com/10m_cultural/ne_10m_ports.zip') - Network accessline 159
| Dataset | Type | Access | |---------|------|--------| | **GADM** | Admin boundaries | https://gadm.org/ | - Network accessline 160
|---------|------|--------| | **GADM** | Admin boundaries | https://gadm.org/ | | **HydroSHEDS** | Rivers, basins | https://www.hydrosheds.org/ | - Network accessline 161
| **GADM** | Admin boundaries | https://gadm.org/ | - Network accessline 162
| **HydroSHEDS** | Rivers, basins | https://www.hydrosheds.org/ | - Network accessline 163
| **Global Power Plant** | Power plants | https://datasets.wri.org/ | - Network accessline 164
| **WorldPop** | Population | https://www.worldpop.org/ | - Network accessline 165
| **GPW** | Population | https://sedac.ciesin.columbia.edu/ | - Network accessline 166
| **HDX** | Humanitarian data | https://data.humdata.org/ | - Network accessline 175
# Geocoding url = "https://maps.googleapis.com/maps/api/geocode/json" - Network accessline 176
url = "https://maps.googleapis.com/maps/api/geocode/json" - Access to secretsline 179
[Credential-related source omitted] - Network accessline 182
response = requests.get(url, params=params) - Network accessline 193
url = "https://api.mapbox.com/geocoding/v5/mapbox.places/Golden%20Gate%20Bridge.json" - Access to secretsline 194
[Credential-related source omitted] - Network accessline 196
response = requests.get(url, params=params) - Network accessline 203
# Current weather url = "https://api.openweathermap.org/data/2.5/weather" - Network accessline 204
url = "https://api.openweathermap.org/data/2.5/weather" - Access to secretsline 208
[Credential-related source omitted] - Network accessline 211
response = requests.get(url, params=params) - Network accessline 222
# Connect to STAC catalog catalog = pystac_client.Client.open("https://earth-search.aws.element84.com/v1") - Network accessline 223
catalog = pystac_client.Client.open("https://earth-search.aws.element84.com/v1") - Network accessline 242
catalog = pystac_client.Client.open( "https://planetarycomputer.microsoft.com/api/stac/v1", - Network accessline 243
"https://planetarycomputer.microsoft.com/api/stac/v1", - Network accessline 265
""" # Initialize API api = SentinelAPI('user', 'password', 'https://scihub.copernicus.eu/dhus') - Network accessline 267
api = SentinelAPI('user', 'password', 'https://scihub.copernicus.eu/dhus')
skills/16-地理空间与遥感/geomaster/references/programming-languages.md
- Network accessline 234
// Add tile layer L.tileLayer('https://{s}.tile.openstreetmap.org/{z}/{x}/{y}.png', { - Network accessline 235
L.tileLayer('https://{s}.tile.openstreetmap.org/{z}/{x}/{y}.png', { - Network accessline 239
// Add GeoJSON layer fetch('data.geojson') - Network accessline 240
fetch('data.geojson')
skills/16-地理空间与遥感/geomaster/references/remote-sensing.md
- Network accessline 27
# Search Sentinel-2 collection catalog = pystac_client.Client.open( "https://planetarycomputer.microsoft.com/api/stac/v1", - Network accessline 28
catalog = pystac_client.Client.open( "https://planetarycomputer.microsoft.com/api/stac/v1", modifier=planetary_computer.sign_inplace, - Network accessline 29
"https://planetarycomputer.microsoft.com/api/stac/v1",
skills/16-地理空间与遥感/geomaster/references/troubleshooting.md
- Dangerous shell operationline 16
sudo apt-get install gdal-bin libgdal-dev - Network accessline 21
# Solution 3: Wheel files pip install rasterio --find-links=https://gis.wheelwrights.com/ - Network accessline 22
pip install rasterio --find-links=https://gis.wheelwrights.com/ - Network accessline 435
- **GeoPandas docs**: https://geopandas.org/ - Network accessline 436
- **Rasterio docs**: https://rasterio.readthedocs.io/ - Network accessline 437
- **PROJ datab**: https://epsg.org/ - Network accessline 439
- **GIS Stack Exchange**: https://gis.stackexchange.com/
skills/16-地理空间与遥感/geopandas/references/data-io.md
- Network accessline 15
# Read from URL gdf = gpd.read_file("https://example.com/data.geojson") - Network accessline 16
gdf = gpd.read_file("https://example.com/data.geojson") - Network accessline 163
# HTTP/HTTPS gdf = gpd.read_file("https://example.com/data.geojson") - Network accessline 164
gdf = gpd.read_file("https://example.com/data.geojson")
skills/17-平台与基础设施/get-available-resources/scripts/detect_resources.py
- Executable scriptline 1
#!/usr/bin/env python3
skills/17-平台与基础设施/iso-13485-certification/scripts/gap_analyzer.py
- Executable scriptline 1
#!/usr/bin/env python3
skills/17-平台与基础设施/modal/SKILL.md
- Network accessline 15
Modal is particularly suited for AI/ML workloads, high-performance batch processing, scheduled jobs, GPU inference, and serverless APIs. Sign up for free at https://modal.com and receive $30/month in credits. - Access to secretsline 213
[Credential-related source omitted] - Network accessline 380
For additional help, see Modal documentation at https://modal.com/docs or join Modal Slack community.
skills/17-平台与基础设施/modal/references/examples.md
skills/17-平台与基础设施/modal/references/getting-started.md
skills/17-平台与基础设施/modal/references/gpu.md
- Network accessline 34
See https://modal.com/pricing for pricing.
skills/17-平台与基础设施/modal/references/images.md
- Network accessline 15
.uv_pip_install("torch<3") .env({"HALT_AND_CATCH_FIRE": "0"}) .run_commands("git clone https://github.com/modal-labs/agi") - Network accessline 16
.env({"HALT_AND_CATCH_FIRE": "0"}) .run_commands("git clone https://github.com/modal-labs/agi") ) - Network accessline 17
.run_commands("git clone https://github.com/modal-labs/agi") - Network accessline 75
modal.Image.debian_slim() .apt_install("git") .run_commands("git clone https://github.com/modal-labs/gpu-glossary") - Network accessline 76
.apt_install("git") .run_commands("git clone https://github.com/modal-labs/gpu-glossary") ) - Network accessline 77
.run_commands("git clone https://github.com/modal-labs/gpu-glossary")
skills/17-平台与基础设施/modal/references/resources.md
- Network accessline 129
Access via https://modal.com/apps
skills/17-平台与基础设施/modal/references/scheduled-jobs.md
skills/17-平台与基础设施/modal/references/secrets.md
- Network accessline 7
Create secrets at https://modal.com/secrets - Access to secretsline 37
[Credential-related source omitted] - Access to secretsline 44
[Credential-related source omitted] - Access to secretsline 47
[Credential-related source omitted] - Access to secretsline 53
[Credential-related source omitted] - Access to secretsline 64
[Credential-related source omitted] - Access to secretsline 111
[Credential-related source omitted] - Access to secretsline 117
[Credential-related source omitted] - Access to secretsline 122
[Credential-related source omitted] - Access to secretsline 160
[Credential-related source omitted] - Access to secretsline 161
[Credential-related source omitted] - Access to secretsline 162
[Credential-related source omitted] - Access to secretsline 163
[Credential-related source omitted]
skills/17-平台与基础设施/modal/references/volumes.md
- Dangerous shell operationline 215
modal volume rm my-volume file.txt
skills/17-平台与基础设施/modal/references/web-endpoints.md
- Network accessline 47
curl "https://workspace--app-square.modal.run?x=42" - Network accessline 61
curl -X POST -H 'Content-Type: application/json' \ - Network accessline 63
https://workspace--app-square.modal.run - Access to secretsline 240
[Credential-related source omitted] - Network accessline 272
def handler(): ... # URL: https://workspace--api.modal.run - Network accessline 274
# URL: https://workspace--api.modal.run
skills/沁言学术skills/qinyan-citation/SKILL.md
- Network accessline 20
使用前请确保： 1. 已设置 `QINYAN_API_KEY` 环境变量（前往 https://platform.qinyanai.com/ 申请） 2. 环境中可用 `curl` 和 `python3` - Network accessline 21
[Credential-related source omitted] - Access to secretsline 21
[Credential-related source omitted] - Access to secretsline 25
[Credential-related source omitted] - Network accessline 126
作者1, A. B., & 作者2, C. D. (年份). 论文标题. 期刊名(斜体), 卷号(期号), 起始页–终止页. https://doi.org/DOI
skills/沁言学术skills/qinyan-citation/scripts/search.sh
- Executable scriptline 1
#!/usr/bin/env bash - Network accessline 10
API_BASE="https://api.qinyanai.com" - Network accessline 12
[Credential-related source omitted] - Access to secretsline 12
[Credential-related source omitted] - Network accessline 13
[Credential-related source omitted] - Access to secretsline 13
[Credential-related source omitted] - Network accessline 14
echo "请前往 https://platform.qinyanai.com/ 申请API Key" - Access to secretsline 15
[Credential-related source omitted] - Network accessline 47
[Credential-related source omitted] - Access to secretsline 47
[Credential-related source omitted] - Network accessline 48
-X POST "${API_BASE}${ENDPOINT}" \ - Access to secretsline 50
[Credential-related source omitted]
skills/沁言学术skills/qinyan-nature-figures/scripts/figure_preflight.py
- Executable scriptline 1
#!/usr/bin/env python3
skills/沁言学术skills/qinyan-nature-polishing/scripts/style_audit.py
- Executable scriptline 1
#!/usr/bin/env python3
skills/沁言学术skills/qinyan-nature-review/scripts/review_consistency.py
- Executable scriptline 1
#!/usr/bin/env python3
skills/沁言学术skills/qinyan-nature-statistics/scripts/reporting_audit.py
- Executable scriptline 1
#!/usr/bin/env python3
skills/沁言学术skills/qinyan-nature-writing/scripts/manuscript_audit.py
- Executable scriptline 1
#!/usr/bin/env python3
skills/沁言学术skills/qinyan-paper-analysis/SKILL.md
- Network accessline 16
使用前请确保： 1. 已设置 `QINYAN_API_KEY` 环境变量（前往 https://platform.qinyanai.com/ 申请） 2. 环境中可用 `curl` 和 `python3` - Network accessline 17
[Credential-related source omitted] - Access to secretsline 17
[Credential-related source omitted] - Access to secretsline 21
[Credential-related source omitted] - Network accessline 26
**接口地址：** `https://api.qinyanai.com/v1/paper-search/analyze` - Network accessline 59
bash scripts/analyze.sh '{"title": "BERT: Pre-training of Deep Bidirectional Transformers", "authors": ["Jacob Devlin"], "pdf_url": "https://arxiv.org/pdf/1810.04805.pdf", "language": "中文"}'
skills/沁言学术skills/qinyan-paper-analysis/scripts/analyze.sh
- Executable scriptline 1
#!/usr/bin/env bash - Network accessline 9
API_BASE="https://api.qinyanai.com" - Network accessline 12
[Credential-related source omitted] - Access to secretsline 12
[Credential-related source omitted] - Network accessline 13
[Credential-related source omitted] - Access to secretsline 13
[Credential-related source omitted] - Network accessline 14
echo "请前往 https://platform.qinyanai.com/ 申请API Key" - Access to secretsline 15
[Credential-related source omitted] - Network accessline 34
[Credential-related source omitted] - Access to secretsline 34
[Credential-related source omitted] - Network accessline 35
-X POST "${API_BASE}${ENDPOINT}" \ - Access to secretsline 37
[Credential-related source omitted]
skills/沁言学术skills/qinyan-paper-search/SKILL.md
skills/沁言学术skills/qinyan-paper-search/scripts/search.sh
- Executable scriptline 1
#!/usr/bin/env bash - Network accessline 10
API_BASE="https://api.qinyanai.com" - Network accessline 12
[Credential-related source omitted] - Access to secretsline 12
[Credential-related source omitted] - Network accessline 13
[Credential-related source omitted] - Access to secretsline 13
[Credential-related source omitted] - Network accessline 14
echo "请前往 https://platform.qinyanai.com/ 申请API Key" - Access to secretsline 15
[Credential-related source omitted] - Network accessline 47
[Credential-related source omitted] - Access to secretsline 47
[Credential-related source omitted] - Network accessline 48
-X POST "${API_BASE}${ENDPOINT}" \ - Access to secretsline 50
[Credential-related source omitted]
skills/沁言学术skills/qinyan-topic-analysis/SKILL.md
skills/沁言学术skills/qinyan-topic-analysis/scripts/search.sh
- Executable scriptline 1
#!/usr/bin/env bash - Network accessline 10
API_BASE="https://api.qinyanai.com" - Network accessline 12
[Credential-related source omitted] - Access to secretsline 12
[Credential-related source omitted] - Network accessline 13
[Credential-related source omitted] - Access to secretsline 13
[Credential-related source omitted] - Network accessline 14
echo "请前往 https://platform.qinyanai.com/ 申请API Key" - Access to secretsline 15
[Credential-related source omitted] - Network accessline 47
[Credential-related source omitted] - Access to secretsline 47
[Credential-related source omitted] - Network accessline 48
-X POST "${API_BASE}${ENDPOINT}" \ - Access to secretsline 50
[Credential-related source omitted]
How the signals fit together
Facts from a static read of the files at the pinned commit. They describe what the code does, not why.
Some files could not be read; the facts below still hold.
Signal confidence: 1 fact
- Broad permissions in the frontmatter of the SKILL.mdbgpt-paper-search/SKILL.md:4High confidence
Works with
| Agent | Status | Evidence | Install to |
|---|---|---|---|
| Claude Code | Compatible | Standard SKILL.md skills/01-论文检索与文献管理/bgpt-paper-search/SKILL.md | .claude/skills/<skill-name>/ |
| Codex | Compatible | Standard SKILL.md skills/01-论文检索与文献管理/bgpt-paper-search/SKILL.md | .agents/skills/<skill-name>/ |
| Cursor | Compatible | Standard SKILL.md skills/01-论文检索与文献管理/bgpt-paper-search/SKILL.md | .cursor/skills/<skill-name>/ |
| Gemini CLI | Compatible | Standard SKILL.md skills/01-论文检索与文献管理/bgpt-paper-search/SKILL.md | .gemini/skills/<skill-name>/ |
| GitHub Copilot | Compatible | Standard SKILL.md skills/01-论文检索与文献管理/bgpt-paper-search/SKILL.md | .github/skills/<skill-name>/ |
| OpenCode | Compatible | Standard SKILL.md skills/01-论文检索与文献管理/bgpt-paper-search/SKILL.md | .opencode/skills/<skill-name>/ |
- Native The repository ships a manifest, folder or frontmatter field for this agent.
- Compatible A standard SKILL.md that this agent can read.
- Unknown No evidence either way.
Derived automatically from paths, manifests and frontmatter. Not tested on each agent.
Add this badge to your README
It updates every night and links to this page.
[](https://hotskills.dev/skill/leonchaox-qinyan-academic-skills/)