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TobiasBlask/open-paper-machine
An autonomous LLM research agent that executes the full academic paper pipeline — from literature search to compiled PDF
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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scientific-skills/citation-management/SKILL.md
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scientific-skills/scholar-evaluation/SKILL.md
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scientific-skills/scientific-critical-thinking/SKILL.md
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scientific-skills/scientific-visualization/SKILL.md
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scientific-skills/scientific-writing/SKILL.md
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scientific-skills/seaborn/SKILL.md
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scientific-skills/statistical-analysis/SKILL.md
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scientific-skills/venue-templates/SKILL.md
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skills/audit-engine/SKILL.md
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skills/figure-engine/SKILL.md
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skills/qualitative-engine/SKILL.md
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skills/verification-engine/SKILL.md
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Verified commit 160df4d13d266411f0850c4de7ecdbd93e8b40be
git clone --filter=blob:none --no-checkout https://github.com/TobiasBlask/open-paper-machine.git .hotskills/tobiasblask-open-paper-machine && git -C .hotskills/tobiasblask-open-paper-machine checkout --detach 160df4d13d266411f0850c4de7ecdbd93e8b40be
# Copy each selected skill directory from .hotskills/tobiasblask-open-paper-machine into .claude/skills/<skill-name>/Heat
0.4- Star growth, last 7 daysNot available
- Star growth, last 24 hoursNot available
- Rank movement, last 7 daysNot available
- Commits, last 30 daysNot available
- Accounts asking about it through MCP, last 7 daysNot available
- Hot votes this weekNot available
Heat never changes the order by stars, and sponsorship never touches it. How heat works
Stars over time
30-day change not yet available: not enough history.
Individual skills in this repository
- citation-management
scientific-skills/citation-management - consciousness-council
scientific-skills/consciousness-council - exploratory-data-analysis
scientific-skills/exploratory-data-analysis - get-available-resources
scientific-skills/get-available-resources - hypothesis-generation
scientific-skills/hypothesis-generation - literature-review
scientific-skills/literature-review - markdown-mermaid-writing
scientific-skills/markdown-mermaid-writing - matplotlib
scientific-skills/matplotlib - networkx
scientific-skills/networkx - peer-review
scientific-skills/peer-review - plotly
scientific-skills/plotly - pyzotero
scientific-skills/pyzotero - research-grants
scientific-skills/research-grants - scholar-evaluation
scientific-skills/scholar-evaluation - scientific-brainstorming
scientific-skills/scientific-brainstorming - scientific-critical-thinking
scientific-skills/scientific-critical-thinking - scientific-schematics
scientific-skills/scientific-schematics - scientific-visualization
scientific-skills/scientific-visualization - scientific-writing
scientific-skills/scientific-writing - seaborn
scientific-skills/seaborn - statistical-analysis
scientific-skills/statistical-analysis - statsmodels
scientific-skills/statsmodels - venue-templates
scientific-skills/venue-templates - what-if-oracle
scientific-skills/what-if-oracle - audit-engine
skills/audit-engine - coauthor-engine
skills/coauthor-engine - figure-engine
skills/figure-engine - idea-engine
skills/idea-engine - latex-engine
skills/latex-engine - literature-engine
skills/literature-engine - method-engine
skills/method-engine - peer-review-engine
skills/peer-review-engine - positioning-engine
skills/positioning-engine - presentation-engine
skills/presentation-engine - qualitative-engine
skills/qualitative-engine - review-engine
skills/review-engine - screening-engine
skills/screening-engine - submission-engine
skills/submission-engine - theory-engine
skills/theory-engine - verification-engine
skills/verification-engine - writing-engine
skills/writing-engine
How it works
citation-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
../../.claude-plugin/marketplace.jsonasset562 bytes../../.claude-plugin/plugin.jsonasset1,837 bytesSKILL.mdcore32,596 bytesSKILL.mdcore32,596 bytesSKILL.mdcore32,596 bytesSKILL.mdcore32,596 bytesSKILL.mdcore32,596 bytesSKILL.mdcore32,596 bytesSKILL.mdcore32,596 bytesSKILL.mdcore32,596 bytesSKILL.mdcore32,596 bytesSKILL.mdcore32,596 bytesSKILL.mdcore32,596 bytesSKILL.mdcore32,596 bytesSKILL.mdcore32,596 bytesSKILL.mdcore32,596 bytesSKILL.mdcore32,596 bytesSKILL.mdcore32,596 bytesSKILL.mdcore32,596 bytesSKILL.mdcore32,596 bytesSKILL.mdcore32,596 bytesSKILL.mdcore32,596 bytesSKILL.mdcore32,596 bytesSKILL.mdcore32,596 bytesSKILL.mdcore32,596 bytesSKILL.mdcore32,596 bytesSKILL.mdcore32,596 bytesSKILL.mdcore32,596 bytesassets/bibtex_template.bibasset9,201 bytesassets/citation_checklist.mddoc10,348 bytesreferences/bibtex_formatting.mddoc19,286 bytesreferences/citation_validation.mddoc16,075 bytesreferences/google_scholar_search.mddoc17,045 bytesreferences/metadata_extraction.mddoc19,182 bytesreferences/pubmed_search.mddoc17,735 bytesscripts/doi_to_bibtex.pyruns6,299 bytesscripts/extract_metadata.pyruns20,794 bytesscripts/format_bibtex.pyruns11,444 bytesscripts/search_google_scholar.pyruns8,982 bytesscripts/search_pubmed.pyruns12,832 bytesscripts/validate_citations.pyruns17,482 bytes
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.)View the complete source on GitHubconsciousness-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:
View the complete source on GitHubexploratory-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
Read SKILL.md
# 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 generationView the complete source on GitHubget-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 strategiesView the complete source on GitHubhypothesis-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 generationView the complete source on GitHubliterature-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 reviewsView the complete source on GitHubmarkdown-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
60,858 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
- 🎨 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 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 isView the complete source on GitHubmatplotlib
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
Read SKILL.md
# 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.)View the complete source on GitHubnetworkx
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
Read SKILL.md
# 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 simulationView the complete source on GitHubpeer-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 availabilityView the complete source on GitHubplotly
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
Read SKILL.md
# 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 pxView the complete source on GitHubpyzotero
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_idView the complete source on GitHubresearch-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
Read SKILL.md
# 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 statementsView the complete source on GitHubscholar-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
Read SKILL.md
# 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 presentationView the complete source on GitHubscientific-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
Read SKILL.md
# 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 opportunitiesView the complete source on GitHubscientific-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-analysesView the complete source on GitHubscientific-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
View the complete source on GitHubscientific-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
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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 formatView the complete source on GitHubscientific-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
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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)View the complete source on GitHubseaborn
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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Read SKILL.md
# 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 boxView the complete source on GitHubstatistical-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
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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 analysesView the complete source on GitHubstatsmodels
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
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Read SKILL.md
# 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 diagnosticsView the complete source on GitHubvenue-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
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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 conferencesView the complete source on GitHubwhat-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
Read SKILL.md
# 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)View the complete source on GitHubaudit-engine
When your agent uses it
Activate when the user wants to audit a paper's empirical or technical claims against a linked code repository — checking whether experiments, datasets, models, metrics, and hyperparameters described in the paper actually exist and match the code. Produces a structured audit report classifying each claim as CONFIRMED, PARTIAL, MISSING, or MISMATCH, with file/line evidence. Useful for reproducibility checks, reviewer due diligence, and pre-submission self-audits of ML/CS/empirical papers that ship code.
What it loads into context
Frontmatter
126 tokensAlways available to the agent
SKILL.md
4,074 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
- Core Principle
- When to Activate
- When NOT to Activate
- Inputs
- Step 1: Extract Auditable Claims
- Step 2: Map the Repository
- Step 3: Search for Evidence (per claim)
- Step 4: Classify Each Claim
- Step 5: Generate Audit Report
- Prioritization Strategy
- Limitations
- Relationship to Other Skills
Files
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> **Orchestration Log**: When this skill is activated, append a log entry to `outputs/orchestration_log.md`:
> ```
> ### Skill Activation: Audit Engine
> **Timestamp:** [current date/time]
> **Actor:** AI Agent (audit-engine)
> **Input:** [paper + repo being audited]
> **Output:** [brief summary — e.g., "Audited 18 claims: 12 CONFIRMED, 3 PARTIAL, 2 MISSING, 1 MISMATCH"]
> ```
# Audit Engine
## Core Principle
Papers make claims. Code embodies what was actually done. This engine systematicallyView the complete source on GitHubcoauthor-engine
When your agent uses it
Activate when the user needs to manage multi-author collaboration on a paper. Tracks author contributions using the CRediT taxonomy, manages responsibility assignments, documents the human-AI division of labor, and produces an author contribution statement ready for submission.
What it loads into context
Frontmatter
63 tokensAlways available to the agent
SKILL.md
1,605 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
- Core Principle
- When to Activate
- Step 1: DEFINE Author Roster
- Step 2: ASSIGN CRediT Roles
- Step 3: TRACK Responsibilities
- Step 4: GENERATE Output
- Integration
Files
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> **Orchestration Log**: When this skill is activated, append a log entry to `outputs/orchestration_log.md`:
> ```
> ### Skill Activation: Co-Author Engine
> **Timestamp:** [current date/time]
> **Actor:** AI Agent (coauthor-engine)
> **Input:** [N] authors, task: [contribution tracking / responsibility assignment / CRediT statement]
> **Output:** author_contributions.md saved
> ```
# Co-Author Engine
## Core Principle
Modern academic publishing increasingly requires transparent documentation of whoView the complete source on GitHubfigure-engine
When your agent uses it
Activate when the user needs to generate, refine, or evaluate academic figures, diagrams, or statistical plots. Uses PaperBanana to transform text descriptions or data files into publication-quality illustrations via direct Python API call. Fallback: matplotlib/seaborn.
What it loads into context
Frontmatter
63 tokensAlways available to the agent
SKILL.md
2,330 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
- Core Principle
- Prerequisites
- Method Priority
- Method 1: Direct Python API (PRIMARY — Always Use This)
- Method 2: Python matplotlib/seaborn (FALLBACK)
- When the User Says "Make Me a Figure" — What to Do
- Diagram Prompting Guide
- Academic Figure Standards
- Integration with Paper Machine
Scripts executed separately
../../scripts/paperbanana_direct.py
Files
../../scripts/paperbanana_direct.pyruns11,766 bytesSKILL.mdcore9,154 bytesSKILL.mdcore9,154 bytesSKILL.mdcore9,154 bytesSKILL.mdcore9,154 bytesSKILL.mdcore9,154 bytesSKILL.mdcore9,154 bytesSKILL.mdcore9,154 bytesSKILL.mdcore9,154 bytesSKILL.mdcore9,154 bytesSKILL.mdcore9,154 bytesSKILL.mdcore9,154 bytesSKILL.mdcore9,154 bytesSKILL.mdcore9,154 bytesSKILL.mdcore9,154 bytesSKILL.mdcore9,154 bytesSKILL.mdcore9,154 bytes
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> **Orchestration Log**: When this skill is activated, append a log entry to `outputs/orchestration_log.md`:
> ```
> ### Skill Activation: Figure Engine
> **Timestamp:** [current date/time]
> **Actor:** AI Agent (figure-engine)
> **Input:** [brief description of the figure request]
> **Output:** [brief description of what was produced — e.g., "Generated methodology diagram (fig03_methodology.png), 3 iterations"]
> ```
# Figure Engine
## Core Principle
Academic papers need professional figures. This skill eliminates manual design work
by using PaperBanana to generate publication-quality diagrams and plots from textView the complete source on GitHubidea-engine
When your agent uses it
Activate when the user needs to evaluate whether a research idea is worth pursuing, brainstorm new research directions, or stress-test a paper concept before committing. This is Phase 0 of the paper machine — the gate that decides whether the full 8-phase pipeline should run. Also activates standalone via /evaluate-idea. Integrates Carlini's research philosophy (conclusion-first test, taste for problems, kill conditions, unreasonable effort) with structured evaluation (7 dimensions, RS1-RS8 principles, 3 specialist agents).
What it loads into context
Frontmatter
120 tokensAlways available to the agent
SKILL.md
2,083 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
- Purpose
- When to Activate
- The 8 Research Strategy Principles (RS1-RS8)
- The 6-Phase Orchestration Pipeline
- Orchestration Rules
- Standalone vs. Pipeline Mode
- Integration with Paper Machine
Files
SKILL.mdcore8,717 bytes
Read SKILL.md
# Idea Engine — Phase 0: Is This Paper Worth Writing?
## Purpose
Most AI tools help you *write* papers. This engine helps you decide *which* papers to write.
The current paper machine takes any topic and produces. This engine adds the missing
gate: **Phase 0 — Is this paper the right use of my time?**
Great research starts with taste, strategic problem selection, honest self-evaluation,
and knowing when to kill your darlings.
## When to Activate
View the complete source on GitHublatex-engine
When your agent uses it
Activate when the user wants to export a completed paper draft to production-ready LaTeX (.tex) and PDF. Converts draft.md + references.bib + figures/ into a complete arxiv-style LaTeX project with properly resolved \citep/\citet citations, booktabs tables, figure environments, and compiled PDF output.
What it loads into context
Frontmatter
81 tokensAlways available to the agent
SKILL.md
2,354 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
- Core Principle
- Prerequisites
- When to Activate
- Conversion Pipeline
- Citation Conversion Details
- Author Block
- Table Conversion
- Figure Handling
- Troubleshooting
- arXiv Submission Preparation
Scripts executed separately
../../scripts/md_to_latex.py
Files
../../scripts/md_to_latex.pyruns25,029 bytesSKILL.mdcore8,742 bytesSKILL.mdcore8,742 bytesSKILL.mdcore8,742 bytesSKILL.mdcore8,742 bytesSKILL.mdcore8,742 bytes
Read SKILL.md
> **Orchestration Log**: When this skill is activated, append a log entry to `outputs/orchestration_log.md`:
> ```
> ### Skill Activation: LaTeX Engine
> **Timestamp:** [current date/time]
> **Actor:** AI Agent (latex-engine)
> **Input:** [brief description of the export request]
> **Output:** [brief description of what was produced — e.g., "Compiled paper.tex to PDF (24 pages), no errors"]
> ```
# LaTeX Engine
## Core Principle
The paper machine works in markdown throughout Phases 1-5 for speed and flexibility.
This engine is the **final production step**: it takes the completed draft.md,View the complete source on GitHubliterature-engine
When your agent uses it
ALWAYS activate when the user needs to find, organize, review, or synthesize academic literature. Uses academic APIs (Semantic Scholar, OpenAlex, CrossRef, arXiv) via scripts/academic_search.py. Handles search strategy, snowballing, screening, concept matrices, narrative synthesis, and literature monitoring (detecting new publications since last search). NEVER use web scraping for paper discovery — APIs first, web search only for verification.
What it loads into context
Frontmatter
104 tokensAlways available to the agent
SKILL.md
3,124 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
- Data Source Priority — STRICT
- Search Strategy
- Screening
- Organizing Literature: The Concept Matrix
- Narrative Synthesis
- Quality Indicators for Paper Selection
- Monitoring / Living Literature Review
Files
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> **Orchestration Log**: When this skill is activated, append a log entry to `outputs/orchestration_log.md`:
> ```
> ### Skill Activation: Literature Engine
> **Timestamp:** [current date/time]
> **Actor:** AI Agent (literature-engine)
> **Input:** [brief description of the search/synthesis request]
> **Output:** [brief description of results — e.g., "47 papers found across 4 databases, deduplicated to 38"]
> ```
# Literature Engine
## Data Source Priority — STRICT
1. **Academic APIs** via `scripts/academic_search.py` — ALWAYS firstView the complete source on GitHubmethod-engine
When your agent uses it
Activate when the user needs to select, justify, describe, or execute a research methodology. Provides method selection guidance, complete method section templates, quality criteria, and tool recommendations. Covers SLR, qualitative (case study, Gioia, Mayring, Grounded Theory), quantitative (SEM, regression, survey, experiment), DSR, mixed methods, action research, ethnography, Delphi study, and simulation. Also includes Research Data Management (RDM) guidance for FAIR-compliant data handling.
What it loads into context
Frontmatter
114 tokensAlways available to the agent
SKILL.md
7,392 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
- Method Selection Guide
- Section A: Systematic Literature Review
- Section B: Qualitative Methods
- Section C: Quantitative Methods
- Section D: Design Science Research
- Section E: Mixed Methods
- Section F: Experiment / Randomized Controlled Trial
- Section G: Action Research
- Section H: Ethnography
- Section I: Delphi Study
- Section J: Simulation / Agent-Based Modeling
- Research Data Management (RDM)
- Quality Criteria Quick Reference
Files
SKILL.mdcore31,552 bytesSKILL.mdcore31,552 bytes
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> **Orchestration Log**: When this skill is activated, append a log entry to `outputs/orchestration_log.md`:
> ```
> ### Skill Activation: Method Engine
> **Timestamp:** [current date/time]
> **Actor:** AI Agent (method-engine)
> **Input:** [brief description of the methodology request]
> **Output:** [brief description of what was produced — e.g., "DSR method section drafted with 3 evaluation criteria"]
> ```
# Method Engine
## Method Selection Guide
### Decision TreeView the complete source on GitHubpeer-review-engine
When your agent uses it
Activate when the user wants to simulate a double-blind peer review of their paper before submission or before sharing with co-authors. Reads the current draft (draft.md or paper.tex), generates 2 independent reviewer reports in the style of top IS/CS conferences (ICIS, ECIS, MISQ level), and saves the output as simulated_reviews.md. The output is formatted to serve as direct input for /respond-reviewers (review-engine feedback loop).
What it loads into context
Frontmatter
112 tokensAlways available to the agent
SKILL.md
4,438 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
- Core Principle
- When to Activate
- Prerequisites
- Step 1: LOCATE & READ the Paper
- Step 2: ASSESS Against Review Dimensions
- Step 3: GENERATE Reviewer 1 — "The Methodologist"
- Step 4: GENERATE Reviewer 2 — "The Theorist"
- Step 5: CALIBRATE Recommendations
- Step 6: COMPILE & SAVE Output
- Venue-Specific Criteria
- Error Recovery
- Integration with Other Engines
Files
SKILL.mdcore20,315 bytes
Read SKILL.md
> **Orchestration Log**: When this skill is activated, append a log entry to `outputs/orchestration_log.md`:
> ```
> ### Skill Activation: Peer Review Engine
> **Timestamp:** [current date/time]
> **Actor:** AI Agent (peer-review-engine)
> **Input:** [paper source: draft.md / paper.tex, word count, number of sections]
> **Output:** 2 simulated reviewer reports saved to simulated_reviews.md
> **Recommendation distribution:** [R1: recommendation, R2: recommendation]
> ```
# Peer Review Engine
## Core Principle
View the complete source on GitHubpositioning-engine
When your agent uses it
Activate when the user needs to analyze how their paper positions itself relative to the closest existing work. Builds a differentiation matrix, identifies unique positioning, and generates a positioning statement that strengthens the contribution argumentation. Complements the theory-engine by focusing on the competitive landscape of related papers.
What it loads into context
Frontmatter
71 tokensAlways available to the agent
SKILL.md
2,386 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
- Core Principle
- When to Activate
- Prerequisites
- Step 1: IDENTIFY Closest Competitors
- Step 2: BUILD Differentiation Matrix
- Step 3: ANALYZE Positioning
- Step 4: GENERATE Positioning Statement
- Step 5: SAVE Output
- Error Recovery
- Integration
Files
SKILL.mdcore10,595 bytes
Read SKILL.md
> **Orchestration Log**: When this skill is activated, append a log entry to `outputs/orchestration_log.md`:
> ```
> ### Skill Activation: Positioning Engine
> **Timestamp:** [current date/time]
> **Actor:** AI Agent (positioning-engine)
> **Input:** Paper draft + [N] comparison papers identified
> **Output:** Differentiation matrix with [N] dimensions, positioning_analysis.md saved
> ```
# Positioning Engine
## Core Principle
"How is your paper different from X?" is the question every reviewer asks. ThisView the complete source on GitHubpresentation-engine
When your agent uses it
Activate when the user needs to create conference presentation slides from a completed paper. Extracts key content, designs a slide structure, generates slide content with speaker notes, and produces a presentation-ready markdown file. Supports IS/CS conference formats (15-20 min presentations).
What it loads into context
Frontmatter
68 tokensAlways available to the agent
SKILL.md
1,915 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
- Core Principle
- When to Activate
- Prerequisites
- Step 1: EXTRACT Key Content
- Step 2: DESIGN Slide Structure
- Step 3: GENERATE Slide Content
- Step 4: GENERATE Speaker Notes
- Step 5: SAVE Output
- Error Recovery
- Integration
Files
SKILL.mdcore7,867 bytes
Read SKILL.md
> **Orchestration Log**: When this skill is activated, append a log entry to `outputs/orchestration_log.md`:
> ```
> ### Skill Activation: Presentation Engine
> **Timestamp:** [current date/time]
> **Actor:** AI Agent (presentation-engine)
> **Input:** [paper source], target format: [conference talk / workshop / poster]
> **Output:** presentation.md with [N] slides + speaker notes
> ```
# Presentation Engine
## Core Principle
A conference presentation is NOT a paper read aloud. It's a persuasion exercise:View the complete source on GitHubqualitative-engine
When your agent uses it
Activate when the user needs to analyze qualitative data — interview transcripts, field notes, or open-ended survey responses. Handles structured summarization, thematic coding, cross-case analysis, theme matrices, and evidence retrieval. Designed to solve the context-window problem: generates compact summaries first, then works from summaries instead of full transcripts. Only loads full text when specific quotes are needed. Supports Gioia, Mayring, Grounded Theory, and general thematic analysis workflows.
What it loads into context
Frontmatter
106 tokensAlways available to the agent
SKILL.md
2,847 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
- Core Principle: Summary-First, Full-Text-on-Demand
- Utility Script
- Phase 1: Structured Summarization
- Phase 2: Initial Coding (First-Order Codes)
- Phase 3: Thematic Grouping (Second-Order Themes)
- Phase 4: Cross-Case Analysis
- Phase 5: Evidence Retrieval (Quote Mining)
- Phase 6: Integration with Writing Engine
- Quality Assurance Checklist
- Activation Triggers
Scripts executed separately
../../scripts/process_interviews.py
Files
../../scripts/process_interviews.pyruns6,818 bytesSKILL.mdcore11,661 bytesSKILL.mdcore11,661 bytesSKILL.mdcore11,661 bytesSKILL.mdcore11,661 bytesSKILL.mdcore11,661 bytes
Read SKILL.md
> **Orchestration Log**: When this skill is activated, append a log entry to `outputs/orchestration_log.md`:
> ```
> ### Skill Activation: Qualitative Engine
> **Timestamp:** [current date/time]
> **Actor:** AI Agent (qualitative-engine)
> **Input:** [brief description of the analysis request]
> **Output:** [brief description — e.g., "23 interviews summarized, 14 first-order codes identified"]
> ```
# Qualitative Engine
## Core Principle: Summary-First, Full-Text-on-Demand
**CRITICAL:** Qualitative data (interview transcripts) can easily overflow the context window.View the complete source on GitHubreview-engine
When your agent uses it
Activate when the user provides reviewer or co-author feedback (annotated PDF, pasted comments, or reviewer report) and wants to implement revisions. Extracts review points, maps them to paper.tex locations, classifies actions, implements changes, recompiles, and generates a change log + latexdiff. Handles the full revision loop from feedback to committed changes.
What it loads into context
Frontmatter
87 tokensAlways available to the agent
SKILL.md
5,028 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
- Core Principle
- When to Activate
- Prerequisites
- Step 1: EXTRACT Review Points
- Step 2: MAP to Source
- Step 3: CLASSIFY Actions
- Step 4: PLAN -- Present Change Plan
- Step 5: IMPLEMENT Changes
- Step 6: VERIFY
- Step 7: DOCUMENT
- Error Recovery
- Integration with Other Engines
- Iteration
Scripts executed separately
../../scripts/extract_annotations.py
Files
../../scripts/extract_annotations.pyruns4,610 bytesSKILL.mdcore20,408 bytesSKILL.mdcore20,408 bytesSKILL.mdcore20,408 bytesSKILL.mdcore20,408 bytesSKILL.mdcore20,408 bytesSKILL.mdcore20,408 bytesSKILL.mdcore20,408 bytesreview_engine_workflow.pngasset4,773,594 bytes
Read SKILL.md
> **Orchestration Log**: When this skill is activated, append a log entry to `outputs/orchestration_log.md`:
> ```
> ### Skill Activation: Review Engine (Round [N])
> **Timestamp:** [current date/time]
> **Actor:** AI Agent (review-engine)
> **Input:** [N] review points from [source] (annotated PDF / pasted text / reviewer report)
> **Output:** [N] changes implemented, paper recompiled, latexdiff generated
> **Human decisions:** [list of QUESTION items resolved by orchestrator]
> ```
# Review Engine
## Core Principle
View the complete source on GitHubscreening-engine
When your agent uses it
Activate when the user needs to systematically screen papers for a Systematic Literature Review (SLR). Implements the PRISMA-compliant screening pipeline: define inclusion/exclusion criteria, title/abstract screening, full-text screening, quality assessment, and PRISMA flow diagram generation. Takes the literature_base.csv from Phase 1 (Reconnaissance) and produces a filtered, documented, auditable set of included studies.
What it loads into context
Frontmatter
100 tokensAlways available to the agent
SKILL.md
3,377 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
- Core Principle
- When to Activate
- Prerequisites
- Step 1: DEFINE Inclusion/Exclusion Criteria
- Step 2: TITLE/ABSTRACT SCREENING
- Step 3: FULL-TEXT SCREENING
- Step 4: QUALITY ASSESSMENT
- Step 5: GENERATE PRISMA Flow Diagram
- Step 6: SAVE & DOCUMENT
- Error Recovery
- Integration with Pipeline
Files
SKILL.mdcore13,526 bytes
Read SKILL.md
> **Orchestration Log**: When this skill is activated, append a log entry to `outputs/orchestration_log.md`:
> ```
> ### Skill Activation: Screening Engine
> **Timestamp:** [current date/time]
> **Actor:** AI Agent (screening-engine)
> **Input:** [N] papers from literature_base.csv, [N] inclusion criteria, [N] exclusion criteria
> **Output:** [N] papers included after screening, PRISMA diagram generated, screening_log.md saved
> ```
# Screening Engine
## Core Principle
A systematic literature review without documented screening is not systematic. ThisView the complete source on GitHubsubmission-engine
When your agent uses it
Activate when the user wants to prepare a paper for submission to a specific venue. Handles venue-specific formatting validation, anonymization checks for double-blind review, cover letter generation, suggested reviewer identification, and submission checklist completion. Produces a submission-ready package.
What it loads into context
Frontmatter
65 tokensAlways available to the agent
SKILL.md
3,539 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
- Core Principle
- When to Activate
- Prerequisites
- Step 1: IDENTIFY Target Venue & Load Requirements
- Step 2: ANONYMIZATION CHECK (for Double-Blind)
- Step 3: GENERATE Cover Letter
- Step 4: SUGGEST Reviewers
- Step 5: FORMATTING VALIDATION
- Step 6: SUBMISSION CHECKLIST
- Step 7: COMPILE Submission Package
- Error Recovery
- Integration
Files
SKILL.mdcore14,832 bytesSKILL.mdcore14,832 bytesSKILL.mdcore14,832 bytes
Read SKILL.md
> **Orchestration Log**: When this skill is activated, append a log entry to `outputs/orchestration_log.md`:
> ```
> ### Skill Activation: Submission Engine
> **Timestamp:** [current date/time]
> **Actor:** AI Agent (submission-engine)
> **Input:** Target venue: [venue], paper: [paper.tex / draft.md]
> **Output:** Submission package prepared: cover letter, anonymization report, checklist
> ```
# Submission Engine
## Core Principle
A paper is only as good as its submission package. Many strong papers receive deskView the complete source on GitHubtheory-engine
When your agent uses it
Activate when the user needs to select a theoretical lens, formulate a research gap, derive hypotheses or design principles, or write a contribution statement. Provides concrete theory-to-paper templates, not abstract advice.
What it loads into context
Frontmatter
50 tokensAlways available to the agent
SKILL.md
2,037 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
- Purpose
- Step 1: Match Topic to Theory
- Step 2: Formulate the Gap
- Step 3: Derive Hypotheses or Propositions
- Step 4: Write the Contribution Statement
Files
SKILL.mdcore9,323 bytes
Read SKILL.md
> **Orchestration Log**: When this skill is activated, append a log entry to `outputs/orchestration_log.md`:
> ```
> ### Skill Activation: Theory Engine
> **Timestamp:** [current date/time]
> **Actor:** AI Agent (theory-engine)
> **Input:** [brief description of the theory selection/gap formulation request]
> **Output:** [brief description of what was produced — e.g., "Selected sociotechnical systems theory, drafted theoretical framing (800 words)"]
> ```
# Theory Engine
## Purpose
Turn "I don't know which theory to use" into a concrete theoretical framing
with drafted paragraphs ready for the paper.View the complete source on GitHubverification-engine
When your agent uses it
Activate when the user needs to verify citations, check source accuracy, or validate that referenced papers actually support the claims attributed to them. Retrieves source abstracts and full text (for open-access papers), compares each citation claim against actual source content, and produces a structured verification report with classifications (VERIFIED, PLAUSIBLE, MISMATCH, UNVERIFIABLE, NOT FOUND).
What it loads into context
Frontmatter
91 tokensAlways available to the agent
SKILL.md
3,442 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
- Core Principle
- When to Activate
- Step 1: Extract Citation Claims
- Step 2: Match to BibTeX Entries
- Step 3: Fetch Source Material (3-Tier Retrieval)
- Step 4: Verify Claims Against Sources
- Step 5: Generate Verification Report
- Prioritization Strategy (Tiered Processing)
- Limitations & Extension Points
Files
SKILL.mdcore14,391 bytesSKILL.mdcore14,391 bytesSKILL.mdcore14,391 bytesSKILL.mdcore14,391 bytesSKILL.mdcore14,391 bytesSKILL.mdcore14,391 bytesSKILL.mdcore14,391 bytesSKILL.mdcore14,391 bytesSKILL.mdcore14,391 bytesSKILL.mdcore14,391 bytesSKILL.mdcore14,391 bytesSKILL.mdcore14,391 bytesSKILL.mdcore14,391 bytesSKILL.mdcore14,391 bytesSKILL.mdcore14,391 bytes
Read SKILL.md
> **Orchestration Log**: When this skill is activated, append a log entry to `outputs/orchestration_log.md`:
> ```
> ### Skill Activation: Verification Engine
> **Timestamp:** [current date/time]
> **Actor:** AI Agent (verification-engine)
> **Input:** [brief description of the verification request]
> **Output:** [brief description of results — e.g., "Verified 42 citations: 35 VERIFIED, 5 PLAUSIBLE, 2 MISMATCH"]
> ```
# Verification Engine
## Core Principle
A citation is only as good as its accuracy. This engine systematically checks whetherView the complete source on GitHubwriting-engine
When your agent uses it
ALWAYS activate when the user writes, drafts, or revises any part of an academic paper. This is the core skill for overcoming writer's block and producing complete first drafts. Provides concrete sentence-level templates, paragraph formulas, and section blueprints for IS/WI/BWL research papers. Works for journal papers (MISQ, BISE, EJIS), conference papers (ICIS, ECIS, WI), and working papers.
What it loads into context
Frontmatter
101 tokensAlways available to the agent
SKILL.md
5,094 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
- Core Principle
- When the User Says "Help Me Write" — What to Do
- INTRODUCTION (Target: 1500-2000 words for journal, 800-1000 for conference)
- THEORETICAL BACKGROUND / LITERATURE REVIEW (Target: 3000-5000 words journal, 1500-2500 conference)
- METHODOLOGY (Target: 2000-3000 words journal, 1000-1500 conference)
- RESULTS / FINDINGS (Target: 2000-4000 words)
- DISCUSSION (Target: 2000-3000 words journal, 1000-1500 conference)
- ABSTRACT (150-250 words, write LAST)
- STYLE RULES
- STYLE ANALYSIS MODE
Files
SKILL.mdcore21,390 bytesSKILL.mdcore21,390 bytes
Read SKILL.md
> **Orchestration Log**: When this skill is activated, append a log entry to `outputs/orchestration_log.md`:
> ```
> ### Skill Activation: Writing Engine
> **Timestamp:** [current date/time]
> **Actor:** AI Agent (writing-engine)
> **Input:** [brief description of what the user asked to write/draft/revise]
> **Output:** [brief description of what was produced — e.g., "Complete draft of Introduction (1,200 words)"]
> ```
# Writing Engine
## Core Principle
The biggest barrier to writing is the blank page. This skill eliminates it by providing
fill-in-the-blank templates at every level: sentence, paragraph, section, and full paper.View the complete source on GitHubSkills included
- citation-management
- consciousness-council
- exploratory-data-analysis
- get-available-resources
- hypothesis-generation
- literature-review
- markdown-mermaid-writing
- matplotlib
- networkx
- peer-review
- plotly
- pyzotero
- research-grants
- scholar-evaluation
- scientific-brainstorming
- scientific-critical-thinking
- scientific-schematics
- scientific-visualization
- scientific-writing
- seaborn
- statistical-analysis
- statsmodels
- venue-templates
- what-if-oracle
- audit-engine
- coauthor-engine
- figure-engine
- idea-engine
- latex-engine
- literature-engine
- method-engine
- peer-review-engine
- positioning-engine
- presentation-engine
- qualitative-engine
- review-engine
- screening-engine
- submission-engine
- theory-engine
- verification-engine
- writing-engine
Repository facts
- License
- MIT
- Created
- Feb 21, 2026
- Last pushed
- Apr 10, 2026
- Forks
- 3
- Open issues
- 0
- Project activity
- Slowing
Before you install
Network or broad shellAutomated signals, not a guarantee
Checked Nov 6, 2026
Not fully analyzed: some files were too long, unreadable or missing. This never lowers the level.
Show 316 notes
scientific-skills/citation-management/SKILL.md:1Analysis incomplete: Referenced executable target unavailable; not analyzedscientific-skills/citation-management/SKILL.md:1Analysis incomplete: Referenced executable target unavailable; not analyzedscientific-skills/citation-management/SKILL.md:1Analysis incomplete: Referenced executable target unavailable; not analyzedscientific-skills/citation-management/SKILL.md:1Analysis incomplete: Referenced executable target unavailable; not analyzedscientific-skills/citation-management/SKILL.md:1Analysis incomplete: Referenced executable target unavailable; not analyzedscientific-skills/citation-management/SKILL.md:1Analysis incomplete: Referenced executable target unavailable; not analyzedscientific-skills/citation-management/SKILL.md:1Analysis incomplete: Referenced executable target unavailable; not analyzedscientific-skills/citation-management/SKILL.md:1Analysis incomplete: Referenced executable target unavailable; not analyzedscientific-skills/citation-management/SKILL.md:1Analysis incomplete: Referenced executable target unavailable; not analyzedscientific-skills/citation-management/SKILL.md:1Analysis incomplete: Referenced executable target unavailable; not analyzedscientific-skills/citation-management/SKILL.md:1Analysis incomplete: Referenced executable target unavailable; not analyzedscientific-skills/citation-management/SKILL.md:1Analysis incomplete: Referenced executable target unavailable; not analyzedscientific-skills/citation-management/SKILL.md:1Analysis incomplete: Referenced executable target unavailable; not analyzedscientific-skills/citation-management/SKILL.md:1Analysis incomplete: Referenced executable target unavailable; not analyzedscientific-skills/citation-management/SKILL.md:1Analysis incomplete: Referenced executable target unavailable; not analyzedscientific-skills/citation-management/SKILL.md:1Analysis incomplete: Referenced executable target unavailable; not analyzedscientific-skills/citation-management/SKILL.md:1Analysis incomplete: Referenced executable target unavailable; not analyzedscientific-skills/citation-management/SKILL.md:1Analysis incomplete: Referenced executable target unavailable; not analyzedscientific-skills/citation-management/SKILL.md:1Analysis incomplete: Referenced executable target unavailable; not analyzedscientific-skills/citation-management/SKILL.md:1Analysis incomplete: Referenced executable target unavailable; not analyzed
- 11 broad permissionsin 11 files: citation-management/SKILL.md:1 hypothesis-generation/SKILL.md:1 literature-review/SKILL.md:1 and 8 more
- 1 dangerous shell commandin 1 file: venue-templates/scripts/validate_format.py:76
- 609 network referencesin 103 files: scientific-writing/references/citation_styles.md:204 citation-management/references/metadata_extraction.md:121 citation-management/SKILL.md:176 and 100 more
- 5 obfuscation signalsin 2 files: scientific-schematics/scripts/generate_schematic_ai.py:283 markdown-mermaid-writing/references/markdown_style_guide.md:529
- No instruction overrides
- 28 executable scriptsin 28 files: citation-management/scripts/doi_to_bibtex.py:1 citation-management/scripts/extract_metadata.py:1 citation-management/scripts/format_bibtex.py:1 and 25 more
- 109 references to secretsin 17 files: scientific-schematics/scripts/generate_schematic_ai.py:12 scripts/paperbanana_direct.py:43 citation-management/scripts/search_pubmed.py:20 and 14 more
- No requests to vote in Hot
Show all 763 signals by file
scientific-skills/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
scientific-skills/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/},
scientific-skills/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/
scientific-skills/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/},
scientific-skills/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
scientific-skills/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},
scientific-skills/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]
scientific-skills/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}'
scientific-skills/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}'
scientific-skills/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/', '')
scientific-skills/citation-management/scripts/search_google_scholar.py
- Executable scriptline 1
#!/usr/bin/env python3
scientific-skills/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]
scientific-skills/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}'
scientific-skills/consciousness-council/SKILL.md
scientific-skills/exploratory-data-analysis/references/chemistry_molecular_formats.md
- Network accessline 290
### .nc / .netcdf - Amber NetCDF Trajectory
scientific-skills/exploratory-data-analysis/references/general_scientific_formats.md
- Network accessline 376
### .nc / .netcdf - Network Common Data Form
scientific-skills/exploratory-data-analysis/scripts/eda_analyzer.py
- Executable scriptline 1
#!/usr/bin/env python3
scientific-skills/get-available-resources/scripts/detect_resources.py
- Executable scriptline 1
#!/usr/bin/env python3
scientific-skills/hypothesis-generation/SKILL.md
- Broad permissionsline 1
Read Write Edit Bash
scientific-skills/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)
scientific-skills/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
scientific-skills/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/
scientific-skills/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)
scientific-skills/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/
scientific-skills/literature-review/scripts/generate_pdf.py
- Executable scriptline 1
#!/usr/bin/env python3
scientific-skills/literature-review/scripts/search_databases.py
scientific-skills/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}"
scientific-skills/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/
scientific-skills/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
scientific-skills/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) -->
scientific-skills/markdown-mermaid-writing/references/diagrams/block.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) -->
scientific-skills/markdown-mermaid-writing/references/diagrams/c4.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) -->
scientific-skills/markdown-mermaid-writing/references/diagrams/class.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) -->
scientific-skills/markdown-mermaid-writing/references/diagrams/complex_examples.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) -->
scientific-skills/markdown-mermaid-writing/references/diagrams/er.md
scientific-skills/markdown-mermaid-writing/references/diagrams/flowchart.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) -->
scientific-skills/markdown-mermaid-writing/references/diagrams/gantt.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) -->
scientific-skills/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) -->
scientific-skills/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) -->
scientific-skills/markdown-mermaid-writing/references/diagrams/mindmap.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) -->
scientific-skills/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) -->
scientific-skills/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) -->
scientific-skills/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) -->
scientific-skills/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) -->
scientific-skills/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) -->
scientific-skills/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) -->
scientific-skills/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) -->
scientific-skills/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) -->
scientific-skills/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) -->
scientific-skills/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) -->
scientific-skills/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) -->
scientific-skills/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) -->
scientific-skills/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) -->
scientific-skills/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
scientific-skills/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/)
scientific-skills/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)
scientific-skills/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]
scientific-skills/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)
scientific-skills/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) -->
scientific-skills/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
scientific-skills/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]
scientific-skills/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)
scientific-skills/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>
scientific-skills/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
scientific-skills/matplotlib/references/api_reference.md
scientific-skills/matplotlib/scripts/plot_template.py
- Executable scriptline 1
#!/usr/bin/env python3
scientific-skills/matplotlib/scripts/style_configurator.py
- Executable scriptline 1
#!/usr/bin/env python3
scientific-skills/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
scientific-skills/peer-review/SKILL.md
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Read Write Edit Bash
scientific-skills/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/
scientific-skills/plotly/SKILL.md
scientific-skills/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]
scientific-skills/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:
scientific-skills/pyzotero/references/exports.md
- Network accessline 45
Pass any valid CSL style name from the [Zotero style repository](https://www.zotero.org/styles):
scientific-skills/research-grants/SKILL.md
- Broad permissionsline 1
Read Write Edit Bash
scientific-skills/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
scientific-skills/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
scientific-skills/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
scientific-skills/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
scientific-skills/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
scientific-skills/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/
scientific-skills/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/**
scientific-skills/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. [
scientific-skills/scholar-evaluation/scripts/calculate_scores.py
- Executable scriptline 1
#!/usr/bin/env python3
scientific-skills/scientific-critical-thinking/SKILL.md
- Broad permissionsline 1
Read Write Edit Bash
scientific-skills/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
scientific-skills/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
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[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
scientific-skills/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
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[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
scientific-skills/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/
scientific-skills/scientific-schematics/scripts/example_usage.sh
- Executable scriptline 1
#!/bin/bash - Access to secretsline 5
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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]
scientific-skills/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
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[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]
scientific-skills/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
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[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]
scientific-skills/scientific-visualization/assets/color_palettes.py
scientific-skills/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
scientific-skills/scientific-visualization/references/matplotlib_examples.md
scientific-skills/scientific-visualization/scripts/figure_export.py
- Executable scriptline 1
#!/usr/bin/env python3
scientific-skills/scientific-visualization/scripts/style_presets.py
- Executable scriptline 1
#!/usr/bin/env python3
scientific-skills/scientific-writing/SKILL.md
- Broad permissionsline 1
Read Write Edit Bash
scientific-skills/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
scientific-skills/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
scientific-skills/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
scientific-skills/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/
scientific-skills/statistical-analysis/SKILL.md
scientific-skills/statistical-analysis/references/reporting_standards.md
scientific-skills/statistical-analysis/scripts/assumption_checks.py
- Executable scriptline 1
Executable file
scientific-skills/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
scientific-skills/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
scientific-skills/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}
scientific-skills/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}\\
scientific-skills/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/
scientific-skills/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/
scientific-skills/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
scientific-skills/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/
scientific-skills/venue-templates/scripts/customize_template.py
- Executable scriptline 1
#!/usr/bin/env python3
scientific-skills/venue-templates/scripts/query_template.py
- Executable scriptline 1
#!/usr/bin/env python3
scientific-skills/venue-templates/scripts/validate_format.py
scientific-skills/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)
scripts/extract_annotations.py
- Executable scriptline 1
Executable file
scripts/md_to_latex.py
- Executable scriptline 1
#!/usr/bin/env python3
scripts/paperbanana_direct.py
- Executable scriptline 1
#!/usr/bin/env python3 - Access to secretsline 43
[Credential-related source omitted] - Access to secretsline 44
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scripts/process_interviews.py
- Executable scriptline 1
Executable file
skills/figure-engine/SKILL.md
- Network accessline 27
> **Based on:** Zhu, D., Meng, R., Song, Y., Wei, X., Li, S., Pfister, T., & Yoon, J. (2026). > *PaperBanana: Automating Academic Illustration for AI Scientists.* [arXiv:2601.23265](https://arxiv.org/abs/2601.23265). - Network accessline 28
> *PaperBanana: Automating Academic Illustration for AI Scientists.* [arXiv:2601.23265](https://arxiv.org/abs/2601.23265). - Network accessline 29
> The pipeline uses a 5-agent, 2-phase architecture: Retriever → Planner → Stylist (Phase 1: planning), > then Visualizer ↔ Critic iterative refinement (Phase 2: generation) with VLM-as-Judge evaluation. > Official research repo: [`dwzhu-pk - Network accessline 31
> Official research repo: [`dwzhu-pku/PaperBanana`](https://github.com/dwzhu-pku/PaperBanana). - Access to secretsline 37
[Credential-related source omitted] - Access to secretsline 38
[Credential-related source omitted] - Network accessline 42
Get a free key at https://aistudio.google.com/apikey
skills/latex-engine/SKILL.md
- Network accessline 23
This engine is the **final production step**: it takes the completed draft.md, references.bib, and figures/ and produces a submission-ready LaTeX project with compiled PDF using the arxiv-style template (https://github.com/kourgeorge/arxiv- - Network accessline 25
compiled PDF using the arxiv-style template (https://github.com/kourgeorge/arxiv-style).
skills/method-engine/SKILL.md
skills/verification-engine/SKILL.md
- Network accessline 125
- OpenAlex: `open_access.oa_url` field 2. **arXiv preprints:** If DOI starts with `10.48550/arxiv.` or BibTeX key suggests arXiv, construct the PDF URL: `https://arxiv.org/pdf/{arxiv_id}` - Network accessline 126
2. **arXiv preprints:** If DOI starts with `10.48550/arxiv.` or BibTeX key suggests arXiv, construct the PDF URL: `https://arxiv.org/pdf/{arxiv_id}` 3. **Fetch the PDF:** - Network accessline 127
construct the PDF URL: `https://arxiv.org/pdf/{arxiv_id}` 3. **Fetch the PDF:** - Use `WebFetch` with the PDF URL to get content, OR - Access to secretsline 370
[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.
Declared and executed: 1 fact
- Contacts www.w3.org, which SKILL.md, the README and the plugin manifest do not mentioncitation-management/scripts/extract_metadata.py:235High confidence
Signal confidence: 499 facts
- Broad permissions in the frontmatter of the SKILL.mdcitation-management/SKILL.md:1High confidence
- Network access in code of the SKILL.mdcitation-management/SKILL.md:176High confidence
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- Network access in code of documentationcitation-management/references/bibtex_formatting.md:317Low confidence
- Network access in code of documentationcitation-management/references/bibtex_formatting.md:388Low confidence
- Network access in code of documentationcitation-management/references/bibtex_formatting.md:389Low confidence
- Network access in code of documentationcitation-management/references/bibtex_formatting.md:390Low confidence
- Network access in code of documentationcitation-management/references/bibtex_formatting.md:589Low confidence
- Network access in code of documentationcitation-management/references/bibtex_formatting.md:600Low confidence
- Network access in code of documentationcitation-management/references/bibtex_formatting.md:610Low confidence
- Network access in code of documentationcitation-management/references/bibtex_formatting.md:612Low confidence
- Network access in code of documentationcitation-management/references/bibtex_formatting.md:805Low confidence
- Network access in code of documentationcitation-management/references/bibtex_formatting.md:883Low confidence
- Network access in code of documentationcitation-management/references/bibtex_formatting.md:884Low confidence
- Network access in code of documentationcitation-management/references/bibtex_formatting.md:885Low confidence
- Network access in prose of documentationcitation-management/references/citation_validation.md:37Low confidence
- Network access in prose of documentationcitation-management/references/citation_validation.md:38Low confidence
- Network access in prose of documentationcitation-management/references/citation_validation.md:50Low confidence
- Network access in prose of documentationcitation-management/references/citation_validation.md:51Low confidence
- Network access in prose of documentationcitation-management/references/citation_validation.md:52Low confidence
- Network access in code of documentationcitation-management/references/citation_validation.md:294Low confidence
and 449 more
Only the first 500 facts are listed.
Works with
| Agent | Status | Evidence | Install to |
|---|---|---|---|
| Claude Code | Native | Manifest .claude-plugin/marketplace.json | .claude/skills/<skill-name>/ |
| Codex | Compatible | Standard SKILL.md scientific-skills/citation-management/SKILL.md | .agents/skills/<skill-name>/ |
| Cursor | Compatible | Standard SKILL.md scientific-skills/citation-management/SKILL.md | .cursor/skills/<skill-name>/ |
| Gemini CLI | Compatible | Standard SKILL.md scientific-skills/citation-management/SKILL.md | .gemini/skills/<skill-name>/ |
| GitHub Copilot | Compatible | Standard SKILL.md scientific-skills/citation-management/SKILL.md | .github/skills/<skill-name>/ |
| OpenCode | Compatible | Standard SKILL.md scientific-skills/citation-management/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.