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wentorai/research-plugins
350+ academic research skills, MCP configs, and plugins for Research-Claw and AI agents
History
Review weekly changes to SKILL.md before updating. Dates mark the weeks in which we observed each version; checks apply to the recorded commits.
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skills/analysis/dataviz/SKILL.md
Week of Oct 26, 2026 · Line counts unavailable
Before Not checked yet → After Instructions only
First observation → bf44b3cd617f
No earlier version was observed for comparison.
skills/analysis/dataviz/algorithm-visualizer-guide/SKILL.md
Week of Oct 26, 2026 · Line counts unavailable
Before Not checked yet → After Instructions only
First observation → bf44b3cd617f
No earlier version was observed for comparison.
skills/analysis/dataviz/bokeh-visualization-guide/SKILL.md
Week of Oct 26, 2026 · Line counts unavailable
Before Not checked yet → After Instructions only
First observation → bf44b3cd617f
No earlier version was observed for comparison.
skills/analysis/dataviz/chart-image-generator/SKILL.md
Week of Oct 26, 2026 · Line counts unavailable
Before Not checked yet → After Instructions only
First observation → bf44b3cd617f
No earlier version was observed for comparison.
skills/analysis/dataviz/color-accessibility-guide/SKILL.md
Week of Oct 26, 2026 · Line counts unavailable
Before Not checked yet → After Instructions only
First observation → bf44b3cd617f
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skills/analysis/dataviz/d3-visualization-guide/SKILL.md
Week of Oct 26, 2026 · Line counts unavailable
Before Not checked yet → After Instructions only
First observation → bf44b3cd617f
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skills/analysis/dataviz/echarts-visualization-guide/SKILL.md
Week of Oct 26, 2026 · Line counts unavailable
Before Not checked yet → After Network or broad shell
First observation → bf44b3cd617f
No earlier version was observed for comparison.
skills/analysis/dataviz/geospatial-viz-guide/SKILL.md
Week of Oct 26, 2026 · Line counts unavailable
Before Not checked yet → After Instructions only
First observation → bf44b3cd617f
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skills/analysis/dataviz/interactive-viz-guide/SKILL.md
Week of Oct 26, 2026 · Line counts unavailable
Before Not checked yet → After Instructions only
First observation → bf44b3cd617f
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skills/analysis/dataviz/metabase-analytics-guide/SKILL.md
Week of Oct 26, 2026 · Line counts unavailable
Before Not checked yet → After Instructions only
First observation → bf44b3cd617f
No earlier version was observed for comparison.
skills/analysis/dataviz/network-visualization-guide/SKILL.md
Week of Oct 26, 2026 · Line counts unavailable
Before Not checked yet → After Instructions only
First observation → bf44b3cd617f
No earlier version was observed for comparison.
skills/analysis/dataviz/plotly-interactive-guide/SKILL.md
Week of Oct 26, 2026 · Line counts unavailable
Before Not checked yet → After Instructions only
First observation → bf44b3cd617f
No earlier version was observed for comparison.
skills/analysis/dataviz/publication-figures-guide/SKILL.md
Week of Oct 26, 2026 · Line counts unavailable
Before Not checked yet → After Instructions only
First observation → bf44b3cd617f
No earlier version was observed for comparison.
skills/analysis/dataviz/python-dataviz-guide/SKILL.md
Week of Oct 26, 2026 · Line counts unavailable
Before Not checked yet → After Instructions only
First observation → bf44b3cd617f
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skills/analysis/dataviz/redash-analytics-guide/SKILL.md
Week of Oct 26, 2026 · Line counts unavailable
Before Not checked yet → After Network or broad shell
First observation → bf44b3cd617f
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skills/analysis/econometrics/SKILL.md
Week of Oct 26, 2026 · Line counts unavailable
Before Not checked yet → After Instructions only
First observation → bf44b3cd617f
No earlier version was observed for comparison.
skills/analysis/econometrics/causal-inference-guide/SKILL.md
Week of Oct 26, 2026 · Line counts unavailable
Before Not checked yet → After Instructions only
First observation → bf44b3cd617f
No earlier version was observed for comparison.
skills/analysis/econometrics/econml-causal-guide/SKILL.md
Week of Oct 26, 2026 · Line counts unavailable
Before Not checked yet → After Instructions only
First observation → bf44b3cd617f
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skills/analysis/econometrics/iv-regression-guide/SKILL.md
Week of Oct 26, 2026 · Line counts unavailable
Before Not checked yet → After Instructions only
First observation → bf44b3cd617f
No earlier version was observed for comparison.
skills/analysis/econometrics/mostly-harmless-guide/SKILL.md
Week of Oct 26, 2026 · Line counts unavailable
Before Not checked yet → After Instructions only
First observation → bf44b3cd617f
No earlier version was observed for comparison.
skills/analysis/econometrics/panel-data-guide/SKILL.md
Week of Oct 26, 2026 · Line counts unavailable
Before Not checked yet → After Instructions only
First observation → bf44b3cd617f
No earlier version was observed for comparison.
skills/analysis/econometrics/python-causality-guide/SKILL.md
Week of Oct 26, 2026 · Line counts unavailable
Before Not checked yet → After Instructions only
First observation → bf44b3cd617f
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skills/analysis/econometrics/robustness-checks/SKILL.md
Week of Oct 26, 2026 · Line counts unavailable
Before Not checked yet → After Instructions only
First observation → bf44b3cd617f
No earlier version was observed for comparison.
skills/analysis/econometrics/stata-analyst-guide/SKILL.md
Week of Oct 26, 2026 · Line counts unavailable
Before Not checked yet → After Instructions only
First observation → bf44b3cd617f
No earlier version was observed for comparison.
skills/analysis/econometrics/stata-reference-guide/SKILL.md
Week of Oct 26, 2026 · Line counts unavailable
Before Not checked yet → After Instructions only
First observation → bf44b3cd617f
No earlier version was observed for comparison.
skills/analysis/econometrics/time-series-guide/SKILL.md
Week of Oct 26, 2026 · Line counts unavailable
Before Not checked yet → After Instructions only
First observation → bf44b3cd617f
No earlier version was observed for comparison.
skills/analysis/statistics/SKILL.md
Week of Oct 26, 2026 · Line counts unavailable
Before Not checked yet → After Instructions only
First observation → bf44b3cd617f
No earlier version was observed for comparison.
skills/analysis/statistics/bayesian-statistics-guide/SKILL.md
Week of Oct 26, 2026 · Line counts unavailable
Before Not checked yet → After Instructions only
First observation → bf44b3cd617f
No earlier version was observed for comparison.
skills/analysis/statistics/data-anomaly-detection/SKILL.md
Week of Oct 26, 2026 · Line counts unavailable
Before Not checked yet → After Instructions only
First observation → bf44b3cd617f
No earlier version was observed for comparison.
skills/analysis/statistics/hypothesis-testing-guide/SKILL.md
Week of Oct 26, 2026 · Line counts unavailable
Before Not checked yet → After Instructions only
First observation → bf44b3cd617f
No earlier version was observed for comparison.
skills/analysis/statistics/meta-analysis-guide/SKILL.md
Week of Oct 26, 2026 · Line counts unavailable
Before Not checked yet → After Instructions only
First observation → bf44b3cd617f
No earlier version was observed for comparison.
skills/analysis/statistics/ml-experiment-tracker/SKILL.md
Week of Oct 26, 2026 · Line counts unavailable
Before Not checked yet → After Instructions only
First observation → bf44b3cd617f
No earlier version was observed for comparison.
skills/analysis/statistics/modeling-strategy-guide/SKILL.md
Week of Oct 26, 2026 · Line counts unavailable
Before Not checked yet → After Instructions only
First observation → bf44b3cd617f
No earlier version was observed for comparison.
skills/analysis/statistics/nonparametric-tests-guide/SKILL.md
Week of Oct 26, 2026 · Line counts unavailable
Before Not checked yet → After Instructions only
First observation → bf44b3cd617f
No earlier version was observed for comparison.
skills/analysis/statistics/power-analysis-guide/SKILL.md
Week of Oct 26, 2026 · Line counts unavailable
Before Not checked yet → After Instructions only
First observation → bf44b3cd617f
No earlier version was observed for comparison.
skills/analysis/statistics/sem-guide/SKILL.md
Week of Oct 26, 2026 · Line counts unavailable
Before Not checked yet → After Instructions only
First observation → bf44b3cd617f
No earlier version was observed for comparison.
skills/analysis/statistics/survival-analysis-guide/SKILL.md
Week of Oct 26, 2026 · Line counts unavailable
Before Not checked yet → After Instructions only
First observation → bf44b3cd617f
No earlier version was observed for comparison.
skills/analysis/wrangling/SKILL.md
Week of Oct 26, 2026 · Line counts unavailable
Before Not checked yet → After Instructions only
First observation → bf44b3cd617f
No earlier version was observed for comparison.
skills/analysis/wrangling/csv-data-analyzer/SKILL.md
Week of Oct 26, 2026 · Line counts unavailable
Before Not checked yet → After Instructions only
First observation → bf44b3cd617f
No earlier version was observed for comparison.
skills/analysis/wrangling/data-cleaning-pipeline/SKILL.md
Week of Oct 26, 2026 · Line counts unavailable
Before Not checked yet → After Instructions only
First observation → bf44b3cd617f
No earlier version was observed for comparison.
skills/analysis/wrangling/data-cog-guide/SKILL.md
Week of Oct 26, 2026 · Line counts unavailable
Before Not checked yet → After Instructions only
First observation → bf44b3cd617f
No earlier version was observed for comparison.
skills/analysis/wrangling/missing-data-handling/SKILL.md
Week of Oct 26, 2026 · Line counts unavailable
Before Not checked yet → After Instructions only
First observation → bf44b3cd617f
No earlier version was observed for comparison.
skills/analysis/wrangling/pandas-data-wrangling/SKILL.md
Week of Oct 26, 2026 · Line counts unavailable
Before Not checked yet → After Instructions only
First observation → bf44b3cd617f
No earlier version was observed for comparison.
skills/analysis/wrangling/questionnaire-design-guide/SKILL.md
Week of Oct 26, 2026 · Line counts unavailable
Before Not checked yet → After Instructions only
First observation → bf44b3cd617f
No earlier version was observed for comparison.
skills/analysis/wrangling/stata-data-cleaning/SKILL.md
Week of Oct 26, 2026 · Line counts unavailable
Before Not checked yet → After Instructions only
First observation → bf44b3cd617f
No earlier version was observed for comparison.
skills/analysis/wrangling/streamline-analyst-guide/SKILL.md
Week of Oct 26, 2026 · Line counts unavailable
Before Not checked yet → After Instructions only
First observation → bf44b3cd617f
No earlier version was observed for comparison.
skills/analysis/wrangling/survey-data-processing/SKILL.md
Week of Oct 26, 2026 · Line counts unavailable
Before Not checked yet → After Instructions only
First observation → bf44b3cd617f
No earlier version was observed for comparison.
skills/analysis/wrangling/text-mining-guide/SKILL.md
Week of Oct 26, 2026 · Line counts unavailable
Before Not checked yet → After Instructions only
First observation → bf44b3cd617f
No earlier version was observed for comparison.
skills/domains/ai-ml/SKILL.md
Week of Oct 26, 2026 · Line counts unavailable
Before Not checked yet → After Instructions only
First observation → bf44b3cd617f
No earlier version was observed for comparison.
skills/domains/ai-ml/ai-agent-papers-guide/SKILL.md
Week of Oct 26, 2026 · Line counts unavailable
Before Not checked yet → After Instructions only
First observation → bf44b3cd617f
No earlier version was observed for comparison.
skills/domains/ai-ml/ai-model-benchmarking/SKILL.md
Week of Oct 26, 2026 · Line counts unavailable
Before Not checked yet → After Instructions only
First observation → bf44b3cd617f
No earlier version was observed for comparison.
skills/domains/ai-ml/anomaly-detection-papers-guide/SKILL.md
Week of Oct 26, 2026 · Line counts unavailable
Before Not checked yet → After Instructions only
First observation → bf44b3cd617f
No earlier version was observed for comparison.
skills/domains/ai-ml/autonomous-agents-papers-guide/SKILL.md
Week of Oct 26, 2026 · Line counts unavailable
Before Not checked yet → After Instructions only
First observation → bf44b3cd617f
No earlier version was observed for comparison.
skills/domains/ai-ml/computer-vision-guide/SKILL.md
Week of Oct 26, 2026 · Line counts unavailable
Before Not checked yet → After Instructions only
First observation → bf44b3cd617f
No earlier version was observed for comparison.
skills/domains/ai-ml/deep-learning-papers-guide/SKILL.md
Week of Oct 26, 2026 · Line counts unavailable
Before Not checked yet → After Instructions only
First observation → bf44b3cd617f
No earlier version was observed for comparison.
skills/domains/ai-ml/dl-transformer-finetune/SKILL.md
Week of Oct 26, 2026 · Line counts unavailable
Before Not checked yet → After Instructions only
First observation → bf44b3cd617f
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skills/domains/ai-ml/domain-adaptation-papers-guide/SKILL.md
Week of Oct 26, 2026 · Line counts unavailable
Before Not checked yet → After Instructions only
First observation → bf44b3cd617f
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skills/domains/ai-ml/generative-ai-guide/SKILL.md
Week of Oct 26, 2026 · Line counts unavailable
Before Not checked yet → After Instructions only
First observation → bf44b3cd617f
No earlier version was observed for comparison.
skills/domains/ai-ml/graph-learning-papers-guide/SKILL.md
Week of Oct 26, 2026 · Line counts unavailable
Before Not checked yet → After Instructions only
First observation → bf44b3cd617f
No earlier version was observed for comparison.
skills/domains/ai-ml/huggingface-api/SKILL.md
Week of Oct 26, 2026 · Line counts unavailable
Before Not checked yet → After Network or broad shell
First observation → bf44b3cd617f
No earlier version was observed for comparison.
skills/domains/ai-ml/huggingface-inference-guide/SKILL.md
Week of Oct 26, 2026 · Line counts unavailable
Before Not checked yet → After Network or broad shell
First observation → bf44b3cd617f
No earlier version was observed for comparison.
skills/domains/ai-ml/keras-deep-learning/SKILL.md
Week of Oct 26, 2026 · Line counts unavailable
Before Not checked yet → After Instructions only
First observation → bf44b3cd617f
No earlier version was observed for comparison.
skills/domains/ai-ml/kolmogorov-arnold-networks-guide/SKILL.md
Week of Oct 26, 2026 · Line counts unavailable
Before Not checked yet → After Instructions only
First observation → bf44b3cd617f
No earlier version was observed for comparison.
skills/domains/ai-ml/llm-evaluation-guide/SKILL.md
Week of Oct 26, 2026 · Line counts unavailable
Before Not checked yet → After Instructions only
First observation → bf44b3cd617f
No earlier version was observed for comparison.
skills/domains/ai-ml/llm-from-scratch-guide/SKILL.md
Week of Oct 26, 2026 · Line counts unavailable
Before Not checked yet → After Instructions only
First observation → bf44b3cd617f
No earlier version was observed for comparison.
skills/domains/ai-ml/ml-pipeline-guide/SKILL.md
Week of Oct 26, 2026 · Line counts unavailable
Before Not checked yet → After Network or broad shell
First observation → bf44b3cd617f
No earlier version was observed for comparison.
skills/domains/ai-ml/nlp-toolkit-guide/SKILL.md
Week of Oct 26, 2026 · Line counts unavailable
Before Not checked yet → After Instructions only
First observation → bf44b3cd617f
No earlier version was observed for comparison.
skills/domains/ai-ml/npcpy-research-guide/SKILL.md
Week of Oct 26, 2026 · Line counts unavailable
Before Not checked yet → After Instructions only
First observation → bf44b3cd617f
No earlier version was observed for comparison.
skills/domains/ai-ml/prompt-engineering-research/SKILL.md
Week of Oct 26, 2026 · Line counts unavailable
Before Not checked yet → After Instructions only
First observation → bf44b3cd617f
No earlier version was observed for comparison.
skills/domains/ai-ml/pytorch-guide/SKILL.md
Week of Oct 26, 2026 · Line counts unavailable
Before Not checked yet → After Instructions only
First observation → bf44b3cd617f
No earlier version was observed for comparison.
skills/domains/ai-ml/pytorch-lightning-guide/SKILL.md
Week of Oct 26, 2026 · Line counts unavailable
Before Not checked yet → After Instructions only
First observation → bf44b3cd617f
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skills/domains/ai-ml/reinforcement-learning-guide/SKILL.md
Week of Oct 26, 2026 · Line counts unavailable
Before Not checked yet → After Instructions only
First observation → bf44b3cd617f
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skills/domains/ai-ml/responsible-ai-guide/SKILL.md
Week of Oct 26, 2026 · Line counts unavailable
Before Not checked yet → After Instructions only
First observation → bf44b3cd617f
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skills/domains/ai-ml/tensorflow-guide/SKILL.md
Week of Oct 26, 2026 · Line counts unavailable
Before Not checked yet → After Instructions only
First observation → bf44b3cd617f
No earlier version was observed for comparison.
skills/domains/ai-ml/transformer-architecture-guide/SKILL.md
Week of Oct 26, 2026 · Line counts unavailable
Before Not checked yet → After Instructions only
First observation → bf44b3cd617f
No earlier version was observed for comparison.
skills/domains/ai-ml/vmas-simulator-guide/SKILL.md
Week of Oct 26, 2026 · Line counts unavailable
Before Not checked yet → After Instructions only
First observation → bf44b3cd617f
No earlier version was observed for comparison.
skills/domains/biomedical/SKILL.md
Week of Oct 26, 2026 · Line counts unavailable
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First observation → bf44b3cd617f
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skills/domains/biomedical/alphafold-api/SKILL.md
Week of Oct 26, 2026 · Line counts unavailable
Before Not checked yet → After Network or broad shell
First observation → bf44b3cd617f
No earlier version was observed for comparison.
skills/domains/biomedical/bioagents-guide/SKILL.md
Week of Oct 26, 2026 · Line counts unavailable
Before Not checked yet → After Instructions only
First observation → bf44b3cd617f
No earlier version was observed for comparison.
skills/domains/biomedical/biothings-api/SKILL.md
Week of Oct 26, 2026 · Line counts unavailable
Before Not checked yet → After Network or broad shell
First observation → bf44b3cd617f
No earlier version was observed for comparison.
skills/domains/biomedical/clawbio-guide/SKILL.md
Week of Oct 26, 2026 · Line counts unavailable
Before Not checked yet → After Instructions only
First observation → bf44b3cd617f
No earlier version was observed for comparison.
skills/domains/biomedical/clinical-dialogue-agents-guide/SKILL.md
Week of Oct 26, 2026 · Line counts unavailable
Before Not checked yet → After Instructions only
First observation → bf44b3cd617f
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skills/domains/biomedical/clinical-research-guide/SKILL.md
Week of Oct 26, 2026 · Line counts unavailable
Before Not checked yet → After Instructions only
First observation → bf44b3cd617f
No earlier version was observed for comparison.
skills/domains/biomedical/clinicaltrials-api/SKILL.md
Week of Oct 26, 2026 · Line counts unavailable
Before Not checked yet → After Network or broad shell
First observation → bf44b3cd617f
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skills/domains/biomedical/clinicaltrials-api-v2/SKILL.md
Week of Oct 26, 2026 · Line counts unavailable
Before Not checked yet → After Network or broad shell
First observation → bf44b3cd617f
No earlier version was observed for comparison.
skills/domains/biomedical/ena-sequence-api/SKILL.md
Week of Oct 26, 2026 · Line counts unavailable
Before Not checked yet → After Network or broad shell
First observation → bf44b3cd617f
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skills/domains/biomedical/enrichr-api/SKILL.md
Week of Oct 26, 2026 · Line counts unavailable
Before Not checked yet → After Network or broad shell
First observation → bf44b3cd617f
No earlier version was observed for comparison.
skills/domains/biomedical/ensembl-rest-api/SKILL.md
Week of Oct 26, 2026 · Line counts unavailable
Before Not checked yet → After Network or broad shell
First observation → bf44b3cd617f
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skills/domains/biomedical/epidemiology-guide/SKILL.md
Week of Oct 26, 2026 · Line counts unavailable
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First observation → bf44b3cd617f
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skills/domains/biomedical/genomas-guide/SKILL.md
Week of Oct 26, 2026 · Line counts unavailable
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First observation → bf44b3cd617f
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skills/domains/biomedical/genomics-analysis-guide/SKILL.md
Week of Oct 26, 2026 · Line counts unavailable
Before Not checked yet → After Instructions only
First observation → bf44b3cd617f
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skills/domains/biomedical/genotex-benchmark-guide/SKILL.md
Week of Oct 26, 2026 · Line counts unavailable
Before Not checked yet → After Instructions only
First observation → bf44b3cd617f
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skills/domains/biomedical/med-researcher-guide/SKILL.md
Week of Oct 26, 2026 · Line counts unavailable
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First observation → bf44b3cd617f
No earlier version was observed for comparison.
skills/domains/biomedical/med-researcher-r1-guide/SKILL.md
Week of Oct 26, 2026 · Line counts unavailable
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First observation → bf44b3cd617f
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skills/domains/biomedical/medgeclaw-guide/SKILL.md
Week of Oct 26, 2026 · Line counts unavailable
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First observation → bf44b3cd617f
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skills/domains/biomedical/medical-data-api/SKILL.md
Week of Oct 26, 2026 · Line counts unavailable
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First observation → bf44b3cd617f
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skills/domains/biomedical/medical-imaging-guide/SKILL.md
Week of Oct 26, 2026 · Line counts unavailable
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First observation → bf44b3cd617f
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skills/domains/biomedical/ncbi-blast-api/SKILL.md
Week of Oct 26, 2026 · Line counts unavailable
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First observation → bf44b3cd617f
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skills/domains/biomedical/ncbi-datasets-api/SKILL.md
Week of Oct 26, 2026 · Line counts unavailable
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First observation → bf44b3cd617f
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skills/domains/biomedical/pdb-structure-api/SKILL.md
Week of Oct 26, 2026 · Line counts unavailable
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First observation → bf44b3cd617f
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skills/domains/biomedical/quickgo-api/SKILL.md
Week of Oct 26, 2026 · Line counts unavailable
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First observation → bf44b3cd617f
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skills/domains/business/SKILL.md
Week of Oct 26, 2026 · Line counts unavailable
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First observation → bf44b3cd617f
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skills/domains/business/architecture-design-guide/SKILL.md
Week of Oct 26, 2026 · Line counts unavailable
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First observation → bf44b3cd617f
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skills/domains/business/innovation-management-guide/SKILL.md
Week of Oct 26, 2026 · Line counts unavailable
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First observation → bf44b3cd617f
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skills/domains/business/market-analysis-guide/SKILL.md
Week of Oct 26, 2026 · Line counts unavailable
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First observation → bf44b3cd617f
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skills/domains/business/operations-research-guide/SKILL.md
Week of Oct 26, 2026 · Line counts unavailable
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First observation → bf44b3cd617f
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skills/domains/business/strategic-management-guide/SKILL.md
Week of Oct 26, 2026 · Line counts unavailable
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First observation → bf44b3cd617f
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skills/domains/chemistry/SKILL.md
Week of Oct 26, 2026 · Line counts unavailable
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First observation → bf44b3cd617f
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skills/domains/chemistry/cactus-cheminformatics-guide/SKILL.md
Week of Oct 26, 2026 · Line counts unavailable
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First observation → bf44b3cd617f
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skills/domains/chemistry/catalysis-hub-api/SKILL.md
Week of Oct 26, 2026 · Line counts unavailable
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First observation → bf44b3cd617f
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skills/domains/chemistry/chemeagle-guide/SKILL.md
Week of Oct 26, 2026 · Line counts unavailable
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First observation → bf44b3cd617f
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skills/domains/chemistry/chemgraph-agent-guide/SKILL.md
Week of Oct 26, 2026 · Line counts unavailable
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First observation → bf44b3cd617f
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skills/domains/chemistry/computational-chemistry-guide/SKILL.md
Week of Oct 26, 2026 · Line counts unavailable
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First observation → bf44b3cd617f
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skills/domains/chemistry/molecular-dynamics-guide/SKILL.md
Week of Oct 26, 2026 · Line counts unavailable
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First observation → bf44b3cd617f
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skills/domains/chemistry/pubchem-api-guide/SKILL.md
Week of Oct 26, 2026 · Line counts unavailable
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First observation → bf44b3cd617f
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skills/domains/chemistry/retrosynthesis-guide/SKILL.md
Week of Oct 26, 2026 · Line counts unavailable
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First observation → bf44b3cd617f
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skills/domains/chemistry/spectroscopy-analysis-guide/SKILL.md
Week of Oct 26, 2026 · Line counts unavailable
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First observation → bf44b3cd617f
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skills/domains/cs/SKILL.md
Week of Oct 26, 2026 · Line counts unavailable
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First observation → bf44b3cd617f
No earlier version was observed for comparison.
skills/domains/cs/ai-security-papers-guide/SKILL.md
Week of Oct 26, 2026 · Line counts unavailable
Before Not checked yet → After Instructions only
First observation → bf44b3cd617f
No earlier version was observed for comparison.
skills/domains/cs/algorithms-complexity-guide/SKILL.md
Week of Oct 26, 2026 · Line counts unavailable
Before Not checked yet → After Instructions only
First observation → bf44b3cd617f
No earlier version was observed for comparison.
skills/domains/cs/code-llm-papers-guide/SKILL.md
Week of Oct 26, 2026 · Line counts unavailable
Before Not checked yet → After Instructions only
First observation → bf44b3cd617f
No earlier version was observed for comparison.
skills/domains/cs/dblp-api/SKILL.md
Week of Oct 26, 2026 · Line counts unavailable
Before Not checked yet → After Network or broad shell
First observation → bf44b3cd617f
No earlier version was observed for comparison.
skills/domains/cs/distributed-systems-guide/SKILL.md
Week of Oct 26, 2026 · Line counts unavailable
Before Not checked yet → After Instructions only
First observation → bf44b3cd617f
No earlier version was observed for comparison.
skills/domains/cs/formal-verification-guide/SKILL.md
Week of Oct 26, 2026 · Line counts unavailable
Before Not checked yet → After Local scripts
First observation → bf44b3cd617f
No earlier version was observed for comparison.
skills/domains/cs/gaussian-splatting-papers-guide/SKILL.md
Week of Oct 26, 2026 · Line counts unavailable
Before Not checked yet → After Instructions only
First observation → bf44b3cd617f
No earlier version was observed for comparison.
skills/domains/cs/llm-aiops-guide/SKILL.md
Week of Oct 26, 2026 · Line counts unavailable
Before Not checked yet → After Instructions only
First observation → bf44b3cd617f
No earlier version was observed for comparison.
skills/domains/cs/software-engineering-research/SKILL.md
Week of Oct 26, 2026 · Line counts unavailable
Before Not checked yet → After Instructions only
First observation → bf44b3cd617f
No earlier version was observed for comparison.
skills/domains/cs/software-heritage-api/SKILL.md
Week of Oct 26, 2026 · Line counts unavailable
Before Not checked yet → After Network or broad shell
First observation → bf44b3cd617f
No earlier version was observed for comparison.
skills/domains/ecology/SKILL.md
Week of Oct 26, 2026 · Line counts unavailable
Before Not checked yet → After Instructions only
First observation → bf44b3cd617f
No earlier version was observed for comparison.
skills/domains/ecology/biodiversity-data-guide/SKILL.md
Week of Oct 26, 2026 · Line counts unavailable
Before Not checked yet → After Instructions only
First observation → bf44b3cd617f
No earlier version was observed for comparison.
skills/domains/ecology/conservation-biology-guide/SKILL.md
Week of Oct 26, 2026 · Line counts unavailable
Before Not checked yet → After Network or broad shell
First observation → bf44b3cd617f
No earlier version was observed for comparison.
skills/domains/ecology/gbif-api/SKILL.md
Week of Oct 26, 2026 · Line counts unavailable
Before Not checked yet → After Network or broad shell
First observation → bf44b3cd617f
No earlier version was observed for comparison.
skills/domains/ecology/inaturalist-api/SKILL.md
Week of Oct 26, 2026 · Line counts unavailable
Before Not checked yet → After Network or broad shell
First observation → bf44b3cd617f
No earlier version was observed for comparison.
skills/domains/ecology/species-distribution-guide/SKILL.md
Week of Oct 26, 2026 · Line counts unavailable
Before Not checked yet → After Instructions only
First observation → bf44b3cd617f
No earlier version was observed for comparison.
skills/domains/economics/SKILL.md
Week of Oct 26, 2026 · Line counts unavailable
Before Not checked yet → After Instructions only
First observation → bf44b3cd617f
No earlier version was observed for comparison.
skills/domains/economics/behavioral-economics-guide/SKILL.md
Week of Oct 26, 2026 · Line counts unavailable
Before Not checked yet → After Instructions only
First observation → bf44b3cd617f
No earlier version was observed for comparison.
skills/domains/economics/development-economics-guide/SKILL.md
Week of Oct 26, 2026 · Line counts unavailable
Before Not checked yet → After Instructions only
First observation → bf44b3cd617f
No earlier version was observed for comparison.
skills/domains/economics/fred-api/SKILL.md
Week of Oct 26, 2026 · Line counts unavailable
Before Not checked yet → After Network or broad shell
First observation → bf44b3cd617f
No earlier version was observed for comparison.
skills/domains/economics/imf-data-api-guide/SKILL.md
Week of Oct 26, 2026 · Line counts unavailable
Before Not checked yet → After Network or broad shell
First observation → bf44b3cd617f
No earlier version was observed for comparison.
skills/domains/economics/nber-working-papers-api/SKILL.md
Week of Oct 26, 2026 · Line counts unavailable
Before Not checked yet → After Network or broad shell
First observation → bf44b3cd617f
No earlier version was observed for comparison.
skills/domains/economics/post-labor-economics/SKILL.md
Week of Oct 26, 2026 · Line counts unavailable
Before Not checked yet → After Instructions only
First observation → bf44b3cd617f
No earlier version was observed for comparison.
skills/domains/economics/pricing-psychology-guide/SKILL.md
Week of Oct 26, 2026 · Line counts unavailable
Before Not checked yet → After Instructions only
First observation → bf44b3cd617f
No earlier version was observed for comparison.
skills/domains/economics/repec-economics-api/SKILL.md
Week of Oct 26, 2026 · Line counts unavailable
Before Not checked yet → After Network or broad shell
First observation → bf44b3cd617f
No earlier version was observed for comparison.
skills/domains/economics/world-bank-data-guide/SKILL.md
Week of Oct 26, 2026 · Line counts unavailable
Before Not checked yet → After Network or broad shell
First observation → bf44b3cd617f
No earlier version was observed for comparison.
skills/domains/education/SKILL.md
Week of Oct 26, 2026 · Line counts unavailable
Before Not checked yet → After Instructions only
First observation → bf44b3cd617f
No earlier version was observed for comparison.
skills/domains/education/academic-study-methods/SKILL.md
Week of Oct 26, 2026 · Line counts unavailable
Before Not checked yet → After Instructions only
First observation → bf44b3cd617f
No earlier version was observed for comparison.
skills/domains/education/assessment-design-guide/SKILL.md
Week of Oct 26, 2026 · Line counts unavailable
Before Not checked yet → After Instructions only
First observation → bf44b3cd617f
No earlier version was observed for comparison.
skills/domains/education/curriculum-design-guide/SKILL.md
Week of Oct 26, 2026 · Line counts unavailable
Before Not checked yet → After Instructions only
First observation → bf44b3cd617f
No earlier version was observed for comparison.
skills/domains/education/educational-research-methods/SKILL.md
Week of Oct 26, 2026 · Line counts unavailable
Before Not checked yet → After Instructions only
First observation → bf44b3cd617f
No earlier version was observed for comparison.
skills/domains/education/learning-science-guide/SKILL.md
Week of Oct 26, 2026 · Line counts unavailable
Before Not checked yet → After Instructions only
First observation → bf44b3cd617f
No earlier version was observed for comparison.
skills/domains/education/mooc-analytics-guide/SKILL.md
Week of Oct 26, 2026 · Line counts unavailable
Before Not checked yet → After Instructions only
First observation → bf44b3cd617f
No earlier version was observed for comparison.
skills/domains/education/open-syllabus-api/SKILL.md
Week of Oct 26, 2026 · Line counts unavailable
Before Not checked yet → After Network or broad shell
First observation → bf44b3cd617f
No earlier version was observed for comparison.
skills/domains/finance/SKILL.md
Week of Oct 26, 2026 · Line counts unavailable
Before Not checked yet → After Instructions only
First observation → bf44b3cd617f
No earlier version was observed for comparison.
skills/domains/finance/akshare-finance-data/SKILL.md
Week of Oct 26, 2026 · Line counts unavailable
Before Not checked yet → After Instructions only
First observation → bf44b3cd617f
No earlier version was observed for comparison.
skills/domains/finance/financial-data-analysis/SKILL.md
Week of Oct 26, 2026 · Line counts unavailable
Before Not checked yet → After Instructions only
First observation → bf44b3cd617f
No earlier version was observed for comparison.
skills/domains/finance/finsight-research-guide/SKILL.md
Week of Oct 26, 2026 · Line counts unavailable
Before Not checked yet → After Instructions only
First observation → bf44b3cd617f
No earlier version was observed for comparison.
skills/domains/finance/options-analytics-agent-guide/SKILL.md
Week of Oct 26, 2026 · Line counts unavailable
Before Not checked yet → After Instructions only
First observation → bf44b3cd617f
No earlier version was observed for comparison.
skills/domains/finance/portfolio-optimization-guide/SKILL.md
Week of Oct 26, 2026 · Line counts unavailable
Before Not checked yet → After Instructions only
First observation → bf44b3cd617f
No earlier version was observed for comparison.
skills/domains/finance/quantitative-finance-guide/SKILL.md
Week of Oct 26, 2026 · Line counts unavailable
Before Not checked yet → After Instructions only
First observation → bf44b3cd617f
No earlier version was observed for comparison.
skills/domains/finance/risk-modeling-guide/SKILL.md
Week of Oct 26, 2026 · Line counts unavailable
Before Not checked yet → After Instructions only
First observation → bf44b3cd617f
No earlier version was observed for comparison.
skills/domains/finance/stata-accounting-research/SKILL.md
Week of Oct 26, 2026 · Line counts unavailable
Before Not checked yet → After Instructions only
First observation → bf44b3cd617f
No earlier version was observed for comparison.
skills/domains/geoscience/SKILL.md
Week of Oct 26, 2026 · Line counts unavailable
Before Not checked yet → After Instructions only
First observation → bf44b3cd617f
No earlier version was observed for comparison.
skills/domains/geoscience/climate-modeling-guide/SKILL.md
Week of Oct 26, 2026 · Line counts unavailable
Before Not checked yet → After Instructions only
First observation → bf44b3cd617f
No earlier version was observed for comparison.
skills/domains/geoscience/climate-science-guide/SKILL.md
Week of Oct 26, 2026 · Line counts unavailable
Before Not checked yet → After Instructions only
First observation → bf44b3cd617f
No earlier version was observed for comparison.
skills/domains/geoscience/gis-remote-sensing-guide/SKILL.md
Week of Oct 26, 2026 · Line counts unavailable
Before Not checked yet → After Instructions only
First observation → bf44b3cd617f
No earlier version was observed for comparison.
skills/domains/geoscience/pangaea-data-api/SKILL.md
Week of Oct 26, 2026 · Line counts unavailable
Before Not checked yet → After Network or broad shell
First observation → bf44b3cd617f
No earlier version was observed for comparison.
skills/domains/geoscience/satellite-remote-sensing/SKILL.md
Week of Oct 26, 2026 · Line counts unavailable
Before Not checked yet → After Instructions only
First observation → bf44b3cd617f
No earlier version was observed for comparison.
skills/domains/geoscience/seismology-data-guide/SKILL.md
Week of Oct 26, 2026 · Line counts unavailable
Before Not checked yet → After Instructions only
First observation → bf44b3cd617f
No earlier version was observed for comparison.
skills/domains/humanities/SKILL.md
Week of Oct 26, 2026 · Line counts unavailable
Before Not checked yet → After Instructions only
First observation → bf44b3cd617f
No earlier version was observed for comparison.
skills/domains/humanities/digital-humanities-guide/SKILL.md
Week of Oct 26, 2026 · Line counts unavailable
Before Not checked yet → After Instructions only
First observation → bf44b3cd617f
No earlier version was observed for comparison.
skills/domains/humanities/ethical-philosophy-guide/SKILL.md
Week of Oct 26, 2026 · Line counts unavailable
Before Not checked yet → After Instructions only
First observation → bf44b3cd617f
No earlier version was observed for comparison.
skills/domains/humanities/history-research-guide/SKILL.md
Week of Oct 26, 2026 · Line counts unavailable
Before Not checked yet → After Instructions only
First observation → bf44b3cd617f
No earlier version was observed for comparison.
skills/domains/humanities/philosophy-research-guide/SKILL.md
Week of Oct 26, 2026 · Line counts unavailable
Before Not checked yet → After Instructions only
First observation → bf44b3cd617f
No earlier version was observed for comparison.
skills/domains/humanities/political-history-guide/SKILL.md
Week of Oct 26, 2026 · Line counts unavailable
Before Not checked yet → After Instructions only
First observation → bf44b3cd617f
No earlier version was observed for comparison.
skills/domains/law/SKILL.md
Week of Oct 26, 2026 · Line counts unavailable
Before Not checked yet → After Instructions only
First observation → bf44b3cd617f
No earlier version was observed for comparison.
skills/domains/law/caselaw-access-api/SKILL.md
Week of Oct 26, 2026 · Line counts unavailable
Before Not checked yet → After Network or broad shell
First observation → bf44b3cd617f
No earlier version was observed for comparison.
skills/domains/law/courtlistener-api/SKILL.md
Week of Oct 26, 2026 · Line counts unavailable
Before Not checked yet → After Network or broad shell
First observation → bf44b3cd617f
No earlier version was observed for comparison.
skills/domains/law/legal-agent-skills-guide/SKILL.md
Week of Oct 26, 2026 · Line counts unavailable
Before Not checked yet → After Instructions only
First observation → bf44b3cd617f
No earlier version was observed for comparison.
skills/domains/law/legal-nlp-guide/SKILL.md
Week of Oct 26, 2026 · Line counts unavailable
Before Not checked yet → After Instructions only
First observation → bf44b3cd617f
No earlier version was observed for comparison.
skills/domains/law/legal-research-guide/SKILL.md
Week of Oct 26, 2026 · Line counts unavailable
Before Not checked yet → After Network or broad shell
First observation → bf44b3cd617f
No earlier version was observed for comparison.
skills/domains/law/opencontracts-guide/SKILL.md
Week of Oct 26, 2026 · Line counts unavailable
Before Not checked yet → After Instructions only
First observation → bf44b3cd617f
No earlier version was observed for comparison.
skills/domains/law/patent-analysis-guide/SKILL.md
Week of Oct 26, 2026 · Line counts unavailable
Before Not checked yet → After Network or broad shell
First observation → bf44b3cd617f
No earlier version was observed for comparison.
skills/domains/law/regulatory-compliance-guide/SKILL.md
Week of Oct 26, 2026 · Line counts unavailable
Before Not checked yet → After Network or broad shell
First observation → bf44b3cd617f
No earlier version was observed for comparison.
skills/domains/law/uk-legislation-api/SKILL.md
Week of Oct 26, 2026 · Line counts unavailable
Before Not checked yet → After Network or broad shell
First observation → bf44b3cd617f
No earlier version was observed for comparison.
skills/domains/math/SKILL.md
Week of Oct 26, 2026 · Line counts unavailable
Before Not checked yet → After Instructions only
First observation → bf44b3cd617f
No earlier version was observed for comparison.
skills/domains/math/lean-theorem-proving-guide/SKILL.md
Week of Oct 26, 2026 · Line counts unavailable
Before Not checked yet → After Instructions only
First observation → bf44b3cd617f
No earlier version was observed for comparison.
skills/domains/math/linear-algebra-applications/SKILL.md
Week of Oct 26, 2026 · Line counts unavailable
Before Not checked yet → After Instructions only
First observation → bf44b3cd617f
No earlier version was observed for comparison.
skills/domains/math/numerical-methods-guide/SKILL.md
Week of Oct 26, 2026 · Line counts unavailable
Before Not checked yet → After Instructions only
First observation → bf44b3cd617f
No earlier version was observed for comparison.
skills/domains/math/oeis-api/SKILL.md
Week of Oct 26, 2026 · Line counts unavailable
Before Not checked yet → After Network or broad shell
First observation → bf44b3cd617f
No earlier version was observed for comparison.
skills/domains/math/symbolic-computation-guide/SKILL.md
Week of Oct 26, 2026 · Line counts unavailable
Before Not checked yet → After Instructions only
First observation → bf44b3cd617f
No earlier version was observed for comparison.
skills/domains/math/topology-data-analysis/SKILL.md
Week of Oct 26, 2026 · Line counts unavailable
Before Not checked yet → After Instructions only
First observation → bf44b3cd617f
No earlier version was observed for comparison.
skills/domains/pharma/SKILL.md
Week of Oct 26, 2026 · Line counts unavailable
Before Not checked yet → After Instructions only
First observation → bf44b3cd617f
No earlier version was observed for comparison.
skills/domains/pharma/clinical-pharmacology-guide/SKILL.md
Week of Oct 26, 2026 · Line counts unavailable
Before Not checked yet → After Instructions only
First observation → bf44b3cd617f
No earlier version was observed for comparison.
skills/domains/pharma/clinical-trial-design-guide/SKILL.md
Week of Oct 26, 2026 · Line counts unavailable
Before Not checked yet → After Instructions only
First observation → bf44b3cd617f
No earlier version was observed for comparison.
skills/domains/pharma/drug-development-guide/SKILL.md
Week of Oct 26, 2026 · Line counts unavailable
Before Not checked yet → After Instructions only
First observation → bf44b3cd617f
No earlier version was observed for comparison.
skills/domains/pharma/drug-target-interaction/SKILL.md
Week of Oct 26, 2026 · Line counts unavailable
Before Not checked yet → After Instructions only
First observation → bf44b3cd617f
No earlier version was observed for comparison.
skills/domains/pharma/madd-drug-discovery-guide/SKILL.md
Week of Oct 26, 2026 · Line counts unavailable
Before Not checked yet → After Instructions only
First observation → bf44b3cd617f
No earlier version was observed for comparison.
skills/domains/pharma/pharmacovigilance-guide/SKILL.md
Week of Oct 26, 2026 · Line counts unavailable
Before Not checked yet → After Instructions only
First observation → bf44b3cd617f
No earlier version was observed for comparison.
skills/domains/physics/SKILL.md
Week of Oct 26, 2026 · Line counts unavailable
Before Not checked yet → After Instructions only
First observation → bf44b3cd617f
No earlier version was observed for comparison.
skills/domains/physics/astrophysics-data-guide/SKILL.md
Week of Oct 26, 2026 · Line counts unavailable
Before Not checked yet → After Instructions only
First observation → bf44b3cd617f
No earlier version was observed for comparison.
skills/domains/physics/computational-physics-guide/SKILL.md
Week of Oct 26, 2026 · Line counts unavailable
Before Not checked yet → After Instructions only
First observation → bf44b3cd617f
No earlier version was observed for comparison.
skills/domains/physics/nasa-ads-api/SKILL.md
Week of Oct 26, 2026 · Line counts unavailable
Before Not checked yet → After Network or broad shell
First observation → bf44b3cd617f
No earlier version was observed for comparison.
skills/domains/physics/particle-physics-guide/SKILL.md
Week of Oct 26, 2026 · Line counts unavailable
Before Not checked yet → After Instructions only
First observation → bf44b3cd617f
No earlier version was observed for comparison.
skills/domains/physics/quantum-computing-guide/SKILL.md
Week of Oct 26, 2026 · Line counts unavailable
Before Not checked yet → After Instructions only
First observation → bf44b3cd617f
No earlier version was observed for comparison.
skills/domains/social-science/SKILL.md
Week of Oct 26, 2026 · Line counts unavailable
Before Not checked yet → After Instructions only
First observation → bf44b3cd617f
No earlier version was observed for comparison.
skills/domains/social-science/ipums-microdata-api/SKILL.md
Week of Oct 26, 2026 · Line counts unavailable
Before Not checked yet → After Network or broad shell
First observation → bf44b3cd617f
No earlier version was observed for comparison.
skills/domains/social-science/network-analysis-guide/SKILL.md
Week of Oct 26, 2026 · Line counts unavailable
Before Not checked yet → After Instructions only
First observation → bf44b3cd617f
No earlier version was observed for comparison.
skills/domains/social-science/psychology-research-guide/SKILL.md
Week of Oct 26, 2026 · Line counts unavailable
Before Not checked yet → After Instructions only
First observation → bf44b3cd617f
No earlier version was observed for comparison.
skills/domains/social-science/social-research-methods/SKILL.md
Week of Oct 26, 2026 · Line counts unavailable
Before Not checked yet → After Instructions only
First observation → bf44b3cd617f
No earlier version was observed for comparison.
skills/domains/social-science/sociology-research-methods/SKILL.md
Week of Oct 26, 2026 · Line counts unavailable
Before Not checked yet → After Instructions only
First observation → bf44b3cd617f
No earlier version was observed for comparison.
skills/domains/social-science/survey-research-guide/SKILL.md
Week of Oct 26, 2026 · Line counts unavailable
Before Not checked yet → After Instructions only
First observation → bf44b3cd617f
No earlier version was observed for comparison.
skills/literature/discovery/SKILL.md
Week of Oct 26, 2026 · Line counts unavailable
Before Not checked yet → After Instructions only
First observation → bf44b3cd617f
No earlier version was observed for comparison.
skills/literature/discovery/citation-alert-guide/SKILL.md
Week of Oct 26, 2026 · Line counts unavailable
Before Not checked yet → After Instructions only
First observation → bf44b3cd617f
No earlier version was observed for comparison.
skills/literature/discovery/conference-proceedings-guide/SKILL.md
Week of Oct 26, 2026 · Line counts unavailable
Before Not checked yet → After Instructions only
First observation → bf44b3cd617f
No earlier version was observed for comparison.
skills/literature/discovery/literature-mapping-guide/SKILL.md
Week of Oct 26, 2026 · Line counts unavailable
Before Not checked yet → After Network or broad shell
First observation → bf44b3cd617f
No earlier version was observed for comparison.
skills/literature/discovery/paper-recommendation-guide/SKILL.md
Week of Oct 26, 2026 · Line counts unavailable
Before Not checked yet → After Network or broad shell
First observation → bf44b3cd617f
No earlier version was observed for comparison.
skills/literature/discovery/papers-we-love-guide/SKILL.md
Week of Oct 26, 2026 · Line counts unavailable
Before Not checked yet → After Instructions only
First observation → bf44b3cd617f
No earlier version was observed for comparison.
skills/literature/discovery/rss-paper-feeds/SKILL.md
Week of Oct 26, 2026 · Line counts unavailable
Before Not checked yet → After Network or broad shell
First observation → bf44b3cd617f
No earlier version was observed for comparison.
skills/literature/discovery/semantic-paper-radar/SKILL.md
Week of Oct 26, 2026 · Line counts unavailable
Before Not checked yet → After Network or broad shell
First observation → bf44b3cd617f
No earlier version was observed for comparison.
skills/literature/discovery/semantic-scholar-recs-guide/SKILL.md
Week of Oct 26, 2026 · Line counts unavailable
Before Not checked yet → After Network or broad shell
First observation → bf44b3cd617f
No earlier version was observed for comparison.
skills/literature/discovery/zotero-arxiv-daily-guide/SKILL.md
Week of Oct 26, 2026 · Line counts unavailable
Before Not checked yet → After Instructions only
First observation → bf44b3cd617f
No earlier version was observed for comparison.
skills/literature/fulltext/SKILL.md
Week of Oct 26, 2026 · Line counts unavailable
Before Not checked yet → After Instructions only
First observation → bf44b3cd617f
No earlier version was observed for comparison.
skills/literature/fulltext/arxiv-latex-source/SKILL.md
Week of Oct 26, 2026 · Line counts unavailable
Before Not checked yet → After Network or broad shell
First observation → bf44b3cd617f
No earlier version was observed for comparison.
skills/literature/fulltext/bioc-pmc-api/SKILL.md
Week of Oct 26, 2026 · Line counts unavailable
Before Not checked yet → After Network or broad shell
First observation → bf44b3cd617f
No earlier version was observed for comparison.
skills/literature/fulltext/core-api-guide/SKILL.md
Week of Oct 26, 2026 · Line counts unavailable
Before Not checked yet → After Network or broad shell
First observation → bf44b3cd617f
No earlier version was observed for comparison.
skills/literature/fulltext/dataverse-api/SKILL.md
Week of Oct 26, 2026 · Line counts unavailable
Before Not checked yet → After Network or broad shell
First observation → bf44b3cd617f
No earlier version was observed for comparison.
skills/literature/fulltext/doaj-api/SKILL.md
Week of Oct 26, 2026 · Line counts unavailable
Before Not checked yet → After Network or broad shell
First observation → bf44b3cd617f
No earlier version was observed for comparison.
skills/literature/fulltext/hal-archive-api/SKILL.md
Week of Oct 26, 2026 · Line counts unavailable
Before Not checked yet → After Network or broad shell
First observation → bf44b3cd617f
No earlier version was observed for comparison.
skills/literature/fulltext/institutional-repository-guide/SKILL.md
Week of Oct 26, 2026 · Line counts unavailable
Before Not checked yet → After Network or broad shell
First observation → bf44b3cd617f
No earlier version was observed for comparison.
skills/literature/fulltext/interlibrary-loan-guide/SKILL.md
Week of Oct 26, 2026 · Line counts unavailable
Before Not checked yet → After Instructions only
First observation → bf44b3cd617f
No earlier version was observed for comparison.
skills/literature/fulltext/open-access-guide/SKILL.md
Week of Oct 26, 2026 · Line counts unavailable
Before Not checked yet → After Network or broad shell
First observation → bf44b3cd617f
No earlier version was observed for comparison.
skills/literature/fulltext/open-access-mining-guide/SKILL.md
Week of Oct 26, 2026 · Line counts unavailable
Before Not checked yet → After Network or broad shell
First observation → bf44b3cd617f
No earlier version was observed for comparison.
skills/literature/fulltext/osf-api/SKILL.md
Week of Oct 26, 2026 · Line counts unavailable
Before Not checked yet → After Network or broad shell
First observation → bf44b3cd617f
No earlier version was observed for comparison.
skills/literature/fulltext/pmc-ftp-bulk-download/SKILL.md
Week of Oct 26, 2026 · Line counts unavailable
Before Not checked yet → After Network or broad shell
First observation → bf44b3cd617f
No earlier version was observed for comparison.
skills/literature/fulltext/pmc-oai-api/SKILL.md
Week of Oct 26, 2026 · Line counts unavailable
Before Not checked yet → After Network or broad shell
First observation → bf44b3cd617f
No earlier version was observed for comparison.
skills/literature/fulltext/preprint-servers-guide/SKILL.md
Week of Oct 26, 2026 · Line counts unavailable
Before Not checked yet → After Network or broad shell
First observation → bf44b3cd617f
No earlier version was observed for comparison.
skills/literature/fulltext/unpaywall-api/SKILL.md
Week of Oct 26, 2026 · Line counts unavailable
Before Not checked yet → After Network or broad shell
First observation → bf44b3cd617f
No earlier version was observed for comparison.
skills/literature/fulltext/zotero-ai-butler-guide/SKILL.md
Week of Oct 26, 2026 · Line counts unavailable
Before Not checked yet → After Instructions only
First observation → bf44b3cd617f
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skills/literature/metadata/SKILL.md
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skills/literature/metadata/academic-paper-summarizer/SKILL.md
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skills/literature/metadata/altmetrics-guide/SKILL.md
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skills/literature/metadata/bibliometrix-guide/SKILL.md
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skills/literature/metadata/datacite-api/SKILL.md
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skills/literature/metadata/doi-content-negotiation/SKILL.md
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skills/literature/metadata/journal-metrics-guide/SKILL.md
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skills/literature/metadata/opencitations-api/SKILL.md
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skills/literature/metadata/orcid-api/SKILL.md
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skills/literature/metadata/ror-organization-api/SKILL.md
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skills/literature/search/SKILL.md
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skills/literature/search/arxiv-api/SKILL.md
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skills/literature/search/findpapers-guide/SKILL.md
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Heat
2.8- 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
- dataviz-skills
skills/analysis/dataviz - algorithm-visualizer-guide
skills/analysis/dataviz/algorithm-visualizer-guide - bokeh-visualization-guide
skills/analysis/dataviz/bokeh-visualization-guide - chart-image-generator
skills/analysis/dataviz/chart-image-generator - color-accessibility-guide
skills/analysis/dataviz/color-accessibility-guide - d3-visualization-guide
skills/analysis/dataviz/d3-visualization-guide - echarts-visualization-guide
skills/analysis/dataviz/echarts-visualization-guide - geospatial-viz-guide
skills/analysis/dataviz/geospatial-viz-guide - interactive-viz-guide
skills/analysis/dataviz/interactive-viz-guide - metabase-analytics-guide
skills/analysis/dataviz/metabase-analytics-guide - network-visualization-guide
skills/analysis/dataviz/network-visualization-guide - plotly-interactive-guide
skills/analysis/dataviz/plotly-interactive-guide - publication-figures-guide
skills/analysis/dataviz/publication-figures-guide - python-dataviz-guide
skills/analysis/dataviz/python-dataviz-guide - redash-analytics-guide
skills/analysis/dataviz/redash-analytics-guide - econometrics-skills
skills/analysis/econometrics - causal-inference-guide
skills/analysis/econometrics/causal-inference-guide - econml-causal-guide
skills/analysis/econometrics/econml-causal-guide - iv-regression-guide
skills/analysis/econometrics/iv-regression-guide - mostly-harmless-guide
skills/analysis/econometrics/mostly-harmless-guide - panel-data-guide
skills/analysis/econometrics/panel-data-guide - python-causality-guide
skills/analysis/econometrics/python-causality-guide - robustness-checks
skills/analysis/econometrics/robustness-checks - stata-analyst-guide
skills/analysis/econometrics/stata-analyst-guide - stata-reference-guide
skills/analysis/econometrics/stata-reference-guide - time-series-guide
skills/analysis/econometrics/time-series-guide - statistics-skills
skills/analysis/statistics - bayesian-statistics-guide
skills/analysis/statistics/bayesian-statistics-guide - data-anomaly-detection
skills/analysis/statistics/data-anomaly-detection - hypothesis-testing-guide
skills/analysis/statistics/hypothesis-testing-guide - meta-analysis-guide
skills/analysis/statistics/meta-analysis-guide - ml-experiment-tracker
skills/analysis/statistics/ml-experiment-tracker - modeling-strategy-guide
skills/analysis/statistics/modeling-strategy-guide - nonparametric-tests-guide
skills/analysis/statistics/nonparametric-tests-guide - power-analysis-guide
skills/analysis/statistics/power-analysis-guide - sem-guide
skills/analysis/statistics/sem-guide - survival-analysis-guide
skills/analysis/statistics/survival-analysis-guide - wrangling-skills
skills/analysis/wrangling - csv-data-analyzer
skills/analysis/wrangling/csv-data-analyzer - data-cleaning-pipeline
skills/analysis/wrangling/data-cleaning-pipeline - data-cog-guide
skills/analysis/wrangling/data-cog-guide - missing-data-handling
skills/analysis/wrangling/missing-data-handling - pandas-data-wrangling
skills/analysis/wrangling/pandas-data-wrangling - questionnaire-design-guide
skills/analysis/wrangling/questionnaire-design-guide - stata-data-cleaning
skills/analysis/wrangling/stata-data-cleaning - streamline-analyst-guide
skills/analysis/wrangling/streamline-analyst-guide - survey-data-processing
skills/analysis/wrangling/survey-data-processing - text-mining-guide
skills/analysis/wrangling/text-mining-guide - ai-ml-skills
skills/domains/ai-ml - ai-agent-papers-guide
skills/domains/ai-ml/ai-agent-papers-guide - ai-model-benchmarking
skills/domains/ai-ml/ai-model-benchmarking - anomaly-detection-papers-guide
skills/domains/ai-ml/anomaly-detection-papers-guide - autonomous-agents-papers-guide
skills/domains/ai-ml/autonomous-agents-papers-guide - computer-vision-guide
skills/domains/ai-ml/computer-vision-guide - deep-learning-papers-guide
skills/domains/ai-ml/deep-learning-papers-guide - dl-transformer-finetune
skills/domains/ai-ml/dl-transformer-finetune - domain-adaptation-papers-guide
skills/domains/ai-ml/domain-adaptation-papers-guide - generative-ai-guide
skills/domains/ai-ml/generative-ai-guide - graph-learning-papers-guide
skills/domains/ai-ml/graph-learning-papers-guide - huggingface-api
skills/domains/ai-ml/huggingface-api - huggingface-inference-guide
skills/domains/ai-ml/huggingface-inference-guide - keras-deep-learning
skills/domains/ai-ml/keras-deep-learning - kolmogorov-arnold-networks-guide
skills/domains/ai-ml/kolmogorov-arnold-networks-guide - llm-evaluation-guide
skills/domains/ai-ml/llm-evaluation-guide - llm-from-scratch-guide
skills/domains/ai-ml/llm-from-scratch-guide - ml-pipeline-guide
skills/domains/ai-ml/ml-pipeline-guide - nlp-toolkit-guide
skills/domains/ai-ml/nlp-toolkit-guide - npcpy-research-guide
skills/domains/ai-ml/npcpy-research-guide - prompt-engineering-research
skills/domains/ai-ml/prompt-engineering-research - pytorch-guide
skills/domains/ai-ml/pytorch-guide - pytorch-lightning-guide
skills/domains/ai-ml/pytorch-lightning-guide - reinforcement-learning-guide
skills/domains/ai-ml/reinforcement-learning-guide - responsible-ai-guide
skills/domains/ai-ml/responsible-ai-guide - tensorflow-guide
skills/domains/ai-ml/tensorflow-guide - transformer-architecture-guide
skills/domains/ai-ml/transformer-architecture-guide - vmas-simulator-guide
skills/domains/ai-ml/vmas-simulator-guide - biomedical-skills
skills/domains/biomedical - alphafold-api
skills/domains/biomedical/alphafold-api - bioagents-guide
skills/domains/biomedical/bioagents-guide - biothings-api
skills/domains/biomedical/biothings-api - clawbio-guide
skills/domains/biomedical/clawbio-guide - clinical-dialogue-agents-guide
skills/domains/biomedical/clinical-dialogue-agents-guide - clinical-research-guide
skills/domains/biomedical/clinical-research-guide - clinicaltrials-api
skills/domains/biomedical/clinicaltrials-api - clinicaltrials-api-v2
skills/domains/biomedical/clinicaltrials-api-v2 - ena-sequence-api
skills/domains/biomedical/ena-sequence-api - enrichr-api
skills/domains/biomedical/enrichr-api - ensembl-rest-api
skills/domains/biomedical/ensembl-rest-api - epidemiology-guide
skills/domains/biomedical/epidemiology-guide - genomas-guide
skills/domains/biomedical/genomas-guide - genomics-analysis-guide
skills/domains/biomedical/genomics-analysis-guide - genotex-benchmark-guide
skills/domains/biomedical/genotex-benchmark-guide - med-researcher-guide
skills/domains/biomedical/med-researcher-guide - med-researcher-r1-guide
skills/domains/biomedical/med-researcher-r1-guide - medgeclaw-guide
skills/domains/biomedical/medgeclaw-guide - medical-data-api
skills/domains/biomedical/medical-data-api - medical-imaging-guide
skills/domains/biomedical/medical-imaging-guide - ncbi-blast-api
skills/domains/biomedical/ncbi-blast-api - ncbi-datasets-api
skills/domains/biomedical/ncbi-datasets-api - pdb-structure-api
skills/domains/biomedical/pdb-structure-api - quickgo-api
skills/domains/biomedical/quickgo-api - business-skills
skills/domains/business - architecture-design-guide
skills/domains/business/architecture-design-guide - innovation-management-guide
skills/domains/business/innovation-management-guide - market-analysis-guide
skills/domains/business/market-analysis-guide - operations-research-guide
skills/domains/business/operations-research-guide - strategic-management-guide
skills/domains/business/strategic-management-guide - chemistry-skills
skills/domains/chemistry - cactus-cheminformatics-guide
skills/domains/chemistry/cactus-cheminformatics-guide - catalysis-hub-api
skills/domains/chemistry/catalysis-hub-api - chemeagle-guide
skills/domains/chemistry/chemeagle-guide - chemgraph-agent-guide
skills/domains/chemistry/chemgraph-agent-guide - computational-chemistry-guide
skills/domains/chemistry/computational-chemistry-guide - molecular-dynamics-guide
skills/domains/chemistry/molecular-dynamics-guide - pubchem-api-guide
skills/domains/chemistry/pubchem-api-guide - retrosynthesis-guide
skills/domains/chemistry/retrosynthesis-guide - spectroscopy-analysis-guide
skills/domains/chemistry/spectroscopy-analysis-guide - cs-skills
skills/domains/cs - ai-security-papers-guide
skills/domains/cs/ai-security-papers-guide - algorithms-complexity-guide
skills/domains/cs/algorithms-complexity-guide - code-llm-papers-guide
skills/domains/cs/code-llm-papers-guide - dblp-api
skills/domains/cs/dblp-api - distributed-systems-guide
skills/domains/cs/distributed-systems-guide - formal-verification-guide
skills/domains/cs/formal-verification-guide - gaussian-splatting-papers-guide
skills/domains/cs/gaussian-splatting-papers-guide - llm-aiops-guide
skills/domains/cs/llm-aiops-guide - software-engineering-research
skills/domains/cs/software-engineering-research - software-heritage-api
skills/domains/cs/software-heritage-api - ecology-skills
skills/domains/ecology - biodiversity-data-guide
skills/domains/ecology/biodiversity-data-guide - conservation-biology-guide
skills/domains/ecology/conservation-biology-guide - gbif-api
skills/domains/ecology/gbif-api - inaturalist-api
skills/domains/ecology/inaturalist-api - species-distribution-guide
skills/domains/ecology/species-distribution-guide - economics-skills
skills/domains/economics - behavioral-economics-guide
skills/domains/economics/behavioral-economics-guide - development-economics-guide
skills/domains/economics/development-economics-guide - fred-api
skills/domains/economics/fred-api - imf-data-api-guide
skills/domains/economics/imf-data-api-guide - nber-working-papers-api
skills/domains/economics/nber-working-papers-api - post-labor-economics
skills/domains/economics/post-labor-economics - pricing-psychology-guide
skills/domains/economics/pricing-psychology-guide - repec-economics-api
skills/domains/economics/repec-economics-api - world-bank-data-guide
skills/domains/economics/world-bank-data-guide - education-skills
skills/domains/education - academic-study-methods
skills/domains/education/academic-study-methods - assessment-design-guide
skills/domains/education/assessment-design-guide - curriculum-design-guide
skills/domains/education/curriculum-design-guide - educational-research-methods
skills/domains/education/educational-research-methods - learning-science-guide
skills/domains/education/learning-science-guide - mooc-analytics-guide
skills/domains/education/mooc-analytics-guide - open-syllabus-api
skills/domains/education/open-syllabus-api - finance-skills
skills/domains/finance - akshare-finance-data
skills/domains/finance/akshare-finance-data - financial-data-analysis
skills/domains/finance/financial-data-analysis - finsight-research-guide
skills/domains/finance/finsight-research-guide - options-analytics-agent-guide
skills/domains/finance/options-analytics-agent-guide - portfolio-optimization-guide
skills/domains/finance/portfolio-optimization-guide - quantitative-finance-guide
skills/domains/finance/quantitative-finance-guide - risk-modeling-guide
skills/domains/finance/risk-modeling-guide - stata-accounting-research
skills/domains/finance/stata-accounting-research - geoscience-skills
skills/domains/geoscience - climate-modeling-guide
skills/domains/geoscience/climate-modeling-guide - climate-science-guide
skills/domains/geoscience/climate-science-guide - gis-remote-sensing-guide
skills/domains/geoscience/gis-remote-sensing-guide - pangaea-data-api
skills/domains/geoscience/pangaea-data-api - satellite-remote-sensing
skills/domains/geoscience/satellite-remote-sensing - seismology-data-guide
skills/domains/geoscience/seismology-data-guide - humanities-skills
skills/domains/humanities - digital-humanities-guide
skills/domains/humanities/digital-humanities-guide - ethical-philosophy-guide
skills/domains/humanities/ethical-philosophy-guide - history-research-guide
skills/domains/humanities/history-research-guide - philosophy-research-guide
skills/domains/humanities/philosophy-research-guide - political-history-guide
skills/domains/humanities/political-history-guide - law-skills
skills/domains/law - caselaw-access-api
skills/domains/law/caselaw-access-api - courtlistener-api
skills/domains/law/courtlistener-api - legal-agent-skills-guide
skills/domains/law/legal-agent-skills-guide - legal-nlp-guide
skills/domains/law/legal-nlp-guide - legal-research-guide
skills/domains/law/legal-research-guide - opencontracts-guide
skills/domains/law/opencontracts-guide - patent-analysis-guide
skills/domains/law/patent-analysis-guide - regulatory-compliance-guide
skills/domains/law/regulatory-compliance-guide - uk-legislation-api
skills/domains/law/uk-legislation-api - math-skills
skills/domains/math - lean-theorem-proving-guide
skills/domains/math/lean-theorem-proving-guide - linear-algebra-applications
skills/domains/math/linear-algebra-applications - numerical-methods-guide
skills/domains/math/numerical-methods-guide - oeis-api
skills/domains/math/oeis-api - symbolic-computation-guide
skills/domains/math/symbolic-computation-guide - topology-data-analysis
skills/domains/math/topology-data-analysis - pharma-skills
skills/domains/pharma - clinical-pharmacology-guide
skills/domains/pharma/clinical-pharmacology-guide - clinical-trial-design-guide
skills/domains/pharma/clinical-trial-design-guide - drug-development-guide
skills/domains/pharma/drug-development-guide - drug-target-interaction
skills/domains/pharma/drug-target-interaction - madd-drug-discovery-guide
skills/domains/pharma/madd-drug-discovery-guide - pharmacovigilance-guide
skills/domains/pharma/pharmacovigilance-guide - physics-skills
skills/domains/physics - astrophysics-data-guide
skills/domains/physics/astrophysics-data-guide - computational-physics-guide
skills/domains/physics/computational-physics-guide - nasa-ads-api
skills/domains/physics/nasa-ads-api - particle-physics-guide
skills/domains/physics/particle-physics-guide - quantum-computing-guide
skills/domains/physics/quantum-computing-guide - social-science-skills
skills/domains/social-science - ipums-microdata-api
skills/domains/social-science/ipums-microdata-api - network-analysis-guide
skills/domains/social-science/network-analysis-guide - psychology-research-guide
skills/domains/social-science/psychology-research-guide - social-research-methods
skills/domains/social-science/social-research-methods - sociology-research-methods
skills/domains/social-science/sociology-research-methods - survey-research-guide
skills/domains/social-science/survey-research-guide - discovery-skills
skills/literature/discovery - citation-alert-guide
skills/literature/discovery/citation-alert-guide - conference-proceedings-guide
skills/literature/discovery/conference-proceedings-guide - literature-mapping-guide
skills/literature/discovery/literature-mapping-guide - paper-recommendation-guide
skills/literature/discovery/paper-recommendation-guide - papers-we-love-guide
skills/literature/discovery/papers-we-love-guide - rss-paper-feeds
skills/literature/discovery/rss-paper-feeds - semantic-paper-radar
skills/literature/discovery/semantic-paper-radar - semantic-scholar-recs-guide
skills/literature/discovery/semantic-scholar-recs-guide - zotero-arxiv-daily-guide
skills/literature/discovery/zotero-arxiv-daily-guide - fulltext-skills
skills/literature/fulltext - arxiv-latex-source
skills/literature/fulltext/arxiv-latex-source - bioc-pmc-api
skills/literature/fulltext/bioc-pmc-api - core-api-guide
skills/literature/fulltext/core-api-guide - dataverse-api
skills/literature/fulltext/dataverse-api - doaj-api
skills/literature/fulltext/doaj-api - hal-archive-api
skills/literature/fulltext/hal-archive-api - institutional-repository-guide
skills/literature/fulltext/institutional-repository-guide - interlibrary-loan-guide
skills/literature/fulltext/interlibrary-loan-guide - open-access-guide
skills/literature/fulltext/open-access-guide - open-access-mining-guide
skills/literature/fulltext/open-access-mining-guide - osf-api
skills/literature/fulltext/osf-api - pmc-ftp-bulk-download
skills/literature/fulltext/pmc-ftp-bulk-download - pmc-oai-api
skills/literature/fulltext/pmc-oai-api - preprint-servers-guide
skills/literature/fulltext/preprint-servers-guide - unpaywall-api
skills/literature/fulltext/unpaywall-api - zotero-ai-butler-guide
skills/literature/fulltext/zotero-ai-butler-guide - metadata-skills
skills/literature/metadata - academic-paper-summarizer
skills/literature/metadata/academic-paper-summarizer - altmetrics-guide
skills/literature/metadata/altmetrics-guide - bibliometrix-guide
skills/literature/metadata/bibliometrix-guide - citation-network-guide
skills/literature/metadata/citation-network-guide - crossref-api
skills/literature/metadata/crossref-api - crossref-event-data-api
skills/literature/metadata/crossref-event-data-api - datacite-api
skills/literature/metadata/datacite-api - doi-content-negotiation
skills/literature/metadata/doi-content-negotiation - doi-resolution-guide
skills/literature/metadata/doi-resolution-guide - h-index-guide
skills/literature/metadata/h-index-guide - journal-metrics-guide
skills/literature/metadata/journal-metrics-guide - opencitations-api
skills/literature/metadata/opencitations-api - orcid-api
skills/literature/metadata/orcid-api - orcid-integration-guide
skills/literature/metadata/orcid-integration-guide - orkg-api
skills/literature/metadata/orkg-api - plumx-metrics-api
skills/literature/metadata/plumx-metrics-api - ror-organization-api
skills/literature/metadata/ror-organization-api - sophosia-reference-guide
skills/literature/metadata/sophosia-reference-guide - viaf-authority-api
skills/literature/metadata/viaf-authority-api - wikidata-api-guide
skills/literature/metadata/wikidata-api-guide - zoplicate-dedup-guide
skills/literature/metadata/zoplicate-dedup-guide - zotero-actions-tags-guide
skills/literature/metadata/zotero-actions-tags-guide - zotmoov-guide
skills/literature/metadata/zotmoov-guide - zutilo-guide
skills/literature/metadata/zutilo-guide - search-skills
skills/literature/search - arxiv-api
skills/literature/search/arxiv-api - arxiv-batch-reporting
skills/literature/search/arxiv-batch-reporting - arxiv-cli-tools
skills/literature/search/arxiv-cli-tools - arxiv-paper-processor
skills/literature/search/arxiv-paper-processor - baidu-scholar-guide
skills/literature/search/baidu-scholar-guide - base-academic-search
skills/literature/search/base-academic-search - biorxiv-api
skills/literature/search/biorxiv-api - boolean-search-guide
skills/literature/search/boolean-search-guide - chatpaper-guide
skills/literature/search/chatpaper-guide - citation-chaining-guide
skills/literature/search/citation-chaining-guide - citeseerx-api
skills/literature/search/citeseerx-api - database-comparison-guide
skills/literature/search/database-comparison-guide - deep-literature-search
skills/literature/search/deep-literature-search - eric-education-api
skills/literature/search/eric-education-api - europe-pmc-api
skills/literature/search/europe-pmc-api - findpapers-guide
skills/literature/search/findpapers-guide - google-scholar-guide
skills/literature/search/google-scholar-guide - ieee-xplore-api
skills/literature/search/ieee-xplore-api - lens-scholarly-api
skills/literature/search/lens-scholarly-api - mesh-terms-guide
skills/literature/search/mesh-terms-guide - open-library-api
skills/literature/search/open-library-api - open-semantic-search-guide
skills/literature/search/open-semantic-search-guide - openaire-api
skills/literature/search/openaire-api - openalex-api
skills/literature/search/openalex-api - plos-open-access-api
skills/literature/search/plos-open-access-api - pubmed-api
skills/literature/search/pubmed-api - scielo-api
skills/literature/search/scielo-api - semantic-scholar-api
skills/literature/search/semantic-scholar-api - share-research-api
skills/literature/search/share-research-api - systematic-search-strategy
skills/literature/search/systematic-search-strategy - worldcat-search-api
skills/literature/search/worldcat-search-api - automation-skills
skills/research/automation - ai-scientist-v2-guide
skills/research/automation/ai-scientist-v2-guide - aim-experiment-guide
skills/research/automation/aim-experiment-guide - claude-academic-workflow-guide
skills/research/automation/claude-academic-workflow-guide - data-collection-automation
skills/research/automation/data-collection-automation - datagen-research-guide
skills/research/automation/datagen-research-guide - kedro-pipeline-guide
skills/research/automation/kedro-pipeline-guide - mle-agent-guide
skills/research/automation/mle-agent-guide - paper-to-agent-guide
skills/research/automation/paper-to-agent-guide - rd-agent-guide
skills/research/automation/rd-agent-guide - research-workflow-automation
skills/research/automation/research-workflow-automation - deep-research-skills
skills/research/deep-research - auto-deep-research-guide
skills/research/deep-research/auto-deep-research-guide - deep-searcher-guide
skills/research/deep-research/deep-searcher-guide - gpt-researcher-guide
skills/research/deep-research/gpt-researcher-guide - in-depth-research-guide
skills/research/deep-research/in-depth-research-guide - khoj-research-guide
skills/research/deep-research/khoj-research-guide - kosmos-scientist-guide
skills/research/deep-research/kosmos-scientist-guide - llm-scientific-discovery-guide
skills/research/deep-research/llm-scientific-discovery-guide - local-deep-research-guide
skills/research/deep-research/local-deep-research-guide - meta-synthesis-guide
skills/research/deep-research/meta-synthesis-guide - open-researcher-guide
skills/research/deep-research/open-researcher-guide - scoping-review-guide
skills/research/deep-research/scoping-review-guide - systematic-review-guide
skills/research/deep-research/systematic-review-guide - tongyi-deep-research-guide
skills/research/deep-research/tongyi-deep-research-guide - funding-skills
skills/research/funding - eu-horizon-guide
skills/research/funding/eu-horizon-guide - figshare-api
skills/research/funding/figshare-api - grant-budget-guide
skills/research/funding/grant-budget-guide - grant-writing-guide
skills/research/funding/grant-writing-guide - nih-reporter-api-guide
skills/research/funding/nih-reporter-api-guide - nsf-award-api-guide
skills/research/funding/nsf-award-api-guide - nsf-grant-guide
skills/research/funding/nsf-grant-guide - open-science-guide
skills/research/funding/open-science-guide - zenodo-api
skills/research/funding/zenodo-api - methodology-skills
skills/research/methodology - action-research-guide
skills/research/methodology/action-research-guide - claude-scientific-guide
skills/research/methodology/claude-scientific-guide - experimental-design-guide
skills/research/methodology/experimental-design-guide - grad-school-guide
skills/research/methodology/grad-school-guide - grounded-theory-guide
skills/research/methodology/grounded-theory-guide - mixed-methods-guide
skills/research/methodology/mixed-methods-guide - parsifal-slr-guide
skills/research/methodology/parsifal-slr-guide - qualitative-research-guide
skills/research/methodology/qualitative-research-guide - research-paper-kb
skills/research/methodology/research-paper-kb - research-town-guide
skills/research/methodology/research-town-guide - scientify-idea-generation
skills/research/methodology/scientify-idea-generation - slr-automation-guide
skills/research/methodology/slr-automation-guide - paper-review-skills
skills/research/paper-review - automated-review-guide
skills/research/paper-review/automated-review-guide - latte-review-guide
skills/research/paper-review/latte-review-guide - paper-critique-framework
skills/research/paper-review/paper-critique-framework - paper-reading-assistant
skills/research/paper-review/paper-reading-assistant - peer-review-guide
skills/research/paper-review/peer-review-guide - rebuttal-writing-guide
skills/research/paper-review/rebuttal-writing-guide - review-response-guide
skills/research/paper-review/review-response-guide - scientify-write-review-paper
skills/research/paper-review/scientify-write-review-paper - code-exec-skills
skills/tools/code-exec - google-colab-guide
skills/tools/code-exec/google-colab-guide - jupyter-notebook-guide
skills/tools/code-exec/jupyter-notebook-guide - kaggle-api-guide
skills/tools/code-exec/kaggle-api-guide - overleaf-cli-guide
skills/tools/code-exec/overleaf-cli-guide - python-reproducibility-guide
skills/tools/code-exec/python-reproducibility-guide - r-reproducibility-guide
skills/tools/code-exec/r-reproducibility-guide - sandbox-execution-guide
skills/tools/code-exec/sandbox-execution-guide - diagram-skills
skills/tools/diagram - code-flow-visualizer
skills/tools/diagram/code-flow-visualizer - excalidraw-diagram-guide
skills/tools/diagram/excalidraw-diagram-guide - json-data-visualizer
skills/tools/diagram/json-data-visualizer - kroki-diagram-api
skills/tools/diagram/kroki-diagram-api - mermaid-architect-guide
skills/tools/diagram/mermaid-architect-guide - mermaid-diagram-guide
skills/tools/diagram/mermaid-diagram-guide - plantuml-guide
skills/tools/diagram/plantuml-guide - scientific-illustration-guide
skills/tools/diagram/scientific-illustration-guide - tldraw-whiteboard-guide
skills/tools/diagram/tldraw-whiteboard-guide - document-skills
skills/tools/document - anystyle-api
skills/tools/document/anystyle-api - docsgpt-guide
skills/tools/document/docsgpt-guide - grobid-pdf-parsing
skills/tools/document/grobid-pdf-parsing - large-document-reader
skills/tools/document/large-document-reader - markdown-academic-guide
skills/tools/document/markdown-academic-guide - paper-parse-guide
skills/tools/document/paper-parse-guide - pdf-extraction-guide
skills/tools/document/pdf-extraction-guide - zotero-addon-market-guide
skills/tools/document/zotero-addon-market-guide - zotero-night-theme-guide
skills/tools/document/zotero-night-theme-guide - zotero-style-guide
skills/tools/document/zotero-style-guide - knowledge-graph-skills
skills/tools/knowledge-graph - citation-network-builder
skills/tools/knowledge-graph/citation-network-builder - concept-map-generator
skills/tools/knowledge-graph/concept-map-generator - graphiti-guide
skills/tools/knowledge-graph/graphiti-guide - knowledge-graph-construction
skills/tools/knowledge-graph/knowledge-graph-construction - notero-zotero-notion-guide
skills/tools/knowledge-graph/notero-zotero-notion-guide - ontology-design-guide
skills/tools/knowledge-graph/ontology-design-guide - openspg-guide
skills/tools/knowledge-graph/openspg-guide - rag-methodology-guide
skills/tools/knowledge-graph/rag-methodology-guide - zotero-markdb-connect-guide
skills/tools/knowledge-graph/zotero-markdb-connect-guide - ocr-translate-skills
skills/tools/ocr-translate - handwriting-recognition-guide
skills/tools/ocr-translate/handwriting-recognition-guide - latex-ocr-guide
skills/tools/ocr-translate/latex-ocr-guide - latex-translation-guide
skills/tools/ocr-translate/latex-translation-guide - multilingual-research-guide
skills/tools/ocr-translate/multilingual-research-guide - pdf-math-translate-guide
skills/tools/ocr-translate/pdf-math-translate-guide - zotero-pdf-translate-guide
skills/tools/ocr-translate/zotero-pdf-translate-guide - zotero-pdf2zh-guide
skills/tools/ocr-translate/zotero-pdf2zh-guide - scraping-skills
skills/tools/scraping - academic-web-scraping
skills/tools/scraping/academic-web-scraping - dataset-finder-guide
skills/tools/scraping/dataset-finder-guide - easy-spider-guide
skills/tools/scraping/easy-spider-guide - google-scholar-scraper
skills/tools/scraping/google-scholar-scraper - repository-harvesting-guide
skills/tools/scraping/repository-harvesting-guide - web-scraping-ethics-guide
skills/tools/scraping/web-scraping-ethics-guide - citation-skills
skills/writing/citation - academic-citation-manager
skills/writing/citation/academic-citation-manager - bibtex-management-guide
skills/writing/citation/bibtex-management-guide - citation-assistant-skill
skills/writing/citation/citation-assistant-skill - citation-style-guide
skills/writing/citation/citation-style-guide - jabref-reference-guide
skills/writing/citation/jabref-reference-guide - jasminum-zotero-guide
skills/writing/citation/jasminum-zotero-guide - mendeley-api
skills/writing/citation/mendeley-api - obsidian-citation-guide
skills/writing/citation/obsidian-citation-guide - obsidian-zotero-guide
skills/writing/citation/obsidian-zotero-guide - onecite-reference-guide
skills/writing/citation/onecite-reference-guide - papersgpt-zotero-guide
skills/writing/citation/papersgpt-zotero-guide - papis-cli-guide
skills/writing/citation/papis-cli-guide - reference-manager-comparison
skills/writing/citation/reference-manager-comparison - zotero-api
skills/writing/citation/zotero-api - zotero-better-bibtex-guide
skills/writing/citation/zotero-better-bibtex-guide - zotero-better-notes-guide
skills/writing/citation/zotero-better-notes-guide - zotero-gpt-guide
skills/writing/citation/zotero-gpt-guide - zotero-mcp-guide
skills/writing/citation/zotero-mcp-guide - zotero-mdnotes-guide
skills/writing/citation/zotero-mdnotes-guide - zotero-reference-guide
skills/writing/citation/zotero-reference-guide - zotero-scholar-guide
skills/writing/citation/zotero-scholar-guide - zotfile-attachment-guide
skills/writing/citation/zotfile-attachment-guide - composition-skills
skills/writing/composition - abstract-writing-guide
skills/writing/composition/abstract-writing-guide - discussion-writing-guide
skills/writing/composition/discussion-writing-guide - introduction-writing-guide
skills/writing/composition/introduction-writing-guide - literature-review-writing
skills/writing/composition/literature-review-writing - methods-section-guide
skills/writing/composition/methods-section-guide - ml-paper-writing
skills/writing/composition/ml-paper-writing - research-paper-writer
skills/writing/composition/research-paper-writer - response-to-reviewers
skills/writing/composition/response-to-reviewers - scientific-writing-guide
skills/writing/composition/scientific-writing-guide - latex-skills
skills/writing/latex - academic-writing-latex
skills/writing/latex/academic-writing-latex - bibliography-management-guide
skills/writing/latex/bibliography-management-guide - latex-drawing-collection
skills/writing/latex/latex-drawing-collection - latex-drawing-guide
skills/writing/latex/latex-drawing-guide - latex-ecosystem-guide
skills/writing/latex/latex-ecosystem-guide - latex-templates-collection
skills/writing/latex/latex-templates-collection - math-typesetting-guide
skills/writing/latex/math-typesetting-guide - md-to-pdf-academic
skills/writing/latex/md-to-pdf-academic - overleaf-collaboration-guide
skills/writing/latex/overleaf-collaboration-guide - tex-render-guide
skills/writing/latex/tex-render-guide - tikz-diagrams-guide
skills/writing/latex/tikz-diagrams-guide - polish-skills
skills/writing/polish - academic-tone-guide
skills/writing/polish/academic-tone-guide - academic-translation-guide
skills/writing/polish/academic-translation-guide - academic-writing-refiner
skills/writing/polish/academic-writing-refiner - ai-writing-humanizer
skills/writing/polish/ai-writing-humanizer - chinese-text-humanizer
skills/writing/polish/chinese-text-humanizer - conciseness-editing-guide
skills/writing/polish/conciseness-editing-guide - grammar-checker-guide
skills/writing/polish/grammar-checker-guide - paper-polish-guide
skills/writing/polish/paper-polish-guide - plagiarism-detection-guide
skills/writing/polish/plagiarism-detection-guide - templates-skills
skills/writing/templates - arxiv-preprint-template
skills/writing/templates/arxiv-preprint-template - beamer-presentation-guide
skills/writing/templates/beamer-presentation-guide - conference-paper-template
skills/writing/templates/conference-paper-template - elegant-paper-template
skills/writing/templates/elegant-paper-template - graphical-abstract-guide
skills/writing/templates/graphical-abstract-guide - novathesis-guide
skills/writing/templates/novathesis-guide - scientific-article-pdf
skills/writing/templates/scientific-article-pdf - sjtuthesis-guide
skills/writing/templates/sjtuthesis-guide - thesis-template-guide
skills/writing/templates/thesis-template-guide - thesis-writing-guide
skills/writing/templates/thesis-writing-guide - thuthesis-guide
skills/writing/templates/thuthesis-guide
How it works
dataviz-skills
When your agent uses it
14 data visualization skills. Trigger: charts, plots, figures, publication-quality graphics. Design: one skill per tool with code templates and academic formatting conventions.
What it loads into context
Frontmatter
42 tokensAlways available to the agent
SKILL.md
527 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
Files
SKILL.mdcore2,299 bytesSKILL.mdcore2,299 bytes
Read SKILL.md
# Data Visualization — 14 Skills
Select the skill matching the user's need, then `read` its SKILL.md.
| Skill | Description |
|-------|-------------|
| [algorithm-visualizer-guide](./algorithm-visualizer-guide/SKILL.md) | Guide to Algorithm Visualizer for interactive algorithm exploration |
| [bokeh-visualization-guide](./bokeh-visualization-guide/SKILL.md) | Guide to Bokeh for interactive browser-based research visualizations |
| [chart-image-generator](./chart-image-generator/SKILL.md) | Generate publication-quality chart images from research data |
| [color-accessibility-guide](./color-accessibility-guide/SKILL.md) | Colorblind-friendly palettes and accessible visualization design |
| [d3-visualization-guide](./d3-visualization-guide/SKILL.md) | Guide to D3.js for building custom interactive data visualizations |
| [echarts-visualization-guide](./echarts-visualization-guide/SKILL.md) | Guide to Apache ECharts for interactive research data dashboards |
| [geospatial-viz-guide](./geospatial-viz-guide/SKILL.md) | Create maps, choropleths, and spatial data visualizations for research |
| [interactive-viz-guide](./interactive-viz-guide/SKILL.md) | Interactive data visualization with Plotly, ECharts, and D3 |View the complete source on GitHubalgorithm-visualizer-guide
When your agent uses it
Guide to Algorithm Visualizer for interactive algorithm exploration
What it loads into context
Frontmatter
99 tokensAlways available to the agent
SKILL.md
2,178 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
- Platform Architecture and Setup
- Visualization API for Custom Algorithms
- Research-Relevant Algorithm Categories
- Creating Custom Visualizations for Teaching
- Integration Tips for Researchers
- References
Files
SKILL.mdcore9,604 bytesSKILL.mdcore9,604 bytesSKILL.mdcore9,604 bytes
Read SKILL.md
# Algorithm Visualizer Guide
## Overview
Algorithm Visualizer is an interactive online platform with over 48K stars on GitHub that allows researchers, educators, and students to visualize algorithms through animated graphical representations. The platform provides a web-based environment where algorithm code runs step-by-step alongside a visual canvas that shows data structures being manipulated in real time.
For academic researchers, Algorithm Visualizer serves two primary purposes. First, it is an excellent tool for teaching computational methods in courses and workshops. Complex algorithms in sorting, graph theory, dynamic programming, and numerical methods become immediately intuitive when students can see the step-by-step execution animated on screen. Second, researchers developing new algorithms can use the platform to debug, validate, and communicate their approaches visually, making it easier to explain novel computational contributions in papers and presentations.
The platform supports JavaScript-based algorithm implementations and provides a visualization API with tracer objects for arrays, graphs, logs, and custom 2D canvases. Researchers can create custom visualizations of their own algorithms and share them through the platform's public repository or embed them in course materials.
## Platform Architecture and Setup
Algorithm Visualizer consists of three main components that work together to provide the interactive visualization experience.
View the complete source on GitHubbokeh-visualization-guide
When your agent uses it
Guide to Bokeh for interactive browser-based research visualizations
What it loads into context
Frontmatter
94 tokensAlways available to the agent
SKILL.md
2,435 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
- Getting Started with Bokeh
- Linked Plots for Multi-Dimensional Data Exploration
- Statistical and Scientific Plot Types
- Bokeh Server for Live Interactive Applications
- Export and Embedding
- References
Files
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# Bokeh Visualization Guide
## Overview
Bokeh is a Python library for creating interactive visualizations for modern web browsers, with over 20K stars on GitHub. Developed and maintained by NumFocus, Bokeh generates standalone HTML documents or serves live interactive applications. Its architecture renders graphics in the browser using BokehJS, meaning the resulting visualizations are portable and can be shared as static HTML files without requiring Python on the viewer's end.
For researchers, Bokeh offers a unique advantage: its server-backed interactive applications allow real-time data exploration during analysis. Unlike static plotting libraries, Bokeh lets researchers build tools where they can brush-select data points, link multiple views of the same dataset, and stream live data from instruments or simulations. This makes it invaluable for exploratory data analysis in laboratory and computational research settings.
Bokeh provides multiple levels of API access. The high-level `bokeh.plotting` interface is comparable in convenience to matplotlib, while the low-level `bokeh.models` interface gives fine-grained control over every visual element. The library also integrates with HoloViews and Panel for building complex dashboards with minimal code.
## Getting Started with Bokeh
### Installation and Basic Setup
View the complete source on GitHubchart-image-generator
When your agent uses it
Generate publication-quality chart images from research data
What it loads into context
Frontmatter
86 tokensAlways available to the agent
SKILL.md
2,136 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
- Academic Figure Styling
- Chart Type Selection Guide
- Generating Common Academic Charts
- Export Specifications by Journal
- Best Practices
- References
Files
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# Chart Image Generator
A skill for generating publication-quality chart images from research data using Python visualization libraries. Covers chart type selection, styling for academic journals, multi-panel layouts, color accessibility, and export at the correct resolution and format for submission.
## Overview
Creating figures for academic publications requires more than just plotting data. Journals have specific requirements for resolution (typically 300-600 DPI), file format (TIFF, EPS, PDF, or high-resolution PNG), font sizes (often 8-12pt in the final printed figure), line weights, and color accessibility. This skill automates the production of figures that meet these standards, reducing the time researchers spend on manual formatting and ensuring consistency across all figures in a manuscript.
The skill supports common chart types used in academic research: scatter plots, bar charts, line plots, box plots, violin plots, heatmaps, forest plots, Kaplan-Meier curves, and multi-panel composite figures. All examples use matplotlib and seaborn with a custom academic styling configuration.
## Academic Figure Styling
### Journal-Ready Style Configuration
View the complete source on GitHubcolor-accessibility-guide
When your agent uses it
Colorblind-friendly palettes and accessible visualization design
What it loads into context
Frontmatter
84 tokensAlways available to the agent
SKILL.md
2,121 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
- Color Vision Deficiency Overview
- Recommended Colorblind-Safe Palettes
- Design Principles for Accessible Visualization
- Checking Your Visualizations
- Quick Reference: Do's and Don'ts
Files
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# Color Accessibility Guide
Design data visualizations that are accessible to colorblind readers and follow best practices for clarity, using tested palettes and encoding principles.
## Color Vision Deficiency Overview
Approximately 8% of males and 0.5% of females have some form of color vision deficiency (CVD). The most common types:
| Type | Prevalence (Male) | Affected Colors | Commonly Confused |
|------|-------------------|-----------------|-------------------|
| Deuteranomaly (green-weak) | 5% | Green | Red and green |
| Protanomaly (red-weak) | 1% | Red | Red and green |
| Deuteranopia (no green) | 1% | Green | Red and green |
| Protanopia (no red) | 1% | Red | Red and green |View the complete source on GitHubd3-visualization-guide
When your agent uses it
Guide to D3.js for building custom interactive data visualizations
What it loads into context
Frontmatter
95 tokensAlways available to the agent
SKILL.md
2,448 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
- Core Concepts for Research Visualizations
- Publication-Quality Scientific Charts
- Interactive Techniques for Research Presentations
- Exporting for Publications
- References
Files
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# D3.js Visualization Guide
## Overview
D3.js (Data-Driven Documents) is the most powerful and flexible JavaScript library for producing dynamic, interactive data visualizations in web browsers. With over 112K stars on GitHub, D3 has become the de facto standard for custom data visualization on the web. It uses HTML, SVG, and CSS to bring data to life, giving researchers full control over the final visual output.
Unlike higher-level charting libraries, D3 operates at the level of individual SVG elements and data bindings, which means researchers can create entirely bespoke visualizations tailored to their specific datasets and publication requirements. This makes it particularly valuable for academic work where standard chart types may not adequately represent complex research findings.
D3 provides a comprehensive ecosystem of modules covering everything from scales and axes to geographic projections, force-directed layouts, and hierarchical data structures. The library follows a functional, composable design that allows researchers to combine modules as needed for their specific visualization tasks.
## Core Concepts for Research Visualizations
D3 revolves around the concept of binding data to DOM elements and applying data-driven transformations. The key patterns every researcher should understand are selections, data joins, scales, and axes.
View the complete source on GitHubecharts-visualization-guide
When your agent uses it
Guide to Apache ECharts for interactive research data dashboards
What it loads into context
Frontmatter
94 tokensAlways available to the agent
SKILL.md
2,195 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
- Basic Configuration and Chart Types
- Advanced Research Visualizations
- Responsive Design and Theming
- Data Loading and Integration
- References
Files
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# Apache ECharts Visualization Guide
## Overview
Apache ECharts is a powerful, free, and open-source interactive charting and data visualization library with over 66K stars on GitHub. Originally developed by Baidu and now an Apache Software Foundation top-level project, ECharts provides a declarative configuration-based approach to building rich, interactive visualizations that run smoothly in any modern browser.
For academic researchers, ECharts offers an excellent balance between ease of use and customization depth. Its declarative option-based API means researchers can produce complex multi-series charts, geographic visualizations, and animated transitions without writing low-level rendering code. This is particularly useful when building research dashboards or interactive supplementary materials for publications.
ECharts supports over 20 chart types out of the box, including line, bar, scatter, pie, radar, candlestick, heatmap, treemap, sunburst, parallel coordinates, sankey diagrams, and geographic maps. Its built-in support for large datasets (via progressive rendering and data sampling) makes it suitable for visualizing experimental results with hundreds of thousands of data points.
## Basic Configuration and Chart Types
ECharts uses a declarative JSON configuration object to define charts. This approach makes it straightforward to build visualizations programmatically from research data.
View the complete source on GitHubgeospatial-viz-guide
When your agent uses it
Create maps, choropleths, and spatial data visualizations for research
What it loads into context
Frontmatter
96 tokensAlways available to the agent
SKILL.md
1,606 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
- Geospatial Data Fundamentals
- Choropleth Maps
- Point Maps and Proportional Symbols
- Interactive Maps with Folium
- Cartographic Best Practices
- Free Data Sources
Files
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# Geospatial Visualization Guide
A skill for creating maps, choropleths, and spatial data visualizations for research publications. Covers coordinate systems, choropleth maps, point maps, Python geospatial libraries, and cartographic best practices for academic papers.
## Geospatial Data Fundamentals
### Common Spatial Data Formats
```
Vector data (discrete features):
- Shapefile (.shp): Legacy standard, multi-file
- GeoJSON (.geojson): Web-friendly, single file
- GeoPackage (.gpkg): Modern SQLite-based, recommended
- KML (.kml): Google Earth formatView the complete source on GitHubinteractive-viz-guide
When your agent uses it
Interactive data visualization with Plotly, ECharts, and D3
What it loads into context
Frontmatter
86 tokensAlways available to the agent
SKILL.md
2,318 tokensWhen the skill is activated
Reference documents
0 tokensLoaded on request
Token counts are estimates. Scripts run separately; they are not counted as reference context.
The playbook
- When to Use Interactive Visualizations
- Plotly (Python)
- Altair (Python - Declarative)
- ECharts (JavaScript)
- Network Visualization
- Exporting for Publication
- Best Practices
Files
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# Interactive Visualization Guide
Create interactive, publication-ready visualizations using Plotly, ECharts, Altair, and Bokeh for academic papers, presentations, and supplementary materials.
## When to Use Interactive Visualizations
| Scenario | Static | Interactive |
|----------|--------|-------------|
| Journal PDF figure | Preferred | Not supported |
| Supplementary materials | Optional | Excellent |
| Conference poster (digital) | Common | Increasingly popular |
| Presentation slides | Standard | Engaging |
| Online appendix / project website | Limited | Ideal |
| Exploratory data analysis | Quick | Detailed exploration |View the complete source on GitHubmetabase-analytics-guide
When your agent uses it
Guide to Metabase for open-source research data analytics and dashboards
What it loads into context
Frontmatter
95 tokensAlways available to the agent
SKILL.md
1,903 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
- Installation and Setup
- Connecting Research Databases
- Building Research Dashboards
- Automated Reporting and Alerts
- Embedding Metabase in Research Applications
- Best Practices for Research Teams
- References
Files
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# Metabase Analytics Guide
## Overview
Metabase is a powerful open-source business intelligence and analytics tool with over 46K stars on GitHub. It allows researchers and data analysts to explore data, create visualizations, and build dashboards without writing SQL, though it fully supports custom SQL queries for advanced users. Metabase connects to a wide variety of databases and provides a browser-based interface that makes data exploration accessible to team members regardless of their technical background.
For academic research groups and labs, Metabase serves as an excellent self-hosted platform for tracking experimental data, monitoring research progress, and creating shared dashboards for collaborative projects. Its ability to connect directly to PostgreSQL, MySQL, SQLite, and many other databases means it can be pointed at existing research data stores without data migration. Researchers can set up automated reports, scheduled email digests, and shared dashboards that keep the entire team informed.
Metabase's no-code query builder is particularly valuable in interdisciplinary research teams where not all members are comfortable with SQL. Principal investigators, graduate students, and collaborators can all explore the same datasets through an intuitive visual interface while power users retain full SQL access for complex analyses.
## Installation and Setup
### Docker Deployment (Recommended)
View the complete source on GitHubnetwork-visualization-guide
When your agent uses it
Visualize networks, graphs, citation maps, and relational data
What it loads into context
Frontmatter
94 tokensAlways available to the agent
SKILL.md
1,467 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
- Network Basics
- Building Networks with NetworkX
- Layout Algorithm Selection
- Specialized Tools
- Publication Tips
Files
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# Network Visualization Guide
A skill for visualizing networks, graphs, and relational data in research. Covers NetworkX for analysis, layout algorithms, publication-quality styling, and tools for citation networks, social networks, and knowledge graphs.
## Network Basics
### When to Use Network Visualization
```
Network visualization is appropriate when your data involves relationships:
- Citation networks (papers citing other papers)
- Co-authorship networks (researchers who collaborate)
- Social networks (individuals connected by interactions)
- Biological networks (protein interactions, gene regulation)View the complete source on GitHubplotly-interactive-guide
When your agent uses it
Guide to Plotly.py for interactive scientific visualizations in Python
What it loads into context
Frontmatter
96 tokensAlways available to the agent
SKILL.md
1,929 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
- Plotly Express for Quick Research Figures
- Graph Objects for Fine-Grained Control
- 3D and Specialized Scientific Plots
- Exporting for Publications
- Dash for Interactive Research Dashboards
- References
Files
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# Plotly Interactive Visualization Guide
## Overview
Plotly.py is a high-level, interactive graphing library for Python with over 18K stars on GitHub. Built on top of plotly.js (which itself uses D3.js and WebGL), Plotly enables researchers to create publication-quality interactive figures directly from Python code. The library integrates seamlessly with pandas DataFrames, NumPy arrays, and the broader scientific Python ecosystem.
What sets Plotly apart for academic researchers is its Plotly Express module, which provides a concise, high-level API for creating complex visualizations in a single function call. Researchers can go from a pandas DataFrame to a fully interactive figure in one line of code, then customize it further as needed. Every Plotly figure is inherently interactive, supporting hover tooltips, zoom, pan, and selection out of the box.
Plotly also offers Dash, a framework for building analytical web applications entirely in Python. This allows researchers to create interactive dashboards for exploring experimental data, sharing results with collaborators, or building supplementary interactive materials for publications without needing front-end development skills.
## Plotly Express for Quick Research Figures
Plotly Express provides the fastest path from data to visualization. It works directly with pandas DataFrames and supports faceting, color mapping, animation, and trendlines.
View the complete source on GitHubpublication-figures-guide
When your agent uses it
Create journal-quality scientific figures with proper styling and accessibility
What it loads into context
Frontmatter
86 tokensAlways available to the agent
SKILL.md
2,082 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
- Journal Figure Requirements
- Colorblind-Friendly Palettes
- Common Figure Types
- Multi-Panel Figures
- Export Best Practices
Files
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# Publication Figures Guide
A skill for creating publication-quality scientific figures that meet journal standards for resolution, formatting, accessibility, and visual clarity. Covers matplotlib, seaborn, and ggplot2 workflows with journal-ready export settings.
## Journal Figure Requirements
### Common Standards
| Requirement | Typical Spec | Notes |
|------------|-------------|-------|
| Resolution | 300-600 DPI | 300 DPI minimum for print |
| File format | PDF, EPS, TIFF | Vector (PDF/EPS) preferred |
| Color mode | CMYK for print, RGB for online | Check journal spec |
| Max width | Single column: 3.3in / Double: 6.7in | Varies by journal |View the complete source on GitHubpython-dataviz-guide
When your agent uses it
Publication-quality data visualization with matplotlib, seaborn, and plotly
What it loads into context
Frontmatter
89 tokensAlways available to the agent
SKILL.md
1,816 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
- Matplotlib: The Foundation
- Seaborn: Statistical Visualization
- Plotly: Interactive Visualization
- Chart Type Selection Guide
- Best Practices
- References
Files
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# Python Data Visualization Guide
## Overview
Data visualization is how researchers communicate quantitative findings. A well-designed figure can convey complex relationships instantly, while a poor one buries the signal in clutter. Python's visualization ecosystem -- anchored by matplotlib, seaborn, and plotly -- provides everything needed to produce publication-quality figures for journals, conferences, and presentations.
This guide covers the three major Python visualization libraries, their strengths and trade-offs, and concrete recipes for the chart types researchers use most frequently. Each example is designed to be copy-paste ready and customizable for your specific dataset and venue requirements.
The emphasis is on producing figures that meet journal standards: correct DPI, appropriate font sizes, accessible color palettes, and vector-format exports. We also cover interactive visualization with plotly for exploratory analysis and supplementary materials.
## Matplotlib: The Foundation
Matplotlib is the most flexible Python plotting library. Nearly every other visualization tool in the Python ecosystem builds on it.
View the complete source on GitHubredash-analytics-guide
When your agent uses it
Guide to Redash for SQL-driven research data dashboards and sharing
What it loads into context
Frontmatter
96 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
- Overview
- Installation and Configuration
- Writing Research Queries
- Visualization Types for Research
- Dashboard Design for Research Labs
- Scheduled Queries and Alerts
- API Access for Automation
- References
Files
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# Redash Analytics Guide
## Overview
Redash is an open-source data visualization and dashboarding tool with over 28K stars on GitHub. It is designed for analysts and researchers who prefer writing SQL to explore and visualize data. Redash connects to virtually any data source that supports SQL or has an API, and provides a browser-based query editor with autocomplete, visualization builder, and dashboard composer.
For academic research groups, Redash offers a lightweight, self-hosted alternative to commercial BI tools. Its SQL-first approach is natural for researchers who already work with databases, and its sharing features make it straightforward to create dashboards that the entire lab can access. Unlike Metabase which emphasizes no-code exploration, Redash is specifically designed for users who are comfortable writing queries and want direct control over their data retrieval logic.
Redash supports over 35 data source types, including PostgreSQL, MySQL, SQLite, BigQuery, Elasticsearch, MongoDB, Google Sheets, CSV files, and even custom Python scripts. This versatility means researchers can build unified dashboards that pull data from multiple sources: experiment databases, survey platforms, instrument logs, and cloud storage.
## Installation and Configuration
### Docker Compose Deployment
View the complete source on GitHubeconometrics-skills
When your agent uses it
10 econometrics skills. Trigger: causal analysis, regression models, treatment effects, panel data. Design: method-centric guides with R/Python code and diagnostic tests.
What it loads into context
Frontmatter
43 tokensAlways available to the agent
SKILL.md
402 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
Files
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# Econometrics — 10 Skills
Select the skill matching the user's need, then `read` its SKILL.md.
| Skill | Description |
|-------|-------------|
| [causal-inference-guide](./causal-inference-guide/SKILL.md) | Causal inference methods including DiD, IV, RDD, and synthetic control |
| [econml-causal-guide](./econml-causal-guide/SKILL.md) | Apply EconML for causal inference combining machine learning and econometrics |
| [iv-regression-guide](./iv-regression-guide/SKILL.md) | Apply instrumental variables, 2SLS, and address endogeneity issues |
| [mostly-harmless-guide](./mostly-harmless-guide/SKILL.md) | Replication code and guide for Mostly Harmless Econometrics methods |
| [panel-data-guide](./panel-data-guide/SKILL.md) | Panel data analysis with fixed and random effects models |
| [python-causality-guide](./python-causality-guide/SKILL.md) | Learn causal inference with Python using the Brave and True handbook |
| [robustness-checks](./robustness-checks/SKILL.md) | Sequential robustness checks in Stata with confounder blocks |
| [stata-analyst-guide](./stata-analyst-guide/SKILL.md) | Stata workflows for publication-ready sociology and social science research |View the complete source on GitHubcausal-inference-guide
When your agent uses it
Causal inference methods including DiD, IV, RDD, and synthetic control
What it loads into context
Frontmatter
96 tokensAlways available to the agent
SKILL.md
1,742 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
- Difference-in-Differences (DiD)
- Instrumental Variables (IV)
- Regression Discontinuity Design (RDD)
- Best Practices
Files
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# Causal Inference Guide
A skill for applying quasi-experimental causal inference methods in observational research. Covers difference-in-differences, instrumental variables, regression discontinuity designs, and synthetic control methods with implementation code and diagnostic checks.
## Difference-in-Differences (DiD)
### Classic Two-Period DiD
```python
import numpy as np
import pandas as pd
import statsmodels.formula.api as smf
def did_estimation(df: pd.DataFrame, outcome: str, treatment: str,View the complete source on GitHubeconml-causal-guide
When your agent uses it
Apply EconML for causal inference combining machine learning and econometrics
What it loads into context
Frontmatter
99 tokensAlways available to the agent
SKILL.md
1,574 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
- Installation and Setup
- Core Estimators and Methods
- Research Workflow Integration
- Best Practices for Academic Research
- References
Files
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# EconML Causal Inference Guide
## Overview
EconML is a Python package developed by Microsoft Research as part of the ALICE (Automated Learning and Intelligence for Causation and Economics) project. It provides a comprehensive suite of methods for estimating heterogeneous treatment effects from observational data, bridging the gap between modern machine learning and classical econometric techniques for causal inference.
Traditional econometric approaches to causal inference often rely on strong parametric assumptions and struggle with high-dimensional data. Pure machine learning methods excel at prediction but do not inherently distinguish correlation from causation. EconML combines the strengths of both paradigms, offering methods that leverage the flexibility of ML for nuisance parameter estimation while maintaining the rigorous causal identification guarantees of econometric theory.
The library implements cutting-edge methods from the academic literature including Double Machine Learning (DML), Causal Forests, Doubly Robust Learners, Orthogonal Random Forests, and Instrumental Variable methods with ML first stages. These tools are essential for researchers across economics, public health, education policy, and any field where understanding causal mechanisms from non-experimental data is critical.
## Installation and Setup
Install EconML via pip:
View the complete source on GitHubiv-regression-guide
When your agent uses it
Apply instrumental variables, 2SLS, and address endogeneity issues
What it loads into context
Frontmatter
94 tokensAlways available to the agent
SKILL.md
1,481 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
- The Endogeneity Problem
- Two-Stage Least Squares (2SLS)
- Instrument Validity Tests
- Classic IV Examples
- Reporting IV Results
Files
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# Instrumental Variables Regression Guide
A skill for applying instrumental variables (IV) estimation to address endogeneity in regression models. Covers the logic of IV, two-stage least squares (2SLS), instrument validity tests, weak instrument diagnostics, and reporting standards.
## The Endogeneity Problem
### Why OLS Fails
```
Ordinary Least Squares assumes: E[u | X] = 0
(Regressors are uncorrelated with the error term)
This assumption is violated when:
- Omitted variable bias: A confound affects both X and YView the complete source on GitHubmostly-harmless-guide
When your agent uses it
Replication code and guide for Mostly Harmless Econometrics methods
What it loads into context
Frontmatter
105 tokensAlways available to the agent
SKILL.md
1,553 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
- Regression Fundamentals
- Instrumental Variables
- Difference-in-Differences
- Regression Discontinuity
- Integration with Research-Claw
- Best Practices
Files
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# Mostly Harmless Econometrics Guide
A skill providing replication code, explanations, and practical guidance for the econometric methods presented in Angrist and Pischke's "Mostly Harmless Econometrics" (MHE). Based on the mostly-harmless-replication repository (642 stars), this skill helps researchers understand and correctly apply core causal inference techniques.
## Overview
"Mostly Harmless Econometrics" is one of the most influential applied econometrics textbooks, providing accessible explanations of the methods that dominate modern empirical research in economics and increasingly in other social sciences. This skill translates the book's core methods into practical guidance that the agent can use to help researchers design studies, select appropriate estimators, and interpret results correctly.
The skill covers regression, instrumental variables, difference-in-differences, regression discontinuity, and related methods, with emphasis on the practical decisions researchers face when applying these techniques to real data.
## Regression Fundamentals
**Ordinary Least Squares (OLS)**
- OLS provides the best linear approximation to the conditional expectation functionView the complete source on GitHubpanel-data-guide
When your agent uses it
Panel data analysis with fixed and random effects models
What it loads into context
Frontmatter
92 tokensAlways available to the agent
SKILL.md
2,886 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
- What Is Panel Data?
- Model Specification
- Estimation in Stata
- Estimation in R (plm Package)
- Estimation in Python (linearmodels)
- Diagnostic Tests
- Advanced Panel Models
- Standard Error Options
- Instrumental Variables in Panel Data
- Reporting Results
- References
Files
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# Panel Data Analysis Guide
Estimate and interpret fixed effects, random effects, and dynamic panel models using Stata, R, and Python for longitudinal/panel datasets.
## What Is Panel Data?
Panel data (also called longitudinal or cross-sectional time-series data) tracks the same units (individuals, firms, countries) across multiple time periods. This structure enables:
- Controlling for unobserved heterogeneity (time-invariant omitted variables)
- Studying dynamic relationships (how X at time t affects Y at time t+1)
- Increased statistical power through more observations
### Data Structure
View the complete source on GitHubpython-causality-guide
When your agent uses it
Learn causal inference with Python using the Brave and True handbook
What it loads into context
Frontmatter
101 tokensAlways available to the agent
SKILL.md
1,398 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
- Installation and Setup
- Core Methods Covered
- Research Workflow Integration
- Best Practices Highlighted in the Handbook
- References
Files
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# Causal Inference for the Brave and True
## Overview
Causal Inference for the Brave and True is an open-source, Python-based textbook by Matheus Facure that teaches causal inference methods through practical implementations. The book bridges the gap between theoretical econometrics textbooks and hands-on data science practice, presenting each method with runnable Python code, real-world datasets, and intuitive explanations that demystify the mathematics behind causal reasoning.
The handbook covers the full spectrum of causal inference techniques used in modern empirical research, from foundational concepts like potential outcomes and directed acyclic graphs (DAGs) through advanced methods including instrumental variables, regression discontinuity, difference-in-differences, and synthetic control. Each chapter builds on the previous one, constructing a coherent framework for thinking about causation from observational data.
With over 3,000 GitHub stars, this resource has become a standard reference for graduate students, applied researchers, and data scientists seeking to add causal reasoning to their analytical toolkit. The emphasis on Python implementation makes it directly applicable to modern research workflows.
## Installation and Setup
The handbook runs as Jupyter notebooks. Set up the environment:
View the complete source on GitHubrobustness-checks
When your agent uses it
Sequential robustness checks in Stata with confounder blocks
What it loads into context
Frontmatter
92 tokensAlways available to the agent
SKILL.md
1,776 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
- Quick Start
- Key Patterns
- Interpretation Guide
- Tips
Files
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# Robustness Checks
A skill for conducting sequential robustness checks in Stata, systematically adding blocks of potential confounders to assess estimate stability.
## Quick Start
```stata
* Base model
svy: regress outcome controls treatment
estimates store m1
* Add confounder block
svy: regress outcome controls treatment confounder1 confounder2
estimates store m2View the complete source on GitHubstata-analyst-guide
When your agent uses it
Stata workflows for publication-ready sociology and social science research
What it loads into context
Frontmatter
90 tokensAlways available to the agent
SKILL.md
1,923 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
- Descriptive Statistics
- Regression Analysis
- Interaction Effects
- Multilevel Models
- Visualization for Publication
- Replication Package
- References
Files
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# Stata Analyst Guide for Social Science Research
Complete Stata workflow for sociology and social science research, from survey data preparation through publication-ready regression tables and visualizations. This skill covers the analytical techniques most commonly used in top sociology journals.
## Overview
Stata is the dominant statistical software in sociology, political science, demography, and many social science disciplines. Its command-line interface, reproducible do-file workflow, and comprehensive support for survey data, multilevel models, and categorical data analysis make it the tool of choice for researchers working with complex social datasets.
This skill provides ready-to-use Stata code for the most common analytical tasks in social science research: descriptive statistics for diverse variable types, regression modeling with proper controls and robustness checks, interaction effects with meaningful visualizations, and automated production of APA/ASA-formatted tables suitable for direct inclusion in journal manuscripts.
The examples draw on typical social science data structures: individual-level survey data with sampling weights, nested data (individuals within organizations or regions), longitudinal panels, and event-history data. All code follows the conventions expected by reviewers at journals such as the American Sociological Review, American Journal of Sociology, and Social Forces.
## Descriptive Statistics
View the complete source on GitHubstata-reference-guide
When your agent uses it
Comprehensive Stata reference covering syntax, econometrics, and 20+ packages
What it loads into context
Frontmatter
103 tokensAlways available to the agent
SKILL.md
2,298 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
- Core Syntax and Data Management
- Estimation Commands
- Community Packages (20+)
- Graphics
- Mata Programming
- Workflow Best Practices
- References
Files
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# Stata Comprehensive Reference Guide
## Overview
Stata is the dominant statistical software in economics, political science, public health, and sociology research. This guide provides a comprehensive reference covering core syntax, data management, estimation commands, causal inference methods, graphics, Mata programming, and 20+ community-contributed packages. It is designed as a progressive-disclosure reference: use the section relevant to your current task rather than reading end-to-end.
## Core Syntax and Data Management
### Data Import and Export
```stata
* Import CSV with variable names in first row
import delimited "data.csv", clear varnames(1)
View the complete source on GitHubtime-series-guide
When your agent uses it
Apply ARIMA, VAR, cointegration, and time series econometric methods
What it loads into context
Frontmatter
94 tokensAlways available to the agent
SKILL.md
1,729 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
- Stationarity and Unit Root Tests
- ARIMA Modeling
- Vector Autoregression (VAR)
- Cointegration Analysis
- Diagnostic Checking
Files
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# Time Series Guide
A skill for applying time series econometric methods including ARIMA modeling, VAR systems, cointegration analysis, and unit root tests. Covers stationarity concepts, model selection, forecasting, and diagnostic checking for economic and financial data.
## Stationarity and Unit Root Tests
### Why Stationarity Matters
A time series is stationary when its statistical properties (mean, variance, autocorrelation) do not change over time. Most econometric methods require stationarity. Non-stationary series can produce spurious regressions.
### Testing for Stationarity
```python
from statsmodels.tsa.stattools import adfuller, kpssView the complete source on GitHubstatistics-skills
When your agent uses it
10 statistical analysis skills. Trigger: statistical tests, Bayesian analysis, hypothesis testing, sampling. Design: method guides covering assumptions, code, and result interpretation.
What it loads into context
Frontmatter
40 tokensAlways available to the agent
SKILL.md
382 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
Files
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# Statistical Analysis — 10 Skills
Select the skill matching the user's need, then `read` its SKILL.md.
| Skill | Description |
|-------|-------------|
| [bayesian-statistics-guide](./bayesian-statistics-guide/SKILL.md) | Bayesian inference methods including prior selection, MCMC, and model comparison |
| [data-anomaly-detection](./data-anomaly-detection/SKILL.md) | Detect anomalies and outliers in research data using statistical methods |
| [hypothesis-testing-guide](./hypothesis-testing-guide/SKILL.md) | Statistical hypothesis testing, power analysis, and significance reporting |
| [meta-analysis-guide](./meta-analysis-guide/SKILL.md) | Conduct systematic meta-analyses with effect size pooling and heterogeneity |
| [ml-experiment-tracker](./ml-experiment-tracker/SKILL.md) | Plan reproducible ML experiment runs with parameters and metrics tracking |
| [modeling-strategy-guide](./modeling-strategy-guide/SKILL.md) | Strategic statistical modeling, experimentation, and causal inference |
| [nonparametric-tests-guide](./nonparametric-tests-guide/SKILL.md) | Apply Mann-Whitney, Kruskal-Wallis, and other nonparametric methods |
| [power-analysis-guide](./power-analysis-guide/SKILL.md) | Sample size calculation and statistical power analysis guide |View the complete source on GitHubbayesian-statistics-guide
When your agent uses it
Bayesian inference methods including prior selection, MCMC, and model comparison
What it loads into context
Frontmatter
86 tokensAlways available to the agent
SKILL.md
1,686 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
- Bayesian Framework Overview
- Prior Specification
- MCMC with PyMC
- Diagnostics
- Model Comparison
- Reporting Bayesian Results
- References
Files
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# Bayesian Statistics Guide
A skill for applying Bayesian statistical methods to research data analysis. Covers prior specification, Markov chain Monte Carlo (MCMC) sampling, posterior interpretation, model comparison, and reporting standards.
## Bayesian Framework Overview
### Bayes' Theorem in Practice
```
Posterior = (Likelihood x Prior) / Evidence
P(theta | data) = P(data | theta) * P(theta) / P(data)
In practice:View the complete source on GitHubdata-anomaly-detection
When your agent uses it
Detect anomalies and outliers in research data using statistical methods
What it loads into context
Frontmatter
88 tokensAlways available to the agent
SKILL.md
1,640 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
- Statistical Detection Methods
- Diagnosis Framework
- Time-Series Anomaly Detection
- Reporting Anomaly Handling
- References
Files
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# Data Anomaly Detection
A skill for identifying anomalies, outliers, and suspicious patterns in research datasets. Combines classical statistical methods with modern machine learning approaches to flag data points that deviate significantly from expected distributions, helping researchers maintain data integrity and uncover genuine scientific findings.
## Overview
Anomalous data points in research datasets can arise from measurement errors, instrument malfunction, data entry mistakes, or genuine rare phenomena. Distinguishing between these sources is critical: blindly removing outliers can bias results, while ignoring measurement errors introduces noise. This skill provides a structured framework for detecting, classifying, and handling anomalies in univariate, multivariate, and time-series research data.
The approach follows a three-stage pipeline: detection (flagging candidate anomalies), diagnosis (determining likely cause), and decision (remove, transform, or retain with justification). Every decision is logged for reproducibility and transparent reporting.
## Statistical Detection Methods
### Univariate Outlier Detection
View the complete source on GitHubhypothesis-testing-guide
When your agent uses it
Statistical hypothesis testing, power analysis, and significance reporting
What it loads into context
Frontmatter
85 tokensAlways available to the agent
SKILL.md
2,167 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
- The Hypothesis Testing Framework
- Test Selection Guide
- Running Tests in Python
- Power Analysis and Sample Size
- Multiple Comparison Corrections
- Best Practices
- References
Files
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# Hypothesis Testing Guide
## Overview
Hypothesis testing is the backbone of empirical research. It provides a principled framework for deciding whether observed differences in data reflect genuine effects or merely random variation. Misuse of hypothesis tests -- p-hacking, ignoring assumptions, confusing statistical and practical significance -- is a leading cause of irreproducible findings.
This guide covers the core hypothesis testing framework, the most commonly used tests across disciplines, assumption checking, effect size reporting, power analysis for sample size planning, and multiple comparison corrections. Each test is accompanied by Python code using scipy, statsmodels, and pingouin, ready to integrate into research workflows.
The goal is not just to help you run tests, but to help you run the right test correctly and report results following modern standards (APA 7th edition, journal best practices).
## The Hypothesis Testing Framework
### Step-by-Step Procedure
View the complete source on GitHubmeta-analysis-guide
When your agent uses it
Conduct systematic meta-analyses with effect size pooling and heterogeneity
What it loads into context
Frontmatter
82 tokensAlways available to the agent
SKILL.md
1,937 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
- Effect Size Computation
- Fixed-Effect and Random-Effects Models
- Forest Plot
- Publication Bias Assessment
- Reporting Standards
Files
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# Meta-Analysis Guide
A skill for conducting rigorous meta-analyses: computing and pooling effect sizes, assessing heterogeneity, evaluating publication bias, and generating forest plots. Follows Cochrane Handbook and PRISMA guidelines.
## Effect Size Computation
### Common Effect Size Measures
| Measure | Use Case | Formula | Interpretation |
|---------|----------|---------|----------------|
| Cohen's d | Mean difference (2 groups) | (M1 - M2) / S_pooled | 0.2 small, 0.5 medium, 0.8 large |
| Hedges' g | d with small-sample correction | d * J(df) | Preferred over d for small N |
| Pearson r | Correlation | r | 0.1 small, 0.3 medium, 0.5 large |
| Odds Ratio | Binary outcomes | (a*d)/(b*c) | 1 = no effect |View the complete source on GitHubml-experiment-tracker
When your agent uses it
Plan reproducible ML experiment runs with parameters and metrics tracking
What it loads into context
Frontmatter
91 tokensAlways available to the agent
SKILL.md
1,807 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
- Experiment Design Framework
- Experiment Logging with MLflow
- Results Comparison and Statistical Testing
- Reproducibility Checklist
- References
Files
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# ML Experiment Tracker
A skill for planning, executing, and tracking machine learning experiments with full reproducibility. Covers experiment design, hyperparameter management, metric logging, model versioning, and comparison across runs to support rigorous ML research.
## Overview
Machine learning research involves running dozens or hundreds of experiments with varying architectures, hyperparameters, data splits, and preprocessing pipelines. Without systematic tracking, it becomes impossible to reproduce results, compare configurations, or identify which changes actually improved performance. This skill provides a structured methodology for experiment management that aligns with academic standards for reproducible ML research.
The approach is framework-agnostic but demonstrates integration with MLflow, Weights & Biases, and plain file-based logging. It emphasizes the practices needed for publications: complete hyperparameter documentation, statistical significance testing across runs, and artifact management for model checkpoints and evaluation outputs.
## Experiment Design Framework
### Defining an Experiment Plan
View the complete source on GitHubmodeling-strategy-guide
When your agent uses it
Strategic statistical modeling, experimentation, and causal inference
What it loads into context
Frontmatter
89 tokensAlways available to the agent
SKILL.md
1,983 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
- Strategic Modeling Decisions
- Causal Inference Methods
- Experimentation Design
- Model Validation
- Communication and Reporting
- References
Files
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# Modeling Strategy Guide
A skill for strategic statistical modeling applied to academic research. Covers advanced modeling decisions, experimental design, causal inference, feature engineering, and the critical thinking required to move from data to defensible conclusions.
## Overview
Senior data scientists distinguish themselves not by knowing more algorithms but by asking better questions, designing cleaner experiments, and being honest about what the data can and cannot tell them. This skill translates that professional discipline into a research context, helping academics apply modern data science practices to their empirical work. It covers the strategic decisions that matter most: when to use simple models versus complex ones, how to establish causality rather than mere correlation, and how to communicate uncertainty honestly.
The skill is particularly useful for researchers working with observational data who need causal inference techniques, those designing randomized experiments who need proper power calculations and analysis plans, and anyone building predictive models who needs to avoid common overfitting and leakage pitfalls.
## Strategic Modeling Decisions
### Model Selection Philosophy
View the complete source on GitHubnonparametric-tests-guide
When your agent uses it
Apply Mann-Whitney, Kruskal-Wallis, and other nonparametric methods
What it loads into context
Frontmatter
105 tokensAlways available to the agent
SKILL.md
1,797 tokensWhen the skill is activated
Reference documents
0 tokensLoaded on request
Token counts are estimates. Scripts run separately; they are not counted as reference context.
The playbook
- When to Use Nonparametric Tests
- Mann-Whitney U Test
- Kruskal-Wallis H Test
- Wilcoxon Signed-Rank Test
- Spearman Rank Correlation
- Reporting Nonparametric Results
Files
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# Nonparametric Tests Guide
A skill for selecting and applying nonparametric statistical tests when data violate parametric assumptions. Covers rank-based tests for group comparisons, correlation, and paired data, with implementation examples and guidance on reporting.
## When to Use Nonparametric Tests
### Decision Criteria
```
Use nonparametric tests when:
- Data are ordinal (Likert scales, rankings)
- Distribution is clearly non-normal (heavy skew, outliers)
- Sample size is very small (n < 15-20 per group)
- Homogeneity of variance is violatedView the complete source on GitHubpower-analysis-guide
When your agent uses it
Sample size calculation and statistical power analysis guide
What it loads into context
Frontmatter
77 tokensAlways available to the agent
SKILL.md
2,080 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 Concepts
- Effect Size Conventions
- Power Analysis in Python (statsmodels)
- Power Analysis in R (pwr Package)
- Using G*Power (Desktop Application)
- Practical Recommendations
Files
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# Power Analysis Guide
Calculate appropriate sample sizes for your study using power analysis, understand effect sizes, and avoid underpowered or wastefully overpowered designs.
## Core Concepts
### The Four Parameters of Power Analysis
Every power analysis involves four interrelated quantities. Fix any three to solve for the fourth:
| Parameter | Symbol | Definition | Typical Value |
|-----------|--------|-----------|---------------|
| **Effect size** | d, r, f, etc. | Magnitude of the phenomenon you expect to detect | Varies by field |
| **Significance level** (alpha) | alpha | Probability of Type I error (false positive) | 0.05 |View the complete source on GitHubsem-guide
When your agent uses it
Structural equation modeling with latent variables guide
What it loads into context
Frontmatter
76 tokensAlways available to the agent
SKILL.md
1,899 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
- What Is SEM?
- SEM Components
- Step 1: Confirmatory Factor Analysis (CFA)
- Step 2: Full Structural Model
- Model Fit Assessment
- Model Modification and Comparison
- Common Pitfalls
- Assumptions and Diagnostics
Files
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# Structural Equation Modeling Guide
Build, estimate, and evaluate structural equation models (SEM) with latent variables using Python (semopy) and R (lavaan), including confirmatory factor analysis and path analysis.
## What Is SEM?
Structural Equation Modeling is a multivariate statistical framework that combines factor analysis and path analysis to test complex theoretical models involving:
- **Observed (manifest) variables**: Directly measured (e.g., survey items, test scores)
- **Latent (unobserved) variables**: Theoretical constructs measured indirectly through observed indicators (e.g., "motivation," "intelligence")
- **Structural paths**: Directional relationships between variables (regression-like)
- **Measurement model**: How latent variables relate to their indicators (CFA)
- **Structural model**: How latent variables relate to each other (path analysis)
View the complete source on GitHubsurvival-analysis-guide
When your agent uses it
Conduct Kaplan-Meier, Cox regression, and time-to-event analyses
What it loads into context
Frontmatter
96 tokensAlways available to the agent
SKILL.md
1,423 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 Concepts
- Kaplan-Meier Estimation
- Log-Rank Test
- Cox Proportional Hazards Regression
- Reporting Standards
Files
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# Survival Analysis Guide
A skill for conducting time-to-event analyses including Kaplan-Meier estimation, log-rank tests, and Cox proportional hazards regression. Covers censoring concepts, assumption checking, and reporting standards for clinical and social science research.
## Core Concepts
### What Is Survival Analysis?
Survival analysis studies the time until an event of interest occurs. Despite the name, the "event" need not be death -- it can be any well-defined transition:
```
Medical: Time to disease recurrence, death, or recovery
Engineering: Time to equipment failure
Social: Time to job termination, divorce, or graduationView the complete source on GitHubwrangling-skills
When your agent uses it
10 data wrangling skills. Trigger: messy data, format conversion, missing values, data reshaping. Design: pipeline-oriented recipes for common data cleaning and transformation tasks.
What it loads into context
Frontmatter
46 tokensAlways available to the agent
SKILL.md
390 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
Files
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# Data Wrangling — 10 Skills
Select the skill matching the user's need, then `read` its SKILL.md.
| Skill | Description |
|-------|-------------|
| [csv-data-analyzer](./csv-data-analyzer/SKILL.md) | Load, explore, clean, and analyze CSV data with statistical summaries |
| [data-cleaning-pipeline](./data-cleaning-pipeline/SKILL.md) | Systematic data cleaning workflows for research datasets |
| [data-cog-guide](./data-cog-guide/SKILL.md) | Upload messy CSVs with minimal prompting for deep automated analysis |
| [missing-data-handling](./missing-data-handling/SKILL.md) | Diagnose missing data patterns and apply appropriate imputation strategies |
| [pandas-data-wrangling](./pandas-data-wrangling/SKILL.md) | Data cleaning, transformation, and exploratory analysis with pandas |
| [questionnaire-design-guide](./questionnaire-design-guide/SKILL.md) | Questionnaire and survey design with Likert scales and coding |
| [stata-data-cleaning](./stata-data-cleaning/SKILL.md) | Clean, transform, and validate messy research data using Stata |
| [streamline-analyst-guide](./streamline-analyst-guide/SKILL.md) | End-to-end data analysis AI agent with Streamlit UI |View the complete source on GitHubcsv-data-analyzer
When your agent uses it
Load, explore, clean, and analyze CSV data with statistical summaries
What it loads into context
Frontmatter
93 tokensAlways available to the agent
SKILL.md
1,633 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
- Data Loading and Initial Profiling
- Data Cleaning Pipeline
- Statistical Summary Generation
- Best Practices for Reproducibility
- References
Files
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# CSV Data Analyzer
A comprehensive skill for loading, exploring, cleaning, and analyzing CSV datasets within research workflows. Designed for researchers who need to quickly understand the structure, quality, and statistical properties of tabular data before conducting deeper analysis.
## Overview
Research datasets commonly arrive as CSV files from instrument exports, survey platforms, government repositories, and collaborator handoffs. This skill provides a structured approach to the entire CSV analysis pipeline: ingestion, profiling, quality assessment, cleaning, transformation, and summary statistics. It emphasizes reproducibility by generating audit logs of every transformation applied to the raw data.
The skill supports datasets of varying complexity, from single-table survey results to multi-file longitudinal study exports with hundreds of columns. It works with standard Python data science libraries (pandas, numpy, scipy) and produces outputs suitable for inclusion in methods sections and supplementary materials.
## Data Loading and Initial Profiling
### Loading Strategies
View the complete source on GitHubdata-cleaning-pipeline
When your agent uses it
Systematic data cleaning workflows for research datasets
What it loads into context
Frontmatter
89 tokensAlways available to the agent
SKILL.md
2,001 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
- The Data Cleaning Workflow
- Initial Data Assessment
- Missing Value Treatment
- Outlier Detection
- Reproducibility and Documentation
Files
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# Data Cleaning Pipeline
A skill for building systematic, reproducible data cleaning pipelines for research datasets. Covers common data quality issues, step-by-step cleaning workflows, handling missing values, detecting and treating outliers, validating data integrity, and documenting cleaning decisions for reproducibility.
## The Data Cleaning Workflow
### Pipeline Overview
Data cleaning should follow a consistent, documented order. Each step builds on the previous one, and the entire pipeline should be scripted for reproducibility.
```
Data Cleaning Pipeline (recommended order):
1. Initial AssessmentView the complete source on GitHubdata-cog-guide
When your agent uses it
Upload messy CSVs with minimal prompting for deep automated analysis
What it loads into context
Frontmatter
93 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
- Overview
- Automated Ingestion Pipeline
- Deep Automated Profiling
- Interactive Analysis Workflow
- Best Practices
- References
Files
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# Data Cog Guide
An intelligent data analysis assistant that accepts messy, poorly documented CSV files and automatically infers structure, cleans anomalies, and produces deep analytical reports with minimal user prompting. Designed for researchers who need quick insights from unfamiliar or inherited datasets without spending hours on manual data preparation.
## Overview
Researchers frequently receive datasets from collaborators, public repositories, or legacy systems that lack documentation, use inconsistent formatting, and contain mixed data quality. Traditional analysis requires significant upfront effort to understand and prepare such data. Data Cog automates this process by applying heuristic inference, pattern recognition, and iterative cleaning to produce analysis-ready data along with a comprehensive profile report.
The skill implements a "zero-configuration" philosophy: provide the CSV file path and an optional research question, and it handles encoding detection, delimiter inference, type casting, missingness assessment, and initial exploratory statistics automatically.
## Automated Ingestion Pipeline
### Smart Loading
View the complete source on GitHubmissing-data-handling
When your agent uses it
Diagnose missing data patterns and apply appropriate imputation strategies
What it loads into context
Frontmatter
87 tokensAlways available to the agent
SKILL.md
1,876 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
- Missing Data Mechanisms
- Imputation Methods
- Outlier Detection
- Reporting Standards
Files
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# Missing Data Handling
A skill for diagnosing missing data mechanisms, selecting appropriate imputation strategies, and conducting sensitivity analyses. Covers everything from simple imputation to multiple imputation and modern machine learning approaches.
## Missing Data Mechanisms
### Rubin's Classification
Understanding the mechanism determines the appropriate handling strategy:
| Mechanism | Definition | Example | Implication |
|-----------|-----------|---------|-------------|
| MCAR | Missingness unrelated to any variable | Lab sample randomly contaminated | Listwise deletion is unbiased (but loses power) |
| MAR | Missingness related to observed variables | Higher-income respondents skip income question less | Multiple imputation appropriate |View the complete source on GitHubpandas-data-wrangling
When your agent uses it
Data cleaning, transformation, and exploratory analysis with pandas
What it loads into context
Frontmatter
90 tokensAlways available to the agent
SKILL.md
1,913 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
- Loading and Inspecting Data
- Handling Missing Data
- Data Transformation
- Exploratory Data Analysis (EDA)
- Performance Optimization
- Best Practices
- References
Files
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# Pandas Data Wrangling Guide
## Overview
Data wrangling -- the process of cleaning, transforming, and preparing raw data for analysis -- typically consumes 60-80% of a data scientist's time. Pandas is the de facto standard library for tabular data manipulation in Python, and mastering its idioms directly translates to faster, more reliable research workflows.
This guide covers the essential pandas operations that researchers encounter daily: loading heterogeneous data sources, diagnosing data quality issues, handling missing values, reshaping data for analysis, and performing exploratory data analysis (EDA). Each section includes copy-paste code examples designed for real-world research datasets.
Whether you are cleaning survey responses, preprocessing experimental logs, merging datasets from multiple sources, or preparing features for machine learning, the patterns here will save hours of trial and error.
## Loading and Inspecting Data
### Reading Common Formats
View the complete source on GitHubquestionnaire-design-guide
When your agent uses it
Questionnaire and survey design with Likert scales and coding
What it loads into context
Frontmatter
82 tokensAlways available to the agent
SKILL.md
2,169 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
- Survey Design Principles
- Likert Scale Design
- Constructing a Multi-Item Scale
- Data Coding and Preparation
- Reliability Analysis
- Validity Assessment
- Common Design Mistakes
- Survey Platform Comparison
Files
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# Questionnaire Design Guide
Design valid and reliable survey instruments with proper question types, Likert scale construction, response coding, and data preparation for analysis.
## Survey Design Principles
### Question Types
| Type | Example | Best For | Analysis |
|------|---------|----------|----------|
| **Likert scale** | "Rate your agreement: 1-5" | Attitudes, perceptions | Ordinal/interval statistics |
| **Multiple choice** | "Select your field" | Demographics, categories | Frequencies, chi-square |
| **Ranking** | "Rank these 5 options" | Preferences, priorities | Rank correlations |
| **Open-ended** | "Describe your experience" | Exploratory, rich data | Qualitative coding |View the complete source on GitHubstata-data-cleaning
When your agent uses it
Clean, transform, and validate messy research data using Stata
What it loads into context
Frontmatter
96 tokensAlways available to the agent
SKILL.md
2,092 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
- Initial Data Assessment
- String Cleaning
- Missing Data Handling
- Variable Construction
- Data Validation
- Saving and Documentation
- Best Practices
- References
Files
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# Stata Data Cleaning
Clean, transform, and validate messy research datasets in Stata. This skill covers the complete data preparation pipeline from raw survey or administrative data to analysis-ready datasets, with emphasis on documentation, reproducibility, and handling the common data quality issues encountered in social science, economics, and health research.
## Overview
Data cleaning typically consumes 60-80% of research time in empirical studies, yet it is often under-documented and poorly reproducible. Stata provides a powerful set of commands for data manipulation, but knowing which commands to use and in what order requires experience with common data quality issues: inconsistent coding, duplicate observations, string formatting problems, implausible values, and complex missing data patterns.
This skill provides a systematic, step-by-step data cleaning workflow in Stata. Each step produces a log of changes made, enabling full reproducibility and audit trails. The workflow is organized around the principle that raw data should never be modified in place -- instead, cleaning scripts transform raw data into processed datasets while preserving the original.
The approach follows best practices from the World Bank's DIME Analytics team and the J-PAL research transparency guidelines, making it suitable for projects that require rigorous data documentation for peer review, replication packages, or regulatory compliance.
## Initial Data Assessment
View the complete source on GitHubstreamline-analyst-guide
When your agent uses it
End-to-end data analysis AI agent with Streamlit UI
What it loads into context
Frontmatter
102 tokensAlways available to the agent
SKILL.md
890 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
- Installation
- Workflow
- Features
- Natural Language Interface
- Use Cases
- References
Files
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# Streamline Analyst Guide
## Overview
Streamline Analyst is an end-to-end data analysis AI agent with a Streamlit web interface. Upload a dataset and describe your analysis goal in natural language — the agent handles data cleaning, EDA, feature engineering, model training, evaluation, and report generation. Provides an interactive UI for reviewing each step and adjusting parameters.
## Installation
```bash
git clone https://github.com/Wilson-ZheLin/Streamline-Analyst.git
cd Streamline-Analyst
pip install -r requirements.txt
streamlit run app.py
```View the complete source on GitHubsurvey-data-processing
When your agent uses it
Clean, recode, and prepare survey response data for analysis
What it loads into context
Frontmatter
91 tokensAlways available to the agent
SKILL.md
2,223 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
- Survey Data Quality Assessment
- Recoding and Transformation
- Open-Ended Response Processing
- Data Reshaping for Analysis
- Export for Statistical Software
Files
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# Survey Data Processing
A skill for cleaning, recoding, and preparing survey response data for statistical analysis. Covers handling common survey data issues such as incomplete responses, attention check failures, reverse-coded items, scale construction, open-ended response coding, and export to analysis-ready formats compatible with SPSS, Stata, and R.
## Survey Data Quality Assessment
### Initial Inspection Workflow
Survey data from platforms like Qualtrics, SurveyMonkey, REDCap, and Google Forms each have their own export formats and quirks. The first step is always standardization.
```python
import pandas as pd
import numpy as np
View the complete source on GitHubtext-mining-guide
When your agent uses it
Apply NLP and text mining techniques to research text data
What it loads into context
Frontmatter
90 tokensAlways available to the agent
SKILL.md
1,652 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
- Text Preprocessing Pipeline
- Topic Modeling
- Sentiment Analysis
- Research Applications
- Validation and Reporting
Files
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# Text Mining Guide
A skill for applying natural language processing (NLP) and text mining techniques to research data. Covers text preprocessing, feature extraction, topic modeling, sentiment analysis, and named entity recognition for analyzing surveys, abstracts, social media, and document corpora.
## Text Preprocessing Pipeline
### Standard Cleaning Steps
```python
import re
from collections import Counter
def preprocess_text(text: str, lowercase: bool = True,View the complete source on GitHubai-ml-skills
When your agent uses it
27 ai & machine learning skills. Trigger: ML experiments, model training, deep learning, NLP, computer vision. Design: covers frameworks, benchmarks, paper reproduction, and AI research workflows.
What it loads into context
Frontmatter
49 tokensAlways available to the agent
SKILL.md
950 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
Files
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# AI & Machine Learning — 27 Skills
Select the skill matching the user's need, then `read` its SKILL.md.
| Skill | Description |
|-------|-------------|
| [ai-agent-papers-guide](./ai-agent-papers-guide/SKILL.md) | Curated 2024-2026 AI agent research papers collection |
| [ai-model-benchmarking](./ai-model-benchmarking/SKILL.md) | Benchmark AI models across 60+ academic evaluation suites and metrics |
| [anomaly-detection-papers-guide](./anomaly-detection-papers-guide/SKILL.md) | Industrial anomaly detection methods and benchmark papers |
| [autonomous-agents-papers-guide](./autonomous-agents-papers-guide/SKILL.md) | Daily-updated collection of autonomous AI agent papers |
| [computer-vision-guide](./computer-vision-guide/SKILL.md) | Apply computer vision research methods, models, and evaluation tools |
| [deep-learning-papers-guide](./deep-learning-papers-guide/SKILL.md) | Annotated deep learning paper implementations with code walkthroughs |
| [dl-transformer-finetune](./dl-transformer-finetune/SKILL.md) | Build transformer fine-tuning plans for classification and generation |
| [domain-adaptation-papers-guide](./domain-adaptation-papers-guide/SKILL.md) | Comprehensive collection of domain adaptation research papers |View the complete source on GitHubai-agent-papers-guide
When your agent uses it
Curated 2024-2026 AI agent research papers collection
What it loads into context
Frontmatter
100 tokensAlways available to the agent
SKILL.md
1,198 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
- Paper Categories
- Highlighted Papers (2024-2025)
- Tracking New Papers
- Framework Comparison
- Use Cases
- References
Files
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# AI Agent Papers Guide (2024-2026)
## Overview
A focused collection of AI agent research papers from 2024-2026, tracking the latest developments in LLM-based agent systems. Unlike broader collections, this focuses on recent breakthroughs — new architectures, benchmarks, multi-agent coordination, and real-world applications. Updated frequently as the field evolves rapidly.
## Paper Categories
```
Recent AI Agent Research
├── Agent Architectures
│ ├── Planning (o1-style reasoning, search-augmented)
│ ├── Memory (long-term, episodic, working)
│ └── Tool use (function calling, code execution)View the complete source on GitHubai-model-benchmarking
When your agent uses it
Benchmark AI models across 60+ academic evaluation suites and metrics
What it loads into context
Frontmatter
103 tokensAlways available to the agent
SKILL.md
2,113 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
- The lm-evaluation-harness
- Benchmark Selection by Research Claim
- Core Benchmarks Deep Dive
- Evaluation Pitfalls
- Reporting Results for Publication
- Best Practices
- References
Files
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# AI Model Benchmarking Guide
## Overview
Rigorous evaluation is the backbone of machine learning research. A model is only as credible as its evaluation protocol: which benchmarks were used, how metrics were computed, whether results are reproducible, and how they compare to baselines. The proliferation of LLMs has made this both more important and more complex, with over 60 established benchmarks and a rapidly evolving landscape.
This guide covers the practical side of model benchmarking: how to use the EleutherAI Language Model Evaluation Harness (lm-evaluation-harness), how to select benchmarks for different research claims, how to avoid common evaluation pitfalls, and how to present results for publication. The focus is on academic rigor rather than leaderboard chasing.
Whether you are evaluating a fine-tuned model for a paper, comparing architectures for an ablation study, or reviewing a submitted manuscript's evaluation section, these patterns will help ensure the evaluation is sound.
## The lm-evaluation-harness
The EleutherAI lm-evaluation-harness is the de facto standard for LLM evaluation in academic research, supporting 60+ tasks and used by most major LLM papers.
View the complete source on GitHubanomaly-detection-papers-guide
When your agent uses it
Industrial anomaly detection methods and benchmark papers
What it loads into context
Frontmatter
102 tokensAlways available to the agent
SKILL.md
1,386 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
- Overview
- Method Taxonomy
- Key Methods
- Benchmark Datasets
- Quick Implementation
- Evaluation Metrics
- Research Frontiers
- Use Cases
- References
Files
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# Industrial Anomaly Detection Papers Guide
## Overview
Industrial anomaly detection uses machine learning to identify defects, faults, and anomalies in manufacturing and quality inspection. This curated collection covers methods from reconstruction-based (autoencoders) to memory-bank approaches (PatchCore), normalizing flows, knowledge distillation, and foundation model-based detectors. Includes benchmark datasets, evaluation metrics, and real-world deployment considerations.
## Method Taxonomy
```
Anomaly Detection Methods
├── Reconstruction-based
│ ├── Autoencoder (AE, VAE)
│ ├── GAN-based (AnoGAN, GANomaly)
│ └── Diffusion-based (AnoDDPM)View the complete source on GitHubautonomous-agents-papers-guide
When your agent uses it
Daily-updated collection of autonomous AI agent papers
What it loads into context
Frontmatter
97 tokensAlways available to the agent
SKILL.md
1,522 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
- Agent Taxonomy
- Landmark Papers
- Paper Tracking
- Agent Benchmarks
- Reading Roadmap
- Use Cases
- References
Files
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# Autonomous Agents Papers Guide
## Overview
A daily-updated collection of research papers on autonomous AI agents — systems that use LLMs for planning, reasoning, tool use, and multi-step task execution. Covers the full agent stack from foundational prompting techniques (ReAct, Chain-of-Thought) to multi-agent systems, memory architectures, and real-world deployments. Organized chronologically with category tags for easy navigation.
## Agent Taxonomy
```
Autonomous Agents
├── Planning & Reasoning
│ ├── Chain-of-Thought (CoT, ToT, GoT)
│ ├── ReAct (Reasoning + Acting)
│ ├── Reflexion (Self-reflection)View the complete source on GitHubcomputer-vision-guide
When your agent uses it
Apply computer vision research methods, models, and evaluation tools
What it loads into context
Frontmatter
89 tokensAlways available to the agent
SKILL.md
1,509 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 Tasks and Architectures
- Dataset Preparation
- Training Pipeline
- Evaluation Metrics
- Reproducibility Checklist
- Ethical Considerations
Files
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# Computer Vision Guide
A skill for conducting computer vision research, covering model architectures, dataset preparation, training pipelines, evaluation metrics, and common experimental protocols for image classification, object detection, and segmentation tasks.
## Core Tasks and Architectures
### Computer Vision Task Taxonomy
```
Image Classification:
Input: Single image
Output: Class label(s)
Models: ResNet, EfficientNet, ViT, ConvNeXt
View the complete source on GitHubdeep-learning-papers-guide
When your agent uses it
Annotated deep learning paper implementations with code walkthroughs
What it loads into context
Frontmatter
101 tokensAlways available to the agent
SKILL.md
2,068 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
- Core Architecture Families
- Key Architecture Comparison
- Training Patterns
- From Paper to Code: A Methodology
- Best Practices
- References
Files
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# Deep Learning Papers Guide
## Overview
Understanding deep learning architectures requires more than reading papers -- it requires reading and writing code. The annotated_deep_learning_paper_implementations repository (65,800+ stars) provides line-by-line annotated implementations of seminal deep learning papers in PyTorch, making it one of the most valuable learning resources in the field.
This guide organizes the key architectures by category, provides implementation patterns for the most important building blocks, and offers strategies for going from paper to working code. Whether you are implementing a Transformer variant for your research, understanding a GAN architecture for your experiments, or teaching a deep learning course, these patterns accelerate the process.
The focus is on practical understanding: what each component does, why it is designed that way, and how to implement it correctly in PyTorch.
## Core Architecture Families
### Transformer Architectures
View the complete source on GitHubdl-transformer-finetune
When your agent uses it
Build transformer fine-tuning plans for classification and generation
What it loads into context
Frontmatter
100 tokensAlways available to the agent
SKILL.md
2,306 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
- Full Fine-Tuning
- Parameter-Efficient Fine-Tuning (PEFT)
- PEFT Method Comparison
- Avoiding Catastrophic Forgetting
- Fine-Tuning Checklist for Papers
- Best Practices
- References
Files
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# Transformer Fine-Tuning Guide
## Overview
Fine-tuning pretrained transformers is the dominant paradigm in modern NLP and increasingly in vision, audio, and multimodal research. The core idea is simple: take a model pretrained on massive data, then adapt it to your specific task with a comparatively small labeled dataset. But the practical details -- which layers to freeze, which optimizer and learning rate to use, how to handle catastrophic forgetting, when to use parameter-efficient methods -- determine whether fine-tuning succeeds or fails.
This guide covers the full spectrum of fine-tuning approaches: full fine-tuning for maximum performance, parameter-efficient fine-tuning (PEFT) for resource-constrained settings, and the decision framework for choosing between them. The patterns are drawn from hundreds of published papers and the Hugging Face ecosystem that supports them.
Whether you are fine-tuning BERT for text classification in a domain-specific corpus, adapting a large language model with LoRA for instruction following, or building a multi-task model for your research pipeline, this guide provides the recipes you need.
## Full Fine-Tuning
### Text Classification with BERT
View the complete source on GitHubdomain-adaptation-papers-guide
When your agent uses it
Comprehensive collection of domain adaptation research papers
What it loads into context
Frontmatter
95 tokensAlways available to the agent
SKILL.md
1,547 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
- Taxonomy of Methods
- Key Methods by Era
- Paper Tracking
- Benchmark Datasets
- Application Areas
- Reading Roadmap
- Use Cases
- References
Files
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# Domain Adaptation Papers Guide
## Overview
Domain adaptation addresses the problem of training models on one data distribution (source domain) and deploying them on a different distribution (target domain). This curated collection covers the full spectrum — from unsupervised domain adaptation (UDA) and domain generalization to partial, open-set, and source-free adaptation. Organized by methodology and application area with regularly updated paper lists.
## Taxonomy of Methods
```
Domain Adaptation
├── Unsupervised DA (UDA)
│ ├── Discrepancy-based (MMD, CORAL, CDD)
│ ├── Adversarial-based (DANN, ADDA, CDAN)
│ ├── Reconstruction-based (DRCN, DSN)View the complete source on GitHubgenerative-ai-guide
When your agent uses it
Curated guide to generative AI covering LLMs and diffusion models
What it loads into context
Frontmatter
100 tokensAlways available to the agent
SKILL.md
1,489 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
- Large Language Models
- Diffusion Models
- Prompt Engineering
- Evaluation and Benchmarks
- Integration with Research-Claw
- Best Practices
Files
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# Generative AI Guide
A skill providing a comprehensive, curated guide to generative AI research and practice, covering large language models (LLMs), diffusion models, transformer architectures, prompt engineering, and evaluation methodologies. Based on the awesome-generative-ai-guide repository (25K stars), this skill equips researchers with structured knowledge of the rapidly evolving generative AI landscape.
## Overview
Generative AI has become one of the most active areas of research across computer science, with implications spanning natural language processing, computer vision, audio synthesis, code generation, scientific discovery, and creative applications. The pace of development makes it challenging for researchers to maintain a current understanding of the field. This skill provides a structured map of the generative AI landscape, organized by topic and application area, with guidance on key papers, methods, and practical considerations.
Whether you are an AI researcher staying current with the field, a domain scientist exploring how generative AI can accelerate your work, or a student entering the field, this skill provides the orientation and resources needed to navigate the space effectively.
## Large Language Models
**Architecture Foundations**
- Transformer architecture: self-attention mechanism, positional encoding, layer normalizationView the complete source on GitHubgraph-learning-papers-guide
When your agent uses it
Conference papers on graph neural networks and graph learning
What it loads into context
Frontmatter
104 tokensAlways available to the agent
SKILL.md
1,101 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
- Topic Taxonomy
- Key Models
- Paper Search
- Benchmark Datasets
- Use Cases
- References
Files
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# Graph Learning Papers Guide
## Overview
A curated list of graph learning papers from top AI/ML conferences (NeurIPS, ICML, ICLR, KDD, WWW, AAAI). Covers graph neural networks, graph transformers, spectral methods, message passing, and applications in molecular science, social networks, and recommendation systems. Organized by venue, year, and topic for systematic tracking.
## Topic Taxonomy
```
Graph Learning
├── Graph Neural Networks
│ ├── Message Passing (GCN, GAT, GraphSAGE, GIN)
│ ├── Spectral (ChebNet, CayleyNet)
│ ├── Graph Transformers (Graphormer, GPS)View the complete source on GitHubhuggingface-api
When your agent uses it
Search and discover ML models, datasets, and Spaces on Hugging Face
What it loads into context
Frontmatter
100 tokensAlways available to the agent
SKILL.md
2,169 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
- Authentication
- Core Endpoints
- Advanced Filters
- Rate Limits
- Academic Use Cases
- Code Examples
- References
Files
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# Hugging Face Hub API
## Overview
The Hugging Face Hub is the largest open-source ML ecosystem, hosting over 1 million models, 200,000+ datasets, and 400,000+ Spaces (demo apps). The Hub API at `https://huggingface.co/api` provides programmatic access to search, discover, and retrieve metadata for all public resources without authentication.
For academic researchers, the Hub API enables systematic model selection for benchmarking, dataset discovery for experiments, tracking community adoption metrics (downloads, likes), and building reproducible ML pipelines that reference specific model revisions by SHA.
## Authentication
**Read endpoints require no authentication.** All search and metadata queries work without a token.
[Credential-related source omitted]
View the complete source on GitHubhuggingface-inference-guide
When your agent uses it
Run NLP and CV model inference via Hugging Face free-tier API
What it loads into context
Frontmatter
97 tokensAlways available to the agent
SKILL.md
1,905 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
- Authentication
- Core Endpoints
- Common Research Patterns
- Rate Limits and Best Practices
- References
Files
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# Hugging Face Inference API Guide
## Overview
The Hugging Face Inference API provides instant access to thousands of pre-trained machine learning models for natural language processing, computer vision, audio processing, and multimodal tasks. Researchers can run inference on state-of-the-art models without managing infrastructure, GPU resources, or complex deployment pipelines.
The API hosts models from the Hugging Face Hub, which contains over 500,000 models contributed by the research community. This includes transformer models for text classification, named entity recognition, summarization, translation, question answering, text generation, and image classification. For academic researchers, the Inference API is invaluable for rapid prototyping, benchmark evaluation, and integrating ML capabilities into research workflows without dedicated compute resources.
The free tier provides access to a broad selection of models with rate limits suitable for development and small-scale research. An API token is required for authentication, available for free at huggingface.co.
## Authentication
A free Hugging Face API token is required. Create an account and generate a token at https://huggingface.co/settings/tokens.
View the complete source on GitHubkeras-deep-learning
When your agent uses it
Build and debug deep learning models with Keras and TensorFlow backend
What it loads into context
Frontmatter
99 tokensAlways available to the agent
SKILL.md
1,876 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
- Model Architecture Patterns
- Data Pipeline with tf.data
- Training and Callback Orchestration
- Debugging and Common Pitfalls
- Best Practices for Research
- References
Files
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# Keras Deep Learning Guide
## Overview
Keras is the high-level deep learning API that ships as part of TensorFlow 2.x and is the recommended interface for building, training, and deploying neural networks. Its Sequential and Functional APIs provide a progressive disclosure of complexity: beginners can stack layers in minutes, while researchers can build arbitrary DAG architectures, custom training loops, and multi-output models with the same framework.
This guide covers practical patterns for academic research with Keras, from image classification and sequence modeling to custom loss functions and experiment reproducibility. The focus is on patterns that appear repeatedly in published work -- data loading pipelines, callback orchestration, hyperparameter search, and model introspection -- rather than toy examples.
Keras is particularly strong in rapid prototyping for research papers. Its integration with TensorBoard, Weights & Biases, and tf.data pipelines makes it straightforward to go from idea to reproducible experiment to publication-quality results.
## Model Architecture Patterns
### Sequential API for Standard Architectures
View the complete source on GitHubkolmogorov-arnold-networks-guide
When your agent uses it
Papers and tutorials on KAN learnable activation networks
What it loads into context
Frontmatter
107 tokensAlways available to the agent
SKILL.md
1,500 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
- Core Concept
- Key Papers
- Implementation
- KAN vs MLP Comparison
- Extensions and Variants
- Scientific Applications
- Research Landscape
- Use Cases
- References
Files
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# Kolmogorov-Arnold Networks (KAN) Guide
## Overview
Kolmogorov-Arnold Networks (KANs) are a novel neural network architecture that places learnable activation functions on edges (weights) instead of fixed activations on nodes. Based on the Kolmogorov-Arnold representation theorem, KANs use B-spline functions as learnable edge activations, achieving better accuracy and interpretability than MLPs with fewer parameters in certain domains. This collection tracks the rapidly growing KAN literature.
## Core Concept
```
Traditional MLP:
x → [fixed activation(linear transform)] → y
Activations on nodes, weights on edges
KAN:View the complete source on GitHubllm-evaluation-guide
When your agent uses it
Evaluate and benchmark large language models for research applications
What it loads into context
Frontmatter
96 tokensAlways available to the agent
SKILL.md
1,590 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
- Evaluation Taxonomy
- Automatic Metrics
- Benchmark Suites
- Human Evaluation
- Evaluation Pitfalls
- Reporting Standards
Files
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# LLM Evaluation Guide
A skill for evaluating and benchmarking large language models (LLMs) in research settings. Covers automatic metrics, human evaluation protocols, benchmark suites, evaluation pitfalls, and best practices for reporting LLM performance.
## Evaluation Taxonomy
### Types of Evaluation
```
1. Intrinsic evaluation:
Measures model quality on its own terms
- Perplexity, likelihood, calibration
- Useful for comparing architectures and training procedures
View the complete source on GitHubllm-from-scratch-guide
When your agent uses it
Build a ChatGPT-like LLM from scratch using PyTorch step by step
What it loads into context
Frontmatter
104 tokensAlways available to the agent
SKILL.md
1,363 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
- Installation and Setup
- Core Learning Pipeline
- Research Applications
- Integration with Research Workflows
- References
Files
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# LLM From Scratch Guide
## Overview
LLMs-from-scratch is a comprehensive educational repository with over 87,000 stars on GitHub that teaches you how to build a ChatGPT-like large language model from the ground up using PyTorch. Created by Sebastian Raschka, a machine learning researcher and author, the project provides a complete pipeline covering data preparation, tokenization, attention mechanisms, pretraining, and instruction finetuning.
Unlike tutorials that treat LLMs as black boxes, this project demystifies every component by walking through the full implementation. Each chapter corresponds to a Jupyter notebook with clear explanations, diagrams, and runnable code. The repository accompanies the book "Build a Large Language Model (From Scratch)" and serves as a standalone learning resource for researchers and engineers who want deep understanding of transformer-based language models.
The project is particularly valuable for academic researchers who need to understand the internals of LLMs for their own research, whether that involves modifying architectures, running ablation studies, or developing domain-specific language models for scientific applications.
## Installation and Setup
Clone the repository and set up a Python environment with the required dependencies:
View the complete source on GitHubml-pipeline-guide
When your agent uses it
Build and deploy reproducible production ML pipelines for research
What it loads into context
Frontmatter
91 tokensAlways available to the agent
SKILL.md
2,174 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
- Pipeline Architecture
- Experiment Tracking with MLflow
- Data Versioning with DVC
- Configuration Management with Hydra
- Feature Engineering Patterns
- Makefile for Reproducibility
- Best Practices
- References
Files
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# ML Pipeline Guide
## Overview
Machine learning research increasingly demands reproducible, end-to-end pipelines that go beyond a single training script. A research ML pipeline encompasses data ingestion, feature engineering, model training, evaluation, experiment tracking, and artifact management. Without a structured pipeline, research results become difficult to reproduce, ablation studies become error-prone, and collaborators cannot build on prior work.
This guide covers the practical tools and patterns for building ML pipelines in an academic research context. The focus is on reproducibility, experiment tracking, and the transition from notebook prototyping to structured experiments. The patterns use MLflow, DVC, and standard Python tooling -- chosen because they are open source, widely adopted in published research, and require minimal infrastructure.
Unlike industry MLOps guides that emphasize deployment at scale, this guide prioritizes the research workflow: running many experiments, tracking what changed between runs, and producing results that reviewers can verify.
## Pipeline Architecture
A research ML pipeline typically has five stages:
View the complete source on GitHubnlp-toolkit-guide
When your agent uses it
NLP analysis with perplexity scoring, burstiness, and entropy metrics
What it loads into context
Frontmatter
98 tokensAlways available to the agent
SKILL.md
2,203 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
- Perplexity Scoring
- Burstiness Analysis
- Entropy and Information-Theoretic Metrics
- AI Text Detection Pipeline
- Tokenization Patterns
- Best Practices
- References
Files
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# NLP Toolkit Guide
## Overview
Natural Language Processing research requires a diverse set of analytical tools beyond standard model training. Text quality assessment, AI-generated text detection, linguistic feature extraction, and corpus analysis all depend on well-understood metrics: perplexity, burstiness, entropy, and their variants.
This guide provides practical implementations of these core NLP metrics alongside patterns for tokenization, embedding analysis, and text feature engineering. The focus is on metrics used in active research areas -- AI text detection (perplexity + burstiness classifiers), information-theoretic analysis of corpora, and linguistic diversity measurement.
These tools are framework-agnostic where possible, but leverage Hugging Face Transformers for language model operations and standard Python scientific computing libraries for statistical analysis.
## Perplexity Scoring
Perplexity measures how well a language model predicts a text. Lower perplexity means the text is more predictable to the model -- a key signal in AI text detection, model evaluation, and domain adaptation.
View the complete source on GitHubnpcpy-research-guide
When your agent uses it
All-in-one Python library for NLP, agents, and knowledge graphs
What it loads into context
Frontmatter
97 tokensAlways available to the agent
SKILL.md
773 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
- Installation
- Core Modules
- Research Workflows
- Use Cases
- References
Files
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# npcpy Research Guide
## Overview
npcpy is an all-in-one Python library that combines NLP, agent orchestration, and knowledge graph capabilities in a single package. It provides tools for text processing, entity extraction, agent creation, graph-based reasoning, and research automation. Designed as a Swiss Army knife for AI researchers who need quick access to diverse NLP and agent capabilities without juggling many dependencies.
## Installation
```bash
pip install npcpy
```
## Core Modules
View the complete source on GitHubprompt-engineering-research
When your agent uses it
Systematic prompt engineering methods for AI-assisted academic research workf...
What it loads into context
Frontmatter
87 tokensAlways available to the agent
SKILL.md
1,699 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
- Prompt Design Patterns
- Chain-of-Thought for Complex Research Tasks
- Evaluation and Reliability
- Research Workflow Integration
- Ethical Considerations
- Key References
Files
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# Prompt Engineering for Research
A skill for applying systematic prompt engineering techniques in academic research contexts. Covers prompt design patterns, evaluation methodologies, and practical workflows for using large language models (LLMs) as research tools.
## Prompt Design Patterns
### Core Prompting Strategies
| Strategy | Description | Best For | Reliability |
|----------|------------|---------|-------------|
| Zero-shot | Direct instruction, no examples | Simple, well-defined tasks | Moderate |
| Few-shot | Include 2-5 examples in prompt | Pattern matching, formatting | High |
| Chain-of-thought | "Think step by step" | Reasoning, math, analysis | High |
| Role prompting | "You are an expert in..." | Domain-specific tasks | Moderate |View the complete source on GitHubpytorch-guide
When your agent uses it
Avoid common PyTorch mistakes and apply robust training patterns
What it loads into context
Frontmatter
87 tokensAlways available to the agent
SKILL.md
2,295 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
- Common Mistakes and Fixes
- Robust Training Template
- Performance Optimization
- Reproducibility Checklist
- Best Practices
- References
Files
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# PyTorch Guide
## Overview
PyTorch is the dominant deep learning framework in academic research, used in the majority of papers at NeurIPS, ICML, and ICLR. Its eager execution model, Pythonic API, and seamless integration with the Python scientific stack make it the default choice for prototyping and publishing research code.
However, PyTorch's flexibility is a double-edged sword. Subtle bugs -- forgetting `model.eval()`, accumulating gradients across batches, incorrect device placement, memory leaks from detached tensors -- can silently corrupt results without raising errors. These issues are especially dangerous in research settings where ground truth is unknown.
This guide catalogs the most common PyTorch mistakes, provides battle-tested training patterns, and covers performance optimization techniques that every researcher should know. The patterns here are drawn from top-tier ML research codebases and the PyTorch team's own best practice recommendations.
## Common Mistakes and Fixes
### The Big Five Mistakes
View the complete source on GitHubpytorch-lightning-guide
When your agent uses it
PyTorch Lightning framework for scalable model training and research
What it loads into context
Frontmatter
96 tokensAlways available to the agent
SKILL.md
1,782 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
- Installation and Setup
- Core Architecture
- Advanced Research Features
- Experiment Reproducibility
- References
Files
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# PyTorch Lightning Guide
## Overview
PyTorch Lightning is a deep learning framework with over 31,000 GitHub stars that provides a high-level interface for PyTorch, enabling researchers to focus on model design rather than engineering boilerplate. Developed by Lightning AI, it decouples the science (model architecture, loss functions, data processing) from the engineering (distributed training, mixed precision, gradient accumulation, checkpointing) through a structured `LightningModule` abstraction.
For academic researchers, Lightning eliminates the need to write repetitive training loops, device management code, and distributed training logic. You define your model, training step, and data loaders, and Lightning handles everything else -- from single GPU to multi-node distributed training, from FP32 to mixed precision, from local development to cloud deployment. This means faster iteration on research ideas with production-quality training infrastructure.
Lightning is used extensively in AI research labs and has become a standard tool for reproducible deep learning experiments. It integrates seamlessly with experiment tracking tools like Weights & Biases, MLflow, and TensorBoard, and supports all PyTorch-compatible model architectures.
## Installation and Setup
```bash
# Install PyTorch LightningView the complete source on GitHubreinforcement-learning-guide
When your agent uses it
Reinforcement learning fundamentals, algorithms, and research
What it loads into context
Frontmatter
81 tokensAlways available to the agent
SKILL.md
2,355 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
- RL Fundamentals
- Algorithm Taxonomy
- Implementation: DQN
- Implementation: PPO
- Research Environments
- Top Venues
- Key Research Directions (2024-2025)
Files
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# Reinforcement Learning Guide
Understand and implement reinforcement learning algorithms from tabular methods through deep RL, including policy gradients, actor-critic, and model-based approaches.
## RL Fundamentals
### The RL Framework
An agent interacts with an environment to maximize cumulative reward:
```
Agent Environment
| |
|--- action a_t ---------->|View the complete source on GitHubresponsible-ai-guide
When your agent uses it
Resources for trustworthy, fair, and ethical AI research
What it loads into context
Frontmatter
96 tokensAlways available to the agent
SKILL.md
1,022 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
- Topic Taxonomy
- Key Tools
- Fairness Assessment
- Reading Roadmap
- Use Cases
- References
Files
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# Responsible AI Guide
## Overview
A comprehensive collection of resources for building trustworthy, fair, and ethical AI systems. Covers fairness metrics, bias detection and mitigation, explainability methods, privacy-preserving techniques, robustness testing, and governance frameworks. Essential reading for researchers working on AI safety, alignment, and deploying models in high-stakes domains.
## Topic Taxonomy
```
Responsible AI
├── Fairness
│ ├── Bias detection (data, model, outcome)
│ ├── Fairness metrics (demographic parity, equalized odds)
│ ├── Bias mitigation (pre/in/post-processing)View the complete source on GitHubtensorflow-guide
When your agent uses it
TensorFlow best practices for tf.function, GPU memory, and deployment
What it loads into context
Frontmatter
93 tokensAlways available to the agent
SKILL.md
1,951 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
- tf.function: The Critical Abstraction
- GPU Memory Management
- Distributed Training Strategies
- Model Export and Serving
- Performance Optimization with XLA
- Debugging and Profiling
- Best Practices
- References
Files
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# TensorFlow Guide
## Overview
TensorFlow is a production-grade machine learning framework that excels at deployment, distributed training, and hardware acceleration. While PyTorch dominates pure research prototyping, TensorFlow remains the standard in industry ML systems and is heavily used in applied research where models must move from experiment to production.
TensorFlow 2.x unified eager execution with graph-mode performance through `tf.function`, but this hybrid approach introduces subtle pitfalls. Understanding when and how TensorFlow traces functions, manages GPU memory, and distributes computation is essential for writing correct and efficient code.
This guide covers the key patterns that trip up researchers: `tf.function` tracing semantics, GPU memory management, distributed strategies, model export, and the ecosystem of tools (TFX, TensorBoard, TF Serving) that make TensorFlow uniquely powerful for end-to-end ML workflows.
## tf.function: The Critical Abstraction
### How Tracing Works
View the complete source on GitHubtransformer-architecture-guide
When your agent uses it
Guide to Transformer architectures for NLP and computer vision
What it loads into context
Frontmatter
84 tokensAlways available to the agent
SKILL.md
2,125 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
- The Original Transformer
- Major Transformer Variants
- Vision Transformers (ViT)
- Efficient Transformer Variants
- Model Scaling Laws
- Research Resources
Files
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# Transformer Architecture Guide
Understand, implement, and adapt Transformer architectures for NLP, computer vision, and multimodal research, from the original attention mechanism to modern variants.
## The Original Transformer
The Transformer (Vaswani et al., 2017, "Attention Is All You Need") replaced recurrence and convolution with self-attention as the primary sequence modeling mechanism.
### Core Components
| Component | Function | Key Parameters |
|-----------|----------|---------------|
| Multi-Head Self-Attention | Computes attention weights across all positions | d_model, n_heads, d_k, d_v |
| Feed-Forward Network | Position-wise nonlinear transformation | d_model, d_ff |View the complete source on GitHubvmas-simulator-guide
When your agent uses it
Vectorized multi-agent reinforcement learning simulator
What it loads into context
Frontmatter
96 tokensAlways available to the agent
SKILL.md
947 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
- Installation
- Quick Start
- Scenarios
- Integration with MARL Libraries
- Custom Scenarios
- Use Cases
- References
Files
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# VMAS: Vectorized Multi-Agent Simulator Guide
## Overview
VMAS is a vectorized simulator for multi-agent reinforcement learning (MARL) that runs thousands of parallel environments on GPU via PyTorch. It provides a diverse set of 2D cooperative, competitive, and mixed scenarios for benchmarking multi-agent algorithms. Orders of magnitude faster than CPU-based simulators, enabling rapid research iteration on multi-agent coordination problems.
## Installation
```bash
pip install vmas
```
## Quick Start
View the complete source on GitHubbiomedical-skills
When your agent uses it
24 biomedical research skills. Trigger: medical research, clinical trials, genomics, bioinformatics. Design: domain databases, wet-lab/dry-lab methods, and ethical compliance guides.
What it loads into context
Frontmatter
47 tokensAlways available to the agent
SKILL.md
844 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
Files
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# Biomedical Research — 24 Skills
Select the skill matching the user's need, then `read` its SKILL.md.
| Skill | Description |
|-------|-------------|
| [alphafold-api](./alphafold-api/SKILL.md) | Query AlphaFold protein structure predictions by UniProt accession |
| [bioagents-guide](./bioagents-guide/SKILL.md) | AI scientist framework for autonomous biological research workflows |
| [biothings-api](./biothings-api/SKILL.md) | Query gene, variant, and drug annotations via BioThings APIs |
| [clawbio-guide](./clawbio-guide/SKILL.md) | OpenClaw bioinformatics skill library for genomics pipelines |
| [clinical-dialogue-agents-guide](./clinical-dialogue-agents-guide/SKILL.md) | Papers on AI agents for clinical dialogue and medical QA |
| [clinical-research-guide](./clinical-research-guide/SKILL.md) | Design clinical studies and report using CONSORT, STROBE guidelines |
| [clinicaltrials-api](./clinicaltrials-api/SKILL.md) | Clinical trial registry database search API |
| [clinicaltrials-api-v2](./clinicaltrials-api-v2/SKILL.md) | Search and analyze clinical trials via the ClinicalTrials.gov v2 API |View the complete source on GitHubalphafold-api
When your agent uses it
Query AlphaFold protein structure predictions by UniProt accession
What it loads into context
Frontmatter
93 tokensAlways available to the agent
SKILL.md
2,348 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
- Authentication
- Core Endpoints
- Key Response Fields
- Rate Limits
- Python Example
- Academic Use Cases
- References
Files
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# AlphaFold Protein Structure Database API
## Overview
The AlphaFold DB, maintained by EMBL-EBI and DeepMind, provides open access to over 200 million protein structure predictions. The REST API enables programmatic lookup of predicted structures, confidence metrics (pLDDT, PAE), and downloadable structure files (PDB, mmCIF, BinaryCIF) keyed on UniProt accessions. Free, no authentication required.
## Authentication
None. All endpoints are publicly accessible without API keys or tokens.
## Core Endpoints
Base URL: `https://alphafold.ebi.ac.uk/api`
View the complete source on GitHubbioagents-guide
When your agent uses it
AI scientist framework for autonomous biological research workflows
What it loads into context
Frontmatter
93 tokensAlways available to the agent
SKILL.md
2,746 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
- BioAgent Architecture
- Biological Research Tasks for AI Agents
- Integration with Lab Automation
- Evaluation and Validation
- Ethical Considerations
- Best Practices
- References
Files
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# BioAgents Guide
## Overview
BioAgents -- AI agent systems for biological research -- represent a paradigm shift in how life science experiments are conceived, designed, executed, and analyzed. Building on the foundation of large language models, these systems integrate literature search, hypothesis generation, experimental design, data analysis, and manuscript drafting into semi-autonomous or fully autonomous research pipelines.
The AI Scientist framework (Sakana AI, 2024) demonstrated that language models can conduct end-to-end research: generating ideas, writing code, running experiments, and producing papers. In biology, this approach is being applied to drug discovery, protein engineering, genomics analysis, and systems biology -- domains where the combinatorial complexity of experimental space makes AI-assisted exploration particularly valuable.
This guide covers the architecture of bioagent systems, the biological research tasks they can automate, integration with wet-lab automation, and the methodological considerations for researchers building or evaluating these systems. The focus is on practical patterns that connect AI capabilities to real biological research problems.
## BioAgent Architecture
### System Components
View the complete source on GitHubbiothings-api
When your agent uses it
Query gene, variant, and drug annotations via BioThings APIs
What it loads into context
Frontmatter
94 tokensAlways available to the agent
SKILL.md
2,571 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
- Authentication
- MyGene.info — Gene Annotations
- MyVariant.info — Variant Annotations
- MyChem.info — Drug & Chemical Annotations
- Query Syntax (Shared Across All Three APIs)
- Rate Limits
- Python Example: Cross-API Gene-Variant-Drug Lookup
- Academic Use Cases
- References
Files
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# BioThings API Suite
## Overview
BioThings is a family of high-performance biomedical annotation APIs developed at the Scripps Research Institute. The suite provides unified, up-to-date access to gene, variant, and chemical/drug annotations aggregated from dozens of authoritative sources. Three primary services cover the core entities in translational research:
- **MyGene.info** — Gene annotations from NCBI Entrez, Ensembl, UniProt, GO, KEGG, Reactome, and 20+ sources.
- **MyVariant.info** — Variant annotations from dbSNP, ClinVar, gnomAD, CADD, COSMIC, and 15+ sources.
- **MyChem.info** — Drug and chemical annotations from NDC, DrugBank, ChEMBL, FDA, PubChem, and 10+ sources.
All three share identical query syntax, require no authentication, and return JSON. Free for academic and commercial use.
## Authentication
View the complete source on GitHubclawbio-guide
When your agent uses it
OpenClaw bioinformatics skill library for genomics pipelines
What it loads into context
Frontmatter
96 tokensAlways available to the agent
SKILL.md
1,181 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
- Installation
- Available Skills
- Usage Examples
- Configuration
- Skill Development
- Use Cases
- References
Files
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# ClawBio Guide
## Overview
ClawBio is a bioinformatics skill library for OpenClaw that provides pre-built skills for common genomics and biological analysis tasks — sequence alignment, variant calling, differential expression, pathway analysis, and more. Each skill encapsulates best-practice bioinformatics pipelines as conversational agent capabilities, making complex analyses accessible through natural language.
## Installation
```bash
# Install as OpenClaw plugin
openclaw plugins install @clawbio/clawbio
# Or add to your OpenClaw configuration
# In openclaw.config.json:View the complete source on GitHubclinical-dialogue-agents-guide
When your agent uses it
Papers on AI agents for clinical dialogue and medical QA
What it loads into context
Frontmatter
104 tokensAlways available to the agent
SKILL.md
1,155 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
- Research Landscape
- Key Systems
- Benchmarks
- Safety Considerations
- Reading Roadmap
- Use Cases
- References
Files
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# Agentic Clinical Dialogue Guide
## Overview
A curated collection of papers on AI agents for clinical dialogue — systems that conduct patient interviews, perform differential diagnosis, explain medical information, and support clinical decision-making through conversation. Covers medical QA benchmarks, patient simulation, clinical reasoning chains, and safety considerations unique to healthcare AI.
## Research Landscape
```
Agentic Clinical Dialogue
├── Patient-Facing Agents
│ ├── Symptom checkers
│ ├── Triage systems
│ ├── Health informationView the complete source on GitHubclinical-research-guide
When your agent uses it
Design clinical studies and report using CONSORT, STROBE guidelines
What it loads into context
Frontmatter
92 tokensAlways available to the agent
SKILL.md
1,627 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
- Study Design Selection
- CONSORT for Randomized Trials
- STROBE for Observational Studies
- Sample Size and Power
- Other Reporting Guidelines
Files
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# Clinical Research Guide
A skill for designing clinical studies and reporting results according to established guidelines. Covers randomized controlled trials (CONSORT), observational studies (STROBE), diagnostic studies (STARD), and systematic reviews (PRISMA).
## Study Design Selection
### Hierarchy of Evidence
```
Systematic Reviews / Meta-analyses
|
Randomized Controlled Trials (RCTs)
|
Cohort Studies (prospective)View the complete source on GitHubclinicaltrials-api
When your agent uses it
Clinical trial registry database search API
What it loads into context
Frontmatter
80 tokensAlways available to the agent
SKILL.md
1,834 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
- Authentication
- Core Endpoints
- Rate Limits
- Common Patterns
- References
Files
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# ClinicalTrials API Guide
## Overview
ClinicalTrials.gov is a registry and results database of publicly and privately supported clinical studies conducted around the world, maintained by the U.S. National Library of Medicine (NLM) at the National Institutes of Health (NIH). It contains registration records for over 500,000 clinical trials from more than 220 countries, making it the largest and most comprehensive clinical trial registry in the world.
The ClinicalTrials.gov API provides programmatic access to this vast repository of clinical trial data. Researchers can search for trials by condition, intervention, sponsor, location, phase, status, and many other criteria. The API returns detailed structured data including study design, eligibility criteria, outcome measures, enrollment information, study contacts, and results when available.
Clinical researchers, pharmaceutical companies, systematic reviewers, epidemiologists, public health officials, patient advocacy groups, and health policy analysts use the ClinicalTrials.gov API to monitor the clinical trial landscape, identify recruiting studies, conduct meta-analyses, analyze research trends, and ensure comprehensive evidence coverage in systematic reviews. The database is a critical resource for evidence-based medicine and regulatory compliance.
## Authentication
No authentication required. The ClinicalTrials.gov API is freely accessible without any API key, token, or registration. All endpoints are publicly available. Users are expected to comply with the NCBI usage policies and make requests at a reasonable rate.
View the complete source on GitHubclinicaltrials-api-v2
When your agent uses it
Search and analyze clinical trials via the ClinicalTrials.gov v2 API
What it loads into context
Frontmatter
96 tokensAlways available to the agent
SKILL.md
2,174 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
- Authentication
- Core Endpoints
- Rate Limits
- Academic Use Cases
- Code Examples
- References
Files
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# ClinicalTrials.gov v2 API Guide
## Overview
ClinicalTrials.gov is the world's largest clinical trial registry, maintained by the U.S. National Library of Medicine (NLM) at NIH. It contains over 576,000 study records from 220+ countries covering interventional trials, observational studies, and expanded access programs. The v2 API provides structured JSON access with field-level filtering, cursor-based pagination, and statistics endpoints.
Key v2 improvements over the legacy API: JSON-native responses, sparse field selection via the `fields` parameter, `nextPageToken` pagination, and dedicated statistics endpoints. Study data is organized into `protocolSection` (sponsor-submitted) and `derivedSection` (NLM-computed).
## Authentication
No authentication required. All endpoints are publicly accessible without API keys or registration. Users should comply with NCBI usage policies and maintain reasonable request rates.
## Core Endpoints
View the complete source on GitHubena-sequence-api
When your agent uses it
Access nucleotide sequence data from the European Nucleotide Archive
What it loads into context
Frontmatter
94 tokensAlways available to the agent
SKILL.md
1,566 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
- API Endpoints
- Python Usage
- Data Access
- References
Files
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# European Nucleotide Archive (ENA) API
## Overview
The European Nucleotide Archive (ENA) at EMBL-EBI is one of the three global nucleotide sequence databases (with NCBI GenBank and DDBJ). It provides access to raw sequencing reads, assembled sequences, and functional annotations from all organisms. The API supports accession lookup, text search, and bulk data retrieval. Free, no authentication required.
## API Endpoints
### Portal API (Search)
```bash
# Search for studies
curl "https://www.ebi.ac.uk/ena/portal/api/search?query=CRISPR+cas9&result=study&limit=20&format=json"
View the complete source on GitHubenrichr-api
When your agent uses it
Perform gene set enrichment analysis using the Enrichr API
What it loads into context
Frontmatter
94 tokensAlways available to the agent
SKILL.md
2,508 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
- Two-Step Workflow
- Core Endpoints
- Available Libraries (225 Total)
- Rate Limits
- Academic Use Cases
- Python Usage
- References
Files
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# Enrichr Gene Set Enrichment Analysis API
## Overview
Enrichr is the most widely used gene set enrichment analysis tool, developed by the Ma'ayan Lab at the Icahn School of Medicine at Mount Sinai. It tests whether a user-supplied gene list is statistically over-represented in curated gene set libraries spanning pathways, ontologies, transcription factor targets, disease associations, and cell types. The API provides access to 225 background libraries covering over 500,000 annotated gene sets. Free, no authentication required.
## Two-Step Workflow
Enrichr uses a submit-then-query pattern:
1. **POST gene list** to `/addList` -- returns a `userListId` token
2. **GET enrichment** from `/enrich` using that token and a chosen library
The `userListId` persists on the server, so you can run multiple library queries against the same submission without re-uploading.View the complete source on GitHubensembl-rest-api
When your agent uses it
Query gene, sequence, and variant data via the Ensembl REST API
What it loads into context
Frontmatter
94 tokensAlways available to the agent
SKILL.md
2,208 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
- Authentication
- Core Endpoints
- Rate Limits
- Academic Use Cases
- Code Examples (Python)
- References
Files
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# Ensembl REST API Guide
## Overview
Ensembl is a genome browser and annotation system maintained by EMBL-EBI and the Wellcome Sanger Institute, providing reference assemblies, gene annotations, variant data, and comparative genomics for over 300 vertebrate genomes. It is the genomic reference underpinning gget, PyEnsembl, and BioMart.
The REST API exposes Ensembl data via stateless HTTP. Researchers can look up genes by symbol or stable ID, retrieve genomic/cDNA/protein sequences, query variant annotations (rsIDs, clinical significance, consequences), access cross-references (HGNC, UniProt, RefSeq, OMIM), and obtain assembly metadata. Responses in JSON or XML.
## Authentication
No authentication required. All endpoints are publicly accessible. Users needing higher throughput can register for an API token.
## Core Endpoints
View the complete source on GitHubepidemiology-guide
When your agent uses it
Epidemiological study designs, measures of association, and public health ana...
What it loads into context
Frontmatter
88 tokensAlways available to the agent
SKILL.md
1,905 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
- Study Design Selection
- Measures of Disease Frequency
- Measures of Association
- Bias Assessment
- Survival Analysis
- Reporting Standards
Files
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# Epidemiology Guide
A skill for designing and analyzing epidemiological studies. Covers study design selection, measures of disease frequency and association, bias assessment, and public health data analysis methods.
## Study Design Selection
### Design Hierarchy
```
Evidence Strength
|
Systematic Review / Meta-Analysis (Highest)
|
Randomized Controlled TrialView the complete source on GitHubgenomas-guide
When your agent uses it
Automate gene expression analysis with the GenoMAS multi-agent system
What it loads into context
Frontmatter
92 tokensAlways available to the agent
SKILL.md
941 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
- Installation
- Core Workflow
- Supported Analyses
- Data Sources
- References
Files
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# GenoMAS Guide
## Overview
GenoMAS (Genomics Multi-Agent System) is a minimalist multi-agent framework for automating scientific analysis workflows, particularly gene expression analysis. It orchestrates specialized agents for data retrieval, preprocessing, differential expression analysis, pathway enrichment, and visualization — turning a natural language research question into a complete bioinformatics pipeline.
## Installation
```bash
pip install genomas
# Or from source
git clone https://github.com/futianfan/GenoMAS.git
cd GenoMAS && pip install -e .
```View the complete source on GitHubgenomics-analysis-guide
When your agent uses it
Workflows for RNA-seq, GWAS, and variant calling in genomic research
What it loads into context
Frontmatter
95 tokensAlways available to the agent
SKILL.md
2,051 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
- RNA-seq Analysis Pipeline
- GWAS Pipeline
- Variant Calling Pipeline
- Best Practices
- References
Files
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# Genomics Analysis Guide
## Overview
Genomic data analysis is the computational backbone of modern molecular biology. From identifying disease-associated variants through Genome-Wide Association Studies (GWAS) to quantifying gene expression with RNA-seq, these workflows transform raw sequencing data into biological insights that drive discoveries in medicine, agriculture, and evolutionary biology.
This guide covers the three most common genomic analysis workflows: RNA-seq differential expression analysis, GWAS for variant-trait associations, and variant calling from whole-genome sequencing (WGS) data. Each workflow is described with tool recommendations, command-line examples, and downstream analysis steps in R and Python.
The emphasis is on reproducibility and best practices. Genomic analyses involve many sequential steps, and errors in early stages propagate through the entire pipeline. Following standardized workflows -- like those from the Broad Institute, ENCODE, and Bioconductor -- reduces the risk of methodological errors.
## RNA-seq Analysis Pipeline
### Workflow Overview
View the complete source on GitHubgenotex-benchmark-guide
When your agent uses it
Benchmark for LLM agents on gene expression data analysis
What it loads into context
Frontmatter
98 tokensAlways available to the agent
SKILL.md
986 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
- Benchmark Structure
- Usage
- Running Evaluations
- Task Examples
- Use Cases
- References
Files
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# GenoTEX Benchmark Guide
## Overview
GenoTEX is a benchmark for evaluating LLM-based agents on gene expression data analysis tasks. It provides curated datasets from GEO (Gene Expression Omnibus) with ground-truth analysis pipelines, testing agents on data preprocessing, differential expression, enrichment analysis, and biological interpretation. Published at MLCB 2025 as an oral presentation.
## Benchmark Structure
```
GenoTEX Benchmark
├── Data Collection
│ └── Curated GEO datasets with ground truth
├── Task Categories
│ ├── Data preprocessing (QC, normalization)View the complete source on GitHubmed-researcher-guide
When your agent uses it
Multi-agent system for biomedical literature review and synthesis
What it loads into context
Frontmatter
90 tokensAlways available to the agent
SKILL.md
1,146 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
- Architecture
- Usage
- PICO Framework Integration
- Evidence Grading
- Search Configuration
- Output Formats
- Clinical Use Cases
- References
Files
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# Med-Researcher Guide
## Overview
Med-Researcher is a multi-agent system designed specifically for biomedical literature review. It orchestrates specialized agents for searching PubMed and other medical databases, extracting structured evidence from clinical papers, and synthesizing findings into evidence-graded summaries. Particularly useful for clinical evidence reviews, drug interaction research, and systematic reviews in medicine.
## Architecture
### Agent Roles
```
Query → Planning Agent (decomposes clinical question)
↓
Search Agent (PubMed, PMC, clinical trials)View the complete source on GitHubmed-researcher-r1-guide
When your agent uses it
Medical deep research agent with reasoning chain analysis
What it loads into context
Frontmatter
99 tokensAlways available to the agent
SKILL.md
1,136 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
- Architecture
- Usage
- Reasoning Chain
- Evidence Grading
- Medical Search Configuration
- Clinical Use Cases
- References
Files
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# MedResearcher-R1 Guide
## Overview
MedResearcher-R1 is a medical deep research agent that combines clinical reasoning chains with iterative literature search to answer complex medical questions. Unlike general research agents, it is specialized for medical evidence — understanding clinical trial designs, PICO frameworks, evidence hierarchies, and medical terminology. Uses reasoning chain analysis (R1) to decompose clinical questions and systematically gather evidence.
## Architecture
```
Clinical Question
↓
R1 Reasoning Chain (decompose into sub-questions)
↓
Medical Search AgentView the complete source on GitHubmedgeclaw-guide
When your agent uses it
AI research assistant for biomedicine, RNA-seq, and drug discovery
What it loads into context
Frontmatter
95 tokensAlways available to the agent
SKILL.md
2,813 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
- Medical NLP and Literature Mining
- RNA-seq Analysis
- Drug Discovery Computational Methods
- Clinical AI Applications
- Best Practices
- References
Files
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# MedgeClaw Guide
## Overview
MedgeClaw is a conceptual framework for AI-powered biomedical research assistance, integrating natural language processing for medical literature, computational biology pipelines, and drug discovery workflows. The name reflects the integration of Medical knowledge Edge (cutting-edge biomedical AI) with the Claw agent pattern for autonomous research execution.
Biomedical research is uniquely suited for AI augmentation because it generates massive, heterogeneous data -- genomic sequences, clinical records, imaging data, molecular structures, and published literature -- that exceeds the capacity of individual researchers to synthesize. AI systems that can navigate across these data types, identify patterns, and suggest hypotheses accelerate the pace of discovery.
This guide covers the key computational methods in biomedical AI research: medical NLP for literature mining, RNA-seq analysis pipelines, drug discovery computational workflows, and the integration patterns that connect these components into coherent research workflows. The focus is on methods that are reproducible, validated, and suitable for publication in biomedical journals.
## Medical NLP and Literature Mining
### Biomedical Named Entity Recognition
View the complete source on GitHubmedical-data-api
When your agent uses it
Access FDA drug data and WHO global health statistics for research
What it loads into context
Frontmatter
93 tokensAlways available to the agent
SKILL.md
2,254 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
- Authentication
- Core Endpoints
- Rate Limits
- Academic Use Cases
- Key Indicator Codes (WHO GHO)
- References
Files
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# Medical Data API Guide (openFDA + WHO GHO)
## Overview
This skill covers two major open medical data APIs for academic research:
**openFDA** is the U.S. Food and Drug Administration's public API providing access to drug labeling (SPL), adverse event reports (FAERS), recalls, and NDC directory. The FAERS dataset contains over 722,000 reports for common drugs like aspirin, making it a primary pharmacovigilance resource.
**WHO Global Health Observatory (GHO)** is the WHO's OData v4 API serving over 2,000 health indicators across 194 member states -- life expectancy, mortality, disease burden, health system coverage, risk factors, and SDG targets. Returns structured JSON with numeric values, confidence intervals, and dimensional breakdowns by country, sex, and year.
Both APIs are free, require no authentication, and return JSON.
## Authentication
View the complete source on GitHubmedical-imaging-guide
When your agent uses it
Medical image analysis with deep learning for research applications
What it loads into context
Frontmatter
90 tokensAlways available to the agent
SKILL.md
2,561 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
- Imaging Modalities and Data Characteristics
- Preprocessing Pipeline
- Model Architecture Selection
- Handling Small Datasets
- Evaluation Metrics
- Ethical and Regulatory Considerations
Files
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# Medical Imaging Guide
A skill for applying deep learning to medical image analysis in research settings. Covers common imaging modalities, preprocessing pipelines, architecture selection for classification and segmentation tasks, handling small datasets with transfer learning and data augmentation, evaluation metrics specific to medical imaging, and regulatory and ethical considerations for clinical translation.
## Imaging Modalities and Data Characteristics
### Common Modalities in Research
```
Modality Overview:
X-ray / Radiography:
- 2D grayscale images
- Resolution: typically 2000x2000 to 4000x4000 pixelsView the complete source on GitHubncbi-blast-api
When your agent uses it
Run sequence similarity searches via the NCBI BLAST REST API
What it loads into context
Frontmatter
97 tokensAlways available to the agent
SKILL.md
1,823 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
- API Workflow
- Python Usage
- Rate Limits
- References
Files
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# NCBI BLAST REST API
## Overview
BLAST (Basic Local Alignment Search Tool) is the most widely used bioinformatics tool, comparing nucleotide or protein sequences against databases to find regions of similarity. The NCBI BLAST REST API enables programmatic submission of searches, status polling, and result retrieval. Free, no authentication required (but rate-limited).
## API Workflow
BLAST searches are asynchronous: submit → poll → retrieve.
### Step 1: Submit Search
```bash
# Nucleotide BLAST (blastn)View the complete source on GitHubncbi-datasets-api
When your agent uses it
Access genomes, genes, and taxonomy data via NCBI Datasets v2 API
What it loads into context
Frontmatter
101 tokensAlways available to the agent
SKILL.md
1,774 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
- API Endpoints
- Response Structure (Gene)
- Python Usage
- CLI Tool
- References
Files
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# NCBI Datasets v2 API
## Overview
NCBI Datasets is the modern API for accessing NCBI's genomic, gene, and taxonomic data — replacing older E-utilities for sequence data retrieval. It provides clean REST endpoints for genome assemblies, gene records, taxonomy trees, and sequence downloads. Covers all organisms in NCBI's databases including RefSeq and GenBank. Free, no authentication required.
## API Endpoints
### Base URL
```
https://api.ncbi.nlm.nih.gov/datasets/v2
```
View the complete source on GitHubpdb-structure-api
When your agent uses it
Search and retrieve 3D protein structures from the RCSB Protein Data Bank
What it loads into context
Frontmatter
97 tokensAlways available to the agent
SKILL.md
2,027 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
- Authentication
- Core Endpoints
- Rate Limits
- Academic Use Cases
- Code Examples
- References
Files
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# RCSB Protein Data Bank API Guide
## Overview
The RCSB Protein Data Bank (PDB) is the single global archive for experimentally determined 3D structures of biological macromolecules. It hosts over 200,000 structures resolved by X-ray crystallography, cryo-EM, NMR spectroscopy, and other methods. Each entry includes atomic coordinates, experimental metadata, polymer sequences, bound ligands, and literature references.
Two complementary APIs are available. The **Data API** (`data.rcsb.org`) serves structured entry metadata, polymer entities, and chemical components via RESTful GET endpoints. The **Search API** (`search.rcsb.org`) supports full-text, attribute-based, sequence similarity, and structure similarity searches.
## Authentication
No authentication required. Both APIs are freely accessible without API keys, tokens, or registration.
## Core Endpoints
View the complete source on GitHubquickgo-api
When your agent uses it
Browse and search Gene Ontology annotations via the QuickGO API
What it loads into context
Frontmatter
95 tokensAlways available to the agent
SKILL.md
1,452 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
- API Endpoints
- Python Usage
- References
Files
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# QuickGO API
## Overview
QuickGO is the EBI's fast browser and API for Gene Ontology (GO) annotations — the standard framework for describing gene/protein functions across all organisms. It provides access to 800M+ GO annotations covering biological processes, molecular functions, and cellular components. Essential for functional genomics, pathway analysis, and gene set enrichment. Free, no authentication.
## API Endpoints
### Base URL
```
https://www.ebi.ac.uk/QuickGO/services
```
View the complete source on GitHubbusiness-skills
When your agent uses it
5 business research skills. Trigger: business strategy, market analysis, competitive intelligence. Design: analytical frameworks and methods for management and innovation research.
What it loads into context
Frontmatter
37 tokensAlways available to the agent
SKILL.md
218 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
Files
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# Business Research — 5 Skills
Select the skill matching the user's need, then `read` its SKILL.md.
| Skill | Description |
|-------|-------------|
| [architecture-design-guide](./architecture-design-guide/SKILL.md) | Design principles of form, function, sustainability in architecture |
| [innovation-management-guide](./innovation-management-guide/SKILL.md) | Innovation metrics, R&D management research, and technology forecasting |
| [market-analysis-guide](./market-analysis-guide/SKILL.md) | Structured frameworks for market sizing, competitive analysis, and strategic ... |
| [operations-research-guide](./operations-research-guide/SKILL.md) | Optimization and operations research methods for business and logistics |
| [strategic-management-guide](./strategic-management-guide/SKILL.md) | Frameworks for strategic planning, resource allocation, and organizational an... |View the complete source on GitHubarchitecture-design-guide
When your agent uses it
Design principles of form, function, sustainability in architecture
What it loads into context
Frontmatter
86 tokensAlways available to the agent
SKILL.md
2,612 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
- Fundamental Design Principles
- Structural Systems
- Sustainability and Performance
- Computational Design Methods
- Best Practices
- References
Files
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# Architecture Design Guide
## Overview
Architectural research spans the technical, aesthetic, social, and environmental dimensions of the built environment. From structural engineering and building physics to urban theory and computational design, the discipline demands integration across multiple domains. Academic architecture research increasingly focuses on sustainability, computational methods, and evidence-based design -- areas where rigorous methodology is essential.
This guide covers the core principles that inform architectural research: the interplay of form and function, structural logic, environmental performance, and human experience. It also addresses the computational tools (BIM, parametric design, simulation) that have transformed how architects investigate and validate design decisions.
Whether you are conducting research on building performance, urban morphology, computational design methods, or the social impact of built environments, this guide provides the frameworks and tools to ground your work in established architectural theory and current best practices.
## Fundamental Design Principles
### The Vitruvian Triad (Updated)
View the complete source on GitHubinnovation-management-guide
When your agent uses it
Innovation metrics, R&D management research, and technology forecasting
What it loads into context
Frontmatter
82 tokensAlways available to the agent
SKILL.md
2,244 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
- Innovation Measurement
- Technology Diffusion Models
- Bibliometric Analysis of R&D Portfolios
- Technology Forecasting Methods
- Open Innovation Research
- Key Journals and Conferences
- Tools and Resources
Files
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# Innovation Management Guide
A skill for conducting research on innovation management, technology strategy, and R&D performance. Covers innovation measurement, technology forecasting, diffusion modeling, patent-publication linkage, and bibliometric analysis of research portfolios.
## Innovation Measurement
### Key Innovation Metrics
| Metric | Definition | Data Source |
|--------|-----------|-------------|
| R&D intensity | R&D spending / Revenue | Annual reports, Compustat |
| Patent count | Granted patents per year | USPTO, EPO |
| Citation-weighted patents | Patents weighted by forward citations | PatentsView |
| New product revenue share | Revenue from products < 3 years old | Internal data |View the complete source on GitHubmarket-analysis-guide
When your agent uses it
Structured frameworks for market sizing, competitive analysis, and strategic ...
What it loads into context
Frontmatter
79 tokensAlways available to the agent
SKILL.md
958 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
- Market Sizing Methodologies
- Competitive Analysis Frameworks
- Data Collection and Validation
- Reporting and Visualization
Files
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# Market Analysis Guide
A comprehensive skill for conducting rigorous market analysis in academic and applied research contexts. This guide covers quantitative market sizing, competitive landscape mapping, and strategic positioning frameworks grounded in peer-reviewed methodologies.
## Market Sizing Methodologies
Market sizing is the foundation of any credible market analysis. There are two primary approaches, and robust research typically employs both for triangulation.
**Top-Down Approach (TAM/SAM/SOM)**
Start with the total addressable market and narrow systematically:
```
TAM (Total Addressable Market)View the complete source on GitHuboperations-research-guide
When your agent uses it
Optimization and operations research methods for business and logistics
What it loads into context
Frontmatter
83 tokensAlways available to the agent
SKILL.md
2,142 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
- Linear Programming
- Integer and Mixed-Integer Programming
- Queuing Theory
- Simulation Methods
- Decision Analysis
- Tools and Libraries
Files
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# Operations Research Guide
A skill for applying operations research (OR) methods to business, logistics, and resource allocation problems. Covers linear programming, integer programming, scheduling, network optimization, simulation, and decision analysis using Python optimization libraries.
## Linear Programming
### Problem Formulation and Solving
```python
from scipy.optimize import linprog
import numpy as np
def solve_production_planning():
"""View the complete source on GitHubstrategic-management-guide
When your agent uses it
Frameworks for strategic planning, resource allocation, and organizational an...
What it loads into context
Frontmatter
82 tokensAlways available to the agent
SKILL.md
1,234 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 Strategic Frameworks
- Strategic Planning Process
- Balanced Scorecard Implementation
- Quantitative Strategy Tools
- References
Files
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# Strategic Management Guide
A research-oriented skill for applying strategic management theories and frameworks to academic case studies, organizational analysis, and business research. Covers classical and contemporary strategy frameworks with rigorous analytical methods.
## Core Strategic Frameworks
### Resource-Based View (RBV)
The RBV framework (Barney, 1991) evaluates competitive advantage through internal resources and capabilities. Apply the VRIO test:
| Criterion | Question | If "No" |
|-----------|----------|---------|
| Valuable | Does it exploit opportunity or neutralize threat? | Competitive disadvantage |
| Rare | Is it controlled by few firms? | Competitive parity |View the complete source on GitHubchemistry-skills
When your agent uses it
9 chemistry skills. Trigger: chemical structure analysis, reaction prediction, molecular modeling. Design: computational chemistry tools and cheminformatics workflows.
What it loads into context
Frontmatter
36 tokensAlways available to the agent
SKILL.md
363 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
Files
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# Chemistry — 9 Skills
Select the skill matching the user's need, then `read` its SKILL.md.
| Skill | Description |
|-------|-------------|
| [cactus-cheminformatics-guide](./cactus-cheminformatics-guide/SKILL.md) | PNNL cheminformatics LLM agent for molecular analysis |
| [catalysis-hub-api](./catalysis-hub-api/SKILL.md) | Query computational catalysis reaction data via Catalysis Hub GraphQL |
| [chemeagle-guide](./chemeagle-guide/SKILL.md) | Multi-agent system for chemical literature information extraction |
| [chemgraph-agent-guide](./chemgraph-agent-guide/SKILL.md) | Automate molecular simulations with the ChemGraph agentic framework |
| [computational-chemistry-guide](./computational-chemistry-guide/SKILL.md) | DFT, molecular simulation, and reaction prediction tools for chemists |
| [molecular-dynamics-guide](./molecular-dynamics-guide/SKILL.md) | Molecular dynamics simulation setup, execution, and trajectory analysis |
| [pubchem-api-guide](./pubchem-api-guide/SKILL.md) | Search PubChem for chemical compounds, structures, and bioassay data |
| [retrosynthesis-guide](./retrosynthesis-guide/SKILL.md) | Retrosynthetic analysis and computational reaction prediction |View the complete source on GitHubcactus-cheminformatics-guide
When your agent uses it
PNNL cheminformatics LLM agent for molecular analysis
What it loads into context
Frontmatter
98 tokensAlways available to the agent
SKILL.md
718 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
- Usage
- Similarity Search
- Substructure Analysis
- Use Cases
- References
Files
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# CACTUS Cheminformatics Agent Guide
## Overview
CACTUS is a cheminformatics LLM agent developed at Pacific Northwest National Laboratory (PNNL) that provides AI-assisted molecular analysis, property prediction, and chemical reasoning. It wraps RDKit, molecular databases, and ML models behind a conversational interface, enabling researchers to query molecular properties, perform similarity searches, and run cheminformatics workflows using natural language.
## Usage
```python
from cactus import ChemAgent
agent = ChemAgent(llm_provider="anthropic")
# Natural language chemistry queriesView the complete source on GitHubcatalysis-hub-api
When your agent uses it
Query computational catalysis reaction data via Catalysis Hub GraphQL
What it loads into context
Frontmatter
96 tokensAlways available to the agent
SKILL.md
1,871 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
- Authentication
- GraphQL Schema
- Core Queries
- Rate Limits
- Academic Use Cases
- Python Example
- References
Files
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# Catalysis Hub GraphQL API Guide
## Overview
Catalysis Hub is an open-access database of DFT-calculated reaction energies and activation barriers for heterogeneous catalysis, developed at SUNCAT Center (Stanford/SLAC). It aggregates computational results from published studies, enabling researchers to search, compare, and reuse DFT data for catalyst screening and mechanism validation.
The GraphQL endpoint provides structured access to reactions, publications, and atomic structures. All data is linked to peer-reviewed publications and includes computational details (DFT code, XC functional, surface facet, coverage).
## Authentication
No authentication required. Catalysis Hub is a free public service with no API keys.
## GraphQL Schema
View the complete source on GitHubchemeagle-guide
When your agent uses it
Multi-agent system for chemical literature information extraction
What it loads into context
Frontmatter
97 tokensAlways available to the agent
SKILL.md
1,120 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
- Agent Pipeline
- Usage
- Batch Processing
- Chemical Entity Recognition
- Database Building
- Use Cases
- References
Files
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# ChemEagle Guide
## Overview
ChemEagle is a multi-agent system for extracting structured chemical information from scientific literature. It uses specialized agents for recognizing chemical entities, extracting reaction conditions, identifying product yields, and building structured databases from unstructured chemistry papers. Particularly useful for building reaction databases and automating systematic reviews in chemistry.
## Agent Pipeline
```
Chemistry Paper (PDF/text)
↓
Document Parser Agent (section identification)
↓
Chemical NER AgentView the complete source on GitHubchemgraph-agent-guide
When your agent uses it
Automate molecular simulations with the ChemGraph agentic framework
What it loads into context
Frontmatter
96 tokensAlways available to the agent
SKILL.md
861 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
- Installation
- Core Capabilities
- Agent Architecture
- Use Cases
- Requirements
- References
Files
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# ChemGraph Agent Guide
## Overview
ChemGraph is an agentic framework from Argonne National Lab that automates molecular simulation workflows using LLMs. Built on LangGraph and ASE (Atomic Simulation Environment), it enables natural language control of computational chemistry tasks — structure generation, geometry optimization, thermochemistry, and more. Supports DFT (NWChem, ORCA), semi-empirical (xTB), and ML potentials (MACE).
## Installation
```bash
pip install chemgraph
# Or via Docker
docker pull ghcr.io/argonne-lcf/chemgraph:latest
```View the complete source on GitHubcomputational-chemistry-guide
When your agent uses it
DFT, molecular simulation, and reaction prediction tools for chemists
What it loads into context
Frontmatter
85 tokensAlways available to the agent
SKILL.md
2,385 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
- Density Functional Theory (DFT)
- Molecular Dynamics Simulations
- Machine Learning in Computational Chemistry
- Reaction Prediction
- Best Practices
- References
Files
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# Computational Chemistry Guide
## Overview
Computational chemistry bridges quantum mechanics and practical chemistry, enabling researchers to predict molecular properties, reaction mechanisms, and material behaviors without stepping into a wet lab. From drug design to catalyst optimization, computational methods accelerate discovery by screening thousands of candidates before committing to synthesis.
This guide covers the major computational chemistry paradigms: Density Functional Theory (DFT) for electronic structure calculations, molecular dynamics (MD) for simulating atomic motion, machine learning potentials for scaling up simulations, and reaction prediction tools for retrosynthesis and mechanism elucidation. Each section includes tool recommendations, typical workflows, and code examples.
Whether you are a chemistry PhD student running your first Gaussian calculations, a materials scientist exploring new alloys with VASP, or a medicinal chemist using ML-based property prediction, this skill provides the conceptual framework and practical recipes to get productive quickly.
## Density Functional Theory (DFT)
### When to Use DFT
View the complete source on GitHubmolecular-dynamics-guide
When your agent uses it
Molecular dynamics simulation setup, execution, and trajectory analysis
What it loads into context
Frontmatter
86 tokensAlways available to the agent
SKILL.md
2,138 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
- System Preparation
- Force Field Selection
- Trajectory Analysis
- Free Energy Methods
- Tools and Software
Files
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# Molecular Dynamics Guide
A skill for setting up, running, and analyzing molecular dynamics (MD) simulations. Covers force field selection, system preparation, simulation protocols, trajectory analysis, and free energy calculations using GROMACS, OpenMM, and MDAnalysis.
## System Preparation
### Building a Simulation System
The standard workflow for preparing an MD simulation:
```
1. Obtain structure (PDB, homology model, or docking pose)
2. Clean structure (add missing atoms, fix protonation states)
3. Assign force field parametersView the complete source on GitHubpubchem-api-guide
When your agent uses it
Search PubChem for chemical compounds, structures, and bioassay data
What it loads into context
Frontmatter
101 tokensAlways available to the agent
SKILL.md
1,962 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
- Authentication
- Core Endpoints
- Common Research Patterns
- Rate Limits and Best Practices
- References
Files
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# PubChem PUG REST API Guide
## Overview
PubChem is the world's largest free chemistry database, maintained by the National Center for Biotechnology Information (NCBI) at the U.S. National Library of Medicine. It contains information on over 115 million chemical compounds, 300 million substances from hundreds of data sources, and over 1.5 million bioassay experiments. PubChem is a critical resource for researchers in chemistry, pharmacology, drug discovery, toxicology, and related life sciences.
The PUG REST (Power User Gateway RESTful) API provides programmatic access to PubChem's three primary databases: Compound (standardized chemical structures), Substance (depositor-provided records), and BioAssay (biological screening results). The API supports searches by name, molecular formula, structure similarity, substructure, and various identifiers including CID, SID, InChI, and SMILES.
PUG REST is entirely free, requires no authentication, and returns data in JSON, XML, CSV, SDF, and other formats. It is designed for both simple lookups and complex cheminformatics workflows.
## Authentication
No authentication is required. PubChem PUG REST is a free public service.
View the complete source on GitHubretrosynthesis-guide
When your agent uses it
Retrosynthetic analysis and computational reaction prediction
What it loads into context
Frontmatter
79 tokensAlways available to the agent
SKILL.md
2,006 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
- What Is Retrosynthesis?
- Corey's Retrosynthetic Strategies
- Computational Retrosynthesis Tools
- SMILES Notation for Chemistry
- Reaction Databases
- Best Practices for Route Planning
Files
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# Retrosynthesis Guide
Plan synthetic routes for target molecules using retrosynthetic analysis principles and computational tools, from Corey's logic to modern AI-driven approaches.
## What Is Retrosynthesis?
Retrosynthesis works backward from a target molecule to identify simpler, commercially available precursors:
```
Target Molecule (TM)
|
[Disconnection 1] ← Apply transform (reverse of a known reaction)
|
Synthon A + Synthon BView the complete source on GitHubspectroscopy-analysis-guide
When your agent uses it
Spectral data analysis for NMR, IR, mass spectrometry, and UV-Vis
What it loads into context
Frontmatter
95 tokensAlways available to the agent
SKILL.md
2,651 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
- Spectral Data Formats
- NMR Spectroscopy
- Mass Spectrometry
- Infrared Spectroscopy
- Spectral Processing Utilities
- Tools and Software
Files
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# Spectroscopy Analysis Guide
A skill for processing and interpreting spectroscopic data in chemistry research. Covers NMR, IR, mass spectrometry, and UV-Vis spectroscopy including data formats, baseline correction, peak detection, spectral matching, and structure elucidation workflows.
## Spectral Data Formats
### Common File Formats
| Format | Spectroscopy | Description |
|--------|-------------|-------------|
| JCAMP-DX (.jdx, .dx) | All types | IUPAC standard exchange format |
| Bruker (1r, fid, acqu) | NMR | Raw and processed Bruker data |
| mzML / mzXML | MS | Open mass spectrometry format |
| SPC (.spc) | IR, UV-Vis | Galactic/Thermo spectral format |View the complete source on GitHubcs-skills
When your agent uses it
10 computer science skills. Trigger: algorithms, systems research, software engineering, security papers. Design: theory, complexity analysis, code-centric research, and security methods.
What it loads into context
Frontmatter
42 tokensAlways available to the agent
SKILL.md
398 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
Files
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# Computer Science — 10 Skills
Select the skill matching the user's need, then `read` its SKILL.md.
| Skill | Description |
|-------|-------------|
| [ai-security-papers-guide](./ai-security-papers-guide/SKILL.md) | AI security papers from top-4 security conferences |
| [algorithms-complexity-guide](./algorithms-complexity-guide/SKILL.md) | Analyze algorithm complexity and computational efficiency for research |
| [code-llm-papers-guide](./code-llm-papers-guide/SKILL.md) | Survey and paper collection on LLMs for code generation |
| [dblp-api](./dblp-api/SKILL.md) | Search computer science publications, authors, and venues via DBLP |
| [distributed-systems-guide](./distributed-systems-guide/SKILL.md) | Distributed systems design patterns and analysis for CS research |
| [formal-verification-guide](./formal-verification-guide/SKILL.md) | Formal methods, theorem proving, and model checking for CS research |
| [gaussian-splatting-papers-guide](./gaussian-splatting-papers-guide/SKILL.md) | Curated papers and resources for 3D Gaussian Splatting |
| [llm-aiops-guide](./llm-aiops-guide/SKILL.md) | Papers on LLMs for IT operations and AIOps research |View the complete source on GitHubai-security-papers-guide
When your agent uses it
AI security papers from top-4 security conferences
What it loads into context
Frontmatter
108 tokensAlways available to the agent
SKILL.md
971 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
- Venues
- Topic Categories
- Key Papers by Year
- Research Trends
- Use Cases
- References
Files
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# AI Security Papers Guide (BIG4 Venues)
## Overview
A curated collection of AI security papers from the top-4 security conferences: IEEE S&P, ACM CCS, USENIX Security, and NDSS. Covers adversarial attacks, model stealing, data poisoning, privacy attacks, deepfake detection, and LLM security. Organized by year and venue, focusing exclusively on peer-reviewed work from these prestigious venues.
## Venues
| Venue | Full Name | Focus |
|-------|-----------|-------|
| **S&P** | IEEE Symposium on Security and Privacy | Broad security + privacy |
| **CCS** | ACM Conference on Computer and Communications Security | Systems security |
| **USENIX** | USENIX Security Symposium | Systems + network security |
| **NDSS** | Network and Distributed System Security | Network security |View the complete source on GitHubalgorithms-complexity-guide
When your agent uses it
Analyze algorithm complexity and computational efficiency for research
What it loads into context
Frontmatter
86 tokensAlways available to the agent
SKILL.md
1,575 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
- Asymptotic Notation
- Complexity Classes
- Algorithm Analysis Techniques
- Presenting Algorithms in Papers
- Dealing with Intractability
Files
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# Algorithms and Complexity Guide
A skill for analyzing algorithm complexity and computational efficiency in research contexts. Covers asymptotic notation, common complexity classes, NP-completeness, amortized analysis, and strategies for presenting algorithmic contributions in papers.
## Asymptotic Notation
### Big-O, Omega, and Theta
```
O(f(n)) -- Upper bound (worst case, "at most")
T(n) is O(f(n)) if T(n) <= c * f(n) for large n
Omega(f(n)) -- Lower bound (best case, "at least")
T(n) is Omega(f(n)) if T(n) >= c * f(n) for large nView the complete source on GitHubcode-llm-papers-guide
When your agent uses it
Survey and paper collection on LLMs for code generation
What it loads into context
Frontmatter
100 tokensAlways available to the agent
SKILL.md
1,342 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
- Taxonomy
- Key Models Timeline
- Benchmark Tracking
- Research Directions
- Paper Search
- Use Cases
- References
Files
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# Code LLM Papers Guide
## Overview
This curated collection covers LLMs for code — from foundational models (Codex, CodeGen, StarCoder) through code generation, completion, repair, translation, and understanding. Accompanies a TMLR survey paper providing systematic categorization. Tracks 500+ papers across pre-training, fine-tuning, evaluation, and application of code-focused language models.
## Taxonomy
```
Code LLMs
├── Pre-training
│ ├── Encoder-only (CodeBERT, GraphCodeBERT)
│ ├── Decoder-only (Codex, CodeGen, StarCoder, DeepSeek-Coder)
│ └── Encoder-Decoder (CodeT5, PLBART)View the complete source on GitHubdblp-api
When your agent uses it
Search computer science publications, authors, and venues via DBLP
What it loads into context
Frontmatter
89 tokensAlways available to the agent
SKILL.md
1,398 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
- Authentication
- Core Endpoints
- Rate Limits
- Common Patterns
- References
Files
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# DBLP API Guide
## Overview
DBLP (Digital Bibliography and Library Project) is the most comprehensive open bibliographic database for computer science. Maintained by Schloss Dagstuhl - Leibniz Center for Informatics, DBLP indexes over 6.5 million publications from thousands of journals, conference proceedings, and informal publication venues in computer science and related fields.
DBLP is widely regarded as the authoritative source for computer science bibliographic data. It is used by researchers for tracking publication records, by hiring committees for evaluating academic productivity, and by bibliometric tools for analyzing the computer science research landscape. Each author in DBLP has a unique profile page with a complete, disambiguated publication list.
The API is entirely free and open, requiring no authentication. There are no published rate limits, though users are expected to be respectful and avoid excessive load on the servers. All data is available under the ODC-BY license.
## Authentication
No authentication required. The DBLP API is fully open and free to use. No API key, registration, or email is needed. Users should include a descriptive User-Agent header and avoid sending excessive concurrent requests.
View the complete source on GitHubdistributed-systems-guide
When your agent uses it
Distributed systems design patterns and analysis for CS research
What it loads into context
Frontmatter
85 tokensAlways available to the agent
SKILL.md
2,191 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
- Consistency Models
- Consensus Algorithms
- Replication Strategies
- Clock Synchronization and Ordering
- Performance Analysis
- Key Research Papers
- Tools and Frameworks
Files
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# Distributed Systems Guide
A skill for researching and designing distributed systems, covering consensus algorithms, replication strategies, consistency models, fault tolerance, and performance analysis. Provides theoretical foundations and practical implementations relevant to systems research.
## Consistency Models
### Consistency Hierarchy
```
Strongest
| Linearizability (atomic, real-time ordering)
| Sequential consistency (program order respected)
| Causal consistency (causally related ops ordered)
| PRAM / FIFO consistency (per-process order)View the complete source on GitHubformal-verification-guide
When your agent uses it
Formal methods, theorem proving, and model checking for CS research
What it loads into context
Frontmatter
88 tokensAlways available to the agent
SKILL.md
2,260 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
- Verification Approaches Overview
- TLA+ Specification
- Interactive Theorem Proving
- SMT Solving
- Model Checking with SPIN
- Verification Workflow
- Tools and Resources
Files
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# Formal Verification Guide
A skill for applying formal methods to verify software and hardware correctness. Covers model checking, interactive theorem proving, specification languages, and practical verification workflows used in systems and programming language research.
## Verification Approaches Overview
### Methods Comparison
| Approach | Technique | Strengths | Limitations |
|----------|-----------|-----------|-------------|
| Model checking | Exhaustive state exploration | Fully automatic, produces counterexamples | State space explosion |
| Theorem proving | Interactive proof construction | Handles infinite state | Requires expert effort |
| Abstract interpretation | Sound static analysis | Automatic, scales well | May report false positives |
| SMT solving | Constraint satisfiability | Powerful automation | Limited to decidable theories |View the complete source on GitHubgaussian-splatting-papers-guide
When your agent uses it
Curated papers and resources for 3D Gaussian Splatting
What it loads into context
Frontmatter
112 tokensAlways available to the agent
SKILL.md
1,483 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
- Core Paper
- Research Landscape
- Tracking New Papers
- Key Methods Comparison
- Implementation Resources
- Survey Papers
- Use Cases
- References
Files
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# 3D Gaussian Splatting Papers Guide
## Overview
3D Gaussian Splatting (3DGS) is a breakthrough technique for real-time radiance field rendering that represents scenes as collections of 3D Gaussians. This curated collection tracks the rapidly evolving 3DGS literature — from the original paper through extensions for dynamic scenes, generation, compression, SLAM, avatars, and more. Essential for researchers in computer vision, graphics, and neural rendering.
## Core Paper
```bibtex
@inproceedings{kerbl3Dgaussians,
title={3D Gaussian Splatting for Real-Time Radiance Field Rendering},
author={Kerbl, Bernhard and Kopanas, Georgios and Leimk{\"u}hler, Thomas
and Drettakis, George},
booktitle={ACM SIGGRAPH 2023},View the complete source on GitHubllm-aiops-guide
When your agent uses it
Papers on LLMs for IT operations and AIOps research
What it loads into context
Frontmatter
108 tokensAlways available to the agent
SKILL.md
3,128 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
- Research Areas
- Key Practices for LLM Operations
- Toolchain Overview
- Typical LLMOps Pipeline Architecture
- Cost Optimization Strategies
- Key Papers
- Use Cases
- References
Files
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# LLM for AIOps Guide
## Overview
A curated collection of research on applying LLMs to IT Operations (AIOps) — log analysis, anomaly detection, incident management, root cause analysis, and automated remediation. Tracks how foundation models are transforming traditional rule-based operations tooling into intelligent, adaptive systems. Relevant for CS researchers at the intersection of systems, NLP, and operations.
## Research Areas
```
LLM for AIOps
├── Log Analysis
│ ├── Log parsing (template extraction)
│ ├── Anomaly detection (from log sequences)
│ ├── Log summarizationView the complete source on GitHubsoftware-engineering-research
When your agent uses it
Guide to software engineering research topics and methodologies
What it loads into context
Frontmatter
78 tokensAlways available to the agent
SKILL.md
1,910 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
- SE Research Subfields
- Research Methodologies in SE
- Key Datasets and Benchmarks
- Static Analysis Tools for Research
- Code Metrics
- Top Venues and Impact
- Research Tools Ecosystem
Files
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# Software Engineering Research Guide
Navigate the landscape of software engineering research, including key subfields, methodologies, datasets, benchmarks, and top venues.
## SE Research Subfields
| Subfield | Key Topics | Major Venues |
|----------|-----------|-------------|
| **Software Testing** | Test generation, fuzzing, mutation testing, flaky tests | ISSTA, ICST, ASE |
| **Program Analysis** | Static analysis, abstract interpretation, symbolic execution | PLDI, POPL, OOPSLA |
| **Software Maintenance** | Code refactoring, technical debt, code smells, evolution | ICSME, MSR, SANER |
| **SE for AI/ML** | ML pipeline testing, data quality, model debugging | ICSE-SEIP, FSE |
| **AI for SE** | Code generation, bug detection, program repair | ICSE, FSE, ASE |
| **Distributed Systems** | Consensus, fault tolerance, scalability, microservices | SOSP, OSDI, EuroSys |View the complete source on GitHubsoftware-heritage-api
When your agent uses it
Archive and retrieve source code history via Software Heritage API
What it loads into context
Frontmatter
91 tokensAlways available to the agent
SKILL.md
1,591 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
- API Endpoints
- SWHID Format
- Python Usage
- Use Cases
- Rate Limits
- References
Files
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# Software Heritage API
## Overview
Software Heritage is the universal archive of software source code, preserving 18B+ source files from 280M+ software origins (GitHub, GitLab, Bitbucket, CRAN, PyPI, Debian, etc.). Each artifact receives a persistent SWHID (SoftWare Heritage IDentifier) for reliable citation in research. The API enables code search, provenance tracking, and historical analysis. Free, no authentication for read access.
## API Endpoints
### Base URL
```
https://archive.softwareheritage.org/api/1
```
View the complete source on GitHubecology-skills
When your agent uses it
5 ecology & environmental science skills. Trigger: biodiversity surveys, species data, environmental monitoring. Design: field data collection, spatial analysis, and conservation biology workflows.
What it loads into context
Frontmatter
41 tokensAlways available to the agent
SKILL.md
229 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
Files
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# Ecology & Environmental Science — 5 Skills
Select the skill matching the user's need, then `read` its SKILL.md.
| Skill | Description |
|-------|-------------|
| [biodiversity-data-guide](./biodiversity-data-guide/SKILL.md) | Biodiversity data access, species occurrence, and ecological tools |
| [conservation-biology-guide](./conservation-biology-guide/SKILL.md) | Apply conservation biology methods, databases, and assessment tools |
| [gbif-api](./gbif-api/SKILL.md) | Global biodiversity data API for species occurrences and datasets |
| [inaturalist-api](./inaturalist-api/SKILL.md) | Citizen science platform API for biodiversity observations |
| [species-distribution-guide](./species-distribution-guide/SKILL.md) | Species distribution modeling with MaxEnt, SDM methods, and GBIF data |View the complete source on GitHubbiodiversity-data-guide
When your agent uses it
Biodiversity data access, species occurrence, and ecological tools
What it loads into context
Frontmatter
82 tokensAlways available to the agent
SKILL.md
2,540 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
- Major Biodiversity Data Sources
- Querying GBIF (Species Occurrences)
- Species Distribution Modeling
- Phylogenetic Analysis
- Ecological Data Analysis
- Data Standards and Best Practices
Files
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# Biodiversity Data Guide
Access, analyze, and visualize biodiversity data from global databases including GBIF, iNaturalist, and GenBank for ecological and evolutionary research.
## Major Biodiversity Data Sources
| Database | Content | Records | API | Cost |
|----------|---------|---------|-----|------|
| GBIF | Species occurrence records | 2.4B+ | Yes | Free |
| iNaturalist | Citizen science observations | 180M+ | Yes | Free |
| GenBank (NCBI) | Genetic sequences | 250M+ | Yes | Free |
| BOLD Systems | DNA barcode records | 15M+ | Yes | Free |
| eBird | Bird observations | 1.3B+ | Yes | Free |
| IUCN Red List | Conservation status | 160,000+ | Yes | Free (with key) |View the complete source on GitHubconservation-biology-guide
When your agent uses it
Apply conservation biology methods, databases, and assessment tools
What it loads into context
Frontmatter
93 tokensAlways available to the agent
SKILL.md
1,561 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
- Species Assessment and Red List
- Habitat Modeling
- Population Viability Analysis (PVA)
- Key Databases
- Conservation Prioritization
- Reporting Conservation Research
Files
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# Conservation Biology Guide
A skill for conducting conservation biology research, covering species assessment methods, habitat modeling, population viability analysis, key biodiversity databases, and frameworks for conservation prioritization.
## Species Assessment and Red List
### IUCN Red List Categories
```
Extinction Risk Categories (from highest to lowest):
EX - Extinct
EW - Extinct in the Wild
CR - Critically EndangeredView the complete source on GitHubgbif-api
When your agent uses it
Global biodiversity data API for species occurrences and datasets
What it loads into context
Frontmatter
82 tokensAlways available to the agent
SKILL.md
1,667 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
- Authentication
- Core Endpoints
- Rate Limits
- Common Patterns
- References
Files
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# GBIF API Guide
## Overview
The Global Biodiversity Information Facility (GBIF) is an international network and data infrastructure funded by governments worldwide, aimed at providing open access to biodiversity data. GBIF aggregates hundreds of millions of species occurrence records from natural history collections, citizen science platforms, monitoring networks, and published literature across the globe.
The GBIF API provides programmatic access to this vast repository of biodiversity data. Researchers can search for species occurrences by taxonomy, geography, time period, and dataset. The API also supports taxonomic name matching, dataset discovery, and species profile lookups. It serves as a foundational resource for ecological research, conservation planning, biogeography, and environmental impact assessments.
Ecologists, conservation biologists, biogeographers, and environmental scientists rely on the GBIF API to retrieve georeferenced occurrence data for species distribution modeling, climate change impact analysis, invasive species tracking, and biodiversity hotspot identification. The data is freely available under open data licenses.
## Authentication
No authentication required for read access. The GBIF API is publicly accessible without any API key or token. All search and retrieval endpoints are open. User authentication is only required for data publishing operations (creating datasets and uploading occurrences), which requires a GBIF account.
View the complete source on GitHubinaturalist-api
When your agent uses it
Citizen science platform API for biodiversity observations
What it loads into context
Frontmatter
83 tokensAlways available to the agent
SKILL.md
1,785 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
- Authentication
- Core Endpoints
- Rate Limits
- Common Patterns
- References
Files
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# iNaturalist API Guide
## Overview
iNaturalist is a citizen science platform and social network for naturalists, jointly operated by the California Academy of Sciences and the National Geographic Society. The platform enables users to record and share observations of organisms in nature, which are then identified by the community and validated by computer vision models. With over 150 million observations of more than 400,000 species, iNaturalist is one of the largest biodiversity data sources in the world.
The iNaturalist API provides programmatic access to this massive observational dataset. Researchers can query observations by taxonomy, geography, time period, observer, and data quality grade. The API also provides access to taxonomic information, place boundaries, and project data. Research-grade observations (those with community-verified identifications) are automatically shared with GBIF for integration into the global biodiversity data infrastructure.
Ecologists, conservation biologists, evolutionary biologists, and citizen science coordinators use the iNaturalist API to analyze species distributions, track phenological patterns, study urban biodiversity, monitor invasive species, and validate species distribution models. The platform's broad geographic and taxonomic coverage makes it particularly valuable for large-scale ecological analyses.
## Authentication
No authentication is required for read-only access to public data. The iNaturalist API v1 allows anonymous queries for observations, taxa, and places. Authentication via OAuth 2.0 is only needed for write operations such as creating observations, adding identifications, or managing projects.
View the complete source on GitHubspecies-distribution-guide
When your agent uses it
Species distribution modeling with MaxEnt, SDM methods, and GBIF data
What it loads into context
Frontmatter
96 tokensAlways available to the agent
SKILL.md
2,694 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
- Occurrence Data
- Environmental Predictors
- Model Fitting
- Model Evaluation
- Climate Change Projections
- Tools and Resources
Files
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# Species Distribution Modeling Guide
A skill for building and evaluating species distribution models (SDMs), covering occurrence data acquisition from biodiversity databases, environmental predictor preparation, model fitting with MaxEnt and ensemble methods, model evaluation, and projection under climate change scenarios.
## Occurrence Data
### Accessing GBIF Data
The Global Biodiversity Information Facility (GBIF) is the primary source of species occurrence records:
```python
from pygbif import occurrences, species
def download_occurrences(species_name: str, country: str = None,View the complete source on GitHubeconomics-skills
When your agent uses it
9 economics skills. Trigger: economic modeling, policy analysis, macroeconomic data, FRED. Design: theory plus empirical methods with standard economics databases.
What it loads into context
Frontmatter
39 tokensAlways available to the agent
SKILL.md
351 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
Files
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# Economics — 9 Skills
Select the skill matching the user's need, then `read` its SKILL.md.
| Skill | Description |
|-------|-------------|
| [behavioral-economics-guide](./behavioral-economics-guide/SKILL.md) | Behavioral economics research methods and key frameworks |
| [development-economics-guide](./development-economics-guide/SKILL.md) | Apply development economics research methods and data sources |
| [fred-api](./fred-api/SKILL.md) | Federal Reserve Economic Data API for US economic indicators |
| [imf-data-api-guide](./imf-data-api-guide/SKILL.md) | Retrieve IMF economic indicators, exchange rates, and country data |
| [nber-working-papers-api](./nber-working-papers-api/SKILL.md) | Access NBER working papers and economic research datasets |
| [post-labor-economics](./post-labor-economics/SKILL.md) | Post-labor economies with automation, UBI, and wealth distribution |
| [pricing-psychology-guide](./pricing-psychology-guide/SKILL.md) | Behavioral economics in pricing strategies and consumer decisions |
| [repec-economics-api](./repec-economics-api/SKILL.md) | Access 4M+ economics working papers and articles via RePEc API |View the complete source on GitHubbehavioral-economics-guide
When your agent uses it
Behavioral economics research methods and key frameworks
What it loads into context
Frontmatter
77 tokensAlways available to the agent
SKILL.md
2,139 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 Theoretical Frameworks
- Key Behavioral Biases and Experimental Tests
- Experimental Methods
- Time Preferences and Discounting
- Data Analysis in Behavioral Economics
- Key Resources
- Top Journals and Venues
Files
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# Behavioral Economics Guide
Conduct behavioral economics research using experimental methods, prospect theory, nudge frameworks, and key empirical tools for studying decision-making under bounded rationality.
## Core Theoretical Frameworks
### Prospect Theory (Kahneman & Tversky, 1979)
People evaluate outcomes relative to a reference point, with losses looming larger than equivalent gains:
```
Key features:
1. Reference dependence: Utility is defined over gains and losses, not absolute wealth
2. Loss aversion: lambda ≈ 2.25 (losses hurt ~2.25x more than equivalent gains)View the complete source on GitHubdevelopment-economics-guide
When your agent uses it
Apply development economics research methods and data sources
What it loads into context
Frontmatter
86 tokensAlways available to the agent
SKILL.md
1,411 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
- Impact Evaluation Methods
- Difference-in-Differences
- Key Data Sources
- Measurement Challenges
- Publishing in Development Economics
Files
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# Development Economics Guide
A skill for conducting development economics research, covering impact evaluation methods, field experiment design, household survey analysis, key data sources, and the methodological toolkit used to study poverty, education, health, and institutions in developing countries.
## Impact Evaluation Methods
### The Identification Problem
```
Fundamental question: What is the causal effect of a program/policy?
Challenge: We observe outcomes for treated individuals, but we cannot
observe what would have happened to them without treatment
(the counterfactual).View the complete source on GitHubfred-api
When your agent uses it
Federal Reserve Economic Data API for US economic indicators
What it loads into context
Frontmatter
97 tokensAlways available to the agent
SKILL.md
1,814 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
- Authentication
- Core Endpoints
- Rate Limits
- Common Patterns
- References
Files
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# FRED API Guide
## Overview
FRED (Federal Reserve Economic Data) is a database maintained by the Research Division of the Federal Reserve Bank of St. Louis. It contains over 800,000 economic time series from dozens of national and international sources, covering macroeconomic indicators, financial markets, employment, trade, monetary policy, and more.
The FRED API provides programmatic access to this extensive economic data repository. Researchers can retrieve time series observations, search for data series by keyword or category, explore release schedules, and access vintage (real-time) data for historical analysis. The data spans decades and in some cases centuries, making it invaluable for longitudinal economic research.
Economists, financial analysts, policy researchers, data scientists, and academic institutions rely on the FRED API for econometric modeling, macroeconomic forecasting, policy analysis, and teaching. It is one of the most widely used economic data APIs in academic research and is cited in thousands of peer-reviewed publications.
## Authentication
Authentication requires a free API key from the Federal Reserve Bank of St. Louis.
View the complete source on GitHubimf-data-api-guide
When your agent uses it
Retrieve IMF economic indicators, exchange rates, and country data
What it loads into context
Frontmatter
107 tokensAlways available to the agent
SKILL.md
1,694 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
- Authentication
- Core Endpoints
- Common Research Patterns
- Rate Limits and Best Practices
- References
Files
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# IMF Data API Guide
## Overview
The International Monetary Fund (IMF) provides a free JSON-based REST API for accessing its extensive collection of macroeconomic and financial datasets. The API covers data from virtually every country and territory, spanning indicators such as GDP, inflation, trade balances, exchange rates, government finance statistics, and balance of payments.
For economics researchers, the IMF API is an essential tool for accessing authoritative international economic data without manual downloads. The data powers research in macroeconomics, development economics, international finance, and policy analysis. The API provides access to key datasets including the World Economic Outlook (WEO), International Financial Statistics (IFS), Balance of Payments Statistics (BOP), and the Direction of Trade Statistics (DOTS).
The API requires no authentication and returns JSON data. It uses a hierarchical structure of datasets, indicators, and country/time dimensions.
## Authentication
No authentication is required. The IMF Data API is completely free and open.
View the complete source on GitHubnber-working-papers-api
When your agent uses it
Access NBER working papers and economic research datasets
What it loads into context
Frontmatter
92 tokensAlways available to the agent
SKILL.md
1,421 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
- Working Papers Access
- NBER Programs
- Python Usage
- Key Datasets
- References
Files
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# NBER Working Papers and Data API
## Overview
The National Bureau of Economic Research (NBER) is the leading U.S. economics research organization, publishing 1,200+ working papers annually by top economists. NBER papers are among the most cited in economics. The website provides structured access to working papers, researcher profiles, and macroeconomic datasets. Free metadata access; some full text requires subscription.
## Working Papers Access
### RSS/Atom Feeds
```bash
# Latest working papers feed
curl "https://www.nber.org/papers.rss"
View the complete source on GitHubpost-labor-economics
When your agent uses it
Post-labor economies with automation, UBI, and wealth distribution
What it loads into context
Frontmatter
93 tokensAlways available to the agent
SKILL.md
2,591 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
- Theoretical Frameworks
- Empirical Evidence
- Policy Proposals
- Modeling Automation Impact
- Research Methods
- Best Practices
- References
Files
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# Post-Labor Economics Guide
## Overview
Post-labor economics studies the economic consequences of advanced automation -- the possibility that AI and robotics will displace human labor at a scale and speed that overwhelms traditional adjustment mechanisms. While technological unemployment is an old concern (dating to the Luddites and Keynes's "Economic Possibilities for Our Grandchildren"), the current wave of AI capabilities has made the question urgent: what happens to labor markets, income distribution, and economic growth when machines can perform most cognitive and physical tasks?
This is not science fiction. The academic literature on task displacement, skill-biased technological change, and automation risk has produced substantial empirical findings and theoretical frameworks. Researchers from economics, political science, sociology, and computer science are converging on these questions.
This guide covers the key theoretical models, empirical evidence, policy proposals (UBI, robot taxes, stakeholder funds), and methodological approaches for studying the economics of automation. It is designed for researchers entering this rapidly growing field and for those in adjacent disciplines who need to engage with the economic arguments.
## Theoretical Frameworks
### The Task-Based Model of Automation
View the complete source on GitHubpricing-psychology-guide
When your agent uses it
Behavioral economics in pricing strategies and consumer decisions
What it loads into context
Frontmatter
90 tokensAlways available to the agent
SKILL.md
2,567 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
- Core Psychological Mechanisms
- Experimental Methods
- Price Elasticity Estimation
- Experimental Design Checklist
- Best Practices
- References
Files
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# Pricing Psychology Guide
## Overview
Pricing psychology sits at the intersection of behavioral economics, marketing science, and consumer research. Classical economics assumes consumers evaluate prices rationally -- comparing marginal utility to marginal cost. Decades of experimental evidence show this is wrong. Consumers use heuristics, are influenced by reference points, respond to framing, and systematically deviate from rational price evaluation.
Understanding these deviations is both scientifically important (they reveal how human cognition processes economic information) and practically consequential (pricing is one of the highest-leverage decisions firms make). This guide covers the key psychological mechanisms in pricing, experimental methods for studying them, and the analytical tools researchers use to measure willingness to pay and price sensitivity.
The focus is on academic rigor: well-identified causal effects, incentive-compatible elicitation methods, and results that replicate. The field has been significantly impacted by the replication crisis, and this guide emphasizes methodological best practices that meet current standards.
## Core Psychological Mechanisms
### Anchoring and Price Perception
View the complete source on GitHubrepec-economics-api
When your agent uses it
Access 4M+ economics working papers and articles via RePEc API
What it loads into context
Frontmatter
96 tokensAlways available to the agent
SKILL.md
1,493 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
- Access Methods
- RePEc Handle Structure
- Python Usage
- Key Data Products
- Rankings
- References
Files
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# RePEc (Research Papers in Economics) API
## Overview
RePEc is the largest decentralized bibliographic database for economics, indexing 4.4M+ items (working papers, journal articles, books, software) from 2,600+ publishers. It powers IDEAS and EconPapers — the two most-used economics paper search engines. The API provides metadata access and citation data. Free, no authentication required for basic access.
## Access Methods
### IDEAS Search API
```bash
# Search economics papers
curl "https://ideas.repec.org/cgi-bin/htsearch?q=monetary+policy+inflation&cmd=Search&fmt=json"
View the complete source on GitHubworld-bank-data-guide
When your agent uses it
Access World Bank development indicators and country statistics
What it loads into context
Frontmatter
97 tokensAlways available to the agent
SKILL.md
1,792 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
- Authentication
- Core Endpoints
- Common Research Patterns
- Rate Limits and Best Practices
- References
Files
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# World Bank Data API Guide
## Overview
The World Bank Data API provides free access to one of the most comprehensive collections of global development data available. The Indicators API covers over 16,000 development indicators across 217 countries and territories, with time series data spanning several decades. Topics include poverty and inequality, health outcomes, education statistics, infrastructure, trade, environmental metrics, and governance indicators.
This API is a cornerstone resource for researchers in development economics, public health, education policy, environmental science, and political science. The World Bank's data is widely cited in academic publications and policy documents, making it an authoritative source for cross-country comparative research.
The API is entirely free, requires no authentication, and returns data in JSON, XML, or other formats. The v2 API supports pagination, filtering by date ranges and income levels, and provides metadata about indicators and data sources.
## Authentication
No authentication is required. The World Bank Data API is free and open.
View the complete source on GitHubeducation-skills
When your agent uses it
7 education research skills. Trigger: pedagogical research, course design, learning analytics, assessment. Design: evidence-based teaching methods and educational measurement tools.
What it loads into context
Frontmatter
39 tokensAlways available to the agent
SKILL.md
287 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
Files
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# Education Research — 7 Skills
Select the skill matching the user's need, then `read` its SKILL.md.
| Skill | Description |
|-------|-------------|
| [academic-study-methods](./academic-study-methods/SKILL.md) | Evidence-based study techniques for academic learning and retention |
| [assessment-design-guide](./assessment-design-guide/SKILL.md) | Psychometrics and educational assessment design for researchers |
| [curriculum-design-guide](./curriculum-design-guide/SKILL.md) | Systematic approaches to curriculum design using backward design and alignment |
| [educational-research-methods](./educational-research-methods/SKILL.md) | Quantitative and qualitative research methods for education studies |
| [learning-science-guide](./learning-science-guide/SKILL.md) | Evidence-based learning science principles for educational research and practice |
| [mooc-analytics-guide](./mooc-analytics-guide/SKILL.md) | Analyzing MOOC data, learning analytics, and online education metrics |
| [open-syllabus-api](./open-syllabus-api/SKILL.md) | Analyze most-taught books and texts via Open Syllabus analytics |View the complete source on GitHubacademic-study-methods
When your agent uses it
Evidence-based study techniques for academic learning and retention
What it loads into context
Frontmatter
87 tokensAlways available to the agent
SKILL.md
1,828 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
- Effectiveness Ranking
- Core Techniques
- Weekly Study Schedule Template
- Reading Academic Papers
- Exam Preparation
- References
Files
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# Evidence-Based Academic Study Methods
## Overview
Decades of cognitive psychology research have identified which study techniques reliably improve learning and retention, and which popular methods are largely ineffective. This guide covers the most effective evidence-based strategies — spaced repetition, active recall, interleaving, elaboration, and concrete examples — with practical implementation advice for graduate students and researchers.
## Effectiveness Ranking
Based on Dunlosky et al. (2013) comprehensive review of 10 learning techniques:
| Technique | Effectiveness | Effort | Why It Works |
|-----------|-------------|--------|-------------|
| **Practice testing** (active recall) | High | Medium | Strengthens retrieval pathways |
| **Distributed practice** (spacing) | High | Low | Exploits spacing effect in memory consolidation |View the complete source on GitHubassessment-design-guide
When your agent uses it
Psychometrics and educational assessment design for researchers
What it loads into context
Frontmatter
83 tokensAlways available to the agent
SKILL.md
1,900 tokensWhen the skill is activated
Reference documents
0 tokensLoaded on request
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The playbook
- Classical Test Theory
- Item Response Theory
- Validity Evidence
- Computerized Adaptive Testing
- Tools and Software
- Key References
Files
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# Assessment Design Guide
A skill for designing, validating, and analyzing educational assessments using modern psychometric methods. Covers classical test theory, item response theory, test construction, validity evidence, and computerized adaptive testing.
## Classical Test Theory
### Reliability Analysis
Classical test theory (CTT) models observed scores as the sum of a true score and error:
```
X = T + E
```
View the complete source on GitHubcurriculum-design-guide
When your agent uses it
Systematic approaches to curriculum design using backward design and alignment
What it loads into context
Frontmatter
80 tokensAlways available to the agent
SKILL.md
1,178 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
- Backward Design Framework
- Constructive Alignment
- Competency-Based Curriculum Mapping
- Quality Assurance
- References
Files
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# Curriculum Design Guide
A structured skill for designing research-informed curricula using backward design, constructive alignment, and competency-based frameworks. Applicable to higher education course design, training program development, and educational research.
## Backward Design Framework
Understanding by Design (Wiggins & McTighe, 2005) reverses the traditional content-first approach:
### Stage 1: Identify Desired Results
Define what students should know, understand, and be able to do:
```yaml
course: "Introduction to Research Methods"View the complete source on GitHubeducational-research-methods
When your agent uses it
Quantitative and qualitative research methods for education studies
What it loads into context
Frontmatter
84 tokensAlways available to the agent
SKILL.md
1,614 tokensWhen the skill is activated
Reference documents
0 tokensLoaded on request
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The playbook
- Study Design Frameworks
- Data Collection Instruments
- Analysis Techniques
- Reporting Standards
- Ethical Considerations
Files
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# Educational Research Methods
A comprehensive skill for conducting rigorous educational research using both quantitative and qualitative methodologies. Covers study design, data collection instruments, analysis techniques, and reporting standards specific to education scholarship.
## Study Design Frameworks
### Quantitative Designs
Educational quantitative research typically follows one of these designs:
| Design | Purpose | Example |
|--------|---------|---------|
| Randomized controlled trial (RCT) | Causal inference | Random assignment to instruction methods |
| Quasi-experimental | Causal inference without randomization | Pre-post comparison with matched control |View the complete source on GitHublearning-science-guide
When your agent uses it
Evidence-based learning science principles for educational research and practice
What it loads into context
Frontmatter
85 tokensAlways available to the agent
SKILL.md
1,350 tokensWhen the skill is activated
Reference documents
0 tokensLoaded on request
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The playbook
- Foundational Learning Theories
- Evidence-Based Study Methods
- Assessment Design
- References
Files
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# Learning Science Guide
A comprehensive skill for applying evidence-based learning science principles to educational research, instructional design, and teaching practice. Grounded in cognitive psychology and educational neuroscience.
## Foundational Learning Theories
### Cognitive Load Theory (Sweller, 1988)
Working memory has limited capacity. Effective instruction manages three types of cognitive load:
| Load Type | Definition | Design Strategy |
|-----------|-----------|-----------------|
| Intrinsic | Complexity inherent to the material | Sequence from simple to complex; chunk information |
| Extraneous | Load from poor instructional design | Eliminate redundancy; use spatial contiguity |View the complete source on GitHubmooc-analytics-guide
When your agent uses it
Analyzing MOOC data, learning analytics, and online education metrics
What it loads into context
Frontmatter
88 tokensAlways available to the agent
SKILL.md
1,832 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 Sources and Formats
- Engagement and Retention Analysis
- Video Analytics
- A/B Testing for Course Design
- Tools and Platforms
- Key References
Files
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# MOOC Analytics Guide
A skill for analyzing Massive Open Online Course data, implementing learning analytics pipelines, and extracting actionable insights from online education platforms. Covers clickstream processing, engagement modeling, dropout prediction, and A/B testing for course design.
## Data Sources and Formats
### Common MOOC Data Schemas
MOOC platforms export several standard data types:
| Data Type | Description | Typical Format |
|-----------|-------------|----------------|
| Clickstream logs | Page views, video plays, pauses, seeks | JSON event logs |
| Forum posts | Discussion text, timestamps, thread structure | CSV/JSON |View the complete source on GitHubopen-syllabus-api
When your agent uses it
Analyze most-taught books and texts via Open Syllabus analytics
What it loads into context
Frontmatter
93 tokensAlways available to the agent
SKILL.md
1,410 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
- Explorer Interface
- Key Metrics
- Python Usage
- Top Assigned Works (Examples)
- Use Cases
- References
Files
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# Open Syllabus API
## Overview
Open Syllabus analyzes 20M+ college course syllabi from 7,000+ institutions in 140+ countries, tracking which books, articles, and media are most frequently assigned in higher education. The Explorer provides teaching frequency rankings and co-assignment patterns. Useful for curriculum research, textbook selection, and understanding disciplinary norms. Free for basic search; institutional subscription for full API access.
## Explorer Interface
### Web Search
```bash
# The primary interface is the web explorer:
# https://explorer.opensyllabus.org/
View the complete source on GitHubfinance-skills
When your agent uses it
8 finance skills. Trigger: financial modeling, market data, risk analysis, quantitative finance. Design: data sources, quantitative methods, and regulatory frameworks.
What it loads into context
Frontmatter
39 tokensAlways available to the agent
SKILL.md
329 tokensWhen the skill is activated
Reference documents
0 tokensLoaded on request
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The playbook
Files
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# Finance — 8 Skills
Select the skill matching the user's need, then `read` its SKILL.md.
| Skill | Description |
|-------|-------------|
| [akshare-finance-data](./akshare-finance-data/SKILL.md) | Access Chinese and global financial data using the AkShare Python library |
| [financial-data-analysis](./financial-data-analysis/SKILL.md) | Methods for acquiring, cleaning, and analyzing financial datasets for research |
| [finsight-research-guide](./finsight-research-guide/SKILL.md) | Deep financial research with the FinSight multi-agent system |
| [options-analytics-agent-guide](./options-analytics-agent-guide/SKILL.md) | AI agent for options pricing, Greeks, and strategy analysis |
| [portfolio-optimization-guide](./portfolio-optimization-guide/SKILL.md) | Portfolio theory, optimization algorithms, and asset allocation methods |
| [quantitative-finance-guide](./quantitative-finance-guide/SKILL.md) | Quantitative methods for financial modeling, derivatives pricing, and risk an... |
| [risk-modeling-guide](./risk-modeling-guide/SKILL.md) | Financial risk modeling including VaR, stress testing, and credit risk |
| [stata-accounting-research](./stata-accounting-research/SKILL.md) | STATA code patterns for empirical accounting and finance research |View the complete source on GitHubakshare-finance-data
When your agent uses it
Access Chinese and global financial data using the AkShare Python library
What it loads into context
Frontmatter
95 tokensAlways available to the agent
SKILL.md
1,727 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
- Installation
- Core Data Categories
- Research Workflow Example
- Common Gotchas
- References
Files
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# AkShare Financial Data Guide
## Overview
AkShare is an open-source Python library providing free access to Chinese and global financial market data. It aggregates data from 50+ sources including Sina Finance, East Money, Tushare, Yahoo Finance, and central bank websites. No API key required for most functions. Essential for financial research, quantitative analysis, and economic studies involving Chinese market data.
## Installation
```bash
pip install akshare --upgrade
# Verify
python -c "import akshare as ak; print(ak.__version__)"
```View the complete source on GitHubfinancial-data-analysis
When your agent uses it
Methods for acquiring, cleaning, and analyzing financial datasets for research
What it loads into context
Frontmatter
80 tokensAlways available to the agent
SKILL.md
1,267 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 Acquisition
- Data Cleaning Pipeline
- Standard Financial Metrics
- Event Studies
- Reproducibility
Files
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# Financial Data Analysis
A practical skill for sourcing, processing, and analyzing financial data in academic research contexts. Covers data acquisition from public APIs, cleaning workflows, and standard analytical techniques used in empirical finance research.
## Data Acquisition
### Public Financial Data Sources
| Source | Data Type | Access | Python Package |
|--------|-----------|--------|---------------|
| Yahoo Finance | Prices, fundamentals | Free | `yfinance` |
| FRED (St. Louis Fed) | Macroeconomic indicators | Free (API key) | `fredapi` |
| SEC EDGAR | Company filings (10-K, 10-Q) | Free | `sec-edgar-downloader` |
| WRDS (Wharton) | CRSP, Compustat, IBES | University subscription | `wrds` |View the complete source on GitHubfinsight-research-guide
When your agent uses it
Deep financial research with the FinSight multi-agent system
What it loads into context
Frontmatter
93 tokensAlways available to the agent
SKILL.md
789 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
- Installation
- Core Capabilities
- Report Structure
- Use Cases
- References
Files
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# FinSight Research Guide
## Overview
FinSight is a deep research agent designed specifically for financial analysis. Developed by RUC-NLPIR, it combines multi-source data retrieval, financial reasoning, and report generation to produce publication-ready financial research. It handles market analysis, company fundamentals, sector comparisons, and macroeconomic assessment through specialized agents.
## Installation
```bash
git clone https://github.com/RUC-NLPIR/FinSight.git
cd FinSight && pip install -e .
```
## Core CapabilitiesView the complete source on GitHuboptions-analytics-agent-guide
When your agent uses it
AI agent for options pricing, Greeks, and strategy analysis
What it loads into context
Frontmatter
89 tokensAlways available to the agent
SKILL.md
808 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
- Core Capabilities
- Greeks Analysis
- Strategy Evaluation
- Implied Volatility
- Use Cases
- References
Files
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# Options Analytics Agent Guide
## Overview
An AI agent for options pricing, risk analysis, and strategy evaluation. It combines Black-Scholes and binomial models, Greeks calculations, implied volatility surfaces, and portfolio risk analytics into a conversational interface. Researchers and quantitative analysts can query options data, price exotic derivatives, and evaluate trading strategies through natural language.
## Core Capabilities
```python
from options_agent import OptionsAgent
agent = OptionsAgent(llm_provider="anthropic")
# Price an optionView the complete source on GitHubportfolio-optimization-guide
When your agent uses it
Portfolio theory, optimization algorithms, and asset allocation methods
What it loads into context
Frontmatter
84 tokensAlways available to the agent
SKILL.md
2,328 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
- Mean-Variance Optimization
- Black-Litterman Model
- Risk Parity
- Factor-Based Portfolio Construction
- Rebalancing and Transaction Costs
- Key Academic References
- Tools and Libraries
Files
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# Portfolio Optimization Guide
A skill for implementing and researching portfolio optimization methods, from classical mean-variance optimization to modern robust and factor-based approaches. Covers Markowitz theory, Black-Litterman, risk parity, and machine learning-enhanced portfolio construction.
## Mean-Variance Optimization
### Classical Markowitz Portfolio
```python
import numpy as np
from scipy.optimize import minimize
def mean_variance_optimize(expected_returns: np.ndarray,
cov_matrix: np.ndarray,View the complete source on GitHubquantitative-finance-guide
When your agent uses it
Quantitative methods for financial modeling, derivatives pricing, and risk an...
What it loads into context
Frontmatter
83 tokensAlways available to the agent
SKILL.md
1,460 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
- Derivatives Pricing
- Portfolio Optimization
- Risk Management
- Time Series Econometrics
- References
Files
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# Quantitative Finance Guide
A rigorous skill for applying quantitative methods to financial research, covering derivatives pricing, portfolio optimization, risk modeling, and time series econometrics. Designed for academic researchers and quantitative analysts.
## Derivatives Pricing
### Black-Scholes-Merton Model
The foundational model for European option pricing:
```python
import numpy as np
from scipy.stats import norm
View the complete source on GitHubrisk-modeling-guide
When your agent uses it
Financial risk modeling including VaR, stress testing, and credit risk
What it loads into context
Frontmatter
87 tokensAlways available to the agent
SKILL.md
2,192 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
- Market Risk: Value at Risk
- Credit Risk Modeling
- Stress Testing
- Regulatory Framework
- Tools and Libraries
Files
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# Risk Modeling Guide
A skill for quantitative financial risk modeling, covering Value at Risk, Expected Shortfall, credit risk, stress testing, and Monte Carlo simulation methods. Essential for financial engineering research and regulatory risk analysis.
## Market Risk: Value at Risk
### VaR Methodologies
| Method | Description | Pros | Cons |
|--------|-------------|------|------|
| Historical simulation | Replay past returns | No distributional assumption | Assumes past repeats |
| Variance-covariance | Assume normal returns | Fast, analytical | Underestimates tail risk |
| Monte Carlo simulation | Simulate from fitted model | Flexible distributions | Computationally expensive |
| Filtered historical simulation | GARCH + historical innovations | Captures volatility clustering | More complex |View the complete source on GitHubstata-accounting-research
When your agent uses it
STATA code patterns for empirical accounting and finance research
What it loads into context
Frontmatter
95 tokensAlways available to the agent
SKILL.md
4,094 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
- Overview
- Data Preparation
- Earnings Quality Models
- Earnings Management Detection
- Instrumental Variables (Two-Stage Least Squares)
- Event Study
- Regression Specifications
- Robustness Tests
- Publication-Ready Output
- Best Practices
- References
Files
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# STATA Accounting Research Guide
## Overview
Empirical accounting research relies heavily on STATA for data manipulation, statistical analysis, and robustness testing. The field has developed standardized methodological approaches -- earnings quality models, event studies, difference-in-differences for regulatory changes, and instrument variable strategies for endogeneity -- that are implemented in a relatively stable set of STATA patterns.
This guide provides the core STATA code patterns used in top accounting journals (The Accounting Review, Journal of Accounting Research, Journal of Accounting and Economics, and Review of Accounting Studies). These patterns are drawn from commonly used research designs in financial reporting, auditing, tax, and managerial accounting research.
Whether you are estimating discretionary accruals, conducting an event study around an earnings announcement, testing the effect of auditor rotation on audit quality, or implementing a regulatory shock analysis, these patterns provide tested, reviewable STATA implementations.
## Data Preparation
### Loading and Cleaning COMPUSTAT Data
View the complete source on GitHubgeoscience-skills
When your agent uses it
6 geoscience & climate skills. Trigger: earth science data, GIS, remote sensing, climate modeling. Design: geospatial tools, satellite data processing, and environmental models.
What it loads into context
Frontmatter
47 tokensAlways available to the agent
SKILL.md
286 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
Files
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# Geoscience & Climate — 6 Skills
Select the skill matching the user's need, then `read` its SKILL.md.
| Skill | Description |
|-------|-------------|
| [climate-modeling-guide](./climate-modeling-guide/SKILL.md) | Climate simulation, modeling tools, and climate data analysis methods |
| [climate-science-guide](./climate-science-guide/SKILL.md) | Climate data analysis, modeling workflows, and carbon neutrality research met... |
| [gis-remote-sensing-guide](./gis-remote-sensing-guide/SKILL.md) | GIS analysis and remote sensing workflows for geospatial research applications |
| [pangaea-data-api](./pangaea-data-api/SKILL.md) | Access earth and environmental science datasets via PANGAEA API |
| [satellite-remote-sensing](./satellite-remote-sensing/SKILL.md) | Satellite imagery analysis and remote sensing for earth science research |
| [seismology-data-guide](./seismology-data-guide/SKILL.md) | Earthquake data analysis, seismogram processing, and seismic research |View the complete source on GitHubclimate-modeling-guide
When your agent uses it
Climate simulation, modeling tools, and climate data analysis methods
What it loads into context
Frontmatter
91 tokensAlways available to the agent
SKILL.md
1,950 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
- Climate Data Standards
- Climate Analysis Techniques
- Statistical Downscaling
- Running Climate Models
- Tools and Resources
Files
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# Climate Modeling Guide
A skill for working with climate models and climate data in research contexts. Covers accessing CMIP archives, processing NetCDF data, running idealized climate simulations, statistical downscaling, and analyzing climate projections with Python tools.
## Climate Data Standards
### NetCDF and CF Conventions
Climate data is stored in NetCDF (Network Common Data Form) files following CF (Climate and Forecast) conventions:
```python
import xarray as xr
import numpy as np
View the complete source on GitHubclimate-science-guide
When your agent uses it
Climate data analysis, modeling workflows, and carbon neutrality research met...
What it loads into context
Frontmatter
88 tokensAlways available to the agent
SKILL.md
1,367 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
- Climate Data Sources
- Temperature Trend Analysis
- Carbon Budget Analysis
- Climate Visualization
- Best Practices
Files
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# Climate Science Guide
A research skill for analyzing climate data, working with climate model outputs, and conducting carbon-related studies. Covers data sources, standard analytical workflows, and visualization techniques used in climate science publications.
## Climate Data Sources
### Observational Datasets
| Dataset | Variables | Resolution | Period | Source |
|---------|-----------|-----------|--------|--------|
| ERA5 | Temperature, precipitation, wind, etc. | 0.25 deg, hourly | 1940-present | ECMWF/Copernicus |
| GPCP | Precipitation | 2.5 deg, monthly | 1979-present | NASA |
| HadCRUT5 | Surface temperature anomaly | 5 deg, monthly | 1850-present | Met Office |
| NOAA GHCN | Station temperature, precipitation | Point data | 1850-present | NOAA |View the complete source on GitHubgis-remote-sensing-guide
When your agent uses it
GIS analysis and remote sensing workflows for geospatial research applications
What it loads into context
Frontmatter
90 tokensAlways available to the agent
SKILL.md
1,224 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
- Satellite Data Sources
- Spatial Analysis with GeoPandas
- Remote Sensing Indices
- Map Production
- Coordinate Reference Systems
Files
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# GIS and Remote Sensing Guide
A comprehensive skill for conducting geospatial analysis and remote sensing research. Covers data acquisition from satellite platforms, spatial analysis with open-source tools, and publication-quality map production.
## Satellite Data Sources
### Key Earth Observation Platforms
| Platform | Provider | Spatial Res. | Revisit | Free? | Use Case |
|----------|----------|-------------|---------|-------|----------|
| Landsat 8/9 | USGS/NASA | 30m (MS), 15m (pan) | 16 days | Yes | Land cover, NDVI time series |
| Sentinel-2 | ESA/Copernicus | 10m | 5 days | Yes | Agriculture, urban mapping |
| MODIS | NASA | 250m-1km | 1-2 days | Yes | Large-scale vegetation, fire |
| Sentinel-1 | ESA | 5-20m | 6 days | Yes | SAR, flood mapping, deformation |View the complete source on GitHubpangaea-data-api
When your agent uses it
Access earth and environmental science datasets via PANGAEA API
What it loads into context
Frontmatter
99 tokensAlways available to the agent
SKILL.md
1,715 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
- API Endpoints
- Python Usage
- Data Topics
- References
Files
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# PANGAEA Data Repository API
## Overview
PANGAEA is the world's leading data repository for earth and environmental sciences, hosting 400K+ datasets with 20B+ data points. It archives research data from oceanography, paleoclimatology, geology, ecology, and atmospheric science. Each dataset has a DOI and is linked to the originating publication. The API provides search, metadata retrieval, and data download. Free, no authentication required.
## API Endpoints
### Search API
```bash
# Search datasets by keyword
curl "https://www.pangaea.de/advanced/search.php?q=ocean+temperature&count=20&type=json"
View the complete source on GitHubsatellite-remote-sensing
When your agent uses it
Satellite imagery analysis and remote sensing for earth science research
What it loads into context
Frontmatter
91 tokensAlways available to the agent
SKILL.md
1,944 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
- Satellite Data Sources
- Preprocessing Pipeline
- Spectral Indices
- Land Cover Classification
- Change Detection
- Tools and Libraries
Files
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# Satellite Remote Sensing
A skill for processing and analyzing satellite imagery for earth science research. Covers data acquisition from major satellite platforms, preprocessing workflows, spectral index computation, land cover classification, and change detection using Python geospatial tools.
## Satellite Data Sources
### Major Earth Observation Missions
| Mission | Operator | Resolution | Revisit | Key Bands | Access |
|---------|----------|-----------|---------|-----------|--------|
| Landsat 8/9 | USGS/NASA | 30m (MS), 15m (pan) | 16 days | 11 bands, OLI+TIRS | Free (USGS EarthExplorer) |
| Sentinel-2 | ESA | 10m-60m | 5 days | 13 bands, MSI | Free (Copernicus Open Access Hub) |
| MODIS | NASA | 250m-1km | 1-2 days | 36 bands | Free (NASA LAADS DAAC) |
| Sentinel-1 | ESA | 5-20m | 6 days | C-band SAR | Free (Copernicus) |View the complete source on GitHubseismology-data-guide
When your agent uses it
Earthquake data analysis, seismogram processing, and seismic research
What it loads into context
Frontmatter
91 tokensAlways available to the agent
SKILL.md
1,839 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
- Seismic Data Sources
- Waveform Processing
- Phase Picking and Location
- Magnitude Estimation
- Focal Mechanisms
- Tools and Software
Files
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# Seismology Data Guide
A skill for processing seismic data, analyzing earthquake catalogs, and working with seismograms using standard tools in observational seismology. Covers data retrieval from global networks, waveform processing with ObsPy, magnitude estimation, focal mechanism analysis, and seismic hazard assessment.
## Seismic Data Sources
### Global Data Centers
| Data Center | Abbreviation | Coverage | Access |
|-------------|-------------|----------|--------|
| IRIS Data Management Center | IRIS DMC | Global broadband | FDSN Web Services |
| European Integrated Data Archive | EIDA | European networks | FDSN Web Services |
| USGS Earthquake Hazards Program | USGS EHP | Global catalog | API + ComCat |
| International Seismological Centre | ISC | Global bulletin | ISC web services |View the complete source on GitHubhumanities-skills
When your agent uses it
5 humanities skills. Trigger: textual analysis, archival research, digital humanities, philosophy. Design: digital tools and qualitative methods for humanities scholarship.
What it loads into context
Frontmatter
37 tokensAlways available to the agent
SKILL.md
222 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
Files
SKILL.mdcore1,071 bytes
Read SKILL.md
# Humanities — 5 Skills
Select the skill matching the user's need, then `read` its SKILL.md.
| Skill | Description |
|-------|-------------|
| [digital-humanities-guide](./digital-humanities-guide/SKILL.md) | Computational methods for humanities research including text mining and netwo... |
| [ethical-philosophy-guide](./ethical-philosophy-guide/SKILL.md) | Applied ethics research methods and major ethical frameworks |
| [history-research-guide](./history-research-guide/SKILL.md) | Historical research from primary sources to scholarly analysis |
| [philosophy-research-guide](./philosophy-research-guide/SKILL.md) | Research methods and analytical frameworks for philosophical inquiry and scho... |
| [political-history-guide](./political-history-guide/SKILL.md) | Chinese and European political struggle history and comparative analysis |View the complete source on GitHubdigital-humanities-guide
When your agent uses it
Computational methods for humanities research including text mining and netwo...
What it loads into context
Frontmatter
88 tokensAlways available to the agent
SKILL.md
1,335 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
- Text Mining and Distant Reading
- Network Analysis for Historical Research
- Spatial Humanities
- Digital Archival Methods
- Ethical Considerations
Files
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# Digital Humanities Guide
A skill for applying computational and quantitative methods to humanities research. Covers text mining, network analysis, spatial humanities, and digital archival methods. Designed for researchers bridging traditional humanities with data-driven approaches.
## Text Mining and Distant Reading
### Corpus Preparation
```python
import re
from collections import Counter
def prepare_corpus(texts: list[str], stopwords: set = None) -> list[list[str]]:
"""View the complete source on GitHubethical-philosophy-guide
When your agent uses it
Applied ethics research methods and major ethical frameworks
What it loads into context
Frontmatter
91 tokensAlways available to the agent
SKILL.md
2,154 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
- Major Ethical Frameworks
- Constructing Ethical Arguments
- Major Subfields
- Writing Ethics Papers
- Best Practices
- References
Files
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# Applied Ethics Research Guide
## Overview
Applied ethics is the branch of philosophy that addresses concrete moral questions in specific domains: medicine, technology, business, environment, law, and research itself. Unlike metaethics (which asks what moral terms mean) or normative ethics (which develops general moral theories), applied ethics works at the intersection of philosophical reasoning and real-world decision-making.
The field has exploded in relevance as emerging technologies -- artificial intelligence, gene editing, autonomous weapons, surveillance systems -- create moral dilemmas that existing law and policy cannot resolve. Applied ethics research requires both philosophical rigor (clear argument structure, engagement with existing frameworks) and empirical grounding (understanding the technology, institution, or practice being evaluated).
This guide covers the major ethical frameworks used in applied ethics, methods for constructing and evaluating ethical arguments, the landscape of subfields (bioethics, AI ethics, research ethics, environmental ethics), and practical guidance for publishing in philosophy and interdisciplinary venues.
## Major Ethical Frameworks
### Framework Comparison
View the complete source on GitHubhistory-research-guide
When your agent uses it
Historical research from primary sources to scholarly analysis
What it loads into context
Frontmatter
84 tokensAlways available to the agent
SKILL.md
2,309 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
- Source Criticism: The Foundation
- Archival Research
- Historiographical Frameworks
- Digital Methods for Historians
- Writing Historical Research
- Best Practices
- References
Files
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# History Research Guide
## Overview
Historical research transforms raw evidence from the past -- documents, artifacts, images, oral testimonies -- into interpretive accounts that explain how and why things happened. The discipline demands a distinctive set of skills: source criticism, archival navigation, contextual reasoning, and the ability to construct arguments that are both evidence-based and theoretically informed.
The digital turn has expanded the historian's toolkit dramatically. Digitized archives, text mining, GIS mapping, and network analysis now complement traditional close reading. But the fundamentals remain unchanged: rigorous engagement with primary sources, awareness of historiographical debates, and transparent argumentation.
This guide covers the full research workflow for historians: finding and evaluating primary sources, navigating archives (physical and digital), applying source criticism, structuring historical arguments, and leveraging digital tools for analysis. It serves researchers at all levels, from graduate students designing their first archival project to established scholars integrating computational methods.
## Source Criticism: The Foundation
### External Criticism (Authenticity)
View the complete source on GitHubphilosophy-research-guide
When your agent uses it
Research methods and analytical frameworks for philosophical inquiry and scho...
What it loads into context
Frontmatter
84 tokensAlways available to the agent
SKILL.md
1,180 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
- Philosophical Argumentation
- Research Methods by Subfield
- Writing Philosophy Papers
- Key Databases
Files
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# Philosophy Research Guide
A skill for conducting rigorous philosophical research, from constructing arguments and analyzing texts to writing publishable philosophy papers. Covers major subfields, argumentation methods, and the distinctive methodology of philosophical inquiry.
## Philosophical Argumentation
### Argument Structure and Evaluation
Every philosophical argument can be reconstructed in standard form:
```
Premise 1: All knowledge requires justification. (epistemic principle)
Premise 2: Sensory experience alone cannot provide certainty. (empirical claim)
Premise 3: If knowledge requires certainty, then sensory experienceView the complete source on GitHubpolitical-history-guide
When your agent uses it
Chinese and European political struggle history and comparative analysis
What it loads into context
Frontmatter
89 tokensAlways available to the agent
SKILL.md
2,393 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
- Chinese Political History: Key Periods
- European Political History: Key Periods
- Comparative Analysis
- Research Methods for Political History
- Writing Comparative Political History
- Best Practices
- References
Files
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# Political History Guide
## Overview
Political history examines the struggle for power, the formation of states, the dynamics of revolution and reform, and the evolution of political ideas across time and space. This guide focuses on comparative political history, drawing on Chinese and European traditions to illuminate how different civilizations have organized political authority, managed conflict, and theorized the relationship between rulers and ruled.
Comparing Chinese and European political development is one of the most productive exercises in historical social science. Both civilizations produced sophisticated state systems, bureaucratic administrations, and political philosophies -- but through radically different paths. Understanding these divergences helps researchers avoid Eurocentric assumptions and develop more robust theories of political change.
This guide covers key periods and themes in both traditions, methods for comparative historical analysis, and the theoretical frameworks that scholars use to explain political transformation. It is designed for researchers working on political history, comparative politics, historical sociology, or area studies who need to engage with both traditions.
## Chinese Political History: Key Periods
### Imperial State Formation
View the complete source on GitHublaw-skills
When your agent uses it
9 legal research skills. Trigger: legal research, case law analysis, regulatory compliance. Design: legal databases, citation networks, and judicial analytics tools.
What it loads into context
Frontmatter
39 tokensAlways available to the agent
SKILL.md
355 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
Files
SKILL.mdcore1,511 bytes
Read SKILL.md
# Legal Research — 9 Skills
Select the skill matching the user's need, then `read` its SKILL.md.
| Skill | Description |
|-------|-------------|
| [caselaw-access-api](./caselaw-access-api/SKILL.md) | Query 360+ years of US case law via the Harvard Caselaw Access Project |
| [courtlistener-api](./courtlistener-api/SKILL.md) | Legal case law database with PACER data and judge profiles |
| [legal-agent-skills-guide](./legal-agent-skills-guide/SKILL.md) | Agent skills collection for legal research and automation |
| [legal-nlp-guide](./legal-nlp-guide/SKILL.md) | NLP techniques for legal text analysis, case law mining, and contracts |
| [legal-research-guide](./legal-research-guide/SKILL.md) | Legal research methods, case law analysis, and compliance tools |
| [opencontracts-guide](./opencontracts-guide/SKILL.md) | Legal document annotation, versioning, and analysis platform |
| [patent-analysis-guide](./patent-analysis-guide/SKILL.md) | Patent search, classification, landscape analysis, and prior art mining |
| [regulatory-compliance-guide](./regulatory-compliance-guide/SKILL.md) | Regulatory text mining, compliance research, and policy analysis tools |View the complete source on GitHubcaselaw-access-api
When your agent uses it
Query 360+ years of US case law via the Harvard Caselaw Access Project
What it loads into context
Frontmatter
96 tokensAlways available to the agent
SKILL.md
1,252 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
- API Endpoints
- Python Usage
- Bulk Data Access
- Key Jurisdictions
- Authentication
- References
Files
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# Caselaw Access Project API
## Overview
The Caselaw Access Project (CAP) by Harvard Law School provides free access to 6.9 million US court opinions spanning 360+ years. The REST API enables searching, filtering, and downloading full-text case opinions from all federal and state courts. No authentication required for metadata; API key (free registration) required for full text of post-1923 cases.
## API Endpoints
### Base URL
```
https://api.case.law/v1/
```
View the complete source on GitHubcourtlistener-api
When your agent uses it
Legal case law database with PACER data and judge profiles
What it loads into context
Frontmatter
88 tokensAlways available to the agent
SKILL.md
2,157 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
- Authentication
- Core Endpoints
- Rate Limits
- Common Patterns
- References
Files
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# CourtListener API Guide
## Overview
CourtListener is a free legal research platform operated by Free Law Project, a non-profit organization dedicated to making legal data freely accessible. The platform hosts one of the largest open collections of U.S. case law, containing millions of court opinions, docket entries, oral arguments, and judge profiles sourced from PACER (Public Access to Court Electronic Records), state court websites, and historical digitization efforts.
The CourtListener REST API provides programmatic access to this extensive legal database, enabling searches across court opinions, dockets, judge biographical data, and court information. The API covers federal courts (Supreme Court, Circuit Courts, District Courts, Bankruptcy Courts) and many state courts, with records spanning from the founding of the republic to the present day.
Legal scholars, law students, practicing attorneys, journalists, policy researchers, and civic technologists use the CourtListener API to conduct legal research, perform empirical legal studies, build litigation analytics tools, track judicial behavior, monitor active litigation, and analyze trends in case law. It serves as a critical resource for computational legal studies and access-to-justice initiatives.
## Authentication
No authentication is required for basic API access. The CourtListener API is publicly accessible for read operations. However, creating an account and using an API token provides higher rate limits and access to additional features. Authentication is recommended for production applications.
View the complete source on GitHublegal-agent-skills-guide
When your agent uses it
Agent skills collection for legal research and automation
What it loads into context
Frontmatter
93 tokensAlways available to the agent
SKILL.md
905 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
- Skill Categories
- Key Skills
- Use Cases
- References
Files
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# Legal Agent Skills Guide
## Overview
A curated collection of agent skills for legal research and automation — contract analysis, case law search, regulatory compliance checking, legal document drafting, and citation verification. Each skill provides structured capabilities that AI agents can use to assist with legal workflows. Designed for legal researchers, law firms, and compliance teams.
## Skill Categories
```
Legal Agent Skills
├── Research Skills
│ ├── Case law search (by jurisdiction, topic)
│ ├── Statute lookup (federal, state, international)
│ ├── Legal commentary searchView the complete source on GitHublegal-nlp-guide
When your agent uses it
NLP techniques for legal text analysis, case law mining, and contracts
What it loads into context
Frontmatter
89 tokensAlways available to the agent
SKILL.md
2,035 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
- Legal Text Characteristics
- Legal Text Classification
- Named Entity Recognition
- Contract Analysis
- Case Similarity and Legal Search
- Legal Datasets and Benchmarks
- Tools and Resources
Files
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# Legal NLP Guide
A skill for applying natural language processing techniques to legal texts. Covers legal document classification, named entity recognition for legal entities, contract clause extraction, case law similarity search, and court opinion summarization using modern NLP tools.
## Legal Text Characteristics
Legal language presents unique NLP challenges:
- **Long documents**: Court opinions average 5,000-20,000 tokens; contracts can exceed 50,000
- **Domain-specific vocabulary**: Terms of art with precise legal meanings (e.g., "consideration", "estoppel")
- **Complex syntax**: Multi-clause sentences with nested qualifications and cross-references
- **Citation networks**: Dense cross-referencing between cases, statutes, and regulations
- **Temporal reasoning**: Effective dates, amendments, and retroactivity
View the complete source on GitHublegal-research-guide
When your agent uses it
Legal research methods, case law analysis, and compliance tools
What it loads into context
Frontmatter
82 tokensAlways available to the agent
SKILL.md
2,306 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
- Legal Research Frameworks
- Legal Research Databases
- Citation Systems
- Computational Legal Research
- Research Areas in Law
- Practical Research Workflow
- Top Academic Venues
Files
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# Legal Research Guide
Conduct systematic legal research across jurisdictions, analyze case law, navigate statutory frameworks, and use computational legal tools for academic and practice-oriented research.
## Legal Research Frameworks
### IRAC Method
The standard analytical framework for legal reasoning:
| Step | Description | Example |
|------|-------------|---------|
| **Issue** | Identify the legal question | "Does web scraping of public data constitute a CFAA violation?" |
| **Rule** | State the applicable legal rule | "The CFAA prohibits accessing a computer 'without authorization' or 'exceeding authorized access'" |View the complete source on GitHubopencontracts-guide
When your agent uses it
Legal document annotation, versioning, and analysis platform
What it loads into context
Frontmatter
92 tokensAlways available to the agent
SKILL.md
1,051 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
- Installation
- Core Features
- Search and Analytics
- Use Cases
- References
Files
SKILL.mdcore4,262 bytesSKILL.mdcore4,262 bytesSKILL.mdcore4,262 bytesSKILL.mdcore4,262 bytesSKILL.mdcore4,262 bytesSKILL.mdcore4,262 bytesSKILL.mdcore4,262 bytes
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# OpenContracts Guide
## Overview
OpenContracts is an open-source platform for legal document annotation, versioning, and analysis. It provides collaborative annotation tools for legal text, version tracking across document drafts, NLP-powered clause extraction, and integration with AI agents via MCP. Designed for legal researchers, law firms, and teams managing large document collections that need structured annotation and analysis.
## Installation
```bash
# Docker deployment
git clone https://github.com/Open-Source-Legal/OpenContracts.git
cd OpenContracts
docker-compose up -d
View the complete source on GitHubpatent-analysis-guide
When your agent uses it
Patent search, classification, landscape analysis, and prior art mining
What it loads into context
Frontmatter
86 tokensAlways available to the agent
SKILL.md
2,073 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
- Patent Data Sources
- Patent Classification Systems
- Patent Landscape Analysis
- Claim Analysis
- Prior Art Search Strategy
- Tools and Resources
Files
SKILL.mdcore8,704 bytesSKILL.mdcore8,704 bytesSKILL.mdcore8,704 bytesSKILL.mdcore8,704 bytesSKILL.mdcore8,704 bytesSKILL.mdcore8,704 bytesSKILL.mdcore8,704 bytes
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# Patent Analysis Guide
A skill for conducting patent research, landscape analysis, and prior art searches. Covers patent database APIs, classification systems, citation network analysis, claim parsing, and technology trend mapping for intellectual property research.
## Patent Data Sources
### Major Patent Databases
| Database | Coverage | API | Cost |
|----------|----------|-----|------|
| USPTO PatentsView | US patents and applications | REST API, bulk download | Free |
| EPO Open Patent Services | EP, WO, and 100+ offices | REST API (OPS) | Free (throttled) |
| Google Patents | 120M+ documents worldwide | BigQuery (Google Patents Public) | Free (BigQuery costs) |
| Lens.org | 130M+ patent records | REST API | Free for researchers |View the complete source on GitHubregulatory-compliance-guide
When your agent uses it
Regulatory text mining, compliance research, and policy analysis tools
What it loads into context
Frontmatter
86 tokensAlways available to the agent
SKILL.md
2,163 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
- Regulatory Data Sources
- Regulatory Text Parsing
- Regulatory Change Detection
- Compliance Gap Analysis
- Regulatory Domains
- Tools and Resources
Files
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# Regulatory Compliance Guide
A skill for mining regulatory texts, tracking regulatory changes, and conducting compliance research. Covers accessing regulatory databases, parsing regulatory language, change detection in regulations, compliance gap analysis, and computational policy analysis.
## Regulatory Data Sources
### US Federal Regulatory Data
| Source | Content | Format | Access |
|--------|---------|--------|--------|
| Federal Register API | Proposed and final rules | JSON API | Free (federalregister.gov) |
| eCFR (Electronic CFR) | Current Code of Federal Regulations | XML + API | Free (ecfr.gov) |
| Regulations.gov | Public comments on rulemakings | JSON API | Free |
| Congress.gov | Bills and legislative history | API + bulk | Free |View the complete source on GitHubuk-legislation-api
When your agent uses it
Access UK laws and statutory instruments via the Legislation.gov.uk API
What it loads into context
Frontmatter
95 tokensAlways available to the agent
SKILL.md
1,904 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
- Authentication
- URL Structure
- Core Endpoints
- Data Formats
- Academic Use Cases
- Python Usage
- References
Files
SKILL.mdcore7,007 bytesSKILL.mdcore7,007 bytesSKILL.mdcore7,007 bytesSKILL.mdcore7,007 bytesSKILL.mdcore7,007 bytesSKILL.mdcore7,007 bytesSKILL.mdcore7,007 bytesSKILL.mdcore7,007 bytes
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# UK Legislation API (legislation.gov.uk)
## Overview
The Legislation.gov.uk API, maintained by The National Archives, provides open access to the full text of UK legislation from 1267 to the present day. The database covers over 100,000 items including Acts of Parliament, Statutory Instruments, Scottish and Welsh legislation, and Northern Ireland statutes. All data is available as structured XML with Dublin Core metadata, Atom feeds, and HTML. The API supports point-in-time versions showing how legislation read at any given date, plus amendment tracking.
## Authentication
No authentication or API key is required. The API is fully open. Respect rate limits by adding reasonable delays between bulk requests.
## URL Structure
All legislation follows the pattern:
View the complete source on GitHubmath-skills
When your agent uses it
6 mathematics skills. Trigger: mathematical proofs, theorem proving, numerical methods, linear algebra. Design: formal verification tools and computational mathematics guides.
What it loads into context
Frontmatter
37 tokensAlways available to the agent
SKILL.md
261 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
Files
SKILL.mdcore1,125 bytes
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# Mathematics — 6 Skills
Select the skill matching the user's need, then `read` its SKILL.md.
| Skill | Description |
|-------|-------------|
| [lean-theorem-proving-guide](./lean-theorem-proving-guide/SKILL.md) | LLM agent for formal theorem proving in Lean 4 |
| [linear-algebra-applications](./linear-algebra-applications/SKILL.md) | Apply linear algebra concepts to research computing and data analysis |
| [numerical-methods-guide](./numerical-methods-guide/SKILL.md) | Apply numerical methods and scientific computing techniques |
| [oeis-api](./oeis-api/SKILL.md) | On-Line Encyclopedia of Integer Sequences API |
| [symbolic-computation-guide](./symbolic-computation-guide/SKILL.md) | Computer algebra systems: SymPy, SageMath, and Mathematica for research |
| [topology-data-analysis](./topology-data-analysis/SKILL.md) | Topological data analysis: persistent homology, Mapper, and TDA tools |View the complete source on GitHublean-theorem-proving-guide
When your agent uses it
LLM agent for formal theorem proving in Lean 4
What it loads into context
Frontmatter
97 tokensAlways available to the agent
SKILL.md
1,012 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
- Architecture
- Usage
- Proof Search Strategies
- Lean 4 Tactic Library
- Batch Proving
- Use Cases
- References
Files
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# Lean Theorem Proving Agent Guide
## Overview
LeanAgent is an LLM-based agent for automated theorem proving in Lean 4, a modern proof assistant. It combines LLM reasoning with formal verification — proposing proof steps that are verified by Lean's type checker. Can prove novel theorems, not just benchmarks, by exploring proof strategies, backtracking on failures, and learning from successful proofs.
## Architecture
```
Theorem Statement (Lean 4)
↓
Goal Analysis Agent (understand proof obligations)
↓
Tactic Suggestion Agent (propose proof steps)View the complete source on GitHublinear-algebra-applications
When your agent uses it
Apply linear algebra concepts to research computing and data analysis
What it loads into context
Frontmatter
89 tokensAlways available to the agent
SKILL.md
1,704 tokensWhen the skill is activated
Reference documents
0 tokensLoaded on request
Token counts are estimates. Scripts run separately; they are not counted as reference context.
The playbook
- Essential Operations
- Matrix Decompositions
- Research Applications
- Numerical Stability
Files
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# Applied Linear Algebra for Research
A skill for applying linear algebra to research computing, data analysis, and scientific modeling. Covers matrix decompositions, eigenvalue problems, least squares, dimensionality reduction, and practical implementation in NumPy/SciPy.
## Essential Operations
### Matrix Multiplication and Solving Systems
```python
import numpy as np
from scipy import linalg
def solve_linear_system(A: np.ndarray, b: np.ndarray) -> dict:View the complete source on GitHubnumerical-methods-guide
When your agent uses it
Apply numerical methods and scientific computing techniques
What it loads into context
Frontmatter
85 tokensAlways available to the agent
SKILL.md
1,652 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
- Root Finding
- Numerical Integration
- Ordinary Differential Equations
- Optimization
- Error Analysis
Files
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# Numerical Methods Guide
A skill for applying numerical methods in scientific computing and research. Covers root finding, numerical integration, ODE solvers, optimization, interpolation, and error analysis with practical implementations in Python.
## Root Finding
### Newton's Method and Alternatives
```python
import numpy as np
def newton_method(f, df, x0: float, tol: float = 1e-10,
max_iter: int = 100) -> dict:View the complete source on GitHuboeis-api
When your agent uses it
On-Line Encyclopedia of Integer Sequences API
What it loads into context
Frontmatter
81 tokensAlways available to the agent
SKILL.md
1,576 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
- Authentication
- Core Endpoints
- Rate Limits
- Common Patterns
- References
Files
SKILL.mdcore6,699 bytesSKILL.mdcore6,699 bytesSKILL.mdcore6,699 bytesSKILL.mdcore6,699 bytesSKILL.mdcore6,699 bytesSKILL.mdcore6,699 bytes
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# OEIS API Guide
## Overview
The On-Line Encyclopedia of Integer Sequences (OEIS) is a comprehensive database of integer sequences, founded by Neil Sloane in 1964 and now containing over 370,000 entries. It is one of the most widely cited resources in mathematics, serving as the definitive reference for identifying, cataloging, and exploring integer sequences that arise in combinatorics, number theory, algebra, analysis, and applied mathematics.
The OEIS provides a web-accessible search interface that can also be used programmatically to query sequences by their terms, by A-number (the unique identifier for each sequence), or by keyword. The API returns structured data in JSON or plain text format, including the sequence terms, formulas, generating functions, references, links to related sequences, and contributed programs in various languages.
Mathematicians, computer scientists, physicists, and researchers in any quantitative discipline use the OEIS to identify unknown sequences encountered in their work, verify conjectures, discover connections between different areas of mathematics, and access formulas and programs for computing sequence terms. It is an indispensable tool for both pure and applied mathematical research.
## Authentication
No authentication required. The OEIS is a free, open resource that does not require any API key, token, or registration. All queries are publicly accessible. The OEIS Foundation operates as a non-profit and relies on voluntary contributions.
View the complete source on GitHubsymbolic-computation-guide
When your agent uses it
Computer algebra systems: SymPy, SageMath, and Mathematica for research
What it loads into context
Frontmatter
93 tokensAlways available to the agent
SKILL.md
2,151 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
- SymPy Fundamentals
- Linear Algebra
- SageMath for Research
- Mathematica / Wolfram Language
- Practical Workflows
- Tools and Resources
Files
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# Symbolic Computation Guide
A skill for using computer algebra systems (CAS) in mathematical research. Covers symbolic differentiation, integration, equation solving, series expansion, linear algebra, and polynomial arithmetic using SymPy, SageMath, and Mathematica, with practical workflows for research mathematics.
## SymPy Fundamentals
### Symbolic Expressions and Manipulation
```python
from sympy import (
symbols, expand, factor, simplify, cancel, apart,
sin, cos, exp, log, sqrt, pi, oo, I,
Rational, Eq, solve, solveset, S
)View the complete source on GitHubtopology-data-analysis
When your agent uses it
Topological data analysis: persistent homology, Mapper, and TDA tools
What it loads into context
Frontmatter
90 tokensAlways available to the agent
SKILL.md
2,588 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 Concepts
- Persistent Homology with Ripser
- Persistence Vectorization
- The Mapper Algorithm
- Stability and Statistical Significance
- Tools and Libraries
Files
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# Topological Data Analysis
A skill for applying topological data analysis (TDA) methods to research data. Covers persistent homology, Vietoris-Rips complexes, persistence diagrams, the Mapper algorithm, and vectorization methods for integrating topological features into machine learning pipelines.
## Core Concepts
### Simplicial Complexes from Data
TDA extracts topological features (connected components, loops, voids) from data by building simplicial complexes at multiple scales:
| Complex | Construction | Computational Cost |
|---------|-------------|-------------------|
| Vietoris-Rips | Edge if distance < epsilon | O(n^d) for d-simplices |
| Cech | Ball intersection (exact) | Computationally expensive |View the complete source on GitHubpharma-skills
When your agent uses it
6 pharmaceutical research skills. Trigger: drug discovery, pharmacology, clinical trial design, regulatory filing. Design: end-to-end pipeline from target identification to clinical trials.
What it loads into context
Frontmatter
42 tokensAlways available to the agent
SKILL.md
272 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
Files
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# Pharmaceutical Research — 6 Skills
Select the skill matching the user's need, then `read` its SKILL.md.
| Skill | Description |
|-------|-------------|
| [clinical-pharmacology-guide](./clinical-pharmacology-guide/SKILL.md) | Clinical pharmacology principles for dosing, drug interactions, and patient s... |
| [clinical-trial-design-guide](./clinical-trial-design-guide/SKILL.md) | Clinical trial methodology, biostatistics, and study design guidance |
| [drug-development-guide](./drug-development-guide/SKILL.md) | End-to-end drug development pipeline from target identification to regulatory... |
| [drug-target-interaction](./drug-target-interaction/SKILL.md) | Computational drug-target interaction prediction and virtual screening |
| [madd-drug-discovery-guide](./madd-drug-discovery-guide/SKILL.md) | Multi-agent system for automated drug discovery pipelines |
| [pharmacovigilance-guide](./pharmacovigilance-guide/SKILL.md) | Adverse drug event detection, safety signal mining, and drug monitoring |View the complete source on GitHubclinical-pharmacology-guide
When your agent uses it
Clinical pharmacology principles for dosing, drug interactions, and patient s...
What it loads into context
Frontmatter
91 tokensAlways available to the agent
SKILL.md
1,661 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
- Pharmacokinetic-Pharmacodynamic (PK/PD) Relationships
- Drug Interaction Assessment
- Therapeutic Drug Monitoring (TDM)
- Special Populations
- Regulatory Considerations
Files
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# Clinical Pharmacology Guide
A skill for applying clinical pharmacology principles to research and practice. Covers pharmacokinetic/pharmacodynamic modeling, drug interaction assessment, therapeutic drug monitoring, and special population dosing.
## Pharmacokinetic-Pharmacodynamic (PK/PD) Relationships
### The Emax Model
The most widely used PK/PD model relates drug concentration to effect:
```python
import numpy as np
import matplotlib.pyplot as plt
View the complete source on GitHubclinical-trial-design-guide
When your agent uses it
Clinical trial methodology, biostatistics, and study design guidance
What it loads into context
Frontmatter
90 tokensAlways available to the agent
SKILL.md
2,257 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
- Clinical Trial Phases
- Study Design Selection
- Sample Size Calculation
- Randomization Methods
- Interim Analysis and Monitoring
- Survival Analysis
- Regulatory Considerations
- Tools and Software
Files
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# Clinical Trial Design Guide
A skill for designing and analyzing clinical trials, covering study design selection, sample size calculation, randomization methods, interim analysis, survival endpoints, and regulatory considerations. Essential for pharmaceutical researchers, biostatisticians, and clinical scientists.
## Clinical Trial Phases
### Phase Overview
| Phase | Objective | Typical N | Duration | Primary Endpoints |
|-------|-----------|----------|----------|-------------------|
| Phase I | Safety, dose-finding | 20-80 | Months | MTD, DLT, PK profile |
| Phase II | Efficacy signal, dosing | 100-300 | 1-2 years | Response rate, biomarker |
| Phase III | Confirmatory efficacy | 300-3,000+ | 2-4 years | OS, PFS, clinical outcome |
| Phase IV | Post-marketing surveillance | 1,000+ | Ongoing | Safety, real-world effectiveness |View the complete source on GitHubdrug-development-guide
When your agent uses it
End-to-end drug development pipeline from target identification to regulatory...
What it loads into context
Frontmatter
82 tokensAlways available to the agent
SKILL.md
1,518 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
- Drug Discovery Pipeline Overview
- Target Identification and Validation
- Lead Optimization
- Pharmacokinetics Modeling
- Clinical Trial Design
- References
Files
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# Drug Development Guide
A comprehensive skill covering the drug development pipeline from target identification through regulatory approval. Designed for pharmaceutical researchers, medicinal chemists, and clinical scientists conducting academic or industry research.
## Drug Discovery Pipeline Overview
```
Target ID -> Hit Finding -> Lead Optimization -> Preclinical -> Phase I -> Phase II -> Phase III -> Regulatory Filing
(1-2 yr) (1-2 yr) (1-3 yr) (1-2 yr) (1 yr) (2 yr) (3 yr) (1-2 yr)
Total timeline: ~10-15 years | Success rate: ~5-10% from Phase I to approval
Estimated cost: $1.3B-$2.8B per approved drug (DiMasi et al., 2016)
```
View the complete source on GitHubdrug-target-interaction
When your agent uses it
Computational drug-target interaction prediction and virtual screening
What it loads into context
Frontmatter
84 tokensAlways available to the agent
SKILL.md
2,125 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
- Drug-Target Interaction Databases
- Molecular Fingerprints and Descriptors
- Machine Learning for DTI Prediction
- Molecular Docking
- Validation and Benchmarking
- Tools and Libraries
Files
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# Drug-Target Interaction Prediction
A skill for computational prediction of drug-target interactions (DTI), covering molecular docking, machine learning-based binding affinity prediction, compound library screening, and target identification using cheminformatics and structural biology tools.
## Drug-Target Interaction Databases
### Key Data Resources
| Database | Content | Access |
|----------|---------|--------|
| ChEMBL | 2.4M compounds, 15M bioactivities | REST API, SQL dump |
| BindingDB | 2.8M binding data points | Bulk download, REST API |
| DrugBank | 15,000+ drug entries with targets | Academic license |
| PDB (Protein Data Bank) | 220,000+ 3D structures | Free download, REST API |View the complete source on GitHubmadd-drug-discovery-guide
When your agent uses it
Multi-agent system for automated drug discovery pipelines
What it loads into context
Frontmatter
94 tokensAlways available to the agent
SKILL.md
1,170 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
- Agent Pipeline
- Usage
- ADMET Prediction
- Molecule Generation
- Lead Optimization
- Use Cases
- References
Files
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# MADD: Multi-Agent Drug Discovery Guide
## Overview
MADD (Multi-Agent Drug Discovery) is a multi-agent system that automates key stages of the drug discovery pipeline — target identification, molecule generation, property prediction (ADMET), docking simulation, and lead optimization. Specialized agents collaborate to propose, evaluate, and refine drug candidates, reducing the manual effort in early-stage drug discovery research.
## Agent Pipeline
```
Target Protein
↓
Target Analysis Agent (binding site, druggability)
↓
Molecule Generation Agent (de novo design)View the complete source on GitHubpharmacovigilance-guide
When your agent uses it
Adverse drug event detection, safety signal mining, and drug monitoring
What it loads into context
Frontmatter
97 tokensAlways available to the agent
SKILL.md
2,118 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
- Adverse Event Data Sources
- Signal Detection Methods
- MedDRA Terminology
- Temporal Pattern Analysis
- Causality Assessment
- Tools and Resources
Files
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# Pharmacovigilance Guide
A skill for computational pharmacovigilance research, covering adverse drug event (ADE) databases, signal detection algorithms, disproportionality analysis, and safety surveillance methods used in post-market drug monitoring.
## Adverse Event Data Sources
### Key Databases
| Database | Operator | Coverage | Access |
|----------|----------|----------|--------|
| FAERS (FDA Adverse Event Reporting System) | FDA | US spontaneous reports | Free quarterly downloads |
| EudraVigilance | EMA | European reports | Research access via application |
| VigiBase | WHO-UMC | Global (150+ countries) | Research license |
| VAERS | CDC/FDA | US vaccine adverse events | Free download |View the complete source on GitHubphysics-skills
When your agent uses it
5 physics & astrophysics skills. Trigger: physics simulations, astronomical data, computational physics. Design: domain databases (NASA ADS, arXiv) and simulation tool guides.
What it loads into context
Frontmatter
44 tokensAlways available to the agent
SKILL.md
252 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
Files
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# Physics & Astrophysics — 5 Skills
Select the skill matching the user's need, then `read` its SKILL.md.
| Skill | Description |
|-------|-------------|
| [astrophysics-data-guide](./astrophysics-data-guide/SKILL.md) | Astronomical data processing with Astropy, FITS files, and sky surveys |
| [computational-physics-guide](./computational-physics-guide/SKILL.md) | Computational physics methods, simulations, and research tools |
| [nasa-ads-api](./nasa-ads-api/SKILL.md) | Search astrophysics and physics literature via NASA ADS bibliographic database |
| [particle-physics-guide](./particle-physics-guide/SKILL.md) | Particle physics data analysis with ROOT, HEPData, and event processing |
| [quantum-computing-guide](./quantum-computing-guide/SKILL.md) | Explore quantum computing research with Qiskit and Cirq frameworks |View the complete source on GitHubastrophysics-data-guide
When your agent uses it
Astronomical data processing with Astropy, FITS files, and sky surveys
What it loads into context
Frontmatter
93 tokensAlways available to the agent
SKILL.md
2,631 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
- Astronomical Data Formats
- Coordinate Systems
- Photometric Analysis
- Spectral Analysis
- Survey Data Access
- Tools and Software
Files
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# Astrophysics Data Guide
A skill for processing and analyzing astronomical data using standard astrophysics tools. Covers FITS file handling, coordinate transformations, photometric analysis, spectral analysis, catalog cross-matching, and accessing major sky survey archives.
## Astronomical Data Formats
### FITS Files
FITS (Flexible Image Transport System) is the standard data format in astronomy:
```python
from astropy.io import fits
import numpy as np
View the complete source on GitHubcomputational-physics-guide
When your agent uses it
Computational physics methods, simulations, and research tools
What it loads into context
Frontmatter
83 tokensAlways available to the agent
SKILL.md
2,731 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
- Computational Methods Overview
- Molecular Dynamics
- Monte Carlo Methods
- Density Functional Theory
- Numerical Methods
- HPC and Parallelization
- Research Resources
Files
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# Computational Physics Guide
Apply computational methods to physics research, including molecular dynamics, Monte Carlo simulations, quantum computing, and numerical methods for solving physical systems.
## Computational Methods Overview
| Method | Application | Scale | Key Software |
|--------|-------------|-------|-------------|
| **Molecular Dynamics (MD)** | Atomic-scale dynamics, materials | Atoms-molecules | LAMMPS, GROMACS, NAMD |
| **Density Functional Theory (DFT)** | Electronic structure, quantum chemistry | Electrons | VASP, Gaussian, Quantum ESPRESSO |
| **Monte Carlo (MC)** | Statistical mechanics, phase transitions | Configurable | Custom, CASINO |
| **Finite Element Method (FEM)** | Continuum mechanics, electrostatics | Macroscopic | COMSOL, FEniCS, Abaqus |
| **Finite Difference (FDTD)** | Electrodynamics, wave propagation | Macroscopic | Meep, Lumerical |
| **N-body Simulation** | Gravitational dynamics, plasma | Stars/particles | GADGET, REBOUND |View the complete source on GitHubnasa-ads-api
When your agent uses it
Search astrophysics and physics literature via NASA ADS bibliographic database
What it loads into context
Frontmatter
99 tokensAlways available to the agent
SKILL.md
1,818 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
- Authentication
- Core Endpoints
- Rate Limits
- Common Patterns
- References
Files
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# NASA ADS API Guide
## Overview
The NASA Astrophysics Data System (ADS) is a digital library operated by the Smithsonian Astrophysical Observatory under a NASA grant. It is the primary bibliographic database for astronomy and astrophysics, also covering significant portions of physics, geophysics, and related disciplines. ADS indexes over 16 million records and provides access to full-text articles, citations, and usage metrics.
ADS is indispensable for astronomers and physicists. Nearly every paper in astrophysics is indexed in ADS, and the system provides powerful search capabilities including full-text search, citation and reference tracking, author disambiguation, and object-level queries (search by astronomical object name). The database integrates with SIMBAD, NED, and other astronomical databases to link publications to the celestial objects they study.
The ADS API requires a free API token and supports 3,000 requests per day. It returns JSON and supports a rich query language with field-specific searches, boolean operators, and positional queries.
## Authentication
Authentication is required via a free API token. Register at https://ui.adsabs.harvard.edu/user/settings/token to generate your token. Include it in every request as a header:
View the complete source on GitHubparticle-physics-guide
When your agent uses it
Particle physics data analysis with ROOT, HEPData, and event processing
What it loads into context
Frontmatter
88 tokensAlways available to the agent
SKILL.md
2,532 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 Formats and Access
- Event Selection and Reconstruction
- Statistical Methods for Discovery
- Histogram Analysis
- Monte Carlo Simulation
- Tools and Software
Files
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# Particle Physics Guide
A skill for analyzing particle physics data, covering event reconstruction, histogram analysis, statistical methods for discovery, and the standard tools used in high-energy physics (HEP) research. Includes ROOT, uproot, pyhf, and HEPData workflows.
## Data Formats and Access
### HEP Data Ecosystem
| Format | Description | Typical Size | Access Tool |
|--------|-------------|-------------|-------------|
| ROOT (.root) | Columnar binary format, HEP standard | GB-TB | ROOT, uproot |
| NanoAOD | Compact analysis format (CMS) | ~1 KB/event | uproot, coffea |
| DAOD_PHYS | Derived analysis format (ATLAS) | ~10 KB/event | ROOT, uproot |
| HepMC | Monte Carlo event record | Variable | pyhepmc |View the complete source on GitHubquantum-computing-guide
When your agent uses it
Explore quantum computing research with Qiskit and Cirq frameworks
What it loads into context
Frontmatter
94 tokensAlways available to the agent
SKILL.md
1,686 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
- Quantum Computing Fundamentals
- Building Quantum Circuits with Qiskit
- Fundamental Quantum Algorithms
- Noise and Error Mitigation
- Running on Real Hardware
- Publishing Quantum Computing Research
Files
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# Quantum Computing Guide
A skill for conducting quantum computing research using Qiskit (IBM) and Cirq (Google) frameworks. Covers quantum circuit construction, fundamental algorithms, noise simulation, and practical considerations for running experiments on quantum hardware.
## Quantum Computing Fundamentals
### Key Concepts
```
Qubit: The basic unit of quantum information
- Superposition: A qubit can be in a state |0>, |1>, or any
linear combination alpha|0> + beta|1> where |alpha|^2 + |beta|^2 = 1
- Measurement: Collapses to |0> with probability |alpha|^2
or |1> with probability |beta|^2View the complete source on GitHubsocial-science-skills
When your agent uses it
6 social science skills. Trigger: survey research, social networks, psychology, behavioral studies. Design: quantitative and qualitative methods for social science research.
What it loads into context
Frontmatter
40 tokensAlways available to the agent
SKILL.md
272 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
Files
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# Social Science — 6 Skills
Select the skill matching the user's need, then `read` its SKILL.md.
| Skill | Description |
|-------|-------------|
| [ipums-microdata-api](./ipums-microdata-api/SKILL.md) | Access harmonized census and survey microdata via the IPUMS API |
| [network-analysis-guide](./network-analysis-guide/SKILL.md) | Social network analysis methods, metrics, and visualization tools |
| [psychology-research-guide](./psychology-research-guide/SKILL.md) | Psychological research methods, experimental design, and analysis |
| [social-research-methods](./social-research-methods/SKILL.md) | Core methods for empirical social science research including surveys and expe... |
| [sociology-research-methods](./sociology-research-methods/SKILL.md) | Sociological research methods from observation to quantitative analysis |
| [survey-research-guide](./survey-research-guide/SKILL.md) | Design, deploy, and analyze surveys for social science and organizational res... |View the complete source on GitHubipums-microdata-api
When your agent uses it
Access harmonized census and survey microdata via the IPUMS API
What it loads into context
Frontmatter
97 tokensAlways available to the agent
SKILL.md
1,647 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
- IPUMS Data Collections
- API Endpoints
- Python Usage
- Key Variables (IPUMS USA)
- Use Cases
- References
Files
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# IPUMS Microdata API
## Overview
IPUMS (Integrated Public Use Microdata Series) provides the world's largest collection of harmonized census and survey microdata. Hosted by the University of Minnesota, it covers demographic, health, labor, and geographic data across 100+ countries and 100+ years. The API enables programmatic extract creation, metadata queries, and data retrieval. Free registration required.
## IPUMS Data Collections
| Collection | Coverage | Records |
|-----------|----------|---------|
| IPUMS USA | U.S. Census & ACS (1850-present) | 16B+ person-records |
| IPUMS CPS | Current Population Survey (1962-present) | Labor force data |
| IPUMS International | Census data from 100+ countries | 2B+ person-records |
| IPUMS NHGIS | U.S. geographic/aggregate data | County-level stats |View the complete source on GitHubnetwork-analysis-guide
When your agent uses it
Social network analysis methods, metrics, and visualization tools
What it loads into context
Frontmatter
88 tokensAlways available to the agent
SKILL.md
2,129 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
- Network Data Fundamentals
- Core Network Metrics
- Community Detection
- Ego Network Analysis
- Visualization Best Practices
Files
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# Network Analysis Guide
A skill for conducting social network analysis (SNA) in research contexts. Covers network data collection and representation, key structural metrics (centrality, density, clustering), community detection algorithms, ego network analysis, longitudinal network models, and visualization best practices using Python NetworkX, igraph, and Gephi.
## Network Data Fundamentals
### Representing Network Data
Networks consist of nodes (actors) and edges (relationships). The first decision in any SNA project is how to represent the data.
```
Network data formats:
Edge List (simplest):View the complete source on GitHubpsychology-research-guide
When your agent uses it
Psychological research methods, experimental design, and analysis
What it loads into context
Frontmatter
97 tokensAlways available to the agent
SKILL.md
2,608 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
- Experimental Design
- Pre-Registration
- Statistical Analysis
- Psychometric Validation
- Reporting Results (APA Format)
- Best Practices
- References
Files
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# Psychology Research Guide
## Overview
Psychology is the scientific study of mind and behavior, spanning cognitive processes, social influence, developmental trajectories, clinical disorders, and neuroscience. The field has undergone a methodological revolution since the replication crisis of the 2010s, with new standards for statistical rigor, pre-registration, transparency, and open science fundamentally reshaping how research is conducted and evaluated.
This guide covers the practical aspects of conducting psychology research in the post-replication-crisis era: experimental design with adequate power, pre-registration, appropriate statistical analysis, effect size reporting, and the tools and platforms that support reproducible psychological science. The focus is on what reviewers and editors at top journals now expect.
Whether you are designing a behavioral experiment, analyzing survey data, conducting a psychometric validation, or reviewing a manuscript, these patterns reflect current best practices in the field.
## Experimental Design
### Between-Subjects vs. Within-Subjects
View the complete source on GitHubsocial-research-methods
When your agent uses it
Core methods for empirical social science research including surveys and expe...
What it loads into context
Frontmatter
86 tokensAlways available to the agent
SKILL.md
1,560 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
- Research Design Fundamentals
- Survey Design
- Experimental Design in Social Science
- Data Analysis Workflow
- Ethical Requirements
- Key References
Files
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# Social Research Methods
A comprehensive skill for designing and conducting empirical social science research. Covers survey methodology, experimental design, qualitative methods, and mixed-methods approaches used across sociology, political science, and psychology.
## Research Design Fundamentals
### Selecting a Research Strategy
```
Research Question Type -> Recommended Design
"What is the prevalence of X?" -> Cross-sectional survey
"Does X cause Y?" -> Randomized experiment or quasi-experiment
"How does X develop over time?" -> Longitudinal panel studyView the complete source on GitHubsociology-research-methods
When your agent uses it
Sociological research methods from observation to quantitative analysis
What it loads into context
Frontmatter
93 tokensAlways available to the agent
SKILL.md
1,743 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
- Core Theoretical Frameworks
- Qualitative Methods
- Quantitative Methods
- Mixed Methods Design
- Ethics in Social Research
- References
Files
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# Sociological Research Methods
## Overview
Sociology studies social structures, institutions, relationships, and change through systematic empirical investigation. This guide covers the major research traditions — from ethnographic fieldwork to large-scale survey analysis — along with the theoretical frameworks that shape research questions. Useful for researchers designing social science studies or analyzing sociological data.
## Core Theoretical Frameworks
Understanding which framework shapes your research question determines your methodology:
| Framework | Core Question | Methods Favored | Key Thinkers |
|-----------|--------------|-----------------|-------------|
| **Structural Functionalism** | How do institutions maintain social order? | Surveys, statistical analysis | Durkheim, Parsons, Merton |
| **Conflict Theory** | How does power inequality shape outcomes? | Historical analysis, critical ethnography | Marx, Weber, Bourdieu |View the complete source on GitHubsurvey-research-guide
When your agent uses it
Design, deploy, and analyze surveys for social science and organizational res...
What it loads into context
Frontmatter
88 tokensAlways available to the agent
SKILL.md
1,493 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
- Survey Design Process
- Survey Administration
- Response Bias Detection
- Analysis Techniques
- Reporting Standards
Files
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# Survey Research Guide
A practical skill for conducting rigorous survey research from instrument design through data analysis. Covers questionnaire construction, sampling strategies, administration methods, response bias mitigation, and analytical techniques commonly used in communication studies, anthropology, management, and sociology.
## Survey Design Process
### Phase 1: Conceptualization
Map your research questions to survey constructs:
```python
def create_survey_blueprint(research_questions: list[dict]) -> dict:
"""
Generate a survey blueprint mapping RQs to constructs and items.View the complete source on GitHubdiscovery-skills
When your agent uses it
9 paper discovery skills. Trigger: finding new relevant papers, tracking citations, staying current. Design: automated monitoring, recommendation engines, and alert setup guides.
What it loads into context
Frontmatter
40 tokensAlways available to the agent
SKILL.md
359 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
Files
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# Paper Discovery — 9 Skills
Select the skill matching the user's need, then `read` its SKILL.md.
| Skill | Description |
|-------|-------------|
| [citation-alert-guide](./citation-alert-guide/SKILL.md) | Set up citation alerts and track new papers citing key references |
| [conference-proceedings-guide](./conference-proceedings-guide/SKILL.md) | Find, access, and cite conference papers and proceedings effectively |
| [literature-mapping-guide](./literature-mapping-guide/SKILL.md) | Visual literature mapping and connected papers exploration |
| [paper-recommendation-guide](./paper-recommendation-guide/SKILL.md) | Systematic paper recommendation and discovery using multiple methods |
| [papers-we-love-guide](./papers-we-love-guide/SKILL.md) | Community-curated directory of influential CS research papers |
| [rss-paper-feeds](./rss-paper-feeds/SKILL.md) | Set up RSS feeds and alerts to track new publications in your research area |
| [semantic-paper-radar](./semantic-paper-radar/SKILL.md) | Semantic literature discovery and synthesis using embeddings |
| [semantic-scholar-recs-guide](./semantic-scholar-recs-guide/SKILL.md) | Paper discovery via recommendation APIs (OpenAlex, CrossRef citation networks) |View the complete source on GitHubcitation-alert-guide
When your agent uses it
Set up citation alerts and track new papers citing key references
What it loads into context
Frontmatter
86 tokensAlways available to the agent
SKILL.md
1,157 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
- Alert Types and Platforms
- Building a Monitoring System
- Managing Alert Overload
- PubMed My NCBI Alerts
Files
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# Citation Alert Guide
A skill for setting up automated citation alerts and tracking systems that notify you when key papers are cited, new articles match your research interests, or important journals publish relevant work.
## Alert Types and Platforms
### Overview of Alert Systems
| Platform | Alert Type | Coverage | Cost |
|----------|-----------|----------|------|
| Google Scholar | Citation alert, keyword alert | Broadest, includes preprints | Free |
| Web of Science | Citation alert, search alert, journal ToC | WoS-indexed journals | Institutional |
| Scopus | Citation alert, search alert, author alert | Scopus-indexed journals | Institutional |
| PubMed | Email alert (My NCBI) | Biomedical literature | Free |View the complete source on GitHubconference-proceedings-guide
When your agent uses it
Find, access, and cite conference papers and proceedings effectively
What it loads into context
Frontmatter
91 tokensAlways available to the agent
SKILL.md
1,184 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
- Major Proceedings Databases
- Navigating Conference Tiers
- Accessing Conference Papers
- Citing Conference Proceedings
Files
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# Conference Proceedings Guide
A skill for finding, accessing, and citing conference papers and proceedings. In many fields -- especially computer science, engineering, and HCI -- conferences are the primary venue for publishing cutting-edge research. This guide covers major proceedings databases, search strategies, and citation practices.
## Major Proceedings Databases
### Database Overview
| Database | Coverage | Access |
|----------|----------|--------|
| ACM Digital Library | ACM conferences (CHI, SIGCOMM, KDD, etc.) | Institutional or ACM membership |
| IEEE Xplore | IEEE conferences (CVPR, ICRA, INFOCOM, etc.) | Institutional or IEEE membership |
| DBLP | CS bibliography, links to proceedings | Free metadata |
| Springer LNCS | Lecture Notes in Computer Science series | Institutional |View the complete source on GitHubliterature-mapping-guide
When your agent uses it
Visual literature mapping and connected papers exploration
What it loads into context
Frontmatter
80 tokensAlways available to the agent
SKILL.md
1,589 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
- What Is Literature Mapping?
- Tools for Visual Literature Mapping
- Building a Custom Literature Map with Python
- Interpreting Literature Maps
- Best Practices
Files
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# Literature Mapping Guide
Build visual maps of scholarly literature to understand research landscapes, identify clusters of related work, and discover hidden connections between papers.
## What Is Literature Mapping?
Literature mapping transforms flat lists of papers into interactive visual networks where nodes represent papers and edges represent citation or similarity relationships. This approach helps researchers:
- See the overall structure of a research field at a glance
- Identify seminal papers (highly connected nodes)
- Discover clusters and subfields
- Find bridging papers that connect disparate areas
- Spot gaps where no papers exist
View the complete source on GitHubpaper-recommendation-guide
When your agent uses it
Systematic paper recommendation and discovery using multiple methods
What it loads into context
Frontmatter
90 tokensAlways available to the agent
SKILL.md
1,417 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
- Algorithmic Recommendation Services
- Citation-Based Discovery Methods
- Building a Reading Pipeline
- Interdisciplinary Discovery
- References
Files
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# Paper Recommendation Guide
## Overview
Finding the right papers to read is a research skill in itself. Beyond keyword searches, modern researchers have access to a rich ecosystem of recommendation tools that use citation networks, semantic similarity, co-authorship patterns, and collaborative filtering to surface relevant papers you might otherwise miss.
This skill provides a systematic approach to paper discovery that goes beyond passive reading. It covers algorithmic recommendation services, citation-based discovery techniques, social and community-driven methods, and strategies for building and maintaining a well-curated reading pipeline. The goal is to minimize the chance that you miss an important paper while avoiding information overload.
Whether you are entering a new field and need foundational papers, tracking the frontier of a mature research area, or looking for interdisciplinary connections, this guide provides concrete methods for each scenario.
## Algorithmic Recommendation Services
### OpenAlex Related Works
View the complete source on GitHubpapers-we-love-guide
When your agent uses it
Community-curated directory of influential CS research papers
What it loads into context
Frontmatter
94 tokensAlways available to the agent
SKILL.md
1,337 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
- Repository Structure
- Topic Categories
- Using PWL for Research
- Reading Group Integration
- Building a Reading List
- Contributing to PWL
- Use Cases
- References
Files
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# Papers We Love Guide
## Overview
Papers We Love (PWL) is a community-driven repository of influential computer science research papers organized by topic, with worldwide reading groups. The repository contains direct links to PDFs and summaries for hundreds of landmark papers across distributed systems, programming languages, machine learning, security, and more. A go-to resource for discovering foundational and impactful research.
## Repository Structure
```
papers-we-love/
├── distributed_systems/
│ ├── README.md # Curated list with descriptions
│ ├── lamport-clocks.pdf
│ └── raft.pdfView the complete source on GitHubrss-paper-feeds
When your agent uses it
Set up RSS feeds and alerts to track new publications in your research area
What it loads into context
Frontmatter
86 tokensAlways available to the agent
SKILL.md
1,740 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
- RSS Feed Sources for Academic Papers
- Citation Alerts
- RSS Reader Configuration
- Automated Filtering and Summarization
- Integration with Reference Managers
Files
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# RSS Paper Feeds
A skill for configuring automated literature monitoring using RSS feeds, email alerts, and citation notifications. Stay current with new publications in your research area without manual searching.
## RSS Feed Sources for Academic Papers
### Journal-Level Feeds
Most major publishers provide RSS feeds for their journals:
| Publisher | Feed URL Pattern | Example |
|-----------|-----------------|---------|
| Nature | `https://www.nature.com/[journal].rss` | `nature.com/nature.rss` |
| Science | `https://www.science.org/action/showFeed?type=etoc&feed=rss&jc=[code]` | jc=science |View the complete source on GitHubsemantic-paper-radar
When your agent uses it
Semantic literature discovery and synthesis using embeddings
What it loads into context
Frontmatter
86 tokensAlways available to the agent
SKILL.md
1,421 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
- Semantic Search Fundamentals
- Discovery Workflows
- Semantic Synthesis
- References
Files
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# Semantic Paper Radar
## Overview
Traditional literature search relies on keyword matching—you find papers that contain the exact terms you search for. Semantic paper discovery goes further by understanding the meaning of research content and finding papers that are conceptually related, even when they use different terminology. This is especially powerful for interdisciplinary research, where the same idea may be expressed in completely different vocabularies across fields.
The Semantic Paper Radar skill provides methods for using embedding-based semantic search, vector databases, and AI-powered synthesis to build a comprehensive, continuously updated view of the literature relevant to your research. It enables you to discover papers you would never find through keyword search alone and to synthesize findings across large bodies of work.
This skill covers setting up a personal semantic search index over your paper collection, querying public semantic search APIs, and using LLM-powered analysis to extract themes and connections from clusters of related papers.
## Semantic Search Fundamentals
### How Embedding-Based Search Works
View the complete source on GitHubsemantic-scholar-recs-guide
When your agent uses it
Paper discovery via recommendation APIs (OpenAlex, CrossRef citation networks)
What it loads into context
Frontmatter
86 tokensAlways available to the agent
SKILL.md
1,559 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
- Finding Related Papers
- Citation Network Traversal
- Building a Reading List Pipeline
- Best Practices
Files
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# Paper Discovery via OpenAlex & CrossRef
Leverage the OpenAlex and CrossRef APIs to discover related papers, traverse citation networks, and build comprehensive reading lists programmatically.
## Overview
OpenAlex indexes over 250 million academic works and provides a free, no-key-required API that supports:
- Work search by title, keyword, or DOI
- Citation and reference graph traversal
- Author profiles and publication histories
- Concept-based discovery across disciplines
- Institutional and venue filtering
View the complete source on GitHubzotero-arxiv-daily-guide
When your agent uses it
Guide to Zotero arXiv Daily for personalized daily paper recommendations
What it loads into context
Frontmatter
96 tokensAlways available to the agent
SKILL.md
1,302 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
- Installation and Setup
- Core Features
- Research Workflow Integration
- Configuring Notification Preferences
- References
Files
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# Zotero arXiv Daily Guide
## Overview
Zotero arXiv Daily is a popular Zotero plugin with over 5,000 GitHub stars that delivers personalized daily paper recommendations from arXiv directly into your Zotero library. By analyzing the papers you already have in your collections, the plugin identifies your research interests and surfaces new preprints that are most relevant to your work.
The challenge of staying current with preprint literature is well known to researchers. ArXiv publishes thousands of new papers daily across dozens of categories, and manually scanning listings or relying solely on keyword alerts often results in information overload or missed relevant work. Zotero arXiv Daily addresses this by using your existing library as a profile of your interests, producing recommendations that improve as your library grows.
The plugin integrates naturally into the Zotero workflow. Recommended papers appear in a dedicated collection where you can review titles and abstracts, save promising papers to your working collections, and dismiss irrelevant suggestions. Over time the recommendation engine learns from your accept and dismiss decisions, refining its model of your interests.
## Installation and Setup
Install Zotero arXiv Daily through the standard Zotero plugin process:
View the complete source on GitHubfulltext-skills
When your agent uses it
16 full-text access skills. Trigger: accessing paper PDFs, bulk downloading, open access, text mining. Design: legal full-text retrieval from open repositories, archives, and preprint servers.
What it loads into context
Frontmatter
48 tokensAlways available to the agent
SKILL.md
582 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
Files
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# Full-Text Access — 16 Skills
Select the skill matching the user's need, then `read` its SKILL.md.
| Skill | Description |
|-------|-------------|
| [arxiv-latex-source](./arxiv-latex-source/SKILL.md) | Download and parse LaTeX source files from arXiv preprints |
| [bioc-pmc-api](./bioc-pmc-api/SKILL.md) | Access PMC Open Access articles in BioC format for text mining |
| [core-api-guide](./core-api-guide/SKILL.md) | Search and retrieve open access research papers via CORE aggregator |
| [dataverse-api](./dataverse-api/SKILL.md) | Deposit and discover research datasets via Harvard Dataverse API |
| [doaj-api](./doaj-api/SKILL.md) | Search open access journals and articles in the DOAJ directory |
| [hal-archive-api](./hal-archive-api/SKILL.md) | Access French and European research via the HAL open archive API |
| [institutional-repository-guide](./institutional-repository-guide/SKILL.md) | Access papers from institutional and subject repositories at scale |
| [interlibrary-loan-guide](./interlibrary-loan-guide/SKILL.md) | Access papers through interlibrary loan and document delivery services |View the complete source on GitHubarxiv-latex-source
When your agent uses it
Download and parse LaTeX source files from arXiv preprints
What it loads into context
Frontmatter
103 tokensAlways available to the agent
SKILL.md
2,055 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
- Authentication
- Core Endpoints
- LaTeX Source Parsing Guide
- Rate Limits
- Academic Use Cases
- Complete Python Example
- References
Files
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# arXiv LaTeX Source Access Guide
## Overview
arXiv stores the original LaTeX source files for the vast majority of its 2.4 million+ preprints. Accessing LaTeX source provides major advantages over PDF parsing: exact mathematical notation as written by the author, structured sections and labels, machine-readable bibliography entries, and intact figure captions, table data, and cross-references.
For formula extraction, citation graph construction, section-level text analysis, or training data curation for scientific language models, LaTeX source is the gold standard. PDF parsing introduces OCR errors in equations, loses structural hierarchy, and mangles complex tables.
The e-print endpoint serves source bundles as gzip-compressed tarballs (`.tar.gz`) containing `.tex` files, figures, `.bib`/`.bbl` bibliography files, style files, and supplementary materials. No authentication is required.
## Authentication
No authentication or API key is required. The e-print endpoint is publicly accessible. However, arXiv asks that automated tools set a descriptive `User-Agent` header and comply with rate limits.
View the complete source on GitHubbioc-pmc-api
When your agent uses it
Access PMC Open Access articles in BioC format for text mining
What it loads into context
Frontmatter
110 tokensAlways available to the agent
SKILL.md
1,260 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
- API Endpoints
- BioC JSON Structure
- Python Usage
- Data Coverage
- Rate Limits
- Citation
- References
Files
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# BioC API for PMC Open Access
## Overview
The BioC API provides full-text articles from PubMed Central (PMC) in the BioC format — a simplified XML/JSON structure designed specifically for biomedical text mining. Unlike the standard PMC OAI service (which returns JATS XML), BioC pre-segments text into passages with offset annotations, making it ideal for NLP pipelines, named entity recognition, relation extraction, and other text mining tasks. Free, no authentication required.
## API Endpoints
### Base URL
```
https://www.ncbi.nlm.nih.gov/research/bionlp/RESTful/pmcoa.cgi/BioC_json/{PMCID}/unicode
```
View the complete source on GitHubcore-api-guide
When your agent uses it
Search and retrieve open access research papers via CORE aggregator
What it loads into context
Frontmatter
85 tokensAlways available to the agent
SKILL.md
1,259 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
- Authentication
- Core Endpoints
- Common Research Patterns
- Rate Limits and Best Practices
- References
Files
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# CORE API Guide
## Overview
CORE (COnnecting REpositories) is the world's largest aggregator of open access research papers, providing access to over 130 million articles harvested from thousands of data providers worldwide. The CORE API enables programmatic search, retrieval, and analysis of scholarly full-text content across repositories, journals, and preprint servers.
The API is particularly valuable for researchers conducting systematic reviews, bibliometric analyses, and literature mining tasks. Unlike many scholarly APIs that only provide metadata, CORE specializes in delivering full-text content, making it essential for text mining and natural language processing workflows in academic research.
CORE's v3 API provides a RESTful interface with JSON responses, supporting complex search queries with Boolean operators, field-specific filtering, and batch operations. It is free for non-commercial academic use, though an API key is required to access the service.
## Authentication
CORE requires a free API key for all requests. Register at https://core.ac.uk/services/api to obtain one.
View the complete source on GitHubdataverse-api
When your agent uses it
Deposit and discover research datasets via Harvard Dataverse API
What it loads into context
Frontmatter
88 tokensAlways available to the agent
SKILL.md
1,702 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
- API Endpoints
- Response Structure
- Python Usage
- Other Dataverse Installations
- References
Files
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# Harvard Dataverse API
## Overview
Dataverse is an open-source research data repository platform developed by Harvard IQSS, hosting 150K+ datasets across 80+ installations worldwide. The Harvard Dataverse alone has 130K+ datasets covering social science, natural science, and humanities. The API supports search, metadata retrieval, file download, and dataset deposit. Free, no authentication for read access.
## API Endpoints
### Base URL
```
https://dataverse.harvard.edu/api
```
View the complete source on GitHubdoaj-api
When your agent uses it
Search open access journals and articles in the DOAJ directory
What it loads into context
Frontmatter
85 tokensAlways available to the agent
SKILL.md
1,476 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
- Authentication
- Core Endpoints
- Rate Limits
- Common Patterns
- References
Files
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# DOAJ API Guide
## Overview
The Directory of Open Access Journals (DOAJ) is a community-curated online directory that indexes and provides access to high quality, open access, peer-reviewed journals. Founded in 2003, DOAJ currently indexes over 20,000 journals and 9 million articles from 130 countries, covering all areas of science, technology, medicine, social sciences, arts, and humanities.
DOAJ serves as a quality filter for open access publishing. Journals must meet strict criteria to be included, including editorial review, transparent policies, and adherence to open access principles. This makes DOAJ particularly valuable for researchers who need to verify whether a journal is a legitimate open access outlet, librarians curating discovery systems, and developers building tools that surface OA content.
The DOAJ API provides free, unauthenticated access to search and retrieve journal and article metadata. All data is available under a CC BY-SA license. The API returns JSON and supports Elasticsearch-style queries for advanced filtering.
## Authentication
No authentication required. The DOAJ API is fully open and free to use. No API key, registration, or email is needed. There are no published rate limits, but users should be respectful and avoid sending excessive concurrent requests. For bulk data access, DOAJ provides data dumps at https://doaj.org/docs/public-data-dump/.
View the complete source on GitHubhal-archive-api
When your agent uses it
Access French and European research via the HAL open archive API
What it loads into context
Frontmatter
94 tokensAlways available to the agent
SKILL.md
1,871 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
- API Endpoints
- Response Structure
- Python Usage
- HAL Domains
- References
Files
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# HAL Open Archive API
## Overview
HAL (Hyper Articles en Ligne) is France's national open archive for scholarly deposits. Managed by CNRS, it hosts 4M+ full-text documents from French research institutions and international collaborators. The API provides Solr-based search with full metadata, PDF links, and OAI-PMH harvesting. Free, no authentication required.
## API Endpoints
### Search API
```bash
# Keyword search
curl "https://api.archives-ouvertes.fr/search/?q=machine+learning&rows=20&wt=json"
View the complete source on GitHubinstitutional-repository-guide
When your agent uses it
Access papers from institutional and subject repositories at scale
What it loads into context
Frontmatter
92 tokensAlways available to the agent
SKILL.md
1,752 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
- Repository Landscape
- OAI-PMH Harvesting from Repositories
- Major Repository Platforms
- Building a Harvesting Pipeline
- Ethical Considerations
- References
Files
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# Institutional Repository Guide
Institutional repositories (IRs) are university-run digital archives that store and provide open access to their researchers' scholarly output — dissertations, journal articles, conference papers, datasets, and technical reports. Subject repositories like arXiv, bioRxiv, SSRN, and RePEc serve similar functions for specific disciplines. Together, they form a distributed network of open scholarship that complements commercial databases.
This guide covers how to discover, access, and systematically harvest content from institutional and subject repositories for literature reviews, meta-analyses, and research data collection.
## Repository Landscape
### Types of Repositories
```
Institutional Repositories (IR):
- Run by universities to archive their researchers' output
- Examples: DSpace, EPrints, Fedora-based systemsView the complete source on GitHubinterlibrary-loan-guide
When your agent uses it
Access papers through interlibrary loan and document delivery services
What it loads into context
Frontmatter
86 tokensAlways available to the agent
SKILL.md
1,262 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
- Understanding Interlibrary Loan
- Step-by-Step ILL Process
- Free Alternatives Before Requesting ILL
- Document Delivery Services
- Tips for Efficient Access
Files
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# Interlibrary Loan Guide
A skill for accessing research papers and books through interlibrary loan (ILL) and document delivery services when your institution does not have a subscription. Covers ILL workflows, alternative free access methods, and strategies for rapid document retrieval.
## Understanding Interlibrary Loan
### What Is ILL?
Interlibrary loan is a service where your library borrows materials from another library on your behalf. Most academic libraries offer ILL free of charge to their students, faculty, and staff. Turnaround time is typically 1-7 business days for articles and 1-3 weeks for books.
### Types of Requests
```
Article/Chapter Request:View the complete source on GitHubopen-access-guide
When your agent uses it
Navigate open access policies, repositories, and legal full-text retrieval me...
What it loads into context
Frontmatter
87 tokensAlways available to the agent
SKILL.md
1,618 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
- Open Access Models
- Legal Full-Text Sources
- Self-Archiving and Green OA
- Institutional Repository Deposit
Files
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# Open Access Guide
A skill for understanding open access publishing models, locating free full-text articles legally, and navigating self-archiving policies. Essential for researchers at institutions with limited journal subscriptions.
## Open Access Models
### Types of Open Access
| Type | Description | Cost to Author | Reader Access |
|------|------------|----------------|---------------|
| Gold OA | Published OA by journal (APC paid) | $1,000-$11,000 | Immediate, permanent |
| Green OA | Self-archived preprint/postprint | Free | After embargo (0-24 months) |
| Diamond/Platinum OA | Journal charges no APC | Free | Immediate, permanent |
| Bronze OA | Free to read on publisher site | Free | No reuse license, may be temporary |View the complete source on GitHubopen-access-mining-guide
When your agent uses it
Mine open access full-text repositories for research data extraction
What it loads into context
Frontmatter
93 tokensAlways available to the agent
SKILL.md
2,397 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
- Legal Framework for Text and Data Mining
- Major Open Access Repositories
- Full-Text Retrieval and Parsing
- Information Extraction from Full Text
- Building Reproducible Pipelines
Files
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# Open Access Mining Guide
A skill for systematically mining open access full-text repositories to extract structured research data at scale. Covers legal frameworks for text and data mining (TDM), major open access repositories and their APIs, full-text retrieval and parsing, section-level extraction, entity recognition in scientific text, and building reproducible mining pipelines.
## Legal Framework for Text and Data Mining
### Rights and Regulations
Text and data mining of published literature operates within a specific legal framework that varies by jurisdiction. Understanding these rules is essential before starting any mining project.
```
Legal landscape for TDM:
EU Directive 2019/790 (DSM Directive):View the complete source on GitHubosf-api
When your agent uses it
Manage open science projects and preprints via the OSF REST API
What it loads into context
Frontmatter
93 tokensAlways available to the agent
SKILL.md
2,087 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
- Authentication
- API Endpoints
- Response Structure (Preprint)
- Python Usage
- Common Research Patterns
- Rate Limits and Best Practices
- References
Files
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# OSF (Open Science Framework) API
## Overview
The Open Science Framework by the Center for Open Science provides infrastructure for the entire research lifecycle — project management, file storage, preprint hosting, and registrations. The API enables search, project creation, file management, and preprint discovery across OSF Preprints, PsyArXiv, SocArXiv, and 25+ community preprint servers. Free, no auth for read access.
## Authentication
Public read access requires no authentication. For creating or modifying resources, generate a personal access token at https://osf.io/settings/tokens.
```bash
# Public access (no auth needed)
curl "https://api.osf.io/v2/nodes/?filter[title]=reproducibility"
View the complete source on GitHubpmc-ftp-bulk-download
When your agent uses it
Bulk download PMC Open Access articles via FTP for large-scale mining
What it loads into context
Frontmatter
102 tokensAlways available to the agent
SKILL.md
1,562 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
- FTP Access Points
- Download Strategies
- PMC ID Cross-Referencing
- Package Contents
- Best Practices
- References
Files
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# PMC FTP Bulk Download
## Overview
The PMC FTP Service provides bulk download access to millions of full-text articles from PubMed Central's Open Access Subset. Unlike the single-article APIs (E-utilities, BioC), the FTP service is designed for large-scale corpus construction — downloading entire collections for text mining, NLP training, systematic reviews, and bibliometric analysis. Free, no authentication required.
**Note**: PMC is migrating to AWS-based Cloud Service in August 2026. FTP paths may change; check official docs for updates.
## FTP Access Points
### Connection
```bash
# FTP (classic)View the complete source on GitHubpmc-oai-api
When your agent uses it
PubMed Central OAI-PMH metadata harvesting
What it loads into context
Frontmatter
89 tokensAlways available to the agent
SKILL.md
2,034 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
- Authentication
- Core Endpoints
- Rate Limits
- Common Patterns
- References
Files
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# PMC-OAI API Guide
## Overview
PubMed Central (PMC) is a free full-text archive of biomedical and life sciences journal literature at the U.S. National Institutes of Health's National Library of Medicine (NIH/NLM). The PMC OAI-PMH (Open Archives Initiative Protocol for Metadata Harvesting) service provides a standardized interface for systematically harvesting metadata and full-text content from the PMC archive.
The OAI-PMH protocol is an internationally recognized standard for metadata harvesting, widely used by libraries, repositories, and research infrastructure. The PMC implementation allows researchers to programmatically discover and retrieve article metadata, including titles, authors, abstracts, MeSH terms, publication dates, and links to full-text XML and PDF versions. This is particularly valuable for building local search indexes, systematic review pipelines, and text mining corpora.
Biomedical researchers, systematic reviewers, bioinformaticians, medical librarians, and text mining specialists use the PMC OAI-PMH service to harvest large collections of open-access biomedical literature for meta-analyses, natural language processing research, knowledge graph construction, and institutional repository enrichment. PMC contains over 9 million full-text articles, making it one of the largest open-access biomedical literature collections in the world.
## Authentication
No authentication required. The PMC OAI-PMH service is freely accessible without any API key, token, or registration. All requests are made via standard HTTP GET requests. However, users must comply with NCBI usage guidelines and respect the rate limits to ensure fair access for all users.
View the complete source on GitHubpreprint-servers-guide
When your agent uses it
Guide to preprint servers across scientific disciplines
What it loads into context
Frontmatter
87 tokensAlways available to the agent
SKILL.md
1,559 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
- What Are Preprints?
- Major Preprint Servers by Discipline
- Submission Workflow
- Licensing Options
- Journal Policies on Preprints
- Programmatic Access Comparison
- Best Practices
Files
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# Preprint Servers Guide
A comprehensive guide to preprint servers across all major academic disciplines, covering submission workflows, licensing, and programmatic access.
## What Are Preprints?
Preprints are complete research manuscripts shared publicly before formal peer review. They enable rapid dissemination of findings, establish priority of discovery, and invite community feedback before journal submission.
Key characteristics:
- **Not peer reviewed** (but increasingly moderated for basic quality)
- **Freely accessible** to anyone
- **Citable** with a DOI
- **Versioned** (authors can post revisions)
- **Compatible** with most journal submissions (check journal policy first)View the complete source on GitHubunpaywall-api
When your agent uses it
Find free legal full-text versions of scholarly articles via Unpaywall
What it loads into context
Frontmatter
102 tokensAlways available to the agent
SKILL.md
1,347 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
- Authentication
- Core Endpoints
- Rate Limits
- Common Patterns
- References
Files
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# Unpaywall API Guide
## Overview
Unpaywall is a free, open database of over 40 million free scholarly articles. Built by the nonprofit OurResearch, Unpaywall indexes legal open access (OA) copies of papers from thousands of institutional repositories, preprint servers, publisher websites, and government archives. It is the most comprehensive source for finding freely available versions of paywalled academic literature.
Researchers, librarians, and tool developers use Unpaywall to locate open access copies of papers they need, assess the OA status of publications, and integrate OA discovery into their workflows. The database is updated daily, scanning repositories and publisher sites for new open access content. Unpaywall categorizes OA into types: gold (published OA), green (repository copy), hybrid (OA in subscription journal), and bronze (free to read on publisher site).
The API requires only an email address for authentication and is free for non-commercial use with a generous rate limit of 100,000 requests per day.
## Authentication
Authentication is via email address passed as a query parameter. No API key or registration is needed -- just provide a valid email:
View the complete source on GitHubzotero-ai-butler-guide
When your agent uses it
AI-powered paper summarization plugin for Zotero
What it loads into context
Frontmatter
104 tokensAlways available to the agent
SKILL.md
1,136 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
- Installation
- Configuration
- Features
- Batch Processing
- Custom Prompts
- Integration with Zotero Workflow
- Use Cases
- References
Files
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# Zotero AI Butler Guide
## Overview
Zotero AI Butler is a Zotero plugin that uses LLMs to summarize, analyze, and annotate academic papers directly within Zotero. It can generate structured summaries, extract key findings, compare papers, and answer questions about documents — all without leaving the reference manager. Supports multiple LLM backends (OpenAI, Claude, local models).
## Installation
```bash
# Download .xpi from GitHub releases
# Zotero 7: Tools → Add-ons → Install Add-on From File
```
## ConfigurationView the complete source on GitHubmetadata-skills
When your agent uses it
24 metadata & bibliometrics skills. Trigger: DOI resolution, citation metrics, author disambiguation, bibliometrics. Design: metadata APIs and bibliometric analysis tools for scholarly records.
What it loads into context
Frontmatter
47 tokensAlways available to the agent
SKILL.md
811 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
Files
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# Metadata & Bibliometrics — 24 Skills
Select the skill matching the user's need, then `read` its SKILL.md.
| Skill | Description |
|-------|-------------|
| [academic-paper-summarizer](./academic-paper-summarizer/SKILL.md) | Summarize academic papers with structured extraction of key elements |
| [altmetrics-guide](./altmetrics-guide/SKILL.md) | Guide to altmetrics and research impact beyond traditional citations |
| [bibliometrix-guide](./bibliometrix-guide/SKILL.md) | Perform science mapping and bibliometric analysis with R bibliometrix |
| [citation-network-guide](./citation-network-guide/SKILL.md) | Analyze citation networks, impact metrics, and bibliometric patterns |
| [crossref-api](./crossref-api/SKILL.md) | Resolve DOIs and retrieve publication metadata from CrossRef registry |
| [crossref-event-data-api](./crossref-event-data-api/SKILL.md) | Track scholarly mentions across the web via Crossref Event Data |
| [datacite-api](./datacite-api/SKILL.md) | Resolve dataset DOIs and query research data metadata via DataCite |
| [doi-content-negotiation](./doi-content-negotiation/SKILL.md) | Retrieve structured metadata from any DOI via HTTP content negotiation |View the complete source on GitHubacademic-paper-summarizer
When your agent uses it
Summarize academic papers with structured extraction of key elements
What it loads into context
Frontmatter
97 tokensAlways available to the agent
SKILL.md
1,148 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
- Structured Extraction Framework
- Batch Processing Workflow
- Tips for High-Quality Summaries
- Output Formats
- References
Files
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# Academic Paper Summarizer
## Overview
Academic papers are dense, technical documents that require significant time to read and understand fully. The Academic Paper Summarizer skill provides a systematic framework for extracting the essential elements from research papers into structured, reusable summaries.
This skill is designed for researchers who need to rapidly process large volumes of literature—whether during a systematic review, when onboarding into a new field, or when preparing a literature review section for their own manuscripts. Rather than producing generic summaries, it enforces a structured template that captures the components most relevant to downstream academic work: research questions, methodology, key findings, limitations, and contributions to the field.
The skill works with any academic paper format (PDF, HTML, plain text) and can be adapted across disciplines from biomedical sciences to social sciences, engineering, and humanities. It emphasizes fidelity to the original text while organizing information into a consistent schema that facilitates comparison across papers.
## Structured Extraction Framework
The core of this skill is a multi-section extraction template. When summarizing a paper, populate each of the following fields:
View the complete source on GitHubaltmetrics-guide
When your agent uses it
Guide to altmetrics and research impact beyond traditional citations
What it loads into context
Frontmatter
82 tokensAlways available to the agent
SKILL.md
1,356 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
- What Are Altmetrics?
- Key Altmetric Providers and Scores
- Querying the Altmetric.com API
- Interpreting Altmetrics Responsibly
- Best Practices for Using Altmetrics
- Tools for Tracking Your Research Impact
Files
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# Altmetrics Guide
Understand and use alternative metrics (altmetrics) to measure the broader impact and reach of research outputs beyond traditional citation counts.
## What Are Altmetrics?
Altmetrics capture the online attention and engagement that research receives across diverse platforms. Unlike citation-based metrics (which can take years to accumulate), altmetrics provide near-real-time signals of how research is being discussed, shared, and used.
| Source Category | Examples | What It Measures |
|----------------|----------|------------------|
| Social media | Twitter/X, Facebook, Reddit, Weibo | Public discussion and sharing |
| News & blogs | Mainstream media, science blogs | Media coverage and science communication |
| Policy documents | Government reports, clinical guidelines | Policy relevance |
| Reference managers | Mendeley, Zotero readership | Academic readership and interest |View the complete source on GitHubbibliometrix-guide
When your agent uses it
Perform science mapping and bibliometric analysis with R bibliometrix
What it loads into context
Frontmatter
96 tokensAlways available to the agent
SKILL.md
1,314 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
- Installation
- Quick Start
- Supported Data Sources
- Key Analysis Types
- References
Files
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# Bibliometrix Guide
## Overview
Bibliometrix is an R package for comprehensive science mapping and bibliometric analysis. It imports data from Scopus, Web of Science, PubMed, and other databases, then performs co-citation analysis, keyword co-occurrence mapping, collaboration networks, thematic evolution tracking, and more. Includes Biblioshiny — a Shiny-based web interface for no-code analysis.
## Installation
```r
install.packages("bibliometrix")
# Or development version
devtools::install_github("massimoaria/bibliometrix")
```View the complete source on GitHubcitation-network-guide
When your agent uses it
Analyze citation networks, impact metrics, and bibliometric patterns
What it loads into context
Frontmatter
80 tokensAlways available to the agent
SKILL.md
2,144 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
- Core Concepts
- Building Citation Networks
- Network Analysis
- Visualization
- Altmetrics
- Best Practices
- References
Files
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# Citation Network Analysis Guide
## Overview
Citation networks encode the intellectual structure of scientific fields. By analyzing who cites whom, how ideas propagate, and where research clusters form, researchers can identify foundational papers, emerging trends, influential authors, and gaps in the literature that represent opportunities for new contributions.
This guide covers the theory and practice of citation network analysis: building networks from bibliographic data, computing standard metrics (h-index, impact factor, PageRank, betweenness centrality), visualizing network structure, and interpreting results. It also covers altmetrics -- alternative impact measures that capture attention beyond traditional citations.
Whether you are conducting a systematic literature review, mapping a new research area, evaluating potential collaborators, or assessing the impact of your own work, citation network analysis provides quantitative tools to complement qualitative judgment.
## Core Concepts
### Types of Citation Networks
View the complete source on GitHubcrossref-api
When your agent uses it
Resolve DOIs and retrieve publication metadata from CrossRef registry
What it loads into context
Frontmatter
85 tokensAlways available to the agent
SKILL.md
1,393 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
- Authentication
- Core Endpoints
- Rate Limits
- Common Patterns
- References
Files
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# CrossRef API Guide
## Overview
CrossRef is the official DOI registration agency for scholarly publishing, maintaining metadata for over 150 million content items including journal articles, books, conference proceedings, preprints, and datasets. The CrossRef REST API provides free, open access to this metadata, making it the authoritative source for resolving Digital Object Identifiers (DOIs) and retrieving standardized publication records.
Publishers deposit metadata with CrossRef when they register DOIs for their content. This makes the CrossRef API a reliable source for bibliographic information, citation counts, licensing data, funder acknowledgments, and links to full-text resources. The API is used extensively in reference management, citation analysis, and research information systems.
The API is free and requires no authentication. Users who include a `mailto` parameter in their requests are placed in the "polite pool" which provides faster and more reliable service.
## Authentication
No authentication required. For best performance, include your email to access the polite pool:
View the complete source on GitHubcrossref-event-data-api
When your agent uses it
Track scholarly mentions across the web via Crossref Event Data
What it loads into context
Frontmatter
94 tokensAlways available to the agent
SKILL.md
1,562 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
- API Endpoints
- Response Structure
- Python Usage
- Use Cases
- References
Files
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# Crossref Event Data API
## Overview
Crossref Event Data tracks where scholarly publications are discussed, shared, and referenced across the open web — Wikipedia citations, Twitter/X mentions, Reddit posts, blog references, policy document citations, and more. Unlike traditional citation counts, Event Data captures real-time online attention to research. Free, no authentication required.
## API Endpoints
### Base URL
```
https://api.eventdata.crossref.org/v1
```
View the complete source on GitHubdatacite-api
When your agent uses it
Resolve dataset DOIs and query research data metadata via DataCite
What it loads into context
Frontmatter
89 tokensAlways available to the agent
SKILL.md
1,548 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
- Authentication
- Core Endpoints
- Rate Limits
- Common Patterns
- References
Files
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# DataCite API Guide
## Overview
DataCite is a leading global DOI registration agency focused on research data. While CrossRef primarily handles DOIs for publications, DataCite specializes in assigning persistent identifiers to datasets, software, samples, instruments, and other research outputs. DataCite has registered over 50 million DOIs from thousands of data repositories worldwide.
The DataCite REST API provides access to metadata for all DataCite DOIs. It is essential for researchers and developers working with research data discovery, data citation, FAIR (Findable, Accessible, Interoperable, Reusable) data practices, and repository integration. The metadata follows the DataCite Metadata Schema, which is designed specifically for describing research data and includes fields for resource types, funding references, geolocation, and related identifiers.
The API is free, open, and requires no authentication. It returns JSON responses following the JSON:API specification, with robust filtering, faceting, and pagination support.
## Authentication
No authentication required. The DataCite API is fully open and free to use. No API key, registration, or email is needed. For write operations (DOI registration and metadata updates), authentication via DataCite member credentials is required, but read-only access is completely open.
View the complete source on GitHubdoi-content-negotiation
When your agent uses it
Retrieve structured metadata from any DOI via HTTP content negotiation
What it loads into context
Frontmatter
89 tokensAlways available to the agent
SKILL.md
1,595 tokensWhen the skill is activated
Reference documents
1,630 tokensLoaded on request
Token counts are estimates. Scripts run separately; they are not counted as reference context.
The playbook
- Overview
- Usage
- Citeproc JSON Response
- Python Usage
- Use Cases
- References
- See Also
Files
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# DOI Content Negotiation
## Overview
Any DOI can return structured metadata in multiple formats through HTTP content negotiation — simply set the `Accept` header when requesting `https://doi.org/{doi}`. This is the most universal way to get citation metadata, BibTeX entries, JSON-LD, and RDF from any publisher without needing a specific API. Works for all 300M+ registered DOIs. Free, no authentication.
## Usage
### Basic Content Negotiation
```bash
# Get JSON citation metadata (Citeproc JSON)
curl -LH "Accept: application/vnd.citationstyles.csl+json" \
"https://doi.org/10.1038/nature14539"View the complete source on GitHubdoi-resolution-guide
When your agent uses it
DOI content negotiation and metadata retrieval techniques
What it loads into context
Frontmatter
74 tokensAlways available to the agent
SKILL.md
1,630 tokensWhen the skill is activated
Reference documents
1,595 tokensLoaded on request
Token counts are estimates. Scripts run separately; they are not counted as reference context.
The playbook
- What Is DOI Content Negotiation?
- Supported Metadata Formats
- Retrieving Metadata via Content Negotiation
- Using the CrossRef API
- DOI Validation and Normalization
- Practical Tips
- See Also
Files
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# DOI Resolution Guide
Master DOI content negotiation to programmatically retrieve structured metadata, citation data, and formatted references from any Digital Object Identifier.
## What Is DOI Content Negotiation?
Every DOI (e.g., `10.1038/s41586-021-03819-2`) resolves to a landing page by default. However, the DOI system supports HTTP content negotiation: by sending different `Accept` headers, you can retrieve structured metadata in various formats instead of an HTML page.
The DOI resolver endpoint is `https://doi.org/{doi}` or equivalently `https://dx.doi.org/{doi}`.
## Supported Metadata Formats
| Accept Header | Format | Use Case |
|---------------|--------|----------|View the complete source on GitHubh-index-guide
When your agent uses it
Understanding and calculating research impact metrics
What it loads into context
Frontmatter
79 tokensAlways available to the agent
SKILL.md
1,612 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 Bibliometric Indicators
- Journal-Level Metrics
- Calculating Your Own H-Index
- Responsible Use of Metrics
Files
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# H-Index and Research Impact Metrics Guide
Understand, calculate, and responsibly interpret bibliometric indicators including h-index, impact factor, and related metrics.
## Core Bibliometric Indicators
### H-Index
The h-index (Hirsch index) is defined as: a researcher has an h-index of h if h of their papers have each been cited at least h times.
**Example**: If a researcher has published 20 papers with citation counts [120, 80, 55, 40, 22, 18, 15, 12, 10, 8, 5, 3, 2, 2, 1, 1, 0, 0, 0, 0], their h-index is 10 (10 papers with at least 10 citations each).
```python
def calculate_h_index(citation_counts):View the complete source on GitHubjournal-metrics-guide
When your agent uses it
Understand journal impact factors, h5-index, CiteScore, and SJR
What it loads into context
Frontmatter
94 tokensAlways available to the agent
SKILL.md
1,538 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 of Major Metrics
- How Key Metrics Are Calculated
- Finding Journal Metrics
- Responsible Use of Metrics
- Predatory Journal Detection
Files
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# Journal Metrics Guide
A skill for understanding, comparing, and responsibly using journal-level metrics including Impact Factor, CiteScore, SJR, SNIP, h5-index, and Eigenfactor. Covers where to find each metric, how they are calculated, their limitations, and how to select appropriate journals for submission.
## Overview of Major Metrics
### Metric Comparison
| Metric | Provider | Formula Basis | Window | Free? |
|--------|----------|--------------|--------|-------|
| Impact Factor (JIF) | Clarivate (JCR) | Citations to articles / citable items | 2 years | No |
| 5-Year IF | Clarivate (JCR) | Same, extended window | 5 years | No |
| CiteScore | Scopus/Elsevier | Citations / documents | 4 years | Yes |
| SJR | Scopus/SCImago | Prestige-weighted citations | 3 years | Yes |View the complete source on GitHubopencitations-api
When your agent uses it
Query open citation data and reference networks via OpenCitations
What it loads into context
Frontmatter
89 tokensAlways available to the agent
SKILL.md
1,439 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
- Authentication
- Core Endpoints
- Rate Limits
- Common Patterns
- References
Files
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# OpenCitations API Guide
## Overview
OpenCitations is an independent infrastructure organization dedicated to open scholarship and the publication of open bibliographic and citation data. Its main product, the OpenCitations Index of Crossref open DOI-to-DOI citations (COCI), contains over 1.6 billion citation relationships harvested from CrossRef metadata. This makes it the largest fully open citation dataset in the world.
The OpenCitations API allows researchers to programmatically access citation and reference data for any DOI in the index. This is valuable for citation network analysis, bibliometric research, impact assessment, and building literature discovery tools. Unlike proprietary citation databases (Web of Science, Scopus), OpenCitations data is fully open under a CC0 public domain dedication.
The API is free, requires no authentication, and has no published rate limits. It returns data in JSON or CSV format, making it easy to integrate into data analysis pipelines.
## Authentication
No authentication required. The OpenCitations API is fully open and free. No API key, registration, or email is needed. There are no published rate limits, but users should implement reasonable request pacing for large-scale queries. For bulk data access, download the complete COCI dataset from https://opencitations.net/download.
View the complete source on GitHuborcid-api
When your agent uses it
Look up researcher profiles and academic identities via the ORCID registry
What it loads into context
Frontmatter
98 tokensAlways available to the agent
SKILL.md
1,500 tokensWhen the skill is activated
Reference documents
1,330 tokensLoaded on request
Token counts are estimates. Scripts run separately; they are not counted as reference context.
The playbook
- Overview
- Authentication
- Core Endpoints
- Rate Limits
- Common Patterns
- References
- See Also
Files
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# ORCID API Guide
## Overview
ORCID (Open Researcher and Contributor ID) is a nonprofit organization that provides unique, persistent digital identifiers to researchers worldwide. With over 18 million registered researchers, ORCID has become the standard for disambiguating author identities across the scholarly ecosystem. Publishers, funders, and institutions use ORCID to reliably connect researchers with their works, grants, and affiliations.
The ORCID Public API provides read access to public data in ORCID records. This includes biographical information, employment history, education, works (publications), funding, and peer review activities. The API is essential for building research information systems, verifying researcher identities, and integrating with institutional repositories.
Authentication is required. The public API uses OAuth 2.0 with a client credentials grant, which provides a bearer token for read-only access to public data. Registration is free and tokens can be obtained programmatically.
## Authentication
[Credential-related source omitted]
View the complete source on GitHuborcid-integration-guide
When your agent uses it
Set up and leverage ORCID for researcher identification and profiles
What it loads into context
Frontmatter
91 tokensAlways available to the agent
SKILL.md
1,330 tokensWhen the skill is activated
Reference documents
1,500 tokensLoaded on request
Token counts are estimates. Scripts run separately; they are not counted as reference context.
The playbook
- What Is ORCID?
- Setting Up Your ORCID Profile
- Integrating ORCID with Publishing Workflows
- Using the ORCID API
- Maintaining Your ORCID Record
- See Also
Files
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# ORCID Integration Guide
A skill for creating, maintaining, and leveraging ORCID (Open Researcher and Contributor ID) to establish a persistent digital identity, automate publication tracking, and integrate with journals, funders, and institutional systems.
## What Is ORCID?
### Overview
ORCID provides a unique 16-digit identifier (e.g., 0000-0002-1825-0097) that distinguishes you from every other researcher, regardless of name similarity, institutional changes, or transliteration variations. Over 19 million researchers have ORCID iDs, and thousands of publishers and funders now require or support ORCID integration.
### Why ORCID Matters
```
Problem: Name ambiguity in scholarly publishingView the complete source on GitHuborkg-api
When your agent uses it
Query the Open Research Knowledge Graph for structured research data
What it loads into context
Frontmatter
89 tokensAlways available to the agent
SKILL.md
1,223 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
- API Endpoints
- Python Usage
- Key Concepts
- ORKG vs Traditional Databases
- Use Cases
- References
Files
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# Open Research Knowledge Graph (ORKG) API
## Overview
The Open Research Knowledge Graph (ORKG) transforms unstructured scholarly articles into structured, machine-readable research contributions. Unlike traditional databases that store metadata (title, authors, DOI), ORKG captures the semantic content — research problems, methods, results, and their relationships. The REST API enables querying, creating, and comparing research contributions programmatically. Free, no authentication required for read operations.
## API Endpoints
### Base URL
```
https://orkg.org/api/
```
View the complete source on GitHubplumx-metrics-api
When your agent uses it
Track research impact beyond citations via PlumX altmetrics API
What it loads into context
Frontmatter
94 tokensAlways available to the agent
SKILL.md
1,539 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
- Overview
- Metric Categories
- API Endpoints
- Response Structure
- Python Usage
- PlumX vs Other Altmetric Services
- References
Files
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# PlumX Metrics API
## Overview
PlumX (by Elsevier/Plum Analytics) tracks 5 categories of research impact metrics beyond traditional citations: Usage, Captures, Mentions, Social Media, and Citations. It covers 130M+ research artifacts including articles, datasets, presentations, and videos. Available via Elsevier's API infrastructure. Requires an Elsevier API key.
## Metric Categories
| Category | What it measures | Examples |
|----------|-----------------|---------|
| **Usage** | Reading/viewing | Abstract views, PDF downloads, HTML views |
| **Captures** | Saving for later | Mendeley readers, CiteULike bookmarks |
| **Mentions** | Commentary | Blog posts, news articles, Wikipedia refs |
| **Social Media** | Sharing/discussion | Tweets, Facebook shares, Reddit posts |View the complete source on GitHubror-organization-api
When your agent uses it
Identify and link research organizations via the ROR registry API
What it loads into context
Frontmatter
86 tokensAlways available to the agent
SKILL.md
1,625 tokensWhen the skill is activated
Reference documents
0 tokensLoaded on request
Token counts are estimates. Scripts run separately; they are not counted as reference context.
The playbook
- Overview
- API Endpoints
- Response Structure
- Python Usage
- Use Cases
- References
Files
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# ROR (Research Organization Registry) API
## Overview
ROR is the community-led registry of open persistent identifiers for research organizations worldwide — 110,000+ entries covering universities, research institutes, government agencies, hospitals, and companies. The API enables affiliation disambiguation, institutional search, and metadata retrieval. Essential for bibliometrics, funder compliance, and research analytics. Free, no authentication required.
## API Endpoints
### Base URL
```
https://api.ror.org/v2
```
View the complete source on GitHubsophosia-reference-guide
When your agent uses it
Reference manager with PDF viewer and Markdown note support
What it loads into context
Frontmatter
92 tokensAlways available to the agent
SKILL.md
787 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
- Installation
- Features
- Workspace Structure
- Note-Taking Features
- Import/Export
- Use Cases
- References
Files
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# Sophosia Reference Manager Guide
## Overview
Sophosia is an open-source reference manager that combines PDF viewing, Markdown note-taking, and Excalidraw visual notes in a single application. Unlike traditional reference managers, it treats each paper as a rich workspace with typed annotations, linked notes, and visual diagrams. Built with modern web technologies (Electron + Vue), it runs on all desktop platforms.
## Installation
```bash
# Download from GitHub releases (Windows/macOS/Linux)
# Or build from source:
git clone https://github.com/sophosia/sophosia.git
cd sophosia
npm install && npm run buildView the complete source on GitHubviaf-authority-api
When your agent uses it
Disambiguate author identities via the VIAF authority file API
What it loads into context
Frontmatter
92 tokensAlways available to the agent
SKILL.md
1,628 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
- API Endpoints
- Response Structure
- Python Usage
- Use Cases
- References
Files
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# VIAF (Virtual International Authority File) API
## Overview
VIAF clusters authority records from 50+ national libraries worldwide, linking different forms of an author's name into a single canonical identity. It covers 50M+ personal, corporate, and geographic name entries. Essential for author disambiguation in bibliometric research — resolving "J. Smith", "John Smith", and "Smith, J.A." to the same person. Free, no authentication.
## API Endpoints
### Base URL
```
https://viaf.org
```
View the complete source on GitHubwikidata-api-guide
When your agent uses it
Query Wikidata SPARQL for scholarly metadata, authors, and entities
What it loads into context
Frontmatter
102 tokensAlways available to the agent
SKILL.md
1,620 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
- Authentication
- Core Endpoints
- Common Research Patterns
- Rate Limits and Best Practices
- References
Files
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# Wikidata SPARQL API Guide
## Overview
Wikidata is a free, collaborative, multilingual knowledge base maintained by the Wikimedia Foundation. It contains structured data about millions of entities including scholarly articles, academic journals, researchers, universities, and scientific concepts. Each entity has a unique QID and properties linking it to other entities, forming a rich knowledge graph.
For academic researchers, Wikidata serves as a powerful tool for bibliometric analysis, disambiguation of author names, mapping institutional relationships, and linking scholarly outputs across different identifier systems (DOI, ORCID, PubMed ID, arXiv ID, etc.). The SPARQL query service provides a flexible, standards-based interface for complex graph queries.
The Wikidata Query Service is entirely free, requires no authentication, and supports the full SPARQL 1.1 query language. It is especially powerful for cross-referencing scholarly metadata that spans multiple databases and identifier systems.
## Authentication
No authentication is required. The Wikidata SPARQL endpoint is free and open.
View the complete source on GitHubzoplicate-dedup-guide
When your agent uses it
Detect and manage duplicate items in Zotero libraries
What it loads into context
Frontmatter
92 tokensAlways available to the agent
SKILL.md
1,156 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
- Installation
- Features
- Configuration
- Usage Workflow
- Matching Examples
- Integration Tips
- Use Cases
- References
Files
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# Zoplicate: Zotero Deduplication Guide
## Overview
Zoplicate is a Zotero plugin that detects and manages duplicate items in your library. It goes beyond Zotero's built-in duplicate detection by offering configurable matching criteria, batch merge operations, automatic deduplication on import, and smart metadata merging that keeps the best version of each field. Essential for researchers who import from multiple databases.
## Installation
```bash
# Download .xpi from GitHub releases
# Zotero 7: Tools → Add-ons → Install Add-on From File
```
## FeaturesView the complete source on GitHubzotero-actions-tags-guide
When your agent uses it
Zotero workflow automation with custom actions and tags
What it loads into context
Frontmatter
91 tokensAlways available to the agent
SKILL.md
1,299 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
- Installation
- Core Concepts
- Action Examples
- Workflow Recipes
- Configuration
- Use Cases
- References
Files
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# Zotero Actions & Tags Guide
## Overview
Zotero Actions & Tags is a powerful Zotero plugin that enables workflow automation through event-triggered actions. Define custom rules that execute when items are added, modified, opened, or tagged — such as auto-tagging by collection, running scripts on PDF open, auto-renaming attachments, or sending notifications. Transforms Zotero from a passive reference manager into an active research workflow engine.
## Installation
```bash
# Download .xpi from GitHub releases
# In Zotero 7: Tools → Add-ons → Install Add-on From File
```
## Core ConceptsView the complete source on GitHubzotmoov-guide
When your agent uses it
Zotero plugin for automatic attachment file organization
What it loads into context
Frontmatter
94 tokensAlways available to the agent
SKILL.md
878 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
- Installation
- Configuration
- Naming Patterns
- Workflow
- Integration with Other Tools
- Use Cases
- References
Files
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# ZotMoov Guide
## Overview
ZotMoov is a Zotero plugin that automatically moves and renames attachment files (PDFs, supplementary materials) to a designated directory with customizable naming patterns. It keeps your PDFs organized in a structured folder hierarchy outside Zotero's storage, using linked files instead of copies. Essential for researchers who want clean file organization alongside Zotero's metadata management.
## Installation
```bash
# Download .xpi from GitHub releases
# Zotero 7: Tools → Add-ons → Install Add-on From File
```
## ConfigurationView the complete source on GitHubzutilo-guide
When your agent uses it
Zotero utility plugin for keyboard shortcuts and batch editing
What it loads into context
Frontmatter
93 tokensAlways available to the agent
SKILL.md
933 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
- Installation
- Key Features
- Configuration
- Workflow Examples
- Use Cases
- References
Files
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# Zutilo Guide
## Overview
Zutilo is a Zotero utility plugin that adds keyboard shortcuts, batch editing capabilities, and clipboard operations for efficient library management. It enables power-user workflows — copying/pasting tags between items, batch-editing fields, assigning keyboard shortcuts to any Zotero action, and quick item manipulation. Essential for researchers managing large collections who need speed and efficiency.
## Installation
```bash
# Download .xpi from GitHub releases
# Zotero 7: Tools → Add-ons → Install Add-on From File
```
## Key FeaturesView the complete source on GitHubsearch-skills
When your agent uses it
31 database search skills. Trigger: finding papers, search strategies, querying academic databases. Design: one skill per database/tool with API details, query syntax, and rate limits.
What it loads into context
Frontmatter
44 tokensAlways available to the agent
SKILL.md
1,030 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
Files
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# Database Search — 31 Skills
Select the skill matching the user's need, then `read` its SKILL.md.
| Skill | Description |
|-------|-------------|
| [arxiv-api](./arxiv-api/SKILL.md) | Search and retrieve preprints from the arXiv open-access repository |
| [arxiv-batch-reporting](./arxiv-batch-reporting/SKILL.md) | Batch search and report generation from arXiv preprint repository |
| [arxiv-cli-tools](./arxiv-cli-tools/SKILL.md) | Command-line tools for searching and batch-downloading arXiv papers |
| [arxiv-paper-processor](./arxiv-paper-processor/SKILL.md) | Process and analyze arXiv papers systematically for research workflows |
| [baidu-scholar-guide](./baidu-scholar-guide/SKILL.md) | Using Baidu Scholar for Chinese and English academic literature search |
| [base-academic-search](./base-academic-search/SKILL.md) | Search 400M+ open access documents via the BASE search engine API |
| [biorxiv-api](./biorxiv-api/SKILL.md) | Preprint server API for biology and medicine papers |
| [boolean-search-guide](./boolean-search-guide/SKILL.md) | Master Boolean operators and advanced search syntax for academic databases |View the complete source on GitHubarxiv-api
When your agent uses it
Search and retrieve preprints from the arXiv open-access repository
What it loads into context
Frontmatter
92 tokensAlways available to the agent
SKILL.md
1,213 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
- Authentication
- Using the `search_arxiv` Tool
- Core Endpoints (Raw API Reference)
- Rate Limits
- Common Patterns
- References
Files
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# arXiv API Guide
## Overview
The arXiv API provides programmatic access to the arXiv preprint repository, one of the most important open-access archives in the sciences. Founded in 1991, arXiv hosts over 2.4 million scholarly articles across physics, mathematics, computer science, quantitative biology, quantitative finance, statistics, electrical engineering and systems science, and economics.
Researchers use the arXiv API to build literature review tools, monitor new submissions in their fields, and integrate preprint search into automated research workflows. The API returns results in Atom XML format with rich metadata including titles, authors, abstracts, categories, DOIs, and journal references.
The API is free to use with no authentication required. It supports complex boolean queries across multiple fields and allows sorting by relevance, submission date, or last-updated date.
## Authentication
No authentication required. The arXiv API is fully open. However, users must respect the rate limit of 3 requests per second. Excessive usage may result in temporary IP-based blocking. Including a descriptive User-Agent header is considered good practice.
View the complete source on GitHubarxiv-batch-reporting
When your agent uses it
Batch search and report generation from arXiv preprint repository
What it loads into context
Frontmatter
92 tokensAlways available to the agent
SKILL.md
1,182 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
- Setting Up Batch Queries
- Report Generation
- Automation and Scheduling
- References
Files
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# arXiv Batch Reporting
## Overview
Keeping up with the flood of new preprints on arXiv is one of the most persistent challenges in fast-moving fields like machine learning, physics, mathematics, and computer science. The arXiv Batch Reporting skill provides a systematic approach to searching, filtering, and generating structured reports from arXiv at scale.
Unlike ad-hoc manual searches, this skill enables researchers to define persistent query profiles, run batch searches across date ranges, and produce formatted reports that highlight the most relevant papers. It is particularly useful for weekly or monthly literature surveillance, lab meeting preparation, and trend analysis across subfields.
The skill leverages the arXiv API and supports advanced query syntax, category filtering, and result ranking by relevance or recency. Reports can be generated in Markdown, HTML, or CSV formats for integration into existing workflows.
## Setting Up Batch Queries
### Query Profile Definition
View the complete source on GitHubarxiv-cli-tools
When your agent uses it
Command-line tools for searching and batch-downloading arXiv papers
What it loads into context
Frontmatter
97 tokensAlways available to the agent
SKILL.md
1,267 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
- Installation
- Searching Papers
- Downloading Papers
- Python API Alternative
- Workflow Integration
- Rate Limits and Etiquette
- References
Files
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# arXiv CLI Tools
## Overview
`arxiv-cli-tools` is a Python command-line interface for searching and downloading papers from arXiv.org. It wraps the `arxiv` Python client library into convenient CLI commands, enabling researchers to search by keyword, author, or category, view abstracts, and batch-download PDFs directly from the terminal. No API key is required.
## Installation
```bash
# Recommended: isolated install with pipx
pipx install arxiv-cli-tools
# Alternative: pip
pip install arxiv-cli-toolsView the complete source on GitHubarxiv-paper-processor
When your agent uses it
Process and analyze arXiv papers systematically for research workflows
What it loads into context
Frontmatter
99 tokensAlways available to the agent
SKILL.md
1,371 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
- Paper Retrieval and Download
- Content Extraction Pipeline
- Analysis and Integration
- References
Files
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# arXiv Paper Processor
## Overview
The arXiv Paper Processor skill provides a complete pipeline for downloading, parsing, and analyzing arXiv papers programmatically. While the arXiv API provides metadata, researchers often need to work with the full text—extracting sections, equations, figures, and references for deeper analysis.
This skill covers the entire processing chain: retrieving papers by ID or search query, downloading PDF and LaTeX source files, extracting structured content, and producing analysis-ready outputs. It is particularly valuable for researchers conducting large-scale literature analysis, building training datasets from academic text, or automating evidence extraction for systematic reviews.
The pipeline handles common challenges in academic PDF processing including multi-column layouts, mathematical notation, table extraction, and reference parsing. It integrates with tools like GROBID for PDF parsing and can work directly with arXiv LaTeX sources for higher-fidelity extraction.
## Paper Retrieval and Download
### Fetching by arXiv ID
View the complete source on GitHubbaidu-scholar-guide
When your agent uses it
Using Baidu Scholar for Chinese and English academic literature search
What it loads into context
Frontmatter
94 tokensAlways available to the agent
SKILL.md
1,299 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
- Search Strategies and Syntax
- Navigating Chinese Academic Databases
- Citation Features and Export
- Best Practices for Bilingual Research
- References
Files
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# Baidu Scholar Guide
## Overview
Baidu Scholar (百度学术, xueshu.baidu.com) is one of the largest academic search engines with particular strength in indexing Chinese-language scholarly publications. For researchers working with Chinese academic literature—or conducting bilingual research that spans both English and Chinese sources—Baidu Scholar provides access to content that is often underrepresented in Western databases like Google Scholar, Scopus, or Web of Science.
Baidu Scholar indexes content from major Chinese academic databases including CNKI (China National Knowledge Infrastructure), Wanfang Data, VIP/CQVIP, as well as international sources like IEEE, Springer, Elsevier, and arXiv. This makes it a valuable complement to English-centric search tools, particularly for fields where Chinese research output is substantial: materials science, traditional medicine, agricultural science, renewable energy, and AI/ML.
This skill covers effective search strategies on Baidu Scholar, navigating its interface, accessing full-text content, and integrating Chinese-language papers into your broader research workflow.
## Search Strategies and Syntax
### Basic Search
View the complete source on GitHubbase-academic-search
When your agent uses it
Search 400M+ open access documents via the BASE search engine API
What it loads into context
Frontmatter
93 tokensAlways available to the agent
SKILL.md
1,732 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
- API Endpoints
- Response Structure
- Python Usage
- BASE vs Other Search Engines
- References
Files
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# BASE (Bielefeld Academic Search Engine) API
## Overview
BASE is one of the world's largest search engines for academic open access web resources. Operated by Bielefeld University Library, it indexes 400M+ documents from 11,000+ content providers including institutional repositories, preprint servers, and digital libraries. Unlike Google Scholar, BASE provides structured metadata, license information, and full-text links. The API is free with registration.
## API Endpoints
### Base URL
```
https://api.base-search.net/cgi-bin/BaseHttpSearchInterface.fcgi
```
View the complete source on GitHubbiorxiv-api
When your agent uses it
Preprint server API for biology and medicine papers
What it loads into context
Frontmatter
83 tokensAlways available to the agent
SKILL.md
1,236 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
- Authentication
- Core Endpoints
- Rate Limits
- Common Patterns
- References
Files
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# bioRxiv API Guide
## Overview
bioRxiv (pronounced "bio-archive") is a free online archive and distribution service for unpublished preprints in the life sciences. Operated by Cold Spring Harbor Laboratory, it provides researchers with immediate access to the latest findings before formal peer review. The bioRxiv API enables programmatic access to preprint metadata, content details, and publication linkage data across biology and medical sciences.
The API serves researchers who need to track emerging research trends, monitor preprint activity in specific subfields, or build automated literature surveillance pipelines. It is particularly valuable for systematic reviewers who want to capture the latest evidence before journal publication, and for bibliometric analysts studying the preprint-to-publication pipeline.
bioRxiv hosts preprints across more than 25 subject areas including neuroscience, genomics, bioinformatics, cell biology, and many more. The API returns structured metadata including titles, authors, abstracts, DOIs, publication dates, and links to corresponding published journal articles when available.
## Authentication
No authentication required. The bioRxiv API is fully open and does not require any API key, token, or registration. All endpoints are publicly accessible without rate limiting restrictions.
View the complete source on GitHubboolean-search-guide
When your agent uses it
Master Boolean operators and advanced search syntax for academic databases
What it loads into context
Frontmatter
87 tokensAlways available to the agent
SKILL.md
1,362 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
- Boolean Operator Fundamentals
- Advanced Search Syntax
- Building a Systematic Search Strategy
- Search Filters and Limits
- Documenting and Reproducing Searches
Files
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# Boolean Search Guide
A skill for constructing precise, reproducible search queries using Boolean operators across major academic databases. Covers AND, OR, NOT logic, proximity operators, truncation, field codes, and strategies for building systematic search strings.
## Boolean Operator Fundamentals
### Core Operators
```
AND — Narrows results. Both terms must appear.
"machine learning" AND "drug discovery"
OR — Broadens results. Either term may appear.
"deep learning" OR "neural network"View the complete source on GitHubchatpaper-guide
When your agent uses it
Use ChatPaper to summarize and search arXiv papers with LLM assistance
What it loads into context
Frontmatter
100 tokensAlways available to the agent
SKILL.md
1,236 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
- Installation and Setup
- Core Features
- Academic Workflow Integration
- Advanced Usage and Tips
- References
Files
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# ChatPaper Guide
## Overview
ChatPaper is an open-source tool that leverages large language models to automatically summarize, search, and analyze academic papers from arXiv. It addresses a fundamental challenge in modern research: the overwhelming volume of new publications makes it nearly impossible for researchers to keep up with developments in their fields through manual reading alone.
The tool connects to the arXiv API to retrieve papers based on keyword queries, then uses LLM capabilities to generate structured summaries covering research motivation, methodology, key findings, and limitations. This enables researchers to rapidly triage large batches of papers and identify the most relevant ones for detailed study.
With over 19,000 GitHub stars, ChatPaper has become a widely adopted tool in the research community. It supports multiple LLM backends and offers both command-line and web-based interfaces, making it accessible to researchers with varying levels of technical expertise.
## Installation and Setup
Clone the repository and install dependencies:
View the complete source on GitHubcitation-chaining-guide
When your agent uses it
Forward and backward citation chaining techniques for literature search
What it loads into context
Frontmatter
79 tokensAlways available to the agent
SKILL.md
1,495 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
- What Is Citation Chaining?
- Step-by-Step Workflow
- Tools for Citation Chaining
- Common Pitfalls
Files
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# Citation Chaining Guide
Master forward and backward citation chaining to systematically discover relevant literature by following the threads of scholarly communication.
## What Is Citation Chaining?
Citation chaining (also called citation tracking, pearl growing, or snowball searching) exploits the connections between papers through their references and citations. Starting from one or more "seed" papers, you trace connections in two directions:
- **Backward chaining**: Examine the reference list of a paper to find older, foundational works it builds upon.
- **Forward chaining**: Find newer papers that have cited the seed paper, discovering subsequent developments.
This approach is especially powerful when keyword searches fail (e.g., when terminology varies across subfields or when concepts predate standardized vocabulary).
## Step-by-Step WorkflowView the complete source on GitHubciteseerx-api
When your agent uses it
Search computer science literature via the CiteSeerX digital library
What it loads into context
Frontmatter
99 tokensAlways available to the agent
SKILL.md
1,482 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
- API Endpoints
- Response Structure
- Python Usage
- Unique Features
- Limitations
- References
Files
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# CiteSeerX API
## Overview
CiteSeerX is a scientific literature digital library focusing on computer and information science, with 10M+ documents and 100M+ citations. It provides autonomous citation indexing — extracting and linking citations without manual curation. The API supports document search, citation lookup, and metadata retrieval. Free, no authentication required.
## API Endpoints
### Base URL
```
https://citeseerx.ist.psu.edu/api
```
View the complete source on GitHubdatabase-comparison-guide
When your agent uses it
Compare major academic databases and when to use each for research
What it loads into context
Frontmatter
86 tokensAlways available to the agent
SKILL.md
1,160 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 of Major Academic Databases
- Field-Specific Database Selection
- Advanced Search Operators by Database
- Cross-Database Search Strategy
- Practical Tips
Files
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# Database Comparison Guide
A comprehensive reference for choosing and querying the right academic database for your research domain, including coverage details, advanced operators, and cross-database strategies.
## Overview of Major Academic Databases
| Database | Coverage | Disciplines | Access Model | Unique Strength |
|----------|----------|-------------|--------------|-----------------|
| Web of Science | 1900-present, 21,000+ journals | Multidisciplinary | Subscription | Citation indexing, Journal Impact Factor |
| Scopus | 1970-present, 27,000+ journals | Multidisciplinary | Subscription | Largest abstract/citation DB, CiteScore |
| PubMed | 1946-present, 35M+ records | Biomedical, life sciences | Free | MeSH controlled vocabulary, clinical filters |
| IEEE Xplore | 1872-present, 6M+ docs | Engineering, CS | Subscription | Conference proceedings, standards |
| Google Scholar | Broad, undisclosed | All fields | Free | Widest coverage, full-text indexing |
| JSTOR | Historical archives | Humanities, social sciences | Subscription | Historical journal runs, primary sources |View the complete source on GitHubdeep-literature-search
When your agent uses it
Multi-source exhaustive literature search across academic databases
What it loads into context
Frontmatter
93 tokensAlways available to the agent
SKILL.md
1,591 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
- Search Strategy Design
- Multi-Database Execution
- Deduplication and Screening
- PRISMA Documentation
- References
Files
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# Deep Literature Search
## Overview
A deep literature search goes beyond a quick Google Scholar query. It is a methodical, multi-source search process designed to identify all relevant publications on a topic with minimal omissions. This level of thoroughness is required for systematic reviews, meta-analyses, grant applications, and dissertation literature reviews where comprehensiveness is not optional—it is a methodological requirement.
This skill provides a structured framework for planning, executing, and documenting exhaustive literature searches across multiple academic databases. It covers query formulation using controlled vocabularies, database selection strategy, deduplication, screening workflows, and PRISMA-compliant documentation of the search process.
The framework is database-agnostic and can be applied across disciplines, from biomedical sciences (PubMed, Cochrane) to social sciences (PsycINFO, ERIC), engineering (IEEE Xplore, Compendex), and multidisciplinary databases (Web of Science, Scopus, OpenAlex).
## Search Strategy Design
### Step 1: Define the Research Question
View the complete source on GitHuberic-education-api
When your agent uses it
Search 2M+ education research records via the ERIC database API
What it loads into context
Frontmatter
92 tokensAlways available to the agent
SKILL.md
1,667 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
- API Endpoints
- Response Structure
- Python Usage
- ERIC Thesaurus
- References
Files
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# ERIC (Education Resources Information Center) API
## Overview
ERIC is the world's largest digital library of education research, sponsored by the U.S. Institute of Education Sciences (IES). It indexes 2M+ records including journal articles, reports, conference papers, and dissertations covering all aspects of education. The API provides free, unauthenticated access to metadata and links to full text where available.
## API Endpoints
### Base URL
```
https://api.ies.ed.gov/eric/
```
View the complete source on GitHubeurope-pmc-api
When your agent uses it
Search biomedical and life sciences literature via Europe PMC
What it loads into context
Frontmatter
87 tokensAlways available to the agent
SKILL.md
1,645 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
- Authentication
- Core Endpoints
- Rate Limits
- Common Patterns
- References
Files
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# Europe PMC API Guide
## Overview
Europe PMC (PubMed Central) is a free, comprehensive biomedical literature database maintained by the European Bioinformatics Institute (EMBL-EBI) as part of a network of 32 European funders. It provides access to over 40 million biomedical and life sciences publications, including abstracts from PubMed/MEDLINE, full-text articles from PubMed Central, patents from the European Patent Office, and preprints from biomedical preprint servers.
Europe PMC extends beyond PubMed by integrating additional European content, preprints, and rich text-mined annotations. It provides links to biological databases (UniProt, Protein Data Bank, etc.), grant information from funders, and citation data. The annotation features include gene/protein mentions, disease names, organism identifiers, and chemical entities extracted via machine learning.
The API is free, requires no authentication, and supports 10 requests per second. It returns JSON or XML and offers advanced query syntax with field-specific searches, boolean operators, and date range filters.
## Authentication
No authentication required. The Europe PMC API is fully open. No API key, registration, or email is needed. The API enforces a rate limit of 10 requests per second per IP address. Including a descriptive User-Agent header is considered good practice.
View the complete source on GitHubfindpapers-guide
When your agent uses it
Search multiple academic databases simultaneously with Findpapers
What it loads into context
Frontmatter
97 tokensAlways available to the agent
SKILL.md
1,102 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
- Installation
- Basic Usage
- Database Configuration
- Systematic Review Workflow
- CLI Usage
- References
Files
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# Findpapers Guide
## Overview
Findpapers is a Python tool for searching multiple academic databases simultaneously — arXiv, bioRxiv, IEEE, medRxiv, PubMed, and Scopus — using a single query. It automates the tedious process of running the same search across multiple platforms, deduplicates results, and exports to structured formats for systematic reviews.
## Installation
```bash
pip install findpapers
```
## Basic Usage
View the complete source on GitHubgoogle-scholar-guide
When your agent uses it
Advanced Google Scholar search techniques for comprehensive literature discovery
What it loads into context
Frontmatter
79 tokensAlways available to the agent
SKILL.md
1,448 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
- Advanced Search Operators
- Citation Tracking Strategies
- Google Scholar Alerts
- Google Scholar Profiles
- Limitations and Complementary Databases
Files
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# Google Scholar Guide
A skill for leveraging Google Scholar's full capabilities for academic literature search. Covers advanced search operators, citation tracking, alert configuration, and strategies for systematic and comprehensive retrieval.
## Advanced Search Operators
### Core Operators
| Operator | Syntax | Example | Effect |
|----------|--------|---------|--------|
| Exact phrase | `"..."` | `"machine learning"` | Matches exact phrase |
| OR | `OR` | `"deep learning" OR "neural network"` | Matches either term |
| Exclude | `-` | `transformer -electrical` | Excludes term |
| Author | `author:` | `author:"Y LeCun"` | Filter by author |View the complete source on GitHubieee-xplore-api
When your agent uses it
Search IEEE's 6M+ engineering and CS publications via the Xplore API
What it loads into context
Frontmatter
93 tokensAlways available to the agent
SKILL.md
1,455 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
- API Endpoints
- Response Structure
- Python Usage
- Access Tiers
- References
Files
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# IEEE Xplore API
## Overview
IEEE Xplore provides access to over 6 million technical documents — journal articles, conference proceedings, technical standards, and books — covering electrical engineering, computer science, and related fields. The API enables metadata search, full-text access (with subscription), and DOI-based batch lookup. Requires an API key (free registration) and institutional subscription for full features.
## API Endpoints
### Base URL
```
https://ieeexploreapi.ieee.org/api/v1/search/articles
```
View the complete source on GitHublens-scholarly-api
When your agent uses it
Search 300M+ scholarly and patent records via the Lens.org API
What it loads into context
Frontmatter
94 tokensAlways available to the agent
SKILL.md
1,597 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
- Authentication
- API Endpoints
- Python Usage
- Unique Features
- Rate Limits
- References
Files
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# Lens.org Scholarly and Patent API
## Overview
Lens.org provides unified access to 300M+ scholarly articles and 150M+ patent records with cross-linkage between them. Uniquely, Lens connects academic research to patent citations, enabling innovation tracking and prior art discovery. The API offers full-text search, citation analysis, and patent-paper linkage. Free for non-commercial use with registration (up to 1,000 requests/day).
## Authentication
```bash
# Register at https://www.lens.org/lens/user/subscriptions
# API token provided in your account settings
[Credential-related source omitted]
```
View the complete source on GitHubmesh-terms-guide
When your agent uses it
Navigate MeSH vocabulary for precise PubMed and MEDLINE searches
What it loads into context
Frontmatter
93 tokensAlways available to the agent
SKILL.md
1,364 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
- Understanding MeSH Vocabulary
- Working with MeSH in PubMed
- Combining MeSH with Free Text
- Validating Your MeSH Strategy
Files
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# MeSH Terms Guide
A skill for using Medical Subject Headings (MeSH) to construct precise and comprehensive biomedical literature searches. Covers MeSH tree structure, subheadings, explosion, major topics, and strategies for combining MeSH with free-text terms.
## Understanding MeSH Vocabulary
### What Is MeSH?
MeSH is a hierarchical controlled vocabulary maintained by the National Library of Medicine. Every article indexed in MEDLINE/PubMed is assigned MeSH terms by trained indexers. Using MeSH ensures you capture articles that discuss a concept even when authors use different terminology.
### Tree Structure and Hierarchy
```
MeSH terms are organized in a tree with up to 13 hierarchical levels.View the complete source on GitHubopen-library-api
When your agent uses it
Search and access book metadata via the Open Library API
What it loads into context
Frontmatter
85 tokensAlways available to the agent
SKILL.md
1,492 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
- API Endpoints
- Response Structure (Search)
- Python Usage
- References
Files
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# Open Library API
## Overview
Open Library (by the Internet Archive) catalogs every book ever published — 40M+ editions, 20M+ unique works. The API provides book search, ISBN/OCLC lookup, cover images, and reading access for 2M+ borrowable ebooks. Particularly useful for monograph discovery, edition tracking, and bibliographic verification. Free, no authentication required.
## API Endpoints
### Search Books
```bash
# Full-text search
curl "https://openlibrary.org/search.json?q=machine+learning&limit=20"
View the complete source on GitHubopen-semantic-search-guide
When your agent uses it
Self-hosted semantic search and text mining platform
What it loads into context
Frontmatter
95 tokensAlways available to the agent
SKILL.md
1,376 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
- Installation
- Architecture
- Indexing Documents
- Search Features
- Python Client
- Entity Extraction Configuration
- Knowledge Graph
- Use Cases
- References
Files
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# Open Semantic Search Guide
## Overview
Open Semantic Search is a self-hosted search and text mining platform that combines full-text search (Apache Solr) with semantic analysis — entity extraction, named entity recognition, text classification, and knowledge graph building. Process and search across documents (PDF, DOCX, emails) with faceted navigation and visual analytics. Ideal for researchers needing private, on-premise document search over large paper collections.
## Installation
```bash
# Docker deployment (recommended)
git clone https://github.com/opensemanticsearch/open-semantic-search.git
cd open-semantic-search
docker-compose up -d
View the complete source on GitHubopenaire-api
When your agent uses it
Search EU-funded research outputs via the OpenAIRE Graph API
What it loads into context
Frontmatter
92 tokensAlways available to the agent
SKILL.md
1,339 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
- API Endpoints
- Python Usage
- Unique Capabilities
- References
Files
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# OpenAIRE Graph API
## Overview
OpenAIRE is the European Open Science infrastructure providing programmatic access to millions of research outputs — publications, datasets, software, and other research products — linked to EU-funded projects, organizations, and researchers. The Graph API is free, requires no authentication, and returns JSON or XML. Uniquely valuable for discovering EU/Horizon-funded research and tracing connections between research outputs, projects, and institutions.
## API Endpoints
### Base URL
```
https://api.openaire.eu
```
View the complete source on GitHubopenalex-api
When your agent uses it
Query the OpenAlex catalog of scholarly works, authors, and institutions
What it loads into context
Frontmatter
88 tokensAlways available to the agent
SKILL.md
1,472 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
- Authentication
- Core Endpoints
- Rate Limits
- Common Patterns
- References
Files
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# OpenAlex API Guide
## Overview
OpenAlex is a free, open catalog of the global research system. It indexes over 250 million scholarly works, 90 million authors, 100,000 institutions, and 65,000 concepts. Created by the nonprofit OurResearch as a replacement for Microsoft Academic Graph, OpenAlex provides comprehensive bibliometric data for academic research analysis.
The API serves as a powerful tool for researchers conducting systematic literature reviews, bibliometric analyses, and research landscape mapping. It covers works across all academic disciplines, linking papers to their authors, institutions, concepts, and citation networks. Each entity in OpenAlex has a persistent identifier (OpenAlex ID) and is enriched with metadata from CrossRef, ORCID, ROR, and other authoritative sources.
OpenAlex is entirely free to use. No API key is required, though providing a contact email in the `mailto` parameter grants access to the polite pool with faster response times and higher rate limits.
## Authentication
No authentication required. For better rate limits and access to the polite pool, include your email in requests:
View the complete source on GitHubplos-open-access-api
When your agent uses it
Search PLOS open access journals with full-text Solr-powered API
What it loads into context
Frontmatter
92 tokensAlways available to the agent
SKILL.md
1,657 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
- API Endpoint
- Python Usage
- Advanced Solr Queries
- Rate Limits
- References
Files
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# PLOS Search API
## Overview
PLOS (Public Library of Science) publishes 7 peer-reviewed open access journals covering biology, medicine, genetics, and more. The Search API provides Solr-powered full-text search across all PLOS content — 350K+ articles, all freely available under CC-BY licenses. No authentication required. Particularly valuable for biomedical and life sciences systematic reviews.
## API Endpoint
### Base URL
```
https://api.plos.org/search
```
View the complete source on GitHubpubmed-api
When your agent uses it
Search biomedical literature and retrieve records via PubMed E-utilities
What it loads into context
Frontmatter
93 tokensAlways available to the agent
SKILL.md
1,759 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
- Authentication
- Core Endpoints
- Rate Limits
- Common Patterns
- References
Files
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# PubMed E-utilities API Guide
## Overview
PubMed is the premier biomedical literature database maintained by the National Center for Biotechnology Information (NCBI) at the US National Library of Medicine. It indexes over 36 million citations and abstracts from MEDLINE, life science journals, and online books. The Entrez Programming Utilities (E-utilities) provide programmatic access to the entire PubMed database and other NCBI databases.
E-utilities consist of a suite of server-side programs that accept URL-based requests and return structured data. These tools are essential for biomedical researchers, systematic reviewers, and developers building health informatics applications. The API supports complex search queries using MeSH (Medical Subject Headings) terms, boolean operators, and field-specific searches.
The API is free and does not require authentication for basic usage. Registering for an NCBI API key raises the rate limit from 3 to 10 requests per second, which is recommended for any automated workflow.
## Authentication
No authentication required for basic usage (3 requests/second). For higher rate limits (10 requests/second), register for a free API key at https://www.ncbi.nlm.nih.gov/account/ and include it in requests:
View the complete source on GitHubscielo-api
When your agent uses it
Access Latin American and developing world research via SciELO API
What it loads into context
Frontmatter
92 tokensAlways available to the agent
SKILL.md
1,476 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
- API Endpoints
- Python Usage
- Subject Areas Covered
- Unique Value
- References
Files
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# SciELO API
## Overview
SciELO (Scientific Electronic Library Online) is the primary open access platform for scholarly journals in Latin America, the Caribbean, Spain, Portugal, and South Africa. It indexes 1,800+ peer-reviewed journals and 900K+ articles, many not indexed elsewhere. All content is open access. The API provides article search, journal metadata, and bibliometric indicators. Free, no authentication required.
## API Endpoints
### ArticleMeta API
Search and retrieve articles:
```bash
# Search articles by keywordView the complete source on GitHubsemantic-scholar-api
When your agent uses it
Search papers and analyze citation graphs via OpenAlex and CrossRef APIs
What it loads into context
Frontmatter
89 tokensAlways available to the agent
SKILL.md
1,372 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
- Authentication
- Core Endpoints
- Rate Limits
- Common Patterns
- References
Files
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# OpenAlex & CrossRef API Guide
## Overview
OpenAlex is a free, open catalog of the global research system, indexing over 250 million academic works across all fields of science. It provides structured access to papers, authors, institutions, concepts, and citation networks. OpenAlex is the successor to Microsoft Academic Graph and is maintained by OurResearch (the team behind Unpaywall).
CrossRef is the official DOI registration agency for scholarly content, providing metadata for over 150 million DOIs across all publishers and disciplines. Together, OpenAlex and CrossRef provide comprehensive coverage for academic search, citation analysis, and bibliometric research.
Both APIs are free to use without authentication. OpenAlex requests a polite `User-Agent` header; CrossRef requests a `User-Agent` with contact email for access to the polite pool (faster rate limits).
## Authentication
No authentication is required for either API.
View the complete source on GitHubshare-research-api
When your agent uses it
Discover open access research outputs via the SHARE notification API
What it loads into context
Frontmatter
86 tokensAlways available to the agent
SKILL.md
1,055 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
- API Endpoints
- Python Usage
- References
Files
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# SHARE Research API
## Overview
SHARE (SHared Access Research Ecosystem) aggregates metadata from 200+ research repositories, preprint servers, and publishers into a unified search API. Operated by the Center for Open Science, it tracks research outputs as they move through the scholarly communication cycle — from preprint to publication. Free, no authentication for search.
## API Endpoints
### Base URL
```
https://share.osf.io/api/v2
```
View the complete source on GitHubsystematic-search-strategy
When your agent uses it
Construct rigorous systematic search strategies for literature reviews
What it loads into context
Frontmatter
79 tokensAlways available to the agent
SKILL.md
1,740 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
- PICO Framework for Search Design
- Database-Specific Search Syntax
- Search Documentation
- Screening Workflow
- Iterating and Refining
Files
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# Systematic Search Strategy
A skill for designing and executing comprehensive, reproducible literature search strategies for systematic reviews, scoping reviews, and meta-analyses. Follows PRISMA 2020 guidelines and Cochrane Handbook best practices.
## PICO Framework for Search Design
Structure your research question using PICO (or variants):
```
P - Population / Problem: Who or what is being studied?
I - Intervention / Exposure: What is the treatment or exposure?
C - Comparison: What is the alternative?
O - Outcome: What is being measured?
View the complete source on GitHubworldcat-search-api
When your agent uses it
Search the world's largest library catalog via OCLC WorldCat API
What it loads into context
Frontmatter
92 tokensAlways available to the agent
SKILL.md
1,869 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
- Authentication
- API Endpoints
- Response Structure
- Python Usage
- Use Cases
- Access Tiers
- References
Files
SKILL.mdcore6,896 bytesSKILL.mdcore6,896 bytesSKILL.mdcore6,896 bytesSKILL.mdcore6,896 bytesSKILL.mdcore6,896 bytesSKILL.mdcore6,896 bytes
Read SKILL.md
# WorldCat Search API
## Overview
WorldCat is the world's largest network of library content, aggregating catalogs from 10,000+ libraries across 170+ countries. The Search API provides access to 500M+ bibliographic records — books, journals, dissertations, media, and more — with holdings information showing which libraries own each item. Essential for interlibrary loan discovery, collection analysis, and comprehensive bibliographic searches. Requires a WSKey (free for non-commercial use).
## Authentication
```bash
# Register at https://platform.worldcat.org/
# Obtain a WSKey (API key) for your application
# OAuth 2.0 client credentials flow
curl -X POST "https://oauth.oclc.org/token" \View the complete source on GitHubautomation-skills
When your agent uses it
10 research automation skills. Trigger: automating experiments, tracking results, reproducible pipelines. Design: ML experiment management, workflow orchestration, and lab automation tools.
What it loads into context
Frontmatter
42 tokensAlways available to the agent
SKILL.md
401 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
Files
SKILL.mdcore1,734 bytes
Read SKILL.md
# Research Automation — 10 Skills
Select the skill matching the user's need, then `read` its SKILL.md.
| Skill | Description |
|-------|-------------|
| [ai-scientist-v2-guide](./ai-scientist-v2-guide/SKILL.md) | Automated scientific discovery via agentic tree search by Sakana AI |
| [aim-experiment-guide](./aim-experiment-guide/SKILL.md) | Track and compare research experiments with Aim experiment tracker |
| [claude-academic-workflow-guide](./claude-academic-workflow-guide/SKILL.md) | Claude Code template for LaTeX, Beamer, and R research workflows |
| [data-collection-automation](./data-collection-automation/SKILL.md) | Automate survey deployment, data collection, and pipeline management |
| [datagen-research-guide](./datagen-research-guide/SKILL.md) | AI-driven multi-agent research assistant for end-to-end studies |
| [kedro-pipeline-guide](./kedro-pipeline-guide/SKILL.md) | Build reproducible data science pipelines with Kedro for research projects |
| [mle-agent-guide](./mle-agent-guide/SKILL.md) | Intelligent companion for ML engineering with arXiv integration |
| [paper-to-agent-guide](./paper-to-agent-guide/SKILL.md) | Transform research papers into interactive AI agents for exploration |View the complete source on GitHubai-scientist-v2-guide
When your agent uses it
Automated scientific discovery via agentic tree search by Sakana AI
What it loads into context
Frontmatter
103 tokensAlways available to the agent
SKILL.md
2,121 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
- Installation and Setup
- Core Research Pipeline
- Research Templates
- Automated Paper Review
- Ethical Considerations and Limitations
- References
Files
SKILL.mdcore9,601 bytesSKILL.mdcore9,601 bytesSKILL.mdcore9,601 bytesSKILL.mdcore9,601 bytesSKILL.mdcore9,601 bytesSKILL.mdcore9,601 bytesSKILL.mdcore9,601 bytesSKILL.mdcore9,601 bytesSKILL.mdcore9,601 bytesSKILL.mdcore9,601 bytesSKILL.mdcore9,601 bytesSKILL.mdcore9,601 bytesSKILL.mdcore9,601 bytesSKILL.mdcore9,601 bytesSKILL.mdcore9,601 bytes
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# AI Scientist v2 Guide
## Overview
AI-Scientist-v2 is an open-source system developed by Sakana AI with over 2,000 GitHub stars that automates the full scientific research pipeline -- from idea generation through experimentation to paper writing. Building on the original AI Scientist, version 2 introduces an agentic tree search approach that systematically explores the space of research ideas, designs and runs experiments, analyzes results, and produces workshop-level scientific papers with minimal human intervention.
The key innovation in v2 is the tree search mechanism. Rather than pursuing a single research direction linearly, the system maintains a tree of possible research trajectories. At each node, the agent can branch into multiple experimental variations, evaluate the results, and prune unpromising directions while doubling down on successful ones. This mirrors how experienced researchers navigate the research landscape -- exploring broadly at first, then focusing resources on the most promising leads.
AI-Scientist-v2 has demonstrated the ability to generate novel, valid research papers in machine learning subfields including diffusion models, language model training, and optimization. While the generated papers are currently at workshop acceptance level, the system represents a significant step toward autonomous scientific discovery and is an invaluable tool for researchers looking to automate the more mechanical aspects of their research workflow.
## Installation and Setup
```bash
# Clone the repositoryView the complete source on GitHubaim-experiment-guide
When your agent uses it
Track and compare research experiments with Aim experiment tracker
What it loads into context
Frontmatter
91 tokensAlways available to the agent
SKILL.md
1,683 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
- Installation and Setup
- Core Features
- Research Workflow Integration
- Storage and Performance
- References
Files
SKILL.mdcore7,605 bytesSKILL.mdcore7,605 bytesSKILL.mdcore7,605 bytesSKILL.mdcore7,605 bytesSKILL.mdcore7,605 bytesSKILL.mdcore7,605 bytesSKILL.mdcore7,605 bytesSKILL.mdcore7,605 bytesSKILL.mdcore7,605 bytesSKILL.mdcore7,605 bytesSKILL.mdcore7,605 bytesSKILL.mdcore7,605 bytesSKILL.mdcore7,605 bytesSKILL.mdcore7,605 bytes
Read SKILL.md
# Aim Experiment Tracker Guide
## Overview
Aim is an open-source experiment tracking platform designed for researchers and ML engineers who need to log, compare, and analyze large numbers of experiments. Unlike cloud-based tracking services that require sending data to external servers, Aim runs entirely on your own infrastructure, making it suitable for research environments with data privacy requirements or institutional restrictions on external services.
The core problem Aim solves is experiment management at scale. A typical research project involves hundreds or thousands of training runs with different hyperparameters, data splits, model architectures, and random seeds. Without systematic tracking, researchers lose track of which configurations produced which results, leading to wasted computation and unreproducible findings. Aim provides a high-performance storage backend and a rich web UI for logging, querying, and visualizing experiment metadata and metrics.
With over 6,000 GitHub stars, Aim has established itself as a compelling self-hosted alternative to tools like Weights and Biases and MLflow. Its Python-native API integrates with minimal friction into existing training loops, and the query language enables sophisticated filtering across thousands of runs.
## Installation and Setup
Install Aim via pip:
View the complete source on GitHubclaude-academic-workflow-guide
When your agent uses it
Claude Code template for LaTeX, Beamer, and R research workflows
What it loads into context
Frontmatter
105 tokensAlways available to the agent
SKILL.md
1,574 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
- Project Structure
- CLAUDE.md Configuration
- LaTeX Paper Workflow
- Beamer Presentations
- R Analysis Integration
- Multi-Agent Review
- Common Tasks
- Use Cases
- References
Files
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# Claude Code Academic Workflow Guide
## Overview
A template and workflow guide for using Claude Code in academic research — managing LaTeX papers, Beamer presentations, R analysis scripts, and multi-agent peer review. Provides structured CLAUDE.md configurations, project templates, and automation patterns for common academic tasks. Designed for economists, social scientists, and quantitative researchers.
## Project Structure
```
research-project/
├── CLAUDE.md # Claude Code instructions
├── paper/
│ ├── main.tex # Main LaTeX document
│ ├── references.bib # BibliographyView the complete source on GitHubdata-collection-automation
When your agent uses it
Automate survey deployment, data collection, and pipeline management
What it loads into context
Frontmatter
89 tokensAlways available to the agent
SKILL.md
1,687 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
- Survey Platform APIs
- Automated Data Quality Checks
- ETL Pipeline for Research Data
- Scheduling and Monitoring
- Ethical Considerations
Files
SKILL.mdcore7,286 bytesSKILL.mdcore7,286 bytesSKILL.mdcore7,286 bytesSKILL.mdcore7,286 bytesSKILL.mdcore7,286 bytesSKILL.mdcore7,286 bytesSKILL.mdcore7,286 bytesSKILL.mdcore7,286 bytesSKILL.mdcore7,286 bytes
Read SKILL.md
# Data Collection Automation Guide
A skill for automating research data collection, survey deployment, and data pipeline management. Covers survey platform APIs, automated data retrieval, quality checks, ETL pipelines, and scheduling for longitudinal studies.
## Survey Platform APIs
### Qualtrics API
```python
import os
import json
import urllib.request
import time
View the complete source on GitHubSkills included
- dataviz-skills
- algorithm-visualizer-guide
- bokeh-visualization-guide
- chart-image-generator
- color-accessibility-guide
- d3-visualization-guide
- echarts-visualization-guide
- geospatial-viz-guide
- interactive-viz-guide
- metabase-analytics-guide
- network-visualization-guide
- plotly-interactive-guide
- publication-figures-guide
- python-dataviz-guide
- redash-analytics-guide
- econometrics-skills
- causal-inference-guide
- econml-causal-guide
- iv-regression-guide
- mostly-harmless-guide
- panel-data-guide
- python-causality-guide
- robustness-checks
- stata-analyst-guide
- stata-reference-guide
- time-series-guide
- statistics-skills
- bayesian-statistics-guide
- data-anomaly-detection
- hypothesis-testing-guide
- meta-analysis-guide
- ml-experiment-tracker
- modeling-strategy-guide
- nonparametric-tests-guide
- power-analysis-guide
- sem-guide
- survival-analysis-guide
- wrangling-skills
- csv-data-analyzer
- data-cleaning-pipeline
- data-cog-guide
- missing-data-handling
- pandas-data-wrangling
- questionnaire-design-guide
- stata-data-cleaning
- streamline-analyst-guide
- survey-data-processing
- text-mining-guide
- ai-ml-skills
- ai-agent-papers-guide
- ai-model-benchmarking
- anomaly-detection-papers-guide
- autonomous-agents-papers-guide
- computer-vision-guide
- deep-learning-papers-guide
- dl-transformer-finetune
- domain-adaptation-papers-guide
- generative-ai-guide
- graph-learning-papers-guide
- huggingface-api
- huggingface-inference-guide
- keras-deep-learning
- kolmogorov-arnold-networks-guide
- llm-evaluation-guide
- llm-from-scratch-guide
- ml-pipeline-guide
- nlp-toolkit-guide
- npcpy-research-guide
- prompt-engineering-research
- pytorch-guide
- pytorch-lightning-guide
- reinforcement-learning-guide
- responsible-ai-guide
- tensorflow-guide
- transformer-architecture-guide
- vmas-simulator-guide
- biomedical-skills
- alphafold-api
- bioagents-guide
- biothings-api
- clawbio-guide
- clinical-dialogue-agents-guide
- clinical-research-guide
- clinicaltrials-api
- clinicaltrials-api-v2
- ena-sequence-api
- enrichr-api
- ensembl-rest-api
- epidemiology-guide
- genomas-guide
- genomics-analysis-guide
- genotex-benchmark-guide
- med-researcher-guide
- med-researcher-r1-guide
- medgeclaw-guide
- medical-data-api
- medical-imaging-guide
- ncbi-blast-api
- ncbi-datasets-api
- pdb-structure-api
- quickgo-api
- business-skills
- architecture-design-guide
- innovation-management-guide
- market-analysis-guide
- operations-research-guide
- strategic-management-guide
- chemistry-skills
- cactus-cheminformatics-guide
- catalysis-hub-api
- chemeagle-guide
- chemgraph-agent-guide
- computational-chemistry-guide
- molecular-dynamics-guide
- pubchem-api-guide
- retrosynthesis-guide
- spectroscopy-analysis-guide
- cs-skills
- ai-security-papers-guide
- algorithms-complexity-guide
- code-llm-papers-guide
- dblp-api
- distributed-systems-guide
- formal-verification-guide
- gaussian-splatting-papers-guide
- llm-aiops-guide
- software-engineering-research
- software-heritage-api
- ecology-skills
- biodiversity-data-guide
- conservation-biology-guide
- gbif-api
- inaturalist-api
- species-distribution-guide
- economics-skills
- behavioral-economics-guide
- development-economics-guide
- fred-api
- imf-data-api-guide
- nber-working-papers-api
- post-labor-economics
- pricing-psychology-guide
- repec-economics-api
- world-bank-data-guide
- education-skills
- academic-study-methods
- assessment-design-guide
- curriculum-design-guide
- educational-research-methods
- learning-science-guide
- mooc-analytics-guide
- open-syllabus-api
- finance-skills
- akshare-finance-data
- financial-data-analysis
- finsight-research-guide
- options-analytics-agent-guide
- portfolio-optimization-guide
- quantitative-finance-guide
- risk-modeling-guide
- stata-accounting-research
- geoscience-skills
- climate-modeling-guide
- climate-science-guide
- gis-remote-sensing-guide
- pangaea-data-api
- satellite-remote-sensing
- seismology-data-guide
- humanities-skills
- digital-humanities-guide
- ethical-philosophy-guide
- history-research-guide
- philosophy-research-guide
- political-history-guide
- law-skills
- caselaw-access-api
- courtlistener-api
- legal-agent-skills-guide
- legal-nlp-guide
- legal-research-guide
- opencontracts-guide
- patent-analysis-guide
- regulatory-compliance-guide
- uk-legislation-api
- math-skills
- lean-theorem-proving-guide
- linear-algebra-applications
- numerical-methods-guide
- oeis-api
- symbolic-computation-guide
- topology-data-analysis
- pharma-skills
- clinical-pharmacology-guide
- clinical-trial-design-guide
- drug-development-guide
- drug-target-interaction
- madd-drug-discovery-guide
- pharmacovigilance-guide
- physics-skills
- astrophysics-data-guide
- computational-physics-guide
- nasa-ads-api
- particle-physics-guide
- quantum-computing-guide
- social-science-skills
- ipums-microdata-api
- network-analysis-guide
- psychology-research-guide
- social-research-methods
- sociology-research-methods
- survey-research-guide
- discovery-skills
- citation-alert-guide
- conference-proceedings-guide
- literature-mapping-guide
- paper-recommendation-guide
- papers-we-love-guide
- rss-paper-feeds
- semantic-paper-radar
- semantic-scholar-recs-guide
- zotero-arxiv-daily-guide
- fulltext-skills
- arxiv-latex-source
- bioc-pmc-api
- core-api-guide
- dataverse-api
- doaj-api
- hal-archive-api
- institutional-repository-guide
- interlibrary-loan-guide
- open-access-guide
- open-access-mining-guide
- osf-api
- pmc-ftp-bulk-download
- pmc-oai-api
- preprint-servers-guide
- unpaywall-api
- zotero-ai-butler-guide
- metadata-skills
- academic-paper-summarizer
- altmetrics-guide
- bibliometrix-guide
- citation-network-guide
- crossref-api
- crossref-event-data-api
- datacite-api
- doi-content-negotiation
- doi-resolution-guide
- h-index-guide
- journal-metrics-guide
- opencitations-api
- orcid-api
- orcid-integration-guide
- orkg-api
- plumx-metrics-api
- ror-organization-api
- sophosia-reference-guide
- viaf-authority-api
- wikidata-api-guide
- zoplicate-dedup-guide
- zotero-actions-tags-guide
- zotmoov-guide
- zutilo-guide
- search-skills
- arxiv-api
- arxiv-batch-reporting
- arxiv-cli-tools
- arxiv-paper-processor
- baidu-scholar-guide
- base-academic-search
- biorxiv-api
- boolean-search-guide
- chatpaper-guide
- citation-chaining-guide
- citeseerx-api
- database-comparison-guide
- deep-literature-search
- eric-education-api
- europe-pmc-api
- findpapers-guide
- google-scholar-guide
- ieee-xplore-api
- lens-scholarly-api
- mesh-terms-guide
- open-library-api
- open-semantic-search-guide
- openaire-api
- openalex-api
- plos-open-access-api
- pubmed-api
- scielo-api
- semantic-scholar-api
- share-research-api
- systematic-search-strategy
- worldcat-search-api
- automation-skills
- ai-scientist-v2-guide
- aim-experiment-guide
- claude-academic-workflow-guide
- data-collection-automation
Showing 300 of 473 skills. See GitHub for the complete repository.
Repository facts
- License
- MIT
- Created
- Mar 9, 2026
- Last pushed
- Jun 19, 2026
- Forks
- 42
- Open issues
- 0
- Project activity
- Slowing
Before you install
Network or broad shellAutomated signals, not a guarantee
Checked Nov 5, 2026
Not fully analyzed: some files were too long, unreadable or missing. This never lowers the level.
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skills/analysis/dataviz/SKILL.md:1Analysis incomplete: Referenced executable target unavailable; not analyzedskills/analysis/dataviz/algorithm-visualizer-guide/SKILL.md:1Analysis incomplete: Referenced executable target unavailable; not analyzedskills/analysis/dataviz/algorithm-visualizer-guide/SKILL.md:1Analysis incomplete: Referenced executable target unavailable; not analyzedskills/analysis/dataviz/bokeh-visualization-guide/SKILL.md:1Analysis incomplete: Referenced executable target unavailable; not analyzedskills/analysis/dataviz/bokeh-visualization-guide/SKILL.md:1Analysis incomplete: Referenced executable target unavailable; not analyzedskills/analysis/dataviz/bokeh-visualization-guide/SKILL.md:1Analysis incomplete: Referenced executable target unavailable; not analyzedskills/analysis/dataviz/bokeh-visualization-guide/SKILL.md:1Analysis incomplete: Referenced executable target unavailable; not analyzedskills/analysis/dataviz/bokeh-visualization-guide/SKILL.md:1Analysis incomplete: Referenced executable target unavailable; not analyzedskills/analysis/dataviz/bokeh-visualization-guide/SKILL.md:1Analysis incomplete: Referenced executable target unavailable; not analyzedskills/analysis/dataviz/bokeh-visualization-guide/SKILL.md:1Analysis incomplete: Referenced executable target unavailable; not analyzedskills/analysis/dataviz/bokeh-visualization-guide/SKILL.md:1Analysis incomplete: Referenced executable target unavailable; not analyzedskills/analysis/dataviz/bokeh-visualization-guide/SKILL.md:1Analysis incomplete: Referenced executable target unavailable; not analyzedskills/analysis/dataviz/bokeh-visualization-guide/SKILL.md:1Analysis incomplete: Referenced executable target unavailable; not analyzedskills/analysis/dataviz/bokeh-visualization-guide/SKILL.md:1Analysis incomplete: Referenced executable target unavailable; not analyzedskills/analysis/dataviz/bokeh-visualization-guide/SKILL.md:1Analysis incomplete: Referenced executable target unavailable; not analyzedskills/analysis/dataviz/bokeh-visualization-guide/SKILL.md:1Analysis incomplete: Referenced executable target unavailable; not analyzedskills/analysis/dataviz/chart-image-generator/SKILL.md:1Analysis incomplete: Referenced executable target unavailable; not analyzedskills/analysis/dataviz/chart-image-generator/SKILL.md:1Analysis incomplete: Referenced executable target unavailable; not analyzedskills/analysis/dataviz/chart-image-generator/SKILL.md:1Analysis incomplete: Referenced executable target unavailable; not analyzedskills/analysis/dataviz/chart-image-generator/SKILL.md:1Analysis incomplete: Referenced executable target unavailable; not analyzed
- No broad tool permissions
- 43 dangerous shell commandsin 21 files: formal-verification-guide/SKILL.md:39 pubchem-api-guide/SKILL.md:41 world-bank-data-guide/SKILL.md:60 and 18 more
- 3,999 network referencesin 304 files: doi-content-negotiation/SKILL.md:8 skills/literature/metadata/doi-resolution-guide/SKILL.md:21 zotero-scholar-guide/SKILL.md:8 and 301 more
- No hidden or obfuscated text
- No instruction overrides
- 1 executable scriptin 1 file: .gitignore:1
- 174 references to secretsin 49 files: fred-api/SKILL.md:12 ipums-microdata-api/SKILL.md:45 huggingface-inference-guide/SKILL.md:30 and 46 more
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Show all 4,217 signals by file
.gitignore
skills/analysis/dataviz/algorithm-visualizer-guide/SKILL.md
- Network accessline 8
subcategory: "dataviz" keywords: ["algorithm visualization", "interactive learning", "code animation", "computational methods", "data structures", "teaching tools"] source: "https://github.com/algorithm-visualizer/algorithm-visualiz - Network accessline 9
keywords: ["algorithm visualization", "interactive learning", "code animation", "computational methods", "data structures", "teaching tools"] source: "https://github.com/algorithm-visualizer/algorithm-visualizer" --- - Network accessline 10
source: "https://github.com/algorithm-visualizer/algorithm-visualizer" - Network accessline 36
# Clone the repository git clone https://github.com/algorithm-visualizer/algorithm-visualizer.git - Network accessline 37
git clone https://github.com/algorithm-visualizer/algorithm-visualizer.git - Network accessline 46
# Access at http://localhost:3000 - Network accessline 52
# Clone all required components git clone https://github.com/algorithm-visualizer/algorithm-visualizer.git - Network accessline 53
git clone https://github.com/algorithm-visualizer/algorithm-visualizer.git - Network accessline 54
git clone https://github.com/algorithm-visualizer/server.git - Network accessline 61
cd ../algorithm-visualizer # Set the server URL in environment configuration echo "REACT_APP_API_URL=http://localhost:8080" > .env.local - Network accessline 62
# Set the server URL in environment configuration echo "REACT_APP_API_URL=http://localhost:8080" > .env.local npm install && npm run build - Network accessline 63
echo "REACT_APP_API_URL=http://localhost:8080" > .env.local - Network accessline 256
- Algorithm Visualizer website: https://algorithm-visualizer.org - Network accessline 257
- Algorithm Visualizer GitHub: https://github.com/algorithm-visualizer/algorithm-visualizer - Network accessline 258
- Algorithms repository: https://github.com/algorithm-visualizer/algorithms - Network accessline 259
- Visualization API documentation: https://github.com/algorithm-visualizer/algorithm-visualizer/wiki
skills/analysis/dataviz/bokeh-visualization-guide/SKILL.md
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subcategory: "dataviz" keywords: ["Bokeh", "interactive plots", "Python visualization", "web dashboards", "scientific charts", "data exploration"] source: "https://github.com/bokeh/bokeh" - Network accessline 9
keywords: ["Bokeh", "interactive plots", "Python visualization", "web dashboards", "scientific charts", "data exploration"] source: "https://github.com/bokeh/bokeh" --- - Network accessline 10
source: "https://github.com/bokeh/bokeh" - Network accessline 267
- Bokeh documentation: https://docs.bokeh.org - Network accessline 268
- Bokeh GitHub repository: https://github.com/bokeh/bokeh - Network accessline 269
- Bokeh gallery: https://docs.bokeh.org/en/latest/docs/gallery.html - Network accessline 270
- HoloViews (high-level Bokeh wrapper): https://holoviews.org
skills/analysis/dataviz/d3-visualization-guide/SKILL.md
- Network accessline 8
subcategory: "dataviz" keywords: ["data visualization", "D3.js", "interactive charts", "SVG", "web visualization", "scientific plots"] source: "https://github.com/d3/d3" - Network accessline 9
keywords: ["data visualization", "D3.js", "interactive charts", "SVG", "web visualization", "scientific plots"] source: "https://github.com/d3/d3" --- - Network accessline 10
source: "https://github.com/d3/d3" - Network accessline 278
- D3.js official documentation: https://d3js.org - Network accessline 279
- D3 GitHub repository: https://github.com/d3/d3 - Network accessline 280
- Observable D3 tutorials: https://observablehq.com/@d3 - Network accessline 281
- D3 Graph Gallery (examples): https://d3-graph-gallery.com
skills/analysis/dataviz/echarts-visualization-guide/SKILL.md
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subcategory: "dataviz" keywords: ["ECharts", "interactive charts", "data dashboard", "Apache ECharts", "scientific charts", "research visualization"] source: "https://github.com/apache/echarts" - Network accessline 9
keywords: ["ECharts", "interactive charts", "data dashboard", "Apache ECharts", "scientific charts", "research visualization"] source: "https://github.com/apache/echarts" --- - Network accessline 10
source: "https://github.com/apache/echarts" - Network accessline 30
<div id="chart" style="width: 800px; height: 500px;"></div> <script src="https://cdn.jsdelivr.net/npm/echarts@5/dist/echarts.min.js"></script> - Network accessline 31
<script src="https://cdn.jsdelivr.net/npm/echarts@5/dist/echarts.min.js"></script> - Network accessline 217
// Load CSV data and convert to ECharts format async function loadExperimentData(csvUrl) { const response = await fetch(csvUrl); - Network accessline 218
async function loadExperimentData(csvUrl) { const response = await fetch(csvUrl); const text = await response.text(); - Network accessline 219
const response = await fetch(csvUrl); - Network accessline 247
- Apache ECharts official site: https://echarts.apache.org - Network accessline 248
- ECharts GitHub repository: https://github.com/apache/echarts - Network accessline 249
- ECharts examples gallery: https://echarts.apache.org/examples - Network accessline 250
- ECharts configuration handbook: https://echarts.apache.org/en/option.html
skills/analysis/dataviz/interactive-viz-guide/SKILL.md
- Network accessline 152
<html> <head> <script src="https://cdn.jsdelivr.net/npm/echarts@5/dist/echarts.min.js"></script> - Network accessline 153
<head> <script src="https://cdn.jsdelivr.net/npm/echarts@5/dist/echarts.min.js"></script> </head> - Network accessline 154
<script src="https://cdn.jsdelivr.net/npm/echarts@5/dist/echarts.min.js"></script>
skills/analysis/dataviz/metabase-analytics-guide/SKILL.md
- Network accessline 8
subcategory: "dataviz" keywords: ["Metabase", "business intelligence", "SQL analytics", "data dashboard", "research analytics", "self-hosted BI"] source: "https://github.com/metabase/metabase" - Network accessline 9
keywords: ["Metabase", "business intelligence", "SQL analytics", "data dashboard", "research analytics", "self-hosted BI"] source: "https://github.com/metabase/metabase" --- - Network accessline 10
source: "https://github.com/metabase/metabase" - Network accessline 40
# Access at http://localhost:3000 - Network accessline 200
<!-- Embed a dashboard in a lab portal --> <iframe src="http://metabase.lab.internal/public/dashboard/abc123-def456" - Network accessline 201
<iframe src="http://metabase.lab.internal/public/dashboard/abc123-def456" frameborder="0" - Network accessline 202
src="http://metabase.lab.internal/public/dashboard/abc123-def456" - Access to secretsline 216
[Credential-related source omitted] - Network accessline 224
signed = jwt.encode(payload, embedding_secret, algorithm="HS256") embed_url = f"http://metabase.lab.internal/embed/dashboard/{signed}" - Network accessline 225
embed_url = f"http://metabase.lab.internal/embed/dashboard/{signed}" - Network accessline 239
- Metabase official documentation: https://www.metabase.com/docs - Network accessline 240
- Metabase GitHub repository: https://github.com/metabase/metabase - Network accessline 241
- Metabase embedding documentation: https://www.metabase.com/docs/latest/embedding - Network accessline 242
- Metabase API reference: https://www.metabase.com/docs/latest/api
skills/analysis/dataviz/plotly-interactive-guide/SKILL.md
- Network accessline 8
subcategory: "dataviz" keywords: ["Plotly", "interactive visualization", "Dash", "Python charts", "scientific plots", "research figures"] source: "https://github.com/plotly/plotly.py" - Network accessline 9
keywords: ["Plotly", "interactive visualization", "Dash", "Python charts", "scientific plots", "research figures"] source: "https://github.com/plotly/plotly.py" --- - Network accessline 10
source: "https://github.com/plotly/plotly.py" - Network accessline 263
- Plotly Python documentation: https://plotly.com/python/ - Network accessline 264
- Plotly.py GitHub repository: https://github.com/plotly/plotly.py - Network accessline 265
- Dash documentation: https://dash.plotly.com - Network accessline 266
- Plotly Express reference: https://plotly.com/python/plotly-express/
skills/analysis/dataviz/python-dataviz-guide/SKILL.md
- Network accessline 191
- [matplotlib Documentation](https://matplotlib.org/stable/) -- Official reference - Network accessline 192
- [seaborn Documentation](https://seaborn.pydata.org/) -- Statistical visualization - Network accessline 193
- [plotly Documentation](https://plotly.com/python/) -- Interactive charts - Network accessline 194
- [Scientific Visualization: Python + Matplotlib](https://github.com/rougier/scientific-visualization-book) -- Nicolas Rougier - Network accessline 195
- [Ten Simple Rules for Better Figures](https://journals.plos.org/ploscompbiol/article?id=10.1371/journal.pcbi.1003833) -- Rougier et al.
skills/analysis/dataviz/redash-analytics-guide/SKILL.md
- Network accessline 8
subcategory: "dataviz" keywords: ["Redash", "SQL dashboards", "data visualization", "query tool", "research analytics", "collaborative BI"] source: "https://github.com/getredash/redash" - Network accessline 9
keywords: ["Redash", "SQL dashboards", "data visualization", "query tool", "research analytics", "collaborative BI"] source: "https://github.com/getredash/redash" --- - Network accessline 10
source: "https://github.com/getredash/redash" - Network accessline 28
# Clone the Redash setup repository git clone https://github.com/getredash/setup.git redash-setup - Network accessline 29
git clone https://github.com/getredash/setup.git redash-setup - Access to secretsline 51
[Credential-related source omitted] - Network accessline 248
REDASH_URL = "http://redash.lab.internal" - Access to secretsline 249
[Credential-related source omitted] - Network accessline 257
response = requests.post(url, json=payload, headers=headers) - Network accessline 260
# Poll for results while job.get("status") not in (3, 4): result = requests.get( - Network accessline 261
while job.get("status") not in (3, 4): result = requests.get( f"{REDASH_URL}/api/jobs/{job['id']}", - Network accessline 262
result = requests.get( - Network accessline 268
# Fetch final results result = requests.get( - Network accessline 269
result = requests.get( - Network accessline 281
- Redash official documentation: https://redash.io/help - Network accessline 282
- Redash GitHub repository: https://github.com/getredash/redash - Network accessline 283
- Redash setup guide: https://redash.io/help/open-source/setup - Network accessline 284
- Redash API reference: https://redash.io/help/user-guide/integrations-and-api/api
skills/analysis/econometrics/econml-causal-guide/SKILL.md
- Network accessline 8
subcategory: "econometrics" keywords: ["causal-inference", "machine-learning", "treatment-effects", "econometrics", "microsoft", "double-ml"] source: "https://github.com/py-why/EconML" - Network accessline 9
keywords: ["causal-inference", "machine-learning", "treatment-effects", "econometrics", "microsoft", "double-ml"] source: "https://github.com/py-why/EconML" --- - Network accessline 10
source: "https://github.com/py-why/EconML" - Network accessline 160
- EconML repository: https://github.com/py-why/EconML - Network accessline 161
- EconML documentation: https://econml.azurewebsites.net/
skills/analysis/econometrics/mostly-harmless-guide/SKILL.md
- Network accessline 8
subcategory: "econometrics" keywords: ["econometrics", "causal-inference", "replication", "regression", "instrumental-variables", "difference-in-differences"] source: "https://github.com/vikjam/mostly-harmless-replication" - Network accessline 9
keywords: ["econometrics", "causal-inference", "replication", "regression", "instrumental-variables", "difference-in-differences"] source: "https://github.com/vikjam/mostly-harmless-replication" --- - Network accessline 10
source: "https://github.com/vikjam/mostly-harmless-replication"
skills/analysis/econometrics/python-causality-guide/SKILL.md
- Network accessline 8
subcategory: "econometrics" keywords: ["causal-inference", "python", "econometrics", "statistics", "treatment-effects", "observational-studies"] source: "https://github.com/matheusfacure/python-causality-handbook" - Network accessline 9
keywords: ["causal-inference", "python", "econometrics", "statistics", "treatment-effects", "observational-studies"] source: "https://github.com/matheusfacure/python-causality-handbook" --- - Network accessline 10
source: "https://github.com/matheusfacure/python-causality-handbook" - Network accessline 28
git clone https://github.com/matheusfacure/python-causality-handbook.git - Network accessline 131
- Python Causality Handbook: https://github.com/matheusfacure/python-causality-handbook - Network accessline 132
- Online version: https://matheusfacure.github.io/python-causality-handbook/
skills/analysis/econometrics/stata-analyst-guide/SKILL.md
- Network accessline 8
subcategory: "econometrics" keywords: ["Stata", "sociology", "social science", "survey data", "regression", "publication tables"] source: "https://www.stata.com" - Network accessline 9
keywords: ["Stata", "sociology", "social science", "survey data", "regression", "publication tables"] source: "https://www.stata.com" --- - Network accessline 10
source: "https://www.stata.com" - Network accessline 241
- Stata Survey Data Reference Manual: https://www.stata.com/manuals/svy.pdf - Network accessline 242
- Mitchell, M. (2021), A Visual Guide to Stata Graphics, 4th ed., Stata Press - Long & Freese (2014), Regression Models for Categorical Dependent Variables Using Stata, 3rd ed. - esttab/estout documentation: http://repec.sowi.unibe.ch/stata - Network accessline 243
- Long & Freese (2014), Regression Models for Categorical Dependent Variables Using Stata, 3rd ed. - esttab/estout documentation: http://repec.sowi.unibe.ch/stata/estout/ - ASA Style Guide: https://www.asanet.org/publications/style-guide/ - Network accessline 244
- esttab/estout documentation: http://repec.sowi.unibe.ch/stata/estout/ - Network accessline 245
- ASA Style Guide: https://www.asanet.org/publications/style-guide/
skills/analysis/econometrics/stata-reference-guide/SKILL.md
- Network accessline 8
subcategory: "econometrics" keywords: ["stata", "econometrics", "panel data", "causal inference", "community packages", "data management", "regression"] source: "https://github.com/dylantmoore/stata-skill" - Network accessline 9
keywords: ["stata", "econometrics", "panel data", "causal inference", "community packages", "data management", "regression"] source: "https://github.com/dylantmoore/stata-skill" --- - Network accessline 10
source: "https://github.com/dylantmoore/stata-skill" - Network accessline 201
* Install from GitHub net install did_multiplegt, from("https://raw.githubusercontent.com/chaisemartinDehejia/did_multiplegt/main") - Network accessline 202
net install did_multiplegt, from("https://raw.githubusercontent.com/chaisemartinDehejia/did_multiplegt/main") - Network accessline 289
- [Stata Official Documentation](https://www.stata.com/manuals/) - Network accessline 290
- [UCLA Stata FAQ](https://stats.oarc.ucla.edu/stata/faq/) - Network accessline 291
- [Stata Journal](https://www.stata-journal.com/) - Network accessline 292
- [SSC Archive](https://ideas.repec.org/s/boc/bocode.html) - Network accessline 293
- [dylantmoore/stata-skill](https://github.com/dylantmoore/stata-skill) — Source for this reference
skills/analysis/statistics/hypothesis-testing-guide/SKILL.md
- Network accessline 206
- [scipy.stats Documentation](https://docs.scipy.org/doc/scipy/reference/stats.html) - Network accessline 207
- [pingouin Documentation](https://pingouin-stats.org/) -- Friendly statistics in Python - Network accessline 208
- [statsmodels Documentation](https://www.statsmodels.org/) - Network accessline 209
- [APA 7th Edition Statistics Reporting Guide](https://apastyle.apa.org/instructional-aids/numbers-statistics-guide.pdf) - Network accessline 210
- [Statistical Tests Cheat Sheet](https://machinelearningmastery.com/statistical-hypothesis-tests-in-python-cheat-sheet/)
skills/analysis/wrangling/data-cog-guide/SKILL.md
- Network accessline 176
- Breck, E., et al. (2019). Data Validation for Machine Learning. *MLSys 2019*. - Hynes, N., et al. (2017). The Data Linter: Lightweight, Automated Sanity Checking for ML Data Sets. *NIPS MLSys Workshop*. - Pandas Development Team (2024). * - Network accessline 178
- Pandas Development Team (2024). *pandas: Powerful Python Data Analysis Toolkit*. https://pandas.pydata.org/
skills/analysis/wrangling/pandas-data-wrangling/SKILL.md
- Network accessline 239
- [pandas Documentation](https://pandas.pydata.org/docs/) -- Official reference - Network accessline 240
- [Python for Data Analysis, 3rd Edition](https://wesmckinney.com/book/) -- Wes McKinney - Network accessline 241
- [Effective Pandas](https://store.metasnake.com/effective-pandas-book) -- Matt Harrison - Network accessline 242
- [pandas Cookbook](https://github.com/jvns/pandas-cookbook) -- Julia Evans
skills/analysis/wrangling/stata-data-cleaning/SKILL.md
- Network accessline 8
subcategory: "wrangling" keywords: ["Stata", "data cleaning", "data wrangling", "missing values", "recoding", "validation"] source: "https://www.stata.com/manuals/d.pdf" - Network accessline 9
keywords: ["Stata", "data cleaning", "data wrangling", "missing values", "recoding", "validation"] source: "https://www.stata.com/manuals/d.pdf" --- - Network accessline 10
source: "https://www.stata.com/manuals/d.pdf" - Network accessline 273
- Stata Data Management Reference Manual: https://www.stata.com/manuals/d.pdf - Network accessline 274
- DIME Analytics Data Management Wiki: https://dimewiki.worldbank.org/Data_Management - Network accessline 275
- J-PAL Research Resources: https://www.povertyactionlab.org/resource/data-cleaning
skills/analysis/wrangling/streamline-analyst-guide/SKILL.md
- Network accessline 8
subcategory: "wrangling" keywords: ["data analysis", "Streamlit", "automated EDA", "machine learning", "data science", "AI analyst"] source: "https://github.com/Wilson-ZheLin/Streamline-Analyst" - Network accessline 9
keywords: ["data analysis", "Streamlit", "automated EDA", "machine learning", "data science", "AI analyst"] source: "https://github.com/Wilson-ZheLin/Streamline-Analyst" --- - Network accessline 10
source: "https://github.com/Wilson-ZheLin/Streamline-Analyst" - Network accessline 22
git clone https://github.com/Wilson-ZheLin/Streamline-Analyst.git - Network accessline 118
- [Streamline-Analyst GitHub](https://github.com/Wilson-ZheLin/Streamline-Analyst) - Network accessline 119
- [Streamlit](https://streamlit.io/)
skills/domains/ai-ml/ai-agent-papers-guide/SKILL.md
- Network accessline 8
subcategory: "ai-ml" keywords: ["AI agents", "agent papers", "2024 research", "LLM agents", "agent frameworks", "survey"] source: "https://github.com/VoltAgent/awesome-ai-agent-papers" - Network accessline 9
keywords: ["AI agents", "agent papers", "2024 research", "LLM agents", "agent frameworks", "survey"] source: "https://github.com/VoltAgent/awesome-ai-agent-papers" --- - Network accessline 10
source: "https://github.com/VoltAgent/awesome-ai-agent-papers" - Network accessline 145
- [awesome-ai-agent-papers](https://github.com/VoltAgent/awesome-ai-agent-papers) - Network accessline 146
- [VoltAgent Framework](https://github.com/VoltAgent/voltagent)
skills/domains/ai-ml/ai-model-benchmarking/SKILL.md
- Network accessline 8
subcategory: "ai-ml" keywords: ["benchmarking", "evaluation", "LLM", "MMLU", "leaderboard", "metrics", "lm-eval"] source: "https://github.com/EleutherAI/lm-evaluation-harness" - Network accessline 9
keywords: ["benchmarking", "evaluation", "LLM", "MMLU", "leaderboard", "metrics", "lm-eval"] source: "https://github.com/EleutherAI/lm-evaluation-harness" --- - Network accessline 10
source: "https://github.com/EleutherAI/lm-evaluation-harness" - Network accessline 205
- [lm-evaluation-harness](https://github.com/EleutherAI/lm-evaluation-harness) -- EleutherAI's evaluation framework (12,000+ stars) - Network accessline 206
- [Open LLM Leaderboard](https://huggingface.co/spaces/HuggingFaceH4/open_llm_leaderboard) -- Hugging Face community leaderboard - Network accessline 207
- [MMLU paper](https://arxiv.org/abs/2009.03300) -- Hendrycks et al., 2021 - Network accessline 208
- [Holistic Evaluation of Language Models (HELM)](https://crfm.stanford.edu/helm/) -- Stanford CRFM - Network accessline 209
- [Chatbot Arena](https://chat.lmsys.org/) -- Human preference-based evaluation (LMSYS)
skills/domains/ai-ml/anomaly-detection-papers-guide/SKILL.md
- Network accessline 8
subcategory: "ai-ml" keywords: ["anomaly detection", "industrial inspection", "defect detection", "MVTec", "unsupervised AD", "visual inspection"] source: "https://github.com/M-3LAB/awesome-industrial-anomaly-detection" - Network accessline 9
keywords: ["anomaly detection", "industrial inspection", "defect detection", "MVTec", "unsupervised AD", "visual inspection"] source: "https://github.com/M-3LAB/awesome-industrial-anomaly-detection" --- - Network accessline 10
source: "https://github.com/M-3LAB/awesome-industrial-anomaly-detection" - Network accessline 165
- [awesome-industrial-anomaly-detection](https://github.com/M-3LAB/awesome-industrial-anomaly-detection) - Network accessline 166
- [Anomalib Library](https://github.com/openvinotoolkit/anomalib) - Network accessline 167
- [MVTec AD Dataset](https://www.mvtec.com/company/research/datasets/mvtec-ad)
skills/domains/ai-ml/autonomous-agents-papers-guide/SKILL.md
- Network accessline 8
subcategory: "ai-ml" keywords: ["autonomous agents", "AI agents", "LLM agents", "agent papers", "planning", "tool use"] source: "https://github.com/tmgthb/Autonomous-Agents" - Network accessline 9
keywords: ["autonomous agents", "AI agents", "LLM agents", "agent papers", "planning", "tool use"] source: "https://github.com/tmgthb/Autonomous-Agents" --- - Network accessline 10
source: "https://github.com/tmgthb/Autonomous-Agents" - Network accessline 176
- [Autonomous-Agents GitHub](https://github.com/tmgthb/Autonomous-Agents) - Network accessline 177
- [LLM-Agent-Paper-List](https://github.com/WooooDyy/LLM-Agent-Paper-List) - Network accessline 178
- [Agent Survey](https://arxiv.org/abs/2308.11432)
skills/domains/ai-ml/deep-learning-papers-guide/SKILL.md
- Network accessline 8
subcategory: "ai-ml" keywords: ["deep learning", "neural network", "Transformer", "CNN", "NLP", "computer vision"] source: "https://github.com/labmlai/annotated_deep_learning_paper_implementations" - Network accessline 9
keywords: ["deep learning", "neural network", "Transformer", "CNN", "NLP", "computer vision"] source: "https://github.com/labmlai/annotated_deep_learning_paper_implementations" --- - Network accessline 10
source: "https://github.com/labmlai/annotated_deep_learning_paper_implementations" - Network accessline 196
- [annotated_deep_learning_paper_implementations](https://github.com/labmlai/annotated_deep_learning_paper_implementations) -- Line-by-line annotated implementations (65,800+ stars) - Network accessline 197
- [Attention Is All You Need](https://arxiv.org/abs/1706.03762) -- Original Transformer paper - Network accessline 198
- [Deep Residual Learning](https://arxiv.org/abs/1512.03385) -- ResNet paper - Network accessline 199
- [The Illustrated Transformer](https://jalammar.github.io/illustrated-transformer/) -- Jay Alammar's visual guide - Network accessline 200
- [Papers With Code](https://paperswithcode.com/) -- Paper-implementation pairs with benchmarks
skills/domains/ai-ml/dl-transformer-finetune/SKILL.md
- Network accessline 8
subcategory: "ai-ml" keywords: ["transformer", "fine-tuning", "BERT", "LoRA", "PEFT", "transfer learning", "NLP"] source: "https://github.com/huggingface/peft" - Network accessline 9
keywords: ["transformer", "fine-tuning", "BERT", "LoRA", "PEFT", "transfer learning", "NLP"] source: "https://github.com/huggingface/peft" --- - Network accessline 10
source: "https://github.com/huggingface/peft" - Network accessline 235
- [LoRA paper](https://arxiv.org/abs/2106.09685) -- Hu et al., 2021 - Network accessline 236
- [QLoRA paper](https://arxiv.org/abs/2305.14314) -- Dettmers et al., 2023 - Network accessline 237
- [PEFT library](https://github.com/huggingface/peft) -- Hugging Face parameter-efficient fine-tuning - Network accessline 238
- [ULMFiT](https://arxiv.org/abs/1801.06146) -- Howard & Ruder, 2018 (gradual unfreezing, discriminative LR) - Network accessline 239
- [Hugging Face Transformers Trainer](https://huggingface.co/docs/transformers/main_classes/trainer) -- Training API documentation
skills/domains/ai-ml/domain-adaptation-papers-guide/SKILL.md
- Network accessline 8
subcategory: "ai-ml" keywords: ["domain adaptation", "transfer learning", "distribution shift", "domain gap", "UDA", "domain generalization"] source: "https://github.com/zhaoxin94/awesome-domain-adaptation" - Network accessline 9
keywords: ["domain adaptation", "transfer learning", "distribution shift", "domain gap", "UDA", "domain generalization"] source: "https://github.com/zhaoxin94/awesome-domain-adaptation" --- - Network accessline 10
source: "https://github.com/zhaoxin94/awesome-domain-adaptation" - Network accessline 171
- [awesome-domain-adaptation](https://github.com/zhaoxin94/awesome-domain-adaptation) - Network accessline 172
- [Transfer Learning Library](https://github.com/thuml/Transfer-Learning-Library) - Network accessline 173
- [DomainBed](https://github.com/facebookresearch/DomainBed)
skills/domains/ai-ml/generative-ai-guide/SKILL.md
- Network accessline 2
name: generative-ai-guide description: "Curated guide to generative AI covering LLMs and diffusion models" source: https://github.com/aishwaryanr/awesome-generative-ai-guide - Network accessline 3
description: "Curated guide to generative AI covering LLMs and diffusion models" source: https://github.com/aishwaryanr/awesome-generative-ai-guide metadata: - Network accessline 4
source: https://github.com/aishwaryanr/awesome-generative-ai-guide
skills/domains/ai-ml/graph-learning-papers-guide/SKILL.md
- Network accessline 8
subcategory: "ai-ml" keywords: ["graph neural network", "GNN", "graph learning", "graph transformer", "message passing", "node classification"] source: "https://github.com/doujiang-zheng/Awesome-Graph-Learning-Papers-List" - Network accessline 9
keywords: ["graph neural network", "GNN", "graph learning", "graph transformer", "message passing", "node classification"] source: "https://github.com/doujiang-zheng/Awesome-Graph-Learning-Papers-List" --- - Network accessline 10
source: "https://github.com/doujiang-zheng/Awesome-Graph-Learning-Papers-List" - Network accessline 122
- [Awesome-Graph-Learning-Papers-List](https://github.com/doujiang-zheng/Awesome-Graph-Learning-Papers-List) - Network accessline 123
- [Open Graph Benchmark](https://ogb.stanford.edu/) - Network accessline 124
- [PyG (PyTorch Geometric)](https://pyg.org/) - Network accessline 125
- [DGL (Deep Graph Library)](https://www.dgl.ai/)
skills/domains/ai-ml/huggingface-api/SKILL.md
- Network accessline 8
subcategory: "ai-ml" keywords: ["Hugging Face", "ML models", "datasets", "model hub", "transformers", "NLP", "computer vision"] source: "https://huggingface.co/docs/hub/api" - Network accessline 9
keywords: ["Hugging Face", "ML models", "datasets", "model hub", "transformers", "NLP", "computer vision"] source: "https://huggingface.co/docs/hub/api" --- - Network accessline 10
source: "https://huggingface.co/docs/hub/api" - Network accessline 17
The Hugging Face Hub is the largest open-source ML ecosystem, hosting over 1 million models, 200,000+ datasets, and 400,000+ Spaces (demo apps). The Hub API at `https://huggingface.co/api` provides programmatic access to search, discover, a - Network accessline 28
[Credential-related source omitted] - Access to secretsline 28
[Credential-related source omitted] - Network accessline 30
[Credential-related source omitted] - Access to secretsline 30
[Credential-related source omitted] - Network accessline 33
Generate tokens at: https://huggingface.co/settings/tokens - Network accessline 40
GET https://huggingface.co/api/models?search={query}&limit={n}&sort={field}&direction={-1|1} - Network accessline 48
curl -s "https://huggingface.co/api/models?search=bert&limit=2&sort=downloads&direction=-1" - Network accessline 76
GET https://huggingface.co/api/models/{owner}/{model_name} - Network accessline 82
curl -s "https://huggingface.co/api/models/google-bert/bert-base-uncased" - Network accessline 102
GET https://huggingface.co/api/datasets?search={query}&limit={n} - Network accessline 108
curl -s "https://huggingface.co/api/datasets?search=squad&limit=2" - Network accessline 128
GET https://huggingface.co/api/datasets/{owner}/{dataset_name} - Network accessline 132
curl -s "https://huggingface.co/api/datasets/rajpurkar/squad_v2" - Network accessline 140
GET https://huggingface.co/api/spaces?search={query}&limit={n} - Network accessline 144
curl -s "https://huggingface.co/api/spaces?search=chatbot&limit=2" - Network accessline 168
# PyTorch text-generation models with 1000+ likes curl -s "https://huggingface.co/api/models?filter=text-generation&library=pytorch&sort=likes&direction=-1&limit=5" - Network accessline 169
curl -s "https://huggingface.co/api/models?filter=text-generation&library=pytorch&sort=likes&direction=-1&limit=5" - Network accessline 171
# Datasets for NER tasks in Chinese curl -s "https://huggingface.co/api/datasets?filter=token-classification&language=zh&limit=10" - Network accessline 172
curl -s "https://huggingface.co/api/datasets?filter=token-classification&language=zh&limit=10" - Network accessline 174
# Gradio Spaces sorted by trending curl -s "https://huggingface.co/api/spaces?filter=gradio&sort=trending&direction=-1&limit=5" - Network accessline 175
curl -s "https://huggingface.co/api/spaces?filter=gradio&sort=trending&direction=-1&limit=5" - Network accessline 200
# Search for top text-classification models resp = requests.get("https://huggingface.co/api/models", params={ - Network accessline 201
resp = requests.get("https://huggingface.co/api/models", params={ - Network accessline 211
# Get specific model metadata detail = requests.get("https://huggingface.co/api/models/google-bert/bert-base-uncased").json() - Network accessline 212
detail = requests.get("https://huggingface.co/api/models/google-bert/bert-base-uncased").json() - Network accessline 246
- Hub API documentation: https://huggingface.co/docs/hub/api - Network accessline 247
- huggingface_hub Python library: https://huggingface.co/docs/huggingface_hub/ - Network accessline 248
- Model Hub: https://huggingface.co/models - Network accessline 249
- Dataset Hub: https://huggingface.co/datasets - Network accessline 250
- Spaces: https://huggingface.co/spaces - Network accessline 251
- OpenAPI spec: https://huggingface.co/docs/hub/api#openapi
skills/domains/ai-ml/huggingface-inference-guide/SKILL.md
- Network accessline 8
subcategory: "ai-ml" keywords: ["huggingface", "inference", "nlp", "machine-learning", "transformers", "models"] source: "https://huggingface.co/docs/api-inference/index" - Network accessline 9
keywords: ["huggingface", "inference", "nlp", "machine-learning", "transformers", "models"] source: "https://huggingface.co/docs/api-inference/index" --- - Network accessline 10
source: "https://huggingface.co/docs/api-inference/index" - Network accessline 25
A free Hugging Face API token is required. Create an account and generate a token at https://huggingface.co/settings/tokens. - Access to secretsline 30
[Credential-related source omitted] - Network accessline 34
[Credential-related source omitted] - Access to secretsline 34
[Credential-related source omitted] - Access to secretsline 35
[Credential-related source omitted] - Network accessline 45
POST https://api-inference.huggingface.co/models/{model_id} - Dangerous shell operationline 49
[Credential-related source omitted] - Network accessline 49
[Credential-related source omitted] - Access to secretsline 49
[Credential-related source omitted] - Network accessline 50
"https://api-inference.huggingface.co/models/distilbert-base-uncased-finetuned-sst-2-english" \ - Access to secretsline 51
[Credential-related source omitted] - Dangerous shell operationline 60
[Credential-related source omitted] - Network accessline 60
[Credential-related source omitted] - Access to secretsline 60
[Credential-related source omitted] - Network accessline 61
"https://api-inference.huggingface.co/models/dslim/bert-base-NER" \ - Access to secretsline 62
[Credential-related source omitted] - Network accessline 71
[Credential-related source omitted] - Access to secretsline 71
[Credential-related source omitted] - Network accessline 72
"https://api-inference.huggingface.co/models/facebook/bart-large-cnn" \ - Access to secretsline 73
[Credential-related source omitted] - Network accessline 86
[Credential-related source omitted] - Access to secretsline 86
[Credential-related source omitted] - Network accessline 87
"https://api-inference.huggingface.co/models/facebook/bart-large-mnli" \ - Access to secretsline 88
[Credential-related source omitted] - Network accessline 103
API_URL = "https://api-inference.huggingface.co/models/distilbert-base-uncased-finetuned-sst-2-english" - Access to secretsline 104
[Credential-related source omitted] - Network accessline 106
def classify_sentiment(texts): """Classify sentiment for a batch of texts.""" response = requests.post(API_URL, headers=HEADERS, json={"inputs": texts}) - Network accessline 107
"""Classify sentiment for a batch of texts.""" response = requests.post(API_URL, headers=HEADERS, json={"inputs": texts}) if response.status_code == 503: - Network accessline 108
response = requests.post(API_URL, headers=HEADERS, json={"inputs": texts}) - Network accessline 112
print(f"Model loading, waiting {wait_time:.0f}s...") time.sleep(wait_time) response = requests.post(API_URL, headers=HEADERS, json={"inputs": texts}) - Network accessline 113
time.sleep(wait_time) response = requests.post(API_URL, headers=HEADERS, json={"inputs": texts}) response.raise_for_status() - Network accessline 114
response = requests.post(API_URL, headers=HEADERS, json={"inputs": texts}) - Network accessline 138
ZSC_URL = "https://api-inference.huggingface.co/models/facebook/bart-large-mnli" - Access to secretsline 139
[Credential-related source omitted] - Network accessline 145
"parameters": {"candidate_labels": categories} } resp = requests.post(ZSC_URL, headers=HEADERS, json=payload) - Network accessline 146
} resp = requests.post(ZSC_URL, headers=HEADERS, json=payload) resp.raise_for_status() - Network accessline 147
resp = requests.post(ZSC_URL, headers=HEADERS, json=payload) - Network accessline 193
- Hugging Face Inference API Documentation: https://huggingface.co/docs/api-inference/index - Network accessline 194
- Hugging Face Model Hub: https://huggingface.co/models - Network accessline 195
- Hugging Face API Token Settings: https://huggingface.co/settings/tokens - Network accessline 196
- Hugging Face Tasks Overview: https://huggingface.co/tasks
skills/domains/ai-ml/keras-deep-learning/SKILL.md
- Network accessline 8
subcategory: "ai-ml" keywords: ["Keras", "deep learning", "neural network", "model training", "TensorFlow", "classification"] source: "https://github.com/fchollet/deep-learning-with-python-notebooks" - Network accessline 9
keywords: ["Keras", "deep learning", "neural network", "model training", "TensorFlow", "classification"] source: "https://github.com/fchollet/deep-learning-with-python-notebooks" --- - Network accessline 10
source: "https://github.com/fchollet/deep-learning-with-python-notebooks" - Access to secretsline 123
[Credential-related source omitted] - Network accessline 206
- [Deep Learning with Python, 2nd Edition](https://www.manning.com/books/deep-learning-with-python-second-edition) -- Francois Chollet (Keras creator) - Network accessline 207
- [Keras documentation](https://keras.io/) -- Official API reference and guides - Network accessline 208
- [TensorFlow tutorials](https://www.tensorflow.org/tutorials) -- End-to-end examples - Network accessline 209
- [fchollet/deep-learning-with-python-notebooks](https://github.com/fchollet/deep-learning-with-python-notebooks) -- Code companion to the book - Network accessline 210
- [Keras examples gallery](https://keras.io/examples/) -- 100+ community-contributed examples
skills/domains/ai-ml/kolmogorov-arnold-networks-guide/SKILL.md
- Network accessline 8
subcategory: "ai-ml" keywords: ["KAN", "Kolmogorov-Arnold", "learnable activations", "spline networks", "neural architecture", "interpretability"] source: "https://github.com/mintisan/awesome-kan" - Network accessline 9
keywords: ["KAN", "Kolmogorov-Arnold", "learnable activations", "spline networks", "neural architecture", "interpretability"] source: "https://github.com/mintisan/awesome-kan" --- - Network accessline 10
source: "https://github.com/mintisan/awesome-kan" - Network accessline 182
- [awesome-kan](https://github.com/mintisan/awesome-kan) - Network accessline 183
- [KAN Paper](https://arxiv.org/abs/2404.19756) - Network accessline 184
- [pykan Implementation](https://github.com/KindXiaoming/pykan) - Network accessline 185
- [KAN 2.0 Paper](https://arxiv.org/abs/2408.10205)
skills/domains/ai-ml/llm-from-scratch-guide/SKILL.md
- Network accessline 8
subcategory: "ai-ml" keywords: ["llm", "pytorch", "transformer", "gpt", "pretraining", "finetuning"] source: "https://github.com/rasbt/LLMs-from-scratch" - Network accessline 9
keywords: ["llm", "pytorch", "transformer", "gpt", "pretraining", "finetuning"] source: "https://github.com/rasbt/LLMs-from-scratch" --- - Network accessline 10
source: "https://github.com/rasbt/LLMs-from-scratch" - Network accessline 28
git clone https://github.com/rasbt/LLMs-from-scratch.git - Network accessline 121
- Repository: https://github.com/rasbt/LLMs-from-scratch - Network accessline 122
- Book: "Build a Large Language Model (From Scratch)" by Sebastian Raschka (Manning, 2024) - Author's blog: https://sebastianraschka.com/blog/ - PyTorch documentation: https://pytorch.org/docs/stable/ - Network accessline 123
- Author's blog: https://sebastianraschka.com/blog/ - Network accessline 124
- PyTorch documentation: https://pytorch.org/docs/stable/
skills/domains/ai-ml/ml-pipeline-guide/SKILL.md
- Network accessline 8
subcategory: "ai-ml" keywords: ["MLOps", "pipeline", "deployment", "reproducibility", "feature engineering", "CI/CD"] source: "https://github.com/mlflow/mlflow" - Network accessline 9
keywords: ["MLOps", "pipeline", "deployment", "reproducibility", "feature engineering", "CI/CD"] source: "https://github.com/mlflow/mlflow" --- - Network accessline 10
source: "https://github.com/mlflow/mlflow" - Dangerous shell operationline 276
rm -rf outputs/ multirun/ __pycache__/ - Network accessline 291
- [MLflow documentation](https://mlflow.org/docs/latest/) -- Experiment tracking and model registry - Network accessline 292
- [DVC documentation](https://dvc.org/doc) -- Data version control for ML - Network accessline 293
- [Hydra documentation](https://hydra.cc/) -- Configuration management framework - Network accessline 294
- [Cookiecutter Data Science](https://drivendata.github.io/cookiecutter-data-science/) -- Project structure template - Network accessline 295
- [Made With ML](https://madewithml.com/) -- MLOps best practices for researchers
skills/domains/ai-ml/nlp-toolkit-guide/SKILL.md
- Network accessline 8
subcategory: "ai-ml" keywords: ["NLP", "perplexity", "burstiness", "entropy", "tokenization", "text analysis"] source: "https://github.com/huggingface/transformers" - Network accessline 9
keywords: ["NLP", "perplexity", "burstiness", "entropy", "tokenization", "text analysis"] source: "https://github.com/huggingface/transformers" --- - Network accessline 10
source: "https://github.com/huggingface/transformers" - Network accessline 243
- [Hugging Face Transformers](https://huggingface.co/docs/transformers/) -- Model hub and tokenizer library - Network accessline 244
- [DetectGPT](https://arxiv.org/abs/2301.11305) -- Perplexity-based AI text detection (Mitchell et al., 2023) - Network accessline 245
- [Burstiness and Memory in Text](https://doi.org/10.1103/PhysRevLett.114.078101) -- Altmann et al., 2015 - Network accessline 246
- [NLTK documentation](https://www.nltk.org/) -- Classic NLP toolkit for feature engineering - Network accessline 247
- [spaCy documentation](https://spacy.io/) -- Industrial-strength NLP for production pipelines
skills/domains/ai-ml/npcpy-research-guide/SKILL.md
- Network accessline 8
subcategory: "ai-ml" keywords: ["npcpy", "NLP", "agents", "knowledge graph", "all-in-one", "Python library"] source: "https://github.com/NPC-Worldwide/npcpy" - Network accessline 9
keywords: ["npcpy", "NLP", "agents", "knowledge graph", "all-in-one", "Python library"] source: "https://github.com/NPC-Worldwide/npcpy" --- - Network accessline 10
source: "https://github.com/NPC-Worldwide/npcpy" - Network accessline 137
- [npcpy GitHub](https://github.com/NPC-Worldwide/npcpy)
skills/domains/ai-ml/pytorch-guide/SKILL.md
- Network accessline 8
subcategory: "ai-ml" keywords: ["PyTorch", "deep learning", "training loop", "GPU", "debugging", "autograd"] source: "https://github.com/pytorch/pytorch" - Network accessline 9
keywords: ["PyTorch", "deep learning", "training loop", "GPU", "debugging", "autograd"] source: "https://github.com/pytorch/pytorch" --- - Network accessline 10
source: "https://github.com/pytorch/pytorch" - Network accessline 277
- [PyTorch documentation](https://pytorch.org/docs/stable/) -- Official API reference - Network accessline 278
- [PyTorch tutorials](https://pytorch.org/tutorials/) -- End-to-end examples from the PyTorch team - Network accessline 279
- [PyTorch best practices](https://pytorch.org/docs/stable/notes/cuda.html) -- CUDA semantics and best practices - Network accessline 280
- [Effective PyTorch](https://github.com/vahidk/EffectivePyTorch) -- Community best practices guide - Network accessline 281
- [PyTorch Lightning](https://lightning.ai/) -- High-level training framework built on PyTorch
skills/domains/ai-ml/pytorch-lightning-guide/SKILL.md
- Network accessline 8
subcategory: "ai-ml" keywords: ["pytorch-lightning", "training", "distributed", "finetuning", "scalability", "research"] source: "https://github.com/Lightning-AI/pytorch-lightning" - Network accessline 9
keywords: ["pytorch-lightning", "training", "distributed", "finetuning", "scalability", "research"] source: "https://github.com/Lightning-AI/pytorch-lightning" --- - Network accessline 10
source: "https://github.com/Lightning-AI/pytorch-lightning" - Network accessline 241
- Repository: https://github.com/Lightning-AI/pytorch-lightning - Network accessline 242
- Documentation: https://lightning.ai/docs/pytorch/stable/ - Network accessline 243
- Lightning AI platform: https://lightning.ai/ - Network accessline 244
- Migration guide from vanilla PyTorch: https://lightning.ai/docs/pytorch/stable/starter/converting.html
skills/domains/ai-ml/responsible-ai-guide/SKILL.md
- Network accessline 8
subcategory: "ai-ml" keywords: ["responsible AI", "AI ethics", "fairness", "trustworthy AI", "AI safety", "bias"] source: "https://github.com/AthenaCore/AwesomeResponsibleAI" - Network accessline 9
keywords: ["responsible AI", "AI ethics", "fairness", "trustworthy AI", "AI safety", "bias"] source: "https://github.com/AthenaCore/AwesomeResponsibleAI" --- - Network accessline 10
source: "https://github.com/AthenaCore/AwesomeResponsibleAI" - Network accessline 124
- [AwesomeResponsibleAI](https://github.com/AthenaCore/AwesomeResponsibleAI) - Network accessline 125
- [Fairlearn](https://fairlearn.org/) - Network accessline 126
- [EU AI Act](https://artificialintelligenceact.eu/)
skills/domains/ai-ml/tensorflow-guide/SKILL.md
- Network accessline 8
subcategory: "ai-ml" keywords: ["TensorFlow", "tf.function", "GPU", "SavedModel", "distributed training", "XLA"] source: "https://github.com/tensorflow/tensorflow" - Network accessline 9
keywords: ["TensorFlow", "tf.function", "GPU", "SavedModel", "distributed training", "XLA"] source: "https://github.com/tensorflow/tensorflow" --- - Network accessline 10
source: "https://github.com/tensorflow/tensorflow" - Network accessline 237
- [TensorFlow documentation](https://www.tensorflow.org/guide) -- Official guides and API reference - Network accessline 238
- [Better performance with tf.function](https://www.tensorflow.org/guide/function) -- Tracing semantics deep dive - Network accessline 239
- [Distributed training guide](https://www.tensorflow.org/guide/distributed_training) -- Multi-GPU and multi-node patterns - Network accessline 240
- [TensorFlow Model Garden](https://github.com/tensorflow/models) -- Reference implementations of SOTA models - Network accessline 241
- [XLA documentation](https://www.tensorflow.org/xla) -- Hardware-accelerated compilation
skills/domains/ai-ml/vmas-simulator-guide/SKILL.md
- Network accessline 8
subcategory: "ai-ml" keywords: ["multi-agent RL", "VMAS", "simulator", "reinforcement learning", "vectorized", "cooperative"] source: "https://github.com/proroklab/VectorizedMultiAgentSimulator" - Network accessline 9
keywords: ["multi-agent RL", "VMAS", "simulator", "reinforcement learning", "vectorized", "cooperative"] source: "https://github.com/proroklab/VectorizedMultiAgentSimulator" --- - Network accessline 10
source: "https://github.com/proroklab/VectorizedMultiAgentSimulator" - Network accessline 127
- [VMAS GitHub](https://github.com/proroklab/VectorizedMultiAgentSimulator) - Network accessline 128
- [VMAS Paper](https://arxiv.org/abs/2207.03530) - Network accessline 129
- [BenchMARL](https://github.com/facebookresearch/BenchMARL)
skills/domains/biomedical/alphafold-api/SKILL.md
- Network accessline 8
subcategory: "biomedical" keywords: ["protein structure", "AlphaFold", "structure prediction", "UniProt", "pLDDT", "bioinformatics"] source: "https://alphafold.ebi.ac.uk" - Network accessline 9
keywords: ["protein structure", "AlphaFold", "structure prediction", "UniProt", "pLDDT", "bioinformatics"] source: "https://alphafold.ebi.ac.uk" --- - Network accessline 10
source: "https://alphafold.ebi.ac.uk" - Network accessline 25
Base URL: `https://alphafold.ebi.ac.uk/api` - Network accessline 32
curl "https://alphafold.ebi.ac.uk/api/prediction/P04637" - Network accessline 54
"sequenceStart": 1, "sequenceEnd": 393, "pdbUrl": "https://alphafold.ebi.ac.uk/files/AF-P04637-F1-model_v6.pdb", - Network accessline 55
"sequenceEnd": 393, "pdbUrl": "https://alphafold.ebi.ac.uk/files/AF-P04637-F1-model_v6.pdb", "cifUrl": "https://alphafold.ebi.ac.uk/files/AF-P04637-F1-model_v6.cif", - Network accessline 56
"pdbUrl": "https://alphafold.ebi.ac.uk/files/AF-P04637-F1-model_v6.pdb", - Network accessline 57
"cifUrl": "https://alphafold.ebi.ac.uk/files/AF-P04637-F1-model_v6.cif", - Network accessline 58
"bcifUrl": "https://alphafold.ebi.ac.uk/files/AF-P04637-F1-model_v6.bcif", - Network accessline 59
"paeImageUrl": "https://alphafold.ebi.ac.uk/files/AF-P04637-F1-predicted_aligned_error_v6.png", - Network accessline 60
"paeDocUrl": "https://alphafold.ebi.ac.uk/files/AF-P04637-F1-predicted_aligned_error_v6.json", - Network accessline 61
"plddtDocUrl": "https://alphafold.ebi.ac.uk/files/AF-P04637-F1-confidence_v6.json", - Network accessline 62
"amAnnotationsUrl": "https://alphafold.ebi.ac.uk/files/AF-P04637-F1-aa-substitutions.csv" - Network accessline 72
curl "https://alphafold.ebi.ac.uk/files/AF-P04637-F1-confidence_v6.json" - Network accessline 92
curl "https://alphafold.ebi.ac.uk/api/uniprot/summary/P04637.json" - Network accessline 106
"summary": { "model_identifier": "AF-P04637-F1", "model_url": "https://alphafold.ebi.ac.uk/files/AF-P04637-F1-model_v6.cif", - Network accessline 107
"model_identifier": "AF-P04637-F1", "model_url": "https://alphafold.ebi.ac.uk/files/AF-P04637-F1-model_v6.cif", "provider": "AlphaFold DB", - Network accessline 108
"model_url": "https://alphafold.ebi.ac.uk/files/AF-P04637-F1-model_v6.cif", - Network accessline 124
# PDB format curl -O "https://alphafold.ebi.ac.uk/files/AF-P04637-F1-model_v6.pdb" - Network accessline 125
curl -O "https://alphafold.ebi.ac.uk/files/AF-P04637-F1-model_v6.pdb" - Network accessline 127
# mmCIF format curl -O "https://alphafold.ebi.ac.uk/files/AF-P04637-F1-model_v6.cif" - Network accessline 128
curl -O "https://alphafold.ebi.ac.uk/files/AF-P04637-F1-model_v6.cif" - Network accessline 130
# Predicted Aligned Error (PAE) matrix curl -O "https://alphafold.ebi.ac.uk/files/AF-P04637-F1-predicted_aligned_error_v6.json" - Network accessline 131
curl -O "https://alphafold.ebi.ac.uk/files/AF-P04637-F1-predicted_aligned_error_v6.json" - Network accessline 154
The AlphaFold DB API has no published per-request rate limits. EMBL-EBI's general fair use policy applies: usage that degrades service for others may result in blocking. For bulk downloads (entire proteomes), use the FTP archive at `https:/ - Network accessline 162
def get_alphafold_prediction(uniprot_id: str) -> dict: """Fetch AlphaFold structure prediction for a UniProt accession.""" url = f"https://alphafold.ebi.ac.uk/api/prediction/{uniprot_id}" - Network accessline 163
"""Fetch AlphaFold structure prediction for a UniProt accession.""" url = f"https://alphafold.ebi.ac.uk/api/prediction/{uniprot_id}" resp = requests.get(url) - Network accessline 164
url = f"https://alphafold.ebi.ac.uk/api/prediction/{uniprot_id}" resp = requests.get(url) resp.raise_for_status() - Network accessline 165
resp = requests.get(url) - Network accessline 172
def get_confidence_scores(prediction: dict) -> dict: """Download per-residue pLDDT confidence scores.""" resp = requests.get(prediction["plddtDocUrl"]) - Network accessline 173
"""Download per-residue pLDDT confidence scores.""" resp = requests.get(prediction["plddtDocUrl"]) resp.raise_for_status() - Network accessline 174
resp = requests.get(prediction["plddtDocUrl"]) - Network accessline 187
resp = requests.get(url) - Network accessline 223
- [AlphaFold Protein Structure Database](https://alphafold.ebi.ac.uk/) - Network accessline 224
- [AlphaFold DB API Documentation](https://alphafold.ebi.ac.uk/api/openapi.json) - Network accessline 225
- [AlphaFold DB FTP (Bulk Downloads)](https://ftp.ebi.ac.uk/pub/databases/alphafold/)
skills/domains/biomedical/bioagents-guide/SKILL.md
- Network accessline 8
subcategory: "biomedical" keywords: ["AI scientist", "biological research", "autonomous lab", "drug discovery", "protein design", "bioinformatics"] source: "https://github.com/SakanaAI/AI-Scientist" - Network accessline 9
keywords: ["AI scientist", "biological research", "autonomous lab", "drug discovery", "protein design", "bioinformatics"] source: "https://github.com/SakanaAI/AI-Scientist" --- - Network accessline 10
source: "https://github.com/SakanaAI/AI-Scientist" - Network accessline 304
- [AI Scientist](https://github.com/SakanaAI/AI-Scientist) -- Sakana AI's autonomous research framework - Network accessline 305
- [ESM-2](https://github.com/facebookresearch/esm) -- Meta's protein language model - Network accessline 306
- [ChemCrow](https://arxiv.org/abs/2304.05376) -- LLM agent for chemistry research - Network accessline 307
- [BioGPT](https://github.com/microsoft/BioGPT) -- Microsoft's biomedical text generation model - Network accessline 308
- [OpenTargets](https://www.opentargets.org/) -- Target identification platform
skills/domains/biomedical/biothings-api/SKILL.md
- Network accessline 8
subcategory: "biomedical" keywords: ["gene annotation", "variant annotation", "drug data", "BioThings", "mygene", "myvariant", "bioinformatics"] source: "https://biothings.io" - Network accessline 9
keywords: ["gene annotation", "variant annotation", "drug data", "BioThings", "mygene", "myvariant", "bioinformatics"] source: "https://biothings.io" --- - Network accessline 10
source: "https://biothings.io" - Network accessline 30
# No API key needed — just query directly curl "https://mygene.info/v3/query?q=BRCA1&size=1" - Network accessline 31
curl "https://mygene.info/v3/query?q=BRCA1&size=1" - Network accessline 39
GET https://mygene.info/v3/query?q={query}&size={n} - Network accessline 45
curl -s "https://mygene.info/v3/query?q=BRCA1&size=1" - Network accessline 70
GET https://mygene.info/v3/gene/{entrez_id} - Network accessline 76
# Full annotation (large response) curl -s "https://mygene.info/v3/gene/1017" - Network accessline 77
curl -s "https://mygene.info/v3/gene/1017" - Network accessline 79
# Selective fields curl -s "https://mygene.info/v3/gene/1017?fields=symbol,name,summary,genomic_pos,go" - Network accessline 80
curl -s "https://mygene.info/v3/gene/1017?fields=symbol,name,summary,genomic_pos,go" - Network accessline 105
GET https://myvariant.info/v1/query?q={query}&size={n} - Network accessline 111
curl -s "https://myvariant.info/v1/query?q=rs58991260&size=1" - Network accessline 150
GET https://myvariant.info/v1/variant/{hgvs_id} - Network accessline 154
curl -s "https://myvariant.info/v1/variant/chr1:g.218631822G>A?fields=dbsnp,cadd,clinvar" - Network accessline 162
GET https://mychem.info/v1/query?q={query}&size={n} - Network accessline 168
curl -s "https://mychem.info/v1/query?q=aspirin&size=1" - Network accessline 206
GET https://mychem.info/v1/chem/{id} - Network accessline 210
curl -s "https://mychem.info/v1/chem/CHEMBL25?fields=drugbank,chembl,pubchem" - Network accessline 241
MYGENE = "https://mygene.info/v3" - Network accessline 242
MYVARIANT = "https://myvariant.info/v1" - Network accessline 243
MYCHEM = "https://mychem.info/v1" - Network accessline 245
def search_gene(symbol): resp = requests.get(f"{MYGENE}/query", - Network accessline 246
resp = requests.get(f"{MYGENE}/query", - Network accessline 252
def search_variants(gene_symbol, size=5): resp = requests.get(f"{MYVARIANT}/query", - Network accessline 253
resp = requests.get(f"{MYVARIANT}/query", - Network accessline 260
def search_drug(name): resp = requests.get(f"{MYCHEM}/query", - Network accessline 261
resp = requests.get(f"{MYCHEM}/query", - Network accessline 292
- BioThings API Hub: https://biothings.io - Network accessline 293
- MyGene.info Documentation: https://docs.mygene.info - Network accessline 294
- MyVariant.info Documentation: https://docs.myvariant.info - Network accessline 295
- MyChem.info Documentation: https://docs.mychem.info - Network accessline 296
- Publication: Xin J et al. *Genome Biology* 17:91 (2016). https://doi.org/10.1186/s13059-016-0953-9
skills/domains/biomedical/clawbio-guide/SKILL.md
- Network accessline 8
subcategory: "biomedical" keywords: ["ClawBio", "bioinformatics", "OpenClaw", "genomics", "pipeline", "biological analysis"] source: "https://github.com/ClawBio/ClawBio" - Network accessline 9
keywords: ["ClawBio", "bioinformatics", "OpenClaw", "genomics", "pipeline", "biological analysis"] source: "https://github.com/ClawBio/ClawBio" --- - Network accessline 10
source: "https://github.com/ClawBio/ClawBio" - Network accessline 165
- [ClawBio GitHub](https://github.com/ClawBio/ClawBio) - Network accessline 166
- [Bioconductor](https://www.bioconductor.org/) - Network accessline 167
- [nf-core Pipelines](https://nf-co.re/)
skills/domains/biomedical/clinical-dialogue-agents-guide/SKILL.md
- Network accessline 8
subcategory: "biomedical" keywords: ["clinical dialogue", "medical QA", "patient interaction", "clinical agents", "healthcare AI", "diagnosis"] source: "https://github.com/xqz614/Awesome-Agentic-Clinical-Dialogue" - Network accessline 9
keywords: ["clinical dialogue", "medical QA", "patient interaction", "clinical agents", "healthcare AI", "diagnosis"] source: "https://github.com/xqz614/Awesome-Agentic-Clinical-Dialogue" --- - Network accessline 10
source: "https://github.com/xqz614/Awesome-Agentic-Clinical-Dialogue" - Network accessline 143
- [Awesome-Agentic-Clinical-Dialogue](https://github.com/xqz614/Awesome-Agentic-Clinical-Dialogue) - Network accessline 144
- [AMIE Paper](https://arxiv.org/abs/2401.05654) - Network accessline 145
- [AgentClinic](https://arxiv.org/abs/2405.07960)
skills/domains/biomedical/clinicaltrials-api-v2/SKILL.md
- Network accessline 8
subcategory: "biomedical" keywords: ["clinical trials", "ClinicalTrials.gov", "medical research", "study design", "FDA", "drug trials"] source: "https://clinicaltrials.gov/data-api/about-api" - Network accessline 9
keywords: ["clinical trials", "ClinicalTrials.gov", "medical research", "study design", "FDA", "drug trials"] source: "https://clinicaltrials.gov/data-api/about-api" --- - Network accessline 10
source: "https://clinicaltrials.gov/data-api/about-api" - Network accessline 29
- **URL**: `GET https://clinicaltrials.gov/api/v2/studies` - Network accessline 50
curl "https://clinicaltrials.gov/api/v2/studies?query.cond=diabetes&query.intr=metformin&pageSize=1&fields=NCTId,BriefTitle,OverallStatus" - Network accessline 74
- **URL**: `GET https://clinicaltrials.gov/api/v2/studies/{nctId}` - Network accessline 77
curl "https://clinicaltrials.gov/api/v2/studies/NCT04280705?fields=NCTId,BriefTitle,OverallStatus,Phase,LeadSponsorName,EnrollmentCount,Condition,InterventionName" - Network accessline 106
- **URL**: `GET https://clinicaltrials.gov/api/v2/stats/size` - Network accessline 109
curl "https://clinicaltrials.gov/api/v2/stats/size" - Network accessline 125
- **URL**: `GET https://clinicaltrials.gov/api/v2/stats/fieldValues/{fieldName}` - Network accessline 128
curl "https://clinicaltrials.gov/api/v2/stats/fieldValues/Phase" - Network accessline 150
No formal rate limits are published for the v2 API. Follow NCBI usage guidelines: stay under 3 requests/second without an API key, up to 10/second with one. For bulk data access, use the AACT relational database (https://aact.ctti-clinicalt - Network accessline 165
def collect_trials(condition, intervention, status="COMPLETED"): base = "https://clinicaltrials.gov/api/v2/studies" - Network accessline 166
base = "https://clinicaltrials.gov/api/v2/studies" - Network accessline 174
[Credential-related source omitted] - Network accessline 175
params["pageToken"] = token data = requests.get(base, params=params).json() studies.extend(data.get("studies", [])) - Network accessline 176
data = requests.get(base, params=params).json() - Network accessline 194
params = {"query.cond": "Alzheimer's Disease", "pageSize": 100, "fields": "NCTId,Phase,LeadSponsorName"} data = requests.get("https://clinicaltrials.gov/api/v2/studies", params=params).json() - Network accessline 196
data = requests.get("https://clinicaltrials.gov/api/v2/studies", params=params).json() - Network accessline 212
- v2 API reference: https://clinicaltrials.gov/data-api/api - Network accessline 213
- About the API: https://clinicaltrials.gov/data-api/about-api - Network accessline 214
- AACT database (bulk access): https://aact.ctti-clinicaltrials.org/ - Network accessline 215
- WHO ICTRP: https://trialsearch.who.int/ - Network accessline 216
- NCBI usage policies: https://www.ncbi.nlm.nih.gov/home/about/policies/
skills/domains/biomedical/clinicaltrials-api/SKILL.md
- Network accessline 8
subcategory: "biomedical" keywords: ["clinical medicine", "epidemiology", "public health", "evidence-based medicine"] source: "https://clinicaltrials.gov/api/" - Network accessline 9
keywords: ["clinical medicine", "epidemiology", "public health", "evidence-based medicine"] source: "https://clinicaltrials.gov/api/" --- - Network accessline 10
source: "https://clinicaltrials.gov/api/" - Network accessline 33
- **URL**: `GET https://clinicaltrials.gov/api/v2/studies` - Network accessline 53
# Search for recruiting cancer immunotherapy trials curl "https://clinicaltrials.gov/api/v2/studies?query.cond=cancer&query.intr=immunotherapy&filter.overallStatus=RECRUITING&pageSize=5" - Network accessline 54
curl "https://clinicaltrials.gov/api/v2/studies?query.cond=cancer&query.intr=immunotherapy&filter.overallStatus=RECRUITING&pageSize=5" - Network accessline 56
# Search by sponsor curl "https://clinicaltrials.gov/api/v2/studies?query.spons=NIH&filter.phase=PHASE3&pageSize=10" - Network accessline 57
curl "https://clinicaltrials.gov/api/v2/studies?query.spons=NIH&filter.phase=PHASE3&pageSize=10" - Network accessline 66
- **URL**: `GET https://clinicaltrials.gov/api/v2/studies/{nctId}` - Network accessline 76
curl "https://clinicaltrials.gov/api/v2/studies/NCT04280705" - Network accessline 83
No formal rate limits are documented for the ClinicalTrials.gov API. However, the service follows NCBI usage guidelines which recommend no more than 3 requests per second without an API key, and up to 10 requests per second with an NCBI API - Network accessline 98
"pageSize": 20 } resp = requests.get("https://clinicaltrials.gov/api/v2/studies", params=params) - Network accessline 99
} resp = requests.get("https://clinicaltrials.gov/api/v2/studies", params=params) data = resp.json() - Network accessline 100
resp = requests.get("https://clinicaltrials.gov/api/v2/studies", params=params) - Network accessline 134
resp = requests.get("https://clinicaltrials.gov/api/v2/studies", params=params) - Network accessline 156
"pageSize": 100 } resp = requests.get("https://clinicaltrials.gov/api/v2/studies", params=params) - Network accessline 157
} resp = requests.get("https://clinicaltrials.gov/api/v2/studies", params=params) data = resp.json() - Network accessline 158
resp = requests.get("https://clinicaltrials.gov/api/v2/studies", params=params) - Network accessline 173
- Official API documentation: https://clinicaltrials.gov/data-api/api - Network accessline 174
- ClinicalTrials.gov homepage: https://clinicaltrials.gov/ - Network accessline 175
- AACT database for bulk analysis: https://aact.ctti-clinicaltrials.org/ - Network accessline 176
- WHO ICTRP (international registry portal): https://trialsearch.who.int/ - Network accessline 177
- NCBI usage policies: https://www.ncbi.nlm.nih.gov/home/about/policies/
skills/domains/biomedical/ena-sequence-api/SKILL.md
- Network accessline 8
subcategory: "biomedical" keywords: ["ENA", "nucleotide sequences", "genomics", "EMBL-EBI", "sequencing data", "NGS"] source: "https://www.ebi.ac.uk/ena/" - Network accessline 9
keywords: ["ENA", "nucleotide sequences", "genomics", "EMBL-EBI", "sequencing data", "NGS"] source: "https://www.ebi.ac.uk/ena/" --- - Network accessline 10
source: "https://www.ebi.ac.uk/ena/" - Network accessline 24
# Search for studies curl "https://www.ebi.ac.uk/ena/portal/api/search?query=CRISPR+cas9&result=study&limit=20&format=json" - Network accessline 25
curl "https://www.ebi.ac.uk/ena/portal/api/search?query=CRISPR+cas9&result=study&limit=20&format=json" - Network accessline 27
# Search for samples curl "https://www.ebi.ac.uk/ena/portal/api/search?query=human+gut+microbiome&result=sample&limit=20&format=json" - Network accessline 28
curl "https://www.ebi.ac.uk/ena/portal/api/search?query=human+gut+microbiome&result=sample&limit=20&format=json" - Network accessline 30
# Search for runs (sequencing data) curl "https://www.ebi.ac.uk/ena/portal/api/search?query=RNA-seq+cancer&result=read_run&limit=20&format=json" - Network accessline 31
curl "https://www.ebi.ac.uk/ena/portal/api/search?query=RNA-seq+cancer&result=read_run&limit=20&format=json" - Network accessline 37
# Get record by accession curl "https://www.ebi.ac.uk/ena/browser/api/xml/PRJEB12345" - Network accessline 38
curl "https://www.ebi.ac.uk/ena/browser/api/xml/PRJEB12345" - Network accessline 40
# Get in JSON format curl "https://www.ebi.ac.uk/ena/browser/api/summary/PRJEB12345" - Network accessline 41
curl "https://www.ebi.ac.uk/ena/browser/api/summary/PRJEB12345" - Network accessline 43
# Get sequence in FASTA curl "https://www.ebi.ac.uk/ena/browser/api/fasta/AF123456" - Network accessline 44
curl "https://www.ebi.ac.uk/ena/browser/api/fasta/AF123456" - Network accessline 46
# Get in EMBL flat file format curl "https://www.ebi.ac.uk/ena/browser/api/embl/AF123456" - Network accessline 47
curl "https://www.ebi.ac.uk/ena/browser/api/embl/AF123456" - Network accessline 53
# Search by organism curl "https://www.ebi.ac.uk/ena/portal/api/search?query=tax_tree(9606)&result=study&limit=20&format=json" - Network accessline 54
curl "https://www.ebi.ac.uk/ena/portal/api/search?query=tax_tree(9606)&result=study&limit=20&format=json" - Network accessline 56
# Get taxonomy details curl "https://www.ebi.ac.uk/ena/taxonomy/rest/tax-id/9606" - Network accessline 57
curl "https://www.ebi.ac.uk/ena/taxonomy/rest/tax-id/9606" - Network accessline 88
PORTAL_URL = "https://www.ebi.ac.uk/ena/portal/api" - Network accessline 89
BROWSER_URL = "https://www.ebi.ac.uk/ena/browser/api" - Network accessline 100
"tax_id,scientific_name,center_name", } resp = requests.get(f"{PORTAL_URL}/search", params=params) - Network accessline 101
} resp = requests.get(f"{PORTAL_URL}/search", params=params) resp.raise_for_status() - Network accessline 102
resp = requests.get(f"{PORTAL_URL}/search", params=params) - Network accessline 115
"library_strategy,read_count,base_count", } resp = requests.get(f"{PORTAL_URL}/search", params=params) - Network accessline 116
} resp = requests.get(f"{PORTAL_URL}/search", params=params) resp.raise_for_status() - Network accessline 117
resp = requests.get(f"{PORTAL_URL}/search", params=params) - Network accessline 122
def get_fasta(accession: str) -> str: """Retrieve sequence in FASTA format.""" resp = requests.get(f"{BROWSER_URL}/fasta/{accession}") - Network accessline 123
"""Retrieve sequence in FASTA format.""" resp = requests.get(f"{BROWSER_URL}/fasta/{accession}") resp.raise_for_status() - Network accessline 124
resp = requests.get(f"{BROWSER_URL}/fasta/{accession}") - Network accessline 136
"limit": 1000, } resp = requests.get(f"{PORTAL_URL}/search", params=params) - Network accessline 137
} resp = requests.get(f"{PORTAL_URL}/search", params=params) resp.raise_for_status() - Network accessline 138
resp = requests.get(f"{PORTAL_URL}/search", params=params) - Network accessline 161
# Download FASTQ files (from run metadata) # The fastq_ftp field provides FTP paths: wget ftp://ftp.sra.ebi.ac.uk/vol1/fastq/ERR123/ERR123456/ERR123456_1.fastq.gz - Network accessline 163
wget ftp://ftp.sra.ebi.ac.uk/vol1/fastq/ERR123/ERR123456/ERR123456_1.fastq.gz - Network accessline 172
- [ENA](https://www.ebi.ac.uk/ena/) - Network accessline 173
- [ENA Portal API](https://www.ebi.ac.uk/ena/portal/api/) - Network accessline 174
- [ENA Browser API](https://www.ebi.ac.uk/ena/browser/api/)
skills/domains/biomedical/enrichr-api/SKILL.md
- Network accessline 8
subcategory: "biomedical" keywords: ["gene set enrichment", "pathway analysis", "GO terms", "KEGG", "Enrichr", "functional analysis"] source: "https://maayanlab.cloud/Enrichr" - Network accessline 9
keywords: ["gene set enrichment", "pathway analysis", "GO terms", "KEGG", "Enrichr", "functional analysis"] source: "https://maayanlab.cloud/Enrichr" --- - Network accessline 10
source: "https://maayanlab.cloud/Enrichr" - Network accessline 33
https://maayanlab.cloud/Enrichr - Network accessline 39
curl -X POST "https://maayanlab.cloud/Enrichr/addList" \ - Network accessline 67
curl "https://maayanlab.cloud/Enrichr/enrich?userListId=124544426&backgroundType=KEGG_2021_Human" - Network accessline 93
curl "https://maayanlab.cloud/Enrichr/view?userListId=124544426" - Network accessline 106
curl "https://maayanlab.cloud/Enrichr/export?userListId=124544426&backgroundType=KEGG_2021_Human&filename=results" \ - Network accessline 113
curl "https://maayanlab.cloud/Enrichr/datasetStatistics" - Network accessline 191
ENRICHR_URL = "https://maayanlab.cloud/Enrichr" - Network accessline 198
"description": (None, description), } resp = requests.post(f"{ENRICHR_URL}/addList", files=payload) - Network accessline 199
} resp = requests.post(f"{ENRICHR_URL}/addList", files=payload) resp.raise_for_status() - Network accessline 200
resp = requests.post(f"{ENRICHR_URL}/addList", files=payload) - Network accessline 205
def get_enrichment(user_list_id: int, library: str) -> list[dict]: """Retrieve enrichment results for a given library.""" resp = requests.get( - Network accessline 206
"""Retrieve enrichment results for a given library.""" resp = requests.get( f"{ENRICHR_URL}/enrich", - Network accessline 207
resp = requests.get( - Network accessline 228
def get_libraries() -> list[dict]: """List all available Enrichr libraries.""" resp = requests.get(f"{ENRICHR_URL}/datasetStatistics") - Network accessline 229
"""List all available Enrichr libraries.""" resp = requests.get(f"{ENRICHR_URL}/datasetStatistics") resp.raise_for_status() - Network accessline 230
resp = requests.get(f"{ENRICHR_URL}/datasetStatistics") - Network accessline 260
- [Enrichr Web App](https://maayanlab.cloud/Enrichr) - Network accessline 261
- [Enrichr API Docs](https://maayanlab.cloud/Enrichr/help#api)
skills/domains/biomedical/ensembl-rest-api/SKILL.md
- Network accessline 8
subcategory: "biomedical" keywords: ["Ensembl", "gene lookup", "sequence retrieval", "genomics", "variant data", "bioinformatics"] source: "https://rest.ensembl.org" - Network accessline 9
keywords: ["Ensembl", "gene lookup", "sequence retrieval", "genomics", "variant data", "bioinformatics"] source: "https://rest.ensembl.org" --- - Network accessline 10
source: "https://rest.ensembl.org" - Network accessline 31
- **URL**: `GET https://rest.ensembl.org/lookup/symbol/{species}/{symbol}` - Network accessline 44
curl "https://rest.ensembl.org/lookup/symbol/homo_sapiens/BRCA1?content-type=application/json" - Network accessline 64
- **URL**: `GET https://rest.ensembl.org/sequence/id/{id}` - Network accessline 78
curl "https://rest.ensembl.org/sequence/id/ENSG00000012048?content-type=application/json&type=genomic" - Network accessline 96
- **URL**: `GET https://rest.ensembl.org/xrefs/symbol/{species}/{symbol}` - Network accessline 101
curl "https://rest.ensembl.org/xrefs/symbol/homo_sapiens/TP53?content-type=application/json" - Network accessline 112
- **URL**: `GET https://rest.ensembl.org/variation/{species}/{id}` - Network accessline 117
curl "https://rest.ensembl.org/variation/homo_sapiens/rs699?content-type=application/json" - Network accessline 134
- **URL**: `GET https://rest.ensembl.org/info/assembly/{species}` - Network accessline 141
- **Response headers**: `X-RateLimit-Limit`, `X-RateLimit-Remaining`, `X-RateLimit-Reset` on every response. - **Batch POST endpoints** (`/lookup/id`, `/sequence/id`): accept up to 1000 IDs per request. - **GRCh37 mirror**: `https://grch37. - Network accessline 143
- **GRCh37 mirror**: `https://grch37.rest.ensembl.org` - Network accessline 160
BASE = "https://rest.ensembl.org" - Network accessline 163
gene = requests.get(f"{BASE}/lookup/symbol/homo_sapiens/BRCA1", headers=HEADERS).json() - Network accessline 166
seq = requests.get(f"{BASE}/sequence/id/{gene['id']}?type=cds", headers=HEADERS).json() - Network accessline 175
ids = ["ENSG00000012048", "ENSG00000141510", "ENSG00000157764"] # BRCA1, TP53, BRAF resp = requests.post( - Network accessline 176
resp = requests.post( - Network accessline 177
"https://rest.ensembl.org/lookup/id", - Network accessline 190
for rsid in ["rs699", "rs1042522", "rs334"]: v = requests.get( - Network accessline 191
v = requests.get( - Network accessline 192
f"https://rest.ensembl.org/variation/homo_sapiens/{rsid}", - Network accessline 201
- REST API docs: https://rest.ensembl.org/documentation - Network accessline 202
- Ensembl browser: https://www.ensembl.org - Network accessline 203
- gget toolkit (built on Ensembl REST): https://pachterlab.github.io/gget/ - Network accessline 204
- GRCh37 archive API: https://grch37.rest.ensembl.org
skills/domains/biomedical/genomas-guide/SKILL.md
- Network accessline 22
pip install genomas # Or from source git clone https://github.com/futianfan/GenoMAS.git - Network accessline 23
# Or from source git clone https://github.com/futianfan/GenoMAS.git cd GenoMAS && pip install -e . - Network accessline 24
git clone https://github.com/futianfan/GenoMAS.git - Network accessline 68
# Step 1: Retrieve data data_agent = DataAgent() dataset = data_agent.fetch("GSE12345", platform="RNA-seq") - Network accessline 70
dataset = data_agent.fetch("GSE12345", platform="RNA-seq") - Network accessline 125
- [GenoMAS GitHub](https://github.com/futianfan/GenoMAS)
skills/domains/biomedical/genomics-analysis-guide/SKILL.md
- Network accessline 266
- [DESeq2 Vignette](https://bioconductor.org/packages/release/bioc/vignettes/DESeq2/inst/doc/DESeq2.html) -- Differential expression analysis - Network accessline 267
- [GATK Best Practices](https://gatk.broadinstitute.org/hc/en-us/sections/360007226651-Best-Practices-Workflows) -- Variant calling - Network accessline 268
- [PLINK2 Documentation](https://www.cog-genomics.org/plink/2.0/) -- Genetic association analysis - Network accessline 269
- [nf-core Pipelines](https://nf-co.re/) -- Community Nextflow workflows - Network accessline 270
- [RNA-seq Analysis Tutorial](https://rnabio.org/) -- Griffith Lab comprehensive tutorial
skills/domains/biomedical/genotex-benchmark-guide/SKILL.md
- Network accessline 8
subcategory: "biomedical" keywords: ["GenoTEX", "gene expression", "benchmark", "LLM agent", "bioinformatics", "GEO"] source: "https://github.com/Liu-Hy/GenoTEX" - Network accessline 9
keywords: ["GenoTEX", "gene expression", "benchmark", "LLM agent", "bioinformatics", "GEO"] source: "https://github.com/Liu-Hy/GenoTEX" --- - Network accessline 10
source: "https://github.com/Liu-Hy/GenoTEX" - Network accessline 123
- [GenoTEX GitHub](https://github.com/Liu-Hy/GenoTEX) - Network accessline 124
- [GEO Database](https://www.ncbi.nlm.nih.gov/geo/) - Network accessline 125
- [MLCB 2025](https://mlcb.github.io/)
skills/domains/biomedical/med-researcher-guide/SKILL.md
skills/domains/biomedical/med-researcher-r1-guide/SKILL.md
- Network accessline 8
subcategory: "biomedical" keywords: ["medical research", "deep research", "clinical reasoning", "PubMed", "medical agent", "evidence-based"] source: "https://github.com/AQ-MedAI/MedResearcher-R1" - Network accessline 9
keywords: ["medical research", "deep research", "clinical reasoning", "PubMed", "medical agent", "evidence-based"] source: "https://github.com/AQ-MedAI/MedResearcher-R1" --- - Network accessline 10
source: "https://github.com/AQ-MedAI/MedResearcher-R1" - Network accessline 144
- [MedResearcher-R1 GitHub](https://github.com/AQ-MedAI/MedResearcher-R1) - Network accessline 145
- [PubMed E-utilities](https://www.ncbi.nlm.nih.gov/books/NBK25501/) - Network accessline 146
- [GRADE Handbook](https://gdt.gradepro.org/app/handbook/handbook.html)
skills/domains/biomedical/medgeclaw-guide/SKILL.md
- Network accessline 294
response = requests.post( - Network accessline 295
"https://api.platform.opentargets.org/api/v4/graphql", - Network accessline 341
- [DESeq2](https://bioconductor.org/packages/DESeq2/) -- Standard RNA-seq differential expression tool - Network accessline 342
- [scispaCy](https://allenai.github.io/scispacy/) -- Biomedical NLP models for spaCy - Network accessline 343
- [Open Targets Platform](https://platform.opentargets.org/) -- Target-disease association evidence - Network accessline 344
- [RDKit](https://www.rdkit.org/) -- Cheminformatics toolkit
skills/domains/biomedical/medical-data-api/SKILL.md
- Network accessline 8
subcategory: "biomedical" keywords: ["FDA", "drug safety", "WHO", "global health", "adverse events", "medical data", "pharmacovigilance"] source: "https://open.fda.gov" - Network accessline 9
keywords: ["FDA", "drug safety", "WHO", "global health", "adverse events", "medical data", "pharmacovigilance"] source: "https://open.fda.gov" --- - Network accessline 10
source: "https://open.fda.gov" - Network accessline 27
[Credential-related source omitted] - Access to secretsline 27
[Credential-related source omitted] - Network accessline 37
- **URL**: `GET https://api.fda.gov/drug/label.json` - Network accessline 50
curl "https://api.fda.gov/drug/label.json?search=aspirin&limit=1" - Network accessline 59
- **URL**: `GET https://api.fda.gov/drug/event.json` - Network accessline 72
curl 'https://api.fda.gov/drug/event.json?search=patient.drug.openfda.brand_name:"aspirin"&limit=1' - Network accessline 81
- **URL**: `GET https://ghoapi.azureedge.net/api/{IndicatorCode}` - Network accessline 95
# Life expectancy at birth (WHOSIS_000001) curl "https://ghoapi.azureedge.net/api/WHOSIS_000001?\$top=2" - Network accessline 96
curl "https://ghoapi.azureedge.net/api/WHOSIS_000001?\$top=2" - Network accessline 105
- **URL**: `GET https://ghoapi.azureedge.net/api/Indicator` - Network accessline 109
curl "https://ghoapi.azureedge.net/api/Indicator?\$top=5" - Network accessline 122
- No documented rate limits; the service runs on Azure CDN (azureedge.net) - Recommended: keep requests under 10/second for courtesy - For bulk downloads, use the GHO data portal CSV exports at https://www.who.int/data/gho - Network accessline 124
- For bulk downloads, use the GHO data portal CSV exports at https://www.who.int/data/gho - Network accessline 135
resp = requests.get("https://api.fda.gov/drug/event.json", params={ - Network accessline 151
resp = requests.get("https://ghoapi.azureedge.net/api/WHOSIS_000001", params={ - Network accessline 167
for drug in ["ibuprofen", "naproxen", "celecoxib"]: resp = requests.get("https://api.fda.gov/drug/label.json", - Network accessline 168
resp = requests.get("https://api.fda.gov/drug/label.json", - Network accessline 190
- openFDA documentation: https://open.fda.gov/apis/ - Network accessline 191
- openFDA API key registration: https://open.fda.gov/apis/authentication/ - Network accessline 192
- openFDA query syntax: https://open.fda.gov/apis/query-syntax/ - Network accessline 193
- openFDA GitHub: https://github.com/FDA/openfda - Network accessline 194
- WHO GHO API documentation: https://www.who.int/data/gho/info/gho-odata-api - Network accessline 195
- WHO GHO OData endpoint: https://ghoapi.azureedge.net/api/ - Network accessline 196
- WHO GHO indicator list: https://ghoapi.azureedge.net/api/Indicator - Network accessline 197
- WHO Global Health Observatory portal: https://www.who.int/data/gho
skills/domains/biomedical/ncbi-blast-api/SKILL.md
- Network accessline 8
subcategory: "biomedical" keywords: ["BLAST", "sequence alignment", "NCBI", "homology search", "protein similarity", "nucleotide search"] source: "https://blast.ncbi.nlm.nih.gov/" - Network accessline 9
keywords: ["BLAST", "sequence alignment", "NCBI", "homology search", "protein similarity", "nucleotide search"] source: "https://blast.ncbi.nlm.nih.gov/" --- - Network accessline 10
source: "https://blast.ncbi.nlm.nih.gov/" - Network accessline 26
# Nucleotide BLAST (blastn) curl -X POST "https://blast.ncbi.nlm.nih.gov/blast/Blast.cgi" \ - Network accessline 27
curl -X POST "https://blast.ncbi.nlm.nih.gov/blast/Blast.cgi" \ - Network accessline 30
# Protein BLAST (blastp) curl -X POST "https://blast.ncbi.nlm.nih.gov/blast/Blast.cgi" \ - Network accessline 31
curl -X POST "https://blast.ncbi.nlm.nih.gov/blast/Blast.cgi" \ - Network accessline 34
# BLAST with specific parameters curl -X POST "https://blast.ncbi.nlm.nih.gov/blast/Blast.cgi" \ - Network accessline 35
curl -X POST "https://blast.ncbi.nlm.nih.gov/blast/Blast.cgi" \ - Network accessline 43
# Poll for completion (returns XML with Status field) curl "https://blast.ncbi.nlm.nih.gov/blast/Blast.cgi?CMD=Get&FORMAT_OBJECT=SearchInfo&RID=YOUR_RID" - Network accessline 44
curl "https://blast.ncbi.nlm.nih.gov/blast/Blast.cgi?CMD=Get&FORMAT_OBJECT=SearchInfo&RID=YOUR_RID" - Network accessline 50
# Get results in XML curl "https://blast.ncbi.nlm.nih.gov/blast/Blast.cgi?CMD=Get&FORMAT_TYPE=XML&RID=YOUR_RID" - Network accessline 51
curl "https://blast.ncbi.nlm.nih.gov/blast/Blast.cgi?CMD=Get&FORMAT_TYPE=XML&RID=YOUR_RID" - Network accessline 53
# Get results in JSON curl "https://blast.ncbi.nlm.nih.gov/blast/Blast.cgi?CMD=Get&FORMAT_TYPE=JSON2_S&RID=YOUR_RID" - Network accessline 54
curl "https://blast.ncbi.nlm.nih.gov/blast/Blast.cgi?CMD=Get&FORMAT_TYPE=JSON2_S&RID=YOUR_RID" - Network accessline 56
# Get results in tabular format curl "https://blast.ncbi.nlm.nih.gov/blast/Blast.cgi?CMD=Get&FORMAT_TYPE=Tabular&RID=YOUR_RID" - Network accessline 57
curl "https://blast.ncbi.nlm.nih.gov/blast/Blast.cgi?CMD=Get&FORMAT_TYPE=Tabular&RID=YOUR_RID" - Network accessline 102
BLAST_URL = "https://blast.ncbi.nlm.nih.gov/blast/Blast.cgi" - Network accessline 107
evalue: float = 0.001) -> str: """Submit a BLAST search, return Request ID.""" resp = requests.post(BLAST_URL, data={ - Network accessline 108
"""Submit a BLAST search, return Request ID.""" resp = requests.post(BLAST_URL, data={ "CMD": "Put", - Network accessline 109
resp = requests.post(BLAST_URL, data={ - Network accessline 128
elapsed = 0 while elapsed < max_wait: resp = requests.get(BLAST_URL, params={ - Network accessline 129
while elapsed < max_wait: resp = requests.get(BLAST_URL, params={ "CMD": "Get", - Network accessline 130
resp = requests.get(BLAST_URL, params={ - Network accessline 144
def get_results(rid: str) -> list: """Retrieve BLAST results as parsed hits.""" resp = requests.get(BLAST_URL, params={ - Network accessline 145
"""Retrieve BLAST results as parsed hits.""" resp = requests.get(BLAST_URL, params={ "CMD": "Get", - Network accessline 146
resp = requests.get(BLAST_URL, params={ - Network accessline 192
- [NCBI BLAST](https://blast.ncbi.nlm.nih.gov/) - Network accessline 193
- [BLAST URL API Guide](https://blast.ncbi.nlm.nih.gov/doc/blast-help/developerinfo.html) - Network accessline 194
- [BLAST Command Line](https://www.ncbi.nlm.nih.gov/books/NBK279690/)
skills/domains/biomedical/ncbi-datasets-api/SKILL.md
- Network accessline 8
subcategory: "biomedical" keywords: ["NCBI", "genome data", "gene data", "taxonomy", "RefSeq", "GenBank"] source: "https://www.ncbi.nlm.nih.gov/datasets/" - Network accessline 9
keywords: ["NCBI", "genome data", "gene data", "taxonomy", "RefSeq", "GenBank"] source: "https://www.ncbi.nlm.nih.gov/datasets/" --- - Network accessline 10
source: "https://www.ncbi.nlm.nih.gov/datasets/" - Network accessline 24
https://api.ncbi.nlm.nih.gov/datasets/v2 - Network accessline 30
# Search genome assemblies by organism curl "https://api.ncbi.nlm.nih.gov/datasets/v2/genome/taxon/9606?page_size=5" - Network accessline 31
curl "https://api.ncbi.nlm.nih.gov/datasets/v2/genome/taxon/9606?page_size=5" - Network accessline 33
# Get assembly by accession curl "https://api.ncbi.nlm.nih.gov/datasets/v2/genome/accession/GCF_000001405.40" - Network accessline 34
curl "https://api.ncbi.nlm.nih.gov/datasets/v2/genome/accession/GCF_000001405.40" - Network accessline 36
# Download genome package curl -o genome.zip \ - Network accessline 37
curl -o genome.zip \ - Network accessline 38
"https://api.ncbi.nlm.nih.gov/datasets/v2/genome/accession/GCF_000001405.40/download?\ - Network accessline 45
# Search genes by symbol curl "https://api.ncbi.nlm.nih.gov/datasets/v2/gene/symbol/TP53/taxon/human" - Network accessline 46
curl "https://api.ncbi.nlm.nih.gov/datasets/v2/gene/symbol/TP53/taxon/human" - Network accessline 48
# Get gene by NCBI Gene ID curl "https://api.ncbi.nlm.nih.gov/datasets/v2/gene/id/7157" - Network accessline 49
curl "https://api.ncbi.nlm.nih.gov/datasets/v2/gene/id/7157" - Network accessline 51
# Search genes by keyword curl "https://api.ncbi.nlm.nih.gov/datasets/v2/gene/search?query=BRCA&taxon=9606&page_size=20" - Network accessline 52
curl "https://api.ncbi.nlm.nih.gov/datasets/v2/gene/search?query=BRCA&taxon=9606&page_size=20" - Network accessline 54
# Download gene data package curl -o gene.zip \ - Network accessline 55
curl -o gene.zip \ - Network accessline 56
"https://api.ncbi.nlm.nih.gov/datasets/v2/gene/id/7157/download?include_annotation_type=FASTA_GENE" - Network accessline 62
# Get taxonomy info curl "https://api.ncbi.nlm.nih.gov/datasets/v2/taxonomy/taxon/9606" - Network accessline 63
curl "https://api.ncbi.nlm.nih.gov/datasets/v2/taxonomy/taxon/9606" - Network accessline 65
# Search taxonomy by name curl "https://api.ncbi.nlm.nih.gov/datasets/v2/taxonomy/name_report?taxon_query=Homo+sapiens" - Network accessline 66
curl "https://api.ncbi.nlm.nih.gov/datasets/v2/taxonomy/name_report?taxon_query=Homo+sapiens" - Network accessline 68
# Get taxonomy tree (subtree) curl "https://api.ncbi.nlm.nih.gov/datasets/v2/taxonomy/taxon/9443/subtree" - Network accessline 69
curl "https://api.ncbi.nlm.nih.gov/datasets/v2/taxonomy/taxon/9443/subtree" - Network accessline 122
BASE_URL = "https://api.ncbi.nlm.nih.gov/datasets/v2" - Network accessline 126
page_size: int = 20) -> list: """Search NCBI genes by keyword.""" resp = requests.get( - Network accessline 127
"""Search NCBI genes by keyword.""" resp = requests.get( f"{BASE_URL}/gene/search", - Network accessline 128
resp = requests.get( - Network accessline 150
def get_gene(gene_id: int) -> dict: """Get detailed gene information.""" resp = requests.get(f"{BASE_URL}/gene/id/{gene_id}") - Network accessline 151
"""Get detailed gene information.""" resp = requests.get(f"{BASE_URL}/gene/id/{gene_id}") resp.raise_for_status() - Network accessline 152
resp = requests.get(f"{BASE_URL}/gene/id/{gene_id}") - Network accessline 165
resp = requests.get( - Network accessline 205
# Install curl -o datasets "https://ftp.ncbi.nlm.nih.gov/pub/datasets/command-line/v2/linux-amd64/datasets" - Network accessline 206
curl -o datasets "https://ftp.ncbi.nlm.nih.gov/pub/datasets/command-line/v2/linux-amd64/datasets" - Network accessline 218
- [NCBI Datasets](https://www.ncbi.nlm.nih.gov/datasets/) - [Datasets API Reference](https://www.ncbi.nlm.nih.gov/datasets/docs/v2/reference-docs/rest-api/) - [Datasets CLI](https://www.ncbi.nlm.nih.gov/datasets/docs/v2/download-and-install - Network accessline 219
- [Datasets API Reference](https://www.ncbi.nlm.nih.gov/datasets/docs/v2/reference-docs/rest-api/) - Network accessline 220
- [Datasets CLI](https://www.ncbi.nlm.nih.gov/datasets/docs/v2/download-and-install/)
skills/domains/biomedical/pdb-structure-api/SKILL.md
- Network accessline 8
subcategory: "biomedical" keywords: ["protein structure", "PDB", "crystallography", "structural biology", "RCSB", "molecular structure"] source: "https://data.rcsb.org" - Network accessline 9
keywords: ["protein structure", "PDB", "crystallography", "structural biology", "RCSB", "molecular structure"] source: "https://data.rcsb.org" --- - Network accessline 10
source: "https://data.rcsb.org" - Network accessline 31
- **URL**: `GET https://data.rcsb.org/rest/v1/core/entry/{pdb_id}` - Network accessline 34
curl "https://data.rcsb.org/rest/v1/core/entry/4HHB" - Network accessline 61
- **URL**: `GET https://data.rcsb.org/rest/v1/core/polymer_entity/{pdb_id}/{entity_id}` - Network accessline 64
curl "https://data.rcsb.org/rest/v1/core/polymer_entity/4HHB/1" - Network accessline 89
- **URL**: `GET https://data.rcsb.org/rest/v1/core/chemcomp/{comp_id}` - Network accessline 92
curl "https://data.rcsb.org/rest/v1/core/chemcomp/HEM" - Network accessline 113
- **URL**: `POST https://search.rcsb.org/rcsbsearch/v2/query` - Network accessline 118
curl -X POST "https://search.rcsb.org/rcsbsearch/v2/query" \ - Network accessline 153
No formal rate limits or rate-limit headers are published. RCSB recommends reasonable request rates. For bulk data, use FTP downloads at `https://files.rcsb.org/pub/pdb/` or `ftp://ftp.wwpdb.org/pub/pdb/` instead of iterative API calls. - Network accessline 174
"paginate": {"start": 0, "rows": 5}} } results = requests.post("https://search.rcsb.org/rcsbsearch/v2/query", - Network accessline 175
} results = requests.post("https://search.rcsb.org/rcsbsearch/v2/query", json=search_body).json() - Network accessline 176
results = requests.post("https://search.rcsb.org/rcsbsearch/v2/query", - Network accessline 181
for hit in results["result_set"]: pdb_id = hit["identifier"] entry = requests.get( - Network accessline 182
pdb_id = hit["identifier"] entry = requests.get( f"https://data.rcsb.org/rest/v1/core/entry/{pdb_id}").json() - Network accessline 183
entry = requests.get( - Network accessline 184
f"https://data.rcsb.org/rest/v1/core/entry/{pdb_id}").json() - Network accessline 196
pdb_id = "4HHB" entry = requests.get( - Network accessline 197
entry = requests.get( - Network accessline 198
f"https://data.rcsb.org/rest/v1/core/entry/{pdb_id}").json() - Network accessline 200
for eid in range(1, entry["rcsb_entry_info"]["polymer_entity_count"] + 1): entity = requests.get( - Network accessline 201
entity = requests.get( - Network accessline 202
f"https://data.rcsb.org/rest/v1/core/polymer_entity/{pdb_id}/{eid}" - Network accessline 214
- Data API docs: https://data.rcsb.org/redoc/index.html - Network accessline 215
- Search API docs: https://search.rcsb.org/index.html - Network accessline 216
- RCSB PDB homepage: https://www.rcsb.org/ - Network accessline 217
- Programmatic access guide: https://www.rcsb.org/docs/programmatic-access - Network accessline 218
- PDB file format: https://www.wwpdb.org/documentation/file-format - Network accessline 219
- Worldwide PDB: https://www.wwpdb.org/
skills/domains/biomedical/quickgo-api/SKILL.md
- Network accessline 8
subcategory: "biomedical" keywords: ["Gene Ontology", "GO annotations", "protein function", "QuickGO", "EBI", "functional genomics"] source: "https://www.ebi.ac.uk/QuickGO/" - Network accessline 9
keywords: ["Gene Ontology", "GO annotations", "protein function", "QuickGO", "EBI", "functional genomics"] source: "https://www.ebi.ac.uk/QuickGO/" --- - Network accessline 10
source: "https://www.ebi.ac.uk/QuickGO/" - Network accessline 24
https://www.ebi.ac.uk/QuickGO/services - Network accessline 30
# Search terms by keyword curl "https://www.ebi.ac.uk/QuickGO/services/ontology/go/search?query=apoptosis&limit=20" - Network accessline 31
curl "https://www.ebi.ac.uk/QuickGO/services/ontology/go/search?query=apoptosis&limit=20" - Network accessline 33
# Get term details curl "https://www.ebi.ac.uk/QuickGO/services/ontology/go/terms/GO:0006915" - Network accessline 34
curl "https://www.ebi.ac.uk/QuickGO/services/ontology/go/terms/GO:0006915" - Network accessline 36
# Get term ancestors/descendants curl "https://www.ebi.ac.uk/QuickGO/services/ontology/go/terms/GO:0006915/ancestors" - Network accessline 37
curl "https://www.ebi.ac.uk/QuickGO/services/ontology/go/terms/GO:0006915/ancestors" - Network accessline 38
curl "https://www.ebi.ac.uk/QuickGO/services/ontology/go/terms/GO:0006915/descendants" - Network accessline 44
# Get annotations for a protein (UniProt ID) curl "https://www.ebi.ac.uk/QuickGO/services/annotation/search?geneProductId=P04637&limit=50" - Network accessline 45
curl "https://www.ebi.ac.uk/QuickGO/services/annotation/search?geneProductId=P04637&limit=50" - Network accessline 47
# Annotations for a GO term curl "https://www.ebi.ac.uk/QuickGO/services/annotation/search?goId=GO:0006915&taxonId=9606&limit=50" - Network accessline 48
curl "https://www.ebi.ac.uk/QuickGO/services/annotation/search?goId=GO:0006915&taxonId=9606&limit=50" - Network accessline 50
# Filter by evidence code curl "https://www.ebi.ac.uk/QuickGO/services/annotation/search?\ - Network accessline 51
curl "https://www.ebi.ac.uk/QuickGO/services/annotation/search?\ - Network accessline 54
# Filter by aspect (ontology branch) curl "https://www.ebi.ac.uk/QuickGO/services/annotation/search?\ - Network accessline 55
curl "https://www.ebi.ac.uk/QuickGO/services/annotation/search?\ - Network accessline 62
# Download as TSV curl "https://www.ebi.ac.uk/QuickGO/services/annotation/downloadSearch?\ - Network accessline 63
curl "https://www.ebi.ac.uk/QuickGO/services/annotation/downloadSearch?\ - Network accessline 91
BASE_URL = "https://www.ebi.ac.uk/QuickGO/services" - Network accessline 94
def search_go_terms(query: str, limit: int = 20) -> list: """Search Gene Ontology terms.""" resp = requests.get( - Network accessline 95
"""Search Gene Ontology terms.""" resp = requests.get( f"{BASE_URL}/ontology/go/search", - Network accessline 96
resp = requests.get( - Network accessline 124
resp = requests.get( - Network accessline 149
"limit": limit, } resp = requests.get( - Network accessline 150
} resp = requests.get( f"{BASE_URL}/annotation/search", - Network accessline 151
resp = requests.get( - Network accessline 178
- [QuickGO](https://www.ebi.ac.uk/QuickGO/) - Network accessline 179
- [QuickGO API Docs](https://www.ebi.ac.uk/QuickGO/api) - Network accessline 180
- [Gene Ontology](http://geneontology.org/)
skills/domains/business/architecture-design-guide/SKILL.md
- Network accessline 275
- Ching, F. D. K. (2014). Architecture: Form, Space, and Order, 4th ed. Wiley. - [ArchDaily](https://www.archdaily.com/) -- Architectural project database and analysis - Network accessline 276
- [ArchDaily](https://www.archdaily.com/) -- Architectural project database and analysis - Network accessline 277
- [Ladybug Tools](https://www.ladybug.tools/) -- Environmental analysis for Grasshopper - Network accessline 279
- [RIBA 2030 Climate Challenge](https://www.architecture.com/about/policy/climate-action/2030-climate-challenge) -- Carbon benchmarks
skills/domains/chemistry/cactus-cheminformatics-guide/SKILL.md
- Network accessline 8
subcategory: "chemistry" keywords: ["CACTUS", "cheminformatics", "PNNL", "molecular analysis", "LLM chemistry", "chemical agent"] source: "https://github.com/pnnl/cactus" - Network accessline 9
keywords: ["CACTUS", "cheminformatics", "PNNL", "molecular analysis", "LLM chemistry", "chemical agent"] source: "https://github.com/pnnl/cactus" --- - Network accessline 10
source: "https://github.com/pnnl/cactus" - Network accessline 87
- [CACTUS GitHub](https://github.com/pnnl/cactus) - Network accessline 88
- [RDKit](https://www.rdkit.org/) - Network accessline 89
- [PNNL](https://www.pnnl.gov/)
skills/domains/chemistry/catalysis-hub-api/SKILL.md
- Network accessline 8
subcategory: "chemistry" keywords: ["catalysis", "DFT", "reaction energy", "surface chemistry", "computational chemistry", "GraphQL"] source: "https://www.catalysis-hub.org" - Network accessline 9
keywords: ["catalysis", "DFT", "reaction energy", "surface chemistry", "computational chemistry", "GraphQL"] source: "https://www.catalysis-hub.org" --- - Network accessline 10
source: "https://www.catalysis-hub.org" - Network accessline 27
**Endpoint:** `https://api.catalysis-hub.org/graphql` - Network accessline 53
curl -s -X POST "https://api.catalysis-hub.org/graphql" \ - Network accessline 70
curl -s -X POST "https://api.catalysis-hub.org/graphql" \ - Network accessline 86
curl -s -X POST "https://api.catalysis-hub.org/graphql" \ - Network accessline 103
curl -s -X POST "https://api.catalysis-hub.org/graphql" \ - Network accessline 133
ENDPOINT = "https://api.catalysis-hub.org/graphql" - Network accessline 135
def query_catalysis_hub(query): """Execute a GraphQL query against Catalysis Hub.""" resp = requests.post(ENDPOINT, json={"query": query}) - Network accessline 136
"""Execute a GraphQL query against Catalysis Hub.""" resp = requests.post(ENDPOINT, json={"query": query}) resp.raise_for_status() - Network accessline 137
resp = requests.post(ENDPOINT, json={"query": query}) - Network accessline 168
- Catalysis Hub: https://www.catalysis-hub.org - Network accessline 169
- GraphQL Endpoint: https://api.catalysis-hub.org/graphql - Network accessline 171
- SUNCAT Center: https://suncat.stanford.edu
skills/domains/chemistry/chemeagle-guide/SKILL.md
- Network accessline 8
subcategory: "chemistry" keywords: ["ChemEagle", "chemical extraction", "literature mining", "reaction extraction", "chemistry NLP", "multi-agent"] source: "https://github.com/CYF2000127/ChemEagle" - Network accessline 9
keywords: ["ChemEagle", "chemical extraction", "literature mining", "reaction extraction", "chemistry NLP", "multi-agent"] source: "https://github.com/CYF2000127/ChemEagle" --- - Network accessline 10
source: "https://github.com/CYF2000127/ChemEagle" - Network accessline 145
- [ChemEagle GitHub](https://github.com/CYF2000127/ChemEagle) - Network accessline 146
- [RDKit](https://www.rdkit.org/) — Chemistry toolkit - Network accessline 147
- [PubChem](https://pubchem.ncbi.nlm.nih.gov/) — Chemical database
skills/domains/chemistry/chemgraph-agent-guide/SKILL.md
- Network accessline 8
subcategory: "chemistry" keywords: ["ChemGraph", "molecular simulation", "computational chemistry", "DFT", "LangGraph", "ASE"] source: "https://github.com/argonne-lcf/ChemGraph" - Network accessline 9
keywords: ["ChemGraph", "molecular simulation", "computational chemistry", "DFT", "LangGraph", "ASE"] source: "https://github.com/argonne-lcf/ChemGraph" --- - Network accessline 10
source: "https://github.com/argonne-lcf/ChemGraph" - Network accessline 118
- [ChemGraph GitHub](https://github.com/argonne-lcf/ChemGraph) - Network accessline 119
- [ASE Documentation](https://wiki.fysik.dtu.dk/ase/) - Network accessline 120
- [LangGraph](https://github.com/langchain-ai/langgraph)
skills/domains/chemistry/computational-chemistry-guide/SKILL.md
- Network accessline 262
- [ORCA Documentation](https://www.faccts.de/docs/orca/6.0/manual/) -- Free quantum chemistry software - Network accessline 263
- [OpenMM Documentation](http://docs.openmm.org/) -- GPU-accelerated molecular dynamics - Network accessline 264
- [RDKit Documentation](https://www.rdkit.org/docs/) -- Cheminformatics toolkit - Network accessline 265
- [Psi4 Documentation](https://psicode.org/) -- Open-source quantum chemistry - Network accessline 266
- [A Hitchhiker's Guide to DFT](https://onlinelibrary.wiley.com/doi/book/10.1002/3527600043) -- Koch and Holthausen
skills/domains/chemistry/pubchem-api-guide/SKILL.md
- Network accessline 8
subcategory: "chemistry" keywords: ["pubchem", "chemistry", "compounds", "structures", "bioassay", "pharmacology"] source: "https://pubchem.ncbi.nlm.nih.gov/docs/pug-rest" - Network accessline 9
keywords: ["pubchem", "chemistry", "compounds", "structures", "bioassay", "pharmacology"] source: "https://pubchem.ncbi.nlm.nih.gov/docs/pug-rest" --- - Network accessline 10
source: "https://pubchem.ncbi.nlm.nih.gov/docs/pug-rest" - Network accessline 28
# No API key needed curl "https://pubchem.ncbi.nlm.nih.gov/rest/pug/compound/name/aspirin/JSON" - Network accessline 29
curl "https://pubchem.ncbi.nlm.nih.gov/rest/pug/compound/name/aspirin/JSON" - Network accessline 37
GET https://pubchem.ncbi.nlm.nih.gov/rest/pug/compound/name/{name}/JSON - Dangerous shell operationline 41
curl -s "https://pubchem.ncbi.nlm.nih.gov/rest/pug/compound/name/caffeine/JSON" | python3 -m json.tool - Network accessline 41
curl -s "https://pubchem.ncbi.nlm.nih.gov/rest/pug/compound/name/caffeine/JSON" \ - Network accessline 50
GET https://pubchem.ncbi.nlm.nih.gov/rest/pug/compound/cid/{cid}/property/{properties}/JSON - Dangerous shell operationline 56
curl -s "https://pubchem.ncbi.nlm.nih.gov/rest/pug/compound/name/ibuprofen/property/MolecularFormula,MolecularWeight,CanonicalSMILES,IUPACName,XLogP/JSON" | python3 -m json.tool - Network accessline 56
curl -s "https://pubchem.ncbi.nlm.nih.gov/rest/pug/compound/name/ibuprofen/property/MolecularFormula,MolecularWeight,CanonicalSMILES,IUPACName,XLogP/JSON" \ - Dangerous shell operationline 63
curl -s "https://pubchem.ncbi.nlm.nih.gov/rest/pug/compound/fastformula/C8H10N4O2/property/IUPACName,MolecularWeight,CanonicalSMILES/JSON" | python3 -m json.tool - Network accessline 63
curl -s "https://pubchem.ncbi.nlm.nih.gov/rest/pug/compound/fastformula/C8H10N4O2/property/IUPACName,MolecularWeight,CanonicalSMILES/JSON" \ - Dangerous shell operationline 72
curl -s "https://pubchem.ncbi.nlm.nih.gov/rest/pug/compound/fastsimilarity_2d/cid/2244/property/IUPACName,MolecularWeight,CanonicalSMILES/JSON?Threshold=90" | python3 -m json.tool - Network accessline 72
curl -s "https://pubchem.ncbi.nlm.nih.gov/rest/pug/compound/fastsimilarity_2d/cid/2244/property/IUPACName,MolecularWeight,CanonicalSMILES/JSON?Threshold=90" \ - Dangerous shell operationline 81
curl -s "https://pubchem.ncbi.nlm.nih.gov/rest/pug/compound/cid/2244/assaysummary/JSON" | python3 -m json.tool - Network accessline 81
curl -s "https://pubchem.ncbi.nlm.nih.gov/rest/pug/compound/cid/2244/assaysummary/JSON" \ - Network accessline 91
PUG_REST = "https://pubchem.ncbi.nlm.nih.gov/rest/pug" - Network accessline 95
props = "MolecularWeight,XLogP,HBondDonorCount,HBondAcceptorCount,TPSA,RotatableBondCount,IUPACName" url = f"{PUG_REST}/compound/name/{name}/property/{props}/JSON" resp = requests.get(url) - Network accessline 96
url = f"{PUG_REST}/compound/name/{name}/property/{props}/JSON" resp = requests.get(url) resp.raise_for_status() - Network accessline 97
resp = requests.get(url) - Network accessline 139
props = "IUPACName,MolecularFormula,MolecularWeight,CanonicalSMILES,XLogP" url = f"{PUG_REST}/compound/cid/{cids}/property/{props}/JSON" resp = requests.get(url) - Network accessline 140
url = f"{PUG_REST}/compound/cid/{cids}/property/{props}/JSON" resp = requests.get(url) resp.raise_for_status() - Network accessline 141
resp = requests.get(url) - Network accessline 177
- PubChem PUG REST Documentation: https://pubchem.ncbi.nlm.nih.gov/docs/pug-rest - Network accessline 178
- PubChem PUG REST Tutorial: https://pubchem.ncbi.nlm.nih.gov/docs/pug-rest-tutorial - Network accessline 179
- PubChem Compound Database: https://pubchem.ncbi.nlm.nih.gov/ - Network accessline 180
- PubChem Power User Gateway: https://pubchem.ncbi.nlm.nih.gov/docs/power-user-gateway
skills/domains/chemistry/retrosynthesis-guide/SKILL.md
skills/domains/cs/ai-security-papers-guide/SKILL.md
- Network accessline 8
subcategory: "cs" keywords: ["AI security", "adversarial ML", "model attacks", "S&P", "CCS", "USENIX", "NDSS"] source: "https://github.com/Zhou-Zi7/Awesome-AI-Security-BIG4" - Network accessline 9
keywords: ["AI security", "adversarial ML", "model attacks", "S&P", "CCS", "USENIX", "NDSS"] source: "https://github.com/Zhou-Zi7/Awesome-AI-Security-BIG4" --- - Network accessline 10
source: "https://github.com/Zhou-Zi7/Awesome-AI-Security-BIG4" - Network accessline 101
- [Awesome-AI-Security-BIG4](https://github.com/Zhou-Zi7/Awesome-AI-Security-BIG4) - Network accessline 102
- [IEEE S&P](https://www.ieee-security.org/TC/SP-Index.html) - Network accessline 103
- [ACM CCS](https://www.sigsac.org/ccs/)
skills/domains/cs/code-llm-papers-guide/SKILL.md
- Network accessline 8
subcategory: "cs" keywords: ["Code LLM", "code generation", "program synthesis", "Codex", "code intelligence", "software engineering"] source: "https://github.com/codefuse-ai/Awesome-Code-LLM" - Network accessline 9
keywords: ["Code LLM", "code generation", "program synthesis", "Codex", "code intelligence", "software engineering"] source: "https://github.com/codefuse-ai/Awesome-Code-LLM" --- - Network accessline 10
source: "https://github.com/codefuse-ai/Awesome-Code-LLM" - Network accessline 128
- [Awesome-Code-LLM](https://github.com/codefuse-ai/Awesome-Code-LLM) - Network accessline 129
- [TMLR Survey Paper](https://arxiv.org/abs/2311.07989) - Network accessline 130
- [HumanEval](https://github.com/openai/human-eval) - Network accessline 131
- [SWE-bench](https://www.swebench.com/)
skills/domains/cs/dblp-api/SKILL.md
- Network accessline 8
subcategory: "cs" keywords: ["algorithms", "data structures", "software engineering", "database", "academic database search"] source: "https://dblp.org/faq/1474707.html" - Network accessline 9
keywords: ["algorithms", "data structures", "software engineering", "database", "academic database search"] source: "https://dblp.org/faq/1474707.html" --- - Network accessline 10
source: "https://dblp.org/faq/1474707.html" - Network accessline 31
- **URL**: `GET https://dblp.org/search/publ/api` - Network accessline 42
curl "https://dblp.org/search/publ/api?q=graph+neural+networks&format=json&h=10&f=0" - Network accessline 48
- **URL**: `GET https://dblp.org/search/author/api` - Network accessline 58
curl "https://dblp.org/search/author/api?q=Yann+LeCun&format=json&h=5" - Network accessline 64
- **URL**: `GET https://dblp.org/search/venue/api` - Network accessline 74
curl "https://dblp.org/search/venue/api?q=NeurIPS&format=json&h=5" - Network accessline 80
- **URL**: `GET https://dblp.org/pid/{pid}.json` - Network accessline 87
curl "https://dblp.org/pid/h/GeoffreyEHinton.json" - Network accessline 102
# Step 1: Find the author's DBLP PID curl "https://dblp.org/search/author/api?q=Jure+Leskovec&format=json&h=1" - Network accessline 103
curl "https://dblp.org/search/author/api?q=Jure+Leskovec&format=json&h=1" - Network accessline 105
# Step 2: Fetch their full profile curl "https://dblp.org/pid/l/JureLeskovec.json" - Network accessline 106
curl "https://dblp.org/pid/l/JureLeskovec.json" - Network accessline 114
curl "https://dblp.org/search/publ/api?q=venue:ICML+year:2024&format=json&h=50" - Network accessline 122
curl "https://dblp.org/search/publ/api?q=attention+is+all+you+need&format=json&h=1" | jq '.result.hits.hit[0].info.doi' - Network accessline 127
- Official documentation: https://dblp.org/faq/1474707.html - Network accessline 128
- DBLP XML dump: https://dblp.org/xml/ - Network accessline 129
- DBLP statistics: https://dblp.org/statistics/
skills/domains/cs/formal-verification-guide/SKILL.md
- Dangerous shell operationline 39
CONSTANTS RM \* Set of resource managers - Dangerous shell operationline 55
\* RM r prepares to commit - Dangerous shell operationline 62
\* TM receives a Prepared message from RM r - Dangerous shell operationline 77
\* Safety property: No RM commits unless TM has committed - Dangerous shell operationline 79
\A r \in RM : rmState[r] = "committed" => tmState = "committed" - Dangerous shell operationline 88
# RM = {"rm1", "rm2", "rm3"}
skills/domains/cs/gaussian-splatting-papers-guide/SKILL.md
- Network accessline 8
subcategory: "cs" keywords: ["3D Gaussian Splatting", "3DGS", "neural rendering", "NeRF", "novel view synthesis", "point cloud"] source: "https://github.com/MrNeRF/awesome-3D-gaussian-splatting" - Network accessline 9
keywords: ["3D Gaussian Splatting", "3DGS", "neural rendering", "NeRF", "novel view synthesis", "point cloud"] source: "https://github.com/MrNeRF/awesome-3D-gaussian-splatting" --- - Network accessline 10
source: "https://github.com/MrNeRF/awesome-3D-gaussian-splatting" - Network accessline 127
# Original implementation git clone https://github.com/graphdeco-inria/gaussian-splatting - Network accessline 128
git clone https://github.com/graphdeco-inria/gaussian-splatting - Network accessline 156
- [awesome-3D-gaussian-splatting](https://github.com/MrNeRF/awesome-3D-gaussian-splatting) - Network accessline 157
- [Original 3DGS](https://repo-sam.inria.fr/fungraph/3d-gaussian-splatting/) - Network accessline 158
- [3DGS Papers Collection](https://3dgaussians.github.io/)
skills/domains/cs/llm-aiops-guide/SKILL.md
- Network accessline 8
subcategory: "cs" keywords: ["AIOps", "LLM operations", "IT automation", "log analysis", "incident management", "DevOps AI"] source: "https://github.com/Jun-jie-Huang/awesome-LLM-AIOps" - Network accessline 9
keywords: ["AIOps", "LLM operations", "IT automation", "log analysis", "incident management", "DevOps AI"] source: "https://github.com/Jun-jie-Huang/awesome-LLM-AIOps" --- - Network accessline 10
source: "https://github.com/Jun-jie-Huang/awesome-LLM-AIOps" - Network accessline 295
- [awesome-LLM-AIOps](https://github.com/Jun-jie-Huang/awesome-LLM-AIOps) - Network accessline 296
- [OpsEval Benchmark](https://arxiv.org/abs/2310.07637) - Network accessline 297
- [vLLM: Easy, Fast, and Cheap LLM Serving](https://arxiv.org/abs/2309.06180) - Network accessline 298
- [LangSmith Documentation](https://docs.smith.langchain.com/) - Network accessline 299
- [MLflow LLM Evaluation](https://mlflow.org/docs/latest/llms/llm-evaluate/index.html) - Network accessline 300
- [FrugalGPT: How to Use LLMs While Reducing Cost](https://arxiv.org/abs/2305.05176) - Network accessline 301
- [RouteLLM: Learning to Route LLMs](https://arxiv.org/abs/2406.18665)
skills/domains/cs/software-engineering-research/SKILL.md
- Network accessline 62
repo_url = "https://github.com/apache/kafka"
skills/domains/cs/software-heritage-api/SKILL.md
- Network accessline 8
subcategory: "cs" keywords: ["Software Heritage", "code archive", "source code preservation", "SWHID", "code provenance", "open source"] source: "https://www.softwareheritage.org/" - Network accessline 9
keywords: ["Software Heritage", "code archive", "source code preservation", "SWHID", "code provenance", "open source"] source: "https://www.softwareheritage.org/" --- - Network accessline 10
source: "https://www.softwareheritage.org/" - Network accessline 24
https://archive.softwareheritage.org/api/1 - Network accessline 30
# Search for repositories curl "https://archive.softwareheritage.org/api/1/origin/search/scikit-learn/?limit=10" - Network accessline 31
curl "https://archive.softwareheritage.org/api/1/origin/search/scikit-learn/?limit=10" - Network accessline 33
# Get origin metadata curl "https://archive.softwareheritage.org/api/1/origin/https://github.com/scikit-learn/scikit-learn/get/" - Network accessline 34
curl "https://archive.softwareheritage.org/api/1/origin/https://github.com/scikit-learn/scikit-learn/get/" - Network accessline 36
# List visits (snapshots) of an origin curl "https://archive.softwareheritage.org/api/1/origin/https://github.com/scikit-learn/scikit-learn/visits/" - Network accessline 37
curl "https://archive.softwareheritage.org/api/1/origin/https://github.com/scikit-learn/scikit-learn/visits/" - Network accessline 43
# Get a specific file by its SHA1 hash curl "https://archive.softwareheritage.org/api/1/content/sha1:adc83b19e793491b1c6ea0fd8b46cd9f32e592fc/" - Network accessline 44
curl "https://archive.softwareheritage.org/api/1/content/sha1:adc83b19e793491b1c6ea0fd8b46cd9f32e592fc/" - Network accessline 46
# Get raw file content curl "https://archive.softwareheritage.org/api/1/content/sha1:adc83b19e793491b1c6ea0fd8b46cd9f32e592fc/raw/" - Network accessline 47
curl "https://archive.softwareheritage.org/api/1/content/sha1:adc83b19e793491b1c6ea0fd8b46cd9f32e592fc/raw/" - Network accessline 49
# Get directory listing curl "https://archive.softwareheritage.org/api/1/directory/{sha1_git}/" - Network accessline 50
curl "https://archive.softwareheritage.org/api/1/directory/{sha1_git}/" - Network accessline 56
# Resolve a SWHID to its object curl "https://archive.softwareheritage.org/api/1/resolve/swh:1:cnt:adc83b19e793491b1c6ea0fd8b46cd9f32e592fc/" - Network accessline 57
curl "https://archive.softwareheritage.org/api/1/resolve/swh:1:cnt:adc83b19e793491b1c6ea0fd8b46cd9f32e592fc/" - Network accessline 59
# Get snapshot curl "https://archive.softwareheritage.org/api/1/snapshot/{sha1_git}/" - Network accessline 60
curl "https://archive.softwareheritage.org/api/1/snapshot/{sha1_git}/" - Network accessline 62
# Get revision (commit) curl "https://archive.softwareheritage.org/api/1/revision/{sha1_git}/" - Network accessline 63
curl "https://archive.softwareheritage.org/api/1/revision/{sha1_git}/" - Network accessline 69
# Request archival of a repository curl -X POST "https://archive.softwareheritage.org/api/1/origin/save/git/url/https://github.com/user/repo/" - Network accessline 70
curl -X POST "https://archive.softwareheritage.org/api/1/origin/save/git/url/https://github.com/user/repo/" - Network accessline 92
With qualifiers: swh:1:cnt:{hash};origin=https://github.com/user/repo;visit=swh:1:snp:{hash};path=/src/main.py - Network accessline 93
swh:1:cnt:{hash};origin=https://github.com/user/repo;visit=swh:1:snp:{hash};path=/src/main.py - Network accessline 102
BASE_URL = "https://archive.softwareheritage.org/api/1" - Network accessline 105
def search_origins(query: str, limit: int = 10) -> list: """Search Software Heritage for archived repositories.""" resp = requests.get( - Network accessline 106
"""Search Software Heritage for archived repositories.""" resp = requests.get( f"{BASE_URL}/origin/search/{query}/", - Network accessline 107
resp = requests.get( - Network accessline 122
def get_origin_visits(origin_url: str) -> list: """Get archival snapshots for a repository.""" resp = requests.get( - Network accessline 123
"""Get archival snapshots for a repository.""" resp = requests.get( f"{BASE_URL}/origin/{origin_url}/visits/", - Network accessline 124
resp = requests.get( - Network accessline 140
def get_directory(sha1_git: str) -> list: """List files in an archived directory.""" resp = requests.get( - Network accessline 141
"""List files in an archived directory.""" resp = requests.get( f"{BASE_URL}/directory/{sha1_git}/", - Network accessline 142
resp = requests.get( - Network accessline 157
def save_code_now(repo_url: str) -> dict: """Request Software Heritage to archive a repository.""" resp = requests.post( - Network accessline 158
"""Request Software Heritage to archive a repository.""" resp = requests.post( f"{BASE_URL}/origin/save/git/url/{repo_url}/", - Network accessline 159
resp = requests.post( - Network accessline 172
# Example: get snapshot history visits = get_origin_visits("https://github.com/pytorch/pytorch") - Network accessline 173
visits = get_origin_visits("https://github.com/pytorch/pytorch") - Network accessline 177
# Example: request archival # result = save_code_now("https://github.com/my-org/my-research-code") - Network accessline 178
# result = save_code_now("https://github.com/my-org/my-research-code") - Network accessline 197
- [Software Heritage](https://www.softwareheritage.org/) - Network accessline 198
- [API Documentation](https://archive.softwareheritage.org/api/) - Network accessline 199
- [SWHID Specification](https://docs.softwareheritage.org/devel/swh-model/persistent-identifiers.html)
skills/domains/ecology/conservation-biology-guide/SKILL.md
skills/domains/ecology/gbif-api/SKILL.md
- Network accessline 8
subcategory: "ecology" keywords: ["ecology", "biodiversity", "taxonomy", "environmental science"] source: "https://www.gbif.org/developer/summary" - Network accessline 9
keywords: ["ecology", "biodiversity", "taxonomy", "environmental science"] source: "https://www.gbif.org/developer/summary" --- - Network accessline 10
source: "https://www.gbif.org/developer/summary" - Network accessline 33
- **URL**: `GET https://api.gbif.org/v1/occurrence/search` - Network accessline 50
curl "https://api.gbif.org/v1/occurrence/search?scientificName=Panthera+tigris&hasCoordinate=true&limit=10" - Network accessline 59
- **URL**: `GET https://api.gbif.org/v1/species/match` - Network accessline 71
curl "https://api.gbif.org/v1/species/match?name=Homo+sapiens" - Network accessline 80
- **URL**: `GET https://api.gbif.org/v1/dataset` - Network accessline 94
curl "https://api.gbif.org/v1/dataset?q=bird+monitoring&type=OCCURRENCE&limit=5" - Network accessline 101
No formal rate limits are enforced on the GBIF API. However, GBIF recommends responsible use patterns. Large data downloads (millions of records) should use the asynchronous download API at `https://api.gbif.org/v1/occurrence/download/reque - Network accessline 115
"limit": 300 } resp = requests.get("https://api.gbif.org/v1/occurrence/search", params=params) - Network accessline 116
} resp = requests.get("https://api.gbif.org/v1/occurrence/search", params=params) data = resp.json() - Network accessline 117
resp = requests.get("https://api.gbif.org/v1/occurrence/search", params=params) - Network accessline 136
for name in names: resp = requests.get("https://api.gbif.org/v1/species/match", params={"name": name}) - Network accessline 137
resp = requests.get("https://api.gbif.org/v1/species/match", params={"name": name}) - Network accessline 147
curl -X POST "https://api.gbif.org/v1/occurrence/download/request" \ - Network accessline 155
- Official API documentation: https://www.gbif.org/developer/summary - Network accessline 156
- GBIF occurrence search API: https://www.gbif.org/developer/occurrence - Network accessline 157
- GBIF species API: https://www.gbif.org/developer/species - Network accessline 158
- GBIF data use guide: https://www.gbif.org/data-use
skills/domains/ecology/inaturalist-api/SKILL.md
- Network accessline 8
subcategory: "ecology" keywords: ["ecology", "biodiversity", "taxonomy", "evolutionary biology"] source: "https://www.inaturalist.org/pages/api+reference" - Network accessline 9
keywords: ["ecology", "biodiversity", "taxonomy", "evolutionary biology"] source: "https://www.inaturalist.org/pages/api+reference" --- - Network accessline 10
source: "https://www.inaturalist.org/pages/api+reference" - Network accessline 27
For authenticated requests, register an application at https://www.inaturalist.org/oauth/applications and use the OAuth 2.0 flow to obtain an access token. - Network accessline 35
- **URL**: `GET https://api.inaturalist.org/v1/observations` - Network accessline 55
curl "https://api.inaturalist.org/v1/observations?taxon_id=3&quality_grade=research&place_id=1&per_page=10" - Network accessline 64
- **URL**: `GET https://api.inaturalist.org/v1/taxa` - Network accessline 78
curl "https://api.inaturalist.org/v1/taxa?q=monarch+butterfly&rank=species" - Network accessline 87
- **URL**: `GET https://api.inaturalist.org/v1/places` - Network accessline 98
curl "https://api.inaturalist.org/v1/places/autocomplete?q=Yellowstone" - Network accessline 105
The iNaturalist API enforces a rate limit of 100 requests per minute. Exceeding this limit returns HTTP 429 responses. For large-scale data exports, iNaturalist recommends using their export tools at https://www.inaturalist.org/observations - Network accessline 123
resp = requests.get("https://api.inaturalist.org/v1/observations", params=params) - Network accessline 147
resp = requests.get("https://api.inaturalist.org/v1/observations", params=params) - Network accessline 164
# Search for Spotted Lanternfly observations in Pennsylvania curl "https://api.inaturalist.org/v1/observations?taxon_id=325295&place_id=46&quality_grade=research&per_page=50" - Network accessline 165
curl "https://api.inaturalist.org/v1/observations?taxon_id=325295&place_id=46&quality_grade=research&per_page=50" - Network accessline 170
- Official API reference: https://www.inaturalist.org/pages/api+reference - Network accessline 171
- API v1 documentation: https://api.inaturalist.org/v1/docs/ - Network accessline 172
- iNaturalist homepage: https://www.inaturalist.org/ - Network accessline 173
- Data export tools: https://www.inaturalist.org/observations/export
skills/domains/economics/fred-api/SKILL.md
- Network accessline 8
subcategory: "economics" keywords: ["microeconomics", "macroeconomics", "econometrics", "development economics"] source: "https://fred.stlouisfed.org/docs/api/fred/" - Network accessline 9
keywords: ["microeconomics", "macroeconomics", "econometrics", "development economics"] source: "https://fred.stlouisfed.org/docs/api/fred/" requires: - Network accessline 10
source: "https://fred.stlouisfed.org/docs/api/fred/" - Access to secretsline 12
[Credential-related source omitted] - Network accessline 29
1. Register for an account at https://fredaccount.stlouisfed.org/login/secure/ 2. Request an API key at https://fredaccount.stlouisfed.org/apikeys 3. Include the key as the `api_key` query parameter in all requests - Network accessline 30
2. Request an API key at https://fredaccount.stlouisfed.org/apikeys - Access to secretsline 31
[Credential-related source omitted] - Network accessline 34
[Credential-related source omitted] - Access to secretsline 34
[Credential-related source omitted] - Network accessline 45
- **URL**: `GET https://api.stlouisfed.org/fred/series` - Access to secretsline 51
[Credential-related source omitted] - Network accessline 57
[Credential-related source omitted] - Access to secretsline 57
[Credential-related source omitted] - Network accessline 66
- **URL**: `GET https://api.stlouisfed.org/fred/series/observations` - Access to secretsline 72
[Credential-related source omitted] - Network accessline 82
[Credential-related source omitted] - Access to secretsline 82
[Credential-related source omitted] - Network accessline 91
- **URL**: `GET https://api.stlouisfed.org/fred/category` - Access to secretsline 97
[Credential-related source omitted] - Network accessline 103
[Credential-related source omitted] - Access to secretsline 103
[Credential-related source omitted] - Network accessline 112
- **URL**: `GET https://api.stlouisfed.org/fred/releases` - Access to secretsline 117
[Credential-related source omitted] - Network accessline 123
[Credential-related source omitted] - Access to secretsline 123
[Credential-related source omitted] - Network accessline 130
The FRED API enforces rate limits that vary by usage. Standard limits allow approximately 120 requests per minute. Exceeding the limit returns HTTP 429 responses. For bulk data retrieval, consider using the FRED Excel add-in or downloading - Access to secretsline 143
[Credential-related source omitted] - Network accessline 145
"observation_start": "2015-01-01" } resp = requests.get("https://api.stlouisfed.org/fred/series/observations", params=params) - Network accessline 146
} resp = requests.get("https://api.stlouisfed.org/fred/series/observations", params=params) data = resp.json() - Network accessline 147
resp = requests.get("https://api.stlouisfed.org/fred/series/observations", params=params) - Access to secretsline 162
[Credential-related source omitted] - Network accessline 164
for sid in series_ids: resp = requests.get("https://api.stlouisfed.org/fred/series/observations", params={ - Network accessline 165
resp = requests.get("https://api.stlouisfed.org/fred/series/observations", params={ - Access to secretsline 167
[Credential-related source omitted] - Network accessline 181
[Credential-related source omitted] - Access to secretsline 181
[Credential-related source omitted] - Network accessline 186
- Official API documentation: https://fred.stlouisfed.org/docs/api/fred/ - Network accessline 187
- FRED homepage: https://fred.stlouisfed.org/ - Network accessline 188
- Series search: https://fred.stlouisfed.org/docs/api/fred/series_search.html - Network accessline 189
- Category tree: https://fred.stlouisfed.org/docs/api/fred/category.html
skills/domains/economics/imf-data-api-guide/SKILL.md
- Network accessline 8
subcategory: "economics" keywords: ["imf", "economics", "macroeconomics", "indicators", "exchange-rates", "gdp"] source: "https://datahelp.imf.org/knowledgebase/articles/667681-using-json-restful-web-service" - Network accessline 9
keywords: ["imf", "economics", "macroeconomics", "indicators", "exchange-rates", "gdp"] source: "https://datahelp.imf.org/knowledgebase/articles/667681-using-json-restful-web-service" --- - Network accessline 10
source: "https://datahelp.imf.org/knowledgebase/articles/667681-using-json-restful-web-service" - Network accessline 28
# No API key needed curl "https://www.imf.org/external/datamapper/api/v1/NGDP_RPCH?periods=2024" - Network accessline 29
curl "https://www.imf.org/external/datamapper/api/v1/NGDP_RPCH?periods=2024" - Network accessline 37
GET https://www.imf.org/external/datamapper/api/v1 - Dangerous shell operationline 41
curl -s "https://www.imf.org/external/datamapper/api/v1" | python3 -m json.tool - Network accessline 41
curl -s "https://www.imf.org/external/datamapper/api/v1" | python3 -m json.tool - Network accessline 47
GET https://www.imf.org/external/datamapper/api/v1/indicators - Network accessline 53
GET https://www.imf.org/external/datamapper/api/v1/{indicator}?periods={year} - Dangerous shell operationline 66
curl -s "https://www.imf.org/external/datamapper/api/v1/NGDP_RPCH?periods=2024" | python3 -m json.tool - Network accessline 66
curl -s "https://www.imf.org/external/datamapper/api/v1/NGDP_RPCH?periods=2024" \ - Network accessline 75
GET http://dataservices.imf.org/REST/SDMX_JSON.svc/CompactData/{database}/{dimensions}?startPeriod={start}&endPeriod={end} - Dangerous shell operationline 81
curl -s "http://dataservices.imf.org/REST/SDMX_JSON.svc/CompactData/IFS/M.US+CN.PCPI_IX?startPeriod=2020&endPeriod=2025" | python3 -m json.tool - Network accessline 81
curl -s "http://dataservices.imf.org/REST/SDMX_JSON.svc/CompactData/IFS/M.US+CN.PCPI_IX?startPeriod=2020&endPeriod=2025" \ - Network accessline 90
DATAMAPPER_URL = "https://www.imf.org/external/datamapper/api/v1" - Network accessline 96
if periods: params["periods"] = ",".join(str(p) for p in periods) resp = requests.get(url, params=params) - Network accessline 97
params["periods"] = ",".join(str(p) for p in periods) resp = requests.get(url, params=params) resp.raise_for_status() - Network accessline 98
resp = requests.get(url, params=params) - Network accessline 128
codes = "+".join(country_codes) url = ( f"http://dataservices.imf.org/REST/SDMX_JSON.svc/" - Network accessline 129
url = ( f"http://dataservices.imf.org/REST/SDMX_JSON.svc/" f"CompactData/IFS/A.{codes}.ENDA_XDC_USD_RATE" - Network accessline 130
f"http://dataservices.imf.org/REST/SDMX_JSON.svc/" - Network accessline 132
f"?startPeriod={start_year}&endPeriod={end_year}" ) resp = requests.get(url) - Network accessline 133
) resp = requests.get(url) resp.raise_for_status() - Network accessline 134
resp = requests.get(url) - Network accessline 171
- IMF Data API Documentation: https://datahelp.imf.org/knowledgebase/articles/667681-using-json-restful-web-service - Network accessline 172
- IMF DataMapper: https://www.imf.org/external/datamapper - Network accessline 173
- IMF Data Portal: https://data.imf.org/ - Network accessline 174
- IMF World Economic Outlook: https://www.imf.org/en/Publications/WEO
skills/domains/economics/nber-working-papers-api/SKILL.md
- Network accessline 8
subcategory: "economics" keywords: ["NBER", "working papers", "economics research", "macroeconomics", "economic policy", "recession dating"] source: "https://www.nber.org/" - Network accessline 9
keywords: ["NBER", "working papers", "economics research", "macroeconomics", "economic policy", "recession dating"] source: "https://www.nber.org/" --- - Network accessline 10
source: "https://www.nber.org/" - Network accessline 24
# Latest working papers feed curl "https://www.nber.org/papers.rss" - Network accessline 25
curl "https://www.nber.org/papers.rss" - Network accessline 27
# Papers by program curl "https://www.nber.org/programs/ef/papers.rss" # Economic Fluctuations - Network accessline 28
curl "https://www.nber.org/programs/ef/papers.rss" # Economic Fluctuations - Network accessline 29
curl "https://www.nber.org/programs/ls/papers.rss" # Labor Studies - Network accessline 30
curl "https://www.nber.org/programs/io/papers.rss" # Industrial Organization - Network accessline 36
# Search via NBER website (HTML scraping needed) curl "https://www.nber.org/api/v1/working_page_listing/contentType/working_paper/?page=1&perPage=20&q=inflation+expectations" - Network accessline 37
curl "https://www.nber.org/api/v1/working_page_listing/contentType/working_paper/?page=1&perPage=20&q=inflation+expectations" - Network accessline 39
# Get specific paper metadata curl "https://www.nber.org/api/v1/working_page_listing/contentType/working_paper/?page=1&perPage=1&q=w28104" - Network accessline 40
curl "https://www.nber.org/api/v1/working_page_listing/contentType/working_paper/?page=1&perPage=1&q=w28104" - Network accessline 46
# Macroeconomic history data # Available at: https://data.nber.org/ - Network accessline 47
# Available at: https://data.nber.org/ - Network accessline 49
# Business cycle dates curl "https://data.nber.org/data/cycles/business_cycle_dates.json" - Network accessline 50
curl "https://data.nber.org/data/cycles/business_cycle_dates.json" - Network accessline 52
# CPS labor data extracts # https://data.nber.org/cps/ - Network accessline 53
# https://data.nber.org/cps/ - Network accessline 87
"""Get latest NBER working papers via RSS.""" if program: url = f"https://www.nber.org/programs/{program}/papers.rss" - Network accessline 88
if program: url = f"https://www.nber.org/programs/{program}/papers.rss" else: - Network accessline 89
url = f"https://www.nber.org/programs/{program}/papers.rss" - Network accessline 91
url = "https://www.nber.org/papers.rss" - Network accessline 93
resp = requests.get(url, timeout=30) - Network accessline 109
per_page: int = 20) -> list: """Search NBER working papers.""" resp = requests.get( - Network accessline 110
"""Search NBER working papers.""" resp = requests.get( "https://www.nber.org/api/v1/working_page_listing/" - Network accessline 111
resp = requests.get( - Network accessline 112
"https://www.nber.org/api/v1/working_page_listing/" - Network accessline 125
"number": item.get("wp_number", ""), "date": item.get("date", ""), "url": f"https://www.nber.org/papers/{item.get('wp_number', '')}", - Network accessline 126
"date": item.get("date", ""), "url": f"https://www.nber.org/papers/{item.get('wp_number', '')}", "abstract": item.get("description", "")[:300], - Network accessline 127
"url": f"https://www.nber.org/papers/{item.get('wp_number', '')}", - Network accessline 134
def get_business_cycle_dates() -> list: """Get NBER official business cycle dates.""" resp = requests.get( - Network accessline 135
"""Get NBER official business cycle dates.""" resp = requests.get( "https://data.nber.org/data/cycles/business_cycle_dates.json", - Network accessline 136
resp = requests.get( - Network accessline 137
"https://data.nber.org/data/cycles/business_cycle_dates.json", - Network accessline 174
- [NBER](https://www.nber.org/) - Network accessline 175
- [NBER Working Papers](https://www.nber.org/papers) - Network accessline 176
- [NBER Data](https://data.nber.org/) - Network accessline 177
- [NBER Programs](https://www.nber.org/programs-projects/programs)
skills/domains/economics/post-labor-economics/SKILL.md
- Network accessline 252
- Frey, C. B. & Osborne, M. A. (2017). The Future of Employment. Technological Forecasting and Social Change, 114, 254-280. - Korinek, A. & Stiglitz, J. E. (2021). Artificial Intelligence, Globalization, and Strategies for Economic Developm - Network accessline 254
- [OECD Future of Work](https://www.oecd.org/future-of-work/) -- Cross-country analysis and policy recommendations
skills/domains/economics/pricing-psychology-guide/SKILL.md
- Network accessline 271
- Rao, V. R. (2009). Handbook of Pricing Research in Marketing. Edward Elgar. - Thomas, M. & Morwitz, V. (2005). Penny Wise and Pound Foolish. JCR, 32(1), 54-64. - [oTree documentation](https://otree.readthedocs.io/) -- Experimental economi - Network accessline 273
- [oTree documentation](https://otree.readthedocs.io/) -- Experimental economics platform
skills/domains/economics/repec-economics-api/SKILL.md
- Network accessline 8
subcategory: "economics" keywords: ["RePEc", "economics papers", "working papers", "IDEAS", "EconPapers", "economic research"] source: "https://ideas.repec.org/" - Network accessline 9
keywords: ["RePEc", "economics papers", "working papers", "IDEAS", "EconPapers", "economic research"] source: "https://ideas.repec.org/" --- - Network accessline 10
source: "https://ideas.repec.org/" - Network accessline 24
# Search economics papers curl "https://ideas.repec.org/cgi-bin/htsearch?q=monetary+policy+inflation&cmd=Search&fmt=json" - Network accessline 25
curl "https://ideas.repec.org/cgi-bin/htsearch?q=monetary+policy+inflation&cmd=Search&fmt=json" - Network accessline 27
# Search specific types # Working papers curl "https://ideas.repec.org/cgi-bin/htsearch?q=fiscal+stimulus&ul=%%2Fp%%2F&fmt=json" - Network accessline 29
curl "https://ideas.repec.org/cgi-bin/htsearch?q=fiscal+stimulus&ul=%%2Fp%%2F&fmt=json" - Network accessline 31
# Journal articles curl "https://ideas.repec.org/cgi-bin/htsearch?q=trade+liberalization&ul=%%2Fa%%2F&fmt=json" - Network accessline 32
curl "https://ideas.repec.org/cgi-bin/htsearch?q=trade+liberalization&ul=%%2Fa%%2F&fmt=json" - Network accessline 38
# Get metadata for a handle curl "https://api.repec.org/handle?handle=RePEc:nbr:nberwo:28104" - Network accessline 39
curl "https://api.repec.org/handle?handle=RePEc:nbr:nberwo:28104" - Network accessline 41
# Author profile by short-id curl "https://api.repec.org/author?short-id=ppi1" - Network accessline 42
curl "https://api.repec.org/author?short-id=ppi1" - Network accessline 44
# List items in a series curl "https://api.repec.org/series?series=RePEc:nbr:nberwo" - Network accessline 45
curl "https://api.repec.org/series?series=RePEc:nbr:nberwo" - Network accessline 51
# Get citations for a paper curl "https://citec.repec.org/api/plain/RePEc:nbr:nberwo:28104" - Network accessline 52
curl "https://citec.repec.org/api/plain/RePEc:nbr:nberwo:28104" - Network accessline 60
# Get download/view statistics curl "https://logec.repec.org/scripts/paperstat.pf?h=RePEc:nbr:nberwo:28104" - Network accessline 61
curl "https://logec.repec.org/scripts/paperstat.pf?h=RePEc:nbr:nberwo:28104" - Network accessline 93
IDEAS_URL = "https://ideas.repec.org/cgi-bin/htsearch" - Network accessline 94
CITEC_URL = "https://citec.repec.org/api/plain" - Network accessline 111
resp = requests.get(IDEAS_URL, params=params, timeout=30) - Network accessline 128
def get_citations(handle: str) -> list: """Get papers citing a given RePEc handle.""" resp = requests.get(f"{CITEC_URL}/{handle}", timeout=30) - Network accessline 129
"""Get papers citing a given RePEc handle.""" resp = requests.get(f"{CITEC_URL}/{handle}", timeout=30) resp.raise_for_status() - Network accessline 130
resp = requests.get(f"{CITEC_URL}/{handle}", timeout=30) - Network accessline 175
# Top economists curl "https://ideas.repec.org/top/top.person.all.html" - Network accessline 176
curl "https://ideas.repec.org/top/top.person.all.html" - Network accessline 178
# Top institutions curl "https://ideas.repec.org/top/top.inst.all.html" - Network accessline 179
curl "https://ideas.repec.org/top/top.inst.all.html" - Network accessline 184
- [IDEAS/RePEc](https://ideas.repec.org/) - Network accessline 185
- [EconPapers](https://econpapers.repec.org/) - Network accessline 186
- [RePEc Author Service](https://authors.repec.org/) - Network accessline 187
- [CitEc](https://citec.repec.org/)
skills/domains/economics/world-bank-data-guide/SKILL.md
- Network accessline 8
subcategory: "economics" keywords: ["world-bank", "development", "indicators", "poverty", "economics", "statistics"] source: "https://datahelpdesk.worldbank.org/knowledgebase/articles/889392-about-the-indicators-api" - Network accessline 9
keywords: ["world-bank", "development", "indicators", "poverty", "economics", "statistics"] source: "https://datahelpdesk.worldbank.org/knowledgebase/articles/889392-about-the-indicators-api" --- - Network accessline 10
source: "https://datahelpdesk.worldbank.org/knowledgebase/articles/889392-about-the-indicators-api" - Network accessline 28
# No API key needed curl "https://api.worldbank.org/v2/country/US/indicator/NY.GDP.MKTP.CD?format=json&date=2020:2024" - Network accessline 29
curl "https://api.worldbank.org/v2/country/US/indicator/NY.GDP.MKTP.CD?format=json&date=2020:2024" - Network accessline 37
GET https://api.worldbank.org/v2/country/{country_code}/indicator/{indicator_code}?format=json - Dangerous shell operationline 60
curl -s "https://api.worldbank.org/v2/country/BR;RU;IN;CN;ZA/indicator/NY.GDP.PCAP.CD?format=json&date=2019:2023&per_page=100" | python3 -m json.tool - Network accessline 60
curl -s "https://api.worldbank.org/v2/country/BR;RU;IN;CN;ZA/indicator/NY.GDP.PCAP.CD?format=json&date=2019:2023&per_page=100" \ - Dangerous shell operationline 67
curl -s "https://api.worldbank.org/v2/indicator?format=json&per_page=50" | python3 -m json.tool - Network accessline 67
curl -s "https://api.worldbank.org/v2/indicator?format=json&per_page=50" \ - Dangerous shell operationline 74
curl -s "https://api.worldbank.org/v2/topic/8/indicator?format=json&per_page=20" | python3 -m json.tool - Network accessline 74
curl -s "https://api.worldbank.org/v2/topic/8/indicator?format=json&per_page=20" \ - Dangerous shell operationline 83
curl -s "https://api.worldbank.org/v2/country/CN?format=json" | python3 -m json.tool - Network accessline 83
curl -s "https://api.worldbank.org/v2/country/CN?format=json" \ - Network accessline 93
BASE_URL = "https://api.worldbank.org/v2" - Network accessline 101
"per_page": per_page } resp = requests.get(url, params=params) - Network accessline 102
} resp = requests.get(url, params=params) resp.raise_for_status() - Network accessline 103
resp = requests.get(url, params=params) - Network accessline 128
panel = {} for ind_code, ind_name in indicators.items(): url = f"https://api.worldbank.org/v2/country/{countries}/indicator/{ind_code}" - Network accessline 129
for ind_code, ind_name in indicators.items(): url = f"https://api.worldbank.org/v2/country/{countries}/indicator/{ind_code}" params = {"format": "json", "date": years, "per_page": 5000} - Network accessline 130
url = f"https://api.worldbank.org/v2/country/{countries}/indicator/{ind_code}" params = {"format": "json", "date": years, "per_page": 5000} resp = requests.get(url, params=params) - Network accessline 131
params = {"format": "json", "date": years, "per_page": 5000} resp = requests.get(url, params=params) data = resp.json() - Network accessline 132
resp = requests.get(url, params=params) - Network accessline 170
- **Country groups:** Use aggregate codes like `EUU` (EU), `WLD` (World), `LIC` (Low Income) for pre-computed aggregates - **MRV parameter:** Use `MRV=1` to get the most recent value for each country when the latest year varies - **Bulk dow - Network accessline 172
- **Bulk downloads:** For large-scale analysis, consider the bulk download options at https://data.worldbank.org/ - Network accessline 176
- World Bank Indicators API Documentation: https://datahelpdesk.worldbank.org/knowledgebase/articles/889392-about-the-indicators-api - Network accessline 177
- World Bank Data Portal: https://data.worldbank.org/ - Network accessline 178
- World Bank Indicator List: https://data.worldbank.org/indicator - Network accessline 179
- World Bank API Query Builder: https://datahelpdesk.worldbank.org/knowledgebase/articles/898581-api-basic-call-structures
skills/domains/education/academic-study-methods/SKILL.md
- Network accessline 226
- Keshav, S. (2007). "How to Read a Paper." *ACM SIGCOMM Computer Communication Review*, 37(3), 83-84. - Brown, P. C., et al. (2014). *Make It Stick: The Science of Successful Learning*. Harvard UP. - [Anki Spaced Repetition Software](https - Network accessline 228
- [Anki Spaced Repetition Software](https://apps.ankiweb.net/)
skills/domains/education/open-syllabus-api/SKILL.md
- Network accessline 8
subcategory: "education" keywords: ["Open Syllabus", "syllabi analytics", "teaching data", "textbook rankings", "curriculum analysis", "higher education"] source: "https://opensyllabus.org/" - Network accessline 9
keywords: ["Open Syllabus", "syllabi analytics", "teaching data", "textbook rankings", "curriculum analysis", "higher education"] source: "https://opensyllabus.org/" --- - Network accessline 10
source: "https://opensyllabus.org/" - Network accessline 24
# The primary interface is the web explorer: # https://explorer.opensyllabus.org/ - Network accessline 25
# https://explorer.opensyllabus.org/ - Network accessline 34
# API requires institutional subscription # Base URL: https://api.opensyllabus.org/v1/ - Network accessline 35
# Base URL: https://api.opensyllabus.org/v1/ - Network accessline 37
[Credential-related source omitted] - Network accessline 38
[Credential-related source omitted] - Access to secretsline 38
[Credential-related source omitted] - Network accessline 39
"https://api.opensyllabus.org/v1/titles?query=republic+plato&limit=20" - Network accessline 41
[Credential-related source omitted] - Network accessline 42
[Credential-related source omitted] - Access to secretsline 42
[Credential-related source omitted] - Network accessline 43
"https://api.opensyllabus.org/v1/titles/12345" - Network accessline 45
[Credential-related source omitted] - Network accessline 46
[Credential-related source omitted] - Access to secretsline 46
[Credential-related source omitted] - Network accessline 47
"https://api.opensyllabus.org/v1/titles/12345/co-assigned?limit=20" - Network accessline 49
[Credential-related source omitted] - Network accessline 50
[Credential-related source omitted] - Access to secretsline 50
[Credential-related source omitted] - Network accessline 51
"https://api.opensyllabus.org/v1/rankings?field=Economics&limit=50" - Network accessline 80
BASE_URL = "https://api.opensyllabus.org/v1" - Network accessline 94
resp = requests.get( - Network accessline 117
[Credential-related source omitted] - Network accessline 118
[Credential-related source omitted] - Network accessline 119
resp = requests.get( - Network accessline 130
[Credential-related source omitted] - Network accessline 131
[Credential-related source omitted] - Network accessline 132
resp = requests.get( - Network accessline 169
- [Open Syllabus](https://opensyllabus.org/) - Network accessline 170
- [Open Syllabus Explorer](https://explorer.opensyllabus.org/)
skills/domains/finance/akshare-finance-data/SKILL.md
- Network accessline 8
subcategory: "finance" keywords: ["akshare", "financial data", "chinese stocks", "market data", "economic indicators", "quantitative finance"] source: "https://github.com/akfamily/akshare" - Network accessline 9
keywords: ["akshare", "financial data", "chinese stocks", "market data", "economic indicators", "quantitative finance"] source: "https://github.com/akfamily/akshare" --- - Network accessline 10
source: "https://github.com/akfamily/akshare" - Network accessline 205
- [AkShare Documentation](https://akshare.akfamily.xyz/) - Network accessline 206
- [AkShare GitHub](https://github.com/akfamily/akshare) - Network accessline 207
- [AkShare API Reference](https://akshare.akfamily.xyz/data/stock/stock.html)
skills/domains/finance/financial-data-analysis/SKILL.md
- Access to secretsline 61
[Credential-related source omitted]
skills/domains/finance/finsight-research-guide/SKILL.md
- Network accessline 8
subcategory: "finance" keywords: ["FinSight", "financial analysis", "deep research", "market analysis", "financial reports", "multi-agent"] source: "https://github.com/RUC-NLPIR/FinSight" - Network accessline 9
keywords: ["FinSight", "financial analysis", "deep research", "market analysis", "financial reports", "multi-agent"] source: "https://github.com/RUC-NLPIR/FinSight" --- - Network accessline 10
source: "https://github.com/RUC-NLPIR/FinSight" - Network accessline 22
git clone https://github.com/RUC-NLPIR/FinSight.git - Network accessline 112
- [FinSight GitHub](https://github.com/RUC-NLPIR/FinSight) - Network accessline 113
- [RUC-NLPIR Lab](http://playbigdata.ruc.edu.cn/)
skills/domains/finance/options-analytics-agent-guide/SKILL.md
skills/domains/finance/stata-accounting-research/SKILL.md
- Network accessline 446
- Dechow, P. M., Sloan, R. G., & Sweeney, A. P. (1995). Detecting Earnings Management. The Accounting Review, 70(2), 193-225. - Kothari, S. P., Leone, A. J., & Wasley, C. E. (2005). Performance Matched Discretionary Accrual Measures. Journa - Network accessline 447
- Kothari, S. P., Leone, A. J., & Wasley, C. E. (2005). Performance Matched Discretionary Accrual Measures. Journal of Accounting and Economics, 39(1), 163-197. - [WRDS (Wharton Research Data Services)](https://wrds-www.wharton.upenn.edu/) - Network accessline 448
- [WRDS (Wharton Research Data Services)](https://wrds-www.wharton.upenn.edu/) -- Standard data platform for accounting/finance research - Network accessline 449
- [reghdfe documentation](https://github.com/sergiocorreia/reghdfe) -- Fast fixed-effects estimation in STATA
skills/domains/geoscience/climate-modeling-guide/SKILL.md
skills/domains/geoscience/climate-science-guide/SKILL.md
- Network accessline 46
import intake catalog = intake.open_esm_datastore( "https://storage.googleapis.com/cmip6/pangeo-cmip6.json" - Network accessline 47
catalog = intake.open_esm_datastore( "https://storage.googleapis.com/cmip6/pangeo-cmip6.json" ) - Network accessline 48
"https://storage.googleapis.com/cmip6/pangeo-cmip6.json"
skills/domains/geoscience/pangaea-data-api/SKILL.md
- Network accessline 8
subcategory: "geoscience" keywords: ["PANGAEA", "earth science data", "oceanography", "paleoclimate", "environmental data", "geoscience repository"] source: "https://www.pangaea.de/" - Network accessline 9
keywords: ["PANGAEA", "earth science data", "oceanography", "paleoclimate", "environmental data", "geoscience repository"] source: "https://www.pangaea.de/" --- - Network accessline 10
source: "https://www.pangaea.de/" - Network accessline 24
# Search datasets by keyword curl "https://www.pangaea.de/advanced/search.php?q=ocean+temperature&count=20&type=json" - Network accessline 25
curl "https://www.pangaea.de/advanced/search.php?q=ocean+temperature&count=20&type=json" - Network accessline 27
# Search with geographic bounding box curl "https://www.pangaea.de/advanced/search.php?\ - Network accessline 28
curl "https://www.pangaea.de/advanced/search.php?\ - Network accessline 31
# Filter by parameter (measurement type) curl "https://www.pangaea.de/advanced/search.php?\ - Network accessline 32
curl "https://www.pangaea.de/advanced/search.php?\ - Network accessline 35
# Filter by date range curl "https://www.pangaea.de/advanced/search.php?\ - Network accessline 36
curl "https://www.pangaea.de/advanced/search.php?\ - Network accessline 43
# Full-text search via Elasticsearch curl -X POST "https://ws.pangaea.de/es/pangaea/panmd/_search" \ - Network accessline 44
curl -X POST "https://ws.pangaea.de/es/pangaea/panmd/_search" \ - Network accessline 64
# Get dataset metadata curl "https://doi.pangaea.de/10.1594/PANGAEA.123456?format=metainfo_json" - Network accessline 65
curl "https://doi.pangaea.de/10.1594/PANGAEA.123456?format=metainfo_json" - Network accessline 67
# Download dataset as tab-delimited text curl "https://doi.pangaea.de/10.1594/PANGAEA.123456?format=textfile" - Network accessline 68
curl "https://doi.pangaea.de/10.1594/PANGAEA.123456?format=textfile" - Network accessline 70
# Download as CSV curl "https://doi.pangaea.de/10.1594/PANGAEA.123456?format=csv" - Network accessline 71
curl "https://doi.pangaea.de/10.1594/PANGAEA.123456?format=csv" - Network accessline 77
# List records curl "https://ws.pangaea.de/oai/provider?verb=ListRecords&metadataPrefix=oai_dc" - Network accessline 78
curl "https://ws.pangaea.de/oai/provider?verb=ListRecords&metadataPrefix=oai_dc" - Network accessline 80
# Get specific record curl "https://ws.pangaea.de/oai/provider?verb=GetRecord&identifier=oai:pangaea.de:doi:10.1594/PANGAEA.123456&metadataPrefix=oai_dc" - Network accessline 81
curl "https://ws.pangaea.de/oai/provider?verb=GetRecord&identifier=oai:pangaea.de:doi:10.1594/PANGAEA.123456&metadataPrefix=oai_dc" - Network accessline 105
SEARCH_URL = "https://www.pangaea.de/advanced/search.php" - Network accessline 106
ES_URL = "https://ws.pangaea.de/es/pangaea/panmd/_search" - Network accessline 121
resp = requests.get(SEARCH_URL, params=params, timeout=30) - Network accessline 138
def download_dataset(doi: str) -> pd.DataFrame: """Download a PANGAEA dataset as a pandas DataFrame.""" url = f"https://doi.pangaea.de/{doi}?format=textfile" - Network accessline 139
"""Download a PANGAEA dataset as a pandas DataFrame.""" url = f"https://doi.pangaea.de/{doi}?format=textfile" resp = requests.get(url, timeout=60) - Network accessline 140
url = f"https://doi.pangaea.de/{doi}?format=textfile" resp = requests.get(url, timeout=60) resp.raise_for_status() - Network accessline 141
resp = requests.get(url, timeout=60) - Network accessline 194
- [PANGAEA](https://www.pangaea.de/) - Network accessline 195
- [PANGAEA API](https://wiki.pangaea.de/wiki/PANGAEA_API) - Network accessline 196
- [PANGAEA Elasticsearch](https://wiki.pangaea.de/wiki/Elasticsearch_API)
skills/domains/geoscience/satellite-remote-sensing/SKILL.md
- Network accessline 36
# Search Sentinel-2 imagery via Microsoft Planetary Computer catalog = pystac_client.Client.open( "https://planetarycomputer.microsoft.com/api/stac/v1", - Network accessline 37
catalog = pystac_client.Client.open( "https://planetarycomputer.microsoft.com/api/stac/v1", modifier=planetary_computer.sign_inplace, - Network accessline 38
"https://planetarycomputer.microsoft.com/api/stac/v1",
skills/domains/humanities/digital-humanities-guide/SKILL.md
- Network accessline 157
<TEI xmlns="http://www.tei-c.org/ns/1.0">
skills/domains/humanities/ethical-philosophy-guide/SKILL.md
- Network accessline 240
- [Stanford Encyclopedia of Philosophy](https://plato.stanford.edu/) -- Peer-reviewed reference for all areas of philosophy - Network accessline 242
- Floridi, L. & Cowls, J. (2019). A Unified Framework of Five Principles for AI in Society. Harvard Data Science Review. - Singer, P. (2011). Practical Ethics, 3rd ed. Cambridge UP. - [PhilPapers](https://philpapers.org/) -- Comprehensive i - Network accessline 244
- [PhilPapers](https://philpapers.org/) -- Comprehensive index of philosophy research
skills/domains/humanities/history-research-guide/SKILL.md
- Network accessline 256
- Bloch, M. (1953). The Historian's Craft. Vintage Books. - Tosh, J. (2015). The Pursuit of History: Aims, Methods and New Directions. Routledge, 6th ed. - [Zotero](https://www.zotero.org/) -- Reference management with Chicago style support - Network accessline 257
- Tosh, J. (2015). The Pursuit of History: Aims, Methods and New Directions. Routledge, 6th ed. - [Zotero](https://www.zotero.org/) -- Reference management with Chicago style support - [Internet Archive](https://archive.org/) -- Universal a - Network accessline 258
- [Zotero](https://www.zotero.org/) -- Reference management with Chicago style support - Network accessline 259
- [Internet Archive](https://archive.org/) -- Universal access to human knowledge - Network accessline 260
- [Programming Historian](https://programminghistorian.org/) -- Digital methods tutorials for historians
skills/domains/humanities/political-history-guide/SKILL.md
- Network accessline 239
- Zhao, D. (2015). The Confucian-Legalist State: A New Theory of Chinese History. Oxford UP. - Fukuyama, F. (2011). The Origins of Political Order. Farrar, Straus and Giroux. - [Cambridge History of China](https://www.cambridge.org/core/ser - Network accessline 241
- [Cambridge History of China](https://www.cambridge.org/core/series/cambridge-history-of-china/) -- Standard reference
skills/domains/law/caselaw-access-api/SKILL.md
- Network accessline 8
subcategory: "law" keywords: ["case law", "legal database", "court opinions", "Harvard law", "judicial data", "legal research"] source: "https://case.law/" - Network accessline 9
keywords: ["case law", "legal database", "court opinions", "Harvard law", "judicial data", "legal research"] source: "https://case.law/" --- - Network accessline 10
source: "https://case.law/" - Network accessline 24
https://api.case.law/v1/ - Network accessline 30
# Search cases by keyword curl "https://api.case.law/v1/cases/?search=free+speech&decision_date_min=2020-01-01" - Network accessline 31
curl "https://api.case.law/v1/cases/?search=free+speech&decision_date_min=2020-01-01" - Network accessline 33
# Get a specific case by ID curl "https://api.case.law/v1/cases/12345678/" - Network accessline 34
curl "https://api.case.law/v1/cases/12345678/" - Network accessline 36
# Filter by jurisdiction and court curl "https://api.case.law/v1/cases/?jurisdiction=us&court=us-sup-ct&search=miranda+rights" - Network accessline 37
curl "https://api.case.law/v1/cases/?jurisdiction=us&court=us-sup-ct&search=miranda+rights" - Network accessline 39
# Filter by date range curl "https://api.case.law/v1/cases/?decision_date_min=2015-01-01&decision_date_max=2025-12-31" - Network accessline 40
curl "https://api.case.law/v1/cases/?decision_date_min=2015-01-01&decision_date_max=2025-12-31" - Network accessline 42
[Credential-related source omitted] - Network accessline 43
[Credential-related source omitted] - Access to secretsline 43
[Credential-related source omitted] - Network accessline 44
"https://api.case.law/v1/cases/12345678/?full_case=true" - Network accessline 65
# List all courts curl "https://api.case.law/v1/courts/" - Network accessline 66
curl "https://api.case.law/v1/courts/" - Network accessline 68
# List all jurisdictions curl "https://api.case.law/v1/jurisdictions/" - Network accessline 69
curl "https://api.case.law/v1/jurisdictions/" - Network accessline 71
# List all reporters (case report series) curl "https://api.case.law/v1/reporters/" - Network accessline 72
curl "https://api.case.law/v1/reporters/" - Network accessline 80
BASE_URL = "https://api.case.law/v1" - Network accessline 91
resp = requests.get(f"{BASE_URL}/cases/", params=params) - Network accessline 118
# Bulk data available at: # https://case.law/bulk/download/ - Network accessline 119
# https://case.law/bulk/download/ - Network accessline 138
[Credential-related source omitted] - Network accessline 147
- [CAP API Documentation](https://case.law/api/) - [CAP Bulk Data](https://case.law/bulk/download/) - Network accessline 148
- [CAP Bulk Data](https://case.law/bulk/download/) - Network accessline 149
- [Harvard Library Innovation Lab](https://lil.law.harvard.edu/)
skills/domains/law/courtlistener-api/SKILL.md
- Network accessline 8
subcategory: "law" keywords: ["law", "case law", "legal theory", "compliance analysis", "jurisprudence"] source: "https://www.courtlistener.com/api/rest-info/" - Network accessline 9
keywords: ["law", "case law", "legal theory", "compliance analysis", "jurisprudence"] source: "https://www.courtlistener.com/api/rest-info/" --- - Network accessline 10
source: "https://www.courtlistener.com/api/rest-info/" - Network accessline 27
[Credential-related source omitted] - Network accessline 28
1. Create a free account at https://www.courtlistener.com/ - Network accessline 34
# Unauthenticated request curl "https://www.courtlistener.com/api/rest/v4/opinions/?q=first+amendment" - Network accessline 35
curl "https://www.courtlistener.com/api/rest/v4/opinions/?q=first+amendment" - Network accessline 37
[Credential-related source omitted] - Network accessline 38
[Credential-related source omitted] - Network accessline 39
"https://www.courtlistener.com/api/rest/v4/opinions/?q=first+amendment" - Network accessline 48
- **URL**: `GET https://www.courtlistener.com/api/rest/v4/opinions/` - Network accessline 64
curl "https://www.courtlistener.com/api/rest/v4/opinions/?q=privacy+fourth+amendment&court=scotus&ordering=-date_filed" - Network accessline 73
- **URL**: `GET https://www.courtlistener.com/api/rest/v4/dockets/` - Network accessline 89
curl "https://www.courtlistener.com/api/rest/v4/dockets/?q=antitrust&court=scotus&ordering=-date_filed" - Network accessline 98
- **URL**: `GET https://www.courtlistener.com/api/rest/v4/courts/` - Network accessline 108
curl "https://www.courtlistener.com/api/rest/v4/courts/?jurisdiction=F" - Network accessline 117
- **URL**: `GET https://www.courtlistener.com/api/rest/v4/people/` - Network accessline 129
curl "https://www.courtlistener.com/api/rest/v4/people/?q=ginsburg" - Network accessline 138
- **URL**: `GET https://www.courtlistener.com/api/rest/v4/search/` - Network accessline 149
curl "https://www.courtlistener.com/api/rest/v4/search/?q=net+neutrality&type=o" - Network accessline 156
The CourtListener API allows up to 5,000 requests per hour for unauthenticated users. Authenticated users with API tokens receive higher limits. The API returns standard HTTP 429 responses when limits are exceeded. For bulk data access, Cou - Network accessline 170
"page_size": 10 } resp = requests.get("https://www.courtlistener.com/api/rest/v4/opinions/", params=params) - Network accessline 171
} resp = requests.get("https://www.courtlistener.com/api/rest/v4/opinions/", params=params) data = resp.json() - Network accessline 172
resp = requests.get("https://www.courtlistener.com/api/rest/v4/opinions/", params=params) - Network accessline 191
"ordering": "-date_filed" } resp = requests.get("https://www.courtlistener.com/api/rest/v4/search/", params={**params, "type": "o"}) - Network accessline 192
} resp = requests.get("https://www.courtlistener.com/api/rest/v4/search/", params={**params, "type": "o"}) results = resp.json() - Network accessline 193
resp = requests.get("https://www.courtlistener.com/api/rest/v4/search/", params={**params, "type": "o"}) - Network accessline 204
curl "https://www.courtlistener.com/api/rest/v4/people/?court=scotus&ordering=-date_nominated" - Network accessline 209
- Official API documentation: https://www.courtlistener.com/api/rest-info/ - Network accessline 210
- CourtListener homepage: https://www.courtlistener.com/ - Network accessline 211
- Bulk data downloads: https://www.courtlistener.com/api/bulk-info/ - Network accessline 212
- Free Law Project: https://free.law/ - Network accessline 213
- RECAP Archive: https://www.courtlistener.com/recap/
skills/domains/law/legal-agent-skills-guide/SKILL.md
- Network accessline 8
subcategory: "law" keywords: ["legal AI", "legal automation", "legal research", "agent skills", "contract analysis", "case law"] source: "https://github.com/lawvable/awesome-legal-skills" - Network accessline 9
keywords: ["legal AI", "legal automation", "legal research", "agent skills", "contract analysis", "case law"] source: "https://github.com/lawvable/awesome-legal-skills" --- - Network accessline 10
source: "https://github.com/lawvable/awesome-legal-skills" - Network accessline 130
- [awesome-legal-skills](https://github.com/lawvable/awesome-legal-skills) - Network accessline 131
- [CourtListener](https://www.courtlistener.com/) — Free case law - Network accessline 132
- [Caselaw Access Project](https://case.law/)
skills/domains/law/legal-research-guide/SKILL.md
- Network accessline 117
# Using the CourtListener API (free, open-source) BASE_URL = "https://www.courtlistener.com/api/rest/v3" - Network accessline 118
BASE_URL = "https://www.courtlistener.com/api/rest/v3" - Network accessline 120
def search_opinions(query, court="scotus", page_size=20): """Search case opinions via CourtListener API.""" response = requests.get( - Network accessline 121
"""Search case opinions via CourtListener API.""" response = requests.get( f"{BASE_URL}/search/", - Network accessline 122
response = requests.get( - Network accessline 136
print(f" Court: {case.get('court', 'N/A')}") print(f" Citation: {case.get('citation', ['N/A'])[0] if case.get('citation') else 'N/A'}") print(f" URL: https://www.courtlistener.com{case.get('absolute_url', '')}") - Network accessline 137
print(f" Citation: {case.get('citation', ['N/A'])[0] if case.get('citation') else 'N/A'}") print(f" URL: https://www.courtlistener.com{case.get('absolute_url', '')}") return results - Network accessline 138
print(f" URL: https://www.courtlistener.com{case.get('absolute_url', '')}") - Network accessline 162
# Get case metadata and citations resp = requests.get(f"{BASE_URL}/opinions/{case_id}/", - Network accessline 163
resp = requests.get(f"{BASE_URL}/opinions/{case_id}/",
skills/domains/law/opencontracts-guide/SKILL.md
- Network accessline 8
subcategory: "law" keywords: ["legal documents", "contract annotation", "document versioning", "legal AI", "NLP legal", "MCP"] source: "https://github.com/Open-Source-Legal/OpenContracts" - Network accessline 9
keywords: ["legal documents", "contract annotation", "document versioning", "legal AI", "NLP legal", "MCP"] source: "https://github.com/Open-Source-Legal/OpenContracts" --- - Network accessline 10
source: "https://github.com/Open-Source-Legal/OpenContracts" - Network accessline 22
# Docker deployment git clone https://github.com/Open-Source-Legal/OpenContracts.git - Network accessline 23
git clone https://github.com/Open-Source-Legal/OpenContracts.git - Network accessline 27
# Access at http://localhost:3000 - Network accessline 37
client = Client("http://localhost:3000") - Network accessline 130
"args": ["@opencontracts/mcp-server"], "env": { "OPENCONTRACTS_URL": "http://localhost:3000" - Network accessline 131
"env": { "OPENCONTRACTS_URL": "http://localhost:3000" } - Network accessline 132
"OPENCONTRACTS_URL": "http://localhost:3000" - Network accessline 167
- [OpenContracts GitHub](https://github.com/Open-Source-Legal/OpenContracts) - Network accessline 168
- [Legal NLP Resources](https://github.com/thunlp/LegalPapers)
skills/domains/law/patent-analysis-guide/SKILL.md
- Network accessline 38
BASE_URL = "https://ops.epo.org/3.2/rest-services" - Network accessline 44
import base64 credentials = base64.b64encode(f"{key}:{secret}".encode()).decode() resp = requests.post( - Network accessline 45
credentials = base64.b64encode(f"{key}:{secret}".encode()).decode() resp = requests.post( "https://ops.epo.org/3.2/auth/accesstoken", - Network accessline 46
resp = requests.post( - Network accessline 47
"https://ops.epo.org/3.2/auth/accesstoken", - Access to secretsline 51
[Credential-related source omitted] - Network accessline 58
pa="university" AND pd>=2020 """ resp = requests.get( - Network accessline 59
""" resp = requests.get( f"{self.BASE_URL}/published-data/search", - Network accessline 60
resp = requests.get(
skills/domains/law/regulatory-compliance-guide/SKILL.md
- Network accessline 38
BASE_URL = "https://www.federalregister.gov/api/v1" - Network accessline 56
resp = requests.get(f"{self.BASE_URL}/documents", params=params) - Network accessline 74
def get_document(self, document_number: str) -> dict: """Retrieve full document details by document number.""" resp = requests.get( - Network accessline 75
"""Retrieve full document details by document number.""" resp = requests.get( f"{self.BASE_URL}/documents/{document_number}.json" - Network accessline 76
resp = requests.get(
skills/domains/law/uk-legislation-api/SKILL.md
- Network accessline 8
subcategory: "law" keywords: ["UK law", "legislation", "statutory instruments", "legal research", "Parliament", "regulatory analysis"] source: "https://www.legislation.gov.uk" - Network accessline 9
keywords: ["UK law", "legislation", "statutory instruments", "legal research", "Parliament", "regulatory analysis"] source: "https://www.legislation.gov.uk" --- - Network accessline 10
source: "https://www.legislation.gov.uk" - Network accessline 28
https://www.legislation.gov.uk/{type}/{year}/{number} - Network accessline 49
# Fetch the Equality Act 2010 — returns structured XML with Dublin Core metadata curl "https://www.legislation.gov.uk/ukpga/2010/15/data.xml" - Network accessline 50
curl "https://www.legislation.gov.uk/ukpga/2010/15/data.xml" - Network accessline 51
# Response: <Legislation DocumentURI="http://www.legislation.gov.uk/ukpga/2010/15" - Network accessline 56
# Fetch Data Protection Act 2018 curl "https://www.legislation.gov.uk/ukpga/2018/12/data.xml" - Network accessline 57
curl "https://www.legislation.gov.uk/ukpga/2018/12/data.xml" - Network accessline 61
# Fetch a Statutory Instrument curl "https://www.legislation.gov.uk/uksi/2020/1374/data.xml" - Network accessline 62
curl "https://www.legislation.gov.uk/uksi/2020/1374/data.xml" - Network accessline 69
# Fetch a single section curl "https://www.legislation.gov.uk/ukpga/2010/15/section/1/data.xml" - Network accessline 70
curl "https://www.legislation.gov.uk/ukpga/2010/15/section/1/data.xml" - Network accessline 72
# Fetch table of contents curl "https://www.legislation.gov.uk/ukpga/2010/15/contents/data.xml" - Network accessline 73
curl "https://www.legislation.gov.uk/ukpga/2010/15/contents/data.xml" - Network accessline 75
# Fetch a specific Part curl "https://www.legislation.gov.uk/ukpga/2010/15/part/1/data.xml" - Network accessline 76
curl "https://www.legislation.gov.uk/ukpga/2010/15/part/1/data.xml" - Network accessline 78
# Fetch a Schedule curl "https://www.legislation.gov.uk/ukpga/2010/15/schedule/1/data.xml" - Network accessline 79
curl "https://www.legislation.gov.uk/ukpga/2010/15/schedule/1/data.xml" - Network accessline 85
# Search by title — returns HTTP 300 with multiple matches curl "https://www.legislation.gov.uk/id?title=equality+act" - Network accessline 86
curl "https://www.legislation.gov.uk/id?title=equality+act" - Network accessline 88
# Browse by type, title keyword, and year curl "https://www.legislation.gov.uk/ukpga?title=data+protection&year=2018" - Network accessline 89
curl "https://www.legislation.gov.uk/ukpga?title=data+protection&year=2018" - Network accessline 91
# List recent Acts via Atom feed curl "https://www.legislation.gov.uk/new/ukpga/data.feed" - Network accessline 92
curl "https://www.legislation.gov.uk/new/ukpga/data.feed" - Network accessline 99
# Changes affecting a specific Act (Atom feed, paginated) curl "https://www.legislation.gov.uk/changes/affected/ukpga/2010/15/data.feed" - Network accessline 100
curl "https://www.legislation.gov.uk/changes/affected/ukpga/2010/15/data.feed" - Network accessline 105
# Changes made BY a specific Act curl "https://www.legislation.gov.uk/changes/affecting/ukpga/2010/15/data.feed" - Network accessline 106
curl "https://www.legislation.gov.uk/changes/affecting/ukpga/2010/15/data.feed" - Network accessline 112
# Legislation as it stood on a specific date curl "https://www.legislation.gov.uk/ukpga/2010/15/2015-01-01/data.xml" - Network accessline 113
curl "https://www.legislation.gov.uk/ukpga/2010/15/2015-01-01/data.xml" - Network accessline 115
# Enacted (original) version curl "https://www.legislation.gov.uk/ukpga/2010/15/enacted/data.xml" - Network accessline 116
curl "https://www.legislation.gov.uk/ukpga/2010/15/enacted/data.xml" - Network accessline 145
BASE = "https://www.legislation.gov.uk" - Network accessline 146
NS = {"leg": "http://www.legislation.gov.uk/namespaces/legislation", - Network accessline 147
"ukm": "http://www.legislation.gov.uk/namespaces/metadata", - Network accessline 148
"dc": "http://purl.org/dc/elements/1.1/", - Network accessline 149
"atom": "http://www.w3.org/2005/Atom"} - Network accessline 152
"""Fetch UK legislation metadata and full XML.""" url = f"{BASE}/{leg_type}/{year}/{number}/data.xml" resp = requests.get(url) - Network accessline 153
url = f"{BASE}/{leg_type}/{year}/{number}/data.xml" resp = requests.get(url) resp.raise_for_status() - Network accessline 154
resp = requests.get(url) - Network accessline 175
- [Legislation.gov.uk API Documentation](https://www.legislation.gov.uk/developer) - Network accessline 176
- [CLML Schema Documentation](https://legislation.github.io/clml-schema/) - Network accessline 177
- [Legislation.gov.uk URI Scheme](https://www.legislation.gov.uk/developer/uris) - Network accessline 178
- [The National Archives](https://www.nationalarchives.gov.uk/) - Network accessline 179
- [Atom Feed Specification](https://www.legislation.gov.uk/developer/formats/atom)
skills/domains/math/lean-theorem-proving-guide/SKILL.md
- Network accessline 8
subcategory: "math" keywords: ["Lean 4", "theorem proving", "formal verification", "LLM math", "proof assistant", "LeanAgent"] source: "https://github.com/lean-dojo/LeanAgent" - Network accessline 9
keywords: ["Lean 4", "theorem proving", "formal verification", "LLM math", "proof assistant", "LeanAgent"] source: "https://github.com/lean-dojo/LeanAgent" --- - Network accessline 10
source: "https://github.com/lean-dojo/LeanAgent" - Network accessline 137
- [LeanAgent GitHub](https://github.com/lean-dojo/LeanAgent) - Network accessline 138
- [Lean 4](https://lean-lang.org/) - Network accessline 139
- [Mathlib4](https://leanprover-community.github.io/mathlib4_docs/) - Network accessline 140
- [LeanDojo](https://leandojo.org/)
skills/domains/math/oeis-api/SKILL.md
- Network accessline 8
subcategory: "math" keywords: ["number theory", "algebra", "probability theory", "applied mathematics"] source: "https://oeis.org/eishelp1.html" - Network accessline 9
keywords: ["number theory", "algebra", "probability theory", "applied mathematics"] source: "https://oeis.org/eishelp1.html" --- - Network accessline 10
source: "https://oeis.org/eishelp1.html" - Network accessline 33
- **URL**: `GET https://oeis.org/search` - Network accessline 45
# Search by sequence terms curl "https://oeis.org/search?q=1,1,2,3,5,8,13,21&fmt=json" - Network accessline 46
curl "https://oeis.org/search?q=1,1,2,3,5,8,13,21&fmt=json" - Network accessline 48
# Search by A-number curl "https://oeis.org/search?q=A000045&fmt=json" - Network accessline 49
curl "https://oeis.org/search?q=A000045&fmt=json" - Network accessline 51
# Search by keyword curl "https://oeis.org/search?q=catalan+numbers&fmt=json" - Network accessline 52
curl "https://oeis.org/search?q=catalan+numbers&fmt=json" - Network accessline 61
- **URL**: `GET https://oeis.org/A{number}` - Network accessline 72
# Retrieve the Fibonacci sequence (A000045) curl "https://oeis.org/A000045?fmt=json" - Network accessline 73
curl "https://oeis.org/A000045?fmt=json" - Network accessline 75
# Retrieve the prime numbers (A000040) curl "https://oeis.org/A000040?fmt=json" - Network accessline 76
curl "https://oeis.org/A000040?fmt=json" - Network accessline 83
No formal rate limits are published for the OEIS. However, the service is run by a non-profit organization with limited infrastructure. Users should be respectful and avoid sending more than a few requests per second. For bulk data access, - Network accessline 97
resp = requests.get("https://oeis.org/search", params={"q": unknown_terms, "fmt": "json"}) - Network accessline 117
# Get the Catalan numbers (A000108) resp = requests.get("https://oeis.org/A000108", params={"fmt": "json"}) - Network accessline 118
resp = requests.get("https://oeis.org/A000108", params={"fmt": "json"}) - Network accessline 141
resp = requests.get("https://oeis.org/search", params={"q": "A000045", "fmt": "json"}) - Network accessline 154
- Official help and documentation: https://oeis.org/eishelp1.html - Network accessline 155
- OEIS homepage: https://oeis.org/ - Network accessline 156
- OEIS Wiki: https://oeis.org/wiki/Welcome - Network accessline 157
- Downloadable data files: https://oeis.org/stripped.gz - Network accessline 158
- OEIS Foundation: https://oeisf.org/
skills/domains/pharma/madd-drug-discovery-guide/SKILL.md
- Network accessline 8
subcategory: "pharma" keywords: ["drug discovery", "multi-agent", "molecular design", "ADMET", "virtual screening", "pharma AI"] source: "https://github.com/sb-ai-lab/MADD" - Network accessline 9
keywords: ["drug discovery", "multi-agent", "molecular design", "ADMET", "virtual screening", "pharma AI"] source: "https://github.com/sb-ai-lab/MADD" --- - Network accessline 10
source: "https://github.com/sb-ai-lab/MADD" - Network accessline 151
- [MADD GitHub](https://github.com/sb-ai-lab/MADD) - Network accessline 152
- [RDKit](https://www.rdkit.org/) — Chemistry toolkit - Network accessline 153
- [AutoDock Vina](https://vina.scripps.edu/) — Molecular docking
skills/domains/physics/nasa-ads-api/SKILL.md
- Network accessline 8
subcategory: "physics" keywords: ["astrophysics", "observational astronomy", "cosmology", "high energy physics"] source: "https://ui.adsabs.harvard.edu/help/api/" - Network accessline 9
keywords: ["astrophysics", "observational astronomy", "cosmology", "high energy physics"] source: "https://ui.adsabs.harvard.edu/help/api/" requires: - Network accessline 10
source: "https://ui.adsabs.harvard.edu/help/api/" - Access to secretsline 12
[Credential-related source omitted] - Network accessline 27
Authentication is required via a free API token. Register at https://ui.adsabs.harvard.edu/user/settings/token to generate your token. Include it in every request as a header: - Network accessline 39
- **URL**: `GET https://api.adsabs.harvard.edu/v1/search/query` - Network accessline 51
[Credential-related source omitted] - Network accessline 52
"https://api.adsabs.harvard.edu/v1/search/query?q=gravitational+waves&fl=title,author,year,bibcode,citation_count,doi&rows=10&sort=citation_count+desc" - Network accessline 58
- **URL**: `GET https://api.adsabs.harvard.edu/v1/search/query` - Network accessline 68
[Credential-related source omitted] - Network accessline 69
"https://api.adsabs.harvard.edu/v1/search/query?q=author:%22Hawking,+S%22&fl=title,year,bibcode,citation_count&rows=20&sort=citation_count+desc" - Network accessline 75
- **URL**: `GET https://api.adsabs.harvard.edu/v1/search/query` - Network accessline 84
[Credential-related source omitted] - Network accessline 85
"https://api.adsabs.harvard.edu/v1/search/query?q=object:%22Sgr+A*%22&fl=title,author,year,bibcode,citation_count&rows=10&sort=date+desc" - Network accessline 91
- **URL**: `POST https://api.adsabs.harvard.edu/v1/metrics` - Network accessline 99
[Credential-related source omitted] - Network accessline 101
"https://api.adsabs.harvard.edu/v1/metrics" \ - Network accessline 117
[Credential-related source omitted] - Network accessline 118
"https://api.adsabs.harvard.edu/v1/search/query?q=dark+energy+AND+year:2023-2026&fl=title,author,year,bibcode,citation_count,doi,abstract&rows=20&sort=citation_count+desc" - Network accessline 126
[Credential-related source omitted] - Network accessline 127
[Credential-related source omitted] - Network accessline 128
"https://api.adsabs.harvard.edu/v1/search/query?q=author:%22Perlmutter,+S%22&fl=title,year,bibcode,citation_count&rows=200&sort=date+desc" - Network accessline 130
[Credential-related source omitted] - Network accessline 131
[Credential-related source omitted] - Network accessline 133
"https://api.adsabs.harvard.edu/v1/metrics" \ - Network accessline 142
[Credential-related source omitted] - Network accessline 143
"https://api.adsabs.harvard.edu/v1/search/query?q=object:%22Crab+Nebula%22+AND+year:2024-2026&fl=title,author,year,bibcode,doi&rows=10&sort=date+desc" - Network accessline 148
- Official documentation: https://ui.adsabs.harvard.edu/help/api/ - Network accessline 149
- ADS query syntax: https://ui.adsabs.harvard.edu/help/search/ - Network accessline 150
- ADS search fields: https://ui.adsabs.harvard.edu/help/search/search-syntax
skills/domains/social-science/ipums-microdata-api/SKILL.md
- Network accessline 8
subcategory: "social-science" keywords: ["IPUMS", "census data", "microdata", "survey data", "demographics", "social science data"] source: "https://www.ipums.org/" - Network accessline 9
keywords: ["IPUMS", "census data", "microdata", "survey data", "demographics", "social science data"] source: "https://www.ipums.org/" --- - Network accessline 10
source: "https://www.ipums.org/" - Network accessline 37
https://api.ipums.org/extracts/ - Network accessline 43
# Register at https://www.ipums.org/ - Access to secretsline 45
[Credential-related source omitted] - Network accessline 51
# Request a data extract (IPUMS USA example) curl -X POST "https://api.ipums.org/extracts/?collection=usa&version=2" \ - Network accessline 52
[Credential-related source omitted] - Access to secretsline 52
[Credential-related source omitted] - Access to secretsline 53
[Credential-related source omitted] - Network accessline 74
[Credential-related source omitted] - Access to secretsline 74
[Credential-related source omitted] - Access to secretsline 75
[Credential-related source omitted] - Network accessline 81
# When status is "completed" curl -O "https://api.ipums.org/extracts/42/download?collection=usa&version=2" \ - Network accessline 82
[Credential-related source omitted] - Access to secretsline 82
[Credential-related source omitted] - Access to secretsline 83
[Credential-related source omitted] - Network accessline 89
# List available variables curl "https://api.ipums.org/metadata/usa/variables?version=2" \ - Network accessline 90
[Credential-related source omitted] - Access to secretsline 90
[Credential-related source omitted] - Access to secretsline 91
[Credential-related source omitted] - Network accessline 93
# Get variable details curl "https://api.ipums.org/metadata/usa/variables/EDUC?version=2" \ - Network accessline 94
[Credential-related source omitted] - Access to secretsline 94
[Credential-related source omitted] - Access to secretsline 95
[Credential-related source omitted] - Network accessline 97
# List available samples curl "https://api.ipums.org/metadata/usa/samples?version=2" \ - Network accessline 98
[Credential-related source omitted] - Access to secretsline 98
[Credential-related source omitted] - Access to secretsline 99
[Credential-related source omitted] - Network accessline 109
BASE_URL = "https://api.ipums.org" - Access to secretsline 110
[Credential-related source omitted] - Network accessline 127
resp = requests.post( - Network accessline 138
"""Poll until extract is ready, return download URL.""" while True: resp = requests.get( - Network accessline 139
while True: resp = requests.get( f"{BASE_URL}/extracts/{extract_id}" - Network accessline 140
resp = requests.get( - Network accessline 157
def get_variable_info(collection: str, variable: str) -> dict: """Get metadata about a variable.""" resp = requests.get( - Network accessline 158
"""Get metadata about a variable.""" resp = requests.get( f"{BASE_URL}/metadata/{collection}/variables/{variable}" - Network accessline 159
resp = requests.get( - Network accessline 208
- [IPUMS](https://www.ipums.org/) - Network accessline 209
- [IPUMS API Documentation](https://developer.ipums.org/) - Network accessline 210
- [IPUMS USA](https://usa.ipums.org/)
skills/domains/social-science/psychology-research-guide/SKILL.md
- Network accessline 8
subcategory: "social-science" keywords: ["psychology", "experimental design", "cognitive science", "psychometrics", "replication", "effect size"] source: "https://github.com/psych-ds/psych-DS" - Network accessline 9
keywords: ["psychology", "experimental design", "cognitive science", "psychometrics", "replication", "effect size"] source: "https://github.com/psych-ds/psych-DS" --- - Network accessline 10
source: "https://github.com/psych-ds/psych-DS" - Network accessline 266
- [Open Science Framework (OSF)](https://osf.io/) -- Pre-registration, data sharing, collaboration - Network accessline 267
- Simmons, J. P., Nelson, L. D., & Simonsohn, U. (2011). False-Positive Psychology. Psychological Science, 22(11), 1359-1366. - Cumming, G. (2014). The New Statistics: Why and How. Psychological Science, 25(1), 7-29. - [pingouin](https://pi - Network accessline 268
- Cumming, G. (2014). The New Statistics: Why and How. Psychological Science, 25(1), 7-29. - [pingouin](https://pingouin-stats.org/) -- Python statistical package for psychology - [PsychDS](https://psych-ds.github.io/) -- Data standard for - Network accessline 269
- [pingouin](https://pingouin-stats.org/) -- Python statistical package for psychology - Network accessline 270
- [PsychDS](https://psych-ds.github.io/) -- Data standard for psychology datasets
skills/domains/social-science/sociology-research-methods/SKILL.md
- Network accessline 178
- Geertz, C. (1973). *The Interpretation of Cultures*. Basic Books. - Braun, V., & Clarke, V. (2006). "Using thematic analysis in psychology." *Qualitative Research in Psychology*, 3(2), 77-101. - [ICPSR Data Archive](https://www.icpsr.umic - Network accessline 179
- Braun, V., & Clarke, V. (2006). "Using thematic analysis in psychology." *Qualitative Research in Psychology*, 3(2), 77-101. - [ICPSR Data Archive](https://www.icpsr.umich.edu/) - [GSS Data Explorer](https://gssdataexplorer.norc.org/) - Network accessline 180
- [ICPSR Data Archive](https://www.icpsr.umich.edu/) - Network accessline 181
- [GSS Data Explorer](https://gssdataexplorer.norc.org/)
skills/literature/discovery/literature-mapping-guide/SKILL.md
- Network accessline 117
# Get paper metadata meta_resp = requests.get( - Network accessline 118
meta_resp = requests.get( - Network accessline 119
f"https://api.semanticscholar.org/graph/v1/paper/{paper_id}", - Network accessline 128
# Get references (backward) refs_resp = requests.get( - Network accessline 129
refs_resp = requests.get( - Network accessline 130
f"https://api.semanticscholar.org/graph/v1/paper/{paper_id}/references",
skills/literature/discovery/paper-recommendation-guide/SKILL.md
- Network accessline 30
# Find works related to a specific paper via its concepts and citations curl "https://api.openalex.org/works?filter=cites:W2741809807&sort=cited_by_count:desc&per_page=10" - Network accessline 31
curl "https://api.openalex.org/works?filter=cites:W2741809807&sort=cited_by_count:desc&per_page=10" - Network accessline 110
- OpenAlex API: https://api.openalex.org - Network accessline 111
- Connected Papers: https://www.connectedpapers.com - Network accessline 112
- Research Rabbit: https://www.researchrabbitapp.com - Network accessline 113
- Paper Recommendation: https://github.com/pengzhenghao/paper-recommendation - Network accessline 114
- VOSviewer: https://www.vosviewer.com
skills/literature/discovery/papers-we-love-guide/SKILL.md
- Network accessline 8
subcategory: "discovery" keywords: ["papers we love", "CS papers", "reading groups", "classic papers", "paper recommendations", "curated list"] source: "https://github.com/papers-we-love/papers-we-love" - Network accessline 9
keywords: ["papers we love", "CS papers", "reading groups", "classic papers", "paper recommendations", "curated list"] source: "https://github.com/papers-we-love/papers-we-love" --- - Network accessline 10
source: "https://github.com/papers-we-love/papers-we-love" - Network accessline 57
# Clone the repository git clone https://github.com/papers-we-love/papers-we-love.git - Network accessline 58
git clone https://github.com/papers-we-love/papers-we-love.git - Network accessline 167
- [Papers We Love GitHub](https://github.com/papers-we-love/papers-we-love) - Network accessline 168
- [Papers We Love Website](https://paperswelove.org/) - Network accessline 169
- [PWL YouTube Channel](https://www.youtube.com/@PapersWeLove)
skills/literature/discovery/rss-paper-feeds/SKILL.md
- Network accessline 23
| Publisher | Feed URL Pattern | Example | |-----------|-----------------|---------| | Nature | `https://www.nature.com/[journal].rss` | `nature.com/nature.rss` | - Network accessline 24
|-----------|-----------------|---------| | Nature | `https://www.nature.com/[journal].rss` | `nature.com/nature.rss` | | Science | `https://www.science.org/action/showFeed?type=etoc&feed=rss&jc=[code]` | jc=science | - Network accessline 25
| Nature | `https://www.nature.com/[journal].rss` | `nature.com/nature.rss` | - Network accessline 26
| Science | `https://www.science.org/action/showFeed?type=etoc&feed=rss&jc=[code]` | jc=science | - Network accessline 27
| Elsevier | `https://rss.sciencedirect.com/publication/science/[ISSN]` | ISSN 0004-3702 for AI | - Network accessline 28
| Springer | `https://link.springer.com/search.rss?search-within=Journal&facet-journal-id=[id]` | id=10994 | - Network accessline 29
| IEEE | `https://ieeexplore.ieee.org/rss/TOC[journal_number].XML` | | - Network accessline 30
| arXiv | `https://rss.arxiv.org/rss/[category]` | cs.AI, stat.ML | - Network accessline 48
for category in categories: feed_url = f"https://rss.arxiv.org/rss/{category}" - Network accessline 49
feed_url = f"https://rss.arxiv.org/rss/{category}" - Network accessline 108
work_id: OpenAlex work ID (e.g., 'W2741809807') or DOI """ headers = {"User-Agent": "ResearchPlugins/1.0 (https://wentor.ai)"} - Network accessline 109
""" headers = {"User-Agent": "ResearchPlugins/1.0 (https://wentor.ai)"} response = requests.get( - Network accessline 110
headers = {"User-Agent": "ResearchPlugins/1.0 (https://wentor.ai)"} response = requests.get( f"https://api.openalex.org/works/{work_id}", - Network accessline 111
response = requests.get( - Network accessline 112
f"https://api.openalex.org/works/{work_id}", - Network accessline 117
# Get recent citing works citing_resp = requests.get( - Network accessline 118
citing_resp = requests.get( - Network accessline 119
"https://api.openalex.org/works",
skills/literature/discovery/semantic-paper-radar/SKILL.md
- Network accessline 39
# Search works via the OpenAlex API curl "https://api.openalex.org/works?search=attention+mechanisms+for+graph+neural+networks&per_page=20" - Network accessline 40
curl "https://api.openalex.org/works?search=attention+mechanisms+for+graph+neural+networks&per_page=20" - Network accessline 140
- OpenAlex API: https://api.openalex.org - Network accessline 141
- SPECTER2 model: https://huggingface.co/allenai/specter2 - Network accessline 142
- ChromaDB: https://www.trychroma.com - Network accessline 143
- ResearchGPT: https://github.com/mukulpatnaik/researchgpt - Network accessline 144
- OpenAlex: https://openalex.org
skills/literature/discovery/semantic-scholar-recs-guide/SKILL.md
- Network accessline 27
Base URL: `https://api.openalex.org` - Network accessline 28
CrossRef URL: `https://api.crossref.org` - Network accessline 39
HEADERS = {"User-Agent": "ResearchPlugins/1.0 (https://wentor.ai)"} - Network accessline 42
# Get the seed paper's concepts response = requests.get( - Network accessline 43
response = requests.get( - Network accessline 44
f"https://api.openalex.org/works/{WORK_ID}", - Network accessline 50
# Find works sharing the same concepts, sorted by citations for concept_id in concepts: related = requests.get( - Network accessline 51
for concept_id in concepts: related = requests.get( "https://api.openalex.org/works", - Network accessline 52
related = requests.get( - Network accessline 53
"https://api.openalex.org/works", - Network accessline 66
def search_crossref(query, limit=10, sort="is-referenced-by-count"): """Search CrossRef for papers sorted by citation count.""" resp = requests.get( - Network accessline 67
"""Search CrossRef for papers sorted by citation count.""" resp = requests.get( "https://api.crossref.org/works", - Network accessline 68
resp = requests.get( - Network accessline 69
"https://api.crossref.org/works", - Network accessline 70
params={"query": query, "rows": limit, "sort": sort, "order": "desc"}, headers={"User-Agent": "ResearchPlugins/1.0 (https://wentor.ai; mailto:dev@wentor.ai)"} ) - Network accessline 71
headers={"User-Agent": "ResearchPlugins/1.0 (https://wentor.ai; mailto:dev@wentor.ai)"} - Network accessline 90
response = requests.get( - Network accessline 91
"https://api.openalex.org/works", - Network accessline 107
response = requests.get( - Network accessline 108
f"https://api.openalex.org/works/{work_id}", - Network accessline 114
# Fetch details for referenced works for ref_id in ref_ids[:20]: ref = requests.get(f"https://api.openalex.org/works/{ref_id.split('/')[-1]}", headers=HEADERS).json() - Network accessline 115
for ref_id in ref_ids[:20]: ref = requests.get(f"https://api.openalex.org/works/{ref_id.split('/')[-1]}", headers=HEADERS).json() print(f" [{ref.get('publication_year')}] {ref.get('title')} ({ref.get('cited_by_count')} cites)") - Network accessline 116
ref = requests.get(f"https://api.openalex.org/works/{ref_id.split('/')[-1]}", headers=HEADERS).json() - Network accessline 139
for seed_id in seed_ids: # Get concepts from seed paper paper = requests.get(f"https://api.openalex.org/works/{seed_id}", headers=HEADERS).json() - Network accessline 140
# Get concepts from seed paper paper = requests.get(f"https://api.openalex.org/works/{seed_id}", headers=HEADERS).json() concept_ids = [c["id"] for c in paper.get("concepts", [])[:2]] - Network accessline 141
paper = requests.get(f"https://api.openalex.org/works/{seed_id}", headers=HEADERS).json() - Network accessline 144
# Find related works via concepts for cid in concept_ids: related = requests.get( - Network accessline 145
for cid in concept_ids: related = requests.get( "https://api.openalex.org/works", - Network accessline 146
related = requests.get( - Network accessline 147
"https://api.openalex.org/works", - Network accessline 157
# Get citing works citing = requests.get( - Network accessline 158
citing = requests.get( - Network accessline 159
"https://api.openalex.org/works",
skills/literature/discovery/zotero-arxiv-daily-guide/SKILL.md
- Network accessline 91
- GitHub Repository: wentor-research-plugins - arXiv API Documentation: https://info.arxiv.org/help/api - Network accessline 92
- arXiv API Documentation: https://info.arxiv.org/help/api - Network accessline 93
- Zotero Plugin Directory: https://www.zotero.org/support/plugins - Network accessline 94
- arXiv Category Taxonomy: https://arxiv.org/category_taxonomy
skills/literature/fulltext/arxiv-latex-source/SKILL.md
- Network accessline 8
subcategory: "fulltext" keywords: ["arXiv", "LaTeX source", "paper parsing", "formula extraction", "full text", "preprint"] source: "https://info.arxiv.org/help/bulk_data_s3.html" - Network accessline 9
keywords: ["arXiv", "LaTeX source", "paper parsing", "formula extraction", "full text", "preprint"] source: "https://info.arxiv.org/help/bulk_data_s3.html" --- - Network accessline 10
source: "https://info.arxiv.org/help/bulk_data_s3.html" - Network accessline 31
- **URL**: `GET https://arxiv.org/e-print/{arxiv_id}` - Network accessline 40
# Download source archive (response: 200, application/gzip, ~1.3 MB) curl -sL -o source.tar.gz "https://arxiv.org/e-print/2301.00001" - Network accessline 41
curl -sL -o source.tar.gz "https://arxiv.org/e-print/2301.00001" - Network accessline 60
curl -sL "https://arxiv.org/e-print/{arxiv_id}" -o source.gz - Network accessline 68
- **URL**: `GET https://export.arxiv.org/api/query?id_list={arxiv_id}` - **Response**: Atom XML with `<title>`, `<author>`, `<summary>`, `<category>`, `<published>` - **Example**: `curl -s "https://export.arxiv.org/api/query?id_list=2301.00 - Network accessline 70
- **Example**: `curl -s "https://export.arxiv.org/api/query?id_list=2301.00001"` - Network accessline 164
def download_arxiv_source(arxiv_id, delay=1.0): """Download and extract all .tex files from an arXiv paper's source.""" url = f"https://arxiv.org/e-print/{arxiv_id}" - Network accessline 165
"""Download and extract all .tex files from an arXiv paper's source.""" url = f"https://arxiv.org/e-print/{arxiv_id}" headers = {"User-Agent": "ResearchTool/1.0 (mailto:user@example.com)"} - Network accessline 166
url = f"https://arxiv.org/e-print/{arxiv_id}" headers = {"User-Agent": "ResearchTool/1.0 (mailto:user@example.com)"} resp = requests.get(url, headers=headers) - Network accessline 167
headers = {"User-Agent": "ResearchTool/1.0 (mailto:user@example.com)"} resp = requests.get(url, headers=headers) resp.raise_for_status() - Network accessline 168
resp = requests.get(url, headers=headers) - Network accessline 192
- arXiv e-print access: https://info.arxiv.org/help/bulk_data_s3.html - Network accessline 193
- arXiv API documentation: https://info.arxiv.org/help/api/index.html - Network accessline 194
- arXiv terms of use: https://info.arxiv.org/help/api/tou.html - Network accessline 195
- arXiv S3 bulk data: https://info.arxiv.org/help/bulk_data_s3.html
skills/literature/fulltext/bioc-pmc-api/SKILL.md
- Network accessline 8
subcategory: "fulltext" keywords: ["bioc", "pmc", "text mining", "biomedical nlp", "full text", "pubmed central"] source: "https://www.ncbi.nlm.nih.gov/research/bionlp/APIs/BioC-PMC/" - Network accessline 9
keywords: ["bioc", "pmc", "text mining", "biomedical nlp", "full text", "pubmed central"] source: "https://www.ncbi.nlm.nih.gov/research/bionlp/APIs/BioC-PMC/" --- - Network accessline 10
source: "https://www.ncbi.nlm.nih.gov/research/bionlp/APIs/BioC-PMC/" - Network accessline 24
https://www.ncbi.nlm.nih.gov/research/bionlp/RESTful/pmcoa.cgi/BioC_json/{PMCID}/unicode - Network accessline 30
# JSON format (recommended for programmatic use) curl "https://www.ncbi.nlm.nih.gov/research/bionlp/RESTful/pmcoa.cgi/BioC_json/PMC6267067/unicode" - Network accessline 31
curl "https://www.ncbi.nlm.nih.gov/research/bionlp/RESTful/pmcoa.cgi/BioC_json/PMC6267067/unicode" - Network accessline 33
# XML format curl "https://www.ncbi.nlm.nih.gov/research/bionlp/RESTful/pmcoa.cgi/BioC_xml/PMC6267067/unicode" - Network accessline 34
curl "https://www.ncbi.nlm.nih.gov/research/bionlp/RESTful/pmcoa.cgi/BioC_xml/PMC6267067/unicode" - Network accessline 36
# ASCII encoding (strips non-ASCII characters) curl "https://www.ncbi.nlm.nih.gov/research/bionlp/RESTful/pmcoa.cgi/BioC_json/PMC6267067/ascii" - Network accessline 37
curl "https://www.ncbi.nlm.nih.gov/research/bionlp/RESTful/pmcoa.cgi/BioC_json/PMC6267067/ascii" - Network accessline 43
# Convert PMID to PMCID first, then query curl "https://www.ncbi.nlm.nih.gov/pmc/utils/idconv/v1.0/?ids=29346600&format=json" - Network accessline 44
curl "https://www.ncbi.nlm.nih.gov/pmc/utils/idconv/v1.0/?ids=29346600&format=json" - Network accessline 100
def get_bioc_article(pmcid: str, fmt: str = "json") -> dict: """Fetch a PMC article in BioC format.""" url = f"https://www.ncbi.nlm.nih.gov/research/bionlp/RESTful/pmcoa.cgi/BioC_{fmt}/{pmcid}/unicode" - Network accessline 101
"""Fetch a PMC article in BioC format.""" url = f"https://www.ncbi.nlm.nih.gov/research/bionlp/RESTful/pmcoa.cgi/BioC_{fmt}/{pmcid}/unicode" resp = requests.get(url, timeout=30) - Network accessline 102
url = f"https://www.ncbi.nlm.nih.gov/research/bionlp/RESTful/pmcoa.cgi/BioC_{fmt}/{pmcid}/unicode" resp = requests.get(url, timeout=30) resp.raise_for_status() - Network accessline 103
resp = requests.get(url, timeout=30) - Network accessline 144
- [BioC-PMC API Documentation](https://www.ncbi.nlm.nih.gov/research/bionlp/APIs/BioC-PMC/) - Network accessline 145
- [BioC Format Specification](http://bioc.sourceforge.net/) - Network accessline 146
- [PMC Open Access Subset](https://www.ncbi.nlm.nih.gov/pmc/tools/openftlist/)
skills/literature/fulltext/core-api-guide/SKILL.md
- Network accessline 8
subcategory: "fulltext" keywords: ["open-access", "fulltext", "research-papers", "aggregator", "CORE"] source: "https://core.ac.uk/documentation/api" - Network accessline 9
keywords: ["open-access", "fulltext", "research-papers", "aggregator", "CORE"] source: "https://core.ac.uk/documentation/api" --- - Network accessline 10
source: "https://core.ac.uk/documentation/api" - Network accessline 25
CORE requires a free API key for all requests. Register at https://core.ac.uk/services/api to obtain one. - Access to secretsline 30
[Credential-related source omitted] - Network accessline 36
[Credential-related source omitted] - Access to secretsline 36
[Credential-related source omitted] - Network accessline 37
"https://api.core.ac.uk/v3/search/works?q=machine+learning" - Network accessline 47
GET https://api.core.ac.uk/v3/search/works?q={query}&limit={n}&offset={n} - Dangerous shell operationline 59
[Credential-related source omitted] - Network accessline 59
[Credential-related source omitted] - Access to secretsline 59
[Credential-related source omitted] - Network accessline 60
"https://api.core.ac.uk/v3/search/works?q=climate+change+adaptation&limit=5" \ - Access to secretsline 70
[Credential-related source omitted] - Network accessline 74
"offset": 0 } resp = requests.get("https://api.core.ac.uk/v3/search/works", headers=headers, params=params) - Network accessline 75
} resp = requests.get("https://api.core.ac.uk/v3/search/works", headers=headers, params=params) data = resp.json() - Network accessline 76
resp = requests.get("https://api.core.ac.uk/v3/search/works", headers=headers, params=params) - Network accessline 92
GET https://api.core.ac.uk/v3/works/{core_id} - Dangerous shell operationline 96
[Credential-related source omitted] - Network accessline 96
[Credential-related source omitted] - Access to secretsline 96
[Credential-related source omitted] - Network accessline 97
"https://api.core.ac.uk/v3/works/doi:10.1234/example.doi" \ - Network accessline 106
[Credential-related source omitted] - Access to secretsline 106
[Credential-related source omitted] - Network accessline 107
-H "Content-Type: application/json" \ -d '[12345, 67890, 11111]' \ "https://api.core.ac.uk/v3/works" - Network accessline 109
"https://api.core.ac.uk/v3/works" - Network accessline 117
GET https://api.core.ac.uk/v3/data-providers?q={query} - Network accessline 141
- CORE API v3 Documentation: https://core.ac.uk/documentation/api - Network accessline 142
- CORE Dashboard and Key Registration: https://core.ac.uk/services/api - Network accessline 143
- CORE Data Dumps (for bulk access): https://core.ac.uk/documentation/dataset - Network accessline 144
- CORE GitHub: https://github.com/oacore
skills/literature/fulltext/dataverse-api/SKILL.md
- Network accessline 8
subcategory: "fulltext" keywords: ["Dataverse", "research data", "data repository", "Harvard", "dataset deposit", "data sharing"] source: "https://dataverse.org/" - Network accessline 9
keywords: ["Dataverse", "research data", "data repository", "Harvard", "dataset deposit", "data sharing"] source: "https://dataverse.org/" --- - Network accessline 10
source: "https://dataverse.org/" - Network accessline 24
https://dataverse.harvard.edu/api - Network accessline 30
# Search datasets curl "https://dataverse.harvard.edu/api/search?q=climate+change&type=dataset&per_page=20" - Network accessline 31
curl "https://dataverse.harvard.edu/api/search?q=climate+change&type=dataset&per_page=20" - Network accessline 33
# Search files within datasets curl "https://dataverse.harvard.edu/api/search?q=temperature+data&type=file&per_page=20" - Network accessline 34
curl "https://dataverse.harvard.edu/api/search?q=temperature+data&type=file&per_page=20" - Network accessline 36
# Filter by subject curl "https://dataverse.harvard.edu/api/search?q=survey+data&type=dataset&\ - Network accessline 37
curl "https://dataverse.harvard.edu/api/search?q=survey+data&type=dataset&\ - Network accessline 40
# Filter by publication date curl "https://dataverse.harvard.edu/api/search?q=genomics&type=dataset&\ - Network accessline 41
curl "https://dataverse.harvard.edu/api/search?q=genomics&type=dataset&\ - Network accessline 44
# Sort by relevance or date curl "https://dataverse.harvard.edu/api/search?q=machine+learning&type=dataset&\ - Network accessline 45
curl "https://dataverse.harvard.edu/api/search?q=machine+learning&type=dataset&\ - Network accessline 52
# By persistent ID (DOI) curl "https://dataverse.harvard.edu/api/datasets/:persistentId/?persistentId=doi:10.7910/DVN/EXAMPLE" - Network accessline 53
curl "https://dataverse.harvard.edu/api/datasets/:persistentId/?persistentId=doi:10.7910/DVN/EXAMPLE" - Network accessline 55
# By dataset ID curl "https://dataverse.harvard.edu/api/datasets/12345" - Network accessline 56
curl "https://dataverse.harvard.edu/api/datasets/12345" - Network accessline 58
# Get dataset versions curl "https://dataverse.harvard.edu/api/datasets/:persistentId/versions?persistentId=doi:10.7910/DVN/EXAMPLE" - Network accessline 59
curl "https://dataverse.harvard.edu/api/datasets/:persistentId/versions?persistentId=doi:10.7910/DVN/EXAMPLE" - Network accessline 65
# Download a specific file by ID curl -O "https://dataverse.harvard.edu/api/access/datafile/67890" - Network accessline 66
curl -O "https://dataverse.harvard.edu/api/access/datafile/67890" - Network accessline 68
# Download with original format curl -O "https://dataverse.harvard.edu/api/access/datafile/67890?format=original" - Network accessline 69
curl -O "https://dataverse.harvard.edu/api/access/datafile/67890?format=original" - Network accessline 71
# Download all files in a dataset (as zip) curl -O "https://dataverse.harvard.edu/api/access/dataset/:persistentId/?persistentId=doi:10.7910/DVN/EXAMPLE" - Network accessline 72
curl -O "https://dataverse.harvard.edu/api/access/dataset/:persistentId/?persistentId=doi:10.7910/DVN/EXAMPLE" - Network accessline 97
"name": "Global Temperature Dataset 2024", "type": "dataset", "url": "https://doi.org/10.7910/DVN/EXAMPLE", - Network accessline 98
"type": "dataset", "url": "https://doi.org/10.7910/DVN/EXAMPLE", "global_id": "doi:10.7910/DVN/EXAMPLE", - Network accessline 99
"url": "https://doi.org/10.7910/DVN/EXAMPLE", - Network accessline 118
BASE_URL = "https://dataverse.harvard.edu/api" - Network accessline 134
resp = requests.get(f"{BASE_URL}/search", params=params) - Network accessline 152
def get_dataset_files(doi: str) -> list: """List files in a dataset.""" resp = requests.get( - Network accessline 153
"""List files in a dataset.""" resp = requests.get( f"{BASE_URL}/datasets/:persistentId/", - Network accessline 154
resp = requests.get( - Network accessline 175
def download_file(file_id: int, output_path: str): """Download a file from Dataverse.""" resp = requests.get( - Network accessline 176
"""Download a file from Dataverse.""" resp = requests.get( f"{BASE_URL}/access/datafile/{file_id}", - Network accessline 177
resp = requests.get( - Network accessline 212
- [Harvard Dataverse](https://dataverse.harvard.edu/) - Network accessline 213
- [Dataverse API Guide](https://guides.dataverse.org/en/latest/api/) - Network accessline 214
- [Dataverse Project](https://dataverse.org/)
skills/literature/fulltext/doaj-api/SKILL.md
- Network accessline 8
subcategory: "fulltext" keywords: ["full-text retrieval", "open access", "open access rights", "scholarly database"] source: "https://doaj.org/api/docs" - Network accessline 9
keywords: ["full-text retrieval", "open access", "open access rights", "scholarly database"] source: "https://doaj.org/api/docs" --- - Network accessline 10
source: "https://doaj.org/api/docs" - Network accessline 25
No authentication required. The DOAJ API is fully open and free to use. No API key, registration, or email is needed. There are no published rate limits, but users should be respectful and avoid sending excessive concurrent requests. For bu - Network accessline 31
- **URL**: `GET https://doaj.org/api/search/articles/{search_query}` - Network accessline 41
curl "https://doaj.org/api/search/articles/climate+change?page=1&pageSize=10" - Network accessline 47
- **URL**: `GET https://doaj.org/api/search/journals/{search_query}` - Network accessline 56
curl "https://doaj.org/api/search/journals/bioinformatics?page=1&pageSize=5" - Network accessline 62
- **URL**: `GET https://doaj.org/api/search/articles/doi:{doi}` - Network accessline 69
curl "https://doaj.org/api/search/articles/doi:10.1371/journal.pone.0213676" - Network accessline 75
- **URL**: `GET https://doaj.org/api/search/journals/issn:{issn}` - Network accessline 82
curl "https://doaj.org/api/search/journals/issn:1932-6203" - Network accessline 97
curl -s "https://doaj.org/api/search/journals/issn:2045-2322" | jq '{total: .total, title: .results[0].bibjson.title, publisher: .results[0].bibjson.publisher.name, license: .results[0].bibjson.license[0].type}' - Network accessline 105
curl -s "https://doaj.org/api/search/articles/bibjson.subject.term:neuroscience?pageSize=20" | jq '.results[] | {title: .bibjson.title, journal: .bibjson.journal.title, url: .bibjson.link[0].url, year: .bibjson.year}' - Network accessline 113
curl -s "https://doaj.org/api/search/journals/issn:2041-1723" | jq '.results[0].bibjson | {title: .title, has_apc: .apc.has_apc, apc_amount: .apc.max[0].price, currency: .apc.max[0].currency}' - Network accessline 118
- Official documentation: https://doaj.org/api/docs - Network accessline 119
- DOAJ public data dump: https://doaj.org/docs/public-data-dump/ - Network accessline 120
- DOAJ journal application criteria: https://doaj.org/apply/guide/
skills/literature/fulltext/hal-archive-api/SKILL.md
- Network accessline 8
subcategory: "fulltext" keywords: ["HAL", "French research", "open archive", "CNRS", "European research", "institutional repository"] source: "https://api.archives-ouvertes.fr/" - Network accessline 9
keywords: ["HAL", "French research", "open archive", "CNRS", "European research", "institutional repository"] source: "https://api.archives-ouvertes.fr/" --- - Network accessline 10
source: "https://api.archives-ouvertes.fr/" - Network accessline 24
# Keyword search curl "https://api.archives-ouvertes.fr/search/?q=machine+learning&rows=20&wt=json" - Network accessline 25
curl "https://api.archives-ouvertes.fr/search/?q=machine+learning&rows=20&wt=json" - Network accessline 27
# Search specific fields curl "https://api.archives-ouvertes.fr/search/?q=title_s:\"deep learning\"&wt=json" - Network accessline 28
curl "https://api.archives-ouvertes.fr/search/?q=title_s:\"deep learning\"&wt=json" - Network accessline 30
# Filter by document type curl "https://api.archives-ouvertes.fr/search/?q=neural+networks&\ - Network accessline 31
curl "https://api.archives-ouvertes.fr/search/?q=neural+networks&\ - Network accessline 34
# Filter by year and language curl "https://api.archives-ouvertes.fr/search/?q=climate+change&\ - Network accessline 35
curl "https://api.archives-ouvertes.fr/search/?q=climate+change&\ - Network accessline 38
# Filter by institution curl "https://api.archives-ouvertes.fr/search/?q=robotics&\ - Network accessline 39
curl "https://api.archives-ouvertes.fr/search/?q=robotics&\ - Network accessline 42
# Return specific fields curl "https://api.archives-ouvertes.fr/search/?q=CRISPR&\ - Network accessline 43
curl "https://api.archives-ouvertes.fr/search/?q=CRISPR&\ - Network accessline 101
"docType_s": "ART", "journalTitle_s": "Environmental Modelling", "uri_s": "https://hal.science/hal-01234567", - Network accessline 102
"journalTitle_s": "Environmental Modelling", "uri_s": "https://hal.science/hal-01234567", "files_s": ["https://hal.science/hal-01234567/document"], - Network accessline 103
"uri_s": "https://hal.science/hal-01234567", - Network accessline 104
"files_s": ["https://hal.science/hal-01234567/document"], - Network accessline 119
BASE_URL = "https://api.archives-ouvertes.fr/search/" - Network accessline 145
resp = requests.get(BASE_URL, params=params) - Network accessline 182
"sort": "producedDateY_i desc", } resp = requests.get(BASE_URL, params=params) - Network accessline 183
} resp = requests.get(BASE_URL, params=params) resp.raise_for_status() - Network accessline 184
resp = requests.get(BASE_URL, params=params) - Network accessline 216
- [HAL Open Archive](https://hal.science/) - Network accessline 217
- [HAL API Documentation](https://api.archives-ouvertes.fr/docs) - Network accessline 218
- [HAL Search Guide](https://doc.archives-ouvertes.fr/en/)
skills/literature/fulltext/institutional-repository-guide/SKILL.md
- Network accessline 54
country: ISO country code (e.g., "US", "GB", "CN") """ base_url = "https://v2.sherpa.ac.uk/cgi/retrieve" - Network accessline 55
""" base_url = "https://v2.sherpa.ac.uk/cgi/retrieve" params = "?item-type=repository&format=Json" - Network accessline 56
base_url = "https://v2.sherpa.ac.uk/cgi/retrieve" - Network accessline 63
req = urllib.request.Request(base_url + params) - Network accessline 64
response = urllib.request.urlopen(req) - Network accessline 109
while url: response = urllib.request.urlopen(url) - Network accessline 110
response = urllib.request.urlopen(url) - Network accessline 111
tree = ET.parse(response) root = tree.getroot() ns = {"oai": "http://www.openarchives.org/OAI/2.0/"} - Network accessline 113
ns = {"oai": "http://www.openarchives.org/OAI/2.0/"} - Network accessline 208
- OpenDOAR: https://v2.sherpa.ac.uk/opendoar/ - Network accessline 209
- OAI-PMH specification: http://www.openarchives.org/OAI/openarchivesprotocol.html - Network accessline 210
- CORE: https://core.ac.uk - Network accessline 211
- BASE: https://www.base-search.net - Network accessline 212
- DSpace documentation: https://wiki.lyrasis.org/display/DSPACE
skills/literature/fulltext/open-access-guide/SKILL.md
- Network accessline 42
""" email = "researcher@university.edu" # Required by Unpaywall API url = f"https://api.unpaywall.org/v2/{doi}?email={email}" - Network accessline 44
url = f"https://api.unpaywall.org/v2/{doi}?email={email}" - Network accessline 46
response = requests.get(url) - Access to secretsline 127
[Credential-related source omitted] - Network accessline 133
[Credential-related source omitted] - Access to secretsline 133
[Credential-related source omitted] - Network accessline 135
[Credential-related source omitted] - Access to secretsline 135
[Credential-related source omitted] - Network accessline 137
response = requests.get(url)
skills/literature/fulltext/open-access-mining-guide/SKILL.md
- Network accessline 120
Parsed article as structured dictionary """ base_url = "https://eutils.ncbi.nlm.nih.gov/entrez/eutils/efetch.fcgi" - Network accessline 121
""" base_url = "https://eutils.ncbi.nlm.nih.gov/entrez/eutils/efetch.fcgi" params = { - Network accessline 122
base_url = "https://eutils.ncbi.nlm.nih.gov/entrez/eutils/efetch.fcgi" - Network accessline 129
response = requests.get(base_url, params=params, timeout=30)
skills/literature/fulltext/osf-api/SKILL.md
- Network accessline 8
subcategory: "fulltext" keywords: ["OSF", "Open Science Framework", "preprints", "open data", "reproducibility", "preregistration"] source: "https://osf.io/" - Network accessline 9
keywords: ["OSF", "Open Science Framework", "preprints", "open data", "reproducibility", "preregistration"] source: "https://osf.io/" --- - Network accessline 10
source: "https://osf.io/" - Network accessline 21
Public read access requires no authentication. For creating or modifying resources, generate a personal access token at https://osf.io/settings/tokens. - Network accessline 24
# Public access (no auth needed) curl "https://api.osf.io/v2/nodes/?filter[title]=reproducibility" - Network accessline 25
curl "https://api.osf.io/v2/nodes/?filter[title]=reproducibility" - Network accessline 27
[Credential-related source omitted] - Network accessline 28
[Credential-related source omitted] - Access to secretsline 28
[Credential-related source omitted] - Network accessline 29
[Credential-related source omitted] - Access to secretsline 29
[Credential-related source omitted] - Network accessline 30
"https://api.osf.io/v2/users/me/" - Network accessline 38
https://api.osf.io/v2 - Network accessline 44
# Search across all OSF content curl "https://api.osf.io/v2/search/?q=replication+crisis&page[size]=20" - Network accessline 45
curl "https://api.osf.io/v2/search/?q=replication+crisis&page[size]=20" - Network accessline 47
# Search preprints curl "https://api.osf.io/v2/preprints/?filter[q]=machine+learning&page[size]=20" - Network accessline 48
curl "https://api.osf.io/v2/preprints/?filter[q]=machine+learning&page[size]=20" - Network accessline 50
# Filter by preprint provider curl "https://api.osf.io/v2/preprints/?filter[provider]=psyarxiv&filter[q]=cognitive+bias" - Network accessline 51
curl "https://api.osf.io/v2/preprints/?filter[provider]=psyarxiv&filter[q]=cognitive+bias" - Network accessline 53
# Search registrations (pre-registered studies) curl "https://api.osf.io/v2/registrations/?filter[q]=randomized+controlled+trial" - Network accessline 54
curl "https://api.osf.io/v2/registrations/?filter[q]=randomized+controlled+trial" - Network accessline 60
# Get public projects curl "https://api.osf.io/v2/nodes/?filter[public]=true&filter[q]=neuroimaging" - Network accessline 61
curl "https://api.osf.io/v2/nodes/?filter[public]=true&filter[q]=neuroimaging" - Network accessline 63
# Get project details curl "https://api.osf.io/v2/nodes/{node_id}/" - Network accessline 64
curl "https://api.osf.io/v2/nodes/{node_id}/" - Network accessline 66
# Get project files curl "https://api.osf.io/v2/nodes/{node_id}/files/" - Network accessline 67
curl "https://api.osf.io/v2/nodes/{node_id}/files/" - Network accessline 69
# Get project contributors curl "https://api.osf.io/v2/nodes/{node_id}/contributors/" - Network accessline 70
curl "https://api.osf.io/v2/nodes/{node_id}/contributors/" - Network accessline 129
BASE_URL = "https://api.osf.io/v2" - Network accessline 142
resp = requests.get(f"{BASE_URL}/preprints/", params=params) - Network accessline 154
"date": attrs.get("date_published", "")[:10], "tags": attrs.get("tags", []), "url": f"https://osf.io/{item.get('id')}/", - Network accessline 155
"tags": attrs.get("tags", []), "url": f"https://osf.io/{item.get('id')}/", }) - Network accessline 156
"url": f"https://osf.io/{item.get('id')}/", - Network accessline 166
"page[size]": page_size, } resp = requests.get(f"{BASE_URL}/registrations/", params=params) - Network accessline 167
} resp = requests.get(f"{BASE_URL}/registrations/", params=params) resp.raise_for_status() - Network accessline 168
resp = requests.get(f"{BASE_URL}/registrations/", params=params) - Network accessline 185
def get_project_files(node_id: str) -> list: """List files in an OSF project.""" resp = requests.get(f"{BASE_URL}/nodes/{node_id}/files/") - Network accessline 186
"""List files in an OSF project.""" resp = requests.get(f"{BASE_URL}/nodes/{node_id}/files/") resp.raise_for_status() - Network accessline 187
resp = requests.get(f"{BASE_URL}/nodes/{node_id}/files/") - Network accessline 232
- [OSF](https://osf.io/) - Network accessline 233
- [OSF API Documentation](https://developer.osf.io/) - Network accessline 234
- [OSF Preprints](https://osf.io/preprints/) - Network accessline 235
- [OSF Registrations (Preregistration)](https://osf.io/registries/) - Network accessline 236
- [Center for Open Science](https://cos.io/)
skills/literature/fulltext/pmc-ftp-bulk-download/SKILL.md
- Network accessline 8
subcategory: "fulltext" keywords: ["pmc", "bulk download", "ftp", "text mining", "open access", "pubmed central"] source: "https://www.ncbi.nlm.nih.gov/pmc/tools/ftp/" - Network accessline 9
keywords: ["pmc", "bulk download", "ftp", "text mining", "open access", "pubmed central"] source: "https://www.ncbi.nlm.nih.gov/pmc/tools/ftp/" --- - Network accessline 10
source: "https://www.ncbi.nlm.nih.gov/pmc/tools/ftp/" - Network accessline 30
# HTTPS alternative (recommended) # Base: https://ftp.ncbi.nlm.nih.gov/pub/pmc/ - Network accessline 31
# Base: https://ftp.ncbi.nlm.nih.gov/pub/pmc/ - Network accessline 37
|---------|------|---------|--------| | **OA Commercial** | `/pub/pmc/oa_comm/` | CC BY/CC0 articles (commercial use OK) | .tar.gz packages | | **OA Non-Commercial** | `/pub/pmc/oa_noncomm/` | CC BY-NC articles | .tar.gz packages | - Network accessline 38
| **OA Commercial** | `/pub/pmc/oa_comm/` | CC BY/CC0 articles (commercial use OK) | .tar.gz packages | | **OA Non-Commercial** | `/pub/pmc/oa_noncomm/` | CC BY-NC articles | .tar.gz packages | | **OA Other** | `/pub/pmc/oa_other/` | Other - Network accessline 39
| **OA Non-Commercial** | `/pub/pmc/oa_noncomm/` | CC BY-NC articles | .tar.gz packages | - Network accessline 50
# Download the OA file list (CSV, ~200MB) wget https://ftp.ncbi.nlm.nih.gov/pub/pmc/oa_file_list.csv - Network accessline 51
wget https://ftp.ncbi.nlm.nih.gov/pub/pmc/oa_file_list.csv - Network accessline 67
def download_article_package(pmcid: str, base_url: str = "https://ftp.ncbi.nlm.nih.gov/pub/pmc"): """Download and extract a specific PMC article package.""" - Network accessline 74
# Download the tar.gz package resp = requests.get(f"{base_url}/{file_path}", stream=True) - Network accessline 75
resp = requests.get(f"{base_url}/{file_path}", stream=True) - Network accessline 94
# Download the baseline (all current articles) wget -r -np -nH --cut-dirs=3 \ - Network accessline 95
wget -r -np -nH --cut-dirs=3 \ - Network accessline 96
https://ftp.ncbi.nlm.nih.gov/pub/pmc/oa_comm/xml/ - Network accessline 98
# Incremental updates (run periodically) wget -r -np -nH --cut-dirs=3 -N \ - Network accessline 99
wget -r -np -nH --cut-dirs=3 -N \ - Network accessline 100
https://ftp.ncbi.nlm.nih.gov/pub/pmc/oa_comm/xml/ - Network accessline 114
output = Path(output_dir) output.mkdir(parents=True, exist_ok=True) base = "https://ftp.ncbi.nlm.nih.gov/pub/pmc" - Network accessline 115
output.mkdir(parents=True, exist_ok=True) base = "https://ftp.ncbi.nlm.nih.gov/pub/pmc" downloaded = 0 - Network accessline 116
base = "https://ftp.ncbi.nlm.nih.gov/pub/pmc" - Network accessline 134
resp = requests.get(url, stream=True, timeout=60) - Network accessline 149
# PMID → PMCID → DOI conversion curl "https://www.ncbi.nlm.nih.gov/pmc/utils/idconv/v1.0/?ids=29346600&format=json" - Network accessline 150
curl "https://www.ncbi.nlm.nih.gov/pmc/utils/idconv/v1.0/?ids=29346600&format=json" - Network accessline 152
# Batch conversion (up to 200 IDs) curl "https://www.ncbi.nlm.nih.gov/pmc/utils/idconv/v1.0/?ids=29346600,30266829,31048553&format=json" - Network accessline 153
curl "https://www.ncbi.nlm.nih.gov/pmc/utils/idconv/v1.0/?ids=29346600,30266829,31048553&format=json" - Network accessline 180
- [PMC FTP Documentation](https://www.ncbi.nlm.nih.gov/pmc/tools/ftp/) - Network accessline 181
- [PMC Open Access Subset](https://www.ncbi.nlm.nih.gov/pmc/tools/openftlist/) - Network accessline 182
- [PMC ID Converter API](https://www.ncbi.nlm.nih.gov/pmc/tools/id-converter-api/)
skills/literature/fulltext/pmc-oai-api/SKILL.md
- Network accessline 8
subcategory: "fulltext" keywords: ["full-text retrieval", "open access", "PDF download", "preprint server"] source: "https://pmc.ncbi.nlm.nih.gov/tools/oai/" - Network accessline 9
keywords: ["full-text retrieval", "open access", "PDF download", "preprint server"] source: "https://pmc.ncbi.nlm.nih.gov/tools/oai/" --- - Network accessline 10
source: "https://pmc.ncbi.nlm.nih.gov/tools/oai/" - Network accessline 33
- **URL**: `GET https://pmc.ncbi.nlm.nih.gov/api/oai/v1/mh/` - Network accessline 48
# Harvest recent open-access records in Dublin Core format curl "https://pmc.ncbi.nlm.nih.gov/api/oai/v1/mh/?verb=ListRecords&metadataPrefix=oai_dc&from=2024-06-01&until=2024-06-07" - Network accessline 49
curl "https://pmc.ncbi.nlm.nih.gov/api/oai/v1/mh/?verb=ListRecords&metadataPrefix=oai_dc&from=2024-06-01&until=2024-06-07" - Network accessline 51
# Harvest PMC full metadata format curl "https://pmc.ncbi.nlm.nih.gov/api/oai/v1/mh/?verb=ListRecords&metadataPrefix=pmc_fm&from=2024-06-01&until=2024-06-02" - Network accessline 52
curl "https://pmc.ncbi.nlm.nih.gov/api/oai/v1/mh/?verb=ListRecords&metadataPrefix=pmc_fm&from=2024-06-01&until=2024-06-02" - Network accessline 61
- **URL**: `GET https://pmc.ncbi.nlm.nih.gov/api/oai/v1/mh/` - Network accessline 73
curl "https://pmc.ncbi.nlm.nih.gov/api/oai/v1/mh/?verb=GetRecord&identifier=oai:pubmedcentral.nih.gov:7096803&metadataPrefix=oai_dc" - Network accessline 82
- **URL**: `GET https://pmc.ncbi.nlm.nih.gov/api/oai/v1/mh/` - Network accessline 92
curl "https://pmc.ncbi.nlm.nih.gov/api/oai/v1/mh/?verb=ListSets" - Network accessline 114
base_url = "https://pmc.ncbi.nlm.nih.gov/api/oai/v1/mh/" - Network accessline 122
all_records = [] while True: resp = requests.get(base_url, params=params) - Network accessline 123
while True: resp = requests.get(base_url, params=params) root = ET.fromstring(resp.text) - Network accessline 124
resp = requests.get(base_url, params=params) - Network accessline 126
ns = {"oai": "http://www.openarchives.org/OAI/2.0/"} - Network accessline 149
url = "https://pmc.ncbi.nlm.nih.gov/api/oai/v1/mh/" - Network accessline 156
resp = requests.get(url, params=params) - Network accessline 159
dc_ns = "http://purl.org/dc/elements/1.1/" - Network accessline 160
oai_ns = "http://www.openarchives.org/OAI/2.0/" - Network accessline 175
curl "https://pmc.ncbi.nlm.nih.gov/api/oai/v1/mh/?verb=ListSets" | head -100 - Network accessline 180
- Official PMC OAI documentation: https://pmc.ncbi.nlm.nih.gov/tools/oai/ - Network accessline 181
- PMC homepage: https://pmc.ncbi.nlm.nih.gov/ - Network accessline 182
- OAI-PMH protocol specification: https://www.openarchives.org/OAI/openarchivesprotocol.html - Network accessline 183
- NCBI usage guidelines: https://www.ncbi.nlm.nih.gov/home/about/policies/ - Network accessline 184
- PMC Open Access subset: https://pmc.ncbi.nlm.nih.gov/tools/openftlist/
skills/literature/fulltext/preprint-servers-guide/SKILL.md
- Network accessline 60
# Download arXiv paper PDF programmatically curl -o paper.pdf https://arxiv.org/pdf/2401.12345 - Network accessline 61
curl -o paper.pdf https://arxiv.org/pdf/2401.12345 - Network accessline 63
# Get metadata via arXiv API curl "http://export.arxiv.org/api/query?id_list=2401.12345" - Network accessline 64
curl "http://export.arxiv.org/api/query?id_list=2401.12345" - Network accessline 80
response = requests.get( - Network accessline 81
"https://api.biorxiv.org/details/biorxiv/2024-01-01/2024-01-31", - Network accessline 92
|---------|-------------|-------------------|--------------------|---------------------| | CC-BY | Yes | Yes | Yes | Yes | | CC-BY-NC | Yes | No | Yes | Yes | - Network accessline 93
| CC-BY | Yes | Yes | Yes | Yes | | CC-BY-NC | Yes | No | Yes | Yes | | CC-BY-ND | Yes | Yes | No | Yes | - Network accessline 94
| CC-BY-NC | Yes | No | Yes | Yes | - Network accessline 99
**Recommendation**: Use CC-BY for maximum openness and compatibility with funder mandates (NIH, Wellcome Trust, ERC). Use CC-BY-NC if you want to restrict commercial reuse.
skills/literature/fulltext/unpaywall-api/SKILL.md
- Network accessline 8
subcategory: "fulltext" keywords: ["full-text retrieval", "open access", "journal copyright policy", "self-archiving"] source: "https://unpaywall.org/products/api" - Network accessline 9
keywords: ["full-text retrieval", "open access", "journal copyright policy", "self-archiving"] source: "https://unpaywall.org/products/api" requires: - Network accessline 10
source: "https://unpaywall.org/products/api" - Network accessline 39
- **URL**: `GET https://api.unpaywall.org/v2/{doi}` - Network accessline 47
curl "https://api.unpaywall.org/v2/10.1038/nature12373?email=user@example.com" - Network accessline 58
- **URL**: `GET https://api.unpaywall.org/v2/{doi}` (repeated per DOI) - Network accessline 66
# Process multiple DOIs sequentially for doi in "10.1038/nature12373" "10.1126/science.aaa8685" "10.1016/j.cell.2015.05.002"; do curl -s "https://api.unpaywall.org/v2/$doi?email=user@example.com" | jq '{doi: .doi, is_oa: .is_oa, oa_st - Network accessline 67
for doi in "10.1038/nature12373" "10.1126/science.aaa8685" "10.1016/j.cell.2015.05.002"; do curl -s "https://api.unpaywall.org/v2/$doi?email=user@example.com" | jq '{doi: .doi, is_oa: .is_oa, oa_status: .oa_status, best_url: .best_oa_lo - Network accessline 68
curl -s "https://api.unpaywall.org/v2/$doi?email=user@example.com" | jq '{doi: .doi, is_oa: .is_oa, oa_status: .oa_status, best_url: .best_oa_location.url}' - Network accessline 76
For large-scale analyses, Unpaywall provides a complete database snapshot and a weekly data feed, rather than requiring millions of individual API calls. Access is available at https://unpaywall.org/products/data-feed for registered users. - Network accessline 89
curl -s "https://api.unpaywall.org/v2/10.1038/s41586-021-03819-2?email=user@example.com" | jq '{title: .title, is_oa: .is_oa, oa_status: .oa_status, pdf: .best_oa_location.url_for_pdf}' - Network accessline 97
curl -s "https://api.unpaywall.org/v2/10.1145/3292500.3330672?email=user@example.com" | jq '.best_oa_location | {url: .url, pdf: .url_for_pdf, version: .version, license: .license}' - Network accessline 105
# For each publication DOI from the grant curl -s "https://api.unpaywall.org/v2/10.1038/nature12373?email=user@example.com" | jq '{doi: .doi, title: .title, is_oa: .is_oa, oa_status: .oa_status, locations: [.oa_locations[] | {host: .host_ty - Network accessline 106
curl -s "https://api.unpaywall.org/v2/10.1038/nature12373?email=user@example.com" | jq '{doi: .doi, title: .title, is_oa: .is_oa, oa_status: .oa_status, locations: [.oa_locations[] | {host: .host_type, license: .license, version: .version}] - Network accessline 111
- Official documentation: https://unpaywall.org/products/api - Network accessline 112
- Unpaywall data format: https://unpaywall.org/data-format - Network accessline 113
- OurResearch: https://ourresearch.org/
skills/literature/fulltext/zotero-ai-butler-guide/SKILL.md
- Network accessline 8
subcategory: "fulltext" keywords: ["Zotero", "AI summary", "paper summarization", "LLM", "abstract generation", "reading assistant"] source: "https://github.com/steven-jianhao-li/zotero-AI-Butler" - Network accessline 9
keywords: ["Zotero", "AI summary", "paper summarization", "LLM", "abstract generation", "reading assistant"] source: "https://github.com/steven-jianhao-li/zotero-AI-Butler" --- - Network accessline 10
source: "https://github.com/steven-jianhao-li/zotero-AI-Butler" - Network accessline 40
**Option 3: Local (Ollama)** - Provider: Ollama - Endpoint: http://localhost:11434 - Network accessline 41
- Provider: Ollama - Endpoint: http://localhost:11434 - Model: llama3.1 - Network accessline 42
- Endpoint: http://localhost:11434 - Network accessline 165
- [Zotero AI Butler GitHub](https://github.com/steven-jianhao-li/zotero-AI-Butler) - Network accessline 166
- [Zotero Plugin Development](https://www.zotero.org/support/dev/client_coding/plugin_development)
skills/literature/metadata/academic-paper-summarizer/SKILL.md
- Network accessline 8
subcategory: "metadata" keywords: ["paper summary", "structured extraction", "abstract", "key findings", "research synthesis", "literature review"] source: "https://github.com/lifan0127/ai-research-assistant" - Network accessline 9
keywords: ["paper summary", "structured extraction", "abstract", "key findings", "research synthesis", "literature review"] source: "https://github.com/lifan0127/ai-research-assistant" --- - Network accessline 10
source: "https://github.com/lifan0127/ai-research-assistant" - Network accessline 99
- Keshav, S. (2007). "How to Read a Paper." ACM SIGCOMM Computer Communication Review. - Pautasso, M. (2013). "Ten Simple Rules for Writing a Literature Review." PLOS Computational Biology. - AI Research Assistant: https://github.com/lifan0 - Network accessline 101
- AI Research Assistant: https://github.com/lifan0127/ai-research-assistant
skills/literature/metadata/altmetrics-guide/SKILL.md
- Network accessline 67
# Look up altmetrics by DOI doi = "10.1038/s41586-021-03819-2" response = requests.get(f"https://api.altmetric.com/v1/doi/{doi}") - Network accessline 69
response = requests.get(f"https://api.altmetric.com/v1/doi/{doi}") - Network accessline 91
# Look up by PubMed ID pmid = "34234348" response = requests.get(f"https://api.altmetric.com/v1/pmid/{pmid}") - Network accessline 93
response = requests.get(f"https://api.altmetric.com/v1/pmid/{pmid}") - Network accessline 95
# Look up by arXiv ID arxiv_id = "2103.14030" response = requests.get(f"https://api.altmetric.com/v1/arxiv/{arxiv_id}") - Network accessline 97
response = requests.get(f"https://api.altmetric.com/v1/arxiv/{arxiv_id}")
skills/literature/metadata/bibliometrix-guide/SKILL.md
- Network accessline 8
subcategory: "metadata" keywords: ["bibliometrix", "bibliometrics", "science mapping", "R", "citation analysis", "research trends"] source: "https://github.com/massimoaria/bibliometrix" - Network accessline 9
keywords: ["bibliometrix", "bibliometrics", "science mapping", "R", "citation analysis", "research trends"] source: "https://github.com/massimoaria/bibliometrix" --- - Network accessline 10
source: "https://github.com/massimoaria/bibliometrix" - Network accessline 162
- [Bibliometrix](https://www.bibliometrix.org/) - Network accessline 163
- [Bibliometrix GitHub](https://github.com/massimoaria/bibliometrix)
skills/literature/metadata/citation-network-guide/SKILL.md
- Network accessline 64
def get_citations(doi, max_results=100): """Fetch papers that cite a given DOI using OpenAlex.""" work_url = f"https://api.openalex.org/works/doi:{doi}" - Network accessline 65
"""Fetch papers that cite a given DOI using OpenAlex.""" work_url = f"https://api.openalex.org/works/doi:{doi}" resp = requests.get(work_url) - Network accessline 66
work_url = f"https://api.openalex.org/works/doi:{doi}" resp = requests.get(work_url) work = resp.json() - Network accessline 67
resp = requests.get(work_url) - Network accessline 71
# Get citing works citing_url = ( f"https://api.openalex.org/works" - Network accessline 72
citing_url = ( f"https://api.openalex.org/works" f"?filter=cites:{openalex_id}" - Network accessline 73
f"https://api.openalex.org/works" - Network accessline 76
f"&sort=cited_by_count:desc" ) citing_resp = requests.get(citing_url) - Network accessline 77
) citing_resp = requests.get(citing_url) citing_works = citing_resp.json()['results'] - Network accessline 78
citing_resp = requests.get(citing_url) - Network accessline 231
- [OpenAlex API](https://docs.openalex.org/) -- Free, open bibliographic data - Network accessline 232
- [CrossRef API](https://api.crossref.org/) -- DOI resolution and metadata - Network accessline 233
- [VOSviewer](https://www.vosviewer.com/) -- Bibliometric visualization tool - Network accessline 234
- [bibliometrix R package](https://www.bibliometrix.org/) -- Comprehensive bibliometric analysis - Network accessline 235
- [Altmetric](https://www.altmetric.com/) -- Alternative impact metrics - Network accessline 236
- [NetworkX Documentation](https://networkx.org/) -- Python graph analysis library
skills/literature/metadata/crossref-api/SKILL.md
- Network accessline 8
subcategory: "metadata" keywords: ["DOI resolution", "digital object identifier", "CrossRef", "citation statistics", "citation tracking"] source: "https://api.crossref.org" - Network accessline 9
keywords: ["DOI resolution", "digital object identifier", "CrossRef", "citation statistics", "citation tracking"] source: "https://api.crossref.org" --- - Network accessline 10
source: "https://api.crossref.org" - Network accessline 28
https://api.crossref.org/works?mailto=your@email.com - Network accessline 37
- **URL**: `GET https://api.crossref.org/works` - Network accessline 52
curl "https://api.crossref.org/works?query.title=attention+is+all+you+need&rows=5&mailto=user@example.com" - Network accessline 58
- **URL**: `GET https://api.crossref.org/works/{doi}` - Network accessline 66
curl "https://api.crossref.org/works/10.1038/s41586-021-03819-2?mailto=user@example.com" - Network accessline 72
- **URL**: `GET https://api.crossref.org/members` - Network accessline 80
curl "https://api.crossref.org/members?query=springer&rows=5&mailto=user@example.com" - Network accessline 86
- **URL**: `GET https://api.crossref.org/journals` - Network accessline 94
curl "https://api.crossref.org/journals?query=nature+machine+intelligence&rows=5&mailto=user@example.com" - Network accessline 109
# Single DOI resolution with full metadata curl "https://api.crossref.org/works/10.1145/3292500.3330672?mailto=user@example.com" - Network accessline 110
curl "https://api.crossref.org/works/10.1145/3292500.3330672?mailto=user@example.com" - Network accessline 118
curl "https://api.crossref.org/works?filter=from-pub-date:2023,type:journal-article&query=machine+learning&sort=is-referenced-by-count&order=desc&rows=20&mailto=user@example.com" - Network accessline 126
curl "https://api.crossref.org/works/10.1038/s41586-021-03819-2?mailto=user@example.com" | jq '.message["is-referenced-by-count"]' - Network accessline 131
- Official documentation: https://api.crossref.org - Network accessline 132
- API documentation: https://api.crossref.org/swagger-ui/index.html - Network accessline 133
- CrossRef metadata best practices: https://www.crossref.org/documentation/
skills/literature/metadata/crossref-event-data-api/SKILL.md
- Network accessline 8
subcategory: "metadata" keywords: ["Crossref", "event data", "altmetrics", "social media mentions", "scholarly impact", "web mentions"] source: "https://www.eventdata.crossref.org/" - Network accessline 9
keywords: ["Crossref", "event data", "altmetrics", "social media mentions", "scholarly impact", "web mentions"] source: "https://www.eventdata.crossref.org/" --- - Network accessline 10
source: "https://www.eventdata.crossref.org/" - Network accessline 24
https://api.eventdata.crossref.org/v1 - Network accessline 30
# Get events for a specific DOI curl "https://api.eventdata.crossref.org/v1/events?obj-id=10.1038/nature14539&rows=20" - Network accessline 31
curl "https://api.eventdata.crossref.org/v1/events?obj-id=10.1038/nature14539&rows=20" - Network accessline 33
# Filter by source curl "https://api.eventdata.crossref.org/v1/events?\ - Network accessline 34
curl "https://api.eventdata.crossref.org/v1/events?\ - Network accessline 37
# Filter by date range curl "https://api.eventdata.crossref.org/v1/events?\ - Network accessline 38
curl "https://api.eventdata.crossref.org/v1/events?\ - Network accessline 41
# Get events about a DOI prefix (publisher level) curl "https://api.eventdata.crossref.org/v1/events?obj-id.prefix=10.1371&rows=50" - Network accessline 42
curl "https://api.eventdata.crossref.org/v1/events?obj-id.prefix=10.1371&rows=50" - Network accessline 44
# Events from a specific source curl "https://api.eventdata.crossref.org/v1/events?source=reddit&rows=50" - Network accessline 45
curl "https://api.eventdata.crossref.org/v1/events?source=reddit&rows=50" - Network accessline 83
"events": [ { "obj_id": "https://doi.org/10.1038/nature14539", - Network accessline 84
{ "obj_id": "https://doi.org/10.1038/nature14539", "source_id": "wikipedia", - Network accessline 85
"obj_id": "https://doi.org/10.1038/nature14539", - Network accessline 86
"source_id": "wikipedia", "subj_id": "https://en.wikipedia.org/wiki/Deep_learning", "relation_type_id": "references", - Network accessline 87
"subj_id": "https://en.wikipedia.org/wiki/Deep_learning", - Network accessline 90
"subj": { "title": "Deep learning - Wikipedia", "url": "https://en.wikipedia.org/wiki/Deep_learning" - Network accessline 91
"title": "Deep learning - Wikipedia", "url": "https://en.wikipedia.org/wiki/Deep_learning" } - Network accessline 92
"url": "https://en.wikipedia.org/wiki/Deep_learning" - Network accessline 107
BASE_URL = "https://api.eventdata.crossref.org/v1" - Network accessline 117
resp = requests.get(f"{BASE_URL}/events", params=params) - Network accessline 181
- [Crossref Event Data](https://www.eventdata.crossref.org/) - Network accessline 182
- [Event Data API Guide](https://www.eventdata.crossref.org/guide/) - Network accessline 183
- [Event Data User Guide](https://www.crossref.org/services/event-data/)
skills/literature/metadata/datacite-api/SKILL.md
- Network accessline 8
subcategory: "metadata" keywords: ["DOI resolution", "digital object identifier", "FAIR data principles", "data sharing"] source: "https://support.datacite.org/docs/api" - Network accessline 9
keywords: ["DOI resolution", "digital object identifier", "FAIR data principles", "data sharing"] source: "https://support.datacite.org/docs/api" --- - Network accessline 10
source: "https://support.datacite.org/docs/api" - Network accessline 31
- **URL**: `GET https://api.datacite.org/dois` - Network accessline 44
curl "https://api.datacite.org/dois?query=climate+change+dataset&resource-type-id=dataset&page[size]=10&sort=-created" - Network accessline 50
- **URL**: `GET https://api.datacite.org/dois/{doi}` - Network accessline 57
curl "https://api.datacite.org/dois/10.5281/zenodo.3678171" - Network accessline 63
- **URL**: `GET https://api.datacite.org/providers` - Network accessline 72
curl "https://api.datacite.org/providers?query=CERN&page[size]=5" - Network accessline 78
- **URL**: `GET https://api.datacite.org/clients` - Network accessline 88
curl "https://api.datacite.org/clients?query=zenodo&page[size]=5" - Network accessline 94
No published rate limits. DataCite does not enforce strict API quotas for read access. However, the service is operated by a nonprofit organization, so users should implement reasonable request pacing. For large-scale data mining, use the D - Network accessline 103
curl -s "https://api.datacite.org/dois?query=CRISPR+genome+editing&resource-type-id=dataset&page[size]=5&sort=-created" | jq '.data[] | {doi: .attributes.doi, title: .attributes.titles[0].title, year: .attributes.publicationYear, publisher: - Network accessline 111
curl -s "https://api.datacite.org/dois?query=python+machine+learning&resource-type-id=software&page[size]=10" | jq '.data[] | {doi: .attributes.doi, title: .attributes.titles[0].title, year: .attributes.publicationYear}' - Network accessline 119
curl -s "https://api.datacite.org/dois/10.5281/zenodo.3678171" | jq '.data.attributes.relatedIdentifiers[] | select(.relationType == "IsSupplementTo" or .relationType == "IsReferencedBy") | {type: .relationType, id: .relatedIdentifier}' - Network accessline 124
- Official documentation: https://support.datacite.org/docs/api - Network accessline 125
- DataCite Metadata Schema: https://schema.datacite.org/ - Network accessline 126
- JSON:API specification: https://jsonapi.org/
skills/literature/metadata/doi-content-negotiation/SKILL.md
- Network accessline 8
subcategory: "metadata" keywords: ["DOI", "content negotiation", "metadata retrieval", "BibTeX", "citation metadata", "linked data"] source: "https://citation.crosscite.org/" - Network accessline 8
subcategory: "metadata" keywords: ["DOI", "content negotiation", "metadata retrieval", "BibTeX", "citation metadata", "linked data"] source: "https://citation.crosscite.org/" - Network accessline 9
keywords: ["DOI", "content negotiation", "metadata retrieval", "BibTeX", "citation metadata", "linked data"] source: "https://citation.crosscite.org/" --- - Network accessline 9
keywords: ["DOI", "content negotiation", "metadata retrieval", "BibTeX", "citation metadata", "linked data"] source: "https://citation.crosscite.org/" --- - Network accessline 10
source: "https://citation.crosscite.org/" - Network accessline 10
source: "https://citation.crosscite.org/" - Network accessline 17
Any DOI can return structured metadata in multiple formats through HTTP content negotiation — simply set the `Accept` header when requesting `https://doi.org/{doi}`. This is the most universal way to get citation metadata, BibTeX entries, J - Network accessline 17
Any DOI can return structured metadata in multiple formats through HTTP content negotiation — simply set the `Accept` header when requesting `https://doi.org/{doi}`. This is the most universal way to get citation metadata, BibTeX entries, J - Network accessline 24
# Get JSON citation metadata (Citeproc JSON) curl -LH "Accept: application/vnd.citationstyles.csl+json" \ - Network accessline 24
# Get JSON citation metadata (Citeproc JSON) curl -LH "Accept: application/vnd.citationstyles.csl+json" \ - Network accessline 25
curl -LH "Accept: application/vnd.citationstyles.csl+json" \ - Network accessline 25
curl -LH "Accept: application/vnd.citationstyles.csl+json" \ - Network accessline 26
"https://doi.org/10.1038/nature14539" - Network accessline 26
"https://doi.org/10.1038/nature14539" - Network accessline 28
# Get BibTeX curl -LH "Accept: application/x-bibtex" \ - Network accessline 28
# Get BibTeX curl -LH "Accept: application/x-bibtex" \ - Network accessline 29
curl -LH "Accept: application/x-bibtex" \ - Network accessline 29
curl -LH "Accept: application/x-bibtex" \ - Network accessline 30
"https://doi.org/10.1038/nature14539" - Network accessline 30
"https://doi.org/10.1038/nature14539" - Network accessline 32
# Get RIS format curl -LH "Accept: application/x-research-info-systems" \ - Network accessline 32
# Get RIS format curl -LH "Accept: application/x-research-info-systems" \ - Network accessline 33
curl -LH "Accept: application/x-research-info-systems" \ - Network accessline 33
curl -LH "Accept: application/x-research-info-systems" \ - Network accessline 34
"https://doi.org/10.1038/nature14539" - Network accessline 34
"https://doi.org/10.1038/nature14539" - Network accessline 36
# Get formatted citation (APA style) curl -LH "Accept: text/x-bibliography; style=apa" \ - Network accessline 36
# Get formatted citation (APA style) curl -LH "Accept: text/x-bibliography; style=apa" \ - Network accessline 37
curl -LH "Accept: text/x-bibliography; style=apa" \ - Network accessline 37
curl -LH "Accept: text/x-bibliography; style=apa" \ - Network accessline 38
"https://doi.org/10.1038/nature14539" - Network accessline 38
"https://doi.org/10.1038/nature14539" - Network accessline 40
# Get formatted citation (Chicago style) curl -LH "Accept: text/x-bibliography; style=chicago-author-date" \ - Network accessline 40
# Get formatted citation (Chicago style) curl -LH "Accept: text/x-bibliography; style=chicago-author-date" \ - Network accessline 41
curl -LH "Accept: text/x-bibliography; style=chicago-author-date" \ - Network accessline 41
curl -LH "Accept: text/x-bibliography; style=chicago-author-date" \ - Network accessline 42
"https://doi.org/10.1038/nature14539" - Network accessline 42
"https://doi.org/10.1038/nature14539" - Network accessline 103
"publisher": "Springer Science and Business Media LLC", "ISSN": ["0028-0836", "1476-4687"], "URL": "http://dx.doi.org/10.1038/nature14539", - Network accessline 103
"publisher": "Springer Science and Business Media LLC", "ISSN": ["0028-0836", "1476-4687"], "URL": "http://dx.doi.org/10.1038/nature14539", - Network accessline 104
"ISSN": ["0028-0836", "1476-4687"], "URL": "http://dx.doi.org/10.1038/nature14539", "abstract": "Deep learning allows computational models..." - Network accessline 104
"ISSN": ["0028-0836", "1476-4687"], "URL": "http://dx.doi.org/10.1038/nature14539", "abstract": "Deep learning allows computational models..." - Network accessline 105
"URL": "http://dx.doi.org/10.1038/nature14539", - Network accessline 105
"URL": "http://dx.doi.org/10.1038/nature14539", - Network accessline 116
def get_metadata(doi: str) -> dict: """Get structured metadata for a DOI.""" resp = requests.get( - Network accessline 116
def get_metadata(doi: str) -> dict: """Get structured metadata for a DOI.""" resp = requests.get( - Network accessline 117
"""Get structured metadata for a DOI.""" resp = requests.get( f"https://doi.org/{doi}", - Network accessline 117
"""Get structured metadata for a DOI.""" resp = requests.get( f"https://doi.org/{doi}", - Network accessline 118
resp = requests.get( - Network accessline 118
resp = requests.get( - Network accessline 119
f"https://doi.org/{doi}", - Network accessline 119
f"https://doi.org/{doi}", - Network accessline 127
def get_bibtex(doi: str) -> str: """Get BibTeX entry for a DOI.""" resp = requests.get( - Network accessline 127
def get_bibtex(doi: str) -> str: """Get BibTeX entry for a DOI.""" resp = requests.get( - Network accessline 128
"""Get BibTeX entry for a DOI.""" resp = requests.get( f"https://doi.org/{doi}", - Network accessline 128
"""Get BibTeX entry for a DOI.""" resp = requests.get( f"https://doi.org/{doi}", - Network accessline 129
resp = requests.get( - Network accessline 129
resp = requests.get( - Network accessline 130
f"https://doi.org/{doi}", - Network accessline 130
f"https://doi.org/{doi}", - Network accessline 139
style: str = "apa") -> str: """Get a formatted citation string.""" resp = requests.get( - Network accessline 139
style: str = "apa") -> str: """Get a formatted citation string.""" resp = requests.get( - Network accessline 140
"""Get a formatted citation string.""" resp = requests.get( f"https://doi.org/{doi}", - Network accessline 140
"""Get a formatted citation string.""" resp = requests.get( f"https://doi.org/{doi}", - Network accessline 141
resp = requests.get( - Network accessline 141
resp = requests.get( - Network accessline 142
f"https://doi.org/{doi}", - Network accessline 142
f"https://doi.org/{doi}", - Network accessline 200
- [DOI Content Negotiation](https://citation.crosscite.org/docs.html) - Network accessline 200
- [DOI Content Negotiation](https://citation.crosscite.org/docs.html) - Network accessline 201
- [Crossref Content Negotiation](https://www.crossref.org/documentation/retrieve-metadata/content-negotiation/) - Network accessline 201
- [Crossref Content Negotiation](https://www.crossref.org/documentation/retrieve-metadata/content-negotiation/) - Network accessline 202
- [CSL Style Repository](https://github.com/citation-style-language/styles) - Network accessline 202
- [CSL Style Repository](https://github.com/citation-style-language/styles)
skills/literature/metadata/doi-resolution-guide/SKILL.md
- Network accessline 21
The DOI resolver endpoint is `https://doi.org/{doi}` or equivalently `https://dx.doi.org/{doi}`. - Network accessline 21
The DOI resolver endpoint is `https://doi.org/{doi}` or equivalently `https://dx.doi.org/{doi}`. - Network accessline 40
curl -LH "Accept: application/vnd.citationstyles.csl+json" \ - Network accessline 40
curl -LH "Accept: application/vnd.citationstyles.csl+json" \ - Network accessline 41
https://doi.org/10.1038/s41586-021-03819-2 - Network accessline 41
https://doi.org/10.1038/s41586-021-03819-2 - Network accessline 47
doi = "10.1038/s41586-021-03819-2" headers = {"Accept": "application/vnd.citationstyles.csl+json"} response = requests.get(f"https://doi.org/{doi}", headers=headers, allow_redirects=True) - Network accessline 47
doi = "10.1038/s41586-021-03819-2" headers = {"Accept": "application/vnd.citationstyles.csl+json"} response = requests.get(f"https://doi.org/{doi}", headers=headers, allow_redirects=True) - Network accessline 49
response = requests.get(f"https://doi.org/{doi}", headers=headers, allow_redirects=True) - Network accessline 49
response = requests.get(f"https://doi.org/{doi}", headers=headers, allow_redirects=True) - Network accessline 62
# APA format curl -LH "Accept: text/x-bibliography; style=apa" \ - Network accessline 62
# APA format curl -LH "Accept: text/x-bibliography; style=apa" \ - Network accessline 63
curl -LH "Accept: text/x-bibliography; style=apa" \ - Network accessline 63
curl -LH "Accept: text/x-bibliography; style=apa" \ - Network accessline 64
https://doi.org/10.1038/s41586-021-03819-2 - Network accessline 64
https://doi.org/10.1038/s41586-021-03819-2 - Network accessline 66
# Chicago format curl -LH "Accept: text/x-bibliography; style=chicago-author-date" \ - Network accessline 66
# Chicago format curl -LH "Accept: text/x-bibliography; style=chicago-author-date" \ - Network accessline 67
curl -LH "Accept: text/x-bibliography; style=chicago-author-date" \ - Network accessline 67
curl -LH "Accept: text/x-bibliography; style=chicago-author-date" \ - Network accessline 68
https://doi.org/10.1038/s41586-021-03819-2 - Network accessline 68
https://doi.org/10.1038/s41586-021-03819-2 - Network accessline 70
# Harvard format curl -LH "Accept: text/x-bibliography; style=harvard-cite-them-right" \ - Network accessline 70
# Harvard format curl -LH "Accept: text/x-bibliography; style=harvard-cite-them-right" \ - Network accessline 71
curl -LH "Accept: text/x-bibliography; style=harvard-cite-them-right" \ - Network accessline 71
curl -LH "Accept: text/x-bibliography; style=harvard-cite-them-right" \ - Network accessline 72
https://doi.org/10.1038/s41586-021-03819-2 - Network accessline 72
https://doi.org/10.1038/s41586-021-03819-2 - Network accessline 78
curl -LH "Accept: application/x-bibtex" \ - Network accessline 78
curl -LH "Accept: application/x-bibtex" \ - Network accessline 79
https://doi.org/10.1038/s41586-021-03819-2 - Network accessline 79
https://doi.org/10.1038/s41586-021-03819-2 - Network accessline 105
doi = "10.1038/s41586-021-03819-2" response = requests.get( - Network accessline 105
doi = "10.1038/s41586-021-03819-2" response = requests.get( - Network accessline 106
response = requests.get( - Network accessline 106
response = requests.get( - Network accessline 107
f"https://api.crossref.org/works/{doi}", - Network accessline 107
f"https://api.crossref.org/works/{doi}", - Network accessline 128
results = [] for doi in dois: resp = requests.get( - Network accessline 128
results = [] for doi in dois: resp = requests.get( - Network accessline 129
for doi in dois: resp = requests.get( f"https://api.crossref.org/works/{doi}", - Network accessline 129
for doi in dois: resp = requests.get( f"https://api.crossref.org/works/{doi}", - Network accessline 130
resp = requests.get( - Network accessline 130
resp = requests.get( - Network accessline 131
f"https://api.crossref.org/works/{doi}", - Network accessline 131
f"https://api.crossref.org/works/{doi}", - Network accessline 156
# Examples normalize_doi("https://doi.org/10.1038/s41586-021-03819-2") # 10.1038/s41586-021-03819-2 - Network accessline 156
# Examples normalize_doi("https://doi.org/10.1038/s41586-021-03819-2") # 10.1038/s41586-021-03819-2 - Network accessline 157
normalize_doi("https://doi.org/10.1038/s41586-021-03819-2") # 10.1038/s41586-021-03819-2 - Network accessline 157
normalize_doi("https://doi.org/10.1038/s41586-021-03819-2") # 10.1038/s41586-021-03819-2 - Network accessline 164
- **Polite pool**: CrossRef provides faster responses to requests with a `User-Agent` header that includes a `mailto:` contact. This is their "polite pool" with higher rate limits. - **OpenAlex alternative**: OpenAlex (https://api.openalex. - Network accessline 164
- **Polite pool**: CrossRef provides faster responses to requests with a `User-Agent` header that includes a `mailto:` contact. This is their "polite pool" with higher rate limits. - **OpenAlex alternative**: OpenAlex (https://api.openalex. - Network accessline 165
- **OpenAlex alternative**: OpenAlex (https://api.openalex.org/works/doi:10.xxx/yyy) provides similar metadata for free, with richer entity linking. - **Handle redirects**: Always use `allow_redirects=True` (or `-L` in curl) as DOIs redirec - Network accessline 165
- **OpenAlex alternative**: OpenAlex (https://api.openalex.org/works/doi:10.xxx/yyy) provides similar metadata for free, with richer entity linking. - **Handle redirects**: Always use `allow_redirects=True` (or `-L` in curl) as DOIs redirec
skills/literature/metadata/h-index-guide/SKILL.md
- Network accessline 96
# Using the OpenAlex API to get journal/source information journal_name = "Nature" response = requests.get( - Network accessline 97
journal_name = "Nature" response = requests.get( "https://api.openalex.org/sources", - Network accessline 98
response = requests.get( - Network accessline 99
"https://api.openalex.org/sources", - Network accessline 121
# OpenAlex provides h-index directly in author profiles author_name = "Geoffrey Hinton" response = requests.get( - Network accessline 122
author_name = "Geoffrey Hinton" response = requests.get( "https://api.openalex.org/authors", - Network accessline 123
response = requests.get( - Network accessline 124
"https://api.openalex.org/authors",
skills/literature/metadata/opencitations-api/SKILL.md
- Network accessline 8
subcategory: "metadata" keywords: ["citation statistics", "citation tracking", "citation analysis", "bibliometrics", "citation network"] source: "https://opencitations.net/index/api/v1" - Network accessline 9
keywords: ["citation statistics", "citation tracking", "citation analysis", "bibliometrics", "citation network"] source: "https://opencitations.net/index/api/v1" --- - Network accessline 10
source: "https://opencitations.net/index/api/v1" - Network accessline 25
No authentication required. The OpenCitations API is fully open and free. No API key, registration, or email is needed. There are no published rate limits, but users should implement reasonable request pacing for large-scale queries. For bu - Network accessline 31
- **URL**: `GET https://api.opencitations.net/index/v1/citations/{doi}` - Network accessline 39
curl "https://api.opencitations.net/index/v1/citations/10.1038/nature12373" - Network accessline 52
- **URL**: `GET https://api.opencitations.net/index/v1/references/{doi}` - Network accessline 59
curl "https://api.opencitations.net/index/v1/references/10.1038/nature12373" - Network accessline 65
- **URL**: `GET https://api.opencitations.net/index/v1/metadata/{doi}` - Network accessline 72
curl "https://api.opencitations.net/index/v1/metadata/10.1038/nature12373" - Network accessline 78
- **URL**: `GET https://api.opencitations.net/index/v1/citation-count/{doi}` - Network accessline 85
curl "https://api.opencitations.net/index/v1/citation-count/10.1038/nature12373" - Network accessline 100
# Get all papers citing the target paper curl -s "https://api.opencitations.net/index/v1/citations/10.1145/3292500.3330672" | jq '.[].citing' - Network accessline 101
curl -s "https://api.opencitations.net/index/v1/citations/10.1145/3292500.3330672" | jq '.[].citing' - Network accessline 103
# Get all papers referenced by the target paper curl -s "https://api.opencitations.net/index/v1/references/10.1145/3292500.3330672" | jq '.[].cited' - Network accessline 104
curl -s "https://api.opencitations.net/index/v1/references/10.1145/3292500.3330672" | jq '.[].cited' - Network accessline 112
curl -s "https://api.opencitations.net/index/v1/citations/10.1038/nature12373" | jq '[.[] | select(.author_sc == "no")] | length' - Network accessline 120
# Multiple DOIs separated by double underscore curl -s "https://api.opencitations.net/index/v1/metadata/10.1038/nature12373__10.1126/science.aaa8685__10.1016/j.cell.2015.05.002" | jq '.[] | {doi: .doi, title: .title, citations: .citation_co - Network accessline 121
curl -s "https://api.opencitations.net/index/v1/metadata/10.1038/nature12373__10.1126/science.aaa8685__10.1016/j.cell.2015.05.002" | jq '.[] | {doi: .doi, title: .title, citations: .citation_count}' - Network accessline 126
- Official documentation: https://opencitations.net/index/api/v1 - OpenCitations data model: https://opencitations.net/model - COCI dataset download: https://opencitations.net/download - Network accessline 127
- OpenCitations data model: https://opencitations.net/model - Network accessline 128
- COCI dataset download: https://opencitations.net/download
skills/literature/metadata/orcid-api/SKILL.md
- Network accessline 8
subcategory: "metadata" keywords: ["researcher ID", "ORCID", "academic identity", "academic metrics"] source: "https://info.orcid.org/documentation/api-tutorial/" - Network accessline 8
subcategory: "metadata" keywords: ["researcher ID", "ORCID", "academic identity", "academic metrics"] source: "https://info.orcid.org/documentation/api-tutorial/" - Network accessline 9
keywords: ["researcher ID", "ORCID", "academic identity", "academic metrics"] source: "https://info.orcid.org/documentation/api-tutorial/" requires: - Network accessline 9
keywords: ["researcher ID", "ORCID", "academic identity", "academic metrics"] source: "https://info.orcid.org/documentation/api-tutorial/" requires: - Network accessline 10
source: "https://info.orcid.org/documentation/api-tutorial/" - Network accessline 10
source: "https://info.orcid.org/documentation/api-tutorial/" - Access to secretsline 12
[Credential-related source omitted] - Access to secretsline 12
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[Credential-related source omitted] - Access to secretsline 30
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[Credential-related source omitted] - Access to secretsline 41
[Credential-related source omitted] - Access to secretsline 41
[Credential-related source omitted] - Network accessline 50
- **URL**: `GET https://pub.orcid.org/v3.0/{orcid-id}/record` - Network accessline 50
- **URL**: `GET https://pub.orcid.org/v3.0/{orcid-id}/record` - Network accessline 58
[Credential-related source omitted] - Network accessline 58
[Credential-related source omitted] - Network accessline 60
"https://pub.orcid.org/v3.0/0000-0002-1825-0097/record" - Network accessline 60
"https://pub.orcid.org/v3.0/0000-0002-1825-0097/record" - Network accessline 66
- **URL**: `GET https://pub.orcid.org/v3.0/{orcid-id}/works` - Network accessline 66
- **URL**: `GET https://pub.orcid.org/v3.0/{orcid-id}/works` - Network accessline 73
[Credential-related source omitted] - Network accessline 73
[Credential-related source omitted] - Network accessline 75
"https://pub.orcid.org/v3.0/0000-0002-1825-0097/works" - Network accessline 75
"https://pub.orcid.org/v3.0/0000-0002-1825-0097/works" - Network accessline 81
- **URL**: `GET https://pub.orcid.org/v3.0/search` - Network accessline 81
- **URL**: `GET https://pub.orcid.org/v3.0/search` - Network accessline 90
[Credential-related source omitted] - Network accessline 90
[Credential-related source omitted] - Network accessline 92
"https://pub.orcid.org/v3.0/search?q=family-name:Hinton+AND+affiliation-org-name:Toronto&rows=10" - Network accessline 92
"https://pub.orcid.org/v3.0/search?q=family-name:Hinton+AND+affiliation-org-name:Toronto&rows=10" - Network accessline 107
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[Credential-related source omitted] - Network accessline 109
"https://pub.orcid.org/v3.0/0000-0001-5109-3700/person" - Network accessline 109
"https://pub.orcid.org/v3.0/0000-0001-5109-3700/person" - Network accessline 117
[Credential-related source omitted] - Network accessline 117
[Credential-related source omitted] - Network accessline 119
"https://pub.orcid.org/v3.0/0000-0002-1825-0097/works" | jq '.group[].work-summary[0] | {title: .title.title.value, doi: (.["external-ids"]["external-id"][] | select(.["external-id-type"] == "doi") | .["external-id-value"])}' - Network accessline 119
"https://pub.orcid.org/v3.0/0000-0002-1825-0097/works" | jq '.group[].work-summary[0] | {title: .title.title.value, doi: (.["external-ids"]["external-id"][] | select(.["external-id-type"] == "doi") | .["external-id-value"])}' - Network accessline 127
[Credential-related source omitted] - Network accessline 127
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"https://pub.orcid.org/v3.0/search?q=affiliation-org-name:Stanford+AND+current-institution-affiliation-name:Stanford&rows=50" - Network accessline 129
"https://pub.orcid.org/v3.0/search?q=affiliation-org-name:Stanford+AND+current-institution-affiliation-name:Stanford&rows=50" - Network accessline 134
- Official documentation: https://info.orcid.org/documentation/api-tutorial/ - Network accessline 134
- Official documentation: https://info.orcid.org/documentation/api-tutorial/ - Network accessline 135
- API reference: https://pub.orcid.org/v3.0/ - Network accessline 135
- API reference: https://pub.orcid.org/v3.0/ - Network accessline 136
- ORCID integration guide: https://info.orcid.org/documentation/integration-guide/ - Network accessline 136
- ORCID integration guide: https://info.orcid.org/documentation/integration-guide/
skills/literature/metadata/orcid-integration-guide/SKILL.md
- Network accessline 146
orcid_id: The ORCID iD (e.g., '0000-0002-1825-0097') """ url = f"https://pub.orcid.org/v3.0/{orcid_id}/works" - Network accessline 146
orcid_id: The ORCID iD (e.g., '0000-0002-1825-0097') """ url = f"https://pub.orcid.org/v3.0/{orcid_id}/works" - Network accessline 147
""" url = f"https://pub.orcid.org/v3.0/{orcid_id}/works" req = urllib.request.Request(url, headers={ - Network accessline 147
""" url = f"https://pub.orcid.org/v3.0/{orcid_id}/works" req = urllib.request.Request(url, headers={ - Network accessline 148
url = f"https://pub.orcid.org/v3.0/{orcid_id}/works" req = urllib.request.Request(url, headers={ "Accept": "application/json" - Network accessline 148
url = f"https://pub.orcid.org/v3.0/{orcid_id}/works" req = urllib.request.Request(url, headers={ "Accept": "application/json" - Network accessline 149
req = urllib.request.Request(url, headers={ - Network accessline 149
req = urllib.request.Request(url, headers={ - Network accessline 150
"Accept": "application/json" }) response = urllib.request.urlopen(req) - Network accessline 150
"Accept": "application/json" }) response = urllib.request.urlopen(req) - Network accessline 151
}) response = urllib.request.urlopen(req) data = json.loads(response.read()) - Network accessline 151
}) response = urllib.request.urlopen(req) data = json.loads(response.read()) - Network accessline 152
response = urllib.request.urlopen(req) - Network accessline 152
response = urllib.request.urlopen(req) - Network accessline 176
- Include peer review activity (via Publons/Web of Science integration) - Use your ORCID iD in email signatures, CVs, and personal websites - Format as a full URL: https://orcid.org/0000-0002-1825-0097 - Network accessline 176
- Include peer review activity (via Publons/Web of Science integration) - Use your ORCID iD in email signatures, CVs, and personal websites - Format as a full URL: https://orcid.org/0000-0002-1825-0097 - Network accessline 178
- Format as a full URL: https://orcid.org/0000-0002-1825-0097 - Network accessline 178
- Format as a full URL: https://orcid.org/0000-0002-1825-0097
skills/literature/metadata/orkg-api/SKILL.md
- Network accessline 8
subcategory: "metadata" keywords: ["knowledge graph", "research data", "structured research", "ORKG", "research contributions", "scholarly graph"] source: "https://orkg.org/" - Network accessline 9
keywords: ["knowledge graph", "research data", "structured research", "ORKG", "research contributions", "scholarly graph"] source: "https://orkg.org/" --- - Network accessline 10
source: "https://orkg.org/" - Network accessline 24
https://orkg.org/api/ - Network accessline 30
# Search papers in ORKG curl "https://orkg.org/api/papers?q=climate+change+adaptation&size=20" - Network accessline 31
curl "https://orkg.org/api/papers?q=climate+change+adaptation&size=20" - Network accessline 33
# Get paper details by ID curl "https://orkg.org/api/papers/R12345" - Network accessline 34
curl "https://orkg.org/api/papers/R12345" - Network accessline 40
# Search any resource (papers, predicates, comparisons) curl "https://orkg.org/api/resources?q=machine+learning&size=20" - Network accessline 41
curl "https://orkg.org/api/resources?q=machine+learning&size=20" - Network accessline 43
# Filter by class curl "https://orkg.org/api/resources?q=BERT&exact=false&classes=Paper" - Network accessline 44
curl "https://orkg.org/api/resources?q=BERT&exact=false&classes=Paper" - Network accessline 52
# List comparisons curl "https://orkg.org/api/comparisons?size=10" - Network accessline 53
curl "https://orkg.org/api/comparisons?size=10" - Network accessline 55
# Get a specific comparison curl "https://orkg.org/api/comparisons/R54321" - Network accessline 56
curl "https://orkg.org/api/comparisons/R54321" - Network accessline 58
# Search comparisons curl "https://orkg.org/api/comparisons?q=sentiment+analysis" - Network accessline 59
curl "https://orkg.org/api/comparisons?q=sentiment+analysis" - Network accessline 65
# Get contributions of a paper curl "https://orkg.org/api/papers/R12345/contributions" - Network accessline 66
curl "https://orkg.org/api/papers/R12345/contributions" - Network accessline 80
BASE_URL = "https://orkg.org/api" - Network accessline 82
def search_orkg_papers(query: str, size: int = 20) -> list: """Search papers in the Open Research Knowledge Graph.""" resp = requests.get(f"{BASE_URL}/papers", params={"q": query, "size": size}) - Network accessline 83
"""Search papers in the Open Research Knowledge Graph.""" resp = requests.get(f"{BASE_URL}/papers", params={"q": query, "size": size}) resp.raise_for_status() - Network accessline 84
resp = requests.get(f"{BASE_URL}/papers", params={"q": query, "size": size}) - Network accessline 98
def get_paper_contributions(paper_id: str) -> dict: """Get structured research contributions for a paper.""" resp = requests.get(f"{BASE_URL}/papers/{paper_id}/contributions") - Network accessline 99
"""Get structured research contributions for a paper.""" resp = requests.get(f"{BASE_URL}/papers/{paper_id}/contributions") resp.raise_for_status() - Network accessline 100
resp = requests.get(f"{BASE_URL}/papers/{paper_id}/contributions") - Network accessline 104
def search_comparisons(topic: str) -> list: """Find structured paper comparisons on a topic.""" resp = requests.get(f"{BASE_URL}/comparisons", params={"q": topic, "size": 10}) - Network accessline 105
"""Find structured paper comparisons on a topic.""" resp = requests.get(f"{BASE_URL}/comparisons", params={"q": topic, "size": 10}) resp.raise_for_status() - Network accessline 106
resp = requests.get(f"{BASE_URL}/comparisons", params={"q": topic, "size": 10}) - Network accessline 150
- [ORKG Website](https://orkg.org/) - Network accessline 151
- [ORKG API Documentation](https://orkg.org/api/) - Network accessline 152
- [ORKG Help Center](https://orkg.org/help-center)
skills/literature/metadata/plumx-metrics-api/SKILL.md
- Network accessline 8
subcategory: "metadata" keywords: ["PlumX", "altmetrics", "research impact", "social media metrics", "usage statistics", "scholarly metrics"] source: "https://plumanalytics.com/" - Network accessline 9
keywords: ["PlumX", "altmetrics", "research impact", "social media metrics", "usage statistics", "scholarly metrics"] source: "https://plumanalytics.com/" --- - Network accessline 10
source: "https://plumanalytics.com/" - Network accessline 34
https://api.elsevier.com/analytics/plumx/ - Network accessline 40
[Credential-related source omitted] - Access to secretsline 40
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"https://api.elsevier.com/analytics/plumx/doi/10.1038/nature14539" - Network accessline 47
[Credential-related source omitted] - Network accessline 48
[Credential-related source omitted] - Access to secretsline 48
[Credential-related source omitted] - Network accessline 49
"https://api.elsevier.com/analytics/plumx/pmid/25428114" - Network accessline 51
[Credential-related source omitted] - Network accessline 52
[Credential-related source omitted] - Access to secretsline 52
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"https://api.elsevier.com/analytics/plumx/isbn/9780262035613" - Network accessline 55
[Credential-related source omitted] - Network accessline 56
[Credential-related source omitted] - Access to secretsline 56
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"https://api.elsevier.com/analytics/plumx/scopusId/84920765826" - Network accessline 117
[Credential-related source omitted] - Access to secretsline 117
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BASE_URL = "https://api.elsevier.com/analytics/plumx" - Access to secretsline 119
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def get_plumx_metrics(doi: str) -> dict: """Get PlumX metrics for a paper by DOI.""" resp = requests.get( - Network accessline 123
"""Get PlumX metrics for a paper by DOI.""" resp = requests.get( f"{BASE_URL}/doi/{doi}", - Network accessline 124
resp = requests.get( - Network accessline 186
- [PlumX Metrics](https://plumanalytics.com/learn/about-metrics/) - Network accessline 187
- [Elsevier Developer Portal](https://dev.elsevier.com/) - Network accessline 188
- [PlumX API Documentation](https://dev.elsevier.com/documentation/PlumXMetricsAPI.wadl)
skills/literature/metadata/ror-organization-api/SKILL.md
- Network accessline 8
subcategory: "metadata" keywords: ["ROR", "research organizations", "institution registry", "affiliation", "organization identifiers", "ISNI"] source: "https://ror.org/" - Network accessline 9
keywords: ["ROR", "research organizations", "institution registry", "affiliation", "organization identifiers", "ISNI"] source: "https://ror.org/" --- - Network accessline 10
source: "https://ror.org/" - Network accessline 24
https://api.ror.org/v2 - Network accessline 30
# Text search curl "https://api.ror.org/v2/organizations?query=MIT" - Network accessline 31
curl "https://api.ror.org/v2/organizations?query=MIT" - Network accessline 33
# Affiliation matching (fuzzy match for messy affiliation strings) curl "https://api.ror.org/v2/organizations?affiliation=Dept+of+CS,+Massachusetts+Inst+of+Technology" - Network accessline 34
curl "https://api.ror.org/v2/organizations?affiliation=Dept+of+CS,+Massachusetts+Inst+of+Technology" - Network accessline 36
# Filter by country curl "https://api.ror.org/v2/organizations?query=university&filter=locations.geonames_details.country_code:US" - Network accessline 37
curl "https://api.ror.org/v2/organizations?query=university&filter=locations.geonames_details.country_code:US" - Network accessline 39
# Filter by organization type curl "https://api.ror.org/v2/organizations?query=research&filter=types:facility" - Network accessline 40
curl "https://api.ror.org/v2/organizations?query=research&filter=types:facility" - Network accessline 46
# Retrieve full record curl "https://api.ror.org/v2/organizations/https://ror.org/042nb2s44" - Network accessline 47
curl "https://api.ror.org/v2/organizations/https://ror.org/042nb2s44" - Network accessline 49
# Also works with just the ID portion curl "https://api.ror.org/v2/organizations/042nb2s44" - Network accessline 50
curl "https://api.ror.org/v2/organizations/042nb2s44" - Network accessline 80
"items": [ { "id": "https://ror.org/042nb2s44", - Network accessline 81
{ "id": "https://ror.org/042nb2s44", "names": [ - Network accessline 82
"id": "https://ror.org/042nb2s44", - Network accessline 100
{"type": "wikidata", "all": ["Q49108"]} ], "links": [{"type": "website", "value": "https://www.mit.edu/"}], - Network accessline 101
], "links": [{"type": "website", "value": "https://www.mit.edu/"}], "relationships": [ - Network accessline 102
"links": [{"type": "website", "value": "https://www.mit.edu/"}], - Network accessline 103
"relationships": [ {"id": "https://ror.org/01a8ajp77", "label": "Lincoln Laboratory", "type": "child"} ], - Network accessline 104
{"id": "https://ror.org/01a8ajp77", "label": "Lincoln Laboratory", "type": "child"} - Network accessline 118
BASE_URL = "https://api.ror.org/v2/organizations" - Network accessline 135
resp = requests.get(BASE_URL, params=params) - Network accessline 162
def match_affiliation(affiliation_string: str) -> dict: """Disambiguate a messy affiliation string to a ROR record.""" resp = requests.get( - Network accessline 163
"""Disambiguate a messy affiliation string to a ROR record.""" resp = requests.get( BASE_URL, - Network accessline 164
resp = requests.get( - Network accessline 175
def get_organization(ror_id: str) -> dict: """Get full ROR record for an organization.""" resp = requests.get(f"{BASE_URL}/{ror_id}") - Network accessline 176
"""Get full ROR record for an organization.""" resp = requests.get(f"{BASE_URL}/{ror_id}") resp.raise_for_status() - Network accessline 177
resp = requests.get(f"{BASE_URL}/{ror_id}") - Network accessline 206
- [ROR](https://ror.org/) - Network accessline 207
- [ROR API Documentation](https://ror.readme.io/) - Network accessline 208
- [ROR Data Dump](https://doi.org/10.5281/zenodo.6347574)
skills/literature/metadata/sophosia-reference-guide/SKILL.md
- Network accessline 8
subcategory: "metadata" keywords: ["reference manager", "PDF viewer", "Markdown notes", "Excalidraw", "paper organization", "open source"] source: "https://github.com/sophosia/sophosia" - Network accessline 9
keywords: ["reference manager", "PDF viewer", "Markdown notes", "Excalidraw", "paper organization", "open source"] source: "https://github.com/sophosia/sophosia" --- - Network accessline 10
source: "https://github.com/sophosia/sophosia" - Network accessline 22
# Download from GitHub releases (Windows/macOS/Linux) # Or build from source: git clone https://github.com/sophosia/sophosia.git - Network accessline 23
# Or build from source: git clone https://github.com/sophosia/sophosia.git cd sophosia - Network accessline 24
git clone https://github.com/sophosia/sophosia.git - Network accessline 109
- [Sophosia GitHub](https://github.com/sophosia/sophosia) - Network accessline 110
- [Excalidraw](https://excalidraw.com/)
skills/literature/metadata/viaf-authority-api/SKILL.md
- Network accessline 8
subcategory: "metadata" keywords: ["VIAF", "authority control", "author disambiguation", "name authority", "OCLC", "library catalog"] source: "https://viaf.org/" - Network accessline 9
keywords: ["VIAF", "authority control", "author disambiguation", "name authority", "OCLC", "library catalog"] source: "https://viaf.org/" --- - Network accessline 10
source: "https://viaf.org/" - Network accessline 24
https://viaf.org - Network accessline 30
# Search by name (AutoSuggest) curl "https://viaf.org/viaf/AutoSuggest?query=einstein+albert" - Network accessline 31
curl "https://viaf.org/viaf/AutoSuggest?query=einstein+albert" - Network accessline 33
# Search via SRU (structured query) curl "https://viaf.org/viaf/search?query=local.personalNames+all+\"hinton+geoffrey\"&\ - Network accessline 34
curl "https://viaf.org/viaf/search?query=local.personalNames+all+\"hinton+geoffrey\"&\ - Network accessline 37
# Search corporate names curl "https://viaf.org/viaf/search?query=local.corporateNames+all+\"MIT\"&\ - Network accessline 38
curl "https://viaf.org/viaf/search?query=local.corporateNames+all+\"MIT\"&\ - Network accessline 45
# JSON format curl "https://viaf.org/viaf/75121530/viaf.json" - Network accessline 46
curl "https://viaf.org/viaf/75121530/viaf.json" - Network accessline 48
# Linked data formats curl -H "Accept: application/json" "https://viaf.org/viaf/75121530" - Network accessline 49
curl -H "Accept: application/json" "https://viaf.org/viaf/75121530" - Network accessline 51
# Cluster data (all linked identities) curl "https://viaf.org/viaf/75121530/justlinks.json" - Network accessline 52
curl "https://viaf.org/viaf/75121530/justlinks.json" - Network accessline 58
# Look up by Library of Congress ID curl "https://viaf.org/viaf/lccn/n79021164/viaf.json" - Network accessline 59
curl "https://viaf.org/viaf/lccn/n79021164/viaf.json" - Network accessline 61
# Look up by ISNI curl "https://viaf.org/viaf/isni/0000000121174331/viaf.json" - Network accessline 62
curl "https://viaf.org/viaf/isni/0000000121174331/viaf.json" - Network accessline 64
# Look up by ORCID curl "https://viaf.org/viaf/sourceID/ORCID|0000-0002-1825-0097/viaf.json" - Network accessline 65
curl "https://viaf.org/viaf/sourceID/ORCID|0000-0002-1825-0097/viaf.json" - Network accessline 67
# Look up by Wikidata QID curl "https://viaf.org/viaf/sourceID/WKP|Q937/viaf.json" - Network accessline 68
curl "https://viaf.org/viaf/sourceID/WKP|Q937/viaf.json" - Network accessline 119
BASE_URL = "https://viaf.org/viaf" - Network accessline 122
def search_person(name: str, limit: int = 10) -> list: """Search VIAF for personal name authorities.""" resp = requests.get( - Network accessline 123
"""Search VIAF for personal name authorities.""" resp = requests.get( f"{BASE_URL}/AutoSuggest", - Network accessline 124
resp = requests.get( - Network accessline 142
def get_authority(viaf_id: str) -> dict: """Get full VIAF authority record.""" resp = requests.get(f"{BASE_URL}/{viaf_id}/viaf.json") - Network accessline 143
"""Get full VIAF authority record.""" resp = requests.get(f"{BASE_URL}/{viaf_id}/viaf.json") resp.raise_for_status() - Network accessline 144
resp = requests.get(f"{BASE_URL}/{viaf_id}/viaf.json") - Network accessline 172
def resolve_by_orcid(orcid: str) -> dict: """Resolve ORCID to VIAF authority record.""" resp = requests.get( - Network accessline 173
"""Resolve ORCID to VIAF authority record.""" resp = requests.get( f"{BASE_URL}/sourceID/ORCID|{orcid}/viaf.json" - Network accessline 174
resp = requests.get( - Network accessline 207
- [VIAF](https://viaf.org/) - Network accessline 208
- [VIAF API Documentation](https://www.oclc.org/developer/api/oclc-apis/viaf.en.html) - Network accessline 209
- [VIAF Guidelines](https://www.oclc.org/research/activities/viaf.html)
skills/literature/metadata/wikidata-api-guide/SKILL.md
- Network accessline 8
subcategory: "metadata" keywords: ["wikidata", "sparql", "linked-data", "metadata", "knowledge-graph", "scholarly"] source: "https://www.wikidata.org/wiki/Wikidata:SPARQL_query_service" - Network accessline 9
keywords: ["wikidata", "sparql", "linked-data", "metadata", "knowledge-graph", "scholarly"] source: "https://www.wikidata.org/wiki/Wikidata:SPARQL_query_service" --- - Network accessline 10
source: "https://www.wikidata.org/wiki/Wikidata:SPARQL_query_service" - Network accessline 28
# No API key needed -- set a descriptive User-Agent header as courtesy curl -G "https://query.wikidata.org/sparql" \ - Network accessline 29
curl -G "https://query.wikidata.org/sparql" \ - Network accessline 40
GET https://query.wikidata.org/sparql?query={SPARQL}&format=json - Network accessline 50
curl -G "https://query.wikidata.org/sparql" \ - Network accessline 81
SPARQL_URL = "https://query.wikidata.org/sparql" - Network accessline 87
def query_wikidata(sparql_query): """Execute a SPARQL query against Wikidata.""" resp = requests.get( - Network accessline 88
"""Execute a SPARQL query against Wikidata.""" resp = requests.get( SPARQL_URL, - Network accessline 89
resp = requests.get( - Network accessline 153
- Wikidata SPARQL Query Service: https://query.wikidata.org/ - Network accessline 154
- Wikidata SPARQL Tutorial: https://www.wikidata.org/wiki/Wikidata:SPARQL_tutorial - Network accessline 155
- Wikidata Properties for Scholarly Articles: https://www.wikidata.org/wiki/Wikidata:WikiProject_Source_MetaData - Network accessline 156
- Wikidata REST API: https://www.wikidata.org/wiki/Wikidata:REST_API
skills/literature/metadata/zoplicate-dedup-guide/SKILL.md
- Network accessline 8
subcategory: "metadata" keywords: ["Zotero", "deduplication", "duplicate detection", "library cleanup", "metadata merge"] source: "https://github.com/ChenglongMa/zoplicate" - Network accessline 9
keywords: ["Zotero", "deduplication", "duplicate detection", "library cleanup", "metadata merge"] source: "https://github.com/ChenglongMa/zoplicate" --- - Network accessline 10
source: "https://github.com/ChenglongMa/zoplicate" - Network accessline 146
- [Zoplicate GitHub](https://github.com/ChenglongMa/zoplicate) - Network accessline 147
- [Zotero Duplicate Detection](https://www.zotero.org/support/duplicate_detection)
skills/literature/metadata/zotero-actions-tags-guide/SKILL.md
- Network accessline 8
subcategory: "metadata" keywords: ["Zotero", "automation", "tags", "workflow", "actions", "productivity"] source: "https://github.com/windingwind/zotero-actions-tags" - Network accessline 9
keywords: ["Zotero", "automation", "tags", "workflow", "actions", "productivity"] source: "https://github.com/windingwind/zotero-actions-tags" --- - Network accessline 10
source: "https://github.com/windingwind/zotero-actions-tags" - Network accessline 210
- [Zotero Actions & Tags GitHub](https://github.com/windingwind/zotero-actions-tags) - Network accessline 211
- [Zotero JavaScript API](https://www.zotero.org/support/dev/client_coding/javascript_api) - Network accessline 212
- [Zotero Plugin Development](https://www.zotero.org/support/dev/client_coding/plugin_development)
skills/literature/metadata/zotmoov-guide/SKILL.md
- Network accessline 8
subcategory: "metadata" keywords: ["Zotero", "file management", "attachment", "auto-move", "organization", "linked files"] source: "https://github.com/wileyyugioh/zotmoov" - Network accessline 9
keywords: ["Zotero", "file management", "attachment", "auto-move", "organization", "linked files"] source: "https://github.com/wileyyugioh/zotmoov" --- - Network accessline 10
source: "https://github.com/wileyyugioh/zotmoov" - Network accessline 119
- [ZotMoov GitHub](https://github.com/wileyyugioh/zotmoov) - Network accessline 120
- [Zotero Linked Files](https://www.zotero.org/support/attaching_files)
skills/literature/metadata/zutilo-guide/SKILL.md
- Network accessline 8
subcategory: "metadata" keywords: ["Zotero", "shortcuts", "batch editing", "utility", "productivity", "copy paste"] source: "https://github.com/wshanks/Zutilo" - Network accessline 9
keywords: ["Zotero", "shortcuts", "batch editing", "utility", "productivity", "copy paste"] source: "https://github.com/wshanks/Zutilo" --- - Network accessline 10
source: "https://github.com/wshanks/Zutilo" - Network accessline 139
- [Zutilo GitHub](https://github.com/wshanks/Zutilo) - Network accessline 140
- [Zotero Keyboard Shortcuts](https://www.zotero.org/support/kb)
skills/literature/search/arxiv-api/SKILL.md
- Network accessline 8
subcategory: "search" keywords: ["academic database search", "literature search", "scholarly database", "semantic search", "preprint server"] source: "https://info.arxiv.org/help/api/" - Network accessline 9
keywords: ["academic database search", "literature search", "scholarly database", "semantic search", "preprint server"] source: "https://info.arxiv.org/help/api/" --- - Network accessline 10
source: "https://info.arxiv.org/help/api/" - Network accessline 40
- **URL**: `GET http://export.arxiv.org/api/query` - Network accessline 52
curl "http://export.arxiv.org/api/query?search_query=au:hinton+AND+ti:deep+learning&start=0&max_results=5&sortBy=submittedDate&sortOrder=descending" - Network accessline 58
- **URL**: `GET http://export.arxiv.org/api/query` - Network accessline 66
curl "http://export.arxiv.org/api/query?id_list=2301.07041&max_results=1" - Network accessline 81
curl "http://export.arxiv.org/api/query?search_query=cat:cs.AI+AND+submittedDate:[202603010000+TO+202603092359]&sortBy=submittedDate&sortOrder=descending&max_results=50" - Network accessline 89
curl "http://export.arxiv.org/api/query?search_query=(ti:transformer+OR+abs:transformer)+AND+cat:cs.CL&max_results=20&sortBy=relevance" - Network accessline 97
curl "http://export.arxiv.org/api/query?id_list=2301.07041,2302.13971,2303.08774&max_results=3" - Network accessline 102
- Official documentation: https://info.arxiv.org/help/api/ - Network accessline 103
- arXiv API user manual: https://info.arxiv.org/help/api/user-manual.html - Network accessline 104
- arXiv bulk data access: https://info.arxiv.org/help/bulk_data.html
skills/literature/search/arxiv-batch-reporting/SKILL.md
skills/literature/search/arxiv-cli-tools/SKILL.md
- Network accessline 8
subcategory: "search" keywords: ["arxiv", "command line", "paper download", "preprint search", "batch download", "literature retrieval"] source: "https://pypi.org/project/arxiv-cli-tools/" - Network accessline 9
keywords: ["arxiv", "command line", "paper download", "preprint search", "batch download", "literature retrieval"] source: "https://pypi.org/project/arxiv-cli-tools/" --- - Network accessline 10
source: "https://pypi.org/project/arxiv-cli-tools/" - Network accessline 156
# Get BibTeX for a specific paper curl -s "https://arxiv.org/bibtex/1706.03762" - Network accessline 157
curl -s "https://arxiv.org/bibtex/1706.03762" - Network accessline 163
- For bulk downloads, add delays between requests - The CLI tool respects rate limits by default - See [arXiv API Terms of Use](https://info.arxiv.org/help/api/tou.html) - Network accessline 165
- See [arXiv API Terms of Use](https://info.arxiv.org/help/api/tou.html) - Network accessline 169
- [arxiv-cli-tools on PyPI](https://pypi.org/project/arxiv-cli-tools/) - Network accessline 170
- [arxiv Python Client](https://github.com/lukasschwab/arxiv.py) - Network accessline 171
- [arXiv API Documentation](https://info.arxiv.org/help/api/) - Network accessline 172
- [arXiv Category Taxonomy](https://arxiv.org/category_taxonomy)
skills/literature/search/arxiv-paper-processor/SKILL.md
- Network accessline 8
subcategory: "search" keywords: ["arxiv", "paper processing", "PDF parsing", "metadata extraction", "preprint analysis", "research pipeline"] source: "https://github.com/tatsu-lab/gpt_paper_assistant" - Network accessline 9
keywords: ["arxiv", "paper processing", "PDF parsing", "metadata extraction", "preprint analysis", "research pipeline"] source: "https://github.com/tatsu-lab/gpt_paper_assistant" --- - Network accessline 10
source: "https://github.com/tatsu-lab/gpt_paper_assistant" - Network accessline 33
# Fetch metadata via Atom feed arxiv_id = "2301.07041" url = f"http://export.arxiv.org/api/query?id_list={arxiv_id}" - Network accessline 34
arxiv_id = "2301.07041" url = f"http://export.arxiv.org/api/query?id_list={arxiv_id}" response = urllib.request.urlopen(url) - Network accessline 35
url = f"http://export.arxiv.org/api/query?id_list={arxiv_id}" response = urllib.request.urlopen(url) feed = feedparser.parse(response.read()) - Network accessline 36
response = urllib.request.urlopen(url) - Network accessline 51
# Download LaTeX source (typically a .tar.gz) wget https://arxiv.org/e-print/2301.07041 -O paper_source.tar.gz - Network accessline 52
wget https://arxiv.org/e-print/2301.07041 -O paper_source.tar.gz - Network accessline 77
# Process a PDF curl -X POST "http://localhost:8070/api/processFulltextDocument" \ - Network accessline 78
curl -X POST "http://localhost:8070/api/processFulltextDocument" \ - Network accessline 138
- arXiv API: https://info.arxiv.org/help/api/index.html - Network accessline 139
- GROBID: https://github.com/kermitt2/grobid - Network accessline 140
- GPT Paper Assistant: https://github.com/tatsu-lab/gpt_paper_assistant - Network accessline 141
- arXiv bulk data access: https://info.arxiv.org/help/bulk_data/index.html
skills/literature/search/baidu-scholar-guide/SKILL.md
- Network accessline 8
subcategory: "search" keywords: ["baidu scholar", "chinese literature", "academic search", "CNKI", "bilingual research", "chinese journals"] source: "https://xueshu.baidu.com" - Network accessline 9
keywords: ["baidu scholar", "chinese literature", "academic search", "CNKI", "bilingual research", "chinese journals"] source: "https://xueshu.baidu.com" --- - Network accessline 10
source: "https://xueshu.baidu.com" - Network accessline 27
Navigate to `https://xueshu.baidu.com` and enter your search terms. Baidu Scholar supports both Chinese and English queries. For bilingual research, run parallel searches: - Network accessline 107
- Baidu Scholar: https://xueshu.baidu.com - Network accessline 108
- CNKI: https://www.cnki.net - Network accessline 109
- ChinaXiv: https://chinaxiv.org - Network accessline 110
- GB/T 7714-2015 citation standard: https://std.samr.gov.cn
skills/literature/search/base-academic-search/SKILL.md
- Network accessline 8
subcategory: "search" keywords: ["BASE", "academic search", "open access", "Bielefeld", "OAI-PMH", "repository aggregator"] source: "https://www.base-search.net/" - Network accessline 9
keywords: ["BASE", "academic search", "open access", "Bielefeld", "OAI-PMH", "repository aggregator"] source: "https://www.base-search.net/" --- - Network accessline 10
source: "https://www.base-search.net/" - Network accessline 24
https://api.base-search.net/cgi-bin/BaseHttpSearchInterface.fcgi - Network accessline 30
# Basic keyword search (JSON response) curl "https://api.base-search.net/cgi-bin/BaseHttpSearchInterface.fcgi?\ - Network accessline 31
curl "https://api.base-search.net/cgi-bin/BaseHttpSearchInterface.fcgi?\ - Network accessline 34
# Search with field filters curl "https://api.base-search.net/cgi-bin/BaseHttpSearchInterface.fcgi?\ - Network accessline 35
curl "https://api.base-search.net/cgi-bin/BaseHttpSearchInterface.fcgi?\ - Network accessline 38
# Filter by document type and year curl "https://api.base-search.net/cgi-bin/BaseHttpSearchInterface.fcgi?\ - Network accessline 39
curl "https://api.base-search.net/cgi-bin/BaseHttpSearchInterface.fcgi?\ - Network accessline 42
# Open access only curl "https://api.base-search.net/cgi-bin/BaseHttpSearchInterface.fcgi?\ - Network accessline 43
curl "https://api.base-search.net/cgi-bin/BaseHttpSearchInterface.fcgi?\ - Network accessline 98
"dcsubject": ["machine learning", "attention mechanism"], "dcdescription": "The dominant sequence transduction models...", "dcidentifier": "https://arxiv.org/abs/1706.03762", - Network accessline 99
"dcdescription": "The dominant sequence transduction models...", "dcidentifier": "https://arxiv.org/abs/1706.03762", "dcsource": "arXiv.org", - Network accessline 100
"dcidentifier": "https://arxiv.org/abs/1706.03762", - Network accessline 118
BASE_URL = "https://api.base-search.net/cgi-bin/BaseHttpSearchInterface.fcgi" - Network accessline 138
resp = requests.get(BASE_URL, params=params) - Network accessline 194
- [BASE Search Engine](https://www.base-search.net/) - Network accessline 195
- [BASE API Documentation](https://www.base-search.net/about/en/about_develop.php) - Network accessline 196
- [BASE Content Providers](https://www.base-search.net/about/en/about_sources.php)
skills/literature/search/biorxiv-api/SKILL.md
- Network accessline 8
subcategory: "search" keywords: ["preprint server", "literature search", "academic database search", "full-text retrieval"] source: "https://api.biorxiv.org/" - Network accessline 9
keywords: ["preprint server", "literature search", "academic database search", "full-text retrieval"] source: "https://api.biorxiv.org/" --- - Network accessline 10
source: "https://api.biorxiv.org/" - Network accessline 33
- **URL**: `GET https://api.biorxiv.org/details/{server}/{interval}/{cursor}` - Network accessline 45
curl "https://api.biorxiv.org/details/biorxiv/2024-01-01/2024-01-31/0" - Network accessline 54
- **URL**: `GET https://api.biorxiv.org/pubs/{server}/{interval}/{cursor}` - Network accessline 66
curl "https://api.biorxiv.org/pubs/biorxiv/2024-01-01/2024-06-30/0" - Network accessline 82
# Fetch recent neuroscience preprints curl "https://api.biorxiv.org/details/biorxiv/2024-06-01/2024-06-07/0" \ - Network accessline 83
curl "https://api.biorxiv.org/details/biorxiv/2024-06-01/2024-06-07/0" \ - Network accessline 92
# Check publication status for recent preprints curl "https://api.biorxiv.org/pubs/biorxiv/2024-01-01/2024-06-30/0" \ - Network accessline 93
curl "https://api.biorxiv.org/pubs/biorxiv/2024-01-01/2024-06-30/0" \ - Network accessline 104
base = "https://api.biorxiv.org/details/biorxiv/2024-06-01/2024-06-07" - Network accessline 108
while True: resp = requests.get(f"{base}/{cursor}").json() - Network accessline 109
resp = requests.get(f"{base}/{cursor}").json() - Network accessline 121
- Official documentation: https://api.biorxiv.org/ - Network accessline 122
- bioRxiv homepage: https://www.biorxiv.org/ - Network accessline 123
- medRxiv API (same structure): https://api.biorxiv.org/ (use `medrxiv` as server parameter)
skills/literature/search/chatpaper-guide/SKILL.md
- Network accessline 8
subcategory: "search" keywords: ["arxiv", "paper-summarization", "literature-search", "chatgpt", "research-acceleration"] source: "https://github.com/kaixindelele/ChatPaper" - Network accessline 9
keywords: ["arxiv", "paper-summarization", "literature-search", "chatgpt", "research-acceleration"] source: "https://github.com/kaixindelele/ChatPaper" --- - Network accessline 10
source: "https://github.com/kaixindelele/ChatPaper" - Network accessline 28
git clone https://github.com/kaixindelele/ChatPaper.git - Access to secretsline 37
[Credential-related source omitted] - Network accessline 120
- ChatPaper repository: https://github.com/kaixindelele/ChatPaper - Network accessline 121
- arXiv API documentation: https://info.arxiv.org/help/api/
skills/literature/search/citation-chaining-guide/SKILL.md
- Network accessline 43
HEADERS = {"User-Agent": "ResearchPlugins/1.0 (https://wentor.ai)"} - Network accessline 45
def get_references(work_id): """Get all references of a paper via OpenAlex.""" url = f"https://api.openalex.org/works/{work_id}" - Network accessline 46
"""Get all references of a paper via OpenAlex.""" url = f"https://api.openalex.org/works/{work_id}" response = requests.get(url, headers=HEADERS) - Network accessline 47
url = f"https://api.openalex.org/works/{work_id}" response = requests.get(url, headers=HEADERS) paper = response.json() - Network accessline 48
response = requests.get(url, headers=HEADERS) - Network accessline 52
references = [] for ref_id in ref_ids: ref = requests.get(f"https://api.openalex.org/works/{ref_id.split('/')[-1]}", headers=HEADERS).json() - Network accessline 53
for ref_id in ref_ids: ref = requests.get(f"https://api.openalex.org/works/{ref_id.split('/')[-1]}", headers=HEADERS).json() if ref.get("title"): - Network accessline 54
ref = requests.get(f"https://api.openalex.org/works/{ref_id.split('/')[-1]}", headers=HEADERS).json() - Network accessline 77
page = 1 while len(all_citations) < limit: response = requests.get( - Network accessline 78
while len(all_citations) < limit: response = requests.get( "https://api.openalex.org/works", - Network accessline 79
response = requests.get( - Network accessline 80
"https://api.openalex.org/works",
skills/literature/search/citeseerx-api/SKILL.md
- Network accessline 8
subcategory: "search" keywords: ["CiteSeerX", "computer science", "citation index", "academic search", "CS literature", "autonomous citation"] source: "https://citeseerx.ist.psu.edu/" - Network accessline 9
keywords: ["CiteSeerX", "computer science", "citation index", "academic search", "CS literature", "autonomous citation"] source: "https://citeseerx.ist.psu.edu/" --- - Network accessline 10
source: "https://citeseerx.ist.psu.edu/" - Network accessline 24
https://citeseerx.ist.psu.edu/api - Network accessline 30
# Keyword search curl "https://citeseerx.ist.psu.edu/api/search?q=graph+neural+networks&start=0&rows=20" - Network accessline 31
curl "https://citeseerx.ist.psu.edu/api/search?q=graph+neural+networks&start=0&rows=20" - Network accessline 33
# Search by title curl "https://citeseerx.ist.psu.edu/api/search?q=title:attention+is+all+you+need" - Network accessline 34
curl "https://citeseerx.ist.psu.edu/api/search?q=title:attention+is+all+you+need" - Network accessline 36
# Search by author curl "https://citeseerx.ist.psu.edu/api/search?q=author:hinton&rows=25" - Network accessline 37
curl "https://citeseerx.ist.psu.edu/api/search?q=author:hinton&rows=25" - Network accessline 39
# Filter by year curl "https://citeseerx.ist.psu.edu/api/search?q=federated+learning&year=2024" - Network accessline 40
curl "https://citeseerx.ist.psu.edu/api/search?q=federated+learning&year=2024" - Network accessline 42
# Sort by citation count curl "https://citeseerx.ist.psu.edu/api/search?q=reinforcement+learning&sort=citationCount+desc" - Network accessline 43
curl "https://citeseerx.ist.psu.edu/api/search?q=reinforcement+learning&sort=citationCount+desc" - Network accessline 49
# Get document metadata curl "https://citeseerx.ist.psu.edu/api/document?doi=10.1.1.123.456" - Network accessline 50
curl "https://citeseerx.ist.psu.edu/api/document?doi=10.1.1.123.456" - Network accessline 52
# Get citations for a document curl "https://citeseerx.ist.psu.edu/api/citations?doi=10.1.1.123.456" - Network accessline 53
curl "https://citeseerx.ist.psu.edu/api/citations?doi=10.1.1.123.456" - Network accessline 55
# Get citing documents curl "https://citeseerx.ist.psu.edu/api/citedby?doi=10.1.1.123.456" - Network accessline 56
curl "https://citeseerx.ist.psu.edu/api/citedby?doi=10.1.1.123.456" - Network accessline 83
"venue": "AI Open", "citationCount": 3500, "url": "https://citeseerx.ist.psu.edu/doc/10.1.1.123.456" - Network accessline 84
"citationCount": 3500, "url": "https://citeseerx.ist.psu.edu/doc/10.1.1.123.456" } - Network accessline 85
"url": "https://citeseerx.ist.psu.edu/doc/10.1.1.123.456" - Network accessline 97
BASE_URL = "https://citeseerx.ist.psu.edu/api" - Network accessline 111
resp = requests.get(f"{BASE_URL}/search", params=params, timeout=30) - Network accessline 130
def get_citations(doc_id: str) -> list: """Get papers cited by a document.""" resp = requests.get( - Network accessline 131
"""Get papers cited by a document.""" resp = requests.get( f"{BASE_URL}/citations", - Network accessline 132
resp = requests.get( - Network accessline 141
def get_cited_by(doc_id: str) -> list: """Get papers that cite a document.""" resp = requests.get( - Network accessline 142
"""Get papers that cite a document.""" resp = requests.get( f"{BASE_URL}/citedby", - Network accessline 143
resp = requests.get( - Network accessline 181
- [CiteSeerX](https://citeseerx.ist.psu.edu/) - Network accessline 182
- [CiteSeerX Documentation](https://citeseerx.ist.psu.edu/about)
skills/literature/search/database-comparison-guide/SKILL.md
- Network accessline 37
# arXiv API query for recent ML papers curl "http://export.arxiv.org/api/query?search_query=cat:cs.LG+AND+ti:transformer&start=0&max_results=25&sortBy=submittedDate&sortOrder=descending" - Network accessline 38
curl "http://export.arxiv.org/api/query?search_query=cat:cs.LG+AND+ti:transformer&start=0&max_results=25&sortBy=submittedDate&sortOrder=descending" - Network accessline 44
# PubMed E-utilities search with MeSH curl "https://eutils.ncbi.nlm.nih.gov/entrez/eutils/esearch.fcgi?db=pubmed&term=%22machine+learning%22[MeSH]+AND+%22drug+discovery%22[MeSH]&retmax=50&sort=date" - Network accessline 45
curl "https://eutils.ncbi.nlm.nih.gov/entrez/eutils/esearch.fcgi?db=pubmed&term=%22machine+learning%22[MeSH]+AND+%22drug+discovery%22[MeSH]&retmax=50&sort=date"
skills/literature/search/deep-literature-search/SKILL.md
skills/literature/search/eric-education-api/SKILL.md
- Network accessline 8
subcategory: "search" keywords: ["ERIC", "education research", "IES", "pedagogy", "teaching", "educational technology"] source: "https://eric.ed.gov/" - Network accessline 9
keywords: ["ERIC", "education research", "IES", "pedagogy", "teaching", "educational technology"] source: "https://eric.ed.gov/" --- - Network accessline 10
source: "https://eric.ed.gov/" - Network accessline 24
https://api.ies.ed.gov/eric/ - Network accessline 30
# Basic keyword search curl "https://api.ies.ed.gov/eric/?search=online+learning&format=json&rows=20" - Network accessline 31
curl "https://api.ies.ed.gov/eric/?search=online+learning&format=json&rows=20" - Network accessline 33
# Search in specific fields curl "https://api.ies.ed.gov/eric/?search=title:\"blended learning\"&format=json" - Network accessline 34
curl "https://api.ies.ed.gov/eric/?search=title:\"blended learning\"&format=json" - Network accessline 36
# Filter by publication date curl "https://api.ies.ed.gov/eric/?search=STEM+education&start=0&rows=25&\ - Network accessline 37
curl "https://api.ies.ed.gov/eric/?search=STEM+education&start=0&rows=25&\ - Network accessline 40
# Filter by publication type curl "https://api.ies.ed.gov/eric/?search=formative+assessment&\ - Network accessline 41
curl "https://api.ies.ed.gov/eric/?search=formative+assessment&\ - Network accessline 44
# Filter by descriptor (ERIC thesaurus term) curl "https://api.ies.ed.gov/eric/?search=descriptor:\"Higher Education\"&format=json" - Network accessline 45
curl "https://api.ies.ed.gov/eric/?search=descriptor:\"Higher Education\"&format=json" - Network accessline 47
# Peer-reviewed only curl "https://api.ies.ed.gov/eric/?search=metacognition&peerreviewed=true&format=json" - Network accessline 48
curl "https://api.ies.ed.gov/eric/?search=metacognition&peerreviewed=true&format=json" - Network accessline 106
"educationlevel": ["Higher Education"], "peerreviewed": "T", "url": "https://eric.ed.gov/?id=EJ1389012", - Network accessline 107
"peerreviewed": "T", "url": "https://eric.ed.gov/?id=EJ1389012", "publicationtype": "Journal Articles", - Network accessline 108
"url": "https://eric.ed.gov/?id=EJ1389012", - Network accessline 122
BASE_URL = "https://api.ies.ed.gov/eric/" - Network accessline 142
resp = requests.get(BASE_URL, params=params) - Network accessline 197
- [ERIC Website](https://eric.ed.gov/) - Network accessline 198
- [ERIC API Guide](https://eric.ed.gov/pdf/ERIC_API.pdf) - Network accessline 199
- [ERIC Thesaurus](https://eric.ed.gov/thesaurus)
skills/literature/search/europe-pmc-api/SKILL.md
- Network accessline 8
subcategory: "search" keywords: ["academic database search", "literature search", "citation tracking", "field-specific search"] source: "https://europepmc.org/RestfulWebService" - Network accessline 9
keywords: ["academic database search", "literature search", "citation tracking", "field-specific search"] source: "https://europepmc.org/RestfulWebService" --- - Network accessline 10
source: "https://europepmc.org/RestfulWebService" - Network accessline 31
- **URL**: `GET https://www.ebi.ac.uk/europepmc/webservices/rest/search` - Network accessline 44
curl "https://www.ebi.ac.uk/europepmc/webservices/rest/search?query=CRISPR+AND+cancer&format=json&resultType=core&pageSize=10&sort=CITED+desc" - Network accessline 50
- **URL**: `GET https://www.ebi.ac.uk/europepmc/webservices/rest/{source}/{id}/citations` - Network accessline 61
curl "https://www.ebi.ac.uk/europepmc/webservices/rest/MED/33116299/citations?format=json&pageSize=10" - Network accessline 67
- **URL**: `GET https://www.ebi.ac.uk/europepmc/webservices/rest/{source}/{id}/references` - Network accessline 78
curl "https://www.ebi.ac.uk/europepmc/webservices/rest/MED/33116299/references?format=json&pageSize=50" - Network accessline 93
curl -s "https://www.ebi.ac.uk/europepmc/webservices/rest/search?query=(TITLE:immunotherapy+AND+TITLE:melanoma)+AND+(PUB_YEAR:[2022+TO+2026])&format=json&resultType=core&pageSize=25&sort=CITED+desc" | jq '.resultList.result[] | {title: .tit - Network accessline 101
curl -s "https://www.ebi.ac.uk/europepmc/webservices/rest/search?query=(SRC:PPR)+AND+large+language+models+AND+biology&format=json&pageSize=10" | jq '.resultList.result[] | {title: .title, source: .source, year: .pubYear, doi: .doi}' - Network accessline 109
# Get papers that cite the target curl -s "https://www.ebi.ac.uk/europepmc/webservices/rest/MED/33116299/citations?format=json&pageSize=50" | jq '.citationList.citation | length' - Network accessline 110
curl -s "https://www.ebi.ac.uk/europepmc/webservices/rest/MED/33116299/citations?format=json&pageSize=50" | jq '.citationList.citation | length' - Network accessline 112
# Get papers referenced by the target curl -s "https://www.ebi.ac.uk/europepmc/webservices/rest/MED/33116299/references?format=json&pageSize=100" | jq '.referenceList.reference | length' - Network accessline 113
curl -s "https://www.ebi.ac.uk/europepmc/webservices/rest/MED/33116299/references?format=json&pageSize=100" | jq '.referenceList.reference | length' - Network accessline 118
- Official documentation: https://europepmc.org/RestfulWebService - Network accessline 119
- Europe PMC search syntax: https://europepmc.org/searchsyntax - Network accessline 120
- Europe PMC annotations API: https://europepmc.org/AnnotationsApi
skills/literature/search/findpapers-guide/SKILL.md
- Network accessline 8
subcategory: "search" keywords: ["Findpapers", "multi-database search", "systematic review", "arXiv", "PubMed", "Scopus", "IEEE"] source: "https://github.com/jonatasgrosman/findpapers" - Network accessline 9
keywords: ["Findpapers", "multi-database search", "systematic review", "arXiv", "PubMed", "Scopus", "IEEE"] source: "https://github.com/jonatasgrosman/findpapers" --- - Network accessline 10
source: "https://github.com/jonatasgrosman/findpapers" - Access to secretsline 111
[Credential-related source omitted] - Access to secretsline 112
[Credential-related source omitted] - Network accessline 175
- [Findpapers GitHub](https://github.com/jonatasgrosman/findpapers) - Network accessline 176
- [Findpapers PyPI](https://pypi.org/project/findpapers/)
skills/literature/search/ieee-xplore-api/SKILL.md
- Network accessline 8
subcategory: "search" keywords: ["ieee", "engineering literature", "computer science", "technical standards", "conference papers", "xplore"] source: "https://developer.ieee.org/" - Network accessline 9
keywords: ["ieee", "engineering literature", "computer science", "technical standards", "conference papers", "xplore"] source: "https://developer.ieee.org/" --- - Network accessline 10
source: "https://developer.ieee.org/" - Network accessline 24
https://ieeexploreapi.ieee.org/api/v1/search/articles - Network accessline 30
# Basic keyword search curl "https://ieeexploreapi.ieee.org/api/v1/search/articles?\ - Network accessline 31
[Credential-related source omitted] - Access to secretsline 31
[Credential-related source omitted] - Access to secretsline 32
[Credential-related source omitted] - Network accessline 36
# Search with filters curl "https://ieeexploreapi.ieee.org/api/v1/search/articles?\ - Network accessline 37
[Credential-related source omitted] - Access to secretsline 37
[Credential-related source omitted] - Access to secretsline 38
[Credential-related source omitted] - Network accessline 82
# Look up up to 25 DOIs at once curl "https://ieeexploreapi.ieee.org/api/v1/search/articles?\ - Network accessline 83
[Credential-related source omitted] - Access to secretsline 83
[Credential-related source omitted] - Access to secretsline 84
[Credential-related source omitted] - Network accessline 108
"end_page": "110", "citing_paper_count": 15, "pdf_url": "https://ieeexplore.ieee.org/stamp/stamp.jsp?tp=&arnumber=12345", - Network accessline 109
"citing_paper_count": 15, "pdf_url": "https://ieeexplore.ieee.org/stamp/stamp.jsp?tp=&arnumber=12345", "html_url": "https://ieeexplore.ieee.org/document/12345" - Network accessline 110
"pdf_url": "https://ieeexplore.ieee.org/stamp/stamp.jsp?tp=&arnumber=12345", - Network accessline 111
"html_url": "https://ieeexplore.ieee.org/document/12345" - Network accessline 123
[Credential-related source omitted] - Access to secretsline 123
[Credential-related source omitted] - Network accessline 124
BASE_URL = "https://ieeexploreapi.ieee.org/api/v1/search/articles" - Access to secretsline 130
[Credential-related source omitted] - Network accessline 141
resp = requests.get(BASE_URL, params=params) - Network accessline 175
- [IEEE Xplore API Portal](https://developer.ieee.org/) - Network accessline 176
- [API Documentation](https://developer.ieee.org/docs) - Network accessline 177
- [IEEE Xplore Digital Library](https://ieeexplore.ieee.org/)
skills/literature/search/lens-scholarly-api/SKILL.md
- Network accessline 8
subcategory: "search" keywords: ["Lens.org", "patent search", "scholarly search", "citation linking", "innovation", "prior art"] source: "https://www.lens.org/" - Network accessline 9
keywords: ["Lens.org", "patent search", "scholarly search", "citation linking", "innovation", "prior art"] source: "https://www.lens.org/" --- - Network accessline 10
source: "https://www.lens.org/" - Network accessline 22
# Register at https://www.lens.org/lens/user/subscriptions - Network accessline 32
# POST-based search curl -X POST "https://api.lens.org/scholarly/search" \ - Network accessline 33
[Credential-related source omitted] - Access to secretsline 33
[Credential-related source omitted] - Access to secretsline 34
[Credential-related source omitted] - Network accessline 45
# Boolean query curl -X POST "https://api.lens.org/scholarly/search" \ - Network accessline 46
[Credential-related source omitted] - Access to secretsline 46
[Credential-related source omitted] - Access to secretsline 47
[Credential-related source omitted] - Network accessline 68
[Credential-related source omitted] - Access to secretsline 68
[Credential-related source omitted] - Access to secretsline 69
[Credential-related source omitted] - Network accessline 106
[Credential-related source omitted] - Access to secretsline 106
[Credential-related source omitted] - Network accessline 107
BASE_URL = "https://api.lens.org" - Network accessline 132
resp = requests.post( - Network accessline 171
resp = requests.post( - Network accessline 209
- [Lens.org](https://www.lens.org/) - Network accessline 210
- [Lens API Documentation](https://docs.api.lens.org/) - Network accessline 211
- [Lens Scholarly Search](https://www.lens.org/lens/search/scholar/list)
skills/literature/search/open-library-api/SKILL.md
- Network accessline 8
subcategory: "search" keywords: ["Open Library", "book search", "ISBN lookup", "Internet Archive", "book metadata", "digital library"] source: "https://openlibrary.org/" - Network accessline 9
keywords: ["Open Library", "book search", "ISBN lookup", "Internet Archive", "book metadata", "digital library"] source: "https://openlibrary.org/" --- - Network accessline 10
source: "https://openlibrary.org/" - Network accessline 24
# Full-text search curl "https://openlibrary.org/search.json?q=machine+learning&limit=20" - Network accessline 25
curl "https://openlibrary.org/search.json?q=machine+learning&limit=20" - Network accessline 27
# Search by title curl "https://openlibrary.org/search.json?title=deep+learning&limit=10" - Network accessline 28
curl "https://openlibrary.org/search.json?title=deep+learning&limit=10" - Network accessline 30
# Search by author curl "https://openlibrary.org/search.json?author=goodfellow&title=deep+learning" - Network accessline 31
curl "https://openlibrary.org/search.json?author=goodfellow&title=deep+learning" - Network accessline 33
# Filter by subject curl "https://openlibrary.org/search.json?q=statistics&subject=data+analysis" - Network accessline 34
curl "https://openlibrary.org/search.json?q=statistics&subject=data+analysis" - Network accessline 36
# Filter by publication year curl "https://openlibrary.org/search.json?q=artificial+intelligence&first_publish_year=2020" - Network accessline 37
curl "https://openlibrary.org/search.json?q=artificial+intelligence&first_publish_year=2020" - Network accessline 39
# Sort by edition count curl "https://openlibrary.org/search.json?q=calculus&sort=editions" - Network accessline 40
curl "https://openlibrary.org/search.json?q=calculus&sort=editions" - Network accessline 46
# Get work (canonical book entity) curl "https://openlibrary.org/works/OL45804W.json" - Network accessline 47
curl "https://openlibrary.org/works/OL45804W.json" - Network accessline 49
# Get edition curl "https://openlibrary.org/books/OL7353617M.json" - Network accessline 50
curl "https://openlibrary.org/books/OL7353617M.json" - Network accessline 52
# Get by ISBN curl "https://openlibrary.org/isbn/9780262035613.json" - Network accessline 53
curl "https://openlibrary.org/isbn/9780262035613.json" - Network accessline 55
# Bibliographic data via Books API curl "https://openlibrary.org/api/books?bibkeys=ISBN:9780262035613&format=json&jscmd=data" - Network accessline 56
curl "https://openlibrary.org/api/books?bibkeys=ISBN:9780262035613&format=json&jscmd=data" - Network accessline 62
# By ISBN (S/M/L sizes) https://covers.openlibrary.org/b/isbn/9780262035613-M.jpg - Network accessline 63
https://covers.openlibrary.org/b/isbn/9780262035613-M.jpg - Network accessline 65
# By OLID https://covers.openlibrary.org/b/olid/OL7353617M-L.jpg - Network accessline 66
https://covers.openlibrary.org/b/olid/OL7353617M-L.jpg - Network accessline 72
# Get author curl "https://openlibrary.org/authors/OL34184A.json" - Network accessline 73
curl "https://openlibrary.org/authors/OL34184A.json" - Network accessline 75
# Search authors curl "https://openlibrary.org/search/authors.json?q=hinton" - Network accessline 76
curl "https://openlibrary.org/search/authors.json?q=hinton" - Network accessline 78
# Author's works curl "https://openlibrary.org/authors/OL34184A/works.json?limit=20" - Network accessline 79
curl "https://openlibrary.org/authors/OL34184A/works.json?limit=20" - Network accessline 124
BASE_URL = "https://openlibrary.org" - Network accessline 134
resp = requests.get(f"{BASE_URL}/search.json", params=params) - Network accessline 154
def get_by_isbn(isbn: str) -> dict: """Look up a book by ISBN.""" resp = requests.get( - Network accessline 155
"""Look up a book by ISBN.""" resp = requests.get( f"{BASE_URL}/api/books", - Network accessline 156
resp = requests.get( - Network accessline 169
def get_author_works(author_key: str, limit: int = 20) -> list: """Get works by an author.""" resp = requests.get( - Network accessline 170
"""Get works by an author.""" resp = requests.get( f"{BASE_URL}/authors/{author_key}/works.json", - Network accessline 171
resp = requests.get( - Network accessline 194
- [Open Library](https://openlibrary.org/) - Network accessline 195
- [Open Library API](https://openlibrary.org/developers/api) - Network accessline 196
- [Open Library Data Dumps](https://openlibrary.org/developers/dumps)
skills/literature/search/open-semantic-search-guide/SKILL.md
- Network accessline 8
subcategory: "search" keywords: ["semantic search", "text mining", "self-hosted", "Solr", "NLP", "entity extraction"] source: "https://github.com/opensemanticsearch/open-semantic-search" - Network accessline 9
keywords: ["semantic search", "text mining", "self-hosted", "Solr", "NLP", "entity extraction"] source: "https://github.com/opensemanticsearch/open-semantic-search" --- - Network accessline 10
source: "https://github.com/opensemanticsearch/open-semantic-search" - Network accessline 22
# Docker deployment (recommended) git clone https://github.com/opensemanticsearch/open-semantic-search.git - Network accessline 23
git clone https://github.com/opensemanticsearch/open-semantic-search.git - Network accessline 27
# Access web UI at http://localhost:8080 - Network accessline 28
# Admin panel at http://localhost:8080/admin - Network accessline 53
# Index a directory of papers curl -X POST "http://localhost:8080/api/index" \ - Network accessline 54
curl -X POST "http://localhost:8080/api/index" \ - Network accessline 58
# Index single file curl -X POST "http://localhost:8080/api/index" \ - Network accessline 59
curl -X POST "http://localhost:8080/api/index" \ - Network accessline 95
SEARCH_URL = "http://localhost:8080/api/search" - Network accessline 111
resp = requests.get(SEARCH_URL, params=params) - Network accessline 161
def get_entity_network(entity, depth=2): """Get co-occurring entities for a given entity.""" resp = requests.get( - Network accessline 162
"""Get co-occurring entities for a given entity.""" resp = requests.get( f"{SEARCH_URL}/graph", - Network accessline 163
resp = requests.get( - Network accessline 188
- [Open Semantic Search GitHub](https://github.com/opensemanticsearch/open-semantic-search) - Network accessline 189
- [Apache Solr](https://solr.apache.org/) - Network accessline 190
- [Apache Tika](https://tika.apache.org/)
skills/literature/search/openaire-api/SKILL.md
- Network accessline 8
subcategory: "search" keywords: ["openaire", "european research", "open science", "EU funding", "research outputs", "open access"] source: "https://develop.openaire.eu/" - Network accessline 9
keywords: ["openaire", "european research", "open science", "EU funding", "research outputs", "open access"] source: "https://develop.openaire.eu/" --- - Network accessline 10
source: "https://develop.openaire.eu/" - Network accessline 24
https://api.openaire.eu - Network accessline 30
# Search by keywords curl "https://api.openaire.eu/search/publications?keywords=climate+change+adaptation&format=json&size=10" - Network accessline 31
curl "https://api.openaire.eu/search/publications?keywords=climate+change+adaptation&format=json&size=10" - Network accessline 33
# Filter by open access curl "https://api.openaire.eu/search/publications?keywords=machine+learning&openaccessonly=true&format=json" - Network accessline 34
curl "https://api.openaire.eu/search/publications?keywords=machine+learning&openaccessonly=true&format=json" - Network accessline 36
# Filter by date curl "https://api.openaire.eu/search/publications?keywords=CRISPR&fromDateAccepted=2023-01-01&toDateAccepted=2026-12-31&format=json" - Network accessline 37
curl "https://api.openaire.eu/search/publications?keywords=CRISPR&fromDateAccepted=2023-01-01&toDateAccepted=2026-12-31&format=json" - Network accessline 39
# Filter by EU project curl "https://api.openaire.eu/search/publications?projectID=corda__h2020::123456&format=json" - Network accessline 40
curl "https://api.openaire.eu/search/publications?projectID=corda__h2020::123456&format=json" - Network accessline 42
# Search by DOI curl "https://api.openaire.eu/search/publications?doi=10.1038/s41586-023-05881-4&format=json" - Network accessline 43
curl "https://api.openaire.eu/search/publications?doi=10.1038/s41586-023-05881-4&format=json" - Network accessline 49
# Find research datasets curl "https://api.openaire.eu/search/datasets?keywords=genomics+sequencing&format=json&size=20" - Network accessline 50
curl "https://api.openaire.eu/search/datasets?keywords=genomics+sequencing&format=json&size=20" - Network accessline 52
# Open access datasets only curl "https://api.openaire.eu/search/datasets?keywords=ocean+temperature&openaccessonly=true&format=json" - Network accessline 53
curl "https://api.openaire.eu/search/datasets?keywords=ocean+temperature&openaccessonly=true&format=json" - Network accessline 59
# Search EU-funded projects curl "https://api.openaire.eu/search/projects?keywords=artificial+intelligence&funder=EC&format=json" - Network accessline 60
curl "https://api.openaire.eu/search/projects?keywords=artificial+intelligence&funder=EC&format=json" - Network accessline 62
# Horizon 2020 projects curl "https://api.openaire.eu/search/projects?keywords=renewable+energy&fundingStream=H2020&format=json" - Network accessline 63
curl "https://api.openaire.eu/search/projects?keywords=renewable+energy&fundingStream=H2020&format=json" - Network accessline 65
# Horizon Europe projects curl "https://api.openaire.eu/search/projects?keywords=quantum+computing&fundingStream=HE&format=json" - Network accessline 66
curl "https://api.openaire.eu/search/projects?keywords=quantum+computing&fundingStream=HE&format=json" - Network accessline 90
BASE_URL = "https://api.openaire.eu" - Network accessline 105
resp = requests.get(f"{BASE_URL}/search/publications", params=params) - Network accessline 139
- [OpenAIRE API Documentation](https://develop.openaire.eu/) - Network accessline 140
- [OpenAIRE Graph](https://graph.openaire.eu/) - Network accessline 141
- [OpenAIRE EXPLORE Portal](https://explore.openaire.eu/)
skills/literature/search/openalex-api/SKILL.md
- Network accessline 8
subcategory: "search" keywords: ["academic database search", "literature search", "citation statistics", "academic metrics", "bibliometrics"] source: "https://docs.openalex.org/" - Network accessline 9
keywords: ["academic database search", "literature search", "citation statistics", "academic metrics", "bibliometrics"] source: "https://docs.openalex.org/" --- - Network accessline 10
source: "https://docs.openalex.org/" - Network accessline 28
https://api.openalex.org/works?mailto=your@email.com - Network accessline 37
- **URL**: `GET https://api.openalex.org/works` - Network accessline 49
curl "https://api.openalex.org/works?search=large+language+models&filter=publication_year:2024&sort=cited_by_count:desc&per_page=10&mailto=user@example.com" - Network accessline 55
- **URL**: `GET https://api.openalex.org/authors` - Network accessline 65
curl "https://api.openalex.org/authors?search=Geoffrey+Hinton&mailto=user@example.com" - Network accessline 71
- **URL**: `GET https://api.openalex.org/institutions` - Network accessline 81
curl "https://api.openalex.org/institutions?search=MIT&mailto=user@example.com" - Network accessline 87
- **URL**: `GET https://api.openalex.org/concepts` - Network accessline 96
curl "https://api.openalex.org/concepts?filter=level:0&per_page=50&mailto=user@example.com" - Network accessline 111
curl "https://api.openalex.org/works?filter=cites:W2741809807&sort=cited_by_count:desc&per_page=25&mailto=user@example.com" - Network accessline 119
curl "https://api.openalex.org/works?filter=institutions.id:I136199984,publication_year:2024&group_by=open_access.is_oa&mailto=user@example.com" - Network accessline 127
curl "https://api.openalex.org/concepts/C41008148?mailto=user@example.com" - Network accessline 132
- Official documentation: https://docs.openalex.org/ - Network accessline 133
- OpenAlex data model: https://docs.openalex.org/about-the-data - Network accessline 134
- API filters reference: https://docs.openalex.org/how-to-use-the-api/get-lists-of-entities/filter-entity-lists
skills/literature/search/plos-open-access-api/SKILL.md
- Network accessline 8
subcategory: "search" keywords: ["PLOS", "open access", "biomedical", "Solr search", "full text", "peer reviewed"] source: "https://api.plos.org/" - Network accessline 9
keywords: ["PLOS", "open access", "biomedical", "Solr search", "full text", "peer reviewed"] source: "https://api.plos.org/" --- - Network accessline 10
source: "https://api.plos.org/" - Network accessline 24
https://api.plos.org/search - Network accessline 30
# Basic keyword search curl "https://api.plos.org/search?q=CRISPR+gene+editing&rows=20&wt=json" - Network accessline 31
curl "https://api.plos.org/search?q=CRISPR+gene+editing&rows=20&wt=json" - Network accessline 33
# Search in specific fields curl "https://api.plos.org/search?q=title:\"machine learning\"+AND+abstract:biomarker&wt=json" - Network accessline 34
curl "https://api.plos.org/search?q=title:\"machine learning\"+AND+abstract:biomarker&wt=json" - Network accessline 36
# Filter by journal curl "https://api.plos.org/search?q=microbiome&fq=journal:\"PLOS ONE\"&wt=json" - Network accessline 37
curl "https://api.plos.org/search?q=microbiome&fq=journal:\"PLOS ONE\"&wt=json" - Network accessline 39
# Filter by date range curl "https://api.plos.org/search?q=COVID-19+vaccine&\ - Network accessline 40
curl "https://api.plos.org/search?q=COVID-19+vaccine&\ - Network accessline 43
# Filter by article type curl "https://api.plos.org/search?q=climate+change&fq=article_type:\"Research Article\"&wt=json" - Network accessline 44
curl "https://api.plos.org/search?q=climate+change&fq=article_type:\"Research Article\"&wt=json" - Network accessline 46
# Return specific fields only curl "https://api.plos.org/search?q=deep+learning&fl=id,title,author,publication_date,score&wt=json" - Network accessline 47
curl "https://api.plos.org/search?q=deep+learning&fl=id,title,author,publication_date,score&wt=json" - Network accessline 106
BASE_URL = "https://api.plos.org/search" - Network accessline 131
resp = requests.get(BASE_URL, params=params) - Network accessline 155
"wt": "json", } resp = requests.get(BASE_URL, params=params) - Network accessline 156
} resp = requests.get(BASE_URL, params=params) resp.raise_for_status() - Network accessline 157
resp = requests.get(BASE_URL, params=params) - Network accessline 201
- [PLOS API Documentation](https://api.plos.org/) - Network accessline 202
- [PLOS Search Tips](https://journals.plos.org/plosone/s/search) - Network accessline 203
- [Solr Query Syntax](https://solr.apache.org/guide/solr/latest/query-guide/standard-query-parser.html)
skills/literature/search/pubmed-api/SKILL.md
- Network accessline 8
subcategory: "search" keywords: ["academic database search", "literature search", "scholarly database", "field-specific search"] source: "https://www.ncbi.nlm.nih.gov/books/NBK25501/" - Network accessline 9
keywords: ["academic database search", "literature search", "scholarly database", "field-specific search"] source: "https://www.ncbi.nlm.nih.gov/books/NBK25501/" --- - Network accessline 10
source: "https://www.ncbi.nlm.nih.gov/books/NBK25501/" - Network accessline 25
No authentication required for basic usage (3 requests/second). For higher rate limits (10 requests/second), register for a free API key at https://www.ncbi.nlm.nih.gov/account/ and include it in requests: - Access to secretsline 28
[Credential-related source omitted] - Network accessline 37
- **URL**: `GET https://eutils.ncbi.nlm.nih.gov/entrez/eutils/esearch.fcgi` - Network accessline 53
curl "https://eutils.ncbi.nlm.nih.gov/entrez/eutils/esearch.fcgi?db=pubmed&term=CRISPR+AND+cancer[Title]&retmax=10&retmode=json&sort=pub_date" - Network accessline 59
- **URL**: `GET https://eutils.ncbi.nlm.nih.gov/entrez/eutils/efetch.fcgi` - Network accessline 71
curl "https://eutils.ncbi.nlm.nih.gov/entrez/eutils/efetch.fcgi?db=pubmed&id=33116299,34735795&rettype=abstract&retmode=xml" - Network accessline 77
- **URL**: `GET https://eutils.ncbi.nlm.nih.gov/entrez/eutils/esummary.fcgi` - Network accessline 87
curl "https://eutils.ncbi.nlm.nih.gov/entrez/eutils/esummary.fcgi?db=pubmed&id=33116299&retmode=json&version=2.0" - Network accessline 102
# Search for clinical trials on diabetes treatment from the last 2 years curl "https://eutils.ncbi.nlm.nih.gov/entrez/eutils/esearch.fcgi?db=pubmed&term=diabetes[MeSH]+AND+treatment[Title]+AND+clinical+trial[Publication+Type]&mindate=2024/0 - Network accessline 103
curl "https://eutils.ncbi.nlm.nih.gov/entrez/eutils/esearch.fcgi?db=pubmed&term=diabetes[MeSH]+AND+treatment[Title]+AND+clinical+trial[Publication+Type]&mindate=2024/01/01&maxdate=2026/03/09&datetype=pdat&retmax=100&retmode=json" - Network accessline 111
# Step 1: Search and store results curl "https://eutils.ncbi.nlm.nih.gov/entrez/eutils/esearch.fcgi?db=pubmed&term=machine+learning+AND+radiology&retmax=0&usehistory=y&retmode=json" - Network accessline 112
curl "https://eutils.ncbi.nlm.nih.gov/entrez/eutils/esearch.fcgi?db=pubmed&term=machine+learning+AND+radiology&retmax=0&usehistory=y&retmode=json" - Network accessline 114
# Step 2: Fetch records using WebEnv and query_key from step 1 curl "https://eutils.ncbi.nlm.nih.gov/entrez/eutils/efetch.fcgi?db=pubmed&WebEnv=WEBENV_VALUE&query_key=1&retmax=50&rettype=abstract&retmode=xml" - Network accessline 115
curl "https://eutils.ncbi.nlm.nih.gov/entrez/eutils/efetch.fcgi?db=pubmed&WebEnv=WEBENV_VALUE&query_key=1&retmax=50&rettype=abstract&retmode=xml" - Network accessline 123
curl "https://eutils.ncbi.nlm.nih.gov/entrez/eutils/esummary.fcgi?db=pubmed&id=33116299,34735795,35363452&retmode=json&version=2.0" - Network accessline 128
- Official documentation: https://www.ncbi.nlm.nih.gov/books/NBK25501/ - Network accessline 129
- E-utilities quick start: https://www.ncbi.nlm.nih.gov/books/NBK25500/ - Network accessline 130
- PubMed search help: https://pubmed.ncbi.nlm.nih.gov/help/
skills/literature/search/scielo-api/SKILL.md
- Network accessline 8
subcategory: "search" keywords: ["SciELO", "Latin America", "open access journals", "developing countries", "Brazilian research", "Spanish research"] source: "https://wiki.scielo.org/" - Network accessline 9
keywords: ["SciELO", "Latin America", "open access journals", "developing countries", "Brazilian research", "Spanish research"] source: "https://wiki.scielo.org/" --- - Network accessline 10
source: "https://wiki.scielo.org/" - Network accessline 26
# Search articles by keyword curl "https://articlemeta.scielo.org/api/v1/article/?collection=scl&q=machine+learning" - Network accessline 27
curl "https://articlemeta.scielo.org/api/v1/article/?collection=scl&q=machine+learning" - Network accessline 29
# Get article by PID (SciELO identifier) curl "https://articlemeta.scielo.org/api/v1/article/?code=S0100-204X2024000100001" - Network accessline 30
curl "https://articlemeta.scielo.org/api/v1/article/?code=S0100-204X2024000100001" - Network accessline 32
# Filter by collection (country) curl "https://articlemeta.scielo.org/api/v1/article/?collection=esp&q=climate+change" - Network accessline 33
curl "https://articlemeta.scielo.org/api/v1/article/?collection=esp&q=climate+change" - Network accessline 35
# Filter by journal ISSN curl "https://articlemeta.scielo.org/api/v1/article/?issn=0100-204X&from_date=2024-01-01" - Network accessline 36
curl "https://articlemeta.scielo.org/api/v1/article/?issn=0100-204X&from_date=2024-01-01" - Network accessline 57
# List journals in a collection curl "https://articlemeta.scielo.org/api/v1/journal/?collection=scl" - Network accessline 58
curl "https://articlemeta.scielo.org/api/v1/journal/?collection=scl" - Network accessline 60
# Get journal by ISSN curl "https://articlemeta.scielo.org/api/v1/journal/?issn=0100-204X" - Network accessline 61
curl "https://articlemeta.scielo.org/api/v1/journal/?issn=0100-204X" - Network accessline 63
# Journal indicators curl "https://analytics.scielo.org/api/v1/journal/?issn=0100-204X" - Network accessline 64
curl "https://analytics.scielo.org/api/v1/journal/?issn=0100-204X" - Network accessline 72
# Full-text search curl "https://search.scielo.org/?q=biodiversity+conservation&format=json&count=20" - Network accessline 73
curl "https://search.scielo.org/?q=biodiversity+conservation&format=json&count=20" - Network accessline 75
# Filter by subject area curl "https://search.scielo.org/?q=neural+networks&filter[subject_area]=Computer+Science&format=json" - Network accessline 76
curl "https://search.scielo.org/?q=neural+networks&filter[subject_area]=Computer+Science&format=json" - Network accessline 78
# Filter by year range curl "https://search.scielo.org/?q=CRISPR&filter[year_cluster]=2023-2026&format=json" - Network accessline 79
curl "https://search.scielo.org/?q=CRISPR&filter[year_cluster]=2023-2026&format=json" - Network accessline 81
# Filter by language curl "https://search.scielo.org/?q=epidemiology&filter[la]=en&format=json" - Network accessline 82
curl "https://search.scielo.org/?q=epidemiology&filter[la]=en&format=json" - Network accessline 103
ARTICLE_API = "https://articlemeta.scielo.org/api/v1" - Network accessline 104
SEARCH_API = "https://search.scielo.org" - Network accessline 114
resp = requests.get(f"{SEARCH_API}/", params=params) - Network accessline 126
"pid": doc.get("pid", ""), "language": doc.get("la", []), "url": f"https://scielo.org/article/{doc.get('pid', '')}", - Network accessline 127
"language": doc.get("la", []), "url": f"https://scielo.org/article/{doc.get('pid', '')}", }) - Network accessline 128
"url": f"https://scielo.org/article/{doc.get('pid', '')}", - Network accessline 133
def get_article(pid: str) -> dict: """Get full article metadata by SciELO PID.""" resp = requests.get( - Network accessline 134
"""Get full article metadata by SciELO PID.""" resp = requests.get( f"{ARTICLE_API}/article/", - Network accessline 135
resp = requests.get( - Network accessline 143
def list_journals(collection: str = "scl") -> list: """List journals in a SciELO collection.""" resp = requests.get( - Network accessline 144
"""List journals in a SciELO collection.""" resp = requests.get( f"{ARTICLE_API}/journal/", - Network accessline 145
resp = requests.get( - Network accessline 179
- [SciELO](https://scielo.org/) - Network accessline 180
- [SciELO Analytics](https://analytics.scielo.org/) - Network accessline 181
- [ArticleMeta API](https://articlemeta.scielo.org/) - Network accessline 182
- [SciELO Developer Wiki](https://wiki.scielo.org/)
skills/literature/search/semantic-scholar-api/SKILL.md
- Network accessline 8
subcategory: "search" keywords: ["academic database search", "semantic search", "AI-powered literature search", "citation analysis", "citation network"] source: "https://api.openalex.org/" - Network accessline 9
keywords: ["academic database search", "semantic search", "AI-powered literature search", "citation analysis", "citation network"] source: "https://api.openalex.org/" --- - Network accessline 10
source: "https://api.openalex.org/" - Network accessline 29
User-Agent: ResearchPlugins/1.0 (https://wentor.ai) - Network accessline 34
User-Agent: ResearchPlugins/1.0 (https://wentor.ai; mailto:dev@wentor.ai) - Network accessline 41
- **URL**: `GET https://api.openalex.org/works` - Network accessline 52
curl "https://api.openalex.org/works?search=attention+is+all+you+need&per_page=5" - Network accessline 58
- **URL**: `GET https://api.openalex.org/works/{id}` - Network accessline 65
curl "https://api.openalex.org/works/W2741809807" - Network accessline 71
- **URL**: `GET https://api.openalex.org/authors` - Network accessline 80
curl "https://api.openalex.org/authors?search=Yoshua+Bengio&per_page=5" - Network accessline 86
- **URL**: `GET https://api.crossref.org/works/{doi}` - Network accessline 93
curl "https://api.crossref.org/works/10.18653/v1/N19-1423" - Network accessline 110
# Get paper details curl "https://api.openalex.org/works/W2741809807" - Network accessline 111
curl "https://api.openalex.org/works/W2741809807" - Network accessline 113
# Get works citing this paper, sorted by citation count curl "https://api.openalex.org/works?filter=cites:W2741809807&sort=cited_by_count:desc&per_page=20" - Network accessline 114
curl "https://api.openalex.org/works?filter=cites:W2741809807&sort=cited_by_count:desc&per_page=20" - Network accessline 122
curl "https://api.openalex.org/works?search=graph+neural+networks&sort=cited_by_count:desc&per_page=20" - Network accessline 130
curl "https://api.crossref.org/works?query=graph+neural+networks&sort=is-referenced-by-count&order=desc&rows=20" - Network accessline 135
- OpenAlex documentation: https://docs.openalex.org/ - Network accessline 136
- CrossRef API documentation: https://api.crossref.org/swagger-ui/index.html - Network accessline 137
- OpenAlex source: https://github.com/ourresearch/openalex-guts
skills/literature/search/share-research-api/SKILL.md
- Network accessline 8
subcategory: "search" keywords: ["SHARE", "open access", "research notification", "COS", "repository aggregator", "preprints"] source: "https://share.osf.io/" - Network accessline 9
keywords: ["SHARE", "open access", "research notification", "COS", "repository aggregator", "preprints"] source: "https://share.osf.io/" --- - Network accessline 10
source: "https://share.osf.io/" - Network accessline 24
https://share.osf.io/api/v2 - Network accessline 30
# Text search across all sources curl "https://share.osf.io/api/v2/search/creativeworks/?q=climate+change&page[size]=20" - Network accessline 31
curl "https://share.osf.io/api/v2/search/creativeworks/?q=climate+change&page[size]=20" - Network accessline 33
# Filter by type curl "https://share.osf.io/api/v2/search/creativeworks/?q=neural+networks&filter[type]=preprint" - Network accessline 34
curl "https://share.osf.io/api/v2/search/creativeworks/?q=neural+networks&filter[type]=preprint" - Network accessline 36
# Filter by source curl "https://share.osf.io/api/v2/search/creativeworks/?q=genomics&filter[sources]=PubMed+Central" - Network accessline 37
curl "https://share.osf.io/api/v2/search/creativeworks/?q=genomics&filter[sources]=PubMed+Central" - Network accessline 39
# Filter by date curl "https://share.osf.io/api/v2/search/creativeworks/?q=COVID-19&filter[date][gte]=2024-01-01" - Network accessline 40
curl "https://share.osf.io/api/v2/search/creativeworks/?q=COVID-19&filter[date][gte]=2024-01-01" - Network accessline 42
# Filter by tag/subject curl "https://share.osf.io/api/v2/search/creativeworks/?q=machine+learning&filter[tags]=deep+learning" - Network accessline 43
curl "https://share.osf.io/api/v2/search/creativeworks/?q=machine+learning&filter[tags]=deep+learning" - Network accessline 76
BASE_URL = "https://share.osf.io/api/v2" - Network accessline 92
resp = requests.get( - Network accessline 127
- [SHARE](https://share.osf.io/) - Network accessline 128
- [SHARE API Documentation](https://share.osf.io/api/v2/) - Network accessline 129
- [Center for Open Science](https://cos.io/)
skills/literature/search/worldcat-search-api/SKILL.md
- Network accessline 8
subcategory: "search" keywords: ["worldcat", "library catalog", "OCLC", "book search", "holdings", "interlibrary loan"] source: "https://developer.api.oclc.org/" - Network accessline 9
keywords: ["worldcat", "library catalog", "OCLC", "book search", "holdings", "interlibrary loan"] source: "https://developer.api.oclc.org/" --- - Network accessline 10
source: "https://developer.api.oclc.org/" - Network accessline 22
# Register at https://platform.worldcat.org/ - Network accessline 25
# OAuth 2.0 client credentials flow curl -X POST "https://oauth.oclc.org/token" \ - Network accessline 26
[Credential-related source omitted] - Access to secretsline 26
[Credential-related source omitted] - Access to secretsline 27
[Credential-related source omitted] - Network accessline 36
https://www.worldcat.org/api/search/ - Network accessline 42
[Credential-related source omitted] - Network accessline 43
[Credential-related source omitted] - Network accessline 44
"https://www.worldcat.org/api/search?q=machine+learning&limit=25" - Network accessline 46
[Credential-related source omitted] - Network accessline 47
[Credential-related source omitted] - Network accessline 48
"https://www.worldcat.org/api/search?q=ti:attention+is+all+you+need" - Network accessline 50
[Credential-related source omitted] - Network accessline 51
[Credential-related source omitted] - Network accessline 52
"https://www.worldcat.org/api/search?q=au:hinton+geoffrey" - Network accessline 54
[Credential-related source omitted] - Network accessline 55
[Credential-related source omitted] - Network accessline 56
"https://www.worldcat.org/api/search?q=bn:9780262035613" - Network accessline 58
[Credential-related source omitted] - Network accessline 59
[Credential-related source omitted] - Network accessline 60
"https://www.worldcat.org/api/search?q=su:artificial+intelligence+AND+yr:2020-2026&itemType=book" - Network accessline 93
[Credential-related source omitted] - Network accessline 94
"https://www.worldcat.org/api/search/brief-bibs/{oclc_number}" - Network accessline 100
[Credential-related source omitted] - Network accessline 101
[Credential-related source omitted] - Network accessline 102
"https://www.worldcat.org/api/search/brief-bibs/{oclc_number}/holdings?lat=42.36&lon=-71.06&distance=50" - Network accessline 136
[Credential-related source omitted] - Access to secretsline 136
[Credential-related source omitted] - Access to secretsline 137
[Credential-related source omitted] - Network accessline 138
BASE_URL = "https://www.worldcat.org/api/search" - Network accessline 141
def get_token() -> str: """Obtain OAuth token from OCLC.""" resp = requests.post( - Network accessline 142
"""Obtain OAuth token from OCLC.""" resp = requests.post( "https://oauth.oclc.org/token", - Network accessline 143
resp = requests.post( - Network accessline 144
"https://oauth.oclc.org/token", - Access to secretsline 145
[Credential-related source omitted] - Access to secretsline 149
[Credential-related source omitted] - Network accessline 160
resp = requests.get( - Network accessline 186
[Credential-related source omitted] - Network accessline 187
[Credential-related source omitted] - Network accessline 188
resp = requests.get( - Network accessline 222
- [WorldCat Search API Documentation](https://developer.api.oclc.org/) - Network accessline 223
- [OCLC Developer Network](https://platform.worldcat.org/) - Network accessline 224
- [WorldCat.org](https://www.worldcat.org/)
skills/research/automation/ai-scientist-v2-guide/SKILL.md
- Network accessline 8
subcategory: "automation" keywords: ["scientific-discovery", "automation", "tree-search", "paper-generation", "experiment-design", "sakana-ai"] source: "https://github.com/SakanaAI/AI-Scientist-v2" - Network accessline 9
keywords: ["scientific-discovery", "automation", "tree-search", "paper-generation", "experiment-design", "sakana-ai"] source: "https://github.com/SakanaAI/AI-Scientist-v2" --- - Network accessline 10
source: "https://github.com/SakanaAI/AI-Scientist-v2" - Network accessline 26
# Clone the repository git clone https://github.com/SakanaAI/AI-Scientist-v2.git - Network accessline 27
git clone https://github.com/SakanaAI/AI-Scientist-v2.git - Access to secretsline 44
[Credential-related source omitted] - Access to secretsline 46
[Credential-related source omitted] - Dangerous shell operationline 53
sudo apt-get install texlive-full - Network accessline 281
- Repository: https://github.com/SakanaAI/AI-Scientist-v2 - Network accessline 282
- Original AI Scientist paper: https://arxiv.org/abs/2408.06292 - Network accessline 283
- Sakana AI: https://sakana.ai/ - Network accessline 284
- AI Scientist v1: https://github.com/SakanaAI/AI-Scientist
skills/research/automation/aim-experiment-guide/SKILL.md
- Network accessline 8
subcategory: "automation" keywords: ["experiment-tracking", "visualization", "mlops", "reproducibility", "metrics", "hyperparameters"] source: "https://github.com/aimhubio/aim" - Network accessline 9
keywords: ["experiment-tracking", "visualization", "mlops", "reproducibility", "metrics", "hyperparameters"] source: "https://github.com/aimhubio/aim" --- - Network accessline 10
source: "https://github.com/aimhubio/aim" - Network accessline 44
The dashboard becomes available at `http://localhost:43800`, providing interactive visualizations of all tracked experiments. - Network accessline 231
- Aim repository: https://github.com/aimhubio/aim - Network accessline 232
- Aim documentation: https://aimstack.readthedocs.io/
skills/research/automation/claude-academic-workflow-guide/SKILL.md
- Network accessline 8
subcategory: "automation" keywords: ["Claude Code", "academic workflow", "LaTeX", "Beamer", "R", "multi-agent review"] source: "https://github.com/pedrohcgs/claude-code-my-workflow" - Network accessline 9
keywords: ["Claude Code", "academic workflow", "LaTeX", "Beamer", "R", "multi-agent review"] source: "https://github.com/pedrohcgs/claude-code-my-workflow" --- - Network accessline 10
source: "https://github.com/pedrohcgs/claude-code-my-workflow" - Network accessline 200
- [claude-code-my-workflow](https://github.com/pedrohcgs/claude-code-my-workflow) - Network accessline 201
- [Metropolis Beamer Theme](https://github.com/matze/mtheme) - Network accessline 202
- [modelsummary](https://modelsummary.com/)
skills/research/automation/data-collection-automation/SKILL.md
- Network accessline 37
[Credential-related source omitted] - Access to secretsline 37
[Credential-related source omitted] - Access to secretsline 38
[Credential-related source omitted] - Network accessline 39
base_url = f"https://{data_center}.qualtrics.com/API/v3" - Network accessline 52
req = urllib.request.Request( - Network accessline 55
headers=headers ) response = json.loads(urllib.request.urlopen(req).read()) - Network accessline 56
) response = json.loads(urllib.request.urlopen(req).read()) progress_id = response["result"]["progressId"] - Network accessline 57
response = json.loads(urllib.request.urlopen(req).read()) - Network accessline 62
while status == "inProgress": time.sleep(2) req = urllib.request.Request( - Network accessline 63
time.sleep(2) req = urllib.request.Request( f"{base_url}/surveys/{survey_id}/export-responses/{progress_id}", - Network accessline 64
req = urllib.request.Request( - Network accessline 66
headers=headers ) check = json.loads(urllib.request.urlopen(req).read()) - Network accessline 67
) check = json.loads(urllib.request.urlopen(req).read()) status = check["result"]["status"] - Network accessline 68
check = json.loads(urllib.request.urlopen(req).read()) - Network accessline 73
# Step 3: Download file req = urllib.request.Request( - Network accessline 74
req = urllib.request.Request( - Network accessline 76
headers=headers ) file_data = urllib.request.urlopen(req).read() - Network accessline 78
file_data = urllib.request.urlopen(req).read() - Access to secretsline 98
[Credential-related source omitted] - Network accessline 110
encoded = urllib.parse.urlencode(data).encode("utf-8") req = urllib.request.Request(api_url, data=encoded) - Network accessline 111
req = urllib.request.Request(api_url, data=encoded) - Network accessline 112
response = urllib.request.urlopen(req)
skills/research/automation/datagen-research-guide/SKILL.md
- Network accessline 2
name: datagen-research-guide description: "AI-driven multi-agent research assistant for end-to-end studies" source: https://github.com/DATAGEN-AI/DATAGEN - Network accessline 3
description: "AI-driven multi-agent research assistant for end-to-end studies" source: https://github.com/DATAGEN-AI/DATAGEN metadata: - Network accessline 4
source: https://github.com/DATAGEN-AI/DATAGEN
skills/research/automation/kedro-pipeline-guide/SKILL.md
- Network accessline 8
subcategory: "automation" keywords: ["pipeline", "reproducibility", "data-science", "workflow", "automation", "mlops"] source: "https://github.com/kedro-org/kedro" - Network accessline 9
keywords: ["pipeline", "reproducibility", "data-science", "workflow", "automation", "mlops"] source: "https://github.com/kedro-org/kedro" --- - Network accessline 10
source: "https://github.com/kedro-org/kedro" - Network accessline 213
- Kedro repository: https://github.com/kedro-org/kedro - Network accessline 214
- Kedro documentation: https://docs.kedro.org/ - Network accessline 215
- Kedro-Viz for pipeline visualization: https://github.com/kedro-org/kedro-viz
skills/research/automation/mle-agent-guide/SKILL.md
- Network accessline 2
name: mle-agent-guide description: "Intelligent companion for ML engineering with arXiv integration" source: https://github.com/MLSys-Tools/MLE-agent - Network accessline 3
description: "Intelligent companion for ML engineering with arXiv integration" source: https://github.com/MLSys-Tools/MLE-agent metadata: - Network accessline 4
source: https://github.com/MLSys-Tools/MLE-agent
skills/research/automation/paper-to-agent-guide/SKILL.md
- Network accessline 2
name: paper-to-agent-guide description: "Transform research papers into interactive AI agents for exploration" source: https://github.com/paper2agent/Paper2Agent - Network accessline 3
description: "Transform research papers into interactive AI agents for exploration" source: https://github.com/paper2agent/Paper2Agent metadata: - Network accessline 4
source: https://github.com/paper2agent/Paper2Agent
skills/research/automation/rd-agent-guide/SKILL.md
- Network accessline 8
subcategory: "automation" keywords: ["r-and-d", "microsoft", "automation", "model-development", "data-science", "experiment-automation"] source: "https://github.com/microsoft/RD-Agent" - Network accessline 9
keywords: ["r-and-d", "microsoft", "automation", "model-development", "data-science", "experiment-automation"] source: "https://github.com/microsoft/RD-Agent" --- - Network accessline 10
source: "https://github.com/microsoft/RD-Agent" - Network accessline 26
# Clone the repository git clone https://github.com/microsoft/RD-Agent.git - Network accessline 27
git clone https://github.com/microsoft/RD-Agent.git - Access to secretsline 41
[Credential-related source omitted] - Access to secretsline 45
[Credential-related source omitted] - Network accessline 243
- Repository: https://github.com/microsoft/RD-Agent - Network accessline 244
- Microsoft Research blog post: https://www.microsoft.com/en-us/research/project/rd-agent/ - Network accessline 245
- Qlib quantitative platform: https://github.com/microsoft/qlib - Network accessline 246
- Documentation: https://microsoft.github.io/RD-Agent/
skills/research/automation/research-workflow-automation/SKILL.md
skills/research/deep-research/deep-searcher-guide/SKILL.md
- Network accessline 8
subcategory: "deep-research" keywords: ["deep-search", "private-data", "milvus", "vector-search", "rag", "document-retrieval"] source: "https://github.com/zilliztech/deep-searcher" - Network accessline 9
keywords: ["deep-search", "private-data", "milvus", "vector-search", "rag", "document-retrieval"] source: "https://github.com/zilliztech/deep-searcher" --- - Network accessline 10
source: "https://github.com/zilliztech/deep-searcher" - Network accessline 29
# Or clone for development git clone https://github.com/zilliztech/deep-searcher.git - Network accessline 30
git clone https://github.com/zilliztech/deep-searcher.git - Network accessline 50
[Credential-related source omitted] - Access to secretsline 50
[Credential-related source omitted] - Network accessline 52
export OLLAMA_BASE_URL=http://localhost:11434 - Access to secretsline 200
[Credential-related source omitted] - Network accessline 233
# Query via REST API curl -X POST http://localhost:8000/research \ - Network accessline 234
curl -X POST http://localhost:8000/research \ - Network accessline 250
- Repository: https://github.com/zilliztech/deep-searcher - Network accessline 251
- Milvus vector database: https://milvus.io/ - Network accessline 252
- Zilliz Cloud (managed Milvus): https://zilliz.com/ - Network accessline 253
- Milvus documentation: https://milvus.io/docs/
skills/research/deep-research/gpt-researcher-guide/SKILL.md
- Network accessline 8
subcategory: "deep-research" keywords: ["deep-research", "autonomous-agent", "web-search", "report-generation", "literature-review"] source: "https://github.com/assafelovic/gpt-researcher" - Network accessline 9
keywords: ["deep-research", "autonomous-agent", "web-search", "report-generation", "literature-review"] source: "https://github.com/assafelovic/gpt-researcher" --- - Network accessline 10
source: "https://github.com/assafelovic/gpt-researcher" - Network accessline 29
# Or clone for development git clone https://github.com/assafelovic/gpt-researcher.git - Network accessline 30
git clone https://github.com/assafelovic/gpt-researcher.git - Access to secretsline 39
[Credential-related source omitted] - Access to secretsline 41
[Credential-related source omitted] - Access to secretsline 44
[Credential-related source omitted] - Access to secretsline 46
[Credential-related source omitted] - Network accessline 53
# Use local LLM via Ollama export OPENAI_BASE_URL=http://localhost:11434/v1 - Network accessline 54
export OPENAI_BASE_URL=http://localhost:11434/v1 - Network accessline 59
# Use local search via SearXNG export SEARX_URL=http://localhost:8888 - Network accessline 60
export SEARX_URL=http://localhost:8888 - Network accessline 141
query="CRISPR gene editing safety profiles", source_urls=[ "https://pubmed.ncbi.nlm.nih.gov/", - Network accessline 142
source_urls=[ "https://pubmed.ncbi.nlm.nih.gov/", "https://www.nature.com/", - Network accessline 143
"https://pubmed.ncbi.nlm.nih.gov/", - Network accessline 144
"https://www.nature.com/", - Network accessline 145
"https://www.science.org/", - Network accessline 188
- Repository: https://github.com/assafelovic/gpt-researcher - Network accessline 189
- Documentation: https://docs.gptr.dev/ - Network accessline 190
- Tavily Search API: https://tavily.com/ - Network accessline 191
- Research paper: https://arxiv.org/abs/2305.04091
skills/research/deep-research/khoj-research-guide/SKILL.md
- Network accessline 8
subcategory: "deep-research" keywords: ["second-brain", "knowledge-management", "document-analysis", "rag", "self-hosted", "research-assistant"] source: "https://github.com/khoj-ai/khoj" - Network accessline 9
keywords: ["second-brain", "knowledge-management", "document-analysis", "rag", "self-hosted", "research-assistant"] source: "https://github.com/khoj-ai/khoj" --- - Network accessline 10
source: "https://github.com/khoj-ai/khoj" - Network accessline 44
After starting Khoj, configure it through the web interface at `http://localhost:42110/config`: - Network accessline 52
[Credential-related source omitted] - Access to secretsline 52
[Credential-related source omitted] - Network accessline 54
export OLLAMA_HOST=http://localhost:11434 - Network accessline 61
- **Web interface**: Full-featured browser UI at `http://localhost:42110` - Network accessline 68
# Install the Obsidian plugin # In Obsidian: Settings > Community Plugins > Search "Khoj" # Configure server URL: http://localhost:42110 - Network accessline 70
# Configure server URL: http://localhost:42110 - Network accessline 92
# Add a document directory requests.post("http://localhost:42110/api/config/data/source", json={ - Network accessline 93
requests.post("http://localhost:42110/api/config/data/source", json={ - Network accessline 106
# Trigger deep research via the API response = requests.post("http://localhost:42110/api/chat", json={ - Network accessline 107
response = requests.post("http://localhost:42110/api/chat", json={ - Network accessline 140
# Create a research agent that monitors new papers requests.post("http://localhost:42110/api/agents", json={ - Network accessline 141
requests.post("http://localhost:42110/api/agents", json={ - Network accessline 197
- Repository: https://github.com/khoj-ai/khoj - Network accessline 198
- Documentation: https://docs.khoj.dev/ - Network accessline 199
- Obsidian plugin: https://github.com/khoj-ai/khoj/tree/master/src/interface/obsidian - Network accessline 200
- Self-hosting guide: https://docs.khoj.dev/get-started/setup/
skills/research/deep-research/kosmos-scientist-guide/SKILL.md
- Network accessline 8
subcategory: "deep-research" keywords: ["AI Scientist", "autonomous discovery", "Claude Code", "research automation", "scientific method", "experiment"] source: "https://github.com/jimmc414/Kosmos" - Network accessline 9
keywords: ["AI Scientist", "autonomous discovery", "Claude Code", "research automation", "scientific method", "experiment"] source: "https://github.com/jimmc414/Kosmos" --- - Network accessline 10
source: "https://github.com/jimmc414/Kosmos" - Network accessline 183
- [Kosmos GitHub](https://github.com/jimmc414/Kosmos) - Network accessline 184
- [The AI Scientist](https://arxiv.org/abs/2408.06292) - Network accessline 185
- [Claude Code](https://docs.anthropic.com/en/docs/claude-code)
skills/research/deep-research/llm-scientific-discovery-guide/SKILL.md
- Network accessline 8
subcategory: "deep-research" keywords: ["LLM agents", "scientific discovery", "biomedical AI", "drug discovery", "hypothesis generation", "lab automation"] source: "https://github.com/zjlrock777/Awesome-LLM-Agents-Scientific-Discove - Network accessline 9
keywords: ["LLM agents", "scientific discovery", "biomedical AI", "drug discovery", "hypothesis generation", "lab automation"] source: "https://github.com/zjlrock777/Awesome-LLM-Agents-Scientific-Discovery" --- - Network accessline 10
source: "https://github.com/zjlrock777/Awesome-LLM-Agents-Scientific-Discovery" - Network accessline 176
- [Awesome-LLM-Agents-Scientific-Discovery](https://github.com/zjlrock777/Awesome-LLM-Agents-Scientific-Discovery) - Network accessline 177
- [The AI Scientist](https://arxiv.org/abs/2408.06292) - Network accessline 178
- [ChemCrow](https://arxiv.org/abs/2304.05376)
skills/research/deep-research/local-deep-research-guide/SKILL.md
- Network accessline 8
subcategory: "deep-research" keywords: ["local-llm", "deep-research", "multi-source", "ollama", "privacy", "academic-search"] source: "https://github.com/LearningCircuit/local-deep-research" - Network accessline 9
keywords: ["local-llm", "deep-research", "multi-source", "ollama", "privacy", "academic-search"] source: "https://github.com/LearningCircuit/local-deep-research" --- - Network accessline 10
source: "https://github.com/LearningCircuit/local-deep-research" - Network accessline 29
# Or clone for development git clone https://github.com/LearningCircuit/local-deep-research.git - Network accessline 30
git clone https://github.com/LearningCircuit/local-deep-research.git - Network accessline 40
# Option 1: Local LLM via Ollama (fully private) # First, install Ollama: https://ollama.com/ - Network accessline 41
# First, install Ollama: https://ollama.com/ - Network accessline 46
# Option 2: Local LLM via LM Studio export LDR_LLM_PROVIDER=lmstudio export LDR_LLM_BASE_URL=http://localhost:1234/v1 - Network accessline 48
export LDR_LLM_BASE_URL=http://localhost:1234/v1 - Access to secretsline 52
[Credential-related source omitted] - Access to secretsline 57
[Credential-related source omitted] - Access to secretsline 67
[Credential-related source omitted] - Network accessline 69
[Credential-related source omitted] - Access to secretsline 69
[Credential-related source omitted] - Network accessline 71
export SEARX_URL=http://localhost:8888 # Self-hosted SearXNG - Access to secretsline 74
[Credential-related source omitted] - Network accessline 238
export LDR_LLM_PROVIDER=ollama export LDR_LLM_MODEL=llama3.1:70b export SEARX_URL=http://localhost:8888 - Network accessline 239
export LDR_LLM_MODEL=llama3.1:70b export SEARX_URL=http://localhost:8888 export LDR_SEARCH_SOURCES=searxng,arxiv,pubmed,wikipedia - Network accessline 240
export SEARX_URL=http://localhost:8888 - Network accessline 249
- Repository: https://github.com/LearningCircuit/local-deep-research - Network accessline 250
- Ollama: https://ollama.com/ - Network accessline 251
- SearXNG: https://github.com/searxng/searxng - Network accessline 252
- OpenAlex API: https://api.openalex.org/ - Network accessline 253
- arXiv API: https://info.arxiv.org/help/api/
skills/research/deep-research/open-researcher-guide/SKILL.md
- Network accessline 8
subcategory: "deep-research" keywords: ["OpenResearcher", "deep research", "research trajectory", "open pipeline", "literature synthesis", "LLM research"] source: "https://github.com/GAIR-NLP/OpenResearcher" - Network accessline 9
keywords: ["OpenResearcher", "deep research", "research trajectory", "open pipeline", "literature synthesis", "LLM research"] source: "https://github.com/GAIR-NLP/OpenResearcher" --- - Network accessline 10
source: "https://github.com/GAIR-NLP/OpenResearcher" - Network accessline 137
- [OpenResearcher GitHub](https://github.com/GAIR-NLP/OpenResearcher) - Network accessline 138
- [GAIR-NLP Lab](https://github.com/GAIR-NLP)
skills/research/deep-research/tongyi-deep-research-guide/SKILL.md
- Network accessline 8
subcategory: "deep-research" keywords: ["deep-research", "alibaba", "tongyi", "agentic-rag", "scholarly-search", "open-source"] source: "https://github.com/Alibaba-NLP/DeepResearch" - Network accessline 9
keywords: ["deep-research", "alibaba", "tongyi", "agentic-rag", "scholarly-search", "open-source"] source: "https://github.com/Alibaba-NLP/DeepResearch" --- - Network accessline 10
source: "https://github.com/Alibaba-NLP/DeepResearch" - Network accessline 26
# Clone the repository git clone https://github.com/Alibaba-NLP/DeepResearch.git - Network accessline 27
git clone https://github.com/Alibaba-NLP/DeepResearch.git - Access to secretsline 43
[Credential-related source omitted] - Access to secretsline 48
[Credential-related source omitted] - Network accessline 55
# Use local models export LLM_BASE_URL=http://localhost:11434/v1 - Network accessline 56
export LLM_BASE_URL=http://localhost:11434/v1 - Access to secretsline 58
[Credential-related source omitted] - Network accessline 214
- Repository: https://github.com/Alibaba-NLP/DeepResearch - Network accessline 215
- Qwen model family: https://github.com/QwenLM/Qwen - Network accessline 216
- Alibaba NLP group: https://github.com/Alibaba-NLP - Network accessline 217
- OpenAlex API: https://api.openalex.org/
skills/research/funding/eu-horizon-guide/SKILL.md
skills/research/funding/figshare-api/SKILL.md
- Network accessline 8
subcategory: "funding" keywords: ["open data", "data sharing", "data archiving", "FAIR data principles", "open source code"] source: "https://docs.figshare.com/" - Network accessline 9
keywords: ["open data", "data sharing", "data archiving", "FAIR data principles", "open source code"] source: "https://docs.figshare.com/" requires: - Network accessline 10
source: "https://docs.figshare.com/" - Access to secretsline 12
[Credential-related source omitted] - Network accessline 29
1. Log in to Figshare at https://figshare.com/ - Network accessline 35
[Credential-related source omitted] - Network accessline 46
- **URL**: `GET https://api.figshare.com/v2/articles` - Network accessline 62
curl -X POST "https://api.figshare.com/v2/articles/search" \ - Network accessline 73
- **URL**: `GET https://api.figshare.com/v2/articles/{article_id}` - Network accessline 83
# Get a specific dataset by ID curl "https://api.figshare.com/v2/articles/12345678" - Network accessline 84
curl "https://api.figshare.com/v2/articles/12345678" - Network accessline 86
# List files in a dataset curl "https://api.figshare.com/v2/articles/12345678/files" - Network accessline 87
curl "https://api.figshare.com/v2/articles/12345678/files" - Network accessline 94
Rate limits vary based on authentication status and endpoint. Authenticated requests generally allow up to 100 requests per minute. Unauthenticated requests are limited to approximately 10 requests per minute. The API returns HTTP 429 with - Network accessline 113
resp = requests.post("https://api.figshare.com/v2/articles/search", json=payload) - Access to secretsline 130
[Credential-related source omitted] - Network accessline 139
"categories": [69] # Genetics category } resp = requests.post("https://api.figshare.com/v2/account/articles", - Network accessline 140
} resp = requests.post("https://api.figshare.com/v2/account/articles", headers=headers, json=article_data) - Network accessline 141
resp = requests.post("https://api.figshare.com/v2/account/articles", - Network accessline 145
# Step 2: Upload file article = requests.get(article_url, headers=headers).json() - Network accessline 146
article = requests.get(article_url, headers=headers).json() - Network accessline 155
curl "https://api.figshare.com/v2/oai?verb=ListRecords&metadataPrefix=oai_dc&set=institution_123" - Network accessline 160
- Official API documentation: https://docs.figshare.com/ - Network accessline 161
- Figshare homepage: https://figshare.com/ - Network accessline 162
- API endpoint reference: https://docs.figshare.com/#figshare_documentation_api_description - Network accessline 163
- OAI-PMH endpoint: https://docs.figshare.com/#oai_pmh
skills/research/funding/nih-reporter-api-guide/SKILL.md
- Network accessline 8
subcategory: "funding" keywords: ["nih", "grants", "biomedical", "funding", "reporter", "health-research"] source: "https://api.reporter.nih.gov/" - Network accessline 9
keywords: ["nih", "grants", "biomedical", "funding", "reporter", "health-research"] source: "https://api.reporter.nih.gov/" --- - Network accessline 10
source: "https://api.reporter.nih.gov/" - Network accessline 28
# No API key needed curl -X POST "https://api.reporter.nih.gov/v2/projects/search" \ - Network accessline 29
curl -X POST "https://api.reporter.nih.gov/v2/projects/search" \ - Network accessline 41
POST https://api.reporter.nih.gov/v2/projects/search - Network accessline 57
curl -s -X POST "https://api.reporter.nih.gov/v2/projects/search" \ - Network accessline 78
POST https://api.reporter.nih.gov/v2/publications/search - Network accessline 82
curl -s -X POST "https://api.reporter.nih.gov/v2/publications/search" \ - Network accessline 98
API_URL = "https://api.reporter.nih.gov/v2/projects/search" - Network accessline 117
resp = requests.post(API_URL, json=payload) - Network accessline 163
- NIH RePORTER API v2 Documentation: https://api.reporter.nih.gov/ - Network accessline 164
- NIH RePORTER Web Interface: https://reporter.nih.gov/ - Network accessline 165
- NIH Activity Codes (Grant Mechanisms): https://grants.nih.gov/grants/funding/ac_search_results.htm - Network accessline 166
- NIH Institutes and Centers: https://www.nih.gov/institutes-nih/list-nih-institutes-centers-offices
skills/research/funding/nsf-award-api-guide/SKILL.md
- Network accessline 8
subcategory: "funding" keywords: ["nsf", "grants", "funding", "awards", "national-science-foundation"] source: "https://www.research.gov/common/webapi/awardapisearch-v1.htm" - Network accessline 9
keywords: ["nsf", "grants", "funding", "awards", "national-science-foundation"] source: "https://www.research.gov/common/webapi/awardapisearch-v1.htm" --- - Network accessline 10
source: "https://www.research.gov/common/webapi/awardapisearch-v1.htm" - Network accessline 28
# No API key needed -- just make the request curl "https://api.nsf.gov/services/v1/awards.json?keyword=quantum+computing" - Network accessline 29
curl "https://api.nsf.gov/services/v1/awards.json?keyword=quantum+computing" - Network accessline 37
GET https://api.nsf.gov/services/v1/awards.json?{params} - Dangerous shell operationline 53
curl -s "https://api.nsf.gov/services/v1/awards.json? keyword=machine+learning& awardeeName=Massachusetts+Institute+of+Technology& printFields=id,title,piFirstName,piLastName,startDate,awardeeName,fundsObligatedAmt,abstractText& offset=1" | - Network accessline 53
curl -s "https://api.nsf.gov/services/v1/awards.json?\ - Dangerous shell operationline 65
curl -s "https://api.nsf.gov/services/v1/awards/2345678.json? printFields=id,title,piFirstName,piLastName,startDate,expDate,awardeeName,fundsObligatedAmt,abstractText" | python3 -m json.tool - Network accessline 65
curl -s "https://api.nsf.gov/services/v1/awards/2345678.json?\ - Network accessline 76
base_url = "https://api.nsf.gov/services/v1/awards.json" - Network accessline 89
"offset": offset } resp = requests.get(base_url, params=params) - Network accessline 90
} resp = requests.get(base_url, params=params) data = resp.json() - Network accessline 91
resp = requests.get(base_url, params=params) - Network accessline 125
- **Date formats:** Use MM/DD/YYYY format strictly - **Pagination:** The API uses 1-based offset; increment by 25 for each page - **Large datasets:** For bulk analysis, consider using the NSF data download at https://www.nsf.gov/awardsearch - Network accessline 127
- **Large datasets:** For bulk analysis, consider using the NSF data download at https://www.nsf.gov/awardsearch/download.jsp - Network accessline 131
- NSF Award Search API Documentation: https://www.research.gov/common/webapi/awardapisearch-v1.htm - NSF Award Search Web Interface: https://www.nsf.gov/awardsearch/ - NSF Data Downloads: https://www.nsf.gov/awardsearch/download.jsp - Network accessline 132
- NSF Award Search Web Interface: https://www.nsf.gov/awardsearch/ - Network accessline 133
- NSF Data Downloads: https://www.nsf.gov/awardsearch/download.jsp
skills/research/funding/zenodo-api/SKILL.md
- Network accessline 8
subcategory: "funding" keywords: ["open data", "data sharing", "data archiving", "FAIR data principles"] source: "https://developers.zenodo.org/" - Network accessline 9
keywords: ["open data", "data sharing", "data archiving", "FAIR data principles"] source: "https://developers.zenodo.org/" requires: - Network accessline 10
source: "https://developers.zenodo.org/" - Access to secretsline 12
[Credential-related source omitted] - Network accessline 29
1. Create an account at https://zenodo.org/ - Access to secretsline 32
[Credential-related source omitted] - Network accessline 35
[Credential-related source omitted] - Network accessline 36
[Credential-related source omitted] - Access to secretsline 36
[Credential-related source omitted] - Network accessline 38
[Credential-related source omitted] - Network accessline 39
[Credential-related source omitted] - Network accessline 50
- **URL**: `GET https://zenodo.org/api/records` - Network accessline 65
curl "https://zenodo.org/api/records?q=climate+change&type=dataset&sort=mostrecent&size=5" - Network accessline 74
- **URL**: `GET https://zenodo.org/api/deposit/depositions` - Access to secretsline 79
[Credential-related source omitted] - Network accessline 87
[Credential-related source omitted] - Network accessline 88
[Credential-related source omitted] - Access to secretsline 88
[Credential-related source omitted] - Network accessline 90
[Credential-related source omitted] - Network accessline 91
[Credential-related source omitted] - Access to secretsline 91
[Credential-related source omitted] - Network accessline 115
"size": 20 } resp = requests.get("https://zenodo.org/api/records", params=params) - Network accessline 116
} resp = requests.get("https://zenodo.org/api/records", params=params) data = resp.json() - Network accessline 117
resp = requests.get("https://zenodo.org/api/records", params=params) - Access to secretsline 132
[Credential-related source omitted] - Network accessline 135
# Step 1: Create empty deposition dep = requests.post("https://zenodo.org/api/deposit/depositions", - Network accessline 136
dep = requests.post("https://zenodo.org/api/deposit/depositions", - Network accessline 140
bucket_url = dep["links"]["bucket"] with open("analysis_code.zip", "rb") as f: requests.put(f"{bucket_url}/analysis_code.zip", - Network accessline 141
[Credential-related source omitted] - Network accessline 142
requests.put(f"{bucket_url}/analysis_code.zip", - Network accessline 153
} } requests.put(f"https://zenodo.org/api/deposit/depositions/{dep['id']}", - Network accessline 154
} requests.put(f"https://zenodo.org/api/deposit/depositions/{dep['id']}", headers=headers, json=metadata) - Network accessline 155
requests.put(f"https://zenodo.org/api/deposit/depositions/{dep['id']}", - Network accessline 156
[Credential-related source omitted] - Network accessline 157
requests.post(f"https://zenodo.org/api/deposit/depositions/{dep['id']}/actions/publish", - Network accessline 166
curl "https://zenodo.org/api/records?communities=astronomy&size=100&sort=mostrecent" - Network accessline 171
- Official API documentation: https://developers.zenodo.org/ - Network accessline 172
- Zenodo homepage: https://zenodo.org/ - Network accessline 173
- GitHub integration guide: https://guides.github.com/activities/citable-code/ - Network accessline 174
- DataCite Metadata Schema: https://schema.datacite.org/
skills/research/methodology/claude-scientific-guide/SKILL.md
- Network accessline 2
name: claude-scientific-guide description: "Ready-to-use agent skills for scientific research and engineering" source: https://github.com/zyphlar/claude-scientific-skills - Network accessline 3
description: "Ready-to-use agent skills for scientific research and engineering" source: https://github.com/zyphlar/claude-scientific-skills metadata: - Network accessline 4
source: https://github.com/zyphlar/claude-scientific-skills
skills/research/methodology/grad-school-guide/SKILL.md
- Network accessline 8
subcategory: "methodology" keywords: ["research question formulation", "hypothesis formulation", "theoretical framework", "conceptual model"] source: "https://github.com/poloclub/awesome-grad-school" - Network accessline 9
keywords: ["research question formulation", "hypothesis formulation", "theoretical framework", "conceptual model"] source: "https://github.com/poloclub/awesome-grad-school" --- - Network accessline 10
source: "https://github.com/poloclub/awesome-grad-school" - Network accessline 178
- [awesome-grad-school](https://github.com/poloclub/awesome-grad-school) -- Curated grad school advice (450+ stars) - Network accessline 179
- [A Survival Guide to a PhD](http://karpathy.github.io/2016/09/07/phd/) -- Andrej Karpathy - Network accessline 180
- [Modest Advice for New Graduate Students](https://stearnslab.yale.edu/modest-advice) -- Stephen Stearns, Yale - Network accessline 181
- [De-Mystifying Good Research](https://bigaidream.gitbooks.io/tech-blog/content/2014/de-mystifying-good-research.html) -- Fei-Fei Li - Network accessline 182
- [How to Write a Great Research Paper](https://www.microsoft.com/en-us/research/academic-program/write-great-research-paper/) -- Simon Peyton Jones
skills/research/methodology/parsifal-slr-guide/SKILL.md
- Network accessline 8
subcategory: "methodology" keywords: ["Parsifal", "systematic review", "SLR", "review protocol", "PICO", "research methodology"] source: "https://github.com/vitorfs/parsifal" - Network accessline 9
keywords: ["Parsifal", "systematic review", "SLR", "review protocol", "PICO", "research methodology"] source: "https://github.com/vitorfs/parsifal" --- - Network accessline 10
source: "https://github.com/vitorfs/parsifal" - Network accessline 133
git clone https://github.com/vitorfs/parsifal.git - Network accessline 136
python manage.py migrate python manage.py runserver # Access at http://localhost:8000 - Network accessline 138
# Access at http://localhost:8000 - Network accessline 151
- [Parsifal](https://github.com/vitorfs/parsifal) - Network accessline 152
- Kitchenham, B. & Charters, S. (2007). "Guidelines for performing Systematic Literature Reviews in Software Engineering." - [PRISMA Statement](http://www.prisma-statement.org/) - [PROSPERO Registry](https://www.crd.york.ac.uk/prospero/) - Network accessline 153
- [PRISMA Statement](http://www.prisma-statement.org/) - Network accessline 154
- [PROSPERO Registry](https://www.crd.york.ac.uk/prospero/)
skills/research/methodology/research-paper-kb/SKILL.md
- Network accessline 260
- Obsidian knowledge management: https://obsidian.md - Network accessline 261
- Zettlekasten method: https://zettelkasten.de/introduction - Network accessline 262
- Notion for research: https://www.notion.so/templates/research - Network accessline 263
- YAML specification: https://yaml.org/spec/1.2.2/
skills/research/methodology/research-town-guide/SKILL.md
- Network accessline 259
- Research Town: wentor-research-plugins - The AI Scientist (Sakana AI): https://github.com/SakanaAI/AI-Scientist - Network accessline 260
- The AI Scientist (Sakana AI): https://github.com/SakanaAI/AI-Scientist - Network accessline 261
- AutoGen multi-agent framework: https://github.com/microsoft/autogen - Network accessline 262
- CrewAI agent framework: https://github.com/joaomdmoura/crewAI
skills/research/methodology/scientify-idea-generation/SKILL.md
skills/research/methodology/slr-automation-guide/SKILL.md
- Network accessline 8
subcategory: "methodology" keywords: ["systematic review", "SLR", "automation", "screening", "PRISMA", "evidence synthesis"] source: "https://github.com/asreview/asreview" - Network accessline 9
keywords: ["systematic review", "SLR", "automation", "screening", "PRISMA", "evidence synthesis"] source: "https://github.com/asreview/asreview" --- - Network accessline 10
source: "https://github.com/asreview/asreview" - Network accessline 232
- [ASReview](https://asreview.nl/) — Active learning for systematic reviews - Network accessline 233
- [PRISMA 2020](http://www.prisma-statement.org/) — Reporting guidelines - Network accessline 234
- [Cochrane Handbook](https://training.cochrane.org/handbook) - Network accessline 235
- [Rayyan](https://www.rayyan.ai/) — Collaborative screening platform
skills/research/paper-review/latte-review-guide/SKILL.md
- Network accessline 8
subcategory: "paper-review" keywords: ["LatteReview", "systematic review", "literature screening", "AI review", "title screening", "PRISMA"] source: "https://github.com/PouriaRouzrokh/LatteReview" - Network accessline 9
keywords: ["LatteReview", "systematic review", "literature screening", "AI review", "title screening", "PRISMA"] source: "https://github.com/PouriaRouzrokh/LatteReview" --- - Network accessline 10
source: "https://github.com/PouriaRouzrokh/LatteReview" - Network accessline 143
llm_provider="ollama", model="llama3", base_url="http://localhost:11434", - Network accessline 144
model="llama3", base_url="http://localhost:11434", ) - Network accessline 145
base_url="http://localhost:11434", - Network accessline 173
- [LatteReview GitHub](https://github.com/PouriaRouzrokh/LatteReview) - Network accessline 174
- [LatteReview Documentation](https://lattereview.readthedocs.io/)
skills/research/paper-review/paper-critique-framework/SKILL.md
- Network accessline 178
- Smith, R. (2006). "Peer review: a flawed process at the heart of science and journals." *Journal of the Royal Society of Medicine*, 99(4), 178-182. - [NeurIPS Reviewer Guidelines](https://neurips.cc/Conferences/2025/ReviewerGuidelines) - Network accessline 179
- [NeurIPS Reviewer Guidelines](https://neurips.cc/Conferences/2025/ReviewerGuidelines) - Network accessline 180
- [ACL Reviewer Tutorial](https://aclrollingreview.org/reviewertutorial)
skills/tools/code-exec/google-colab-guide/SKILL.md
- Network accessline 8
subcategory: "code-exec" keywords: ["Google Colab", "Jupyter", "GPU", "machine learning", "Python", "cloud computing"] source: "https://colab.research.google.com" - Network accessline 9
keywords: ["Google Colab", "Jupyter", "GPU", "machine learning", "Python", "cloud computing"] source: "https://colab.research.google.com" --- - Network accessline 10
source: "https://colab.research.google.com" - Network accessline 67
# From URL !wget -q https://example.com/dataset.zip -O /content/dataset.zip - Network accessline 68
!wget -q https://example.com/dataset.zip -O /content/dataset.zip - Network accessline 186
# Install from GitHub !pip install -q git+https://github.com/huggingface/peft.git - Network accessline 187
!pip install -q git+https://github.com/huggingface/peft.git - Network accessline 259
# Clone a research repository !git clone https://github.com/user/research-repo.git /content/repo - Network accessline 260
!git clone https://github.com/user/research-repo.git /content/repo - Network accessline 273
- Google Colab documentation: https://colab.research.google.com/notebooks/intro.ipynb - Network accessline 274
- Colab resource limits FAQ: https://research.google.com/colaboratory/faq.html - Network accessline 275
- PyTorch on Colab: https://pytorch.org/tutorials/beginner/colab - Network accessline 276
- Hugging Face + Colab: https://huggingface.co/docs/transformers/notebooks
skills/tools/code-exec/jupyter-notebook-guide/SKILL.md
- Network accessline 169
# Then open http://localhost:8888 in your local browser
skills/tools/code-exec/kaggle-api-guide/SKILL.md
- Network accessline 8
subcategory: "code-exec" keywords: ["kaggle", "datasets", "competitions", "notebooks", "data-science", "machine-learning"] source: "https://www.kaggle.com/docs/api" - Network accessline 9
keywords: ["kaggle", "datasets", "competitions", "notebooks", "data-science", "machine-learning"] source: "https://www.kaggle.com/docs/api" --- - Network accessline 10
source: "https://www.kaggle.com/docs/api" - Network accessline 25
A free Kaggle API token is required. Generate one from your Kaggle account settings at https://www.kaggle.com/settings. - Access to secretsline 41
[Credential-related source omitted] - Network accessline 212
- Kaggle API Documentation: https://www.kaggle.com/docs/api - Network accessline 213
- Kaggle API GitHub Repository: https://github.com/Kaggle/kaggle-api - Network accessline 214
- Kaggle Datasets: https://www.kaggle.com/datasets - Network accessline 215
- Kaggle Competitions: https://www.kaggle.com/competitions - Network accessline 216
- Kaggle Notebooks: https://www.kaggle.com/code
skills/tools/code-exec/overleaf-cli-guide/SKILL.md
- Network accessline 8
subcategory: "code-exec" keywords: ["Overleaf", "LaTeX", "CLI", "sync", "git", "collaboration", "academic writing"] source: "https://github.com/overleaf/overleaf" - Network accessline 9
keywords: ["Overleaf", "LaTeX", "CLI", "sync", "git", "collaboration", "academic writing"] source: "https://github.com/overleaf/overleaf" --- - Network accessline 10
source: "https://github.com/overleaf/overleaf" - Network accessline 32
# Clone your Overleaf project (requires paid plan) git clone https://git.overleaf.com/YOUR_PROJECT_ID my-paper - Network accessline 33
git clone https://git.overleaf.com/YOUR_PROJECT_ID my-paper - Network accessline 36
# The remote is already configured git remote -v # origin https://git.overleaf.com/YOUR_PROJECT_ID (fetch) - Network accessline 37
git remote -v # origin https://git.overleaf.com/YOUR_PROJECT_ID (fetch) # origin https://git.overleaf.com/YOUR_PROJECT_ID (push) - Network accessline 38
# origin https://git.overleaf.com/YOUR_PROJECT_ID (fetch) - Network accessline 39
# origin https://git.overleaf.com/YOUR_PROJECT_ID (push) - Network accessline 133
# Export from Zotero using Better BibTeX subprocess.run([ "curl", "-s", - Network accessline 134
subprocess.run([ "curl", "-s", f"http://localhost:23119/better-bibtex/export/library?/" - Network accessline 135
"curl", "-s", f"http://localhost:23119/better-bibtex/export/library?/" f"{zotero_lib_id}/library.biblatex", - Network accessline 136
f"http://localhost:23119/better-bibtex/export/library?/" - Network accessline 276
- Overleaf Git bridge documentation: https://www.overleaf.com/learn/how-to/Git_integration - Network accessline 277
- overleaf-sync: https://github.com/moritzgloeckl/overleaf-sync - Network accessline 278
- LaTeX Workshop (VS Code): https://github.com/James-Yu/LaTeX-Workshop - Network accessline 279
- latexmk documentation: https://mg.readthedocs.io/latexmk.html
skills/tools/code-exec/python-reproducibility-guide/SKILL.md
- Access to secretsline 179
[Credential-related source omitted] - Dangerous shell operationline 211
RUN apt-get update && apt-get install -y build-essential git && rm -rf /var/lib/apt/lists/* - Dangerous shell operationline 214
&& rm -rf /var/lib/apt/lists/* - Dangerous shell operationline 290
rm -rf data/processed/ outputs/
skills/tools/code-exec/r-reproducibility-guide/SKILL.md
- Dangerous shell operationline 201
rm -rf output/figures/* output/manuscript.pdf data/processed/*
skills/tools/code-exec/sandbox-execution-guide/SKILL.md
- Dangerous shell operationline 34
RUN apt-get update && apt-get install -y --no-install-recommends build-essential gfortran libopenblas-dev && rm -rf /var/lib/apt/lists/* - Dangerous shell operationline 38
&& rm -rf /var/lib/apt/lists/* - Network accessline 149
# shell.nix for a research project { pkgs ? import (fetchTarball { url = "https://github.com/NixOS/nixpkgs/archive/nixos-23.11.tar.gz"; - Network accessline 150
{ pkgs ? import (fetchTarball { url = "https://github.com/NixOS/nixpkgs/archive/nixos-23.11.tar.gz"; }) {} }: - Network accessline 151
url = "https://github.com/NixOS/nixpkgs/archive/nixos-23.11.tar.gz";
skills/tools/diagram/code-flow-visualizer/SKILL.md
- Network accessline 8
subcategory: "diagram" keywords: ["code visualization", "flowchart", "Mermaid", "Python", "control flow", "AST"] source: "https://github.com/mermaid-js/mermaid" - Network accessline 9
keywords: ["code visualization", "flowchart", "Mermaid", "Python", "control flow", "AST"] source: "https://github.com/mermaid-js/mermaid" --- - Network accessline 10
source: "https://github.com/mermaid-js/mermaid" - Network accessline 194
- Mermaid flowchart syntax: https://mermaid.js.org/syntax/flowchart.html - Network accessline 195
- Python AST module: https://docs.python.org/3/library/ast.html - Network accessline 196
- TypeScript Compiler API: https://github.com/microsoft/TypeScript/wiki/Using-the-Compiler-API - Network accessline 197
- Code2Flow (related tool): https://github.com/scottrogowski/code2flow
skills/tools/diagram/excalidraw-diagram-guide/SKILL.md
- Network accessline 8
subcategory: "diagram" keywords: ["Excalidraw", "diagram", "whiteboard", "hand-drawn", "sketch", "visual"] source: "https://github.com/excalidraw/excalidraw" - Network accessline 9
keywords: ["Excalidraw", "diagram", "whiteboard", "hand-drawn", "sketch", "visual"] source: "https://github.com/excalidraw/excalidraw" --- - Network accessline 10
source: "https://github.com/excalidraw/excalidraw" - Network accessline 105
<iframe src="https://excalidraw.com/#json=YOUR_ENCODED_DATA" - Network accessline 106
src="https://excalidraw.com/#json=YOUR_ENCODED_DATA" - Network accessline 168
- Excalidraw open-source repository: https://github.com/excalidraw/excalidraw - Network accessline 169
- Excalidraw libraries (pre-made shapes): https://libraries.excalidraw.com - Network accessline 170
- Excalidraw file format specification: https://docs.excalidraw.com
skills/tools/diagram/json-data-visualizer/SKILL.md
- Network accessline 8
subcategory: "diagram" keywords: ["JSON visualization", "data structure diagram", "tree visualization", "API response viewer", "schema explorer"] source: "https://github.com/AykutSarac/jsoncrack.com" - Network accessline 9
keywords: ["JSON visualization", "data structure diagram", "tree visualization", "API response viewer", "schema explorer"] source: "https://github.com/AykutSarac/jsoncrack.com" --- - Network accessline 10
source: "https://github.com/AykutSarac/jsoncrack.com" - Network accessline 32
# Clone the repository git clone https://github.com/AykutSarac/jsoncrack.com.git - Network accessline 33
git clone https://github.com/AykutSarac/jsoncrack.com.git - Network accessline 56
# Access at http://localhost:8888 - Network accessline 147
# Fetch metadata from CrossRef API response = requests.get( - Network accessline 148
response = requests.get( - Network accessline 149
"https://api.crossref.org/works/10.1038/nature12373" - Network accessline 192
<iframe src="https://jsoncrack.internal.lab/widget" - Network accessline 193
src="https://jsoncrack.internal.lab/widget" - Network accessline 267
- JSON Crack website: https://jsoncrack.com - Network accessline 268
- JSON Crack GitHub: https://github.com/AykutSarac/jsoncrack.com - Network accessline 269
- JSON Crack React embed: https://www.npmjs.com/package/jsoncrack-react
skills/tools/diagram/kroki-diagram-api/SKILL.md
- Network accessline 8
subcategory: "diagram" keywords: ["Kroki", "diagram generation", "text-to-diagram", "Graphviz", "PlantUML", "Mermaid"] source: "https://kroki.io/" - Network accessline 9
keywords: ["Kroki", "diagram generation", "text-to-diagram", "Graphviz", "PlantUML", "Mermaid"] source: "https://kroki.io/" --- - Network accessline 10
source: "https://kroki.io/" - Network accessline 24
# Graphviz DOT diagram curl "https://kroki.io/graphviz/svg/digraph{A->B->C}" -o diagram.svg - Network accessline 25
curl "https://kroki.io/graphviz/svg/digraph{A->B->C}" -o diagram.svg - Network accessline 27
# Mermaid diagram (base64-encoded) echo "graph TD; A-->B; B-->C;" | base64 | \ curl "https://kroki.io/mermaid/svg/$(cat -)" -o diagram.svg - Network accessline 28
echo "graph TD; A-->B; B-->C;" | base64 | curl "https://kroki.io/mermaid/svg/$(cat -)" -o diagram.svg - Network accessline 29
curl "https://kroki.io/mermaid/svg/$(cat -)" -o diagram.svg - Network accessline 35
# PlantUML sequence diagram curl -X POST "https://kroki.io/plantuml/svg" \ - Network accessline 36
curl -X POST "https://kroki.io/plantuml/svg" \ - Network accessline 43
# Mermaid flowchart curl -X POST "https://kroki.io/mermaid/svg" \ - Network accessline 44
curl -X POST "https://kroki.io/mermaid/svg" \ - Network accessline 53
# Graphviz curl -X POST "https://kroki.io/graphviz/svg" \ - Network accessline 54
curl -X POST "https://kroki.io/graphviz/svg" \ - Network accessline 61
# D2 diagram curl -X POST "https://kroki.io/d2/svg" \ - Network accessline 62
curl -X POST "https://kroki.io/d2/svg" \ - Network accessline 73
https://kroki.io/{diagram_type}/{output_format}/{encoded_source} - Network accessline 75
# Or POST to: https://kroki.io/{diagram_type}/{output_format} - Network accessline 76
https://kroki.io/{diagram_type}/{output_format} - Network accessline 115
KROKI_URL = "https://kroki.io" - Network accessline 119
output_format: str = "svg") -> bytes: """Render a text diagram to image via Kroki.""" resp = requests.post( - Network accessline 120
"""Render a text diagram to image via Kroki.""" resp = requests.post( f"{KROKI_URL}/{diagram_type}/{output_format}", - Network accessline 121
resp = requests.post( - Network accessline 191
# Then use http://localhost:8000 instead of https://kroki.io - Network accessline 196
- [Kroki](https://kroki.io/) - Network accessline 197
- [Kroki Documentation](https://docs.kroki.io/) - Network accessline 198
- [Kroki GitHub](https://github.com/yuzutech/kroki)
skills/tools/diagram/mermaid-architect-guide/SKILL.md
- Network accessline 8
subcategory: "diagram" keywords: ["Mermaid", "architecture", "system design", "C4 model", "diagram", "hand-drawn"] source: "https://github.com/mermaid-js/mermaid" - Network accessline 9
keywords: ["Mermaid", "architecture", "system design", "C4 model", "diagram", "hand-drawn"] source: "https://github.com/mermaid-js/mermaid" --- - Network accessline 10
source: "https://github.com/mermaid-js/mermaid" - Network accessline 216
- Mermaid official documentation: https://mermaid.js.org - Network accessline 217
- C4 model specification: https://c4model.com - Network accessline 218
- Mermaid CLI (mmdc): https://github.com/mermaid-js/mermaid-cli - Network accessline 219
- Mermaid Live Editor: https://mermaid.live
skills/tools/diagram/mermaid-diagram-guide/SKILL.md
- Network accessline 260
- **Export as SVG for papers.** SVG scales perfectly and produces crisp output at any size. - **Version-control your diagrams.** Since Mermaid is text, it diffs cleanly in Git. - **Test in the live editor.** Use [mermaid.live](https://merma - Network accessline 262
- **Test in the live editor.** Use [mermaid.live](https://mermaid.live/) to iterate quickly before committing. - Network accessline 266
- [Mermaid Documentation](https://mermaid.js.org/) -- Official reference - Network accessline 267
- [Mermaid Live Editor](https://mermaid.live/) -- Interactive diagram editor - Network accessline 268
- [Mermaid CLI](https://github.com/mermaid-js/mermaid-cli) -- Command-line rendering tool - Network accessline 269
- [GitHub Mermaid Support](https://github.blog/2022-02-14-include-diagrams-markdown-files-mermaid/) -- Native rendering in GitHub
skills/tools/diagram/plantuml-guide/SKILL.md
- Network accessline 379
server = plantuml.PlantUML(url="http://www.plantuml.com/plantuml/svg/")
skills/tools/diagram/tldraw-whiteboard-guide/SKILL.md
- Network accessline 8
subcategory: "diagram" keywords: ["whiteboard", "infinite canvas", "diagram SDK", "collaborative drawing", "research diagrams", "visual thinking"] source: "https://github.com/tldraw/tldraw" - Network accessline 9
keywords: ["whiteboard", "infinite canvas", "diagram SDK", "collaborative drawing", "research diagrams", "visual thinking"] source: "https://github.com/tldraw/tldraw" --- - Network accessline 10
source: "https://github.com/tldraw/tldraw" - Network accessline 32
# Clone and run locally git clone https://github.com/tldraw/tldraw.git - Network accessline 33
git clone https://github.com/tldraw/tldraw.git - Network accessline 38
# Access at http://localhost:5420 - Network accessline 394
- tldraw official site: https://tldraw.com - Network accessline 395
- tldraw GitHub repository: https://github.com/tldraw/tldraw - Network accessline 396
- tldraw SDK documentation: https://tldraw.dev - Network accessline 397
- tldraw examples: https://github.com/tldraw/tldraw/tree/main/apps/examples
skills/tools/document/anystyle-api/SKILL.md
- Network accessline 8
subcategory: "document" keywords: ["PDF parsing", "document chunking", "format conversion", "PDF extraction"] source: "https://anystyle.io/" - Network accessline 9
keywords: ["PDF parsing", "document chunking", "format conversion", "PDF extraction"] source: "https://anystyle.io/" --- - Network accessline 10
source: "https://anystyle.io/" - Network accessline 25
No authentication required. The AnyStyle web service is freely accessible without any API key, token, or registration. The service can be used via the web interface at https://anystyle.io/ or through its API endpoint. For heavy usage or pri - Network accessline 33
- **URL**: `POST https://anystyle.io/parse` - Network accessline 44
# Parse a single citation curl -X POST "https://anystyle.io/parse" \ - Network accessline 45
curl -X POST "https://anystyle.io/parse" \ - Network accessline 49
# Parse multiple citations (one per line) curl -X POST "https://anystyle.io/parse" \ - Network accessline 50
curl -X POST "https://anystyle.io/parse" \ - Network accessline 98
resp = requests.post( - Network accessline 99
"https://anystyle.io/parse", - Network accessline 121
def parse_references(raw_text): """Parse raw citation text into structured records.""" resp = requests.post( - Network accessline 122
"""Parse raw citation text into structured records.""" resp = requests.post( "https://anystyle.io/parse", - Network accessline 123
resp = requests.post( - Network accessline 124
"https://anystyle.io/parse", - Network accessline 154
resp = requests.post( - Network accessline 155
"https://anystyle.io/parse", - Network accessline 195
- AnyStyle web service: https://anystyle.io/ - Network accessline 196
- AnyStyle GitHub repository: https://github.com/inukshuk/anystyle - Network accessline 197
- AnyStyle Ruby gem: https://rubygems.org/gems/anystyle - Network accessline 198
- AnyStyle CLI documentation: https://github.com/inukshuk/anystyle-cli - Network accessline 199
- CSL (Citation Style Language): https://citationstyles.org/
skills/tools/document/docsgpt-guide/SKILL.md
- Network accessline 8
subcategory: "document" keywords: ["document-analysis", "knowledge-base", "rag", "private-ai", "enterprise-search", "question-answering"] source: "https://github.com/arc53/DocsGPT" - Network accessline 9
keywords: ["document-analysis", "knowledge-base", "rag", "private-ai", "enterprise-search", "question-answering"] source: "https://github.com/arc53/DocsGPT" --- - Network accessline 10
source: "https://github.com/arc53/DocsGPT" - Network accessline 28
git clone https://github.com/arc53/DocsGPT.git - Access to secretsline 32
[Credential-related source omitted] - Access to secretsline 40
[Credential-related source omitted] - Network accessline 42
# Or use a local model via Ollama LLM_NAME=ollama OLLAMA_API_BASE=http://localhost:11434 - Network accessline 43
LLM_NAME=ollama OLLAMA_API_BASE=http://localhost:11434 MODEL_NAME=llama3 - Network accessline 44
OLLAMA_API_BASE=http://localhost:11434 - Network accessline 54
The web interface becomes available at `http://localhost:5173`. For production deployments behind a reverse proxy, configure the appropriate `VITE_API_HOST` environment variable. - Network accessline 69
# Upload documents via the API curl -X POST http://localhost:7091/api/upload \ - Network accessline 70
curl -X POST http://localhost:7091/api/upload \ - Network accessline 80
# Query your document collection via API curl -X POST http://localhost:7091/api/answer \ - Network accessline 81
curl -X POST http://localhost:7091/api/answer \ - Network accessline 128
- DocsGPT repository: https://github.com/arc53/DocsGPT - Network accessline 129
- DocsGPT documentation: https://docs.docsgpt.cloud/
skills/tools/document/grobid-pdf-parsing/SKILL.md
- Network accessline 8
subcategory: "document" keywords: ["PDF parsing", "PDF extraction", "document chunking", "format conversion"] source: "https://github.com/kermitt2/grobid" - Network accessline 9
keywords: ["PDF parsing", "PDF extraction", "document chunking", "format conversion"] source: "https://github.com/kermitt2/grobid" --- - Network accessline 10
source: "https://github.com/kermitt2/grobid" - Network accessline 37
# GROBID is now running at http://localhost:8070 - Network accessline 38
# Web console: http://localhost:8070/console - Network accessline 44
git clone https://github.com/kermitt2/grobid.git - Network accessline 55
# Process a single PDF and get TEI XML curl -v --form input=@paper.pdf \ - Network accessline 56
curl -v --form input=@paper.pdf \ - Network accessline 57
http://localhost:8070/api/processFulltextDocument \ - Network accessline 60
# With options curl -v --form input=@paper.pdf \ - Network accessline 61
curl -v --form input=@paper.pdf \ - Network accessline 63
--form consolidateCitations=1 \ --form includeRawCitations=1 \ http://localhost:8070/api/processFulltextDocument \ - Network accessline 64
--form includeRawCitations=1 \ http://localhost:8070/api/processFulltextDocument \ -o paper.tei.xml - Network accessline 65
http://localhost:8070/api/processFulltextDocument \ - Network accessline 85
class GrobidClient: def __init__(self, base_url='http://localhost:8070'): - Network accessline 86
def __init__(self, base_url='http://localhost:8070'): - Network accessline 96
'consolidateCitations': '1' if consolidate_citations else '0', } response = requests.post(url, files=files, data=data) - Network accessline 97
} response = requests.post(url, files=files, data=data) response.raise_for_status() - Network accessline 98
response = requests.post(url, files=files, data=data) - Network accessline 104
url = f'{self.base_url}/api/processHeaderDocument' files = {'input': open(pdf_path, 'rb')} response = requests.post(url, files=files) - Network accessline 105
files = {'input': open(pdf_path, 'rb')} response = requests.post(url, files=files) response.raise_for_status() - Network accessline 106
response = requests.post(url, files=files) - Network accessline 111
"""Check if GROBID server is running.""" try: resp = requests.get(f'{self.base_url}/api/isalive') - Network accessline 112
try: resp = requests.get(f'{self.base_url}/api/isalive') return resp.status_code == 200 - Network accessline 113
resp = requests.get(f'{self.base_url}/api/isalive') - Network accessline 133
def parse_tei_metadata(tei_xml): """Extract title, authors, abstract from TEI XML.""" ns = {'tei': 'http://www.tei-c.org/ns/1.0'} - Network accessline 134
"""Extract title, authors, abstract from TEI XML.""" ns = {'tei': 'http://www.tei-c.org/ns/1.0'} root = etree.fromstring(tei_xml.encode('utf-8')) - Network accessline 135
ns = {'tei': 'http://www.tei-c.org/ns/1.0'} - Network accessline 169
def parse_tei_sections(tei_xml): """Extract structured sections from TEI XML body.""" ns = {'tei': 'http://www.tei-c.org/ns/1.0'} - Network accessline 170
"""Extract structured sections from TEI XML body.""" ns = {'tei': 'http://www.tei-c.org/ns/1.0'} root = etree.fromstring(tei_xml.encode('utf-8')) - Network accessline 171
ns = {'tei': 'http://www.tei-c.org/ns/1.0'} - Network accessline 194
def parse_tei_references(tei_xml): """Extract structured references from TEI XML.""" ns = {'tei': 'http://www.tei-c.org/ns/1.0'} - Network accessline 195
"""Extract structured references from TEI XML.""" ns = {'tei': 'http://www.tei-c.org/ns/1.0'} root = etree.fromstring(tei_xml.encode('utf-8')) - Network accessline 196
ns = {'tei': 'http://www.tei-c.org/ns/1.0'} - Network accessline 199
refs = [] for bib in root.findall('.//tei:listBibl/tei:biblStruct', ns): ref = {'id': bib.get('{http://www.w3.org/XML/1998/namespace}id', '')} - Network accessline 201
ref = {'id': bib.get('{http://www.w3.org/XML/1998/namespace}id', '')} - Network accessline 290
- [GROBID Documentation](https://grobid.readthedocs.io/) -- Official documentation - Network accessline 291
- [GROBID GitHub](https://github.com/kermitt2/grobid) -- Source code - Network accessline 292
- [TEI Guidelines](https://tei-c.org/release/doc/tei-p5-doc/en/html/) -- TEI XML standard - Network accessline 293
- [grobid-client-python](https://github.com/kermitt2/grobid_client_python) -- Official Python client - Network accessline 294
- [Science Parse](https://github.com/allenai/science-parse) -- Allen AI alternative parser
skills/tools/document/large-document-reader/SKILL.md
skills/tools/document/paper-parse-guide/SKILL.md
- Network accessline 31
| Local PDF | Direct file path | Best quality, no network needed | | DOI | Resolve via CrossRef/Unpaywall | Auto-fetches open access version | | arXiv ID | `https://arxiv.org/pdf/{id}` | Always available | - Network accessline 32
| DOI | Resolve via CrossRef/Unpaywall | Auto-fetches open access version | | arXiv ID | `https://arxiv.org/pdf/{id}` | Always available | | URL | Direct download | May require institutional access | - Network accessline 33
| arXiv ID | `https://arxiv.org/pdf/{id}` | Always available | | URL | Direct download | May require institutional access | | OpenAlex ID | OpenAlex API + OA link | Includes metadata | - Network accessline 83
# Parse a paper curl -X POST "http://localhost:8070/api/processFulltextDocument" \ - Network accessline 84
curl -X POST "http://localhost:8070/api/processFulltextDocument" \ - Network accessline 239
- GROBID: https://github.com/kermitt2/grobid - Network accessline 240
- PyMuPDF: https://pymupdf.readthedocs.io - Network accessline 241
- OpenAlex API: https://api.openalex.org - Network accessline 242
- Unpaywall API: https://unpaywall.org/products/api - Network accessline 243
- S. Keshav, "How to Read a Paper" (2007): http://ccr.sigcomm.org/online/files/p83-keshavA.pdf
skills/tools/document/pdf-extraction-guide/SKILL.md
- Network accessline 167
def parse_with_grobid(pdf_path, grobid_url="http://localhost:8070"): - Network accessline 168
"""Parse a paper PDF using GROBID.""" with open(pdf_path, "rb") as f: response = requests.post( - Network accessline 169
with open(pdf_path, "rb") as f: response = requests.post( f"{grobid_url}/api/processFulltextDocument", - Network accessline 170
response = requests.post( - Network accessline 184
tei_xml = parse_with_grobid("paper.pdf") root = etree.fromstring(tei_xml.encode()) ns = {"tei": "http://www.tei-c.org/ns/1.0"} - Network accessline 186
ns = {"tei": "http://www.tei-c.org/ns/1.0"}
skills/tools/document/zotero-addon-market-guide/SKILL.md
- Network accessline 8
subcategory: "document" keywords: ["Zotero", "plugin marketplace", "add-ons", "plugin discovery", "extension manager"] source: "https://github.com/syt2/zotero-addons" - Network accessline 9
keywords: ["Zotero", "plugin marketplace", "add-ons", "plugin discovery", "extension manager"] source: "https://github.com/syt2/zotero-addons" --- - Network accessline 10
source: "https://github.com/syt2/zotero-addons" - Network accessline 106
- [Zotero Add-ons GitHub](https://github.com/syt2/zotero-addons) - Network accessline 107
- [Zotero Plugin Template](https://github.com/windingwind/zotero-plugin-template) - Network accessline 108
- [Zotero 7 Plugin Development](https://www.zotero.org/support/dev/client_coding/plugin_development)
skills/tools/document/zotero-night-theme-guide/SKILL.md
- Network accessline 8
subcategory: "document" keywords: ["Zotero", "dark mode", "theme", "night mode", "UI customization", "eye comfort"] source: "https://github.com/tefkah/zotero-night" - Network accessline 9
keywords: ["Zotero", "dark mode", "theme", "night mode", "UI customization", "eye comfort"] source: "https://github.com/tefkah/zotero-night" --- - Network accessline 10
source: "https://github.com/tefkah/zotero-night" - Network accessline 141
- [Zotero Night GitHub](https://github.com/tefkah/zotero-night) - Network accessline 142
- [Zotero 7 Theming](https://www.zotero.org/support/preferences)
skills/tools/document/zotero-style-guide/SKILL.md
- Network accessline 8
subcategory: "document" keywords: ["Zotero", "styling", "UI customization", "reading progress", "tags", "columns"] source: "https://github.com/MuiseDestiny/zotero-style" - Network accessline 9
keywords: ["Zotero", "styling", "UI customization", "reading progress", "tags", "columns"] source: "https://github.com/MuiseDestiny/zotero-style" --- - Network accessline 10
source: "https://github.com/MuiseDestiny/zotero-style" - Network accessline 215
- [Zotero Style GitHub](https://github.com/MuiseDestiny/zotero-style) - Network accessline 216
- [Zotero Plugin Development](https://www.zotero.org/support/dev/client_coding/plugin_development) - Network accessline 217
- [Zotero Styles Gallery](https://www.zotero.org/styles)
skills/tools/knowledge-graph/citation-network-builder/SKILL.md
- Network accessline 72
records["doi"] .str.lower() .str.replace("https://doi.org/", "", regex=False) - Network accessline 73
.str.lower() .str.replace("https://doi.org/", "", regex=False) .str.replace("http://dx.doi.org/", "", regex=False) - Network accessline 74
.str.replace("https://doi.org/", "", regex=False) - Network accessline 75
.str.replace("http://dx.doi.org/", "", regex=False)
skills/tools/knowledge-graph/concept-map-generator/SKILL.md
skills/tools/knowledge-graph/graphiti-guide/SKILL.md
- Network accessline 8
subcategory: "knowledge-graph" keywords: ["knowledge-graph", "ai-agents", "temporal-graphs", "neo4j", "memory", "entity-extraction"] source: "https://github.com/getzep/graphiti" - Network accessline 9
keywords: ["knowledge-graph", "ai-agents", "temporal-graphs", "neo4j", "memory", "entity-extraction"] source: "https://github.com/getzep/graphiti" --- - Network accessline 10
source: "https://github.com/getzep/graphiti" - Access to secretsline 48
[Credential-related source omitted] - Network accessline 196
Access the Neo4j browser at `http://localhost:7474` to visually explore your knowledge graph. Use Cypher queries for advanced exploration: - Network accessline 216
- Graphiti repository: https://github.com/getzep/graphiti - Network accessline 217
- Zep documentation: https://docs.getzep.com/ - Network accessline 218
- Neo4j graph database: https://neo4j.com/
skills/tools/knowledge-graph/knowledge-graph-construction/SKILL.md
- Network accessline 302
- [Neo4j Documentation](https://neo4j.com/docs/) -- Graph database - Network accessline 303
- [NetworkX Documentation](https://networkx.org/) -- Python graph library - Network accessline 304
- [OpenAlex API](https://docs.openalex.org/) -- Open bibliographic data - Network accessline 305
- [LlamaIndex Knowledge Graph Guide](https://docs.llamaindex.ai/) -- KG-RAG integration - Network accessline 306
- [Graphiti](https://github.com/getzep/graphiti) -- Temporal knowledge graph library
skills/tools/knowledge-graph/notero-zotero-notion-guide/SKILL.md
- Network accessline 8
subcategory: "knowledge-graph" keywords: ["Zotero", "Notion", "sync", "reference management", "knowledge base", "annotations"] source: "https://github.com/dvanoni/notero" - Network accessline 9
keywords: ["Zotero", "Notion", "sync", "reference management", "knowledge base", "annotations"] source: "https://github.com/dvanoni/notero" --- - Network accessline 10
source: "https://github.com/dvanoni/notero" - Network accessline 185
- [Notero GitHub](https://github.com/dvanoni/notero) - Network accessline 186
- [Notion API](https://developers.notion.com/) - Network accessline 187
- [Zotero Plugin Development](https://www.zotero.org/support/dev/)
skills/tools/knowledge-graph/ontology-design-guide/SKILL.md
skills/tools/knowledge-graph/openspg-guide/SKILL.md
- Network accessline 8
subcategory: "knowledge-graph" keywords: ["OpenSPG", "knowledge graph", "SPG", "KAG", "Ant Group", "semantic reasoning"] source: "https://github.com/OpenSPG/openspg" - Network accessline 9
keywords: ["OpenSPG", "knowledge graph", "SPG", "KAG", "Ant Group", "semantic reasoning"] source: "https://github.com/OpenSPG/openspg" --- - Network accessline 10
source: "https://github.com/OpenSPG/openspg" - Network accessline 22
# Docker deployment git clone https://github.com/OpenSPG/openspg.git - Network accessline 23
git clone https://github.com/OpenSPG/openspg.git - Network accessline 30
# Access KG Studio at http://localhost:8887 - Network accessline 208
- [OpenSPG GitHub](https://github.com/OpenSPG/openspg) - Network accessline 209
- [KAG Framework](https://github.com/OpenSPG/KAG) - Network accessline 210
- [SPG White Paper](https://arxiv.org/abs/2302.09560)
skills/tools/knowledge-graph/zotero-markdb-connect-guide/SKILL.md
- Network accessline 8
subcategory: "knowledge-graph" keywords: ["Zotero", "Obsidian", "Logseq", "markdown", "PKM", "literature notes"] source: "https://github.com/daeh/zotero-markdb-connect" - Network accessline 9
keywords: ["Zotero", "Obsidian", "Logseq", "markdown", "PKM", "literature notes"] source: "https://github.com/daeh/zotero-markdb-connect" --- - Network accessline 10
source: "https://github.com/daeh/zotero-markdb-connect" - Network accessline 63
**Year:** {{year}} **Venue:** {{publicationTitle}} **DOI:** [{{doi}}](https://doi.org/{{doi}}) - Network accessline 65
**DOI:** [{{doi}}](https://doi.org/{{doi}}) - Network accessline 160
- [MarkDB-Connect GitHub](https://github.com/daeh/zotero-markdb-connect) - Network accessline 161
- [Obsidian](https://obsidian.md/) - Network accessline 162
- [Logseq](https://logseq.com/)
skills/tools/ocr-translate/latex-ocr-guide/SKILL.md
- Network accessline 36
# Or install from GitHub for latest version pip install git+https://github.com/lukas-blecher/LaTeX-OCR.git - Network accessline 37
pip install git+https://github.com/lukas-blecher/LaTeX-OCR.git - Network accessline 172
response = requests.post( - Network accessline 173
'https://api.mathpix.com/v3/text',
skills/tools/ocr-translate/latex-translation-guide/SKILL.md
- Network accessline 8
subcategory: "ocr-translate" keywords: ["LaTeX translation", "document translation", "multilingual", "math preservation", "academic translation"] source: "https://github.com/SUSYUSTC/MathTranslate" - Network accessline 9
keywords: ["LaTeX translation", "document translation", "multilingual", "math preservation", "academic translation"] source: "https://github.com/SUSYUSTC/MathTranslate" --- - Network accessline 10
source: "https://github.com/SUSYUSTC/MathTranslate" - Network accessline 174
- [LaTeXTrans](https://github.com/SUSYUSTC/MathTranslate) - Network accessline 175
- [PDFMathTranslate](https://github.com/Byaidu/PDFMathTranslate) - Network accessline 176
- [arXiv LaTeX Cleaner](https://github.com/google-research/arxiv-latex-cleaner)
skills/tools/ocr-translate/pdf-math-translate-guide/SKILL.md
- Network accessline 8
subcategory: "ocr-translate" keywords: ["pdf-translation", "math-formatting", "scientific-papers", "multilingual", "latex-preservation"] source: "https://github.com/Byaidu/PDFMathTranslate" - Network accessline 9
keywords: ["pdf-translation", "math-formatting", "scientific-papers", "multilingual", "latex-preservation"] source: "https://github.com/Byaidu/PDFMathTranslate" --- - Network accessline 10
source: "https://github.com/Byaidu/PDFMathTranslate" - Access to secretsline 41
[Credential-related source omitted] - Access to secretsline 48
[Credential-related source omitted] - Network accessline 139
- PDFMathTranslate repository: https://github.com/Byaidu/PDFMathTranslate - Network accessline 140
- PyPI package: https://pypi.org/project/pdf2zh/
skills/tools/ocr-translate/zotero-pdf-translate-guide/SKILL.md
- Network accessline 8
subcategory: "ocr-translate" keywords: ["zotero", "pdf-translate", "multilingual", "annotation", "academic-translation"] source: "https://github.com/windingwind/zotero-pdf-translate" - Network accessline 9
keywords: ["zotero", "pdf-translate", "multilingual", "annotation", "academic-translation"] source: "https://github.com/windingwind/zotero-pdf-translate" --- - Network accessline 10
source: "https://github.com/windingwind/zotero-pdf-translate" - Network accessline 27
1. Download the latest `.xpi` file from the GitHub releases page at https://github.com/windingwind/zotero-pdf-translate/releases - Access to secretsline 36
[Credential-related source omitted] - Access to secretsline 37
[Credential-related source omitted] - Access to secretsline 38
[Credential-related source omitted] - Network accessline 92
- GitHub Repository: https://github.com/windingwind/zotero-pdf-translate - Network accessline 93
- Zotero Plugin Directory: https://www.zotero.org/support/plugins - Network accessline 94
- Zotero Forums Translation Discussion: https://forums.zotero.org - Network accessline 95
- DeepL API Documentation: https://www.deepl.com/docs-api
skills/tools/ocr-translate/zotero-pdf2zh-guide/SKILL.md
- Network accessline 2
name: zotero-pdf2zh-guide description: "PDF Chinese translation plugin for Zotero reference manager" source: https://github.com/guaguastandup/zotero-pdf2zh - Network accessline 3
description: "PDF Chinese translation plugin for Zotero reference manager" source: https://github.com/guaguastandup/zotero-pdf2zh metadata: - Network accessline 4
source: https://github.com/guaguastandup/zotero-pdf2zh
skills/tools/scraping/academic-web-scraping/SKILL.md
- Network accessline 43
class OpenAlexClient: BASE_URL = "https://api.openalex.org" - Network accessline 44
BASE_URL = "https://api.openalex.org" - Network accessline 46
def __init__(self, email): self.session = requests.Session() - Network accessline 47
self.session = requests.Session() - Access to secretsline 110
[Credential-related source omitted] - Network accessline 172
response = requests.get(url, headers=headers, timeout=30) - Network accessline 226
1. **Check for an API first.** Most academic platforms have one. 2. **Read robots.txt.** `https://example.com/robots.txt` specifies what is allowed. - Network accessline 227
2. **Read robots.txt.** `https://example.com/robots.txt` specifies what is allowed. - Network accessline 320
- [OpenAlex API Documentation](https://docs.openalex.org/) -- Open bibliographic data API - Network accessline 321
- [CrossRef API](https://api.crossref.org/) -- DOI resolution and metadata - Network accessline 322
- [BeautifulSoup Documentation](https://www.crummy.com/software/BeautifulSoup/bs4/doc/) -- HTML parsing - Network accessline 323
- [Scrapy Documentation](https://docs.scrapy.org/) -- Web scraping framework - Network accessline 324
- [Playwright Documentation](https://playwright.dev/python/) -- Browser automation - Network accessline 325
- [Web Scraping Ethics](https://towardsdatascience.com/ethics-in-web-scraping-b96b18136f01) -- Ethical considerations
skills/tools/scraping/dataset-finder-guide/SKILL.md
- Network accessline 114
def search_google_datasets(query, num_results=10): """Search Google Dataset Search and extract results.""" url = f"https://datasetsearch.research.google.com/search" - Network accessline 115
"""Search Google Dataset Search and extract results.""" url = f"https://datasetsearch.research.google.com/search" params = {"query": query, "docid": ""} - Network accessline 116
url = f"https://datasetsearch.research.google.com/search" - Network accessline 129
def search_zenodo(query, resource_type="dataset", size=10): """Search Zenodo for research datasets.""" url = "https://zenodo.org/api/records" - Network accessline 130
"""Search Zenodo for research datasets.""" url = "https://zenodo.org/api/records" params = { - Network accessline 131
url = "https://zenodo.org/api/records" - Network accessline 137
"access_right": "open" } response = requests.get(url, params=params) - Network accessline 138
} response = requests.get(url, params=params) results = response.json() - Network accessline 139
response = requests.get(url, params=params) - Network accessline 170
| CC-BY 4.0 | Yes | Yes | Yes | | CC-BY-SA 4.0 | Yes | Yes (share-alike) | Yes | | CC-BY-NC 4.0 | No | Yes | Yes | - Network accessline 171
| CC-BY-SA 4.0 | Yes | Yes (share-alike) | Yes | | CC-BY-NC 4.0 | No | Yes | Yes | | ODC-ODbL | Yes | Yes (share-alike) | Yes | - Network accessline 172
| CC-BY-NC 4.0 | No | Yes | Yes | - Network accessline 220
DATASETS = { "main_dataset": { "url": "https://zenodo.org/record/12345/files/data.csv", - Network accessline 221
"main_dataset": { "url": "https://zenodo.org/record/12345/files/data.csv", "sha256": "abc123...", - Network accessline 222
"url": "https://zenodo.org/record/12345/files/data.csv", - Network accessline 236
path.parent.mkdir(parents=True, exist_ok=True) print(f"Downloading {name}...") response = requests.get(info["url"]) - Network accessline 237
print(f"Downloading {name}...") response = requests.get(info["url"]) path.write_bytes(response.content) - Network accessline 238
response = requests.get(info["url"]) - Network accessline 249
- Kaggle API documentation: https://github.com/Kaggle/kaggle-api - Network accessline 250
- Hugging Face Datasets: https://huggingface.co/docs/datasets - Network accessline 251
- Zenodo API: https://developers.zenodo.org - Network accessline 252
- Google Dataset Search: https://datasetsearch.research.google.com - Network accessline 253
- Gebru et al., "Datasheets for Datasets" (2021): https://arxiv.org/abs/1803.09010
skills/tools/scraping/easy-spider-guide/SKILL.md
- Network accessline 8
subcategory: "scraping" keywords: ["web scraping", "visual crawler", "no-code scraping", "data collection", "research data", "web automation"] source: "https://github.com/NaiboWang/EasySpider" - Network accessline 9
keywords: ["web scraping", "visual crawler", "no-code scraping", "data collection", "research data", "web automation"] source: "https://github.com/NaiboWang/EasySpider" --- - Network accessline 10
source: "https://github.com/NaiboWang/EasySpider" - Network accessline 28
# Download the latest release for your platform from GitHub releases # https://github.com/NaiboWang/EasySpider/releases - Network accessline 29
# https://github.com/NaiboWang/EasySpider/releases - Network accessline 37
# Or run from source git clone https://github.com/NaiboWang/EasySpider.git - Network accessline 38
git clone https://github.com/NaiboWang/EasySpider.git - Network accessline 247
- EasySpider GitHub repository: https://github.com/NaiboWang/EasySpider - Network accessline 248
- EasySpider documentation: https://github.com/NaiboWang/EasySpider/wiki - Network accessline 249
- EasySpider video tutorials: https://github.com/NaiboWang/EasySpider#tutorials - Network accessline 250
- Web scraping ethics in research: https://doi.org/10.1177/2053951720943006
skills/tools/scraping/repository-harvesting-guide/SKILL.md
- Network accessline 40
Transport: HTTP GET or POST Response format: XML Base URL example: https://arxiv.org/oai2 - Network accessline 42
Base URL example: https://arxiv.org/oai2 - Network accessline 96
OAI_NS = "http://www.openarchives.org/OAI/2.0/" - Network accessline 97
DC_NS = "http://purl.org/dc/elements/1.1/" - Network accessline 128
while True: response = requests.get(base_url, params=params, timeout=30) - Network accessline 129
response = requests.get(base_url, params=params, timeout=30) - Network accessline 297
arXiv: https://export.arxiv.org/oai2 - Network accessline 298
PubMed Central: https://www.ncbi.nlm.nih.gov/pmc/oai/oai.cgi - Network accessline 299
Europeana: https://oai.europeana.eu/oai - Network accessline 300
HAL (France): https://api.archives-ouvertes.fr/oai/hal - Network accessline 301
DBLP: https://dblp.org/oai - Network accessline 302
CiteSeerX: https://citeseerx.ist.psu.edu/oai2 - Network accessline 304
To find more endpoints: - OpenDOAR directory: https://v2.sherpa.ac.uk/opendoar/ - Network accessline 305
- OpenDOAR directory: https://v2.sherpa.ac.uk/opendoar/
skills/tools/scraping/web-scraping-ethics-guide/SKILL.md
- Network accessline 63
Args: base_url: The website's base URL (e.g., 'https://example.com') - Network accessline 64
base_url: The website's base URL (e.g., 'https://example.com') - Network accessline 127
for i, url in enumerate(urls): try: req = urllib.request.Request(url, headers={ - Network accessline 128
try: req = urllib.request.Request(url, headers={ "User-Agent": user_agent, - Network accessline 129
req = urllib.request.Request(url, headers={ - Network accessline 134
response = urllib.request.urlopen(req, timeout=30)
skills/writing/citation/academic-citation-manager/SKILL.md
- Network accessline 32
def get_bibtex_from_doi(doi): """Retrieve BibTeX entry from a DOI via CrossRef.""" url = f"https://doi.org/{doi}" - Network accessline 33
"""Retrieve BibTeX entry from a DOI via CrossRef.""" url = f"https://doi.org/{doi}" headers = {"Accept": "application/x-bibtex"} - Network accessline 34
url = f"https://doi.org/{doi}" headers = {"Accept": "application/x-bibtex"} response = requests.get(url, headers=headers, allow_redirects=True) - Network accessline 35
headers = {"Accept": "application/x-bibtex"} response = requests.get(url, headers=headers, allow_redirects=True) if response.status_code == 200: - Network accessline 36
response = requests.get(url, headers=headers, allow_redirects=True) - Network accessline 58
def get_citation_from_openalex(work_id): """Retrieve citation data from OpenAlex API.""" url = f"https://api.openalex.org/works/{work_id}" - Network accessline 59
"""Retrieve citation data from OpenAlex API.""" url = f"https://api.openalex.org/works/{work_id}" headers = {"User-Agent": "ResearchPlugins/1.0 (https://wentor.ai)"} - Network accessline 60
url = f"https://api.openalex.org/works/{work_id}" headers = {"User-Agent": "ResearchPlugins/1.0 (https://wentor.ai)"} response = requests.get(url, headers=headers) - Network accessline 61
headers = {"User-Agent": "ResearchPlugins/1.0 (https://wentor.ai)"} response = requests.get(url, headers=headers) if response.status_code == 200: - Network accessline 62
response = requests.get(url, headers=headers) - Network accessline 92
"""Retrieve BibTeX from arXiv.""" import feedparser url = f"http://export.arxiv.org/api/query?id_list={arxiv_id}" - Network accessline 93
import feedparser url = f"http://export.arxiv.org/api/query?id_list={arxiv_id}" feed = feedparser.parse(url) - Network accessline 94
url = f"http://export.arxiv.org/api/query?id_list={arxiv_id}" - Network accessline 104
year={{{year}}}, journal={{arXiv preprint arXiv:{arxiv_id}}}, url={{https://arxiv.org/abs/{arxiv_id}}} - Network accessline 105
journal={{arXiv preprint arXiv:{arxiv_id}}}, url={{https://arxiv.org/abs/{arxiv_id}}} }}""" - Network accessline 106
url={{https://arxiv.org/abs/{arxiv_id}}} - Network accessline 137
citation += "." if doi: citation += f" https://doi.org/{doi}" - Network accessline 139
citation += f" https://doi.org/{doi}" - Network accessline 184
**APA 7th:** Jumper, J., Evans, R., & Pritzel, A. (2021). Highly accurate protein structure prediction with AlphaFold. *Nature*, *596*, 583-589. https://doi.org/10.1038/s41586-021-03819-2 - Network accessline 185
Jumper, J., Evans, R., & Pritzel, A. (2021). Highly accurate protein structure prediction with AlphaFold. *Nature*, *596*, 583-589. https://doi.org/10.1038/s41586-021-03819-2 - Network accessline 251
def validate_citation(doi): """Validate a citation against CrossRef metadata.""" url = f"https://api.crossref.org/works/{doi}" - Network accessline 252
"""Validate a citation against CrossRef metadata.""" url = f"https://api.crossref.org/works/{doi}" response = requests.get(url) - Network accessline 253
url = f"https://api.crossref.org/works/{doi}" response = requests.get(url) if response.status_code != 200: - Network accessline 254
response = requests.get(url) - Network accessline 313
- CrossRef API: https://api.crossref.org - Network accessline 314
- OpenAlex API: https://api.openalex.org - Network accessline 315
- APA 7th Edition Manual: https://apastyle.apa.org/products/publication-manual-7th-edition - Network accessline 316
- BibTeX documentation: http://www.bibtex.org - Network accessline 317
- CSL styles repository: https://github.com/citation-style-language/styles
skills/writing/citation/bibtex-management-guide/SKILL.md
- Network accessline 177
if 'doi' in fields: doi = fields['doi'] doi = doi.replace('https://doi.org/', '') - Network accessline 178
doi = fields['doi'] doi = doi.replace('https://doi.org/', '') doi = doi.replace('http://dx.doi.org/', '') - Network accessline 179
doi = doi.replace('https://doi.org/', '') - Network accessline 180
doi = doi.replace('http://dx.doi.org/', '') - Network accessline 199
Retrieve a complete BibTeX entry from a DOI using CrossRef. """ url = f"https://doi.org/{doi}" - Network accessline 200
""" url = f"https://doi.org/{doi}" headers = {'Accept': 'application/x-bibtex'} - Network accessline 201
url = f"https://doi.org/{doi}" headers = {'Accept': 'application/x-bibtex'} response = requests.get(url, headers=headers, allow_redirects=True) - Network accessline 203
response = requests.get(url, headers=headers, allow_redirects=True)
skills/writing/citation/citation-assistant-skill/SKILL.md
- Network accessline 8
subcategory: "citation" keywords: ["citation assistant", "OpenAlex", "Claude Code skill", "reference lookup", "academic citation"] source: "https://github.com/ZhangNy301/citation-assistant" - Network accessline 9
keywords: ["citation assistant", "OpenAlex", "Claude Code skill", "reference lookup", "academic citation"] source: "https://github.com/ZhangNy301/citation-assistant" --- - Network accessline 10
source: "https://github.com/ZhangNy301/citation-assistant" - Network accessline 35
OA_API = "https://api.openalex.org" - Network accessline 37
def search_papers(query, limit=5): """Search OpenAlex for papers.""" resp = requests.get( - Network accessline 38
"""Search OpenAlex for papers.""" resp = requests.get( f"{OA_API}/works", - Network accessline 39
resp = requests.get( - Network accessline 43
"per_page": limit, }, headers={"User-Agent": "ResearchPlugins/1.0 (https://wentor.ai)"}, - Network accessline 44
}, headers={"User-Agent": "ResearchPlugins/1.0 (https://wentor.ai)"}, ) - Network accessline 45
headers={"User-Agent": "ResearchPlugins/1.0 (https://wentor.ai)"}, - Network accessline 60
def get_bibtex(doi): """Get BibTeX for a paper via CrossRef DOI resolution.""" resp = requests.get( - Network accessline 61
"""Get BibTeX for a paper via CrossRef DOI resolution.""" resp = requests.get( f"https://api.crossref.org/works/{doi}", - Network accessline 62
resp = requests.get( - Network accessline 63
f"https://api.crossref.org/works/{doi}", - Network accessline 64
headers={"User-Agent": "ResearchPlugins/1.0 (https://wentor.ai; mailto:dev@wentor.ai)"}, - Network accessline 98
def get_citing_works(openalex_id, limit=10): """Get papers that cite this work via OpenAlex.""" resp = requests.get( - Network accessline 99
"""Get papers that cite this work via OpenAlex.""" resp = requests.get( f"{OA_API}/works", - Network accessline 100
resp = requests.get( - Network accessline 105
"sort": "cited_by_count:desc", }, headers={"User-Agent": "ResearchPlugins/1.0 (https://wentor.ai)"}, - Network accessline 106
}, headers={"User-Agent": "ResearchPlugins/1.0 (https://wentor.ai)"}, ) - Network accessline 107
headers={"User-Agent": "ResearchPlugins/1.0 (https://wentor.ai)"}, - Network accessline 124
"""Find papers related to a given paper via OpenAlex.""" # Get the paper's concepts, then search for similar works resp = requests.get( - Network accessline 125
# Get the paper's concepts, then search for similar works resp = requests.get( f"{OA_API}/works/{openalex_id}", - Network accessline 126
resp = requests.get( - Network accessline 127
f"{OA_API}/works/{openalex_id}", headers={"User-Agent": "ResearchPlugins/1.0 (https://wentor.ai)"}, ) - Network accessline 128
headers={"User-Agent": "ResearchPlugins/1.0 (https://wentor.ai)"}, - Network accessline 133
related_resp = requests.get( - Network accessline 138
"sort": "cited_by_count:desc", }, headers={"User-Agent": "ResearchPlugins/1.0 (https://wentor.ai)"}, - Network accessline 139
}, headers={"User-Agent": "ResearchPlugins/1.0 (https://wentor.ai)"}, ) - Network accessline 140
headers={"User-Agent": "ResearchPlugins/1.0 (https://wentor.ai)"}, - Network accessline 177
if doi and doi not in seen_ids: seen_ids.add(doi) bibtex = get_bibtex(doi.replace("https://doi.org/", "")) - Network accessline 179
bibtex = get_bibtex(doi.replace("https://doi.org/", "")) - Network accessline 204
- [Citation Assistant GitHub](https://github.com/ZhangNy301/citation-assistant) - Network accessline 205
- [OpenAlex API](https://api.openalex.org/) - Network accessline 206
- [CrossRef API](https://api.crossref.org/)
skills/writing/citation/citation-style-guide/SKILL.md
- Network accessline 49
Smith, J. A., & Jones, B. C. (2023). Title of the article in sentence case. Journal Name in Title Case, 45(2), 123-145. https://doi.org/10.1234/example - Network accessline 51
https://doi.org/10.1234/example - Network accessline 53
# Book Williams, R. T. (2022). Title of the book in sentence case (3rd ed.). Publisher Name. https://doi.org/10.1234/example - Network accessline 55
Publisher Name. https://doi.org/10.1234/example - Network accessline 57
# Chapter in edited book Chen, L. (2023). Chapter title. In A. Editor & B. Editor (Eds.), Book title (pp. 50-75). Publisher. https://doi.org/10.1234/example - Network accessline 59
Book title (pp. 50-75). Publisher. https://doi.org/10.1234/example - Network accessline 62
Lee, S., & Park, J. (2023). Paper title. In Proceedings of the 2023 Conference Name (pp. 100-110). Publisher. https://doi.org/10.1234/example - Network accessline 64
https://doi.org/10.1234/example - Network accessline 66
# Webpage National Institutes of Health. (2023, March 15). Page title. https://www.nih.gov/example - Network accessline 68
https://www.nih.gov/example - Network accessline 70
# Preprint Garcia, M. (2023). Title of preprint. arXiv. https://doi.org/10.48550/arXiv.2301.12345 - Network accessline 72
https://doi.org/10.48550/arXiv.2301.12345 - Network accessline 74
# Dataset Johnson, K. (2023). Dataset title (Version 2.0) [Data set]. Repository. https://doi.org/10.1234/example - Network accessline 76
https://doi.org/10.1234/example - Network accessline 95
Smith, John A., and Beth C. Jones. "Article Title in Title Case." Journal Name, vol. 45, no. 2, 2023, pp. 123-45. https://doi.org/10.1234/example. - Network accessline 97
https://doi.org/10.1234/example. - Network accessline 139
# Online source [4] Author, "Page title," Website, date. [Online]. Available: https://www.example.com - Network accessline 141
https://www.example.com - Network accessline 158
<?xml version="1.0" encoding="utf-8"?> <style xmlns="http://purl.org/net/xbiblio/csl" class="in-text" version="1.0"> - Network accessline 159
<style xmlns="http://purl.org/net/xbiblio/csl" class="in-text" version="1.0"> - Network accessline 160
<info> <title>Custom Style</title> <id>http://www.example.com/custom-style</id> - Network accessline 161
<title>Custom Style</title> <id>http://www.example.com/custom-style</id> </info> - Network accessline 162
<id>http://www.example.com/custom-style</id>
skills/writing/citation/jabref-reference-guide/SKILL.md
- Network accessline 8
subcategory: "citation" keywords: ["jabref", "bibtex", "biblatex", "reference-manager", "latex", "bibliography"] source: "https://github.com/JabRef/jabref" - Network accessline 9
keywords: ["jabref", "bibtex", "biblatex", "reference-manager", "latex", "bibliography"] source: "https://github.com/JabRef/jabref" --- - Network accessline 10
source: "https://github.com/JabRef/jabref" - Network accessline 27
**Direct Download**: 1. Visit https://www.jabref.org and download the installer for your platform - Network accessline 28
1. Visit https://www.jabref.org and download the installer for your platform - Dangerous shell operationline 34
- Linux (Snap): `sudo snap install jabref` - Network accessline 123
- Official Website: https://www.jabref.org - Network accessline 124
- GitHub Repository: https://github.com/JabRef/jabref - Network accessline 125
- JabRef User Documentation: https://docs.jabref.org - Network accessline 126
- JabRef Blog: https://blog.jabref.org - Network accessline 127
- CTAN BibTeX Documentation: https://ctan.org/pkg/bibtex
skills/writing/citation/jasminum-zotero-guide/SKILL.md
- Network accessline 8
subcategory: "citation" keywords: ["zotero", "cnki", "chinese-academic", "jasminum", "metadata", "bibliography"] source: "https://github.com/l0o0/jasminum" - Network accessline 9
keywords: ["zotero", "cnki", "chinese-academic", "jasminum", "metadata", "bibliography"] source: "https://github.com/l0o0/jasminum" --- - Network accessline 10
source: "https://github.com/l0o0/jasminum" - Network accessline 27
1. Download the latest `.xpi` file from https://github.com/l0o0/jasminum/releases - Network accessline 99
- GitHub Repository: https://github.com/l0o0/jasminum - Network accessline 100
- CNKI (China National Knowledge Infrastructure): https://www.cnki.net - Network accessline 101
- Zotero Plugin Directory: https://www.zotero.org/support/plugins - Network accessline 102
- GB/T 7714 Citation Standard: https://std.samr.gov.cn - Network accessline 103
- Zotero Chinese User Community: https://zotero-chinese.com
skills/writing/citation/mendeley-api/SKILL.md
- Network accessline 24
# 1. Register app at https://dev.elsevier.com/ # 2. Get access token via client credentials (for catalog search) curl -X POST "https://api.mendeley.com/oauth/token" \ - Network accessline 25
# 2. Get access token via client credentials (for catalog search) curl -X POST "https://api.mendeley.com/oauth/token" \ -d "grant_type=client_credentials" \ - Network accessline 26
[Credential-related source omitted] - Access to secretsline 26
[Credential-related source omitted] - Access to secretsline 30
[Credential-related source omitted] - Access to secretsline 32
[Credential-related source omitted] - Network accessline 40
https://api.mendeley.com - Network accessline 48
[Credential-related source omitted] - Network accessline 49
[Credential-related source omitted] - Network accessline 50
"https://api.mendeley.com/catalog?query=deep+learning+NLP&limit=20" - Network accessline 52
[Credential-related source omitted] - Network accessline 53
[Credential-related source omitted] - Network accessline 54
"https://api.mendeley.com/catalog?doi=10.1038/nature14539" - Network accessline 56
[Credential-related source omitted] - Network accessline 57
[Credential-related source omitted] - Network accessline 58
"https://api.mendeley.com/catalog?title=attention+is+all+you+need" - Network accessline 64
[Credential-related source omitted] - Network accessline 65
[Credential-related source omitted] - Network accessline 66
"https://api.mendeley.com/documents?limit=50&sort=created&order=desc" - Network accessline 68
[Credential-related source omitted] - Network accessline 69
[Credential-related source omitted] - Network accessline 70
"https://api.mendeley.com/documents/{doc_id}" - Network accessline 72
[Credential-related source omitted] - Network accessline 73
[Credential-related source omitted] - Network accessline 74
-H "Content-Type: application/vnd.mendeley-document.1+json" \ -d '{"title":"My Paper","type":"journal","year":2025,"authors":[{"first_name":"A","last_name":"B"}]}' \ "https://api.mendeley.com/documents" - Network accessline 76
"https://api.mendeley.com/documents" - Network accessline 82
[Credential-related source omitted] - Network accessline 83
[Credential-related source omitted] - Network accessline 84
"https://api.mendeley.com/folders" - Network accessline 86
[Credential-related source omitted] - Network accessline 87
[Credential-related source omitted] - Network accessline 88
"https://api.mendeley.com/documents?group_id={group_id}" - Network accessline 94
[Credential-related source omitted] - Network accessline 95
[Credential-related source omitted] - Network accessline 96
"https://api.mendeley.com/annotations?document_id={doc_id}" - Network accessline 131
"abstract": "The dominant sequence transduction models...", "reader_count": 15432, "link": "https://www.mendeley.com/catalogue/..." - Network accessline 132
"reader_count": 15432, "link": "https://www.mendeley.com/catalogue/..." } - Network accessline 133
"link": "https://www.mendeley.com/catalogue/..." - Network accessline 143
[Credential-related source omitted] - Access to secretsline 143
[Credential-related source omitted] - Network accessline 144
[Credential-related source omitted] - Access to secretsline 144
[Credential-related source omitted] - Network accessline 145
TOKEN_URL = "https://api.mendeley.com/oauth/token" - Network accessline 146
BASE_URL = "https://api.mendeley.com" - Network accessline 149
def get_token() -> str: """Obtain access token via client credentials.""" resp = requests.post(TOKEN_URL, data={ - Network accessline 150
"""Obtain access token via client credentials.""" resp = requests.post(TOKEN_URL, data={ "grant_type": "client_credentials", - Network accessline 151
resp = requests.post(TOKEN_URL, data={ - Access to secretsline 155
[Credential-related source omitted] - Access to secretsline 158
[Credential-related source omitted] - Network accessline 169
resp = requests.get( - Network accessline 191
[Credential-related source omitted] - Network accessline 192
[Credential-related source omitted] - Network accessline 193
resp = requests.get( - Network accessline 229
- [Mendeley API Documentation](wentor-research-plugins) - [Mendeley Developer Portal](https://dev.elsevier.com/) - Network accessline 230
- [Mendeley Developer Portal](https://dev.elsevier.com/) - Network accessline 231
- [Mendeley API Reference](https://api.mendeley.com/apidocs/)
skills/writing/citation/obsidian-citation-guide/SKILL.md
- Network accessline 2
name: obsidian-citation-guide description: "Citation plugin for Obsidian note-taking with BibTeX support" source: https://github.com/hans/obsidian-citation-plugin - Network accessline 3
description: "Citation plugin for Obsidian note-taking with BibTeX support" source: https://github.com/hans/obsidian-citation-plugin metadata: - Network accessline 4
source: https://github.com/hans/obsidian-citation-plugin
skills/writing/citation/obsidian-zotero-guide/SKILL.md
- Network accessline 2
name: obsidian-zotero-guide description: "Insert citations and notes from Zotero into Obsidian knowledge bases" source: https://github.com/mgmeyers/obsidian-zotero-integration - Network accessline 3
description: "Insert citations and notes from Zotero into Obsidian knowledge bases" source: https://github.com/mgmeyers/obsidian-zotero-integration metadata: - Network accessline 4
source: https://github.com/mgmeyers/obsidian-zotero-integration
skills/writing/citation/onecite-reference-guide/SKILL.md
- Network accessline 8
subcategory: "citation" keywords: ["OneCite", "reference formatting", "citation parser", "BibTeX", "metadata completion", "MCP"] source: "https://github.com/HzaCode/OneCite" - Network accessline 9
keywords: ["OneCite", "reference formatting", "citation parser", "BibTeX", "metadata completion", "MCP"] source: "https://github.com/HzaCode/OneCite" --- - Network accessline 10
source: "https://github.com/HzaCode/OneCite" - Network accessline 166
- [OneCite GitHub](https://github.com/HzaCode/OneCite) - Network accessline 167
- [CrossRef API](https://api.crossref.org/) - Network accessline 168
- [Citation Style Language](https://citationstyles.org/)
skills/writing/citation/papersgpt-zotero-guide/SKILL.md
- Network accessline 2
name: papersgpt-zotero-guide description: "AI plugin for Zotero with ChatGPT, Claude, and DeepSeek support" source: https://github.com/papersgpt/papersgpt-for-zotero - Network accessline 3
description: "AI plugin for Zotero with ChatGPT, Claude, and DeepSeek support" source: https://github.com/papersgpt/papersgpt-for-zotero metadata: - Network accessline 4
source: https://github.com/papersgpt/papersgpt-for-zotero
skills/writing/citation/papis-cli-guide/SKILL.md
- Network accessline 8
subcategory: "citation" keywords: ["papis", "cli", "bibliography", "document-manager", "terminal", "command-line"] source: "https://github.com/papis/papis" - Network accessline 9
keywords: ["papis", "cli", "bibliography", "document-manager", "terminal", "command-line"] source: "https://github.com/papis/papis" --- - Network accessline 10
source: "https://github.com/papis/papis" - Dangerous shell operationline 118
papis rm "old paper 2015" - Network accessline 210
- GitHub Repository: https://github.com/papis/papis - Network accessline 211
- Papis Documentation: https://papis.readthedocs.io - Network accessline 212
- Papis Wiki: https://github.com/papis/papis/wiki - Network accessline 213
- PyPI Package: https://pypi.org/project/papis
skills/writing/citation/zotero-api/SKILL.md
- Network accessline 8
subcategory: "citation" keywords: ["reference management", "citation management", "bibliography organization", "literature import"] source: "https://www.zotero.org/support/dev/web_api/v3/basics" - Network accessline 9
keywords: ["reference management", "citation management", "bibliography organization", "literature import"] source: "https://www.zotero.org/support/dev/web_api/v3/basics" --- - Network accessline 10
source: "https://www.zotero.org/support/dev/web_api/v3/basics" - Network accessline 27
1. Log in to your Zotero account at https://www.zotero.org/ - Network accessline 28
2. Navigate to https://www.zotero.org/settings/keys - Network accessline 33
[Credential-related source omitted] - Network accessline 34
[Credential-related source omitted] - Access to secretsline 34
[Credential-related source omitted] - Network accessline 36
# Public group library (no auth needed) curl "https://api.zotero.org/groups/GROUP_ID/items" - Network accessline 37
curl "https://api.zotero.org/groups/GROUP_ID/items" - Network accessline 40
Your user ID can be found at https://www.zotero.org/settings/keys alongside your API key settings. - Network accessline 48
- **URL**: `GET https://api.zotero.org/users/{userID}/items` - Network accessline 67
[Credential-related source omitted] - Network accessline 68
[Credential-related source omitted] - Network accessline 69
"https://api.zotero.org/users/12345/items?itemType=journalArticle&tag=machine+learning&format=json" - Network accessline 71
[Credential-related source omitted] - Network accessline 72
[Credential-related source omitted] - Network accessline 73
"https://api.zotero.org/users/12345/items?q=neural+networks&format=json" - Network accessline 82
- **URL**: `GET https://api.zotero.org/groups/{groupID}/items` - Network accessline 99
# List items in a public group library curl "https://api.zotero.org/groups/67890/items?format=json&limit=50" - Network accessline 100
curl "https://api.zotero.org/groups/67890/items?format=json&limit=50" - Network accessline 102
# Get formatted bibliography from a group curl "https://api.zotero.org/groups/67890/items?format=bib&style=apa" - Network accessline 103
curl "https://api.zotero.org/groups/67890/items?format=bib&style=apa" - Network accessline 123
# Get items formatted as APA bibliography resp = requests.get( - Network accessline 124
resp = requests.get( - Network accessline 125
"https://api.zotero.org/users/12345/collections/ABCDEF/items", - Network accessline 142
resp = requests.get( - Network accessline 143
"https://api.zotero.org/users/12345/items", - Network accessline 167
resp = requests.get( - Network accessline 168
"https://api.zotero.org/users/12345/items", - Network accessline 184
- Official Web API documentation: https://www.zotero.org/support/dev/web_api/v3/basics - Network accessline 185
- API key management: https://www.zotero.org/settings/keys - Network accessline 186
- Item types and fields: https://api.zotero.org/itemTypes - Network accessline 187
- Zotero homepage: https://www.zotero.org/ - Network accessline 188
- Citation styles: https://www.zotero.org/styles
skills/writing/citation/zotero-better-bibtex-guide/SKILL.md
- Network accessline 8
subcategory: "citation" keywords: ["zotero", "bibtex", "latex", "citation-keys", "better-bibtex", "bibliography"] source: "https://github.com/retorquere/zotero-better-bibtex" - Network accessline 9
keywords: ["zotero", "bibtex", "latex", "citation-keys", "better-bibtex", "bibliography"] source: "https://github.com/retorquere/zotero-better-bibtex" --- - Network accessline 10
source: "https://github.com/retorquere/zotero-better-bibtex" - Network accessline 27
1. Download the latest `.xpi` file from https://github.com/retorquere/zotero-better-bibtex/releases - Network accessline 72
**Pull Export via HTTP**: BBT runs a local HTTP server that serves your bibliography on demand. Configure your LaTeX build system to pull the latest `.bib` file from `http://127.0.0.1:23119/better-bibtex/export/collection?...` before each c - Network accessline 104
- GitHub Repository: https://github.com/retorquere/zotero-better-bibtex - Network accessline 105
- BBT Documentation: https://retorque.re/zotero-better-bibtex/ - Network accessline 106
- Citation Key Formulas: https://retorque.re/zotero-better-bibtex/citing/ - Network accessline 107
- Zotero Plugin Directory: https://www.zotero.org/support/plugins
skills/writing/citation/zotero-better-notes-guide/SKILL.md
- Network accessline 8
subcategory: "citation" keywords: ["zotero", "note-taking", "better-notes", "research-notes", "knowledge-management"] source: "https://github.com/windingwind/zotero-better-notes" - Network accessline 9
keywords: ["zotero", "note-taking", "better-notes", "research-notes", "knowledge-management"] source: "https://github.com/windingwind/zotero-better-notes" --- - Network accessline 10
source: "https://github.com/windingwind/zotero-better-notes" - Network accessline 27
1. Download the latest `.xpi` release from https://github.com/windingwind/zotero-better-notes/releases - Network accessline 118
- GitHub Repository: https://github.com/windingwind/zotero-better-notes - Network accessline 119
- Better Notes Documentation: https://github.com/windingwind/zotero-better-notes/wiki - Network accessline 120
- Zotero Plugin Directory: https://www.zotero.org/support/plugins - Network accessline 121
- Zettelkasten Method for Researchers: https://zettelkasten.de/introduction
skills/writing/citation/zotero-gpt-guide/SKILL.md
- Network accessline 8
subcategory: "citation" keywords: ["zotero", "gpt", "ai-assistant", "research-ai", "paper-analysis"] source: "https://github.com/MuiseDestiny/zotero-gpt" - Network accessline 9
keywords: ["zotero", "gpt", "ai-assistant", "research-ai", "paper-analysis"] source: "https://github.com/MuiseDestiny/zotero-gpt" --- - Network accessline 10
source: "https://github.com/MuiseDestiny/zotero-gpt" - Network accessline 27
1. Download the latest `.xpi` file from https://github.com/MuiseDestiny/zotero-gpt/releases - Access to secretsline 36
[Credential-related source omitted] - Access to secretsline 37
[Credential-related source omitted] - Access to secretsline 38
[Credential-related source omitted] - Network accessline 108
- GitHub Repository: https://github.com/MuiseDestiny/zotero-gpt - Network accessline 109
- Zotero Plugin Directory: https://www.zotero.org/support/plugins - Network accessline 110
- OpenAI API Documentation: https://platform.openai.com/docs - Network accessline 111
- Responsible AI Use in Research: https://www.nature.com/articles/d41586-023-00056-7
skills/writing/citation/zotero-mcp-guide/SKILL.md
- Network accessline 8
subcategory: "citation" keywords: ["zotero", "mcp", "claude", "ai-assistant", "model-context-protocol"] source: "https://github.com/54yyyu/zotero-mcp" - Network accessline 9
keywords: ["zotero", "mcp", "claude", "ai-assistant", "model-context-protocol"] source: "https://github.com/54yyyu/zotero-mcp" --- - Network accessline 10
source: "https://github.com/54yyyu/zotero-mcp" - Network accessline 35
# Clone the repository git clone https://github.com/54yyyu/zotero-mcp.git - Network accessline 36
git clone https://github.com/54yyyu/zotero-mcp.git - Network accessline 161
- GitHub Repository: https://github.com/54yyyu/zotero-mcp - Network accessline 162
- Model Context Protocol Specification: https://modelcontextprotocol.io - Network accessline 163
- Zotero Local API Documentation: https://www.zotero.org/support/dev/web_api/v3/basics - Network accessline 164
- Claude Desktop MCP Configuration: https://docs.anthropic.com/en/docs/claude-desktop
skills/writing/citation/zotero-mdnotes-guide/SKILL.md
- Network accessline 2
name: zotero-mdnotes-guide description: "Export Zotero items and annotations to Markdown note files" source: https://github.com/argenos/zotero-mdnotes - Network accessline 3
description: "Export Zotero items and annotations to Markdown note files" source: https://github.com/argenos/zotero-mdnotes metadata: - Network accessline 4
source: https://github.com/argenos/zotero-mdnotes
skills/writing/citation/zotero-reference-guide/SKILL.md
- Network accessline 2
name: zotero-reference-guide description: "PDF references add-on for enriching Zotero library metadata" source: https://github.com/MuiseDestiny/zotero-reference - Network accessline 3
description: "PDF references add-on for enriching Zotero library metadata" source: https://github.com/MuiseDestiny/zotero-reference metadata: - Network accessline 4
source: https://github.com/MuiseDestiny/zotero-reference
skills/writing/citation/zotero-scholar-guide/SKILL.md
- Network accessline 8
subcategory: "citation" keywords: ["zotero", "reference manager", "citation management", "API", "metadata", "bibliography", "paper organization"] source: "https://github.com/zotero/zotero" - Network accessline 9
keywords: ["zotero", "reference manager", "citation management", "API", "metadata", "bibliography", "paper organization"] source: "https://github.com/zotero/zotero" --- - Network accessline 10
source: "https://github.com/zotero/zotero" - Network accessline 27
1. Log in to your Zotero account at https://www.zotero.org - Network accessline 28
2. Navigate to Settings > Feeds/API (https://www.zotero.org/settings/keys) - Access to secretsline 38
[Credential-related source omitted] - Network accessline 45
[Credential-related source omitted] - Dangerous shell operationline 46
[Credential-related source omitted] - Network accessline 46
[Credential-related source omitted] - Access to secretsline 46
[Credential-related source omitted] - Network accessline 47
"https://api.zotero.org/users/$ZOTERO_USER_ID/items?limit=5&format=json" \ - Network accessline 63
[Credential-related source omitted] - Access to secretsline 63
[Credential-related source omitted] - Network accessline 64
[Credential-related source omitted] - Access to secretsline 64
[Credential-related source omitted] - Network accessline 65
BASE_URL = f"https://api.zotero.org/users/{USER_ID}" - Access to secretsline 67
[Credential-related source omitted] - Network accessline 72
"""Resolve DOI metadata and add to Zotero.""" # Step 1: Get metadata from DOI via Crossref cr_resp = requests.get( - Network accessline 73
# Step 1: Get metadata from DOI via Crossref cr_resp = requests.get( f"https://api.crossref.org/works/{doi}", - Network accessline 74
cr_resp = requests.get( - Network accessline 75
f"https://api.crossref.org/works/{doi}", - Network accessline 89
"abstractNote": cr_data.get("abstract", ""), "DOI": doi, "url": f"https://doi.org/{doi}", - Network accessline 90
"DOI": doi, "url": f"https://doi.org/{doi}", "date": cr_data.get("published-print", {}).get("date-parts", [[""]])[0][0], - Network accessline 91
"url": f"https://doi.org/{doi}", - Network accessline 100
# Step 3: POST to Zotero resp = requests.post( - Network accessline 101
resp = requests.post( - Network accessline 126
"""Fetch arXiv metadata and add to Zotero.""" feed = feedparser.parse( f"http://export.arxiv.org/api/query?id_list={arxiv_id}" - Network accessline 127
feed = feedparser.parse( f"http://export.arxiv.org/api/query?id_list={arxiv_id}" ) - Network accessline 128
f"http://export.arxiv.org/api/query?id_list={arxiv_id}" - Network accessline 152
resp = requests.post( - Network accessline 184
resp = requests.post( - Network accessline 201
"parentCollection": parent_key or False } resp = requests.post( - Network accessline 202
} resp = requests.post( f"{BASE_URL}/collections", - Network accessline 203
resp = requests.post( - Network accessline 226
""" # Fetch all items items = requests.get( - Network accessline 227
# Fetch all items items = requests.get( f"{BASE_URL}/items?limit=100&format=json", - Network accessline 228
items = requests.get( - Network accessline 243
if len(new_tags) > len(item["data"].get("tags", [])): requests.patch( - Network accessline 244
requests.patch( - Network accessline 263
[Credential-related source omitted] - Network accessline 264
[Credential-related source omitted] - Access to secretsline 264
[Credential-related source omitted] - Network accessline 265
"https://api.zotero.org/users/$ZOTERO_USER_ID/items?format=bibtex&limit=100" \ - Network accessline 268
[Credential-related source omitted] - Network accessline 269
[Credential-related source omitted] - Access to secretsline 269
[Credential-related source omitted] - Network accessline 270
"https://api.zotero.org/users/$ZOTERO_USER_ID/collections/ABCD1234/items?format=csljson" \ - Network accessline 273
[Credential-related source omitted] - Network accessline 274
[Credential-related source omitted] - Access to secretsline 274
[Credential-related source omitted] - Network accessline 275
"https://api.zotero.org/users/$ZOTERO_USER_ID/items?format=ris&limit=100" \ - Network accessline 282
[Credential-related source omitted] - Network accessline 283
[Credential-related source omitted] - Access to secretsline 283
[Credential-related source omitted] - Network accessline 285
"https://api.zotero.org/users/$ZOTERO_USER_ID/items?format=bib&style=apa&limit=50" - Network accessline 290
- Zotero Web API v3 documentation: https://www.zotero.org/support/dev/web_api/v3/start - Network accessline 291
- Zotero source code: https://github.com/zotero/zotero - Network accessline 292
- pyzotero (Python library): https://github.com/urschrei/pyzotero - Network accessline 293
- Crossref API: https://api.crossref.org - Network accessline 294
- Zotero Better BibTeX plugin: https://retorque.re/zotero-better-bibtex/
skills/writing/citation/zotfile-attachment-guide/SKILL.md
- Network accessline 8
subcategory: "citation" keywords: ["zotero", "zotfile", "attachment", "pdf-management", "file-organization", "sync"] source: "https://github.com/jlegewie/zotfile" - Network accessline 9
keywords: ["zotero", "zotfile", "attachment", "pdf-management", "file-organization", "sync"] source: "https://github.com/jlegewie/zotfile" --- - Network accessline 10
source: "https://github.com/jlegewie/zotfile" - Network accessline 15
**WARNING: ZotFile is NOT compatible with Zotero 7.** For Zotero 7, use the built-in attachment management or the [Attanger](https://github.com/MuiseDestiny/zotero-attanger) plugin as a replacement. - Network accessline 29
1. Download the latest `.xpi` file from https://github.com/jlegewie/zotfile/releases - Network accessline 139
- GitHub Repository: https://github.com/jlegewie/zotfile - Network accessline 140
- ZotFile Documentation: http://zotfile.com - Network accessline 141
- Zotero Plugin Directory: https://www.zotero.org/support/plugins - Network accessline 142
- Zotero File Management Guide: https://www.zotero.org/support/attaching_files
skills/writing/composition/ml-paper-writing/SKILL.md
- Network accessline 8
subcategory: "composition" keywords: ["machine learning", "NeurIPS", "ICML", "ICLR", "AI paper", "deep learning", "conference paper"] source: "https://github.com/karpathy/arxiv-sanity-lite" - Network accessline 9
keywords: ["machine learning", "NeurIPS", "ICML", "ICLR", "AI paper", "deep learning", "conference paper"] source: "https://github.com/karpathy/arxiv-sanity-lite" --- - Network accessline 10
source: "https://github.com/karpathy/arxiv-sanity-lite" - Network accessline 159
- NeurIPS 2025 Call for Papers and Reviewer Guidelines: https://neurips.cc - Network accessline 160
- ICML Author Instructions: https://icml.cc - Network accessline 161
- ICLR Submission Guidelines: https://iclr.cc - Network accessline 162
- Karpathy's arXiv Sanity: https://github.com/karpathy/arxiv-sanity-lite
skills/writing/composition/research-paper-writer/SKILL.md
- Network accessline 223
- [IEEE Author Tools](https://journals.ieeeauthorcenter.ieee.org/) - Network accessline 224
- [ACM Primary Article Templates](https://www.acm.org/publications/proceedings-template) - Network accessline 225
- [ACM CCS Tree](https://dl.acm.org/ccs) - Network accessline 226
- [Booktabs Package Documentation](https://ctan.org/pkg/booktabs)
skills/writing/composition/scientific-writing-guide/SKILL.md
- Network accessline 8
subcategory: "composition" keywords: ["scientific writing", "academic writing", "writing tools", "manuscript preparation", "AI writing workflow", "prose quality"] source: "https://github.com/writing-resources/awesome-scientific-writ - Network accessline 9
keywords: ["scientific writing", "academic writing", "writing tools", "manuscript preparation", "AI writing workflow", "prose quality"] source: "https://github.com/writing-resources/awesome-scientific-writing" --- - Network accessline 10
source: "https://github.com/writing-resources/awesome-scientific-writing" - Network accessline 217
- [awesome-scientific-writing](https://github.com/writing-resources/awesome-scientific-writing) -- Curated list of tools and resources (920+ stars) - Network accessline 218
- [How to Write a Great Research Paper](https://www.microsoft.com/en-us/research/academic-program/write-great-research-paper/) -- Simon Peyton Jones, Microsoft Research - Network accessline 219
- [The Science of Scientific Writing](https://www.americanscientist.org/blog/the-long-view/the-science-of-scientific-writing) -- Gopen and Swan, American Scientist - Network accessline 220
- [Improving Your Scientific Writing](https://lijunsun.github.io/files/ScientificWritingV39.pdf) -- Lijun Sun, McGill University - Network accessline 221
- [Three Sins of Authors in CS and Math](http://www.cs.cmu.edu/~jrs/sins.html) -- Jonathan Shewchuk, UC Berkeley
skills/writing/latex/academic-writing-latex/SKILL.md
- Network accessline 23
| Publisher | Template Source | Document Class | |-----------|----------------|---------------| | IEEE | [ieee.org/conferences](https://www.ieee.org/conferences/publishing/templates.html) | `IEEEtran` | - Network accessline 24
|-----------|----------------|---------------| | IEEE | [ieee.org/conferences](https://www.ieee.org/conferences/publishing/templates.html) | `IEEEtran` | | ACM | [acm.org/publications](https://www.acm.org/publications/proceedings-template) - Network accessline 25
| IEEE | [ieee.org/conferences](https://www.ieee.org/conferences/publishing/templates.html) | `IEEEtran` | - Network accessline 26
| ACM | [acm.org/publications](https://www.acm.org/publications/proceedings-template) | `acmart` | - Network accessline 27
| Springer | LNCS template package | `llncs` | | Elsevier | [elsarticle](https://www.elsevier.com/researcher/author/policies-and-guidelines/latex-instructions) | `elsarticle` | | Nature | Author submission guidelines | `nature` | - Network accessline 28
| Elsevier | [elsarticle](https://www.elsevier.com/researcher/author/policies-and-guidelines/latex-instructions) | `elsarticle` | - Network accessline 30
| APS/AIP | [REVTeX](https://journals.aps.org/revtex) | `revtex4-2` | - Network accessline 282
- [Overleaf Documentation](https://www.overleaf.com/learn) - Network accessline 283
- [LaTeX Wikibook](https://en.wikibooks.org/wiki/LaTeX) - Network accessline 284
- [CTAN Package Repository](https://ctan.org/) - Network accessline 285
- [BibTeX Entry Types](https://www.bibtex.com/e/entry-types/)
skills/writing/latex/latex-drawing-collection/SKILL.md
- Network accessline 2
name: latex-drawing-collection description: "LaTeX drawing examples for Bayesian networks, tensors, and diagrams" source: https://github.com/xinychen/awesome-latex-drawing - Network accessline 3
description: "LaTeX drawing examples for Bayesian networks, tensors, and diagrams" source: https://github.com/xinychen/awesome-latex-drawing metadata: - Network accessline 4
source: https://github.com/xinychen/awesome-latex-drawing
skills/writing/latex/latex-drawing-guide/SKILL.md
- Network accessline 8
subcategory: "latex" keywords: ["LaTeX typesetting", "LaTeX figure insertion", "LaTeX custom style", "scientific figure creation"] source: "https://github.com/xinychen/awesome-latex-drawing" - Network accessline 9
keywords: ["LaTeX typesetting", "LaTeX figure insertion", "LaTeX custom style", "scientific figure creation"] source: "https://github.com/xinychen/awesome-latex-drawing" --- - Network accessline 10
source: "https://github.com/xinychen/awesome-latex-drawing" - Network accessline 230
- [awesome-latex-drawing](https://github.com/xinychen/awesome-latex-drawing) -- 30+ LaTeX drawing examples (2,000+ stars) - Network accessline 231
- [TikZ and PGF Manual](https://tikz.dev/) -- Official documentation - Network accessline 232
- [PGFPlots Manual](https://pgfplots.net/) -- Data visualization in LaTeX - Network accessline 233
- [TikZ Examples](https://texample.net/tikz/examples/) -- Community gallery - Network accessline 234
- [LaTeX Drawing Tutorial](https://www.overleaf.com/learn/latex/TikZ_package) -- Overleaf tutorial
skills/writing/latex/latex-ecosystem-guide/SKILL.md
- Network accessline 8
subcategory: "latex" keywords: ["LaTeX typesetting", "LaTeX template", "LaTeX package management", "online LaTeX editor"] source: "https://github.com/egeerardyn/awesome-LaTeX" - Network accessline 9
keywords: ["LaTeX typesetting", "LaTeX template", "LaTeX package management", "online LaTeX editor"] source: "https://github.com/egeerardyn/awesome-LaTeX" --- - Network accessline 10
source: "https://github.com/egeerardyn/awesome-LaTeX" - Network accessline 236
- [awesome-LaTeX](https://github.com/egeerardyn/awesome-LaTeX) -- Curated list of LaTeX resources (1,600+ stars) - Network accessline 237
- [CTAN](https://ctan.org/) -- Comprehensive TeX Archive Network - Network accessline 238
- [LaTeX Wikibook](https://en.wikibooks.org/wiki/LaTeX) -- Community-maintained reference - Network accessline 239
- [Overleaf Documentation](https://www.overleaf.com/learn) -- Tutorials and templates - Network accessline 240
- [LaTeX Workshop for VS Code](https://github.com/James-Yu/LaTeX-Workshop) -- VS Code extension
skills/writing/latex/latex-templates-collection/SKILL.md
- Network accessline 2
name: latex-templates-collection description: "Collection of LaTeX templates for papers, presentations, and CVs" source: https://github.com/deedydas/latex-templates - Network accessline 3
description: "Collection of LaTeX templates for papers, presentations, and CVs" source: https://github.com/deedydas/latex-templates metadata: - Network accessline 4
source: https://github.com/deedydas/latex-templates
skills/writing/latex/md-to-pdf-academic/SKILL.md
- Network accessline 8
subcategory: "latex" keywords: ["markdown", "PDF", "pandoc", "LaTeX rendering", "document conversion", "academic formatting"] source: "https://github.com/Wandmalfarbe/pandoc-latex-template" - Network accessline 9
keywords: ["markdown", "PDF", "pandoc", "LaTeX rendering", "document conversion", "academic formatting"] source: "https://github.com/Wandmalfarbe/pandoc-latex-template" --- - Network accessline 10
source: "https://github.com/Wandmalfarbe/pandoc-latex-template" - Dangerous shell operationline 35
sudo tlmgr install collection-fontsrecommended latexmk - Dangerous shell operationline 38
sudo apt-get install pandoc texlive-full - Network accessline 65
# Download Eisvogel template mkdir -p ~/.pandoc/templates wget https://github.com/Wandmalfarbe/pandoc-latex-template/releases/latest/download/Eisvogel.tar.gz - Network accessline 66
mkdir -p ~/.pandoc/templates wget https://github.com/Wandmalfarbe/pandoc-latex-template/releases/latest/download/Eisvogel.tar.gz tar -xzf Eisvogel.tar.gz -C ~/.pandoc/templates/ - Network accessline 67
wget https://github.com/Wandmalfarbe/pandoc-latex-template/releases/latest/download/Eisvogel.tar.gz - Dangerous shell operationline 192
rm -f $(PAPER).pdf $(PAPER).docx $(PAPER).html - Network accessline 227
- Pandoc User's Guide: https://pandoc.org/MANUAL.html - Network accessline 228
- Eisvogel LaTeX Template: https://github.com/Wandmalfarbe/pandoc-latex-template - Network accessline 229
- pandoc-crossref: https://github.com/lierdakil/pandoc-crossref - Network accessline 230
- Pandoc citations: https://pandoc.org/MANUAL.html#citations
skills/writing/latex/overleaf-collaboration-guide/SKILL.md
skills/writing/latex/tex-render-guide/SKILL.md
- Network accessline 187
# Using the codecogs API (free for reasonable usage) # URL-encode your LaTeX and fetch the image curl "https://latex.codecogs.com/png.image?\dpi{300}\bg{white}\int_{0}^{1}x^2\,dx" \ - Network accessline 188
# URL-encode your LaTeX and fetch the image curl "https://latex.codecogs.com/png.image?\dpi{300}\bg{white}\int_{0}^{1}x^2\,dx" \ -o equation.png - Network accessline 189
curl "https://latex.codecogs.com/png.image?\dpi{300}\bg{white}\int_{0}^{1}x^2\,dx" \ - Network accessline 192
# SVG variant: curl "https://latex.codecogs.com/svg.image?\int_{0}^{1}x^2\,dx" \ - Network accessline 193
curl "https://latex.codecogs.com/svg.image?\int_{0}^{1}x^2\,dx" \ - Network accessline 238
- KaTeX: https://katex.org - Network accessline 239
- MathJax: https://www.mathjax.org - Network accessline 240
- dvipng documentation: https://ctan.org/pkg/dvipng - Network accessline 241
- dvisvgm: https://dvisvgm.de - Network accessline 243
- CodeCogs equation editor: https://latex.codecogs.com
skills/writing/polish/academic-writing-refiner/SKILL.md
- Network accessline 8
subcategory: "polish" keywords: ["academic English grammar check", "readability improvement", "academic writing style", "Chinglish correction"] source: "https://github.com/SYSUSELab/academic-writing-guide" - Network accessline 9
keywords: ["academic English grammar check", "readability improvement", "academic writing style", "Chinglish correction"] source: "https://github.com/SYSUSELab/academic-writing-guide" --- - Network accessline 10
source: "https://github.com/SYSUSELab/academic-writing-guide" - Network accessline 140
- [academic-writing-guide](https://github.com/SYSUSELab/academic-writing-guide) -- Writing checklist and tutorials (327+ stars) - Network accessline 141
- [Common Errors in Technical Writing](https://www.cs.columbia.edu/~hgs/etc/writing-bugs.html) -- Henning Schulzrinne, Columbia University - Network accessline 142
- [Writefull](https://writefull.com/) -- AI writing assistant trained on published papers - Network accessline 143
- [LanguageTool](https://languagetool.org/) -- Open-source grammar checker
skills/writing/polish/chinese-text-humanizer/SKILL.md
skills/writing/polish/grammar-checker-guide/SKILL.md
- Network accessline 62
[Credential-related source omitted] - Access to secretsline 62
[Credential-related source omitted] - Network accessline 63
"LANGUAGETOOL_URL", "https://api.languagetool.org/v2/check" ) - Network accessline 64
"https://api.languagetool.org/v2/check" - Network accessline 74
req = urllib.request.Request(api_url, data=data) - Network accessline 75
response = urllib.request.urlopen(req)
skills/writing/polish/paper-polish-guide/SKILL.md
skills/writing/templates/arxiv-preprint-template/SKILL.md
- Network accessline 8
subcategory: "templates" keywords: ["arXiv", "preprint", "NIPS style", "LaTeX template", "NeurIPS", "manuscript"] source: "https://github.com/kourgeorge/arxiv-style" - Network accessline 9
keywords: ["arXiv", "preprint", "NIPS style", "LaTeX template", "NeurIPS", "manuscript"] source: "https://github.com/kourgeorge/arxiv-style" --- - Network accessline 10
source: "https://github.com/kourgeorge/arxiv-style" - Network accessline 22
git clone https://github.com/kourgeorge/arxiv-style.git - Network accessline 182
- [arxiv-style GitHub](https://github.com/kourgeorge/arxiv-style) - Network accessline 183
- [arXiv Submission Guide](https://info.arxiv.org/help/submit/index.html) - Network accessline 184
- [arXiv LaTeX Cleaner](https://github.com/google-research/arxiv-latex-cleaner)
skills/writing/templates/elegant-paper-template/SKILL.md
- Network accessline 8
subcategory: "templates" keywords: ["ElegantPaper", "LaTeX template", "working paper", "technical report", "elegant design", "academic paper"] source: "https://github.com/ElegantLaTeX/ElegantPaper" - Network accessline 9
keywords: ["ElegantPaper", "LaTeX template", "working paper", "technical report", "elegant design", "academic paper"] source: "https://github.com/ElegantLaTeX/ElegantPaper" --- - Network accessline 10
source: "https://github.com/ElegantLaTeX/ElegantPaper" - Network accessline 22
# Download template git clone https://github.com/ElegantLaTeX/ElegantPaper.git - Network accessline 23
git clone https://github.com/ElegantLaTeX/ElegantPaper.git - Network accessline 139
- [ElegantPaper GitHub](https://github.com/ElegantLaTeX/ElegantPaper) - Network accessline 140
- [ElegantLaTeX](https://elegantlatex.org/) - Network accessline 141
- [ElegantPaper Documentation](https://github.com/ElegantLaTeX/ElegantPaper/blob/master/elegantpaper-en.pdf)
skills/writing/templates/graphical-abstract-guide/SKILL.md
- Network accessline 8
subcategory: "templates" keywords: ["graphical abstract", "SVG", "paper submission", "visual summary", "journal figure", "scientific illustration"] source: "https://github.com/jgraph/drawio" - Network accessline 9
keywords: ["graphical abstract", "SVG", "paper submission", "visual summary", "journal figure", "scientific illustration"] source: "https://github.com/jgraph/drawio" --- - Network accessline 10
source: "https://github.com/jgraph/drawio" - Network accessline 93
1. Open https://app.diagrams.net - Dangerous shell operationline 104
sudo apt install inkscape # Ubuntu - Network accessline 179
- Draw.io: https://app.diagrams.net - Network accessline 180
- Inkscape: https://inkscape.org - Network accessline 181
- Elsevier Graphical Abstract Guidelines: https://www.elsevier.com/authors/tools-and-resources/graphical-abstracts - Network accessline 182
- ACS Author Guidelines: https://pubs.acs.org/page/4authors/submission/graphics.html
skills/writing/templates/novathesis-guide/SKILL.md
- Network accessline 2
name: novathesis-guide description: "LaTeX thesis template supporting multiple universities and formats" source: https://github.com/joaomlourenco/novathesis - Network accessline 3
description: "LaTeX thesis template supporting multiple universities and formats" source: https://github.com/joaomlourenco/novathesis metadata: - Network accessline 4
source: https://github.com/joaomlourenco/novathesis
skills/writing/templates/scientific-article-pdf/SKILL.md
- Network accessline 31
# IEEE conference and journal templates # Download from: https://www.ieee.org/conferences/publishing/templates.html - Network accessline 32
# Download from: https://www.ieee.org/conferences/publishing/templates.html - Network accessline 34
wget https://www.ieee.org/content/dam/ieee-org/ieee/web/org/pubs/conference-latex-template_10-17-19.zip - Network accessline 39
# ACM Primary Article Template # Download from: https://www.acm.org/publications/proceedings-template - Network accessline 40
# Download from: https://www.acm.org/publications/proceedings-template - Network accessline 48
# Download from: https://www.springer.com/gp/computer-science/lncs/conference-proceedings-guidelines - Network accessline 55
# Elsevier article template # Download from: https://www.elsevier.com/authors/policies-and-guidelines/latex-instructions - Network accessline 56
# Download from: https://www.elsevier.com/authors/policies-and-guidelines/latex-instructions - Network accessline 64
# NeurIPS: https://neurips.cc/Conferences/2026/PaperInformation/StyleFiles - Network accessline 65
# ICML: https://icml.cc/Conferences/2026/StyleAuthorInstructions - Dangerous shell operationline 218
rm -f *.aux *.bbl *.blg *.log *.out *.toc *.fls *.fdb_latexmk - Network accessline 257
- IEEE LaTeX templates: https://www.ieee.org/conferences/publishing/templates.html - Network accessline 258
- ACM acmart class: https://www.acm.org/publications/proceedings-template - Network accessline 259
- Springer LNCS: https://www.springer.com/gp/computer-science/lncs - Network accessline 260
- Elsevier elsarticle: https://www.elsevier.com/authors/policies-and-guidelines/latex-instructions - Network accessline 261
- LaTeX Wikibook: https://en.wikibooks.org/wiki/LaTeX
skills/writing/templates/sjtuthesis-guide/SKILL.md
- Network accessline 8
subcategory: "templates" keywords: ["latex", "thesis", "sjtu", "template", "academic-writing", "shanghai-jiaotong"] source: "https://github.com/sjtug/SJTUThesis" - Network accessline 9
keywords: ["latex", "thesis", "sjtu", "template", "academic-writing", "shanghai-jiaotong"] source: "https://github.com/sjtug/SJTUThesis" --- - Network accessline 10
source: "https://github.com/sjtug/SJTUThesis" - Dangerous shell operationline 32
sudo apt-get install texlive-full - Network accessline 40
git clone https://github.com/sjtug/SJTUThesis.git - Network accessline 194
- SJTUThesis repository: https://github.com/sjtug/SJTUThesis - Network accessline 195
- SJTU graduate school formatting guidelines - SJTUG user group: https://sjtug.org/ - LaTeX workshop materials from SJTUG - Network accessline 196
- SJTUG user group: https://sjtug.org/
skills/writing/templates/thesis-writing-guide/SKILL.md
- Network accessline 8
subcategory: "templates" keywords: ["thesis template", "dissertation formatting", "journal formatting requirements", "page limit"] source: "https://github.com/awesome-thesis/awesome-thesis" - Network accessline 9
keywords: ["thesis template", "dissertation formatting", "journal formatting requirements", "page limit"] source: "https://github.com/awesome-thesis/awesome-thesis" --- - Network accessline 10
source: "https://github.com/awesome-thesis/awesome-thesis" - Network accessline 216
- [awesome-thesis](https://github.com/awesome-thesis/awesome-thesis) -- Practical thesis tips (626+ stars) - Network accessline 217
- [How to Write a Thesis](https://www.ldeo.columbia.edu/~martins/sen_res/how_to_thesis.html) -- Columbia University guide - Network accessline 218
- [Dissertate Template](https://github.com/suchow/Dissertate) -- Harvard multi-school LaTeX template - Network accessline 219
- [latexdiff](https://ctan.org/pkg/latexdiff) -- Visual diff tool for LaTeX documents - Network accessline 220
- [Overleaf Thesis Templates](https://www.overleaf.com/latex/templates/tagged/thesis) -- Gallery of institutional templates
skills/writing/templates/thuthesis-guide/SKILL.md
- Network accessline 8
subcategory: "templates" keywords: ["latex", "thesis", "tsinghua", "template", "academic-writing", "typesetting"] source: "https://github.com/tuna/thuthesis" - Network accessline 9
keywords: ["latex", "thesis", "tsinghua", "template", "academic-writing", "typesetting"] source: "https://github.com/tuna/thuthesis" --- - Network accessline 10
source: "https://github.com/tuna/thuthesis" - Dangerous shell operationline 34
sudo apt-get install texlive-full - Network accessline 42
git clone https://github.com/tuna/thuthesis.git - Network accessline 178
- ThuThesis repository: https://github.com/tuna/thuthesis - Network accessline 179
- CTAN package page: https://ctan.org/pkg/thuthesis - Network accessline 181
- TUNA association: https://tuna.moe/
How the signals fit together
Facts from a static read of the files at the pinned commit. They describe what the code does, not why.
Some files could not be read; the facts below still hold.
Signal confidence: 500 facts
- Executable script in code of a file that runs.gitignore:1High confidence
- Access to secrets in code of a file that runs.gitignore:28High confidence
- Network access in the frontmatter of the SKILL.mdalgorithm-visualizer-guide/SKILL.md:8Low confidence
- Network access in the frontmatter of the SKILL.mdalgorithm-visualizer-guide/SKILL.md:9Low confidence
- Network access in the frontmatter of the SKILL.mdalgorithm-visualizer-guide/SKILL.md:10Low confidence
- Network access in code of the SKILL.mdalgorithm-visualizer-guide/SKILL.md:36High confidence
- Network access in code of the SKILL.mdalgorithm-visualizer-guide/SKILL.md:37High confidence
- Network access in code of the SKILL.mdalgorithm-visualizer-guide/SKILL.md:46High confidence
- Network access in code of the SKILL.mdalgorithm-visualizer-guide/SKILL.md:52High confidence
- Network access in code of the SKILL.mdalgorithm-visualizer-guide/SKILL.md:53High confidence
- Network access in code of the SKILL.mdalgorithm-visualizer-guide/SKILL.md:54High confidence
- Network access in code of the SKILL.mdalgorithm-visualizer-guide/SKILL.md:61High confidence
- Network access in code of the SKILL.mdalgorithm-visualizer-guide/SKILL.md:62High confidence
- Network access in code of the SKILL.mdalgorithm-visualizer-guide/SKILL.md:63High confidence
- Network access in prose of the SKILL.mdalgorithm-visualizer-guide/SKILL.md:256Low confidence
- Network access in prose of the SKILL.mdalgorithm-visualizer-guide/SKILL.md:257Low confidence
- Network access in prose of the SKILL.mdalgorithm-visualizer-guide/SKILL.md:258Low confidence
- Network access in prose of the SKILL.mdalgorithm-visualizer-guide/SKILL.md:259Low confidence
- Network access in the frontmatter of the SKILL.mdbokeh-visualization-guide/SKILL.md:8Low confidence
- Network access in the frontmatter of the SKILL.mdbokeh-visualization-guide/SKILL.md:9Low confidence
- Network access in the frontmatter of the SKILL.mdbokeh-visualization-guide/SKILL.md:10Low confidence
- Network access in prose of the SKILL.mdbokeh-visualization-guide/SKILL.md:267Low confidence
- Network access in prose of the SKILL.mdbokeh-visualization-guide/SKILL.md:268Low confidence
- Network access in prose of the SKILL.mdbokeh-visualization-guide/SKILL.md:269Low confidence
- Network access in prose of the SKILL.mdbokeh-visualization-guide/SKILL.md:270Low confidence
- Network access in the frontmatter of the SKILL.mdd3-visualization-guide/SKILL.md:8Low confidence
- Network access in the frontmatter of the SKILL.mdd3-visualization-guide/SKILL.md:9Low confidence
- Network access in the frontmatter of the SKILL.mdd3-visualization-guide/SKILL.md:10Low confidence
- Network access in prose of the SKILL.mdd3-visualization-guide/SKILL.md:278Low confidence
- Network access in prose of the SKILL.mdd3-visualization-guide/SKILL.md:279Low confidence
- Network access in prose of the SKILL.mdd3-visualization-guide/SKILL.md:280Low confidence
- Network access in prose of the SKILL.mdd3-visualization-guide/SKILL.md:281Low confidence
- Network access in the frontmatter of the SKILL.mdecharts-visualization-guide/SKILL.md:8Low confidence
- Network access in the frontmatter of the SKILL.mdecharts-visualization-guide/SKILL.md:9Low confidence
- Network access in the frontmatter of the SKILL.mdecharts-visualization-guide/SKILL.md:10Low confidence
- Network access in code of the SKILL.mdecharts-visualization-guide/SKILL.md:30High confidence
- Network access in code of the SKILL.mdecharts-visualization-guide/SKILL.md:31High confidence
- Network access in code of the SKILL.mdecharts-visualization-guide/SKILL.md:217High confidence
- Network access in code of the SKILL.mdecharts-visualization-guide/SKILL.md:218High confidence
- Network access in code of the SKILL.mdecharts-visualization-guide/SKILL.md:219High confidence
- Network access in prose of the SKILL.mdecharts-visualization-guide/SKILL.md:247Low confidence
- Network access in prose of the SKILL.mdecharts-visualization-guide/SKILL.md:248Low confidence
- Network access in prose of the SKILL.mdecharts-visualization-guide/SKILL.md:249Low confidence
- Network access in prose of the SKILL.mdecharts-visualization-guide/SKILL.md:250Low confidence
- Network access in code of the SKILL.mdinteractive-viz-guide/SKILL.md:152High confidence
- Network access in code of the SKILL.mdinteractive-viz-guide/SKILL.md:153High confidence
- Network access in code of the SKILL.mdinteractive-viz-guide/SKILL.md:154High confidence
- Network access in the frontmatter of the SKILL.mdmetabase-analytics-guide/SKILL.md:8Low confidence
- Network access in the frontmatter of the SKILL.mdmetabase-analytics-guide/SKILL.md:9Low confidence
- Network access in the frontmatter of the SKILL.mdmetabase-analytics-guide/SKILL.md:10Low confidence
and 450 more
Only the first 500 facts are listed.
Works with
| Agent | Status | Evidence | Install to |
|---|---|---|---|
| Claude Code | Compatible | Standard SKILL.md skills/analysis/dataviz/SKILL.md | .claude/skills/<skill-name>/ |
| Codex | Compatible | Standard SKILL.md skills/analysis/dataviz/SKILL.md | .agents/skills/<skill-name>/ |
| Cursor | Compatible | Standard SKILL.md skills/analysis/dataviz/SKILL.md | .cursor/skills/<skill-name>/ |
| Gemini CLI | Compatible | Standard SKILL.md skills/analysis/dataviz/SKILL.md | .gemini/skills/<skill-name>/ |
| GitHub Copilot | Compatible | Standard SKILL.md skills/analysis/dataviz/SKILL.md | .github/skills/<skill-name>/ |
| OpenCode | Compatible | Standard SKILL.md skills/analysis/dataviz/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.