#852 by GitHub stars
wu-yc/LabClaw
LabClaw – Operating Layer for LabOS (Stanford-Princeton AI Co-Scientists)
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/bio/alphafold-database/SKILL.md
Week of Oct 26, 2026 · Line counts unavailable
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First observation → df378022e8b0
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skills/bio/analyze_lab_video_cell_behavior/SKILL.md
Week of Oct 26, 2026 · Line counts unavailable
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First observation → df378022e8b0
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skills/bio/anndata/SKILL.md
Week of Oct 26, 2026 · Line counts unavailable
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skills/bio/arboreto/SKILL.md
Week of Oct 26, 2026 · Line counts unavailable
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skills/bio/benchling-integration/SKILL.md
Week of Oct 26, 2026 · Line counts unavailable
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skills/bio/bioinformatics/SKILL.md
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skills/bio/biomni/SKILL.md
Week of Oct 26, 2026 · Line counts unavailable
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skills/bio/biopython/SKILL.md
Week of Oct 26, 2026 · Line counts unavailable
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skills/bio/brenda-database/SKILL.md
Week of Oct 26, 2026 · Line counts unavailable
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First observation → df378022e8b0
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skills/bio/cellxgene-census/SKILL.md
Week of Oct 26, 2026 · Line counts unavailable
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First observation → df378022e8b0
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skills/bio/clinpgx-database/SKILL.md
Week of Oct 26, 2026 · Line counts unavailable
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First observation → df378022e8b0
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skills/bio/cobrapy/SKILL.md
Week of Oct 26, 2026 · Line counts unavailable
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skills/bio/cosmic-database/SKILL.md
Week of Oct 26, 2026 · Line counts unavailable
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skills/bio/deeptools/SKILL.md
Week of Oct 26, 2026 · Line counts unavailable
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skills/bio/detect_common_wetlab_errors/SKILL.md
Week of Oct 26, 2026 · Line counts unavailable
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First observation → df378022e8b0
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skills/bio/dnanexus-integration/SKILL.md
Week of Oct 26, 2026 · Line counts unavailable
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First observation → df378022e8b0
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skills/bio/egocentric_view_to_structured_log/SKILL.md
Week of Oct 26, 2026 · Line counts unavailable
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First observation → df378022e8b0
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skills/bio/ena-database/SKILL.md
Week of Oct 26, 2026 · Line counts unavailable
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skills/bio/ensembl-database/SKILL.md
Week of Oct 26, 2026 · Line counts unavailable
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skills/bio/esm/SKILL.md
Week of Oct 26, 2026 · Line counts unavailable
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skills/bio/etetoolkit/SKILL.md
Week of Oct 26, 2026 · Line counts unavailable
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skills/bio/extract_experiment_data_from_video/SKILL.md
Week of Oct 26, 2026 · Line counts unavailable
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First observation → df378022e8b0
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skills/bio/flowio/SKILL.md
Week of Oct 26, 2026 · Line counts unavailable
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skills/bio/gene-database/SKILL.md
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skills/bio/geniml/SKILL.md
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skills/bio/geo-database/SKILL.md
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skills/bio/gget/SKILL.md
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skills/bio/gtars/SKILL.md
Week of Oct 26, 2026 · Line counts unavailable
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skills/bio/gwas-database/SKILL.md
Week of Oct 26, 2026 · Line counts unavailable
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skills/bio/histolab/SKILL.md
Week of Oct 26, 2026 · Line counts unavailable
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skills/bio/hmdb-database/SKILL.md
Week of Oct 26, 2026 · Line counts unavailable
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skills/bio/hypogenic/SKILL.md
Week of Oct 26, 2026 · Line counts unavailable
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skills/bio/kegg-database/SKILL.md
Week of Oct 26, 2026 · Line counts unavailable
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skills/bio/labarchive-integration/SKILL.md
Week of Oct 26, 2026 · Line counts unavailable
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skills/bio/lamindb/SKILL.md
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skills/bio/latchbio-integration/SKILL.md
Week of Oct 26, 2026 · Line counts unavailable
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skills/bio/matchms/SKILL.md
Week of Oct 26, 2026 · Line counts unavailable
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skills/bio/metabolomics-workbench-database/SKILL.md
Week of Oct 26, 2026 · Line counts unavailable
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skills/bio/omero-integration/SKILL.md
Week of Oct 26, 2026 · Line counts unavailable
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skills/bio/opentrons-integration/SKILL.md
Week of Oct 26, 2026 · Line counts unavailable
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skills/bio/pathml/SKILL.md
Week of Oct 26, 2026 · Line counts unavailable
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skills/bio/pdb-database/SKILL.md
Week of Oct 26, 2026 · Line counts unavailable
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First observation → df378022e8b0
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skills/bio/protocol_video_matching/SKILL.md
Week of Oct 26, 2026 · Line counts unavailable
Before Not checked yet → After Instructions only
First observation → df378022e8b0
No earlier version was observed for comparison.
skills/bio/protocolsio-integration/SKILL.md
Week of Oct 26, 2026 · Line counts unavailable
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First observation → df378022e8b0
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skills/bio/pydeseq2/SKILL.md
Week of Oct 26, 2026 · Line counts unavailable
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skills/bio/pylabrobot/SKILL.md
Week of Oct 26, 2026 · Line counts unavailable
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First observation → df378022e8b0
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skills/bio/pyopenms/SKILL.md
Week of Oct 26, 2026 · Line counts unavailable
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skills/bio/pysam/SKILL.md
Week of Oct 26, 2026 · Line counts unavailable
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First observation → df378022e8b0
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skills/bio/reactome-database/SKILL.md
Week of Oct 26, 2026 · Line counts unavailable
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skills/bio/realtime_protocol_guidance_prompts/SKILL.md
Week of Oct 26, 2026 · Line counts unavailable
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skills/bio/robot_protocol_step_generator/SKILL.md
Week of Oct 26, 2026 · Line counts unavailable
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skills/bio/scanpy/SKILL.md
Week of Oct 26, 2026 · Line counts unavailable
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skills/bio/scikit-bio/SKILL.md
Week of Oct 26, 2026 · Line counts unavailable
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skills/bio/scikit-survival/SKILL.md
Week of Oct 26, 2026 · Line counts unavailable
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skills/bio/scvi-tools/SKILL.md
Week of Oct 26, 2026 · Line counts unavailable
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First observation → df378022e8b0
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skills/bio/string-database/SKILL.md
Week of Oct 26, 2026 · Line counts unavailable
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skills/bio/tooluniverse-crispr-screen-analysis/SKILL.md
Week of Oct 26, 2026 · Line counts unavailable
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skills/bio/tooluniverse-epigenomics/SKILL.md
Week of Oct 26, 2026 · Line counts unavailable
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skills/bio/tooluniverse-expression-data-retrieval/SKILL.md
Week of Oct 26, 2026 · Line counts unavailable
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skills/bio/tooluniverse-gene-enrichment/SKILL.md
Week of Oct 26, 2026 · Line counts unavailable
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skills/bio/tooluniverse-gwas-drug-discovery/SKILL.md
Week of Oct 26, 2026 · Line counts unavailable
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skills/bio/tooluniverse-gwas-finemapping/SKILL.md
Week of Oct 26, 2026 · Line counts unavailable
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skills/bio/tooluniverse-gwas-snp-interpretation/SKILL.md
Week of Oct 26, 2026 · Line counts unavailable
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First observation → df378022e8b0
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skills/bio/tooluniverse-gwas-study-explorer/SKILL.md
Week of Oct 26, 2026 · Line counts unavailable
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First observation → df378022e8b0
No earlier version was observed for comparison.
skills/bio/tooluniverse-gwas-trait-to-gene/SKILL.md
Week of Oct 26, 2026 · Line counts unavailable
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First observation → df378022e8b0
No earlier version was observed for comparison.
skills/bio/tooluniverse-immune-repertoire-analysis/SKILL.md
Week of Oct 26, 2026 · Line counts unavailable
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First observation → df378022e8b0
No earlier version was observed for comparison.
skills/bio/tooluniverse-metabolomics/SKILL.md
Week of Oct 26, 2026 · Line counts unavailable
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First observation → df378022e8b0
No earlier version was observed for comparison.
skills/bio/tooluniverse-metabolomics-analysis/SKILL.md
Week of Oct 26, 2026 · Line counts unavailable
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skills/bio/tooluniverse-multi-omics-integration/SKILL.md
Week of Oct 26, 2026 · Line counts unavailable
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skills/bio/tooluniverse-multiomic-disease-characterization/SKILL.md
Week of Oct 26, 2026 · Line counts unavailable
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skills/bio/tooluniverse-phylogenetics/SKILL.md
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skills/bio/tooluniverse-polygenic-risk-score/SKILL.md
Week of Oct 26, 2026 · Line counts unavailable
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skills/bio/tooluniverse-protein-interactions/SKILL.md
Week of Oct 26, 2026 · Line counts unavailable
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skills/bio/tooluniverse-protein-structure-retrieval/SKILL.md
Week of Oct 26, 2026 · Line counts unavailable
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skills/bio/tooluniverse-proteomics-analysis/SKILL.md
Week of Oct 26, 2026 · Line counts unavailable
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skills/bio/tooluniverse-rnaseq-deseq2/SKILL.md
Week of Oct 26, 2026 · Line counts unavailable
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skills/bio/tooluniverse-sequence-retrieval/SKILL.md
Week of Oct 26, 2026 · Line counts unavailable
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skills/bio/tooluniverse-single-cell/SKILL.md
Week of Oct 26, 2026 · Line counts unavailable
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skills/bio/tooluniverse-spatial-omics-analysis/SKILL.md
Week of Oct 26, 2026 · Line counts unavailable
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skills/bio/tooluniverse-spatial-transcriptomics/SKILL.md
Week of Oct 26, 2026 · Line counts unavailable
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skills/bio/tooluniverse-structural-variant-analysis/SKILL.md
Week of Oct 26, 2026 · Line counts unavailable
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skills/bio/tooluniverse-systems-biology/SKILL.md
Week of Oct 26, 2026 · Line counts unavailable
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skills/bio/tooluniverse-variant-analysis/SKILL.md
Week of Oct 26, 2026 · Line counts unavailable
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skills/bio/tooluniverse-variant-interpretation/SKILL.md
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skills/bio/umap-learn/SKILL.md
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skills/bio/uniprot-database/SKILL.md
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skills/general/aeon/SKILL.md
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skills/general/article-writing/SKILL.md
Week of Oct 26, 2026 · Line counts unavailable
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skills/general/dask/SKILL.md
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skills/general/exploratory-data-analysis/SKILL.md
Week of Oct 26, 2026 · Line counts unavailable
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skills/general/export_experiment_data_to_excel/SKILL.md
Week of Oct 26, 2026 · Line counts unavailable
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skills/general/fair-data/SKILL.md
Week of Oct 26, 2026 · Line counts unavailable
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skills/general/gaussian_splatting_scene_description/SKILL.md
Week of Oct 26, 2026 · Line counts unavailable
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skills/general/generate_cell_analysis_charts/SKILL.md
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skills/general/generate_double_column_pdf_report/SKILL.md
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skills/general/generate_scientific_method_section/SKILL.md
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skills/general/geopandas/SKILL.md
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skills/general/get-available-resources/SKILL.md
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skills/general/hypothesis-generation/SKILL.md
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skills/general/latex-posters/SKILL.md
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skills/general/markitdown/SKILL.md
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skills/general/matlab/SKILL.md
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skills/general/matplotlib/SKILL.md
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skills/general/networkx/SKILL.md
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skills/general/patent-drafting/SKILL.md
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skills/general/peer-review/SKILL.md
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skills/general/plotly/SKILL.md
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skills/general/polars/SKILL.md
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skills/general/pptx-generation/SKILL.md
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skills/general/pptx-posters/SKILL.md
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skills/general/protocol-writing/SKILL.md
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skills/general/pymc/SKILL.md
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skills/general/pymoo/SKILL.md
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skills/general/pytorch-lightning/SKILL.md
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skills/general/regulatory-submission/SKILL.md
Week of Oct 26, 2026 · Line counts unavailable
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skills/general/reproducibility-checklist/SKILL.md
Week of Oct 26, 2026 · Line counts unavailable
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skills/general/scholar-evaluation/SKILL.md
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skills/general/science-communication/SKILL.md
Week of Oct 26, 2026 · Line counts unavailable
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skills/general/scientific-brainstorming/SKILL.md
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skills/general/scientific-critical-thinking/SKILL.md
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skills/general/scientific-diagram-generation/SKILL.md
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skills/general/scientific-slides/SKILL.md
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skills/general/scientific-visualization/SKILL.md
Week of Oct 26, 2026 · Line counts unavailable
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skills/general/scientific-writing/SKILL.md
Week of Oct 26, 2026 · Line counts unavailable
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First observation → df378022e8b0
No earlier version was observed for comparison.
skills/general/scikit-learn/SKILL.md
Week of Oct 26, 2026 · Line counts unavailable
Before Not checked yet → After Network or broad shell
First observation → df378022e8b0
No earlier version was observed for comparison.
skills/general/scikit-survival/SKILL.md
Week of Oct 26, 2026 · Line counts unavailable
Before Not checked yet → After Network or broad shell
First observation → df378022e8b0
No earlier version was observed for comparison.
skills/general/seaborn/SKILL.md
Week of Oct 26, 2026 · Line counts unavailable
Before Not checked yet → After Network or broad shell
First observation → df378022e8b0
No earlier version was observed for comparison.
skills/general/shap/SKILL.md
Week of Oct 26, 2026 · Line counts unavailable
Before Not checked yet → After Network or broad shell
First observation → df378022e8b0
No earlier version was observed for comparison.
skills/general/statistical-analysis/SKILL.md
Week of Oct 26, 2026 · Line counts unavailable
Before Not checked yet → After Network or broad shell
First observation → df378022e8b0
No earlier version was observed for comparison.
skills/general/statistics/SKILL.md
Week of Oct 26, 2026 · Line counts unavailable
Before Not checked yet → After Instructions only
First observation → df378022e8b0
No earlier version was observed for comparison.
skills/general/statsmodels/SKILL.md
Week of Oct 26, 2026 · Line counts unavailable
Before Not checked yet → After Network or broad shell
First observation → df378022e8b0
No earlier version was observed for comparison.
skills/general/sympy/SKILL.md
Week of Oct 26, 2026 · Line counts unavailable
Before Not checked yet → After Network or broad shell
First observation → df378022e8b0
No earlier version was observed for comparison.
skills/general/tooluniverse-statistical-modeling/SKILL.md
Week of Oct 26, 2026 · Line counts unavailable
Before Not checked yet → After Network or broad shell
First observation → df378022e8b0
No earlier version was observed for comparison.
skills/general/transformers/SKILL.md
Week of Oct 26, 2026 · Line counts unavailable
Before Not checked yet → After Network or broad shell
First observation → df378022e8b0
No earlier version was observed for comparison.
skills/general/vaex/SKILL.md
Week of Oct 26, 2026 · Line counts unavailable
Before Not checked yet → After Network or broad shell
First observation → df378022e8b0
No earlier version was observed for comparison.
skills/general/venue-templates/SKILL.md
Week of Oct 26, 2026 · Line counts unavailable
Before Not checked yet → After Network or broad shell
First observation → df378022e8b0
No earlier version was observed for comparison.
skills/general/visualization/SKILL.md
Week of Oct 26, 2026 · Line counts unavailable
Before Not checked yet → After Instructions only
First observation → df378022e8b0
No earlier version was observed for comparison.
skills/general/voice_command_to_skill/SKILL.md
Week of Oct 26, 2026 · Line counts unavailable
Before Not checked yet → After Network or broad shell
First observation → df378022e8b0
No earlier version was observed for comparison.
skills/general/writing/SKILL.md
Week of Oct 26, 2026 · Line counts unavailable
Before Not checked yet → After Instructions only
First observation → df378022e8b0
No earlier version was observed for comparison.
skills/general/zarr-python/SKILL.md
Week of Oct 26, 2026 · Line counts unavailable
Before Not checked yet → After Network or broad shell
First observation → df378022e8b0
No earlier version was observed for comparison.
skills/literature/academic-literature-search/SKILL.md
Week of Oct 26, 2026 · Line counts unavailable
Before Not checked yet → After Network or broad shell
First observation → df378022e8b0
No earlier version was observed for comparison.
skills/literature/article-writing/SKILL.md
Week of Oct 26, 2026 · Line counts unavailable
Before Not checked yet → After Instructions only
First observation → df378022e8b0
No earlier version was observed for comparison.
skills/literature/arxiv-search/SKILL.md
Week of Oct 26, 2026 · Line counts unavailable
Before Not checked yet → After Network or broad shell
First observation → df378022e8b0
No earlier version was observed for comparison.
skills/literature/biomedical-search/SKILL.md
Week of Oct 26, 2026 · Line counts unavailable
Before Not checked yet → After Network or broad shell
First observation → df378022e8b0
No earlier version was observed for comparison.
skills/literature/biorxiv-database/SKILL.md
Week of Oct 26, 2026 · Line counts unavailable
Before Not checked yet → After Network or broad shell
First observation → df378022e8b0
No earlier version was observed for comparison.
skills/literature/biorxiv-search/SKILL.md
Week of Oct 26, 2026 · Line counts unavailable
Before Not checked yet → After Network or broad shell
First observation → df378022e8b0
No earlier version was observed for comparison.
skills/literature/bioservices/SKILL.md
Week of Oct 26, 2026 · Line counts unavailable
Before Not checked yet → After Network or broad shell
First observation → df378022e8b0
No earlier version was observed for comparison.
skills/literature/citation-management/SKILL.md
Week of Oct 26, 2026 · Line counts unavailable
Before Not checked yet → After Network or broad shell
First observation → df378022e8b0
No earlier version was observed for comparison.
skills/literature/clinical-trials-search/SKILL.md
Week of Oct 26, 2026 · Line counts unavailable
Before Not checked yet → After Network or broad shell
First observation → df378022e8b0
No earlier version was observed for comparison.
skills/literature/clinicaltrials-database/SKILL.md
Week of Oct 26, 2026 · Line counts unavailable
Before Not checked yet → After Network or broad shell
First observation → df378022e8b0
No earlier version was observed for comparison.
skills/literature/clinvar-database/SKILL.md
Week of Oct 26, 2026 · Line counts unavailable
Before Not checked yet → After Network or broad shell
First observation → df378022e8b0
No earlier version was observed for comparison.
skills/literature/gene-database/SKILL.md
Week of Oct 26, 2026 · Line counts unavailable
Before Not checked yet → After Network or broad shell
First observation → df378022e8b0
No earlier version was observed for comparison.
skills/literature/geo-database/SKILL.md
Week of Oct 26, 2026 · Line counts unavailable
Before Not checked yet → After Network or broad shell
First observation → df378022e8b0
No earlier version was observed for comparison.
skills/literature/gwas-database/SKILL.md
Week of Oct 26, 2026 · Line counts unavailable
Before Not checked yet → After Network or broad shell
First observation → df378022e8b0
No earlier version was observed for comparison.
skills/literature/hmdb-database/SKILL.md
Week of Oct 26, 2026 · Line counts unavailable
Before Not checked yet → After Network or broad shell
First observation → df378022e8b0
No earlier version was observed for comparison.
skills/literature/literature/SKILL.md
Week of Oct 26, 2026 · Line counts unavailable
Before Not checked yet → After Network or broad shell
First observation → df378022e8b0
No earlier version was observed for comparison.
skills/literature/literature-review/SKILL.md
Week of Oct 26, 2026 · Line counts unavailable
Before Not checked yet → After Network or broad shell
First observation → df378022e8b0
No earlier version was observed for comparison.
skills/literature/literature-search/SKILL.md
Week of Oct 26, 2026 · Line counts unavailable
Before Not checked yet → After Network or broad shell
First observation → df378022e8b0
No earlier version was observed for comparison.
skills/literature/literature_to_hypothesis/SKILL.md
Week of Oct 26, 2026 · Line counts unavailable
Before Not checked yet → After Instructions only
First observation → df378022e8b0
No earlier version was observed for comparison.
skills/literature/medrxiv-search/SKILL.md
Week of Oct 26, 2026 · Line counts unavailable
Before Not checked yet → After Network or broad shell
First observation → df378022e8b0
No earlier version was observed for comparison.
skills/literature/openalex-database/SKILL.md
Week of Oct 26, 2026 · Line counts unavailable
Before Not checked yet → After Network or broad shell
First observation → df378022e8b0
No earlier version was observed for comparison.
skills/literature/patents-search/SKILL.md
Week of Oct 26, 2026 · Line counts unavailable
Before Not checked yet → After Network or broad shell
First observation → df378022e8b0
No earlier version was observed for comparison.
skills/literature/pdb-database/SKILL.md
Week of Oct 26, 2026 · Line counts unavailable
Before Not checked yet → After Network or broad shell
First observation → df378022e8b0
No earlier version was observed for comparison.
skills/literature/perplexity-search/SKILL.md
Week of Oct 26, 2026 · Line counts unavailable
Before Not checked yet → After Network or broad shell
First observation → df378022e8b0
No earlier version was observed for comparison.
skills/literature/protocol-writing/SKILL.md
Week of Oct 26, 2026 · Line counts unavailable
Before Not checked yet → After Instructions only
First observation → df378022e8b0
No earlier version was observed for comparison.
skills/literature/pubmed-database/SKILL.md
Week of Oct 26, 2026 · Line counts unavailable
Before Not checked yet → After Network or broad shell
First observation → df378022e8b0
No earlier version was observed for comparison.
skills/literature/pubmed-search/SKILL.md
Week of Oct 26, 2026 · Line counts unavailable
Before Not checked yet → After Network or broad shell
First observation → df378022e8b0
No earlier version was observed for comparison.
skills/literature/research-grants/SKILL.md
Week of Oct 26, 2026 · Line counts unavailable
Before Not checked yet → After Network or broad shell
First observation → df378022e8b0
No earlier version was observed for comparison.
skills/literature/research-lookup/SKILL.md
Week of Oct 26, 2026 · Line counts unavailable
Before Not checked yet → After Network or broad shell
First observation → df378022e8b0
No earlier version was observed for comparison.
skills/literature/review-writing/SKILL.md
Week of Oct 26, 2026 · Line counts unavailable
Before Not checked yet → After Network or broad shell
First observation → df378022e8b0
No earlier version was observed for comparison.
skills/literature/scientific-writing/SKILL.md
Week of Oct 26, 2026 · Line counts unavailable
Before Not checked yet → After Network or broad shell
First observation → df378022e8b0
No earlier version was observed for comparison.
skills/literature/tooluniverse-literature-deep-research/SKILL.md
Week of Oct 26, 2026 · Line counts unavailable
Before Not checked yet → After Network or broad shell
First observation → df378022e8b0
No earlier version was observed for comparison.
skills/literature/uspto-database/SKILL.md
Week of Oct 26, 2026 · Line counts unavailable
Before Not checked yet → After Network or broad shell
First observation → df378022e8b0
No earlier version was observed for comparison.
skills/med/clinical/SKILL.md
Week of Oct 26, 2026 · Line counts unavailable
Before Not checked yet → After Instructions only
First observation → df378022e8b0
No earlier version was observed for comparison.
skills/med/clinical-decision-support/SKILL.md
Week of Oct 26, 2026 · Line counts unavailable
Before Not checked yet → After Network or broad shell
First observation → df378022e8b0
No earlier version was observed for comparison.
skills/med/clinical-reports/SKILL.md
Week of Oct 26, 2026 · Line counts unavailable
Before Not checked yet → After Network or broad shell
First observation → df378022e8b0
No earlier version was observed for comparison.
skills/med/clinicaltrials-database/SKILL.md
Week of Oct 26, 2026 · Line counts unavailable
Before Not checked yet → After Network or broad shell
First observation → df378022e8b0
No earlier version was observed for comparison.
skills/med/clinvar-database/SKILL.md
Week of Oct 26, 2026 · Line counts unavailable
Before Not checked yet → After Network or broad shell
First observation → df378022e8b0
No earlier version was observed for comparison.
skills/med/iso-13485-certification/SKILL.md
Week of Oct 26, 2026 · Line counts unavailable
Before Not checked yet → After Network or broad shell
First observation → df378022e8b0
No earlier version was observed for comparison.
skills/med/neurokit2/SKILL.md
Week of Oct 26, 2026 · Line counts unavailable
Before Not checked yet → After Network or broad shell
First observation → df378022e8b0
No earlier version was observed for comparison.
skills/med/neuropixels-analysis/SKILL.md
Week of Oct 26, 2026 · Line counts unavailable
Before Not checked yet → After Network or broad shell
First observation → df378022e8b0
No earlier version was observed for comparison.
skills/med/pydicom/SKILL.md
Week of Oct 26, 2026 · Line counts unavailable
Before Not checked yet → After Network or broad shell
First observation → df378022e8b0
No earlier version was observed for comparison.
skills/med/pyhealth/SKILL.md
Week of Oct 26, 2026 · Line counts unavailable
Before Not checked yet → After Network or broad shell
First observation → df378022e8b0
No earlier version was observed for comparison.
skills/med/tooluniverse-cancer-variant-interpretation/SKILL.md
Week of Oct 26, 2026 · Line counts unavailable
Before Not checked yet → After Network or broad shell
First observation → df378022e8b0
No earlier version was observed for comparison.
skills/med/tooluniverse-clinical-guidelines/SKILL.md
Week of Oct 26, 2026 · Line counts unavailable
Before Not checked yet → After Network or broad shell
First observation → df378022e8b0
No earlier version was observed for comparison.
skills/med/tooluniverse-clinical-trial-design/SKILL.md
Week of Oct 26, 2026 · Line counts unavailable
Before Not checked yet → After Network or broad shell
First observation → df378022e8b0
No earlier version was observed for comparison.
skills/med/tooluniverse-clinical-trial-matching/SKILL.md
Week of Oct 26, 2026 · Line counts unavailable
Before Not checked yet → After Network or broad shell
First observation → df378022e8b0
No earlier version was observed for comparison.
skills/med/tooluniverse-disease-research/SKILL.md
Week of Oct 26, 2026 · Line counts unavailable
Before Not checked yet → After Network or broad shell
First observation → df378022e8b0
No earlier version was observed for comparison.
skills/med/tooluniverse-image-analysis/SKILL.md
Week of Oct 26, 2026 · Line counts unavailable
Before Not checked yet → After Network or broad shell
First observation → df378022e8b0
No earlier version was observed for comparison.
skills/med/tooluniverse-immunotherapy-response-prediction/SKILL.md
Week of Oct 26, 2026 · Line counts unavailable
Before Not checked yet → After Network or broad shell
First observation → df378022e8b0
No earlier version was observed for comparison.
skills/med/tooluniverse-infectious-disease/SKILL.md
Week of Oct 26, 2026 · Line counts unavailable
Before Not checked yet → After Network or broad shell
First observation → df378022e8b0
No earlier version was observed for comparison.
skills/med/tooluniverse-precision-medicine-stratification/SKILL.md
Week of Oct 26, 2026 · Line counts unavailable
Before Not checked yet → After Network or broad shell
First observation → df378022e8b0
No earlier version was observed for comparison.
skills/med/tooluniverse-precision-oncology/SKILL.md
Week of Oct 26, 2026 · Line counts unavailable
Before Not checked yet → After Network or broad shell
First observation → df378022e8b0
No earlier version was observed for comparison.
skills/med/tooluniverse-rare-disease-diagnosis/SKILL.md
Week of Oct 26, 2026 · Line counts unavailable
Before Not checked yet → After Network or broad shell
First observation → df378022e8b0
No earlier version was observed for comparison.
skills/med/treatment-plans/SKILL.md
Week of Oct 26, 2026 · Line counts unavailable
Before Not checked yet → After Network or broad shell
First observation → df378022e8b0
No earlier version was observed for comparison.
skills/pharma/adaptyv/SKILL.md
Week of Oct 26, 2026 · Line counts unavailable
Before Not checked yet → After Network or broad shell
First observation → df378022e8b0
No earlier version was observed for comparison.
skills/pharma/chembl-database/SKILL.md
Week of Oct 26, 2026 · Line counts unavailable
Before Not checked yet → After Network or broad shell
First observation → df378022e8b0
No earlier version was observed for comparison.
skills/pharma/chembl-search/SKILL.md
Week of Oct 26, 2026 · Line counts unavailable
Before Not checked yet → After Network or broad shell
First observation → df378022e8b0
No earlier version was observed for comparison.
skills/pharma/chemistry/SKILL.md
Week of Oct 26, 2026 · Line counts unavailable
Before Not checked yet → After Instructions only
First observation → df378022e8b0
No earlier version was observed for comparison.
skills/pharma/datamol/SKILL.md
Week of Oct 26, 2026 · Line counts unavailable
Before Not checked yet → After Network or broad shell
First observation → df378022e8b0
No earlier version was observed for comparison.
skills/pharma/deepchem/SKILL.md
Week of Oct 26, 2026 · Line counts unavailable
Before Not checked yet → After Network or broad shell
First observation → df378022e8b0
No earlier version was observed for comparison.
skills/pharma/diffdock/SKILL.md
Week of Oct 26, 2026 · Line counts unavailable
Before Not checked yet → After Network or broad shell
First observation → df378022e8b0
No earlier version was observed for comparison.
skills/pharma/drug-discovery-search/SKILL.md
Week of Oct 26, 2026 · Line counts unavailable
Before Not checked yet → After Network or broad shell
First observation → df378022e8b0
No earlier version was observed for comparison.
skills/pharma/drug-labels-search/SKILL.md
Week of Oct 26, 2026 · Line counts unavailable
Before Not checked yet → After Network or broad shell
First observation → df378022e8b0
No earlier version was observed for comparison.
skills/pharma/drugbank-database/SKILL.md
Week of Oct 26, 2026 · Line counts unavailable
Before Not checked yet → After Network or broad shell
First observation → df378022e8b0
No earlier version was observed for comparison.
skills/pharma/drugbank-search/SKILL.md
Week of Oct 26, 2026 · Line counts unavailable
Before Not checked yet → After Network or broad shell
First observation → df378022e8b0
No earlier version was observed for comparison.
skills/pharma/fda-database/SKILL.md
Week of Oct 26, 2026 · Line counts unavailable
Before Not checked yet → After Network or broad shell
First observation → df378022e8b0
No earlier version was observed for comparison.
skills/pharma/medchem/SKILL.md
Week of Oct 26, 2026 · Line counts unavailable
Before Not checked yet → After Network or broad shell
First observation → df378022e8b0
No earlier version was observed for comparison.
skills/pharma/molfeat/SKILL.md
Week of Oct 26, 2026 · Line counts unavailable
Before Not checked yet → After Network or broad shell
First observation → df378022e8b0
No earlier version was observed for comparison.
skills/pharma/open-targets-search/SKILL.md
Week of Oct 26, 2026 · Line counts unavailable
Before Not checked yet → After Network or broad shell
First observation → df378022e8b0
No earlier version was observed for comparison.
skills/pharma/opentargets-database/SKILL.md
Week of Oct 26, 2026 · Line counts unavailable
Before Not checked yet → After Network or broad shell
First observation → df378022e8b0
No earlier version was observed for comparison.
skills/pharma/pubchem-database/SKILL.md
Week of Oct 26, 2026 · Line counts unavailable
Before Not checked yet → After Network or broad shell
First observation → df378022e8b0
No earlier version was observed for comparison.
skills/pharma/pytdc/SKILL.md
Week of Oct 26, 2026 · Line counts unavailable
Before Not checked yet → After Network or broad shell
First observation → df378022e8b0
No earlier version was observed for comparison.
skills/pharma/rdkit/SKILL.md
Week of Oct 26, 2026 · Line counts unavailable
Before Not checked yet → After Network or broad shell
First observation → df378022e8b0
No earlier version was observed for comparison.
skills/pharma/rowan/SKILL.md
Week of Oct 26, 2026 · Line counts unavailable
Before Not checked yet → After Network or broad shell
First observation → df378022e8b0
No earlier version was observed for comparison.
skills/pharma/tooluniverse-adverse-event-detection/SKILL.md
Week of Oct 26, 2026 · Line counts unavailable
Before Not checked yet → After Network or broad shell
First observation → df378022e8b0
No earlier version was observed for comparison.
skills/pharma/tooluniverse-antibody-engineering/SKILL.md
Week of Oct 26, 2026 · Line counts unavailable
Before Not checked yet → After Network or broad shell
First observation → df378022e8b0
No earlier version was observed for comparison.
skills/pharma/tooluniverse-binder-discovery/SKILL.md
Week of Oct 26, 2026 · Line counts unavailable
Before Not checked yet → After Network or broad shell
First observation → df378022e8b0
No earlier version was observed for comparison.
skills/pharma/tooluniverse-chemical-compound-retrieval/SKILL.md
Week of Oct 26, 2026 · Line counts unavailable
Before Not checked yet → After Network or broad shell
First observation → df378022e8b0
No earlier version was observed for comparison.
skills/pharma/tooluniverse-chemical-safety/SKILL.md
Week of Oct 26, 2026 · Line counts unavailable
Before Not checked yet → After Network or broad shell
First observation → df378022e8b0
No earlier version was observed for comparison.
skills/pharma/tooluniverse-drug-drug-interaction/SKILL.md
Week of Oct 26, 2026 · Line counts unavailable
Before Not checked yet → After Instructions only
First observation → df378022e8b0
No earlier version was observed for comparison.
skills/pharma/tooluniverse-drug-repurposing/SKILL.md
Week of Oct 26, 2026 · Line counts unavailable
Before Not checked yet → After Network or broad shell
First observation → df378022e8b0
No earlier version was observed for comparison.
skills/pharma/tooluniverse-drug-research/SKILL.md
Week of Oct 26, 2026 · Line counts unavailable
Before Not checked yet → After Network or broad shell
First observation → df378022e8b0
No earlier version was observed for comparison.
skills/pharma/tooluniverse-drug-target-validation/SKILL.md
Week of Oct 26, 2026 · Line counts unavailable
Before Not checked yet → After Network or broad shell
First observation → df378022e8b0
No earlier version was observed for comparison.
skills/pharma/tooluniverse-network-pharmacology/SKILL.md
Week of Oct 26, 2026 · Line counts unavailable
Before Not checked yet → After Network or broad shell
First observation → df378022e8b0
No earlier version was observed for comparison.
skills/pharma/tooluniverse-pharmacovigilance/SKILL.md
Week of Oct 26, 2026 · Line counts unavailable
Before Not checked yet → After Network or broad shell
First observation → df378022e8b0
No earlier version was observed for comparison.
skills/pharma/tooluniverse-protein-therapeutic-design/SKILL.md
Week of Oct 26, 2026 · Line counts unavailable
Before Not checked yet → After Network or broad shell
First observation → df378022e8b0
No earlier version was observed for comparison.
skills/pharma/tooluniverse-target-research/SKILL.md
Week of Oct 26, 2026 · Line counts unavailable
Before Not checked yet → After Network or broad shell
First observation → df378022e8b0
No earlier version was observed for comparison.
skills/pharma/torch_geometric/SKILL.md
Week of Oct 26, 2026 · Line counts unavailable
Before Not checked yet → After Network or broad shell
First observation → df378022e8b0
No earlier version was observed for comparison.
skills/pharma/torchdrug/SKILL.md
Week of Oct 26, 2026 · Line counts unavailable
Before Not checked yet → After Network or broad shell
First observation → df378022e8b0
No earlier version was observed for comparison.
skills/pharma/zinc-database/SKILL.md
Week of Oct 26, 2026 · Line counts unavailable
Before Not checked yet → After Network or broad shell
First observation → df378022e8b0
No earlier version was observed for comparison.
skills/vision/egohos-segmentation/SKILL.md
Week of Oct 26, 2026 · Line counts unavailable
Before Not checked yet → After Network or broad shell
First observation → df378022e8b0
No earlier version was observed for comparison.
skills/vision/hand-tracking-toolkit/SKILL.md
Week of Oct 26, 2026 · Line counts unavailable
Before Not checked yet → After Network or broad shell
First observation → df378022e8b0
No earlier version was observed for comparison.
skills/vision/hands-3d-pose/SKILL.md
Week of Oct 26, 2026 · Line counts unavailable
Before Not checked yet → After Network or broad shell
First observation → df378022e8b0
No earlier version was observed for comparison.
skills/vision/handtracking/SKILL.md
Week of Oct 26, 2026 · Line counts unavailable
Before Not checked yet → After Network or broad shell
First observation → df378022e8b0
No earlier version was observed for comparison.
skills/vision/hot3d/SKILL.md
Week of Oct 26, 2026 · Line counts unavailable
Before Not checked yet → After Network or broad shell
First observation → df378022e8b0
No earlier version was observed for comparison.
skills/visualization/matplotlib/SKILL.md
Week of Oct 26, 2026 · Line counts unavailable
Before Not checked yet → After Network or broad shell
First observation → df378022e8b0
No earlier version was observed for comparison.
skills/visualization/plotly/SKILL.md
Week of Oct 26, 2026 · Line counts unavailable
Before Not checked yet → After Network or broad shell
First observation → df378022e8b0
No earlier version was observed for comparison.
skills/visualization/scientific-visualization/SKILL.md
Week of Oct 26, 2026 · Line counts unavailable
Before Not checked yet → After Network or broad shell
First observation → df378022e8b0
No earlier version was observed for comparison.
skills/visualization/seaborn/SKILL.md
Week of Oct 26, 2026 · Line counts unavailable
Before Not checked yet → After Network or broad shell
First observation → df378022e8b0
No earlier version was observed for comparison.
Heat
0.3- Star growth, last 7 daysAhead of 6% of tracked repositories
- Star growth, last 24 hoursAhead of 3% of tracked repositories
- Rank movement, last 7 daysAhead of 4% of tracked repositories
- Commits, last 30 daysAhead of 0% of tracked repositories
- Accounts asking about it through MCP, last 7 daysNot available
- Hot votes this weekAhead of 0% of tracked repositories
Heat never changes the order by stars, and sponsorship never touches it. How heat works
Stars over time
Individual skills in this repository
- alphafold-database
skills/bio/alphafold-database - analyze_lab_video_cell_behavior
skills/bio/analyze_lab_video_cell_behavior - anndata
skills/bio/anndata - arboreto
skills/bio/arboreto - benchling-integration
skills/bio/benchling-integration - bioinformatics
skills/bio/bioinformatics - biomni
skills/bio/biomni - biopython
skills/bio/biopython - brenda-database
skills/bio/brenda-database - cellxgene-census
skills/bio/cellxgene-census - clinpgx-database
skills/bio/clinpgx-database - cobrapy
skills/bio/cobrapy - cosmic-database
skills/bio/cosmic-database - deeptools
skills/bio/deeptools - detect_common_wetlab_errors
skills/bio/detect_common_wetlab_errors - dnanexus-integration
skills/bio/dnanexus-integration - egocentric_view_to_structured_log
skills/bio/egocentric_view_to_structured_log - ena-database
skills/bio/ena-database - ensembl-database
skills/bio/ensembl-database - esm
skills/bio/esm - etetoolkit
skills/bio/etetoolkit - extract_experiment_data_from_video
skills/bio/extract_experiment_data_from_video - flowio
skills/bio/flowio - gene-database
skills/bio/gene-database - geniml
skills/bio/geniml - geo-database
skills/bio/geo-database - gget
skills/bio/gget - gtars
skills/bio/gtars - gwas-database
skills/bio/gwas-database - histolab
skills/bio/histolab - hmdb-database
skills/bio/hmdb-database - hypogenic
skills/bio/hypogenic - kegg-database
skills/bio/kegg-database - labarchive-integration
skills/bio/labarchive-integration - lamindb
skills/bio/lamindb - latchbio-integration
skills/bio/latchbio-integration - matchms
skills/bio/matchms - metabolomics-workbench-database
skills/bio/metabolomics-workbench-database - omero-integration
skills/bio/omero-integration - opentrons-integration
skills/bio/opentrons-integration - pathml
skills/bio/pathml - pdb-database
skills/bio/pdb-database - protocol_video_matching
skills/bio/protocol_video_matching - protocolsio-integration
skills/bio/protocolsio-integration - pydeseq2
skills/bio/pydeseq2 - pylabrobot
skills/bio/pylabrobot - pyopenms
skills/bio/pyopenms - pysam
skills/bio/pysam - reactome-database
skills/bio/reactome-database - realtime_protocol_guidance_prompts
skills/bio/realtime_protocol_guidance_prompts - robot_protocol_step_generator
skills/bio/robot_protocol_step_generator - scanpy
skills/bio/scanpy - scikit-bio
skills/bio/scikit-bio - scikit-survival
skills/bio/scikit-survival - scvi-tools
skills/bio/scvi-tools - string-database
skills/bio/string-database - tooluniverse-crispr-screen-analysis
skills/bio/tooluniverse-crispr-screen-analysis - tooluniverse-epigenomics
skills/bio/tooluniverse-epigenomics - tooluniverse-expression-data-retrieval
skills/bio/tooluniverse-expression-data-retrieval - tooluniverse-gene-enrichment
skills/bio/tooluniverse-gene-enrichment - tooluniverse-gwas-drug-discovery
skills/bio/tooluniverse-gwas-drug-discovery - tooluniverse-gwas-finemapping
skills/bio/tooluniverse-gwas-finemapping - tooluniverse-gwas-snp-interpretation
skills/bio/tooluniverse-gwas-snp-interpretation - tooluniverse-gwas-study-explorer
skills/bio/tooluniverse-gwas-study-explorer - tooluniverse-gwas-trait-to-gene
skills/bio/tooluniverse-gwas-trait-to-gene - tooluniverse-immune-repertoire-analysis
skills/bio/tooluniverse-immune-repertoire-analysis - tooluniverse-metabolomics
skills/bio/tooluniverse-metabolomics - tooluniverse-metabolomics-analysis
skills/bio/tooluniverse-metabolomics-analysis - tooluniverse-multi-omics-integration
skills/bio/tooluniverse-multi-omics-integration - tooluniverse-multiomic-disease-characterization
skills/bio/tooluniverse-multiomic-disease-characterization - tooluniverse-phylogenetics
skills/bio/tooluniverse-phylogenetics - tooluniverse-polygenic-risk-score
skills/bio/tooluniverse-polygenic-risk-score - Protein Interaction Network Analysis
skills/bio/tooluniverse-protein-interactions - tooluniverse-protein-structure-retrieval
skills/bio/tooluniverse-protein-structure-retrieval - tooluniverse-proteomics-analysis
skills/bio/tooluniverse-proteomics-analysis - tooluniverse-rnaseq-deseq2
skills/bio/tooluniverse-rnaseq-deseq2 - tooluniverse-sequence-retrieval
skills/bio/tooluniverse-sequence-retrieval - tooluniverse-single-cell
skills/bio/tooluniverse-single-cell - tooluniverse-spatial-omics-analysis
skills/bio/tooluniverse-spatial-omics-analysis - tooluniverse-spatial-transcriptomics
skills/bio/tooluniverse-spatial-transcriptomics - tooluniverse-structural-variant-analysis
skills/bio/tooluniverse-structural-variant-analysis - tooluniverse-systems-biology
skills/bio/tooluniverse-systems-biology - tooluniverse-variant-analysis
skills/bio/tooluniverse-variant-analysis - tooluniverse-variant-interpretation
skills/bio/tooluniverse-variant-interpretation - umap-learn
skills/bio/umap-learn - uniprot-database
skills/bio/uniprot-database - aeon
skills/general/aeon - article-writing
skills/general/article-writing - dask
skills/general/dask - exploratory-data-analysis
skills/general/exploratory-data-analysis - export_experiment_data_to_excel
skills/general/export_experiment_data_to_excel - fair-data
skills/general/fair-data - gaussian_splatting_scene_description
skills/general/gaussian_splatting_scene_description - generate_cell_analysis_charts
skills/general/generate_cell_analysis_charts - generate_double_column_pdf_report
skills/general/generate_double_column_pdf_report - generate_scientific_method_section
skills/general/generate_scientific_method_section - geopandas
skills/general/geopandas - get-available-resources
skills/general/get-available-resources - hypothesis-generation
skills/general/hypothesis-generation - latex-posters
skills/general/latex-posters - markitdown
skills/general/markitdown - matlab
skills/general/matlab - matplotlib
skills/general/matplotlib - networkx
skills/general/networkx - patent-drafting
skills/general/patent-drafting - peer-review
skills/general/peer-review - plotly
skills/general/plotly - polars
skills/general/polars - pptx-generation
skills/general/pptx-generation - pptx-posters
skills/general/pptx-posters - protocol-writing
skills/general/protocol-writing - pymc-bayesian-modeling
skills/general/pymc - pymoo
skills/general/pymoo - pytorch-lightning
skills/general/pytorch-lightning - regulatory-submission
skills/general/regulatory-submission - reproducibility-checklist
skills/general/reproducibility-checklist - scholar-evaluation
skills/general/scholar-evaluation - science-communication
skills/general/science-communication - scientific-brainstorming
skills/general/scientific-brainstorming - scientific-critical-thinking
skills/general/scientific-critical-thinking - scientific-diagram-generation
skills/general/scientific-diagram-generation - scientific-slides
skills/general/scientific-slides - scientific-visualization
skills/general/scientific-visualization - scientific-writing
skills/general/scientific-writing - scikit-learn
skills/general/scikit-learn - scikit-survival
skills/general/scikit-survival - seaborn
skills/general/seaborn - shap
skills/general/shap - statistical-analysis
skills/general/statistical-analysis - statistics
skills/general/statistics - statsmodels
skills/general/statsmodels - sympy
skills/general/sympy - tooluniverse-statistical-modeling
skills/general/tooluniverse-statistical-modeling - transformers
skills/general/transformers - vaex
skills/general/vaex - venue-templates
skills/general/venue-templates - visualization
skills/general/visualization - voice_command_to_skill
skills/general/voice_command_to_skill - writing
skills/general/writing - zarr-python
skills/general/zarr-python - academic-literature-search
skills/literature/academic-literature-search - article-writing
skills/literature/article-writing - arxiv-search
skills/literature/arxiv-search - biomedical-search
skills/literature/biomedical-search - biorxiv-database
skills/literature/biorxiv-database - biorxiv-search
skills/literature/biorxiv-search - bioservices
skills/literature/bioservices - citation-management
skills/literature/citation-management - clinical-trials-search
skills/literature/clinical-trials-search - clinicaltrials-database
skills/literature/clinicaltrials-database - clinvar-database
skills/literature/clinvar-database - gene-database
skills/literature/gene-database - geo-database
skills/literature/geo-database - gwas-database
skills/literature/gwas-database - hmdb-database
skills/literature/hmdb-database - literature
skills/literature/literature - literature-review
skills/literature/literature-review - literature-search
skills/literature/literature-search - literature_to_hypothesis
skills/literature/literature_to_hypothesis - medrxiv-search
skills/literature/medrxiv-search - openalex-database
skills/literature/openalex-database - patents-search
skills/literature/patents-search - pdb-database
skills/literature/pdb-database - perplexity-search
skills/literature/perplexity-search - protocol-writing
skills/literature/protocol-writing - pubmed-database
skills/literature/pubmed-database - pubmed-search
skills/literature/pubmed-search - research-grants
skills/literature/research-grants - research-lookup
skills/literature/research-lookup - review-writing
skills/literature/review-writing - scientific-writing
skills/literature/scientific-writing - tooluniverse-literature-deep-research
skills/literature/tooluniverse-literature-deep-research - uspto-database
skills/literature/uspto-database - clinical
skills/med/clinical - clinical-decision-support
skills/med/clinical-decision-support - clinical-reports
skills/med/clinical-reports - clinicaltrials-database
skills/med/clinicaltrials-database - clinvar-database
skills/med/clinvar-database - iso-13485-certification
skills/med/iso-13485-certification - neurokit2
skills/med/neurokit2 - neuropixels-analysis
skills/med/neuropixels-analysis - pydicom
skills/med/pydicom - pyhealth
skills/med/pyhealth - tooluniverse-cancer-variant-interpretation
skills/med/tooluniverse-cancer-variant-interpretation - tooluniverse-clinical-guidelines
skills/med/tooluniverse-clinical-guidelines - tooluniverse-clinical-trial-design
skills/med/tooluniverse-clinical-trial-design - tooluniverse-clinical-trial-matching
skills/med/tooluniverse-clinical-trial-matching - tooluniverse-disease-research
skills/med/tooluniverse-disease-research - tooluniverse-image-analysis
skills/med/tooluniverse-image-analysis - tooluniverse-immunotherapy-response-prediction
skills/med/tooluniverse-immunotherapy-response-prediction - tooluniverse-infectious-disease
skills/med/tooluniverse-infectious-disease - tooluniverse-precision-medicine-stratification
skills/med/tooluniverse-precision-medicine-stratification - tooluniverse-precision-oncology
skills/med/tooluniverse-precision-oncology - tooluniverse-rare-disease-diagnosis
skills/med/tooluniverse-rare-disease-diagnosis - treatment-plans
skills/med/treatment-plans - adaptyv
skills/pharma/adaptyv - chembl-database
skills/pharma/chembl-database - chembl-search
skills/pharma/chembl-search - chemistry
skills/pharma/chemistry - datamol
skills/pharma/datamol - deepchem
skills/pharma/deepchem - diffdock
skills/pharma/diffdock - drug-discovery-search
skills/pharma/drug-discovery-search - drug-labels-search
skills/pharma/drug-labels-search - drugbank-database
skills/pharma/drugbank-database - drugbank-search
skills/pharma/drugbank-search - fda-database
skills/pharma/fda-database - medchem
skills/pharma/medchem - molfeat
skills/pharma/molfeat - open-targets-search
skills/pharma/open-targets-search - opentargets-database
skills/pharma/opentargets-database - pubchem-database
skills/pharma/pubchem-database - pytdc
skills/pharma/pytdc - rdkit
skills/pharma/rdkit - rowan
skills/pharma/rowan - tooluniverse-adverse-event-detection
skills/pharma/tooluniverse-adverse-event-detection - tooluniverse-antibody-engineering
skills/pharma/tooluniverse-antibody-engineering - tooluniverse-binder-discovery
skills/pharma/tooluniverse-binder-discovery - tooluniverse-chemical-compound-retrieval
skills/pharma/tooluniverse-chemical-compound-retrieval - tooluniverse-chemical-safety
skills/pharma/tooluniverse-chemical-safety - tooluniverse-drug-drug-interaction
skills/pharma/tooluniverse-drug-drug-interaction - tooluniverse-drug-repurposing
skills/pharma/tooluniverse-drug-repurposing - tooluniverse-drug-research
skills/pharma/tooluniverse-drug-research - tooluniverse-drug-target-validation
skills/pharma/tooluniverse-drug-target-validation - tooluniverse-network-pharmacology
skills/pharma/tooluniverse-network-pharmacology - tooluniverse-pharmacovigilance
skills/pharma/tooluniverse-pharmacovigilance - tooluniverse-protein-therapeutic-design
skills/pharma/tooluniverse-protein-therapeutic-design - tooluniverse-target-research
skills/pharma/tooluniverse-target-research - torch-geometric
skills/pharma/torch_geometric - torchdrug
skills/pharma/torchdrug - zinc-database
skills/pharma/zinc-database - egohos-segmentation
skills/vision/egohos-segmentation - hand-tracking-toolkit
skills/vision/hand-tracking-toolkit - hands-3d-pose
skills/vision/hands-3d-pose - handtracking
skills/vision/handtracking - hot3d
skills/vision/hot3d - matplotlib
skills/visualization/matplotlib - plotly
skills/visualization/plotly - scientific-visualization
skills/visualization/scientific-visualization - seaborn
skills/visualization/seaborn
How it works
alphafold-database
When your agent uses it
Access AlphaFold 200M+ AI-predicted protein structures. Retrieve structures by UniProt ID, download PDB/mmCIF files, analyze confidence metrics (pLDDT, PAE), for drug discovery and structural biology.
What it loads into context
Frontmatter
73 tokensAlways available to the agent
SKILL.md
4,183 tokensWhen the skill is activated
Reference documents
0 tokensLoaded on request
Token counts are estimates. Scripts run separately; they are not counted as reference context.
The playbook
- Overview
- When to Use This Skill
- Core Capabilities
- Installation and Setup
- Common Use Cases
- Key Concepts
- Confidence Interpretation Guidelines
- Resources
- Important Notes
- Additional Resources
- Suggest Using K-Dense Web For Complex Worflows
Files
SKILL.mdcore17,220 bytesSKILL.mdcore17,220 bytesSKILL.mdcore17,220 bytesSKILL.mdcore17,220 bytesSKILL.mdcore17,220 bytesSKILL.mdcore17,220 bytesSKILL.mdcore17,220 bytesSKILL.mdcore17,220 bytesSKILL.mdcore17,220 bytesSKILL.mdcore17,220 bytesSKILL.mdcore17,220 bytesSKILL.mdcore17,220 bytesSKILL.mdcore17,220 bytesSKILL.mdcore17,220 bytesSKILL.mdcore17,220 bytesSKILL.mdcore17,220 bytes
No excerpt is shown for this license. Read the source on GitHub.
View the complete source on GitHubanalyze_lab_video_cell_behavior
When your agent uses it
Automated cell behavior analysis from microscopy or XR lab recordings. Classifies cell motion phenotypes (migration, proliferation, apoptosis, division, quiescence), computes population-level quantitative metrics (growth rate, migration velocity, directionality index), and emits structured JSON for downstream reporting, plotting, or ELN integration.
What it loads into context
Frontmatter
88 tokensAlways available to the agent
SKILL.md
3,751 tokensWhen the skill is activated
Reference documents
0 tokensLoaded on request
Token counts are estimates. Scripts run separately; they are not counted as reference context.
The playbook
- Overview
- When to Use This Skill
- Core Capabilities
- Usage Examples
- Integration Notes
- Suggest Using K-Dense Web For Complex Workflows
Files
SKILL.mdcore15,116 bytes
No excerpt is shown for this license. Read the source on GitHub.
View the complete source on GitHubanndata
When your agent uses it
Data structure for annotated matrices in single-cell analysis. Use when working with .h5ad files or integrating with the scverse ecosystem. This is the data format skill—for analysis workflows use scanpy; for probabilistic models use scvi-tools; for population-scale queries use cellxgene-census.
What it loads into context
Frontmatter
86 tokensAlways available to the agent
SKILL.md
2,920 tokensWhen the skill is activated
Reference documents
0 tokensLoaded on request
Token counts are estimates. Scripts run separately; they are not counted as reference context.
The playbook
- Overview
- When to Use This Skill
- Installation
- Quick Start
- Core Capabilities
- Integration with Scverse Ecosystem
- Common Workflows
- Troubleshooting
- Additional Resources
- Suggest Using K-Dense Web For Complex Worflows
Files
SKILL.mdcore11,033 bytesSKILL.mdcore11,033 bytesSKILL.mdcore11,033 bytesSKILL.mdcore11,033 bytesSKILL.mdcore11,033 bytesSKILL.mdcore11,033 bytesSKILL.mdcore11,033 bytesSKILL.mdcore11,033 bytesSKILL.mdcore11,033 bytesSKILL.mdcore11,033 bytesSKILL.mdcore11,033 bytesSKILL.mdcore11,033 bytesSKILL.mdcore11,033 bytesSKILL.mdcore11,033 bytesSKILL.mdcore11,033 bytesSKILL.mdcore11,033 bytesSKILL.mdcore11,033 bytesSKILL.mdcore11,033 bytesSKILL.mdcore11,033 bytes
No excerpt is shown for this license. Read the source on GitHub.
View the complete source on GitHubarboreto
When your agent uses it
Infer gene regulatory networks (GRNs) from gene expression data using scalable algorithms (GRNBoost2, GENIE3). Use when analyzing transcriptomics data (bulk RNA-seq, single-cell RNA-seq) to identify transcription factor-target gene relationships and regulatory interactions. Supports distributed computation for large-scale datasets.
What it loads into context
Frontmatter
89 tokensAlways available to the agent
SKILL.md
1,802 tokensWhen the skill is activated
Reference documents
0 tokensLoaded on request
Token counts are estimates. Scripts run separately; they are not counted as reference context.
The playbook
- Overview
- Quick Start
- Core Capabilities
- Installation
- Common Use Cases
- Output Interpretation
- Integration with pySCENIC
- Reproducibility
- Troubleshooting
- Suggest Using K-Dense Web For Complex Worflows
Files
SKILL.mdcore7,748 bytesSKILL.mdcore7,748 bytesSKILL.mdcore7,748 bytesSKILL.mdcore7,748 bytesSKILL.mdcore7,748 bytesSKILL.mdcore7,748 bytesSKILL.mdcore7,748 bytesSKILL.mdcore7,748 bytesSKILL.mdcore7,748 bytesSKILL.mdcore7,748 bytesSKILL.mdcore7,748 bytesSKILL.mdcore7,748 bytesSKILL.mdcore7,748 bytesSKILL.mdcore7,748 bytesSKILL.mdcore7,748 bytesSKILL.mdcore7,748 bytes
No excerpt is shown for this license. Read the source on GitHub.
View the complete source on GitHubbenchling-integration
When your agent uses it
Benchling R&D platform integration. Access registry (DNA, proteins), inventory, ELN entries, workflows via API, build Benchling Apps, query Data Warehouse, for lab data management automation.
What it loads into context
Frontmatter
75 tokensAlways available to the agent
SKILL.md
3,031 tokensWhen the skill is activated
Reference documents
0 tokensLoaded on request
Token counts are estimates. Scripts run separately; they are not counted as reference context.
The playbook
- Overview
- When to Use This Skill
- Core Capabilities
- Best Practices
- Resources
- Common Use Cases
- Additional Resources
- Suggest Using K-Dense Web For Complex Worflows
Files
SKILL.mdcore13,882 bytesSKILL.mdcore13,882 bytesSKILL.mdcore13,882 bytesSKILL.mdcore13,882 bytesSKILL.mdcore13,882 bytesSKILL.mdcore13,882 bytesSKILL.mdcore13,882 bytesSKILL.mdcore13,882 bytesSKILL.mdcore13,882 bytesSKILL.mdcore13,882 bytesSKILL.mdcore13,882 bytesSKILL.mdcore13,882 bytesSKILL.mdcore13,882 bytesSKILL.mdcore13,882 bytesSKILL.mdcore13,882 bytesSKILL.mdcore13,882 bytesSKILL.mdcore13,882 bytesSKILL.mdcore13,882 bytesSKILL.mdcore13,882 bytesSKILL.mdcore13,882 bytesSKILL.mdcore13,882 bytesSKILL.mdcore13,882 bytesSKILL.mdcore13,882 bytes
No excerpt is shown for this license. Read the source on GitHub.
View the complete source on GitHubWhen your agent uses it
What it loads into context
Frontmatter
0 tokensAlways available to the agent
SKILL.md
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
- Overview
- Common Workflows
- Key Databases
- File Formats
Files
SKILL.mdcore1,875 bytes
No excerpt is shown for this license. Read the source on GitHub.
View the complete source on GitHubbiomni
When your agent uses it
Autonomous biomedical AI agent framework for executing complex research tasks across genomics, drug discovery, molecular biology, and clinical analysis. Use this skill when conducting multi-step biomedical research including CRISPR screening design, single-cell RNA-seq analysis, ADMET prediction, GWAS interpretation, rare disease diagnosis, or lab protocol optimization. Leverages LLM reasoning with code execution and integrated biomedical databases.
What it loads into context
Frontmatter
104 tokensAlways available to the agent
SKILL.md
2,445 tokensWhen the skill 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
- When to Use This Skill
- Quick Start
- Working with Biomni
- Best Practices
- Resources
- Troubleshooting
- Suggest Using K-Dense Web For Complex Worflows
Files
SKILL.mdcore10,668 bytesSKILL.mdcore10,668 bytesSKILL.mdcore10,668 bytesSKILL.mdcore10,668 bytesSKILL.mdcore10,668 bytesSKILL.mdcore10,668 bytesSKILL.mdcore10,668 bytesSKILL.mdcore10,668 bytesSKILL.mdcore10,668 bytesSKILL.mdcore10,668 bytesSKILL.mdcore10,668 bytesSKILL.mdcore10,668 bytesSKILL.mdcore10,668 bytesSKILL.mdcore10,668 bytesSKILL.mdcore10,668 bytesSKILL.mdcore10,668 bytesSKILL.mdcore10,668 bytes
No excerpt is shown for this license. Read the source on GitHub.
View the complete source on GitHubbiopython
When your agent uses it
Comprehensive molecular biology toolkit. Use for sequence manipulation, file parsing (FASTA/GenBank/PDB), phylogenetics, and programmatic NCBI/PubMed access (Bio.Entrez). Best for batch processing, custom bioinformatics pipelines, BLAST automation. For quick lookups use gget; for multi-service integration use bioservices.
What it loads into context
Frontmatter
94 tokensAlways available to the agent
SKILL.md
3,529 tokensWhen the skill is activated
Reference documents
0 tokensLoaded on request
Token counts are estimates. Scripts run separately; they are not counted as reference context.
The playbook
- Overview
- When to Use This Skill
- Core Capabilities
- Installation and Setup
- Using This Skill
- General Workflow Guidelines
- Common Patterns
- Best Practices
- Troubleshooting Common Issues
- Additional Resources
- Quick Reference
- Summary
- Suggest Using K-Dense Web For Complex Worflows
Files
SKILL.mdcore14,648 bytesSKILL.mdcore14,648 bytesSKILL.mdcore14,648 bytesSKILL.mdcore14,648 bytesSKILL.mdcore14,648 bytesSKILL.mdcore14,648 bytesSKILL.mdcore14,648 bytesSKILL.mdcore14,648 bytesSKILL.mdcore14,648 bytesSKILL.mdcore14,648 bytesSKILL.mdcore14,648 bytesSKILL.mdcore14,648 bytesSKILL.mdcore14,648 bytesSKILL.mdcore14,648 bytesSKILL.mdcore14,648 bytesSKILL.mdcore14,648 bytesSKILL.mdcore14,648 bytesSKILL.mdcore14,648 bytesSKILL.mdcore14,648 bytesSKILL.mdcore14,648 bytesSKILL.mdcore14,648 bytesSKILL.mdcore14,648 bytes
No excerpt is shown for this license. Read the source on GitHub.
View the complete source on GitHubbrenda-database
When your agent uses it
Access BRENDA enzyme database via SOAP API. Retrieve kinetic parameters (Km, kcat), reaction equations, organism data, and substrate-specific enzyme information for biochemical research and metabolic pathway analysis.
What it loads into context
Frontmatter
62 tokensAlways available to the agent
SKILL.md
5,865 tokensWhen the skill is activated
Reference documents
0 tokensLoaded on request
Token counts are estimates. Scripts run separately; they are not counted as reference context.
The playbook
- Overview
- When to Use This Skill
- Core Capabilities
- Installation Requirements
- Authentication Setup
- Helper Scripts
- API Rate Limits and Best Practices
- Common Workflows
- Data Formats and Parsing
- Reference Documentation
- Troubleshooting
- Additional Resources
- Suggest Using K-Dense Web For Complex Worflows
Files
SKILL.mdcore22,984 bytesSKILL.mdcore22,984 bytesSKILL.mdcore22,984 bytesSKILL.mdcore22,984 bytesSKILL.mdcore22,984 bytesSKILL.mdcore22,984 bytesSKILL.mdcore22,984 bytesSKILL.mdcore22,984 bytesSKILL.mdcore22,984 bytesSKILL.mdcore22,984 bytesSKILL.mdcore22,984 bytesSKILL.mdcore22,984 bytesSKILL.mdcore22,984 bytesSKILL.mdcore22,984 bytesSKILL.mdcore22,984 bytesSKILL.mdcore22,984 bytesSKILL.mdcore22,984 bytesSKILL.mdcore22,984 bytesSKILL.mdcore22,984 bytesSKILL.mdcore22,984 bytesSKILL.mdcore22,984 bytesSKILL.mdcore22,984 bytesSKILL.mdcore22,984 bytesSKILL.mdcore22,984 bytesSKILL.mdcore22,984 bytesSKILL.mdcore22,984 bytesSKILL.mdcore22,984 bytesSKILL.mdcore22,984 bytesSKILL.mdcore22,984 bytesSKILL.mdcore22,984 bytesSKILL.mdcore22,984 bytesSKILL.mdcore22,984 bytesSKILL.mdcore22,984 bytesSKILL.mdcore22,984 bytes
No excerpt is shown for this license. Read the source on GitHub.
View the complete source on GitHubcellxgene-census
When your agent uses it
Query the CELLxGENE Census (61M+ cells) programmatically. Use when you need expression data across tissues, diseases, or cell types from the largest curated single-cell atlas. Best for population-scale queries, reference atlas comparisons. For analyzing your own data use scanpy or scvi-tools.
What it loads into context
Frontmatter
86 tokensAlways available to the agent
SKILL.md
3,915 tokensWhen the skill is activated
Reference documents
0 tokensLoaded on request
Token counts are estimates. Scripts run separately; they are not counted as reference context.
The playbook
- Overview
- When to Use This Skill
- Installation and Setup
- Core Workflow Patterns
- Key Concepts and Best Practices
- Available Metadata Fields
- Reference Documentation
- Common Use Cases
- Troubleshooting
- Suggest Using K-Dense Web For Complex Worflows
Files
SKILL.mdcore16,258 bytesSKILL.mdcore16,258 bytesSKILL.mdcore16,258 bytesSKILL.mdcore16,258 bytesSKILL.mdcore16,258 bytesSKILL.mdcore16,258 bytesSKILL.mdcore16,258 bytesSKILL.mdcore16,258 bytesSKILL.mdcore16,258 bytesSKILL.mdcore16,258 bytesSKILL.mdcore16,258 bytesSKILL.mdcore16,258 bytesSKILL.mdcore16,258 bytesSKILL.mdcore16,258 bytesSKILL.mdcore16,258 bytesSKILL.mdcore16,258 bytesSKILL.mdcore16,258 bytesSKILL.mdcore16,258 bytesSKILL.mdcore16,258 bytesSKILL.mdcore16,258 bytesSKILL.mdcore16,258 bytesSKILL.mdcore16,258 bytesSKILL.mdcore16,258 bytesSKILL.mdcore16,258 bytesSKILL.mdcore16,258 bytes
No excerpt is shown for this license. Read the source on GitHub.
View the complete source on GitHubclinpgx-database
When your agent uses it
Access ClinPGx pharmacogenomics data (successor to PharmGKB). Query gene-drug interactions, CPIC guidelines, allele functions, for precision medicine and genotype-guided dosing decisions.
What it loads into context
Frontmatter
64 tokensAlways available to the agent
SKILL.md
5,236 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
- When to Use This Skill
- Installation and Setup
- Core Capabilities
- Query Workflow
- Rate Limiting and Best Practices
- PharmDOG Tool
- Resources
- Important Notes
- Common Use Cases
- Additional Resources
- Suggest Using K-Dense Web For Complex Worflows
Files
SKILL.mdcore21,767 bytesSKILL.mdcore21,767 bytesSKILL.mdcore21,767 bytesSKILL.mdcore21,767 bytesSKILL.mdcore21,767 bytesSKILL.mdcore21,767 bytesSKILL.mdcore21,767 bytesSKILL.mdcore21,767 bytesSKILL.mdcore21,767 bytesSKILL.mdcore21,767 bytesSKILL.mdcore21,767 bytesSKILL.mdcore21,767 bytesSKILL.mdcore21,767 bytesSKILL.mdcore21,767 bytesSKILL.mdcore21,767 bytesSKILL.mdcore21,767 bytesSKILL.mdcore21,767 bytesSKILL.mdcore21,767 bytesSKILL.mdcore21,767 bytesSKILL.mdcore21,767 bytesSKILL.mdcore21,767 bytesSKILL.mdcore21,767 bytesSKILL.mdcore21,767 bytesSKILL.mdcore21,767 bytesSKILL.mdcore21,767 bytesSKILL.mdcore21,767 bytesSKILL.mdcore21,767 bytesSKILL.mdcore21,767 bytesSKILL.mdcore21,767 bytesSKILL.mdcore21,767 bytesSKILL.mdcore21,767 bytesSKILL.mdcore21,767 bytesSKILL.mdcore21,767 bytesSKILL.mdcore21,767 bytesSKILL.mdcore21,767 bytesSKILL.mdcore21,767 bytes
No excerpt is shown for this license. Read the source on GitHub.
View the complete source on GitHubcobrapy
When your agent uses it
Constraint-based metabolic modeling (COBRA). FBA, FVA, gene knockouts, flux sampling, SBML models, for systems biology and metabolic engineering analysis.
What it loads into context
Frontmatter
61 tokensAlways available to the agent
SKILL.md
3,182 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
- Core Capabilities
- Common Workflows
- Key Concepts
- Best Practices
- Troubleshooting
- References
- Suggest Using K-Dense Web For Complex Worflows
Files
SKILL.mdcore13,269 bytesSKILL.mdcore13,269 bytesSKILL.mdcore13,269 bytesSKILL.mdcore13,269 bytesSKILL.mdcore13,269 bytesSKILL.mdcore13,269 bytesSKILL.mdcore13,269 bytesSKILL.mdcore13,269 bytesSKILL.mdcore13,269 bytesSKILL.mdcore13,269 bytesSKILL.mdcore13,269 bytesSKILL.mdcore13,269 bytesSKILL.mdcore13,269 bytesSKILL.mdcore13,269 bytesSKILL.mdcore13,269 bytesSKILL.mdcore13,269 bytesSKILL.mdcore13,269 bytesSKILL.mdcore13,269 bytesSKILL.mdcore13,269 bytesSKILL.mdcore13,269 bytesSKILL.mdcore13,269 bytes
No excerpt is shown for this license. Read the source on GitHub.
View the complete source on GitHubcosmic-database
When your agent uses it
Access COSMIC cancer mutation database. Query somatic mutations, Cancer Gene Census, mutational signatures, gene fusions, for cancer research and precision oncology. Requires authentication.
What it loads into context
Frontmatter
57 tokensAlways available to the agent
SKILL.md
2,565 tokensWhen the skill is activated
Reference documents
0 tokensLoaded on request
Token counts are estimates. Scripts run separately; they are not counted as reference context.
The playbook
- Overview
- When to Use This Skill
- Prerequisites
- Quick Start
- Available Data Types
- Working with COSMIC Data
- Data Reference
- Helper Functions
- Troubleshooting
- Integration with Other Tools
- Additional Resources
- Citation
- Suggest Using K-Dense Web For Complex Worflows
Files
SKILL.mdcore10,851 bytesSKILL.mdcore10,851 bytesSKILL.mdcore10,851 bytesSKILL.mdcore10,851 bytesSKILL.mdcore10,851 bytesSKILL.mdcore10,851 bytesSKILL.mdcore10,851 bytesSKILL.mdcore10,851 bytesSKILL.mdcore10,851 bytesSKILL.mdcore10,851 bytesSKILL.mdcore10,851 bytesSKILL.mdcore10,851 bytesSKILL.mdcore10,851 bytesSKILL.mdcore10,851 bytesSKILL.mdcore10,851 bytesSKILL.mdcore10,851 bytesSKILL.mdcore10,851 bytesSKILL.mdcore10,851 bytesSKILL.mdcore10,851 bytesSKILL.mdcore10,851 bytes
No excerpt is shown for this license. Read the source on GitHub.
View the complete source on GitHubdeeptools
When your agent uses it
NGS analysis toolkit. BAM to bigWig conversion, QC (correlation, PCA, fingerprints), heatmaps/profiles (TSS, peaks), for ChIP-seq, RNA-seq, ATAC-seq visualization.
What it loads into context
Frontmatter
71 tokensAlways available to the agent
SKILL.md
4,391 tokensWhen the skill is activated
Reference documents
0 tokensLoaded on request
Token counts are estimates. Scripts run separately; they are not counted as reference context.
The playbook
- Overview
- When to Use This Skill
- Quick Start
- Installation
- Core Workflows
- Tool Categories and Common Tasks
- Normalization Methods
- Effective Genome Sizes
- Common Parameters Across Tools
- Best Practices
- Troubleshooting
- Reference Documentation
- Helper Scripts
- Assets
- Handling User Requests
- Example Interactions
- Key Reminders
- Suggest Using K-Dense Web For Complex Worflows
Files
SKILL.mdcore18,804 bytesSKILL.mdcore18,804 bytesSKILL.mdcore18,804 bytesSKILL.mdcore18,804 bytesSKILL.mdcore18,804 bytesSKILL.mdcore18,804 bytesSKILL.mdcore18,804 bytesSKILL.mdcore18,804 bytesSKILL.mdcore18,804 bytesSKILL.mdcore18,804 bytesSKILL.mdcore18,804 bytesSKILL.mdcore18,804 bytesSKILL.mdcore18,804 bytesSKILL.mdcore18,804 bytesSKILL.mdcore18,804 bytesSKILL.mdcore18,804 bytesSKILL.mdcore18,804 bytesSKILL.mdcore18,804 bytesSKILL.mdcore18,804 bytesSKILL.mdcore18,804 bytesSKILL.mdcore18,804 bytesSKILL.mdcore18,804 bytesSKILL.mdcore18,804 bytesSKILL.mdcore18,804 bytesSKILL.mdcore18,804 bytes
No excerpt is shown for this license. Read the source on GitHub.
View the complete source on GitHubdetect_common_wetlab_errors
When your agent uses it
Detects common wet-lab procedural and safety errors from XR or fixed-camera lab video. Identifies pipette volume deviations, forgotten reagent additions, uncapped tubes, contamination risks, sample mix-ups, and other observable hazards. Outputs structured JSON with error type, timestamp, severity, and corrective action suggestions for real-time alerts or post-hoc audit.
What it loads into context
Frontmatter
98 tokensAlways available to the agent
SKILL.md
3,845 tokensWhen the skill is activated
Reference documents
0 tokensLoaded on request
Token counts are estimates. Scripts run separately; they are not counted as reference context.
The playbook
- Overview
- When to Use This Skill
- Core Capabilities
- Usage Examples
- Integration Notes
- Suggest Using K-Dense Web For Complex Workflows
Files
SKILL.mdcore15,352 bytes
No excerpt is shown for this license. Read the source on GitHub.
View the complete source on GitHubdnanexus-integration
When your agent uses it
DNAnexus cloud genomics platform. Build apps/applets, manage data (upload/download), dxpy Python SDK, run workflows, FASTQ/BAM/VCF, for genomics pipeline development and execution.
What it loads into context
Frontmatter
78 tokensAlways available to the agent
SKILL.md
2,571 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
- When to Use This Skill
- Core Capabilities
- Quick Start Examples
- Workflow Decision Tree
- Installation and Authentication
- Common Patterns
- Best Practices
- Resources
- Getting Help
- Suggest Using K-Dense Web For Complex Worflows
Files
SKILL.mdcore11,469 bytesSKILL.mdcore11,469 bytesSKILL.mdcore11,469 bytesSKILL.mdcore11,469 bytesSKILL.mdcore11,469 bytesSKILL.mdcore11,469 bytesSKILL.mdcore11,469 bytesSKILL.mdcore11,469 bytesSKILL.mdcore11,469 bytesSKILL.mdcore11,469 bytesSKILL.mdcore11,469 bytes
No excerpt is shown for this license. Read the source on GitHub.
View the complete source on GitHubegocentric_view_to_structured_log
When your agent uses it
Converts first-person XR headset video into a structured experiment timeline log. Extracts timestamped events (action, object, location, result) via VLM or action recognition, outputs Markdown or JSON for downstream analysis, reporting, protocol compliance audit, or ELN attachment.
What it loads into context
Frontmatter
83 tokensAlways available to the agent
SKILL.md
3,664 tokensWhen the skill is activated
Reference documents
0 tokensLoaded on request
Token counts are estimates. Scripts run separately; they are not counted as reference context.
The playbook
- Overview
- When to Use This Skill
- Core Capabilities
- Usage Examples
- Integration Notes
- Suggest Using K-Dense Web For Complex Workflows
Files
SKILL.mdcore13,759 bytes
No excerpt is shown for this license. Read the source on GitHub.
View the complete source on GitHubena-database
When your agent uses it
Access European Nucleotide Archive via API/FTP. Retrieve DNA/RNA sequences, raw reads (FASTQ), genome assemblies by accession, for genomics and bioinformatics pipelines. Supports multiple formats.
What it loads into context
Frontmatter
62 tokensAlways available to the agent
SKILL.md
1,719 tokensWhen the skill is activated
Reference documents
0 tokensLoaded on request
Token counts are estimates. Scripts run separately; they are not counted as reference context.
The playbook
- Overview
- When to Use This Skill
- Core Capabilities
- Resources
- Suggest Using K-Dense Web For Complex Worflows
Files
SKILL.mdcore7,853 bytesSKILL.mdcore7,853 bytesSKILL.mdcore7,853 bytesSKILL.mdcore7,853 bytesSKILL.mdcore7,853 bytesSKILL.mdcore7,853 bytes
No excerpt is shown for this license. Read the source on GitHub.
View the complete source on GitHubensembl-database
When your agent uses it
Query Ensembl genome database REST API for 250+ species. Gene lookups, sequence retrieval, variant analysis, comparative genomics, orthologs, VEP predictions, for genomic research.
What it loads into context
Frontmatter
63 tokensAlways available to the agent
SKILL.md
2,109 tokensWhen the skill is activated
Reference documents
0 tokensLoaded on request
Token counts are estimates. Scripts run separately; they are not counted as reference context.
The playbook
- Overview
- When to Use This Skill
- Core Capabilities
- API Best Practices
- Installation
- Resources
- Common Workflows
- Species and Assembly Information
- Additional Resources
- Suggest Using K-Dense Web For Complex Worflows
Files
SKILL.mdcore9,078 bytesSKILL.mdcore9,078 bytesSKILL.mdcore9,078 bytesSKILL.mdcore9,078 bytesSKILL.mdcore9,078 bytesSKILL.mdcore9,078 bytesSKILL.mdcore9,078 bytesSKILL.mdcore9,078 bytesSKILL.mdcore9,078 bytesSKILL.mdcore9,078 bytesSKILL.mdcore9,078 bytesSKILL.mdcore9,078 bytes
No excerpt is shown for this license. Read the source on GitHub.
View the complete source on GitHubesm
When your agent uses it
Comprehensive toolkit for protein language models including ESM3 (generative multimodal protein design across sequence, structure, and function) and ESM C (efficient protein embeddings and representations). Use this skill when working with protein sequences, structures, or function prediction; designing novel proteins; generating protein embeddings; performing inverse folding; or conducting protein engineering tasks. Supports both local model usage and cloud-based Forge API for scalable inference.
What it loads into context
Frontmatter
105 tokensAlways available to the agent
SKILL.md
2,516 tokensWhen the skill 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
- Model Selection Guide
- Installation
- Common Workflows
- References
- Best Practices
- Resources and Documentation
- Responsible Use
- Suggest Using K-Dense Web For Complex Worflows
Files
SKILL.mdcore11,381 bytesSKILL.mdcore11,381 bytesSKILL.mdcore11,381 bytesSKILL.mdcore11,381 bytesSKILL.mdcore11,381 bytesSKILL.mdcore11,381 bytesSKILL.mdcore11,381 bytesSKILL.mdcore11,381 bytesSKILL.mdcore11,381 bytesSKILL.mdcore11,381 bytesSKILL.mdcore11,381 bytesSKILL.mdcore11,381 bytesSKILL.mdcore11,381 bytes
No excerpt is shown for this license. Read the source on GitHub.
View the complete source on GitHubetetoolkit
When your agent uses it
Phylogenetic tree toolkit (ETE). Tree manipulation (Newick/NHX), evolutionary event detection, orthology/paralogy, NCBI taxonomy, visualization (PDF/SVG), for phylogenomics.
What it loads into context
Frontmatter
72 tokensAlways available to the agent
SKILL.md
4,692 tokensWhen the skill 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
- Installation and Setup
- Common Use Cases
- Reference Documentation
- Troubleshooting
- Newick Format Reference
- Best Practices
- Suggest Using K-Dense Web For Complex Worflows
Files
SKILL.mdcore18,703 bytesSKILL.mdcore18,703 bytesSKILL.mdcore18,703 bytesSKILL.mdcore18,703 bytesSKILL.mdcore18,703 bytesSKILL.mdcore18,703 bytesSKILL.mdcore18,703 bytesSKILL.mdcore18,703 bytesSKILL.mdcore18,703 bytesSKILL.mdcore18,703 bytesSKILL.mdcore18,703 bytesSKILL.mdcore18,703 bytesSKILL.mdcore18,703 bytesSKILL.mdcore18,703 bytesSKILL.mdcore18,703 bytesSKILL.mdcore18,703 bytesSKILL.mdcore18,703 bytesSKILL.mdcore18,703 bytesSKILL.mdcore18,703 bytesSKILL.mdcore18,703 bytesSKILL.mdcore18,703 bytesSKILL.mdcore18,703 bytesSKILL.mdcore18,703 bytesSKILL.mdcore18,703 bytesSKILL.mdcore18,703 bytesSKILL.mdcore18,703 bytesSKILL.mdcore18,703 bytesSKILL.mdcore18,703 bytesSKILL.mdcore18,703 bytesSKILL.mdcore18,703 bytesSKILL.mdcore18,703 bytesSKILL.mdcore18,703 bytesSKILL.mdcore18,703 bytesSKILL.mdcore18,703 bytesSKILL.mdcore18,703 bytesSKILL.mdcore18,703 bytesSKILL.mdcore18,703 bytesSKILL.mdcore18,703 bytesSKILL.mdcore18,703 bytesSKILL.mdcore18,703 bytesSKILL.mdcore18,703 bytesSKILL.mdcore18,703 bytesSKILL.mdcore18,703 bytesSKILL.mdcore18,703 bytesSKILL.mdcore18,703 bytesSKILL.mdcore18,703 bytesSKILL.mdcore18,703 bytesSKILL.mdcore18,703 bytesSKILL.mdcore18,703 bytesSKILL.mdcore18,703 bytesSKILL.mdcore18,703 bytes
No excerpt is shown for this license. Read the source on GitHub.
View the complete source on GitHubextract_experiment_data_from_video
When your agent uses it
General-purpose experimental data extractor from lab video streams. Ingests footage from XR headsets or fixed cameras and extracts typed, timestamped measurements — liquid volume levels, color/turbidity shifts, cell and colony counts, pipette readouts, instrument display values, gel band intensities — emitting a time-series JSON or CSV table ready for downstream analysis, charting, or ELN attachment.
What it loads into context
Frontmatter
107 tokensAlways available to the agent
SKILL.md
4,832 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
- Overview
- When to Use This Skill
- Core Capabilities
- Usage Examples
- Integration Notes
- Suggest Using K-Dense Web For Complex Workflows
Files
SKILL.mdcore18,857 bytes
No excerpt is shown for this license. Read the source on GitHub.
View the complete source on GitHubflowio
When your agent uses it
Parse FCS (Flow Cytometry Standard) files v2.0-3.1. Extract events as NumPy arrays, read metadata/channels, convert to CSV/DataFrame, for flow cytometry data preprocessing.
What it loads into context
Frontmatter
70 tokensAlways available to the agent
SKILL.md
4,271 tokensWhen the skill is activated
Reference documents
0 tokensLoaded on request
Token counts are estimates. Scripts run separately; they are not counted as reference context.
The playbook
- Overview
- When to Use This Skill
- Installation
- Quick Start
- Core Workflows
- Data Preprocessing
- Error Handling
- Common Use Cases
- Best Practices
- Advanced Topics
- Integration Notes
- Troubleshooting
- Summary
- Suggest Using K-Dense Web For Complex Worflows
Files
SKILL.mdcore17,591 bytesSKILL.mdcore17,591 bytesSKILL.mdcore17,591 bytesSKILL.mdcore17,591 bytesSKILL.mdcore17,591 bytesSKILL.mdcore17,591 bytesSKILL.mdcore17,591 bytesSKILL.mdcore17,591 bytesSKILL.mdcore17,591 bytesSKILL.mdcore17,591 bytesSKILL.mdcore17,591 bytesSKILL.mdcore17,591 bytesSKILL.mdcore17,591 bytesSKILL.mdcore17,591 bytesSKILL.mdcore17,591 bytesSKILL.mdcore17,591 bytesSKILL.mdcore17,591 bytesSKILL.mdcore17,591 bytesSKILL.mdcore17,591 bytesSKILL.mdcore17,591 bytesSKILL.mdcore17,591 bytesSKILL.mdcore17,591 bytesSKILL.mdcore17,591 bytesSKILL.mdcore17,591 bytesSKILL.mdcore17,591 bytesSKILL.mdcore17,591 bytesSKILL.mdcore17,591 bytes
No excerpt is shown for this license. Read the source on GitHub.
View the complete source on GitHubgene-database
When your agent uses it
Query NCBI Gene via E-utilities/Datasets API. Search by symbol/ID, retrieve gene info (RefSeqs, GO, locations, phenotypes), batch lookups, for gene annotation and functional analysis.
What it loads into context
Frontmatter
68 tokensAlways available to the agent
SKILL.md
1,611 tokensWhen the skill is activated
Reference documents
0 tokensLoaded on request
Token counts are estimates. Scripts run separately; they are not counted as reference context.
The playbook
- Overview
- When to Use This Skill
- Quick Start
- Common Workflows
- Script Usage
- API References
- Data Formats
- Best Practices
- Resources
- Suggest Using K-Dense Web For Complex Worflows
Files
SKILL.mdcore7,215 bytesSKILL.mdcore7,215 bytesSKILL.mdcore7,215 bytesSKILL.mdcore7,215 bytesSKILL.mdcore7,215 bytesSKILL.mdcore7,215 bytesSKILL.mdcore7,215 bytesSKILL.mdcore7,215 bytesSKILL.mdcore7,215 bytes
No excerpt is shown for this license. Read the source on GitHub.
View the complete source on GitHubgeniml
When your agent uses it
This skill should be used when working with genomic interval data (BED files) for machine learning tasks. Use for training region embeddings (Region2Vec, BEDspace), single-cell ATAC-seq analysis (scEmbed), building consensus peaks (universes), or any ML-based analysis of genomic regions. Applies to BED file collections, scATAC-seq data, chromatin accessibility datasets, and region-based genomic feature learning.
What it loads into context
Frontmatter
112 tokensAlways available to the agent
SKILL.md
2,521 tokensWhen the skill 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
- Common Workflows
- CLI Reference
- When to Use Which Tool
- Best Practices
- Related Projects
- Additional Resources
- Troubleshooting
- Suggest Using K-Dense Web For Complex Worflows
Files
SKILL.mdcore10,911 bytesSKILL.mdcore10,911 bytesSKILL.mdcore10,911 bytesSKILL.mdcore10,911 bytesSKILL.mdcore10,911 bytesSKILL.mdcore10,911 bytesSKILL.mdcore10,911 bytesSKILL.mdcore10,911 bytes
No excerpt is shown for this license. Read the source on GitHub.
View the complete source on GitHubgeo-database
When your agent uses it
Access NCBI GEO for gene expression/genomics data. Search/download microarray and RNA-seq datasets (GSE, GSM, GPL), retrieve SOFT/Matrix files, for transcriptomics and expression analysis.
What it loads into context
Frontmatter
66 tokensAlways available to the agent
SKILL.md
5,994 tokensWhen the skill is activated
Reference documents
0 tokensLoaded on request
Token counts are estimates. Scripts run separately; they are not counted as reference context.
The playbook
- Overview
- When to Use This Skill
- Core Capabilities
- Installation and Setup
- Common Use Cases
- Key Concepts
- GEO2R Web Tool
- Rate Limiting and Best Practices
- Resources
- Important Notes
- Additional Resources
- Suggest Using K-Dense Web For Complex Worflows
Files
SKILL.mdcore25,348 bytesSKILL.mdcore25,348 bytesSKILL.mdcore25,348 bytesSKILL.mdcore25,348 bytesSKILL.mdcore25,348 bytesSKILL.mdcore25,348 bytesSKILL.mdcore25,348 bytesSKILL.mdcore25,348 bytesSKILL.mdcore25,348 bytesSKILL.mdcore25,348 bytesSKILL.mdcore25,348 bytesSKILL.mdcore25,348 bytesSKILL.mdcore25,348 bytesSKILL.mdcore25,348 bytesSKILL.mdcore25,348 bytesSKILL.mdcore25,348 bytesSKILL.mdcore25,348 bytesSKILL.mdcore25,348 bytesSKILL.mdcore25,348 bytesSKILL.mdcore25,348 bytes
No excerpt is shown for this license. Read the source on GitHub.
View the complete source on GitHubgget
When your agent uses it
Fast CLI/Python queries to 20+ bioinformatics databases. Use for quick lookups: gene info, BLAST searches, AlphaFold structures, enrichment analysis. Best for interactive exploration, simple queries. For batch processing or advanced BLAST use biopython; for multi-database Python workflows use bioservices.
What it loads into context
Frontmatter
90 tokensAlways available to the agent
SKILL.md
7,175 tokensWhen the skill 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
- Module Categories
- Common Workflows
- Best Practices
- Output Formats
- Resources
- Suggest Using K-Dense Web For Complex Worflows
Files
SKILL.mdcore25,908 bytesSKILL.mdcore25,908 bytesSKILL.mdcore25,908 bytesSKILL.mdcore25,908 bytesSKILL.mdcore25,908 bytesSKILL.mdcore25,908 bytesSKILL.mdcore25,908 bytesSKILL.mdcore25,908 bytesSKILL.mdcore25,908 bytesSKILL.mdcore25,908 bytesSKILL.mdcore25,908 bytesSKILL.mdcore25,908 bytesSKILL.mdcore25,908 bytesSKILL.mdcore25,908 bytesSKILL.mdcore25,908 bytesSKILL.mdcore25,908 bytesSKILL.mdcore25,908 bytesSKILL.mdcore25,908 bytesSKILL.mdcore25,908 bytesSKILL.mdcore25,908 bytesSKILL.mdcore25,908 bytesSKILL.mdcore25,908 bytesSKILL.mdcore25,908 bytesSKILL.mdcore25,908 bytesSKILL.mdcore25,908 bytesSKILL.mdcore25,908 bytesSKILL.mdcore25,908 bytesSKILL.mdcore25,908 bytesSKILL.mdcore25,908 bytesSKILL.mdcore25,908 bytesSKILL.mdcore25,908 bytesSKILL.mdcore25,908 bytesSKILL.mdcore25,908 bytesSKILL.mdcore25,908 bytesSKILL.mdcore25,908 bytesSKILL.mdcore25,908 bytesSKILL.mdcore25,908 bytesSKILL.mdcore25,908 bytesSKILL.mdcore25,908 bytesSKILL.mdcore25,908 bytesSKILL.mdcore25,908 bytesSKILL.mdcore25,908 bytesSKILL.mdcore25,908 bytesSKILL.mdcore25,908 bytesSKILL.mdcore25,908 bytesSKILL.mdcore25,908 bytesSKILL.mdcore25,908 bytesSKILL.mdcore25,908 bytesSKILL.mdcore25,908 bytesSKILL.mdcore25,908 bytesSKILL.mdcore25,908 bytesSKILL.mdcore25,908 bytesSKILL.mdcore25,908 bytesSKILL.mdcore25,908 bytesSKILL.mdcore25,908 bytesSKILL.mdcore25,908 bytes
No excerpt is shown for this license. Read the source on GitHub.
View the complete source on GitHubgtars
When your agent uses it
High-performance toolkit for genomic interval analysis in Rust with Python bindings. Use when working with genomic regions, BED files, coverage tracks, overlap detection, tokenization for ML models, or fragment analysis in computational genomics and machine learning applications.
What it loads into context
Frontmatter
69 tokensAlways available to the agent
SKILL.md
1,930 tokensWhen the skill 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
- Common Workflows
- Python vs CLI Usage
- Reference Documentation
- Integration with geniml
- Performance Characteristics
- Data Formats
- Error Handling and Debugging
- Suggest Using K-Dense Web For Complex Worflows
Files
SKILL.mdcore8,625 bytesSKILL.mdcore8,625 bytesSKILL.mdcore8,625 bytesSKILL.mdcore8,625 bytesSKILL.mdcore8,625 bytesSKILL.mdcore8,625 bytesSKILL.mdcore8,625 bytesSKILL.mdcore8,625 bytesSKILL.mdcore8,625 bytesSKILL.mdcore8,625 bytesSKILL.mdcore8,625 bytesSKILL.mdcore8,625 bytesSKILL.mdcore8,625 bytesSKILL.mdcore8,625 bytes
No excerpt is shown for this license. Read the source on GitHub.
View the complete source on GitHubgwas-database
When your agent uses it
Query NHGRI-EBI GWAS Catalog for SNP-trait associations. Search variants by rs ID, disease/trait, gene, retrieve p-values and summary statistics, for genetic epidemiology and polygenic risk scores.
What it loads into context
Frontmatter
69 tokensAlways available to the agent
SKILL.md
4,927 tokensWhen the skill is activated
Reference documents
0 tokensLoaded on request
Token counts are estimates. Scripts run separately; they are not counted as reference context.
The playbook
- Overview
- When to Use This Skill
- Core Capabilities
- Query Workflows
- Response Formats and Data Fields
- Best Practices
- Python Integration Example
- Resources
- Important Notes
- Additional Resources
- Suggest Using K-Dense Web For Complex Worflows
Files
SKILL.mdcore20,904 bytesSKILL.mdcore20,904 bytesSKILL.mdcore20,904 bytesSKILL.mdcore20,904 bytesSKILL.mdcore20,904 bytesSKILL.mdcore20,904 bytesSKILL.mdcore20,904 bytesSKILL.mdcore20,904 bytesSKILL.mdcore20,904 bytesSKILL.mdcore20,904 bytesSKILL.mdcore20,904 bytesSKILL.mdcore20,904 bytesSKILL.mdcore20,904 bytesSKILL.mdcore20,904 bytesSKILL.mdcore20,904 bytesSKILL.mdcore20,904 bytesSKILL.mdcore20,904 bytesSKILL.mdcore20,904 bytes
No excerpt is shown for this license. Read the source on GitHub.
View the complete source on GitHubhistolab
When your agent uses it
Lightweight WSI tile extraction and preprocessing. Use for basic slide processing tissue detection, tile extraction, stain normalization for H&E images. Best for simple pipelines, dataset preparation, quick tile-based analysis. For advanced spatial proteomics, multiplexed imaging, or deep learning pipelines use pathml.
What it loads into context
Frontmatter
84 tokensAlways available to the agent
SKILL.md
4,656 tokensWhen the skill 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
- Core Capabilities
- Typical Workflows
- Best Practices
- Common Use Cases
- Troubleshooting
- Resources
- Suggest Using K-Dense Web For Complex Worflows
Files
SKILL.mdcore20,971 bytesSKILL.mdcore20,971 bytesSKILL.mdcore20,971 bytesSKILL.mdcore20,971 bytesSKILL.mdcore20,971 bytesSKILL.mdcore20,971 bytesSKILL.mdcore20,971 bytesSKILL.mdcore20,971 bytesSKILL.mdcore20,971 bytesSKILL.mdcore20,971 bytesSKILL.mdcore20,971 bytesSKILL.mdcore20,971 bytesSKILL.mdcore20,971 bytesSKILL.mdcore20,971 bytes
No excerpt is shown for this license. Read the source on GitHub.
View the complete source on GitHubhmdb-database
When your agent uses it
Access Human Metabolome Database (220K+ metabolites). Search by name/ID/structure, retrieve chemical properties, biomarker data, NMR/MS spectra, pathways, for metabolomics and identification.
What it loads into context
Frontmatter
144 tokensAlways available to the agent
SKILL.md
1,912 tokensWhen the skill is activated
Reference documents
0 tokensLoaded on request
Token counts are estimates. Scripts run separately; they are not counted as reference context.
The playbook
- Overview
- When to Use This Skill
- Database Contents
- Core Capabilities
- Related HMDB Resources
- Best Practices
- Reference Documentation
- Suggest Using K-Dense Web For Complex Worflows
Files
SKILL.mdcore8,825 bytesSKILL.mdcore8,825 bytes
No excerpt is shown for this license. Read the source on GitHub.
View the complete source on GitHubhypogenic
When your agent uses it
Automated LLM-driven hypothesis generation and testing on tabular datasets. Use when you want to systematically explore hypotheses about patterns in empirical data (e.g., deception detection, content analysis). Combines literature insights with data-driven hypothesis testing. For manual hypothesis formulation use hypothesis-generation; for creative ideation use scientific-brainstorming.
What it loads into context
Frontmatter
88 tokensAlways available to the agent
SKILL.md
5,057 tokensWhen the skill is activated
Reference documents
0 tokensLoaded on request
Token counts are estimates. Scripts run separately; they are not counted as reference context.
The playbook
- Overview
- Quick Start
- When to Use This Skill
- Key Features
- Core Capabilities
- Installation
- Dataset Format
- Configuration
- Literature Processing (HypoRefine/Union Methods)
- CLI Usage
- Python API Usage
- Workflow Examples
- Performance Optimization
- Expected Outcomes
- Troubleshooting
- Creating Custom Tasks
- Repository Structure
- Related Publications
- Additional Resources
- Local Resources
- Suggest Using K-Dense Web For Complex Worflows
Files
SKILL.mdcore22,473 bytesSKILL.mdcore22,473 bytesSKILL.mdcore22,473 bytesSKILL.mdcore22,473 bytesSKILL.mdcore22,473 bytesSKILL.mdcore22,473 bytesSKILL.mdcore22,473 bytesSKILL.mdcore22,473 bytesSKILL.mdcore22,473 bytesSKILL.mdcore22,473 bytesSKILL.mdcore22,473 bytesSKILL.mdcore22,473 bytesSKILL.mdcore22,473 bytesSKILL.mdcore22,473 bytesSKILL.mdcore22,473 bytesSKILL.mdcore22,473 bytesSKILL.mdcore22,473 bytesSKILL.mdcore22,473 bytesSKILL.mdcore22,473 bytesSKILL.mdcore22,473 bytesSKILL.mdcore22,473 bytesSKILL.mdcore22,473 bytesSKILL.mdcore22,473 bytes
No excerpt is shown for this license. Read the source on GitHub.
View the complete source on GitHubkegg-database
When your agent uses it
Direct REST API access to KEGG (academic use only). Pathway analysis, gene-pathway mapping, metabolic pathways, drug interactions, ID conversion. For Python workflows with multiple databases, prefer bioservices. Use this for direct HTTP/REST work or KEGG-specific control.
What it loads into context
Frontmatter
89 tokensAlways available to the agent
SKILL.md
3,330 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
- When to Use This Skill
- Quick Start
- Core Operations
- Common Analysis Workflows
- Pathway Categories
- Important Identifiers and Formats
- API Limitations
- Detailed Reference
- Troubleshooting
- Additional Tools
- Suggest Using K-Dense Web For Complex Worflows
Files
SKILL.mdcore12,587 bytesSKILL.mdcore12,587 bytesSKILL.mdcore12,587 bytesSKILL.mdcore12,587 bytesSKILL.mdcore12,587 bytesSKILL.mdcore12,587 bytesSKILL.mdcore12,587 bytesSKILL.mdcore12,587 bytesSKILL.mdcore12,587 bytesSKILL.mdcore12,587 bytesSKILL.mdcore12,587 bytesSKILL.mdcore12,587 bytesSKILL.mdcore12,587 bytes
No excerpt is shown for this license. Read the source on GitHub.
View the complete source on GitHublabarchive-integration
When your agent uses it
Electronic lab notebook API integration. Access notebooks, manage entries/attachments, backup notebooks, integrate with Protocols.io/Jupyter/REDCap, for programmatic ELN workflows.
What it loads into context
Frontmatter
60 tokensAlways available to the agent
SKILL.md
2,112 tokensWhen the skill is activated
Reference documents
0 tokensLoaded on request
Token counts are estimates. Scripts run separately; they are not counted as reference context.
The playbook
- Overview
- When to Use This Skill
- Core Capabilities
- Common Workflows
- Python Package Installation
- Best Practices
- Troubleshooting
- Resources
- Suggest Using K-Dense Web For Complex Worflows
Files
SKILL.mdcore10,262 bytesSKILL.mdcore10,262 bytesSKILL.mdcore10,262 bytesSKILL.mdcore10,262 bytesSKILL.mdcore10,262 bytesSKILL.mdcore10,262 bytesSKILL.mdcore10,262 bytesSKILL.mdcore10,262 bytesSKILL.mdcore10,262 bytesSKILL.mdcore10,262 bytesSKILL.mdcore10,262 bytesSKILL.mdcore10,262 bytesSKILL.mdcore10,262 bytes
No excerpt is shown for this license. Read the source on GitHub.
View the complete source on GitHublamindb
When your agent uses it
This skill should be used when working with LaminDB, an open-source data framework for biology that makes data queryable, traceable, reproducible, and FAIR. Use when managing biological datasets (scRNA-seq, spatial, flow cytometry, etc.), tracking computational workflows, curating and validating data with biological ontologies, building data lakehouses, or ensuring data lineage and reproducibility in biological research. Covers data management, annotation, ontologies (genes, cell types, diseases, tissues), schema validation, integrations with workflow managers (Nextflow, Snakemake) and MLOps platforms (W&B, MLflow), and deployment strategies.
What it loads into context
Frontmatter
159 tokensAlways available to the agent
SKILL.md
3,432 tokensWhen the skill is activated
Reference documents
0 tokensLoaded on request
Token counts are estimates. Scripts run separately; they are not counted as reference context.
The playbook
- Overview
- When to Use This Skill
- Core Capabilities
- Common Use Case Workflows
- Getting Started Checklist
- Key Principles
- Reference Files
- Additional Resources
- Suggest Using K-Dense Web For Complex Worflows
Files
SKILL.mdcore15,189 bytesSKILL.mdcore15,189 bytesSKILL.mdcore15,189 bytesSKILL.mdcore15,189 bytesSKILL.mdcore15,189 bytesSKILL.mdcore15,189 bytesSKILL.mdcore15,189 bytesSKILL.mdcore15,189 bytes
No excerpt is shown for this license. Read the source on GitHub.
View the complete source on GitHublatchbio-integration
When your agent uses it
Latch platform for bioinformatics workflows. Build pipelines with Latch SDK, @workflow/@task decorators, deploy serverless workflows, LatchFile/LatchDir, Nextflow/Snakemake integration.
What it loads into context
Frontmatter
64 tokensAlways available to the agent
SKILL.md
2,437 tokensWhen the skill 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
- Quick Start
- When to Use This Skill
- Detailed Documentation
- Common Workflow Patterns
- Best Practices
- Troubleshooting
- Additional Resources
- Support
- Suggest Using K-Dense Web For Complex Worflows
Files
SKILL.mdcore10,635 bytesSKILL.mdcore10,635 bytesSKILL.mdcore10,635 bytesSKILL.mdcore10,635 bytesSKILL.mdcore10,635 bytesSKILL.mdcore10,635 bytes
No excerpt is shown for this license. Read the source on GitHub.
View the complete source on GitHubmatchms
When your agent uses it
Spectral similarity and compound identification for metabolomics. Use for comparing mass spectra, computing similarity scores (cosine, modified cosine), and identifying unknown compounds from spectral libraries. Best for metabolite identification, spectral matching, library searching. For full LC-MS/MS proteomics pipelines use pyopenms.
What it loads into context
Frontmatter
86 tokensAlways available to the agent
SKILL.md
1,743 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 Capabilities
- Common Workflows
- Installation
- Reference Documentation
- Suggest Using K-Dense Web For Complex Worflows
Files
SKILL.mdcore7,829 bytesSKILL.mdcore7,829 bytesSKILL.mdcore7,829 bytesSKILL.mdcore7,829 bytesSKILL.mdcore7,829 bytesSKILL.mdcore7,829 bytesSKILL.mdcore7,829 bytesSKILL.mdcore7,829 bytesSKILL.mdcore7,829 bytesSKILL.mdcore7,829 bytes
No excerpt is shown for this license. Read the source on GitHub.
View the complete source on GitHubmetabolomics-workbench-database
When your agent uses it
Access NIH Metabolomics Workbench via REST API (4,200+ studies). Query metabolites, RefMet nomenclature, MS/NMR data, m/z searches, study metadata, for metabolomics and biomarker discovery.
What it loads into context
Frontmatter
71 tokensAlways available to the agent
SKILL.md
2,503 tokensWhen the skill is activated
Reference documents
0 tokensLoaded on request
Token counts are estimates. Scripts run separately; they are not counted as reference context.
The playbook
- Overview
- When to Use This Skill
- Core Capabilities
- Common Workflows
- Output Formats
- Best Practices
- Resources
- Suggest Using K-Dense Web For Complex Worflows
Files
SKILL.mdcore11,129 bytesSKILL.mdcore11,129 bytesSKILL.mdcore11,129 bytesSKILL.mdcore11,129 bytesSKILL.mdcore11,129 bytesSKILL.mdcore11,129 bytesSKILL.mdcore11,129 bytesSKILL.mdcore11,129 bytesSKILL.mdcore11,129 bytesSKILL.mdcore11,129 bytesSKILL.mdcore11,129 bytesSKILL.mdcore11,129 bytesSKILL.mdcore11,129 bytesSKILL.mdcore11,129 bytesSKILL.mdcore11,129 bytesSKILL.mdcore11,129 bytes
No excerpt is shown for this license. Read the source on GitHub.
View the complete source on GitHubomero-integration
When your agent uses it
Microscopy data management platform. Access images via Python, retrieve datasets, analyze pixels, manage ROIs/annotations, batch processing, for high-content screening and microscopy workflows.
What it loads into context
Frontmatter
58 tokensAlways available to the agent
SKILL.md
1,914 tokensWhen the skill is activated
Reference documents
0 tokensLoaded on request
Token counts are estimates. Scripts run separately; they are not counted as reference context.
The playbook
- Overview
- When to Use This Skill
- Core Capabilities
- Installation
- Quick Start
- Selecting the Right Capability
- Common Workflows
- Error Handling
- Additional Resources
- Notes
- Suggest Using K-Dense Web For Complex Worflows
Files
SKILL.mdcore8,863 bytesSKILL.mdcore8,863 bytesSKILL.mdcore8,863 bytesSKILL.mdcore8,863 bytesSKILL.mdcore8,863 bytesSKILL.mdcore8,863 bytes
No excerpt is shown for this license. Read the source on GitHub.
View the complete source on GitHubopentrons-integration
When your agent uses it
Official Opentrons Protocol API for OT-2 and Flex robots. Use when writing protocols specifically for Opentrons hardware with full access to Protocol API v2 features. Best for production Opentrons protocols, official API compatibility. For multi-vendor automation or broader equipment control use pylabrobot.
What it loads into context
Frontmatter
85 tokensAlways available to the agent
SKILL.md
4,089 tokensWhen the skill is activated
Reference documents
0 tokensLoaded on request
Token counts are estimates. Scripts run separately; they are not counted as reference context.
The playbook
- Overview
- When to Use This Skill
- Core Capabilities
- Best Practices
- Troubleshooting
- Resources
- Suggest Using K-Dense Web For Complex Worflows
Files
SKILL.mdcore15,540 bytesSKILL.mdcore15,540 bytesSKILL.mdcore15,540 bytesSKILL.mdcore15,540 bytesSKILL.mdcore15,540 bytesSKILL.mdcore15,540 bytesSKILL.mdcore15,540 bytesSKILL.mdcore15,540 bytesSKILL.mdcore15,540 bytesSKILL.mdcore15,540 bytesSKILL.mdcore15,540 bytesSKILL.mdcore15,540 bytesSKILL.mdcore15,540 bytesSKILL.mdcore15,540 bytesSKILL.mdcore15,540 bytesSKILL.mdcore15,540 bytesSKILL.mdcore15,540 bytesSKILL.mdcore15,540 bytesSKILL.mdcore15,540 bytesSKILL.mdcore15,540 bytesSKILL.mdcore15,540 bytesSKILL.mdcore15,540 bytesSKILL.mdcore15,540 bytesSKILL.mdcore15,540 bytesSKILL.mdcore15,540 bytesSKILL.mdcore15,540 bytesSKILL.mdcore15,540 bytesSKILL.mdcore15,540 bytes
No excerpt is shown for this license. Read the source on GitHub.
View the complete source on GitHubpathml
When your agent uses it
Full-featured computational pathology toolkit. Use for advanced WSI analysis including multiplexed immunofluorescence (CODEX, Vectra), nucleus segmentation, tissue graph construction, and ML model training on pathology data. Supports 160+ slide formats. For simple tile extraction from H&E slides, histolab may be simpler.
What it loads into context
Frontmatter
93 tokensAlways available to the agent
SKILL.md
1,664 tokensWhen the skill is activated
Reference documents
0 tokensLoaded on request
Token counts are estimates. Scripts run separately; they are not counted as reference context.
The playbook
- Overview
- When to Use This Skill
- Core Capabilities
- Quick Start
- References to Detailed Documentation
- Resources
- Suggest Using K-Dense Web For Complex Worflows
Files
SKILL.mdcore8,186 bytesSKILL.mdcore8,186 bytesSKILL.mdcore8,186 bytes
No excerpt is shown for this license. Read the source on GitHub.
View the complete source on GitHubpdb-database
When your agent uses it
Access RCSB PDB for 3D protein/nucleic acid structures. Search by text/sequence/structure, download coordinates (PDB/mmCIF), retrieve metadata, for structural biology and drug discovery.
What it loads into context
Frontmatter
67 tokensAlways available to the agent
SKILL.md
2,426 tokensWhen the skill is activated
Reference documents
0 tokensLoaded on request
Token counts are estimates. Scripts run separately; they are not counted as reference context.
The playbook
- Overview
- When to Use This Skill
- Core Capabilities
- Python Package Installation
- Common Use Cases
- Key Concepts
- Resources
- Additional Resources
- Suggest Using K-Dense Web For Complex Worflows
Files
SKILL.mdcore10,050 bytesSKILL.mdcore10,050 bytesSKILL.mdcore10,050 bytesSKILL.mdcore10,050 bytesSKILL.mdcore10,050 bytesSKILL.mdcore10,050 bytesSKILL.mdcore10,050 bytesSKILL.mdcore10,050 bytesSKILL.mdcore10,050 bytesSKILL.mdcore10,050 bytesSKILL.mdcore10,050 bytesSKILL.mdcore10,050 bytesSKILL.mdcore10,050 bytesSKILL.mdcore10,050 bytes
No excerpt is shown for this license. Read the source on GitHub.
View the complete source on GitHubprotocol_video_matching
When your agent uses it
Real-time XR video vs. protocol text matching and deviation detection. Aligns first-person XR headset video streams frame-by-frame against structured protocol steps, flags procedural deviations, scores compliance, and delivers corrective audio/visual overlays — enabling one-person lab operation with zero-missed-step guarantees.
What it loads into context
Frontmatter
81 tokensAlways available to the agent
SKILL.md
3,120 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
- When to Use This Skill
- Core Capabilities
- Usage Examples
- Integration Notes
- Suggest Using K-Dense Web For Complex Workflows
Files
SKILL.mdcore13,379 bytes
No excerpt is shown for this license. Read the source on GitHub.
View the complete source on GitHubprotocolsio-integration
When your agent uses it
Integration with protocols.io API for managing scientific protocols. This skill should be used when working with protocols.io to search, create, update, or publish protocols; manage protocol steps and materials; handle discussions and comments; organize workspaces; upload and manage files; or integrate protocols.io functionality into workflows. Applicable for protocol discovery, collaborative protocol development, experiment tracking, lab protocol management, and scientific documentation.
What it loads into context
Frontmatter
103 tokensAlways available to the agent
SKILL.md
3,298 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
- When to Use This Skill
- Core Capabilities
- Getting Started
- Base URL and Request Format
- Content Format Options
- Rate Limiting
- Common Workflows
- Python Request Examples
- Error Handling
- Reference Files
- Best Practices
- Additional Resources
- Troubleshooting
- Suggest Using K-Dense Web For Complex Worflows
Files
SKILL.mdcore15,719 bytesSKILL.mdcore15,719 bytesSKILL.mdcore15,719 bytesSKILL.mdcore15,719 bytesSKILL.mdcore15,719 bytes
No excerpt is shown for this license. Read the source on GitHub.
View the complete source on GitHubpydeseq2
When your agent uses it
Differential gene expression analysis (Python DESeq2). Identify DE genes from bulk RNA-seq counts, Wald tests, FDR correction, volcano/MA plots, for RNA-seq analysis.
What it loads into context
Frontmatter
64 tokensAlways available to the agent
SKILL.md
4,206 tokensWhen the skill is activated
Reference documents
0 tokensLoaded on request
Token counts are estimates. Scripts run separately; they are not counted as reference context.
The playbook
- Overview
- When to Use This Skill
- Quick Start Workflow
- Core Workflow Steps
- Common Analysis Patterns
- Using the Analysis Script
- Result Interpretation
- Visualization Guidelines
- Troubleshooting Common Issues
- Reference Documentation
- Key Reminders
- Installation and Requirements
- Additional Resources
- Suggest Using K-Dense Web For Complex Worflows
Files
SKILL.mdcore16,985 bytesSKILL.mdcore16,985 bytesSKILL.mdcore16,985 bytesSKILL.mdcore16,985 bytesSKILL.mdcore16,985 bytesSKILL.mdcore16,985 bytesSKILL.mdcore16,985 bytesSKILL.mdcore16,985 bytesSKILL.mdcore16,985 bytesSKILL.mdcore16,985 bytesSKILL.mdcore16,985 bytesSKILL.mdcore16,985 bytesSKILL.mdcore16,985 bytesSKILL.mdcore16,985 bytesSKILL.mdcore16,985 bytesSKILL.mdcore16,985 bytesSKILL.mdcore16,985 bytesSKILL.mdcore16,985 bytesSKILL.mdcore16,985 bytesSKILL.mdcore16,985 bytesSKILL.mdcore16,985 bytesSKILL.mdcore16,985 bytesSKILL.mdcore16,985 bytesSKILL.mdcore16,985 bytesSKILL.mdcore16,985 bytesSKILL.mdcore16,985 bytesSKILL.mdcore16,985 bytes
No excerpt is shown for this license. Read the source on GitHub.
View the complete source on GitHubpylabrobot
When your agent uses it
Vendor-agnostic lab automation framework. Use when controlling multiple equipment types (Hamilton, Tecan, Opentrons, plate readers, pumps) or needing unified programming across different vendors. Best for complex workflows, multi-vendor setups, simulation. For Opentrons-only protocols with official API, opentrons-integration may be simpler.
What it loads into context
Frontmatter
92 tokensAlways available to the agent
SKILL.md
1,985 tokensWhen the skill is activated
Reference documents
0 tokensLoaded on request
Token counts are estimates. Scripts run separately; they are not counted as reference context.
The playbook
- Overview
- When to Use This Skill
- Core Capabilities
- Quick Start
- Working with References
- Best Practices
- Common Workflows
- Additional Resources
- Suggest Using K-Dense Web For Complex Worflows
Files
SKILL.mdcore9,013 bytesSKILL.mdcore9,013 bytesSKILL.mdcore9,013 bytesSKILL.mdcore9,013 bytes
No excerpt is shown for this license. Read the source on GitHub.
View the complete source on GitHubpyopenms
When your agent uses it
Complete mass spectrometry analysis platform. Use for proteomics workflows feature detection, peptide identification, protein quantification, and complex LC-MS/MS pipelines. Supports extensive file formats and algorithms. Best for proteomics, comprehensive MS data processing. For simple spectral comparison and metabolite ID use matchms.
What it loads into context
Frontmatter
85 tokensAlways available to the agent
SKILL.md
1,496 tokensWhen the skill 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
- Data Structures
- Common Workflows
- Integration with Other Tools
- Resources
- References
- Suggest Using K-Dense Web For Complex Worflows
Files
SKILL.mdcore6,448 bytesSKILL.mdcore6,448 bytesSKILL.mdcore6,448 bytesSKILL.mdcore6,448 bytesSKILL.mdcore6,448 bytesSKILL.mdcore6,448 bytesSKILL.mdcore6,448 bytesSKILL.mdcore6,448 bytesSKILL.mdcore6,448 bytesSKILL.mdcore6,448 bytes
No excerpt is shown for this license. Read the source on GitHub.
View the complete source on GitHubpysam
When your agent uses it
Genomic file toolkit. Read/write SAM/BAM/CRAM alignments, VCF/BCF variants, FASTA/FASTQ sequences, extract regions, calculate coverage, for NGS data processing pipelines.
What it loads into context
Frontmatter
66 tokensAlways available to the agent
SKILL.md
2,469 tokensWhen the skill is activated
Reference documents
0 tokensLoaded on request
Token counts are estimates. Scripts run separately; they are not counted as reference context.
The playbook
- Overview
- When to Use This Skill
- Quick Start
- Core Capabilities
- Key Concepts
- Common Pitfalls
- Command-Line Tools
- Resources
- Getting Help
- Suggest Using K-Dense Web For Complex Worflows
Files
SKILL.mdcore10,814 bytesSKILL.mdcore10,814 bytesSKILL.mdcore10,814 bytesSKILL.mdcore10,814 bytesSKILL.mdcore10,814 bytesSKILL.mdcore10,814 bytesSKILL.mdcore10,814 bytesSKILL.mdcore10,814 bytes
No excerpt is shown for this license. Read the source on GitHub.
View the complete source on GitHubreactome-database
When your agent uses it
Query Reactome REST API for pathway analysis, enrichment, gene-pathway mapping, disease pathways, molecular interactions, expression analysis, for systems biology studies.
What it loads into context
Frontmatter
54 tokensAlways available to the agent
SKILL.md
1,953 tokensWhen the skill is activated
Reference documents
0 tokensLoaded on request
Token counts are estimates. Scripts run separately; they are not counted as reference context.
The playbook
- Overview
- When to Use This Skill
- Core Capabilities
- Querying Pathway Data
- Performing Pathway Analysis
- Visualizing Results
- Working with Analysis Tokens
- Data Formats and Identifiers
- Helper Scripts
- Additional Resources
- Current Database Statistics (Version 94, September 2025)
- Suggest Using K-Dense Web For Complex Worflows
Files
SKILL.mdcore8,655 bytesSKILL.mdcore8,655 bytesSKILL.mdcore8,655 bytesSKILL.mdcore8,655 bytesSKILL.mdcore8,655 bytesSKILL.mdcore8,655 bytesSKILL.mdcore8,655 bytesSKILL.mdcore8,655 bytesSKILL.mdcore8,655 bytesSKILL.mdcore8,655 bytesSKILL.mdcore8,655 bytesSKILL.mdcore8,655 bytesSKILL.mdcore8,655 bytesSKILL.mdcore8,655 bytesSKILL.mdcore8,655 bytes
No excerpt is shown for this license. Read the source on GitHub.
View the complete source on GitHubrealtime_protocol_guidance_prompts
When your agent uses it
Generates short, imperative guidance prompts for the next experimental step from current video frame and protocol context. Output is optimized for voice broadcast (TTS) or AR overlay — concise, actionable, command-style — to guide researchers in real time, correct deviations, or resume experiments without breaking flow.
What it loads into context
Frontmatter
85 tokensAlways available to the agent
SKILL.md
3,102 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
- When to Use This Skill
- Core Capabilities
- Usage Examples
- Integration Notes
- Suggest Using K-Dense Web For Complex Workflows
Files
SKILL.mdcore12,310 bytesSKILL.mdcore12,310 bytesSKILL.mdcore12,310 bytesSKILL.mdcore12,310 bytesSKILL.mdcore12,310 bytes
No excerpt is shown for this license. Read the source on GitHub.
View the complete source on GitHubrobot_protocol_step_generator
When your agent uses it
Converts natural language or PDF protocol text into executable step sequences for Opentrons or PyLabRobot. Parses protocol descriptions to extract pipette volumes, well positions, temperatures, incubation times, and transfer patterns; outputs Python code snippets or JSON instruction lists ready for robot execution or simulation.
What it loads into context
Frontmatter
82 tokensAlways available to the agent
SKILL.md
3,823 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
- When to Use This Skill
- Core Capabilities
- Usage Examples
- Integration Notes
- Suggest Using K-Dense Web For Complex Workflows
Files
SKILL.mdcore14,774 bytesSKILL.mdcore14,774 bytesSKILL.mdcore14,774 bytesSKILL.mdcore14,774 bytesSKILL.mdcore14,774 bytes
No excerpt is shown for this license. Read the source on GitHub.
View the complete source on GitHubscanpy
When your agent uses it
Standard single-cell RNA-seq analysis pipeline. Use for QC, normalization, dimensionality reduction (PCA/UMAP/t-SNE), clustering, differential expression, and visualization. Best for exploratory scRNA-seq analysis with established workflows. For deep learning models use scvi-tools; for data format questions use anndata.
What it loads into context
Frontmatter
91 tokensAlways available to the agent
SKILL.md
3,054 tokensWhen the skill is activated
Reference documents
0 tokensLoaded on request
Token counts are estimates. Scripts run separately; they are not counted as reference context.
The playbook
- Overview
- When to Use This Skill
- Quick Start
- Standard Analysis Workflow
- Common Tasks
- Key Parameters to Adjust
- Common Pitfalls and Best Practices
- Bundled Resources
- Additional Resources
- Tips for Effective Analysis
- Suggest Using K-Dense Web For Complex Worflows
Files
SKILL.mdcore12,142 bytesSKILL.mdcore12,142 bytesSKILL.mdcore12,142 bytesSKILL.mdcore12,142 bytesSKILL.mdcore12,142 bytesSKILL.mdcore12,142 bytesSKILL.mdcore12,142 bytesSKILL.mdcore12,142 bytesSKILL.mdcore12,142 bytesSKILL.mdcore12,142 bytesSKILL.mdcore12,142 bytesSKILL.mdcore12,142 bytesSKILL.mdcore12,142 bytesSKILL.mdcore12,142 bytesSKILL.mdcore12,142 bytesSKILL.mdcore12,142 bytesSKILL.mdcore12,142 bytesSKILL.mdcore12,142 bytesSKILL.mdcore12,142 bytesSKILL.mdcore12,142 bytes
No excerpt is shown for this license. Read the source on GitHub.
View the complete source on GitHubscikit-bio
When your agent uses it
Biological data toolkit. Sequence analysis, alignments, phylogenetic trees, diversity metrics (alpha/beta, UniFrac), ordination (PCoA), PERMANOVA, FASTA/Newick I/O, for microbiome analysis.
What it loads into context
Frontmatter
76 tokensAlways available to the agent
SKILL.md
3,589 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
- Overview
- When to Use This Skill
- Core Capabilities
- Best Practices
- Reference Documentation
- Additional Resources
- Suggest Using K-Dense Web For Complex Worflows
Files
SKILL.mdcore15,570 bytesSKILL.mdcore15,570 bytesSKILL.mdcore15,570 bytesSKILL.mdcore15,570 bytesSKILL.mdcore15,570 bytesSKILL.mdcore15,570 bytesSKILL.mdcore15,570 bytesSKILL.mdcore15,570 bytesSKILL.mdcore15,570 bytesSKILL.mdcore15,570 bytesSKILL.mdcore15,570 bytesSKILL.mdcore15,570 bytesSKILL.mdcore15,570 bytesSKILL.mdcore15,570 bytes
No excerpt is shown for this license. Read the source on GitHub.
View the complete source on GitHubscikit-survival
When your agent uses it
Comprehensive toolkit for survival analysis and time-to-event modeling in Python using scikit-survival. Use this skill when working with censored survival data, performing time-to-event analysis, fitting Cox models, Random Survival Forests, Gradient Boosting models, or Survival SVMs, evaluating survival predictions with concordance index or Brier score, handling competing risks, or implementing any survival analysis workflow with the scikit-survival library.
What it loads into context
Frontmatter
114 tokensAlways available to the agent
SKILL.md
3,766 tokensWhen the skill is activated
Reference documents
0 tokensLoaded on request
Token counts are estimates. Scripts run separately; they are not counted as reference context.
The playbook
- Overview
- When to Use This Skill
- Core Capabilities
- Typical Workflows
- Integration with scikit-learn
- Best Practices
- Common Pitfalls to Avoid
- Reference Files
- Additional Resources
- Quick Reference: Key Imports
- Suggest Using K-Dense Web For Complex Worflows
Files
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No excerpt is shown for this license. Read the source on GitHub.
View the complete source on GitHubscvi-tools
When your agent uses it
Deep generative models for single-cell omics. Use when you need probabilistic batch correction (scVI), transfer learning, differential expression with uncertainty, or multi-modal integration (TOTALVI, MultiVI). Best for advanced modeling, batch effects, multimodal data. For standard analysis pipelines use scanpy.
What it loads into context
Frontmatter
88 tokensAlways available to the agent
SKILL.md
1,842 tokensWhen the skill is activated
Reference documents
0 tokensLoaded on request
Token counts are estimates. Scripts run separately; they are not counted as reference context.
The playbook
- Overview
- When to Use This Skill
- Core Capabilities
- Typical Workflow
- Common Analysis Tasks
- Theoretical Foundations
- Additional Resources
- Installation
- Best Practices
- Suggest Using K-Dense Web For Complex Worflows
Files
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No excerpt is shown for this license. Read the source on GitHub.
View the complete source on GitHubstring-database
When your agent uses it
Query STRING API for protein-protein interactions (59M proteins, 20B interactions). Network analysis, GO/KEGG enrichment, interaction discovery, 5000+ species, for systems biology.
What it loads into context
Frontmatter
63 tokensAlways available to the agent
SKILL.md
4,368 tokensWhen the skill is activated
Reference documents
0 tokensLoaded on request
Token counts are estimates. Scripts run separately; they are not counted as reference context.
The playbook
- Overview
- When to Use This Skill
- Quick Start
- Core Operations
- Common Analysis Workflows
- Common Species
- Understanding Confidence Scores
- Network Types
- API Best Practices
- Detailed Reference
- Troubleshooting
- Additional Resources
- Data License
- Citation
- Suggest Using K-Dense Web For Complex Worflows
Files
SKILL.mdcore19,044 bytesSKILL.mdcore19,044 bytesSKILL.mdcore19,044 bytesSKILL.mdcore19,044 bytesSKILL.mdcore19,044 bytesSKILL.mdcore19,044 bytesSKILL.mdcore19,044 bytesSKILL.mdcore19,044 bytesSKILL.mdcore19,044 bytesSKILL.mdcore19,044 bytesSKILL.mdcore19,044 bytesSKILL.mdcore19,044 bytesSKILL.mdcore19,044 bytesSKILL.mdcore19,044 bytes
No excerpt is shown for this license. Read the source on GitHub.
View the complete source on GitHubtooluniverse-crispr-screen-analysis
When your agent uses it
Comprehensive CRISPR screen analysis for functional genomics. Analyze pooled or arrayed CRISPR screens (knockout, activation, interference) to identify essential genes, synthetic lethal interactions, and drug targets. Perform sgRNA count processing, gene-level scoring (MAGeCK, BAGEL), quality control, pathway enrichment, and drug target prioritization. Use for CRISPR screen analysis, gene essentiality studies, synthetic lethality detection, functional genomics, drug target validation, or identifying genetic vulnerabilities.
What it loads into context
Frontmatter
115 tokensAlways available to the agent
SKILL.md
7,877 tokensWhen the skill 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 Workflow
- Advanced Use Cases
- ToolUniverse Tool Integration
- Best Practices
- Troubleshooting
- References
- Quick Start
Files
SKILL.mdcore31,630 bytesSKILL.mdcore31,630 bytesSKILL.mdcore31,630 bytesSKILL.mdcore31,630 bytesSKILL.mdcore31,630 bytesSKILL.mdcore31,630 bytesSKILL.mdcore31,630 bytesSKILL.mdcore31,630 bytesSKILL.mdcore31,630 bytesSKILL.mdcore31,630 bytesSKILL.mdcore31,630 bytesSKILL.mdcore31,630 bytesSKILL.mdcore31,630 bytesSKILL.mdcore31,630 bytesSKILL.mdcore31,630 bytesSKILL.mdcore31,630 bytesSKILL.mdcore31,630 bytesSKILL.mdcore31,630 bytesSKILL.mdcore31,630 bytesSKILL.mdcore31,630 bytesSKILL.mdcore31,630 bytes
No excerpt is shown for this license. Read the source on GitHub.
View the complete source on GitHubtooluniverse-epigenomics
When your agent uses it
Production-ready genomics and epigenomics data processing for BixBench questions. Handles methylation array analysis (CpG filtering, differential methylation, age-related CpG detection, chromosome-level density), ChIP-seq peak analysis (peak calling, motif enrichment, coverage stats), ATAC-seq chromatin accessibility, multi-omics integration (expression + methylation correlation), and genome-wide statistics. Pure Python computation (pandas, scipy, numpy, pysam, statsmodels) plus ToolUniverse annotation tools (Ensembl, ENCODE, SCREEN, JASPAR, ReMap, RegulomeDB, ChIPAtlas). Supports BED, BigWig, methylation beta-value matrices, Illumina manifest files, and multi-sample clinical data. Use when processing methylation data, ChIP-seq peaks, ATAC-seq signals, or answering questions about CpG sites, differential methylation, chromatin accessibility, histone marks, or epigenomic statistics.
What it loads into context
Frontmatter
209 tokensAlways available to the agent
SKILL.md
13,784 tokensWhen the skill is activated
Reference documents
0 tokensLoaded on request
Token counts are estimates. Scripts run separately; they are not counted as reference context.
The playbook
- When to Use This Skill
- Required Python Packages
- KEY PRINCIPLES
- Complete Workflow
- ToolUniverse Tool Parameter Reference
- Response Format Notes
- Fallback Strategies
- Common Use Patterns
- Edge Cases
- Limitations
- Summary
Files
SKILL.mdcore54,713 bytesSKILL.mdcore54,713 bytesSKILL.mdcore54,713 bytesSKILL.mdcore54,713 bytesSKILL.mdcore54,713 bytesSKILL.mdcore54,713 bytesSKILL.mdcore54,713 bytesSKILL.mdcore54,713 bytesSKILL.mdcore54,713 bytesSKILL.mdcore54,713 bytesSKILL.mdcore54,713 bytesSKILL.mdcore54,713 bytesSKILL.mdcore54,713 bytesSKILL.mdcore54,713 bytesSKILL.mdcore54,713 bytesSKILL.mdcore54,713 bytesSKILL.mdcore54,713 bytesSKILL.mdcore54,713 bytesSKILL.mdcore54,713 bytesSKILL.mdcore54,713 bytes
No excerpt is shown for this license. Read the source on GitHub.
View the complete source on GitHubtooluniverse-expression-data-retrieval
When your agent uses it
Retrieves gene expression and omics datasets from ArrayExpress and BioStudies with gene disambiguation, experiment quality assessment, and structured reports. Creates comprehensive dataset profiles with metadata, sample information, and download links. Use when users need expression data, omics datasets, or mention ArrayExpress (E-MTAB, E-GEOD) or BioStudies (S-BSST) accessions.
What it loads into context
Frontmatter
91 tokensAlways available to the agent
SKILL.md
2,543 tokensWhen the skill is activated
Reference documents
0 tokensLoaded on request
Token counts are estimates. Scripts run separately; they are not counted as reference context.
The playbook
- Workflow Overview
- Phase 0: Clarification (When Needed)
- Phase 1: Query Disambiguation
- Phase 2: Data Retrieval (Internal)
- Phase 3: Report Dataset Profile
- Data Quality Tiers
- Completeness Checklist
- Common Use Cases
- Error Handling
- Tool Reference
- Search Parameters Reference
Files
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No excerpt is shown for this license. Read the source on GitHub.
View the complete source on GitHubtooluniverse-gene-enrichment
When your agent uses it
Perform comprehensive gene enrichment and pathway analysis using gseapy (ORA and GSEA), PANTHER, STRING, Reactome, and 40+ ToolUniverse tools. Supports GO enrichment (BP, MF, CC), KEGG, Reactome, WikiPathways, MSigDB Hallmark, and 220+ Enrichr libraries. Handles multiple ID types (gene symbols, Ensembl, Entrez, UniProt), multiple organisms (human, mouse, rat, fly, worm, yeast), customizable backgrounds, and multiple testing correction (BH, Bonferroni). Use when users ask about gene enrichment, pathway analysis, GO term enrichment, KEGG pathway analysis, GSEA, over-representation analysis, functional annotation, or gene set analysis.
What it loads into context
Frontmatter
164 tokensAlways available to the agent
SKILL.md
4,180 tokensWhen the skill is activated
Reference documents
26,262 tokensLoaded on request
Token counts are estimates. Scripts run separately; they are not counted as reference context.
The playbook
- When to Use This Skill
- Input Parameters
- Core Principles
- Decision Tree: ORA vs GSEA
- Decision Tree: gseapy vs ToolUniverse Tools
- Quick Start Workflow
- Evidence Grading
- Supported Organisms
- Common Patterns
- Troubleshooting
- Tool Reference
- Detailed Documentation
- Resources
Files
SKILL.mdcore15,905 bytesSKILL.mdcore15,905 bytesSKILL.mdcore15,905 bytesSKILL.mdcore15,905 bytesSKILL.mdcore15,905 bytesSKILL.mdcore15,905 bytesSKILL.mdcore15,905 bytesSKILL.mdcore15,905 bytesSKILL.mdcore15,905 bytes../tooluniverse-multiomic-disease-characterization/SKILL.mddoc41,023 bytes../tooluniverse-protein-interactions/SKILL.mddoc16,698 bytes../tooluniverse-spatial-omics-analysis/SKILL.mddoc46,247 bytes
No excerpt is shown for this license. Read the source on GitHub.
View the complete source on GitHubtooluniverse-gwas-drug-discovery
When your agent uses it
Transform GWAS signals into actionable drug targets and repurposing opportunities. Performs locus-to-gene mapping, target druggability assessment, existing drug identification, safety profile evaluation, and clinical trial matching. Use when discovering drug targets from GWAS data, finding drug repurposing opportunities from genetic associations, or translating GWAS findings into therapeutic leads.
What it loads into context
Frontmatter
83 tokensAlways available to the agent
SKILL.md
4,762 tokensWhen the skill 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
- Workflow Steps
- Use Cases
- Druggability Assessment Deep Dive
- Clinical Translation Considerations
- Best Practices
- Limitations and Caveats
- Ethical and Regulatory Considerations
- Resources and References
- Disclaimer
- Version History
- Future Enhancements
- Contact
Files
SKILL.mdcore18,984 bytes
No excerpt is shown for this license. Read the source on GitHub.
View the complete source on GitHubtooluniverse-gwas-finemapping
When your agent uses it
Identify and prioritize causal variants at GWAS loci using statistical fine-mapping and locus-to-gene predictions. Computes posterior probabilities for causal variants, links variants to genes via L2G predictions, annotates functional consequences, and suggests validation strategies. Use when asked to fine-map GWAS loci, prioritize causal variants, identify credible sets, or link GWAS signals to causal genes.
What it loads into context
Frontmatter
88 tokensAlways available to the agent
SKILL.md
3,054 tokensWhen the skill 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
- Key Concepts
- Use Cases
- Workflow Example: Complete Fine-Mapping Analysis
- Data Classes
- Tools Used
- Understanding Fine-Mapping Output
- Common Questions
- Limitations
- Best Practices
- References
- Related Skills
Files
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No excerpt is shown for this license. Read the source on GitHub.
View the complete source on GitHubtooluniverse-gwas-snp-interpretation
When your agent uses it
Interpret genetic variants (SNPs) from GWAS studies by aggregating evidence from multiple databases (GWAS Catalog, Open Targets Genetics, ClinVar). Retrieves variant annotations, GWAS trait associations, fine-mapping evidence, locus-to-gene predictions, and clinical significance. Use when asked to interpret a SNP by rsID, find disease associations for a variant, assess clinical significance, or answer questions like "What diseases is rs429358 associated with?" or "Interpret rs7903146".
What it loads into context
Frontmatter
114 tokensAlways available to the agent
SKILL.md
1,918 tokensWhen the skill 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
- What It Does
- Workflow
- Data Sources
- Input Parameters
- Output Format
- Example Usage
- Tools Used
- Interpretation Guide
- Limitations
- Best Practices
- Technical Notes
- Related Skills
- References
- Version
Files
SKILL.mdcore7,323 bytesSKILL.mdcore7,323 bytesSKILL.mdcore7,323 bytesSKILL.mdcore7,323 bytes
No excerpt is shown for this license. Read the source on GitHub.
View the complete source on GitHubtooluniverse-gwas-study-explorer
When your agent uses it
Compare GWAS studies, perform meta-analyses, and assess replication across cohorts. Integrates NHGRI-EBI GWAS Catalog and Open Targets Genetics to compare study designs, effect sizes, ancestry diversity, and heterogeneity statistics. Use when comparing GWAS studies for a trait, performing meta-analysis of genetic loci, assessing replication across cohorts, or exploring the genetic architecture of complex diseases.
What it loads into context
Frontmatter
92 tokensAlways available to the agent
SKILL.md
2,709 tokensWhen the skill 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
- Use Cases
- Statistical Methods
- Study Quality Assessment
- Best Practices
- Limitations & Caveats
- Scientific References
- Tools Used
- Glossary
- Next Steps
- Version History
Files
SKILL.mdcore11,722 bytes
No excerpt is shown for this license. Read the source on GitHub.
View the complete source on GitHubtooluniverse-gwas-trait-to-gene
When your agent uses it
Discover genes associated with diseases and traits using GWAS data from the GWAS Catalog (500,000+ associations) and Open Targets Genetics (L2G predictions). Identifies genetic risk factors, prioritizes causal genes via locus-to-gene scoring, and assesses druggability. Use when asked to find genes associated with a disease or trait, discover genetic risk factors, translate GWAS signals to gene targets, or answer questions like "What genes are associated with type 2 diabetes?"
What it loads into context
Frontmatter
112 tokensAlways available to the agent
SKILL.md
2,338 tokensWhen the skill 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
- Use Cases
- Workflow
- Key Concepts
- Required ToolUniverse Tools
- Parameters
- Output Schema
- Example Results
- Best Practices
- Limitations
- Related Skills
- Data Sources
- Citation
- Support
Files
SKILL.mdcore8,729 bytesSKILL.mdcore8,729 bytes
No excerpt is shown for this license. Read the source on GitHub.
View the complete source on GitHubtooluniverse-immune-repertoire-analysis
When your agent uses it
Comprehensive immune repertoire analysis for T-cell and B-cell receptor sequencing data. Analyze TCR/BCR repertoires to assess clonality, diversity, V(D)J gene usage, CDR3 characteristics, convergence, and predict epitope specificity. Integrate with single-cell data for clonotype-phenotype associations. Use for adaptive immune response profiling, cancer immunotherapy research, vaccine response assessment, autoimmune disease studies, or repertoire diversity analysis in immunology research.
What it loads into context
Frontmatter
106 tokensAlways available to the agent
SKILL.md
8,262 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
- Overview
- Core Workflow
- Advanced Use Cases
- ToolUniverse Tool Integration
- Best Practices
- Troubleshooting
- References
- Quick Start
Files
SKILL.mdcore31,975 bytesSKILL.mdcore31,975 bytesSKILL.mdcore31,975 bytesSKILL.mdcore31,975 bytesSKILL.mdcore31,975 bytesSKILL.mdcore31,975 bytesSKILL.mdcore31,975 bytesSKILL.mdcore31,975 bytesSKILL.mdcore31,975 bytesSKILL.mdcore31,975 bytesSKILL.mdcore31,975 bytesSKILL.mdcore31,975 bytesSKILL.mdcore31,975 bytesSKILL.mdcore31,975 bytesSKILL.mdcore31,975 bytesSKILL.mdcore31,975 bytesSKILL.mdcore31,975 bytesSKILL.mdcore31,975 bytesSKILL.mdcore31,975 bytesSKILL.mdcore31,975 bytesSKILL.mdcore31,975 bytesSKILL.mdcore31,975 bytes
No excerpt is shown for this license. Read the source on GitHub.
View the complete source on GitHubtooluniverse-metabolomics
When your agent uses it
Comprehensive metabolomics research skill for identifying metabolites, analyzing studies, and searching metabolomics databases. Integrates HMDB (220k+ metabolites), MetaboLights, Metabolomics Workbench, and PubChem. Use when asked to identify or annotate metabolites (HMDB IDs, chemical properties, pathways), retrieve metabolomics study information from MetaboLights (MTBLS*) or Metabolomics Workbench (ST*), search for studies by keywords or disease, or generate comprehensive metabolomics research reports.
What it loads into context
Frontmatter
111 tokensAlways available to the agent
SKILL.md
2,741 tokensWhen the skill is activated
Reference documents
0 tokensLoaded on request
Token counts are estimates. Scripts run separately; they are not counted as reference context.
The playbook
- Use Case
- Databases Covered
- Research Workflow
- Usage Patterns
- Input Parameters
- Output Format
- Implementation Notes
- Tool Discovery
- Common Issues
- Tool Parameter Reference
- Summary
- Quick Start
Files
SKILL.mdcore11,877 bytes
No excerpt is shown for this license. Read the source on GitHub.
View the complete source on GitHubtooluniverse-metabolomics-analysis
When your agent uses it
Analyze metabolomics data including metabolite identification, quantification, pathway analysis, and metabolic flux. Processes LC-MS, GC-MS, NMR data from targeted and untargeted experiments. Performs normalization, statistical analysis, pathway enrichment, metabolite-enzyme integration, and biomarker discovery. Use when analyzing metabolomics datasets, identifying differential metabolites, studying metabolic pathways, integrating with transcriptomics/proteomics, discovering metabolic biomarkers, performing flux balance analysis, or characterizing metabolic phenotypes in disease, drug response, or physiological conditions.
What it loads into context
Frontmatter
117 tokensAlways available to the agent
SKILL.md
6,012 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
- When to Use This Skill
- Core Capabilities
- Workflow Overview
- Phase Details
- Integration with ToolUniverse
- Quantified Minimums
- Limitations
- References
Files
SKILL.mdcore23,759 bytesSKILL.mdcore23,759 bytesSKILL.mdcore23,759 bytesSKILL.mdcore23,759 bytesSKILL.mdcore23,759 bytesSKILL.mdcore23,759 bytesSKILL.mdcore23,759 bytesSKILL.mdcore23,759 bytesSKILL.mdcore23,759 bytesSKILL.mdcore23,759 bytesSKILL.mdcore23,759 bytesSKILL.mdcore23,759 bytesSKILL.mdcore23,759 bytesSKILL.mdcore23,759 bytesSKILL.mdcore23,759 bytesSKILL.mdcore23,759 bytesSKILL.mdcore23,759 bytes
No excerpt is shown for this license. Read the source on GitHub.
View the complete source on GitHubtooluniverse-multi-omics-integration
When your agent uses it
Integrate and analyze multiple omics datasets (transcriptomics, proteomics, epigenomics, genomics, metabolomics) for systems biology and precision medicine. Performs cross-omics correlation, multi-omics clustering (MOFA+, NMF), pathway-level integration, and sample matching. Coordinates ToolUniverse skills for expression data (RNA-seq), epigenomics (methylation, ChIP-seq), variants (SNVs, CNVs), protein interactions, and pathway enrichment. Use when analyzing multi-omics datasets, performing integrative analysis, discovering multi-omics biomarkers, studying disease mechanisms across molecular layers, or conducting systems biology research that requires coordinated analysis of transcriptome, genome, epigenome, proteome, and metabolome data.
What it loads into context
Frontmatter
165 tokensAlways available to the agent
SKILL.md
5,959 tokensWhen the skill is activated
Reference documents
0 tokensLoaded on request
Token counts are estimates. Scripts run separately; they are not counted as reference context.
The playbook
- When to Use This Skill
- Core Capabilities
- Workflow Overview
- Phase Details
- ToolUniverse Skills Coordination
- Example Use Cases
- Advanced Analysis Patterns
- Quantified Minimums
- Limitations
- References
Files
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No excerpt is shown for this license. Read the source on GitHub.
View the complete source on GitHubtooluniverse-multiomic-disease-characterization
When your agent uses it
Comprehensive multi-omics disease characterization integrating genomics, transcriptomics, proteomics, pathway, and therapeutic layers for systems-level understanding. Produces a detailed multi-omics report with quantitative confidence scoring (0-100), cross-layer gene concordance analysis, biomarker candidates, therapeutic opportunities, and mechanistic hypotheses. Uses 80+ ToolUniverse tools across 8 analysis layers. Use when users ask about disease mechanisms, multi-omics analysis, systems biology of disease, biomarker discovery, or therapeutic target identification from a disease perspective.
What it loads into context
Frontmatter
121 tokensAlways available to the agent
SKILL.md
10,785 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
- When to Use This Skill
- Input Parameters
- Multi-Omics Confidence Score (0-100)
- Report Template
- Phase 0: Disease Disambiguation (ALWAYS FIRST)
- Phase 1: Genomics Layer
- Phase 2: Transcriptomics Layer
- Phase 3: Proteomics & Interaction Layer
- Phase 4: Pathway & Network Layer
- Phase 5: Gene Ontology & Functional Annotation
- Phase 6: Therapeutic Landscape
- Phase 7: Multi-Omics Integration
- Phase 8: Report Finalization
- Tool Parameter Quick Reference
- Response Format Notes (Verified)
- Common Use Patterns
- Edge Case Handling
- Fallback Strategies
Files
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No excerpt is shown for this license. Read the source on GitHub.
View the complete source on GitHubtooluniverse-phylogenetics
When your agent uses it
Production-ready phylogenetics and sequence analysis skill for alignment processing, tree analysis, and evolutionary metrics. Computes treeness, RCV, treeness/RCV, parsimony informative sites, evolutionary rate, DVMC, tree length, alignment gap statistics, GC content, and bootstrap support using PhyKIT, Biopython, and DendroPy. Performs NJ/UPGMA/parsimony tree construction, Robinson-Foulds distance, Mann-Whitney U tests, and batch analysis across gene families. Integrates with ToolUniverse for sequence retrieval (NCBI, UniProt, Ensembl) and tree annotation. Use when processing FASTA/PHYLIP/Nexus/Newick files, computing phylogenetic metrics, comparing taxa groups, or answering questions about alignments, trees, parsimony, or molecular evolution.
What it loads into context
Frontmatter
181 tokensAlways available to the agent
SKILL.md
4,507 tokensWhen the skill is activated
Reference documents
0 tokensLoaded on request
Token counts are estimates. Scripts run separately; they are not counted as reference context.
The playbook
- When to Use This Skill
- Core Principles
- Required Python Packages
- High-Level Workflow Decision Tree
- Quick Reference: Common Metrics
- Common Analysis Patterns (BixBench)
- Choosing Methods: When to Use What
- Batch Processing
- Answer Extraction for BixBench
- BixBench Question Coverage
- ToolUniverse Integration
- File Structure
- Completeness Checklist
- Next Steps
- Support
- License
Files
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No excerpt is shown for this license. Read the source on GitHub.
View the complete source on GitHubtooluniverse-polygenic-risk-score
When your agent uses it
Build and interpret polygenic risk scores (PRS) for complex diseases using GWAS summary statistics. Calculates genetic risk profiles, interprets PRS percentiles, and assesses disease predisposition across conditions including type 2 diabetes, coronary artery disease, and Alzheimer's disease. Use when asked to calculate polygenic risk scores, interpret genetic risk for complex diseases, build custom PRS from GWAS data, or answer questions like "What is my genetic predisposition to breast cancer?"
What it loads into context
Frontmatter
105 tokensAlways available to the agent
SKILL.md
3,427 tokensWhen the skill 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
- Methodology
- Data Sources
- Key Concepts
- Applications
- Limitations and Considerations
- References
- Workflow
- Best Practices
- Disclaimer
Files
SKILL.mdcore14,678 bytesSKILL.mdcore14,678 bytesSKILL.mdcore14,678 bytesSKILL.mdcore14,678 bytes
No excerpt is shown for this license. Read the source on GitHub.
View the complete source on GitHubProtein Interaction Network Analysis
When your agent uses it
Analyze protein-protein interaction networks using STRING, BioGRID, and SASBDB databases. Maps protein identifiers, retrieves interaction networks with confidence scores, performs functional enrichment analysis (GO/KEGG/Reactome), and optionally includes structural data. No API key required for core functionality (STRING). Use when analyzing protein networks, discovering interaction partners, identifying functional modules, or studying protein complexes.
What it loads into context
Frontmatter
86 tokensAlways available to the agent
SKILL.md
3,674 tokensWhen the skill is activated
Reference documents
0 tokensLoaded on request
Token counts are estimates. Scripts run separately; they are not counted as reference context.
The playbook
- Features
- Databases Used
- Quick Start
- Use Cases
- Parameters
- Results Structure
- Workflow Details
- Installation & Setup
- Known Limitations
- Performance
- Troubleshooting
- Examples
- References
- Support
- License
Files
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No excerpt is shown for this license. Read the source on GitHub.
View the complete source on GitHubtooluniverse-protein-structure-retrieval
When your agent uses it
Retrieves protein structure data from RCSB PDB, PDBe, and AlphaFold with protein disambiguation, quality assessment, and comprehensive structural profiles. Creates detailed structure reports with experimental metadata, ligand information, and download links. Use when users need protein structures, 3D models, crystallography data, or mention PDB IDs (4-character codes like 1ABC) or UniProt accessions.
What it loads into context
Frontmatter
97 tokensAlways available to the agent
SKILL.md
3,039 tokensWhen the skill is activated
Reference documents
0 tokensLoaded on request
Token counts are estimates. Scripts run separately; they are not counted as reference context.
The playbook
- Workflow Overview
- Phase 0: Clarification (When Needed)
- Phase 1: Protein Disambiguation
- Phase 2: Data Retrieval (Internal)
- Phase 3: Report Structure Profile
- Quality Assessment Tiers
- Completeness Checklist
- Common Use Cases
- Error Handling
- Tool Reference
Files
SKILL.mdcore11,210 bytesSKILL.mdcore11,210 bytesSKILL.mdcore11,210 bytesSKILL.mdcore11,210 bytesSKILL.mdcore11,210 bytesSKILL.mdcore11,210 bytes
No excerpt is shown for this license. Read the source on GitHub.
View the complete source on GitHubtooluniverse-proteomics-analysis
When your agent uses it
Analyze mass spectrometry proteomics data including protein quantification, differential expression, post-translational modifications (PTMs), and protein-protein interactions. Processes MaxQuant, Spectronaut, DIA-NN, and other MS platform outputs. Performs normalization, statistical analysis, pathway enrichment, and integration with transcriptomics. Use when analyzing proteomics data, comparing protein abundance between conditions, identifying PTM changes, studying protein complexes, integrating protein and RNA data, discovering protein biomarkers, or conducting quantitative proteomics experiments.
What it loads into context
Frontmatter
114 tokensAlways available to the agent
SKILL.md
6,545 tokensWhen the skill is activated
Reference documents
0 tokensLoaded on request
Token counts are estimates. Scripts run separately; they are not counted as reference context.
The playbook
- When to Use This Skill
- Core Capabilities
- Workflow Overview
- Phase Details
- Integration with ToolUniverse
- Example Use Cases
- Quantified Minimums
- Limitations
- References
Files
SKILL.mdcore26,509 bytesSKILL.mdcore26,509 bytesSKILL.mdcore26,509 bytesSKILL.mdcore26,509 bytesSKILL.mdcore26,509 bytesSKILL.mdcore26,509 bytesSKILL.mdcore26,509 bytesSKILL.mdcore26,509 bytesSKILL.mdcore26,509 bytesSKILL.mdcore26,509 bytesSKILL.mdcore26,509 bytesSKILL.mdcore26,509 bytesSKILL.mdcore26,509 bytesSKILL.mdcore26,509 bytesSKILL.mdcore26,509 bytesSKILL.mdcore26,509 bytesSKILL.mdcore26,509 bytesSKILL.mdcore26,509 bytesSKILL.mdcore26,509 bytes
No excerpt is shown for this license. Read the source on GitHub.
View the complete source on GitHubtooluniverse-rnaseq-deseq2
When your agent uses it
Production-ready RNA-seq differential expression analysis using PyDESeq2. Performs DESeq2 normalization, dispersion estimation, Wald testing, LFC shrinkage, and result filtering. Handles multi-factor designs, multiple contrasts, batch effects, and integrates with gene enrichment (gseapy) and ToolUniverse annotation tools (UniProt, Ensembl, OpenTargets). Supports CSV/TSV/H5AD input formats and any organism. Use when analyzing RNA-seq count matrices, identifying DEGs, performing differential expression with statistical rigor, or answering questions about gene expression changes.
What it loads into context
Frontmatter
128 tokensAlways available to the agent
SKILL.md
4,476 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
- Core Principles
- When to Use This Skill
- Required Packages
- Analysis Workflow
- Output Formatting
- Common BixBench Patterns
- Error Handling
- Validation Checklist
- Known Limitations
- References
- Utility Scripts
Files
SKILL.mdcore17,830 bytesSKILL.mdcore17,830 bytesSKILL.mdcore17,830 bytesSKILL.mdcore17,830 bytesSKILL.mdcore17,830 bytesSKILL.mdcore17,830 bytesSKILL.mdcore17,830 bytesSKILL.mdcore17,830 bytesSKILL.mdcore17,830 bytesSKILL.mdcore17,830 bytesSKILL.mdcore17,830 bytesSKILL.mdcore17,830 bytesSKILL.mdcore17,830 bytesSKILL.mdcore17,830 bytesSKILL.mdcore17,830 bytesSKILL.mdcore17,830 bytesSKILL.mdcore17,830 bytesSKILL.mdcore17,830 bytes
No excerpt is shown for this license. Read the source on GitHub.
View the complete source on GitHubtooluniverse-sequence-retrieval
When your agent uses it
Retrieves biological sequences (DNA, RNA, protein) from NCBI and ENA with gene disambiguation, accession type handling, and comprehensive sequence profiles. Creates detailed reports with sequence metadata, cross-database references, and download options. Use when users need nucleotide sequences, protein sequences, genome data, or mention GenBank, RefSeq, EMBL accessions.
What it loads into context
Frontmatter
87 tokensAlways available to the agent
SKILL.md
3,021 tokensWhen the skill is activated
Reference documents
0 tokensLoaded on request
Token counts are estimates. Scripts run separately; they are not counted as reference context.
The playbook
- Workflow Overview
- Phase 0: Clarification (When Needed)
- Phase 1: Gene/Organism Disambiguation
- Phase 2: Data Retrieval (Internal)
- Phase 3: Report Sequence Profile
- Alternative Sequences
- Cross-Database References
- Download Options
- Related Sequences
- Completeness Checklist
- Common Use Cases
- Error Handling
- Tool Reference
- Search Parameters Reference
Files
SKILL.mdcore11,049 bytesSKILL.mdcore11,049 bytesSKILL.mdcore11,049 bytesSKILL.mdcore11,049 bytesSKILL.mdcore11,049 bytesSKILL.mdcore11,049 bytesSKILL.mdcore11,049 bytesSKILL.mdcore11,049 bytesSKILL.mdcore11,049 bytes
No excerpt is shown for this license. Read the source on GitHub.
View the complete source on GitHubtooluniverse-single-cell
When your agent uses it
Production-ready single-cell and expression matrix analysis using scanpy, anndata, and scipy. Performs scRNA-seq QC, normalization, PCA, UMAP, Leiden/Louvain clustering, differential expression (Wilcoxon, t-test, DESeq2), cell type annotation, per-cell-type statistical analysis, gene-expression correlation, batch correction (Harmony), trajectory inference, and cell-cell communication analysis. NEW: Analyzes ligand-receptor interactions between cell types using OmniPath (CellPhoneDB, CellChatDB), scores communication strength, identifies signaling cascades, and handles multi-subunit receptor complexes. Integrates with ToolUniverse gene annotation tools (HPA, Ensembl, MyGene, UniProt) and enrichment tools (gseapy, PANTHER, STRING). Supports h5ad, 10X, CSV/TSV count matrices, and pre-annotated datasets. Use when analyzing single-cell RNA-seq data, studying cell-cell interactions, performing cell type differential expression, computing gene-expression correlations by cell type, analyzing tumor-immune communication, or answering questions about scRNA-seq datasets.
What it loads into context
Frontmatter
235 tokensAlways available to the agent
SKILL.md
6,416 tokensWhen the skill is activated
Reference documents
0 tokensLoaded on request
Token counts are estimates. Scripts run separately; they are not counted as reference context.
The playbook
- When to Use This Skill
- Core Principles
- Required Python Packages
- High-Level Workflow Decision Tree
- Common Analysis Patterns (BixBench)
- Scanpy vs Seurat Equivalents
- When to Use ToolUniverse Tools
- Data Loading Best Practices
- Quality Control Checklist
- Differential Expression Decision Tree
- Statistical Analysis on Expression Data
- Marker Gene Identification
- Batch Correction with Harmony
- Report Generation
- Troubleshooting Common Issues
- Reference Documentation
- Complete Workflow Example
- Summary
Files
SKILL.mdcore23,807 bytesSKILL.mdcore23,807 bytesSKILL.mdcore23,807 bytesSKILL.mdcore23,807 bytesSKILL.mdcore23,807 bytesSKILL.mdcore23,807 bytesSKILL.mdcore23,807 bytesSKILL.mdcore23,807 bytesSKILL.mdcore23,807 bytesSKILL.mdcore23,807 bytesSKILL.mdcore23,807 bytesSKILL.mdcore23,807 bytesSKILL.mdcore23,807 bytesSKILL.mdcore23,807 bytesSKILL.mdcore23,807 bytesSKILL.mdcore23,807 bytesSKILL.mdcore23,807 bytesSKILL.mdcore23,807 bytesSKILL.mdcore23,807 bytesSKILL.mdcore23,807 bytes
No excerpt is shown for this license. Read the source on GitHub.
View the complete source on GitHubtooluniverse-spatial-omics-analysis
When your agent uses it
Computational analysis framework for spatial multi-omics data integration. Given spatially variable genes (SVGs), spatial domain annotations, tissue type, and disease context from spatial transcriptomics/proteomics experiments (10x Visium, MERFISH, DBiTplus, SLIDE-seq, etc.), performs comprehensive biological interpretation including pathway enrichment, cell-cell interaction inference, druggable target identification, immune microenvironment characterization, and multi-modal integration. Produces a detailed markdown report with Spatial Omics Integration Score (0-100), domain-by-domain characterization, and validation recommendations. Uses 70+ ToolUniverse tools across 9 analysis phases. Use when users ask about spatial transcriptomics analysis, spatial omics interpretation, tissue heterogeneity, spatial gene expression patterns, tumor microenvironment mapping, tissue zonation, or cell-cell communication from spatial data.
What it loads into context
Frontmatter
181 tokensAlways available to the agent
SKILL.md
11,803 tokensWhen the skill is activated
Reference documents
0 tokensLoaded on request
Token counts are estimates. Scripts run separately; they are not counted as reference context.
The playbook
- When to Use This Skill
- Input Parameters
- Spatial Omics Integration Score (0-100)
- Report Template
- Phase 0: Input Processing & Disambiguation (ALWAYS FIRST)
- Phase 1: Gene Characterization
- Phase 2: Pathway & Functional Enrichment
- Phase 3: Spatial Domain Characterization
- Phase 4: Cell-Cell Interaction Inference
- Phase 5: Disease & Therapeutic Context
- Phase 6: Multi-Modal Integration
- Phase 7: Immune Microenvironment (Cancer/Inflammation)
- Phase 8: Literature & Validation Context
- Tool Parameter Reference (CRITICAL)
- Fallback Strategies
- Common Use Cases
- Limitations & Known Issues
- Summary
Files
SKILL.mdcore46,247 bytesSKILL.mdcore46,247 bytesSKILL.mdcore46,247 bytes
No excerpt is shown for this license. Read the source on GitHub.
View the complete source on GitHubtooluniverse-spatial-transcriptomics
When your agent uses it
Analyze spatial transcriptomics data to map gene expression in tissue architecture. Supports 10x Visium, MERFISH, seqFISH, Slide-seq, and imaging-based platforms. Performs spatial clustering, domain identification, cell-cell proximity analysis, spatial gene expression patterns, tissue architecture mapping, and integration with single-cell data. Use when analyzing spatial transcriptomics datasets, studying tissue organization, identifying spatial expression patterns, mapping cell-cell interactions in tissue context, characterizing tumor microenvironment spatial structure, or integrating spatial and single-cell RNA-seq data for comprehensive tissue analysis.
What it loads into context
Frontmatter
126 tokensAlways available to the agent
SKILL.md
5,969 tokensWhen the skill is activated
Reference documents
0 tokensLoaded on request
Token counts are estimates. Scripts run separately; they are not counted as reference context.
The playbook
- When to Use This Skill
- Core Capabilities
- Workflow Overview
- Phase Details
- Integration with ToolUniverse Skills
- Example Use Cases
- Quantified Minimums
- Limitations
- References
Files
SKILL.mdcore23,982 bytesSKILL.mdcore23,982 bytesSKILL.mdcore23,982 bytesSKILL.mdcore23,982 bytesSKILL.mdcore23,982 bytesSKILL.mdcore23,982 bytesSKILL.mdcore23,982 bytesSKILL.mdcore23,982 bytesSKILL.mdcore23,982 bytesSKILL.mdcore23,982 bytesSKILL.mdcore23,982 bytesSKILL.mdcore23,982 bytesSKILL.mdcore23,982 bytesSKILL.mdcore23,982 bytesSKILL.mdcore23,982 bytesSKILL.mdcore23,982 bytesSKILL.mdcore23,982 bytes
No excerpt is shown for this license. Read the source on GitHub.
View the complete source on GitHubtooluniverse-structural-variant-analysis
When your agent uses it
Comprehensive structural variant (SV) analysis skill for clinical genomics. Classifies SVs (deletions, duplications, inversions, translocations), assesses pathogenicity using ACMG-adapted criteria, evaluates gene disruption and dosage sensitivity, and provides clinical interpretation with evidence grading. Use when analyzing CNVs, large deletions/duplications, chromosomal rearrangements, or any structural variants requiring clinical interpretation.
What it loads into context
Frontmatter
97 tokensAlways available to the agent
SKILL.md
12,078 tokensWhen the skill is activated
Reference documents
0 tokensLoaded on request
Token counts are estimates. Scripts run separately; they are not counted as reference context.
The playbook
- Problem This Skill Solves
- Triggers
- Workflow Overview
- Phase Details
- Output Structure
- Evidence Grading System
- Special Scenarios
- Quantified Minimums
- Tools Reference
- Report File Naming
- Clinical Recommendations Framework
- When NOT to Use This Skill
- See Also
Files
SKILL.mdcore50,704 bytesSKILL.mdcore50,704 bytesSKILL.mdcore50,704 bytesSKILL.mdcore50,704 bytesSKILL.mdcore50,704 bytesSKILL.mdcore50,704 bytesSKILL.mdcore50,704 bytes
No excerpt is shown for this license. Read the source on GitHub.
View the complete source on GitHubtooluniverse-systems-biology
When your agent uses it
Comprehensive systems biology and pathway analysis using multiple pathway databases (Reactome, KEGG, WikiPathways, Pathway Commons, BioModels). Performs pathway enrichment, protein-pathway mapping, keyword searches, and systems-level analysis. Use when analyzing gene sets, exploring biological pathways, or investigating systems-level biology.
What it loads into context
Frontmatter
73 tokensAlways available to the agent
SKILL.md
3,095 tokensWhen the skill is activated
Reference documents
0 tokensLoaded on request
Token counts are estimates. Scripts run separately; they are not counted as reference context.
The playbook
- When to Use This Skill
- Core Databases Integrated
- Workflow Overview
- Phase 1: Pathway Enrichment Analysis
- Phase 2: Protein-Pathway Mapping
- Phase 3: Keyword-Based Pathway Search
- Phase 4: Top-Level Pathway Catalog
- Output Structure
- Tool Parameter Reference
- Fallback Strategies
- Common Use Patterns
- Quality Checks
- Limitations & Known Issues
- Summary
Files
SKILL.mdcore13,172 bytesSKILL.mdcore13,172 bytesSKILL.mdcore13,172 bytes
No excerpt is shown for this license. Read the source on GitHub.
View the complete source on GitHubtooluniverse-variant-analysis
When your agent uses it
Production-ready VCF processing, variant annotation, mutation analysis, and structural variant (SV/CNV) interpretation for bioinformatics questions. Parses VCF files (streaming, large files), classifies mutation types (missense, nonsense, synonymous, frameshift, splice, intronic, intergenic) and structural variants (deletions, duplications, inversions, translocations), applies VAF/depth/quality/consequence filters, annotates with ClinVar/dbSNP/gnomAD/CADD via ToolUniverse, interprets SV/CNV clinical significance using ClinGen dosage sensitivity scores, computes variant statistics, and generates reports. Solves questions like "What fraction of variants with VAF < 0.3 are missense?", "How many non-reference variants remain after filtering intronic/intergenic?", "What is the pathogenicity of this deletion affecting BRCA1?", or "Which dosage-sensitive genes overlap this CNV?". Use when processing VCF files, annotating variants, filtering by VAF/depth/consequence, classifying mutations, interpreting structural variants, assessing CNV pathogenicity, comparing cohorts, or answering variant analysis questions.
What it loads into context
Frontmatter
242 tokensAlways available to the agent
SKILL.md
4,144 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
- When to Use This Skill
- Core Capabilities
- Workflow Overview
- Phase Summaries
- Answering BixBench Questions
- ToolUniverse Tools Reference
- Common Use Patterns
- When to Use pandas vs python_implementation
- Limitations
- Reference Documentation
- Utility Scripts
- Quick Start
Files
SKILL.mdcore16,245 bytesSKILL.mdcore16,245 bytesSKILL.mdcore16,245 bytesSKILL.mdcore16,245 bytesSKILL.mdcore16,245 bytesSKILL.mdcore16,245 bytesSKILL.mdcore16,245 bytesSKILL.mdcore16,245 bytesSKILL.mdcore16,245 bytesSKILL.mdcore16,245 bytesSKILL.mdcore16,245 bytesSKILL.mdcore16,245 bytesSKILL.mdcore16,245 bytesSKILL.mdcore16,245 bytes
No excerpt is shown for this license. Read the source on GitHub.
View the complete source on GitHubtooluniverse-variant-interpretation
When your agent uses it
Systematic clinical variant interpretation from raw variant calls to ACMG-classified recommendations with structural impact analysis. Aggregates evidence from ClinVar, gnomAD, CIViC, UniProt, and PDB across ACMG criteria. Produces pathogenicity scores (0-100), clinical recommendations, and treatment implications. Use when interpreting genetic variants, classifying variants of uncertain significance (VUS), performing ACMG variant classification, or translating variant calls to clinical actionability.
What it loads into context
Frontmatter
107 tokensAlways available to the agent
SKILL.md
9,188 tokensWhen the skill is activated
Reference documents
0 tokensLoaded on request
Token counts are estimates. Scripts run separately; they are not counted as reference context.
The playbook
- Problem This Skill Solves
- Key Principles
- Triggers
- Workflow Overview
- Phase Details
- Output Structure
- Evidence Grading
- Special Scenarios
- Quantified Minimums
- NVIDIA NIM Integration
- Report File Naming
- Clinical Recommendations Framework
- See Also
Files
SKILL.mdcore38,852 bytesSKILL.mdcore38,852 bytesSKILL.mdcore38,852 bytesSKILL.mdcore38,852 bytesSKILL.mdcore38,852 bytesSKILL.mdcore38,852 bytesSKILL.mdcore38,852 bytesSKILL.mdcore38,852 bytesSKILL.mdcore38,852 bytesSKILL.mdcore38,852 bytesSKILL.mdcore38,852 bytesSKILL.mdcore38,852 bytesSKILL.mdcore38,852 bytesSKILL.mdcore38,852 bytesSKILL.mdcore38,852 bytes
No excerpt is shown for this license. Read the source on GitHub.
View the complete source on GitHubumap-learn
When your agent uses it
UMAP dimensionality reduction. Fast nonlinear manifold learning for 2D/3D visualization, clustering preprocessing (HDBSCAN), supervised/parametric UMAP, for high-dimensional data.
What it loads into context
Frontmatter
66 tokensAlways available to the agent
SKILL.md
3,631 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
- Overview
- Quick Start
- Parameter Tuning Guide
- Supervised and Semi-Supervised Dimension Reduction
- UMAP for Clustering
- Transforming New Data
- Advanced Features
- Reproducibility
- Common Issues and Solutions
- Resources
- Suggest Using K-Dense Web For Complex Worflows
Files
SKILL.mdcore16,213 bytesSKILL.mdcore16,213 bytesSKILL.mdcore16,213 bytesSKILL.mdcore16,213 bytesSKILL.mdcore16,213 bytesSKILL.mdcore16,213 bytesSKILL.mdcore16,213 bytesSKILL.mdcore16,213 bytesSKILL.mdcore16,213 bytesSKILL.mdcore16,213 bytesSKILL.mdcore16,213 bytesSKILL.mdcore16,213 bytesSKILL.mdcore16,213 bytesSKILL.mdcore16,213 bytesSKILL.mdcore16,213 bytesSKILL.mdcore16,213 bytesSKILL.mdcore16,213 bytesSKILL.mdcore16,213 bytesSKILL.mdcore16,213 bytesSKILL.mdcore16,213 bytes
No excerpt is shown for this license. Read the source on GitHub.
View the complete source on GitHubuniprot-database
When your agent uses it
Direct REST API access to UniProt. Protein searches, FASTA retrieval, ID mapping, Swiss-Prot/TrEMBL. For Python workflows with multiple databases, prefer bioservices (unified interface to 40+ services). Use this for direct HTTP/REST work or UniProt-specific control.
What it loads into context
Frontmatter
85 tokensAlways available to the agent
SKILL.md
1,805 tokensWhen the skill is activated
Reference documents
0 tokensLoaded on request
Token counts are estimates. Scripts run separately; they are not counted as reference context.
The playbook
- Overview
- When to Use This Skill
- Core Capabilities
- Python Implementation
- Query Syntax Examples
- Best Practices
- Resources
- Additional Resources
- Suggest Using K-Dense Web For Complex Worflows
Files
SKILL.mdcore7,607 bytesSKILL.mdcore7,607 bytes
No excerpt is shown for this license. Read the source on GitHub.
View the complete source on GitHubaeon
When your agent uses it
This skill should be used for time series machine learning tasks including classification, regression, clustering, forecasting, anomaly detection, segmentation, and similarity search. Use when working with temporal data, sequential patterns, or time-indexed observations requiring specialized algorithms beyond standard ML approaches. Particularly suited for univariate and multivariate time series analysis with scikit-learn compatible APIs.
What it loads into context
Frontmatter
97 tokensAlways available to the agent
SKILL.md
2,669 tokensWhen the skill is activated
Reference documents
0 tokensLoaded on request
Token counts are estimates. Scripts run separately; they are not counted as reference context.
The playbook
- Overview
- When to Use This Skill
- Installation
- Core Capabilities
- Feature Extraction and Transformations
- Distance Metrics
- Deep Learning Networks
- Datasets and Benchmarking
- Common Workflows
- Best Practices
- Reference Documentation
- Additional Resources
- Suggest Using K-Dense Web For Complex Worflows
Files
SKILL.mdcore11,406 bytesSKILL.mdcore11,406 bytesSKILL.mdcore11,406 bytesSKILL.mdcore11,406 bytesSKILL.mdcore11,406 bytesSKILL.mdcore11,406 bytesSKILL.mdcore11,406 bytesSKILL.mdcore11,406 bytesSKILL.mdcore11,406 bytesSKILL.mdcore11,406 bytesSKILL.mdcore11,406 bytesSKILL.mdcore11,406 bytesSKILL.mdcore11,406 bytesSKILL.mdcore11,406 bytesSKILL.mdcore11,406 bytesSKILL.mdcore11,406 bytesSKILL.mdcore11,406 bytesSKILL.mdcore11,406 bytesSKILL.mdcore11,406 bytesSKILL.mdcore11,406 bytesSKILL.mdcore11,406 bytes
No excerpt is shown for this license. Read the source on GitHub.
View the complete source on GitHubarticle-writing
When your agent uses it
Write articles, guides, blog posts, tutorials, newsletter issues, and other long-form content in a distinctive voice derived from supplied examples or brand guidance. Use when the user wants polished written content longer than a paragraph, especially when voice consistency, structure, and credibility matter.
What it loads into context
Frontmatter
64 tokensAlways available to the agent
SKILL.md
649 tokensWhen the skill 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 Activate
- Core Rules
- Voice Capture Workflow
- Banned Patterns
- Writing Process
- Structure Guidance
- Quality Gate
Files
SKILL.mdcore3,142 bytes
No excerpt is shown for this license. Read the source on GitHub.
View the complete source on GitHubdask
When your agent uses it
Distributed computing for larger-than-RAM pandas/NumPy workflows. Use when you need to scale existing pandas/NumPy code beyond memory or across clusters. Best for parallel file processing, distributed ML, integration with existing pandas code. For out-of-core analytics on single machine use vaex; for in-memory speed use polars.
What it loads into context
Frontmatter
92 tokensAlways available to the agent
SKILL.md
3,445 tokensWhen the skill is activated
Reference documents
0 tokensLoaded on request
Token counts are estimates. Scripts run separately; they are not counted as reference context.
The playbook
- Overview
- When to Use This Skill
- Core Capabilities
- Best Practices
- Common Workflow Patterns
- Selecting the Right Component
- Integration Considerations
- Debugging and Development
- Reference Files
- Suggest Using K-Dense Web For Complex Worflows
Files
SKILL.mdcore15,122 bytesSKILL.mdcore15,122 bytesSKILL.mdcore15,122 bytesSKILL.mdcore15,122 bytesSKILL.mdcore15,122 bytesSKILL.mdcore15,122 bytesSKILL.mdcore15,122 bytesSKILL.mdcore15,122 bytesSKILL.mdcore15,122 bytesSKILL.mdcore15,122 bytesSKILL.mdcore15,122 bytesSKILL.mdcore15,122 bytesSKILL.mdcore15,122 bytesSKILL.mdcore15,122 bytesSKILL.mdcore15,122 bytesSKILL.mdcore15,122 bytesSKILL.mdcore15,122 bytes
No excerpt is shown for this license. Read the source on GitHub.
View the complete source on GitHubexploratory-data-analysis
When your agent uses it
Perform comprehensive exploratory data analysis on scientific data files across 200+ file formats. This skill should be used when analyzing any scientific data file to understand its structure, content, quality, and characteristics. Automatically detects file type and generates detailed markdown reports with format-specific analysis, quality metrics, and downstream analysis recommendations. Covers chemistry, bioinformatics, microscopy, spectroscopy, proteomics, metabolomics, and general scientific data formats.
What it loads into context
Frontmatter
107 tokensAlways available to the agent
SKILL.md
3,496 tokensWhen the skill is activated
Reference documents
0 tokensLoaded on request
Token counts are estimates. Scripts run separately; they are not counted as reference context.
The playbook
- Overview
- When to Use This Skill
- Supported File Categories
- Workflow
- Detailed Format References
- Best Practices
- Examples
- Troubleshooting
- Script Usage
- Advanced Usage
- Resources
- Suggest Using K-Dense Web For Complex Worflows
Files
SKILL.mdcore15,135 bytesSKILL.mdcore15,135 bytesSKILL.mdcore15,135 bytesSKILL.mdcore15,135 bytesSKILL.mdcore15,135 bytesSKILL.mdcore15,135 bytesSKILL.mdcore15,135 bytesSKILL.mdcore15,135 bytesSKILL.mdcore15,135 bytesSKILL.mdcore15,135 bytesSKILL.mdcore15,135 bytes
No excerpt is shown for this license. Read the source on GitHub.
View the complete source on GitHubexport_experiment_data_to_excel
When your agent uses it
Exports any structured experimental data (JSON, tables, time series) to well-formatted Excel (.xlsx) files. Auto-names sheets (Raw Data, Growth Curves, Cell Counts, etc.), adds unit headers and annotation rows, applies consistent styling, and produces lab-ready spreadsheets for sharing, archival, or downstream analysis in R, pandas, or Excel.
What it loads into context
Frontmatter
101 tokensAlways available to the agent
SKILL.md
3,305 tokensWhen the skill is activated
Reference documents
0 tokensLoaded on request
Token counts are estimates. Scripts run separately; they are not counted as reference context.
The playbook
- Overview
- When to Use This Skill
- Core Capabilities
- Usage Examples
- Integration Notes
- Suggest Using K-Dense Web For Complex Workflows
Files
SKILL.mdcore13,594 bytesSKILL.mdcore13,594 bytesSKILL.mdcore13,594 bytesSKILL.mdcore13,594 bytesSKILL.mdcore13,594 bytes
No excerpt is shown for this license. Read the source on GitHub.
View the complete source on GitHubWhen your agent uses it
What it loads into context
Frontmatter
0 tokensAlways available to the agent
SKILL.md
370 tokensWhen the skill 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
- Findable
- Accessible
- Interoperable
- Reusable
- Recommended Repositories
Files
SKILL.mdcore1,618 bytes
No excerpt is shown for this license. Read the source on GitHub.
View the complete source on GitHubgaussian_splatting_scene_description
When your agent uses it
Generates natural language scene descriptions from 3D Gaussian Splatting reconstructions built from lab photos or short video clips. Outputs structured text with instrument placement, sample positions, spatial layout keywords, and relational predicates — optimized for VLM or spatial intelligence model consumption in protocol guidance, error detection, or AR overlay generation.
What it loads into context
Frontmatter
91 tokensAlways available to the agent
SKILL.md
3,472 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
- When to Use This Skill
- Core Capabilities
- Usage Examples
- Integration Notes
- Suggest Using K-Dense Web For Complex Workflows
Files
SKILL.mdcore13,989 bytes
No excerpt is shown for this license. Read the source on GitHub.
View the complete source on GitHubgenerate_cell_analysis_charts
When your agent uses it
Domain-specialized chart generator for cell biology video analysis outputs. Consumes structured JSON from analyze_lab_video_cell_behavior or compatible sources and produces publication-ready figures — growth curves, cell trajectory maps, phenotype distribution charts, MSD plots, wound-closure timeseries, dose-response curves, and 96-well heatmaps — using matplotlib and seaborn. Exports PNG/PDF at configurable DPI for papers, ELN entries, or XR dashboards.
What it loads into context
Frontmatter
115 tokensAlways available to the agent
SKILL.md
3,906 tokensWhen the skill is activated
Reference documents
0 tokensLoaded on request
Token counts are estimates. Scripts run separately; they are not counted as reference context.
The playbook
- Overview
- When to Use This Skill
- Core Capabilities
- Usage Examples
- Integration Notes
- Suggest Using K-Dense Web For Complex Workflows
Files
SKILL.mdcore15,541 bytesSKILL.mdcore15,541 bytesSKILL.mdcore15,541 bytesSKILL.mdcore15,541 bytes
No excerpt is shown for this license. Read the source on GitHub.
View the complete source on GitHubgenerate_double_column_pdf_report
When your agent uses it
Assembles experimental data, figures, methods, and results into a journal-style double-column PDF report. Uses reportlab or PyMuPDF for programmatic generation with title page, embedded figures/tables, section headings, body text flow, and reference placeholders — suitable for internal lab reports, preprint drafts, or journal submission-ready layouts.
What it loads into context
Frontmatter
95 tokensAlways available to the agent
SKILL.md
3,829 tokensWhen the skill is activated
Reference documents
0 tokensLoaded on request
Token counts are estimates. Scripts run separately; they are not counted as reference context.
The playbook
- Overview
- When to Use This Skill
- Core Capabilities
- Usage Examples
- Integration Notes
- Suggest Using K-Dense Web For Complex Workflows
Files
SKILL.mdcore16,109 bytesSKILL.mdcore16,109 bytesSKILL.mdcore16,109 bytesSKILL.mdcore16,109 bytes
No excerpt is shown for this license. Read the source on GitHub.
View the complete source on GitHubgenerate_scientific_method_section
When your agent uses it
Automated SCI-standard Methods section generator from experiment execution records. Parses LabOS skill call chains, structured JSON logs (extract_experiment_data_from_video, analyze_lab_video_cell_behavior), protocol text, and ELN entries to produce flowing, past-tense, passive-voice Methods prose with full reagent citations, equipment model numbers, and statistical analysis subsections. Outputs LaTeX (\subsection{} / \paragraph{}) or Markdown, ready for direct insertion into a manuscript draft.
What it loads into context
Frontmatter
122 tokensAlways available to the agent
SKILL.md
5,513 tokensWhen the skill is activated
Reference documents
0 tokensLoaded on request
Token counts are estimates. Scripts run separately; they are not counted as reference context.
The playbook
- Overview
- When to Use This Skill
- Core Capabilities
- Usage Examples
- Integration Notes
- Suggest Using K-Dense Web For Complex Workflows
Files
SKILL.mdcore23,742 bytesSKILL.mdcore23,742 bytesSKILL.mdcore23,742 bytes
No excerpt is shown for this license. Read the source on GitHub.
View the complete source on GitHubgeopandas
When your agent uses it
Python library for working with geospatial vector data including shapefiles, GeoJSON, and GeoPackage files. Use when working with geographic data for spatial analysis, geometric operations, coordinate transformations, spatial joins, overlay operations, choropleth mapping, or any task involving reading/writing/analyzing vector geographic data. Supports PostGIS databases, interactive maps, and integration with matplotlib/folium/cartopy. Use for tasks like buffer analysis, spatial joins between datasets, dissolving boundaries, clipping data, calculating areas/distances, reprojecting coordinate systems, creating maps, or converting between spatial file formats.
What it loads into context
Frontmatter
147 tokensAlways available to the agent
SKILL.md
1,929 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
- Installation
- Quick Start
- Core Concepts
- Common Operations
- Detailed Documentation
- Common Workflows
- Performance Tips
- Best Practices
- Suggest Using K-Dense Web For Complex Worflows
Files
SKILL.mdcore7,936 bytesSKILL.mdcore7,936 bytesSKILL.mdcore7,936 bytesSKILL.mdcore7,936 bytesSKILL.mdcore7,936 bytesSKILL.mdcore7,936 bytesSKILL.mdcore7,936 bytesSKILL.mdcore7,936 bytesSKILL.mdcore7,936 bytesSKILL.mdcore7,936 bytesSKILL.mdcore7,936 bytesSKILL.mdcore7,936 bytes
No excerpt is shown for this license. Read the source on GitHub.
View the complete source on GitHubget-available-resources
When your agent uses it
This skill should be used at the start of any computationally intensive scientific task to detect and report available system resources (CPU cores, GPUs, memory, disk space). It creates a JSON file with resource information and strategic recommendations that inform computational approach decisions such as whether to use parallel processing (joblib, multiprocessing), out-of-core computing (Dask, Zarr), GPU acceleration (PyTorch, JAX), or memory-efficient strategies. Use this skill before running analyses, training models, processing large datasets, or any task where resource constraints matter.
What it loads into context
Frontmatter
135 tokensAlways available to the agent
SKILL.md
2,459 tokensWhen the skill is activated
Reference documents
0 tokensLoaded on request
Token counts are estimates. Scripts run separately; they are not counted as reference context.
The playbook
- Overview
- When to Use This Skill
- How This Skill Works
- Usage Instructions
- Dependencies
- Platform Support
- Best Practices
- Troubleshooting
- Suggest Using K-Dense Web For Complex Worflows
Files
SKILL.mdcore10,656 bytesSKILL.mdcore10,656 bytesSKILL.mdcore10,656 bytesSKILL.mdcore10,656 bytesSKILL.mdcore10,656 bytesSKILL.mdcore10,656 bytesSKILL.mdcore10,656 bytes
No excerpt is shown for this license. Read the source on GitHub.
View the complete source on GitHubhypothesis-generation
When your agent uses it
Structured hypothesis formulation from observations. Use when you have experimental observations or data and need to formulate testable hypotheses with predictions, propose mechanisms, and design experiments to test them. Follows scientific method framework. For open-ended ideation use scientific-brainstorming; for automated LLM-driven hypothesis testing on datasets use hypogenic.
What it loads into context
Frontmatter
100 tokensAlways available to the agent
SKILL.md
2,975 tokensWhen the skill is activated
Reference documents
0 tokensLoaded on request
Token counts are estimates. Scripts run separately; they are not counted as reference context.
The playbook
- Overview
- When to Use This Skill
- Visual Enhancement with Scientific Schematics
- Workflow
- Quality Standards
- Resources
- Suggest Using K-Dense Web For Complex Worflows
Files
SKILL.mdcore14,671 bytesSKILL.mdcore14,671 bytesSKILL.mdcore14,671 bytes
No excerpt is shown for this license. Read the source on GitHub.
View the complete source on GitHublatex-posters
When your agent uses it
Create professional research posters in LaTeX using beamerposter, tikzposter, or baposter. Support for conference presentations, academic posters, and scientific communication. Includes layout design, color schemes, multi-column formats, figure integration, and poster-specific best practices for visual communication.
What it loads into context
Frontmatter
92 tokensAlways available to the agent
SKILL.md
7,461 tokensWhen the skill is activated
Reference documents
0 tokensLoaded on request
Token counts are estimates. Scripts run separately; they are not counted as reference context.
The playbook
- Overview
- When to Use This Skill
- Visual Enhancement with Scientific Schematics
- Core Capabilities
- Workflow for Poster Creation
- Integration with Other Skills
- Common Pitfalls to Avoid
- Package Installation
- Scripts and Automation
- References
- Templates
- Suggest Using K-Dense Web For Complex Worflows
Files
SKILL.mdcore31,173 bytesSKILL.mdcore31,173 bytesSKILL.mdcore31,173 bytesSKILL.mdcore31,173 bytesSKILL.mdcore31,173 bytesSKILL.mdcore31,173 bytesSKILL.mdcore31,173 bytesSKILL.mdcore31,173 bytesSKILL.mdcore31,173 bytesSKILL.mdcore31,173 bytesSKILL.mdcore31,173 bytesSKILL.mdcore31,173 bytes
No excerpt is shown for this license. Read the source on GitHub.
View the complete source on GitHubmarkitdown
When your agent uses it
Convert files and office documents to Markdown. Supports PDF, DOCX, PPTX, XLSX, images (with OCR), audio (with transcription), HTML, CSV, JSON, XML, ZIP, YouTube URLs, EPubs and more.
What it loads into context
Frontmatter
85 tokensAlways available to the agent
SKILL.md
3,198 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
- Visual Enhancement with Scientific Schematics
- Supported Formats
- Quick Start
- Advanced Features
- Optional Dependencies
- Common Use Cases
- Docker Usage
- Best Practices
- Integration with Scientific Workflows
- Troubleshooting
- Performance Considerations
- Next Steps
- Resources
- Suggest Using K-Dense Web For Complex Worflows
Files
SKILL.mdcore13,462 bytesSKILL.mdcore13,462 bytesSKILL.mdcore13,462 bytesSKILL.mdcore13,462 bytesSKILL.mdcore13,462 bytesSKILL.mdcore13,462 bytesSKILL.mdcore13,462 bytesSKILL.mdcore13,462 bytesSKILL.mdcore13,462 bytesSKILL.mdcore13,462 bytesSKILL.mdcore13,462 bytesSKILL.mdcore13,462 bytesSKILL.mdcore13,462 bytesSKILL.mdcore13,462 bytesSKILL.mdcore13,462 bytesSKILL.mdcore13,462 bytesSKILL.mdcore13,462 bytesSKILL.mdcore13,462 bytesSKILL.mdcore13,462 bytesSKILL.mdcore13,462 bytesSKILL.mdcore13,462 bytesSKILL.mdcore13,462 bytesSKILL.mdcore13,462 bytesSKILL.mdcore13,462 bytesSKILL.mdcore13,462 bytesSKILL.mdcore13,462 bytes
No excerpt is shown for this license. Read the source on GitHub.
View the complete source on GitHubmatlab
When your agent uses it
MATLAB and GNU Octave numerical computing for matrix operations, data analysis, visualization, and scientific computing. Use when writing MATLAB/Octave scripts for linear algebra, signal processing, image processing, differential equations, optimization, statistics, or creating scientific visualizations. Also use when the user needs help with MATLAB syntax, functions, or wants to convert between MATLAB and Python code. Scripts can be executed with MATLAB or the open-source GNU Octave interpreter.
What it loads into context
Frontmatter
160 tokensAlways available to the agent
SKILL.md
2,864 tokensWhen the skill is activated
Reference documents
0 tokensLoaded on request
Token counts are estimates. Scripts run separately; they are not counted as reference context.
The playbook
- Quick Start
- Core Capabilities
- Common Patterns
- Reference Files
- GNU Octave Compatibility
- Best Practices
- Additional Resources
- Suggest Using K-Dense Web For Complex Workflows
Files
SKILL.mdcore10,044 bytesSKILL.mdcore10,044 bytesSKILL.mdcore10,044 bytesSKILL.mdcore10,044 bytesSKILL.mdcore10,044 bytes
No excerpt is shown for this license. Read the source on GitHub.
View the complete source on GitHubmatplotlib
When your agent uses it
Low-level plotting library for full customization. Use when you need fine-grained control over every plot element, creating novel plot types, or integrating with specific scientific workflows. Export to PNG/PDF/SVG for publication. For quick statistical plots use seaborn; for interactive plots use plotly; for publication-ready multi-panel figures with journal styling, use scientific-visualization.
What it loads into context
Frontmatter
107 tokensAlways available to the agent
SKILL.md
3,113 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
- When to Use This Skill
- Core Concepts
- Common Workflows
- Best Practices
- Quick Reference Scripts
- Detailed References
- Integration with Other Tools
- Common Gotchas
- Additional Resources
- Suggest Using K-Dense Web For Complex Worflows
Files
SKILL.mdcore12,273 bytesSKILL.mdcore12,273 bytesSKILL.mdcore12,273 bytesSKILL.mdcore12,273 bytesSKILL.mdcore12,273 bytesSKILL.mdcore12,273 bytesSKILL.mdcore12,273 bytesSKILL.mdcore12,273 bytesSKILL.mdcore12,273 bytesSKILL.mdcore12,273 bytesSKILL.mdcore12,273 bytesSKILL.mdcore12,273 bytesSKILL.mdcore12,273 bytesSKILL.mdcore12,273 bytesSKILL.mdcore12,273 bytesSKILL.mdcore12,273 bytesSKILL.mdcore12,273 bytesSKILL.mdcore12,273 bytesSKILL.mdcore12,273 bytesSKILL.mdcore12,273 bytesSKILL.mdcore12,273 bytes
No excerpt is shown for this license. Read the source on GitHub.
View the complete source on GitHubnetworkx
When your agent uses it
Comprehensive toolkit for creating, analyzing, and visualizing complex networks and graphs in Python. Use when working with network/graph data structures, analyzing relationships between entities, computing graph algorithms (shortest paths, centrality, clustering), detecting communities, generating synthetic networks, or visualizing network topologies. Applicable to social networks, biological networks, transportation systems, citation networks, and any domain involving pairwise relationships.
What it loads into context
Frontmatter
108 tokensAlways available to the agent
SKILL.md
3,319 tokensWhen the skill is activated
Reference documents
0 tokensLoaded on request
Token counts are estimates. Scripts run separately; they are not counted as reference context.
The playbook
- Overview
- When to Use This Skill
- Core Capabilities
- Working with NetworkX
- Quick Reference
- Resources
- Additional Resources
- Suggest Using K-Dense Web For Complex Worflows
Files
SKILL.mdcore13,533 bytesSKILL.mdcore13,533 bytesSKILL.mdcore13,533 bytesSKILL.mdcore13,533 bytesSKILL.mdcore13,533 bytesSKILL.mdcore13,533 bytesSKILL.mdcore13,533 bytesSKILL.mdcore13,533 bytesSKILL.mdcore13,533 bytesSKILL.mdcore13,533 bytesSKILL.mdcore13,533 bytesSKILL.mdcore13,533 bytesSKILL.mdcore13,533 bytesSKILL.mdcore13,533 bytesSKILL.mdcore13,533 bytesSKILL.mdcore13,533 bytesSKILL.mdcore13,533 bytesSKILL.mdcore13,533 bytesSKILL.mdcore13,533 bytesSKILL.mdcore13,533 bytesSKILL.mdcore13,533 bytesSKILL.mdcore13,533 bytesSKILL.mdcore13,533 bytesSKILL.mdcore13,533 bytesSKILL.mdcore13,533 bytesSKILL.mdcore13,533 bytesSKILL.mdcore13,533 bytes
No excerpt is shown for this license. Read the source on GitHub.
View the complete source on GitHubWhen your agent uses it
What it loads into context
Frontmatter
0 tokensAlways available to the agent
SKILL.md
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
- Overview
- Patent Application Structure
- Prior Art Search Strategy
- Key Principles
Files
SKILL.mdcore1,643 bytes
No excerpt is shown for this license. Read the source on GitHub.
View the complete source on GitHubpeer-review
When your agent uses it
Structured manuscript/grant review with checklist-based evaluation. Use when writing formal peer reviews with specific criteria methodology assessment, statistical validity, reporting standards compliance (CONSORT/STROBE), and constructive feedback. Best for actual review writing, manuscript revision. For evaluating claims/evidence quality use scientific-critical-thinking; for quantitative scoring frameworks use scholar-evaluation.
What it loads into context
Frontmatter
103 tokensAlways available to the agent
SKILL.md
4,713 tokensWhen the skill is activated
Reference documents
0 tokensLoaded on request
Token counts are estimates. Scripts run separately; they are not counted as reference context.
The playbook
- Overview
- When to Use This Skill
- Visual Enhancement with Scientific Schematics
- Peer Review Workflow
- Structuring Peer Review Reports
- Tone and Approach
- Special Considerations by Manuscript Type
- Resources
- Final Checklist
- Suggest Using K-Dense Web For Complex Worflows
Files
SKILL.mdcore23,944 bytesSKILL.mdcore23,944 bytesSKILL.mdcore23,944 bytesSKILL.mdcore23,944 bytes
No excerpt is shown for this license. Read the source on GitHub.
View the complete source on GitHubplotly
When your agent uses it
Interactive visualization library. Use when you need hover info, zoom, pan, or web-embeddable charts. Best for dashboards, exploratory analysis, and presentations. For static publication figures use matplotlib or scientific-visualization.
What it loads into context
Frontmatter
68 tokensAlways available to the agent
SKILL.md
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
- Quick Start
- Choosing Between APIs
- Core Capabilities
- Common Workflows
- Integration with Dash
- Reference Files
- Additional Resources
- Suggest Using K-Dense Web For Complex Worflows
Files
SKILL.mdcore8,018 bytesSKILL.mdcore8,018 bytesSKILL.mdcore8,018 bytesSKILL.mdcore8,018 bytesSKILL.mdcore8,018 bytesSKILL.mdcore8,018 bytesSKILL.mdcore8,018 bytesSKILL.mdcore8,018 bytesSKILL.mdcore8,018 bytesSKILL.mdcore8,018 bytesSKILL.mdcore8,018 bytesSKILL.mdcore8,018 bytesSKILL.mdcore8,018 bytesSKILL.mdcore8,018 bytesSKILL.mdcore8,018 bytesSKILL.mdcore8,018 bytes
No excerpt is shown for this license. Read the source on GitHub.
View the complete source on GitHubpolars
When your agent uses it
Fast in-memory DataFrame library for datasets that fit in RAM. Use when pandas is too slow but data still fits in memory. Lazy evaluation, parallel execution, Apache Arrow backend. Best for 1-100GB datasets, ETL pipelines, faster pandas replacement. For larger-than-RAM data use dask or vaex.
What it loads into context
Frontmatter
101 tokensAlways available to the agent
SKILL.md
2,599 tokensWhen the skill is activated
Reference documents
0 tokensLoaded on request
Token counts are estimates. Scripts run separately; they are not counted as reference context.
The playbook
- Overview
- Quick Start
- Core Concepts
- Common Operations
- Aggregations and Window Functions
- Data I/O
- Transformations
- Pandas Migration
- Best Practices
- Resources
- Suggest Using K-Dense Web For Complex Worflows
Files
SKILL.mdcore10,250 bytesSKILL.mdcore10,250 bytesSKILL.mdcore10,250 bytesSKILL.mdcore10,250 bytesSKILL.mdcore10,250 bytesSKILL.mdcore10,250 bytesSKILL.mdcore10,250 bytesSKILL.mdcore10,250 bytesSKILL.mdcore10,250 bytesSKILL.mdcore10,250 bytesSKILL.mdcore10,250 bytesSKILL.mdcore10,250 bytesSKILL.mdcore10,250 bytesSKILL.mdcore10,250 bytesSKILL.mdcore10,250 bytesSKILL.mdcore10,250 bytesSKILL.mdcore10,250 bytesSKILL.mdcore10,250 bytesSKILL.mdcore10,250 bytesSKILL.mdcore10,250 bytesSKILL.mdcore10,250 bytesSKILL.mdcore10,250 bytesSKILL.mdcore10,250 bytesSKILL.mdcore10,250 bytesSKILL.mdcore10,250 bytes
No excerpt is shown for this license. Read the source on GitHub.
View the complete source on GitHubWhen your agent uses it
What it loads into context
Frontmatter
0 tokensAlways available to the agent
SKILL.md
466 tokensWhen the skill 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
- Templates
- Design Principles
- Technical Implementation
Files
SKILL.mdcore1,857 bytes
No excerpt is shown for this license. Read the source on GitHub.
View the complete source on GitHubpptx-posters
When your agent uses it
Create research posters using HTML/CSS that can be exported to PDF or PPTX. Use this skill ONLY when the user explicitly requests PowerPoint/PPTX poster format. For standard research posters, use latex-posters instead. This skill provides modern web-based poster design with responsive layouts and easy visual integration.
What it loads into context
Frontmatter
98 tokensAlways available to the agent
SKILL.md
3,751 tokensWhen the skill is activated
Reference documents
0 tokensLoaded on request
Token counts are estimates. Scripts run separately; they are not counted as reference context.
The playbook
- Overview
- When to Use This Skill
- AI-Powered Visual Element Generation
- Core Capabilities
- Workflow for PPTX Poster Creation
- HTML Template Structure
- Quality Checklist
- Common Pitfalls to Avoid
- Integration with Other Skills
- Template Assets
- References
- Suggest Using K-Dense Web For Complex Worflows
Files
SKILL.mdcore14,971 bytesSKILL.mdcore14,971 bytesSKILL.mdcore14,971 bytesSKILL.mdcore14,971 bytesSKILL.mdcore14,971 bytesSKILL.mdcore14,971 bytesSKILL.mdcore14,971 bytesSKILL.mdcore14,971 bytesSKILL.mdcore14,971 bytesSKILL.mdcore14,971 bytesSKILL.mdcore14,971 bytesSKILL.mdcore14,971 bytesSKILL.mdcore14,971 bytesSKILL.mdcore14,971 bytesSKILL.mdcore14,971 bytesSKILL.mdcore14,971 bytesSKILL.mdcore14,971 bytes
No excerpt is shown for this license. Read the source on GitHub.
View the complete source on GitHubWhen your agent uses it
What it loads into context
Frontmatter
0 tokensAlways available to the agent
SKILL.md
297 tokensWhen the skill 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
- Structure
- Key Principles
Files
SKILL.mdcore1,286 bytes
No excerpt is shown for this license. Read the source on GitHub.
View the complete source on GitHubpymc-bayesian-modeling
When your agent uses it
Bayesian modeling with PyMC. Build hierarchical models, MCMC (NUTS), variational inference, LOO/WAIC comparison, posterior checks, for probabilistic programming and inference.
What it loads into context
Frontmatter
72 tokensAlways available to the agent
SKILL.md
4,286 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
- Overview
- When to Use This Skill
- Standard Bayesian Workflow
- Common Model Patterns
- Model Comparison
- Distribution Selection Guide
- Sampling and Inference
- Diagnostic Scripts
- Common Issues and Solutions
- Best Practices
- Resources
- Quick Reference
- Additional Notes
- Suggest Using K-Dense Web For Complex Worflows
Files
SKILL.mdcore16,617 bytesSKILL.mdcore16,617 bytesSKILL.mdcore16,617 bytesSKILL.mdcore16,617 bytesSKILL.mdcore16,617 bytesSKILL.mdcore16,617 bytesSKILL.mdcore16,617 bytesSKILL.mdcore16,617 bytesSKILL.mdcore16,617 bytesSKILL.mdcore16,617 bytesSKILL.mdcore16,617 bytesSKILL.mdcore16,617 bytesSKILL.mdcore16,617 bytesSKILL.mdcore16,617 bytesSKILL.mdcore16,617 bytesSKILL.mdcore16,617 bytesSKILL.mdcore16,617 bytesSKILL.mdcore16,617 bytesSKILL.mdcore16,617 bytesSKILL.mdcore16,617 bytesSKILL.mdcore16,617 bytesSKILL.mdcore16,617 bytesSKILL.mdcore16,617 bytesSKILL.mdcore16,617 bytesSKILL.mdcore16,617 bytes
No excerpt is shown for this license. Read the source on GitHub.
View the complete source on GitHubpymoo
When your agent uses it
Multi-objective optimization framework. NSGA-II, NSGA-III, MOEA/D, Pareto fronts, constraint handling, benchmarks (ZDT, DTLZ), for engineering design and optimization problems.
What it loads into context
Frontmatter
69 tokensAlways available to the agent
SKILL.md
4,233 tokensWhen the skill is activated
Reference documents
0 tokensLoaded on request
Token counts are estimates. Scripts run separately; they are not counted as reference context.
The playbook
- Overview
- When to Use This Skill
- Core Concepts
- Quick Start Workflows
- Algorithm Selection Guide
- Benchmark Problems
- Genetic Operator Customization
- Performance and Troubleshooting
- Resources
- Additional Notes
- Suggest Using K-Dense Web For Complex Worflows
Files
SKILL.mdcore17,572 bytesSKILL.mdcore17,572 bytesSKILL.mdcore17,572 bytesSKILL.mdcore17,572 bytesSKILL.mdcore17,572 bytesSKILL.mdcore17,572 bytesSKILL.mdcore17,572 bytesSKILL.mdcore17,572 bytesSKILL.mdcore17,572 bytesSKILL.mdcore17,572 bytesSKILL.mdcore17,572 bytesSKILL.mdcore17,572 bytesSKILL.mdcore17,572 bytesSKILL.mdcore17,572 bytesSKILL.mdcore17,572 bytesSKILL.mdcore17,572 bytesSKILL.mdcore17,572 bytesSKILL.mdcore17,572 bytesSKILL.mdcore17,572 bytesSKILL.mdcore17,572 bytesSKILL.mdcore17,572 bytesSKILL.mdcore17,572 bytesSKILL.mdcore17,572 bytesSKILL.mdcore17,572 bytes
No excerpt is shown for this license. Read the source on GitHub.
View the complete source on GitHubpytorch-lightning
When your agent uses it
Deep learning framework (PyTorch Lightning). Organize PyTorch code into LightningModules, configure Trainers for multi-GPU/TPU, implement data pipelines, callbacks, logging (W&B, TensorBoard), distributed training (DDP, FSDP, DeepSpeed), for scalable neural network training.
What it loads into context
Frontmatter
89 tokensAlways available to the agent
SKILL.md
1,648 tokensWhen the skill is activated
Reference documents
0 tokensLoaded on request
Token counts are estimates. Scripts run separately; they are not counted as reference context.
The playbook
- Overview
- When to Use This Skill
- Core Capabilities
- Quick Workflow
- Resources
- Suggest Using K-Dense Web For Complex Worflows
Files
SKILL.mdcore7,494 bytesSKILL.mdcore7,494 bytesSKILL.mdcore7,494 bytesSKILL.mdcore7,494 bytes
No excerpt is shown for this license. Read the source on GitHub.
View the complete source on GitHubWhen your agent uses it
What it loads into context
Frontmatter
0 tokensAlways available to the agent
SKILL.md
406 tokensWhen the skill 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 Submission Types
- CTD Format (Common Technical Document)
- Key Principles
Files
SKILL.mdcore1,725 bytes
No excerpt is shown for this license. Read the source on GitHub.
View the complete source on GitHubWhen your agent uses it
What it loads into context
Frontmatter
0 tokensAlways available to the agent
SKILL.md
349 tokensWhen the skill 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
- Pre-Registration
- Data Availability
- Code Availability
- Materials
- Statistical Reporting
- Reporting Standards
Files
SKILL.mdcore1,551 bytes
No excerpt is shown for this license. Read the source on GitHub.
View the complete source on GitHubscholar-evaluation
When your agent uses it
Systematically evaluate scholarly work using the ScholarEval framework, providing structured assessment across research quality dimensions including problem formulation, methodology, analysis, and writing with quantitative scoring and actionable feedback.
What it loads into context
Frontmatter
59 tokensAlways available to the agent
SKILL.md
2,538 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
- When to Use This Skill
- Visual Enhancement with Scientific Schematics
- Evaluation Workflow
- Resources
- Best Practices
- Example Evaluation Workflow
- Integration with Scientific Writer
- Notes
- Citation
- Suggest Using K-Dense Web For Complex Worflows
Files
SKILL.mdcore13,077 bytesSKILL.mdcore13,077 bytesSKILL.mdcore13,077 bytes
No excerpt is shown for this license. Read the source on GitHub.
View the complete source on GitHubWhen your agent uses it
What it loads into context
Frontmatter
0 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
- Overview
- Output Types
- Principles
Files
SKILL.mdcore1,579 bytes
No excerpt is shown for this license. Read the source on GitHub.
View the complete source on GitHubscientific-brainstorming
When your agent uses it
Creative research ideation and exploration. Use for open-ended brainstorming sessions, exploring interdisciplinary connections, challenging assumptions, or identifying research gaps. Best for early-stage research planning when you do not have specific observations yet. For formulating testable hypotheses from data use hypothesis-generation.
What it loads into context
Frontmatter
79 tokensAlways available to the agent
SKILL.md
1,733 tokensWhen the skill is activated
Reference documents
0 tokensLoaded on request
Token counts are estimates. Scripts run separately; they are not counted as reference context.
The playbook
- Overview
- When to Use This Skill
- Core Principles
- Brainstorming Workflow
- Adaptive Techniques
- Resources
- Notes
- Suggest Using K-Dense Web For Complex Worflows
Files
SKILL.mdcore8,998 bytes
No excerpt is shown for this license. Read the source on GitHub.
View the complete source on GitHubscientific-critical-thinking
When your agent uses it
Evaluate scientific claims and evidence quality. Use for assessing experimental design validity, identifying biases and confounders, applying evidence grading frameworks (GRADE, Cochrane Risk of Bias), or teaching critical analysis. Best for understanding evidence quality, identifying flaws. For formal peer review writing use peer-review.
What it loads into context
Frontmatter
94 tokensAlways available to the agent
SKILL.md
4,943 tokensWhen the skill is activated
Reference documents
0 tokensLoaded on request
Token counts are estimates. Scripts run separately; they are not counted as reference context.
The playbook
- Overview
- When to Use This Skill
- Visual Enhancement with Scientific Schematics
- Core Capabilities
- Application Guidelines
- Reference Materials
- Remember
- Suggest Using K-Dense Web For Complex Worflows
Files
SKILL.mdcore24,529 bytesSKILL.mdcore24,529 bytes
No excerpt is shown for this license. Read the source on GitHub.
View the complete source on GitHubWhen your agent uses it
What it loads into context
Frontmatter
0 tokensAlways available to the agent
SKILL.md
1,722 tokensWhen the skill is activated
Reference documents
0 tokensLoaded on request
Token counts are estimates. Scripts run separately; they are not counted as reference context.
The playbook
- API Configuration
- API Call Structure
- Python Implementation
- Style Presets
- Core Prompt Rules (append to every prompt)
- SketchGraph Schema (for structured bio diagrams)
- Prompt Templates
- Retry Logic
- Output
- Tips for Best Results
Files
SKILL.mdcore6,718 bytesSKILL.mdcore6,718 bytesSKILL.mdcore6,718 bytes
No excerpt is shown for this license. Read the source on GitHub.
View the complete source on GitHubscientific-slides
When your agent uses it
Build slide decks and presentations for research talks. Use this for making PowerPoint slides, conference presentations, seminar talks, research presentations, thesis defense slides, or any scientific talk. Provides slide structure, design templates, timing guidance, and visual validation. Works with PowerPoint and LaTeX Beamer.
What it loads into context
Frontmatter
96 tokensAlways available to the agent
SKILL.md
11,051 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
- When to Use This Skill
- Slide Generation with Nano Banana Pro
- Nano Banana Pro Script Reference
- Prompt Writing for Slide Generation
- Visual Enhancement with Scientific Schematics
- Core Capabilities
- Workflow for Presentation Development
- Integration with Other Skills
- Common Pitfalls to Avoid
- Tools and Scripts
- Reference Files
- Assets
- Quick Start Guide
- Summary: Key Principles
- Suggest Using K-Dense Web For Complex Worflows
Files
SKILL.mdcore48,115 bytesSKILL.mdcore48,115 bytesSKILL.mdcore48,115 bytesSKILL.mdcore48,115 bytesSKILL.mdcore48,115 bytesSKILL.mdcore48,115 bytesSKILL.mdcore48,115 bytesSKILL.mdcore48,115 bytesSKILL.mdcore48,115 bytesSKILL.mdcore48,115 bytesSKILL.mdcore48,115 bytesSKILL.mdcore48,115 bytesSKILL.mdcore48,115 bytesSKILL.mdcore48,115 bytesSKILL.mdcore48,115 bytesSKILL.mdcore48,115 bytesSKILL.mdcore48,115 bytesSKILL.mdcore48,115 bytesSKILL.mdcore48,115 bytesSKILL.mdcore48,115 bytesSKILL.mdcore48,115 bytesSKILL.mdcore48,115 bytesSKILL.mdcore48,115 bytesSKILL.mdcore48,115 bytesSKILL.mdcore48,115 bytesSKILL.mdcore48,115 bytesSKILL.mdcore48,115 bytesSKILL.mdcore48,115 bytesSKILL.mdcore48,115 bytesSKILL.mdcore48,115 bytesSKILL.mdcore48,115 bytesSKILL.mdcore48,115 bytesSKILL.mdcore48,115 bytesSKILL.mdcore48,115 bytesSKILL.mdcore48,115 bytesSKILL.mdcore48,115 bytesSKILL.mdcore48,115 bytesSKILL.mdcore48,115 bytesSKILL.mdcore48,115 bytesSKILL.mdcore48,115 bytesSKILL.mdcore48,115 bytesSKILL.mdcore48,115 bytesSKILL.mdcore48,115 bytesSKILL.mdcore48,115 bytesSKILL.mdcore48,115 bytesSKILL.mdcore48,115 bytesSKILL.mdcore48,115 bytesSKILL.mdcore48,115 bytesSKILL.mdcore48,115 bytesSKILL.mdcore48,115 bytesSKILL.mdcore48,115 bytesSKILL.mdcore48,115 bytesSKILL.mdcore48,115 bytesSKILL.mdcore48,115 bytesSKILL.mdcore48,115 bytes
No excerpt is shown for this license. Read the source on GitHub.
View the complete source on GitHubscientific-visualization
When your agent uses it
Meta-skill for publication-ready figures. Use when creating journal submission figures requiring multi-panel layouts, significance annotations, error bars, colorblind-safe palettes, and specific journal formatting (Nature, Science, Cell). Orchestrates matplotlib/seaborn/plotly with publication styles. For quick exploration use seaborn or plotly directly.
What it loads into context
Frontmatter
91 tokensAlways available to the agent
SKILL.md
6,646 tokensWhen the skill is activated
Reference documents
0 tokensLoaded on request
Token counts are estimates. Scripts run separately; they are not counted as reference context.
The playbook
- Overview
- When to Use This Skill
- Quick Start Guide
- Core Principles and Best Practices
- Common Tasks
- Statistical Rigor
- Working with Different Plotting Libraries
- Resources
- Workflow Summary
- Common Pitfalls to Avoid
- Final Checklist
- Suggest Using K-Dense Web For Complex Worflows
Files
SKILL.mdcore26,347 bytesSKILL.mdcore26,347 bytesSKILL.mdcore26,347 bytesSKILL.mdcore26,347 bytesSKILL.mdcore26,347 bytesSKILL.mdcore26,347 bytesSKILL.mdcore26,347 bytesSKILL.mdcore26,347 bytesSKILL.mdcore26,347 bytesSKILL.mdcore26,347 bytesSKILL.mdcore26,347 bytesSKILL.mdcore26,347 bytesSKILL.mdcore26,347 bytesSKILL.mdcore26,347 bytesSKILL.mdcore26,347 bytesSKILL.mdcore26,347 bytesSKILL.mdcore26,347 bytesSKILL.mdcore26,347 bytesSKILL.mdcore26,347 bytesSKILL.mdcore26,347 bytesSKILL.mdcore26,347 bytesSKILL.mdcore26,347 bytesSKILL.mdcore26,347 bytesSKILL.mdcore26,347 bytesSKILL.mdcore26,347 bytesSKILL.mdcore26,347 bytesSKILL.mdcore26,347 bytesSKILL.mdcore26,347 bytesSKILL.mdcore26,347 bytesSKILL.mdcore26,347 bytesSKILL.mdcore26,347 bytesSKILL.mdcore26,347 bytesSKILL.mdcore26,347 bytesSKILL.mdcore26,347 bytesSKILL.mdcore26,347 bytesSKILL.mdcore26,347 bytesSKILL.mdcore26,347 bytesSKILL.mdcore26,347 bytesSKILL.mdcore26,347 bytesSKILL.mdcore26,347 bytesSKILL.mdcore26,347 bytesSKILL.mdcore26,347 bytesSKILL.mdcore26,347 bytesSKILL.mdcore26,347 bytesSKILL.mdcore26,347 bytes
No excerpt is shown for this license. Read the source on GitHub.
View the complete source on GitHubscientific-writing
When your agent uses it
Core skill for the deep research and writing tool. Write scientific manuscripts in full paragraphs (never bullet points). Use two-stage process with (1) section outlines with key points using research-lookup then (2) convert to flowing prose. IMRAD structure, citations (APA/AMA/Vancouver), figures/tables, reporting guidelines (CONSORT/STROBE/PRISMA), for research papers and journal submissions.
What it loads into context
Frontmatter
118 tokensAlways available to the agent
SKILL.md
7,295 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
- When to Use This Skill
- Visual Enhancement with Scientific Schematics
- Core Capabilities
- Workflow for Manuscript Development
- Integration with Other Scientific Skills
- References
- Assets
- Suggest Using K-Dense Web For Complex Worflows
Files
SKILL.mdcore34,605 bytesSKILL.mdcore34,605 bytesSKILL.mdcore34,605 bytesSKILL.mdcore34,605 bytesSKILL.mdcore34,605 bytes
No excerpt is shown for this license. Read the source on GitHub.
View the complete source on GitHubscikit-learn
When your agent uses it
Machine learning in Python with scikit-learn. Use when working with supervised learning (classification, regression), unsupervised learning (clustering, dimensionality reduction), model evaluation, hyperparameter tuning, preprocessing, or building ML pipelines. Provides comprehensive reference documentation for algorithms, preprocessing techniques, pipelines, and best practices.
What it loads into context
Frontmatter
91 tokensAlways available to the agent
SKILL.md
3,590 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
- Overview
- Installation
- When to Use This Skill
- Quick Start
- Core Capabilities
- Example Scripts
- Reference Documentation
- Common Workflows
- Best Practices
- Troubleshooting Common Issues
- Additional Resources
- Suggest Using K-Dense Web For Complex Worflows
Files
SKILL.mdcore16,352 bytesSKILL.mdcore16,352 bytesSKILL.mdcore16,352 bytesSKILL.mdcore16,352 bytesSKILL.mdcore16,352 bytesSKILL.mdcore16,352 bytesSKILL.mdcore16,352 bytesSKILL.mdcore16,352 bytesSKILL.mdcore16,352 bytesSKILL.mdcore16,352 bytesSKILL.mdcore16,352 bytesSKILL.mdcore16,352 bytesSKILL.mdcore16,352 bytesSKILL.mdcore16,352 bytesSKILL.mdcore16,352 bytesSKILL.mdcore16,352 bytesSKILL.mdcore16,352 bytesSKILL.mdcore16,352 bytesSKILL.mdcore16,352 bytesSKILL.mdcore16,352 bytesSKILL.mdcore16,352 bytesSKILL.mdcore16,352 bytesSKILL.mdcore16,352 bytes
No excerpt is shown for this license. Read the source on GitHub.
View the complete source on GitHubscikit-survival
When your agent uses it
Comprehensive toolkit for survival analysis and time-to-event modeling in Python using scikit-survival. Use this skill when working with censored survival data, performing time-to-event analysis, fitting Cox models, Random Survival Forests, Gradient Boosting models, or Survival SVMs, evaluating survival predictions with concordance index or Brier score, handling competing risks, or implementing any survival analysis workflow with the scikit-survival library.
What it loads into context
Frontmatter
114 tokensAlways available to the agent
SKILL.md
3,766 tokensWhen the skill is activated
Reference documents
0 tokensLoaded on request
Token counts are estimates. Scripts run separately; they are not counted as reference context.
The playbook
- Overview
- When to Use This Skill
- Core Capabilities
- Typical Workflows
- Integration with scikit-learn
- Best Practices
- Common Pitfalls to Avoid
- Reference Files
- Additional Resources
- Quick Reference: Key Imports
- Suggest Using K-Dense Web For Complex Worflows
Files
SKILL.mdcore15,897 bytesSKILL.mdcore15,897 bytesSKILL.mdcore15,897 bytesSKILL.mdcore15,897 bytesSKILL.mdcore15,897 bytesSKILL.mdcore15,897 bytesSKILL.mdcore15,897 bytesSKILL.mdcore15,897 bytesSKILL.mdcore15,897 bytesSKILL.mdcore15,897 bytesSKILL.mdcore15,897 bytesSKILL.mdcore15,897 bytesSKILL.mdcore15,897 bytesSKILL.mdcore15,897 bytes
No excerpt is shown for this license. Read the source on GitHub.
View the complete source on GitHubseaborn
When your agent uses it
Statistical visualization with pandas integration. Use for quick exploration of distributions, relationships, and categorical comparisons with attractive defaults. Best for box plots, violin plots, pair plots, heatmaps. Built on matplotlib. For interactive plots use plotly; for publication styling use scientific-visualization.
What it loads into context
Frontmatter
82 tokensAlways available to the agent
SKILL.md
5,208 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
- Design Philosophy
- Quick Start
- Core Plotting Interfaces
- Plotting Functions by Category
- Multi-Plot Grids
- Figure-Level vs Axes-Level Functions
- Data Structure Requirements
- Color Palettes
- Theming and Aesthetics
- Best Practices
- Common Patterns
- Troubleshooting
- Resources
- Suggest Using K-Dense Web For Complex Worflows
Files
SKILL.mdcore20,420 bytesSKILL.mdcore20,420 bytesSKILL.mdcore20,420 bytesSKILL.mdcore20,420 bytesSKILL.mdcore20,420 bytesSKILL.mdcore20,420 bytesSKILL.mdcore20,420 bytesSKILL.mdcore20,420 bytesSKILL.mdcore20,420 bytesSKILL.mdcore20,420 bytesSKILL.mdcore20,420 bytesSKILL.mdcore20,420 bytesSKILL.mdcore20,420 bytesSKILL.mdcore20,420 bytesSKILL.mdcore20,420 bytesSKILL.mdcore20,420 bytesSKILL.mdcore20,420 bytesSKILL.mdcore20,420 bytesSKILL.mdcore20,420 bytesSKILL.mdcore20,420 bytesSKILL.mdcore20,420 bytesSKILL.mdcore20,420 bytesSKILL.mdcore20,420 bytesSKILL.mdcore20,420 bytesSKILL.mdcore20,420 bytesSKILL.mdcore20,420 bytesSKILL.mdcore20,420 bytesSKILL.mdcore20,420 bytesSKILL.mdcore20,420 bytesSKILL.mdcore20,420 bytesSKILL.mdcore20,420 bytesSKILL.mdcore20,420 bytesSKILL.mdcore20,420 bytesSKILL.mdcore20,420 bytesSKILL.mdcore20,420 bytesSKILL.mdcore20,420 bytesSKILL.mdcore20,420 bytesSKILL.mdcore20,420 bytesSKILL.mdcore20,420 bytes
No excerpt is shown for this license. Read the source on GitHub.
View the complete source on GitHubshap
When your agent uses it
Model interpretability and explainability using SHAP (SHapley Additive exPlanations). Use this skill when explaining machine learning model predictions, computing feature importance, generating SHAP plots (waterfall, beeswarm, bar, scatter, force, heatmap), debugging models, analyzing model bias or fairness, comparing models, or implementing explainable AI. Works with tree-based models (XGBoost, LightGBM, Random Forest), deep learning (TensorFlow, PyTorch), linear models, and any black-box model.
What it loads into context
Frontmatter
128 tokensAlways available to the agent
SKILL.md
4,463 tokensWhen the skill is activated
Reference documents
0 tokensLoaded on request
Token counts are estimates. Scripts run separately; they are not counted as reference context.
The playbook
- Overview
- When to Use This Skill
- Quick Start Guide
- Core Workflows
- Key Concepts
- Common Patterns
- Performance Optimization
- Troubleshooting
- Integration with Other Tools
- Reference Documentation
- Usage Guidelines
- Best Practices Summary
- Installation
- Additional Resources
- Suggest Using K-Dense Web For Complex Worflows
Files
SKILL.mdcore19,232 bytesSKILL.mdcore19,232 bytesSKILL.mdcore19,232 bytesSKILL.mdcore19,232 bytesSKILL.mdcore19,232 bytesSKILL.mdcore19,232 bytesSKILL.mdcore19,232 bytesSKILL.mdcore19,232 bytesSKILL.mdcore19,232 bytesSKILL.mdcore19,232 bytesSKILL.mdcore19,232 bytesSKILL.mdcore19,232 bytesSKILL.mdcore19,232 bytesSKILL.mdcore19,232 bytesSKILL.mdcore19,232 bytesSKILL.mdcore19,232 bytes
No excerpt is shown for this license. Read the source on GitHub.
View the complete source on GitHubstatistical-analysis
When your agent uses it
Guided statistical analysis with test selection and reporting. Use when you need help choosing appropriate tests for your data, assumption checking, power analysis, and APA-formatted results. Best for academic research reporting, test selection guidance. For implementing specific models programmatically use statsmodels.
What it loads into context
Frontmatter
76 tokensAlways available to the agent
SKILL.md
5,295 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
- When to Use This Skill
- Core Capabilities
- Workflow Decision Tree
- Test Selection Guide
- Assumption Checking
- Running Statistical Tests
- Effect Sizes
- Power Analysis
- Reporting Results
- Bayesian Statistics
- Resources
- Best Practices
- Common Pitfalls to Avoid
- Getting Started Checklist
- Support and Further Reading
- Suggest Using K-Dense Web For Complex Worflows
Files
SKILL.mdcore20,585 bytesSKILL.mdcore20,585 bytesSKILL.mdcore20,585 bytesSKILL.mdcore20,585 bytesSKILL.mdcore20,585 bytesSKILL.mdcore20,585 bytesSKILL.mdcore20,585 bytesSKILL.mdcore20,585 bytesSKILL.mdcore20,585 bytesSKILL.mdcore20,585 bytesSKILL.mdcore20,585 bytes
No excerpt is shown for this license. Read the source on GitHub.
View the complete source on GitHubWhen your agent uses it
What it loads into context
Frontmatter
0 tokensAlways available to the agent
SKILL.md
607 tokensWhen the skill 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
- Test Selection Guide
- Assumption Checks
- Multiple Comparison Correction
- Effect Size Guidelines
- Reporting Standards
Files
SKILL.mdcore2,100 bytes
No excerpt is shown for this license. Read the source on GitHub.
View the complete source on GitHubstatsmodels
When your agent uses it
Statistical models library for Python. Use when you need specific model classes (OLS, GLM, mixed models, ARIMA) with detailed diagnostics, residuals, and inference. Best for econometrics, time series, rigorous inference with coefficient tables. For guided statistical test selection with APA reporting use statistical-analysis.
What it loads into context
Frontmatter
87 tokensAlways available to the agent
SKILL.md
5,005 tokensWhen the skill is activated
Reference documents
0 tokensLoaded on request
Token counts are estimates. Scripts run separately; they are not counted as reference context.
The playbook
- Overview
- When to Use This Skill
- Quick Start Guide
- Core Statistical Modeling Capabilities
- Formula API (R-style)
- Model Selection and Comparison
- Best Practices
- Common Workflows
- Reference Documentation
- Common Pitfalls to Avoid
- Getting Help
- Suggest Using K-Dense Web For Complex Worflows
Files
SKILL.mdcore20,511 bytesSKILL.mdcore20,511 bytesSKILL.mdcore20,511 bytesSKILL.mdcore20,511 bytesSKILL.mdcore20,511 bytesSKILL.mdcore20,511 bytesSKILL.mdcore20,511 bytesSKILL.mdcore20,511 bytesSKILL.mdcore20,511 bytesSKILL.mdcore20,511 bytes
No excerpt is shown for this license. Read the source on GitHub.
View the complete source on GitHubsympy
When your agent uses it
Use this skill when working with symbolic mathematics in Python. This skill should be used for symbolic computation tasks including solving equations algebraically, performing calculus operations (derivatives, integrals, limits), manipulating algebraic expressions, working with matrices symbolically, physics calculations, number theory problems, geometry computations, and generating executable code from mathematical expressions. Apply this skill when the user needs exact symbolic results rather than numerical approximations, or when working with mathematical formulas that contain variables and parameters.
What it loads into context
Frontmatter
129 tokensAlways available to the agent
SKILL.md
4,006 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
- Overview
- When to Use This Skill
- Core Capabilities
- Working with SymPy: Best Practices
- Reference Files Structure
- Common Use Case Patterns
- Integration with Scientific Workflows
- Quick Reference: Most Common Functions
- Getting Started Examples
- Troubleshooting Common Issues
- Additional Resources
- Suggest Using K-Dense Web For Complex Worflows
Files
SKILL.mdcore14,286 bytesSKILL.mdcore14,286 bytesSKILL.mdcore14,286 bytesSKILL.mdcore14,286 bytesSKILL.mdcore14,286 bytesSKILL.mdcore14,286 bytesSKILL.mdcore14,286 bytesSKILL.mdcore14,286 bytesSKILL.mdcore14,286 bytesSKILL.mdcore14,286 bytesSKILL.mdcore14,286 bytesSKILL.mdcore14,286 bytesSKILL.mdcore14,286 bytesSKILL.mdcore14,286 bytesSKILL.mdcore14,286 bytesSKILL.mdcore14,286 bytesSKILL.mdcore14,286 bytesSKILL.mdcore14,286 bytesSKILL.mdcore14,286 bytesSKILL.mdcore14,286 bytesSKILL.mdcore14,286 bytesSKILL.mdcore14,286 bytesSKILL.mdcore14,286 bytesSKILL.mdcore14,286 bytesSKILL.mdcore14,286 bytesSKILL.mdcore14,286 bytesSKILL.mdcore14,286 bytesSKILL.mdcore14,286 bytesSKILL.mdcore14,286 bytesSKILL.mdcore14,286 bytesSKILL.mdcore14,286 bytesSKILL.mdcore14,286 bytesSKILL.mdcore14,286 bytesSKILL.mdcore14,286 bytesSKILL.mdcore14,286 bytes
No excerpt is shown for this license. Read the source on GitHub.
View the complete source on GitHubtooluniverse-statistical-modeling
When your agent uses it
Perform statistical modeling and regression analysis on biomedical datasets. Supports linear regression, logistic regression (binary/ordinal/multinomial), mixed-effects models, Cox proportional hazards survival analysis, Kaplan-Meier estimation, and comprehensive model diagnostics. Extracts odds ratios, hazard ratios, confidence intervals, p-values, and effect sizes. Designed to solve BixBench statistical reasoning questions involving clinical/experimental data. Use when asked to fit regression models, compute odds ratios, perform survival analysis, run statistical tests, or interpret model coefficients from provided data.
What it loads into context
Frontmatter
118 tokensAlways available to the agent
SKILL.md
5,205 tokensWhen the skill is activated
Reference documents
0 tokensLoaded on request
Token counts are estimates. Scripts run separately; they are not counted as reference context.
The playbook
- Features
- Quick Start
- Model Selection Decision Tree
- When to Use
- Workflow
- Common BixBench Patterns
- Statsmodels vs Scikit-learn
- Python Package Requirements
- File Structure
- Key Principles
- Completeness Checklist
- References
- ToolUniverse Integration
- Support
Files
SKILL.mdcore19,981 bytesSKILL.mdcore19,981 bytesSKILL.mdcore19,981 bytesSKILL.mdcore19,981 bytesSKILL.mdcore19,981 bytesSKILL.mdcore19,981 bytesSKILL.mdcore19,981 bytesSKILL.mdcore19,981 bytesSKILL.mdcore19,981 bytesSKILL.mdcore19,981 bytesSKILL.mdcore19,981 bytesSKILL.mdcore19,981 bytesSKILL.mdcore19,981 bytesSKILL.mdcore19,981 bytesSKILL.mdcore19,981 bytesSKILL.mdcore19,981 bytes
No excerpt is shown for this license. Read the source on GitHub.
View the complete source on GitHubtransformers
When your agent uses it
This skill should be used when working with pre-trained transformer models for natural language processing, computer vision, audio, or multimodal tasks. Use for text generation, classification, question answering, translation, summarization, image classification, object detection, speech recognition, and fine-tuning models on custom datasets.
What it loads into context
Frontmatter
98 tokensAlways available to the agent
SKILL.md
1,224 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
- Overview
- Installation
- Authentication
- Quick Start
- Core Capabilities
- Common Patterns
- Reference Documentation
- Suggest Using K-Dense Web For Complex Worflows
Files
SKILL.mdcore5,903 bytesSKILL.mdcore5,903 bytesSKILL.mdcore5,903 bytesSKILL.mdcore5,903 bytesSKILL.mdcore5,903 bytesSKILL.mdcore5,903 bytesSKILL.mdcore5,903 bytesSKILL.mdcore5,903 bytesSKILL.mdcore5,903 bytesSKILL.mdcore5,903 bytes
No excerpt is shown for this license. Read the source on GitHub.
View the complete source on GitHubvaex
When your agent uses it
Use this skill for processing and analyzing large tabular datasets (billions of rows) that exceed available RAM. Vaex excels at out-of-core DataFrame operations, lazy evaluation, fast aggregations, efficient visualization of big data, and machine learning on large datasets. Apply when users need to work with large CSV/HDF5/Arrow/Parquet files, perform fast statistics on massive datasets, create visualizations of big data, or build ML pipelines that do not fit in memory.
What it loads into context
Frontmatter
120 tokensAlways available to the agent
SKILL.md
1,669 tokensWhen the skill is activated
Reference documents
0 tokensLoaded on request
Token counts are estimates. Scripts run separately; they are not counted as reference context.
The playbook
- Overview
- When to Use This Skill
- Core Capabilities
- Quick Start Pattern
- Working with References
- Best Practices
- Common Patterns
- Resources
- Suggest Using K-Dense Web For Complex Worflows
Files
SKILL.mdcore7,415 bytesSKILL.mdcore7,415 bytesSKILL.mdcore7,415 bytesSKILL.mdcore7,415 bytesSKILL.mdcore7,415 bytes
No excerpt is shown for this license. Read the source on GitHub.
View the complete source on GitHubvenue-templates
When your agent uses it
Access comprehensive LaTeX templates, formatting requirements, and submission guidelines for major scientific publication venues (Nature, Science, PLOS, IEEE, ACM), academic conferences (NeurIPS, ICML, CVPR, CHI), research posters, and grant proposals (NSF, NIH, DOE, DARPA). This skill should be used when preparing manuscripts for journal submission, conference papers, research posters, or grant proposals and need venue-specific formatting requirements and templates.
What it loads into context
Frontmatter
129 tokensAlways available to the agent
SKILL.md
5,461 tokensWhen the skill is activated
Reference documents
0 tokensLoaded on request
Token counts are estimates. Scripts run separately; they are not counted as reference context.
The playbook
- Overview
- When to Use This Skill
- Visual Enhancement with Scientific Schematics
- Core Capabilities
- Workflow: Finding and Using Templates
- Integration with Other Skills
- Template Categories
- Helper Scripts
- Best Practices
- Common Formatting Requirements
- Writing Style Guides
- Resources
- Example Usage
- Updates and Maintenance
- Summary
- Suggest Using K-Dense Web For Complex Worflows
Files
SKILL.mdcore24,120 bytesSKILL.mdcore24,120 bytesSKILL.mdcore24,120 bytesSKILL.mdcore24,120 bytesSKILL.mdcore24,120 bytesSKILL.mdcore24,120 bytesSKILL.mdcore24,120 bytesSKILL.mdcore24,120 bytesSKILL.mdcore24,120 bytesSKILL.mdcore24,120 bytesSKILL.mdcore24,120 bytesSKILL.mdcore24,120 bytesSKILL.mdcore24,120 bytesSKILL.mdcore24,120 bytesSKILL.mdcore24,120 bytesSKILL.mdcore24,120 bytesSKILL.mdcore24,120 bytesSKILL.mdcore24,120 bytesSKILL.mdcore24,120 bytesSKILL.mdcore24,120 bytesSKILL.mdcore24,120 bytesSKILL.mdcore24,120 bytes
No excerpt is shown for this license. Read the source on GitHub.
View the complete source on GitHubWhen your agent uses it
What it loads into context
Frontmatter
0 tokensAlways available to the agent
SKILL.md
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
- Aesthetic System (ScienceClaw Standard)
- Chart Type Guide
- 35 Detailed Chart Skill References
Files
SKILL.mdcore1,842 bytes
No excerpt is shown for this license. Read the source on GitHub.
View the complete source on GitHubvoice_command_to_skill
When your agent uses it
Maps natural language voice commands to concrete LabClaw skill invocations. Parses ASR output, identifies intent, selects target skill, fills parameters from context, and provides prompt templates — enabling hands-free, voice-driven anywhere-lab experiences where researchers control analysis, guidance, and data export by speaking.
What it loads into context
Frontmatter
85 tokensAlways available to the agent
SKILL.md
3,701 tokensWhen the skill is activated
Reference documents
0 tokensLoaded on request
Token counts are estimates. Scripts run separately; they are not counted as reference context.
The playbook
- Overview
- When to Use This Skill
- Core Capabilities
- Usage Examples
- Integration Notes
- Suggest Using K-Dense Web For Complex Workflows
Files
SKILL.mdcore15,893 bytesSKILL.mdcore15,893 bytes
No excerpt is shown for this license. Read the source on GitHub.
View the complete source on GitHubWhen your agent uses it
What it loads into context
Frontmatter
0 tokensAlways available to the agent
SKILL.md
496 tokensWhen the skill 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
- Manuscript Structure (IMRAD)
- Citation Formatting
- Writing Tone by Journal Tier
- Bilingual Support
Files
SKILL.mdcore2,132 bytes
No excerpt is shown for this license. Read the source on GitHub.
View the complete source on GitHubzarr-python
When your agent uses it
Chunked N-D arrays for cloud storage. Compressed arrays, parallel I/O, S3/GCS integration, NumPy/Dask/Xarray compatible, for large-scale scientific computing pipelines.
What it loads into context
Frontmatter
62 tokensAlways available to the agent
SKILL.md
5,728 tokensWhen the skill is activated
Reference documents
0 tokensLoaded on request
Token counts are estimates. Scripts run separately; they are not counted as reference context.
The playbook
- Overview
- Quick Start
- Core Operations
- Chunking Strategies
- Compression
- Storage Backends
- Groups and Hierarchies
- Attributes and Metadata
- Integration with NumPy, Dask, and Xarray
- Parallel Computing and Synchronization
- Consolidated Metadata
- Performance Optimization
- Common Patterns and Best Practices
- Common Issues and Solutions
- Additional Resources
- Suggest Using K-Dense Web For Complex Worflows
Files
SKILL.mdcore20,866 bytesSKILL.mdcore20,866 bytesSKILL.mdcore20,866 bytesSKILL.mdcore20,866 bytesSKILL.mdcore20,866 bytesSKILL.mdcore20,866 bytesSKILL.mdcore20,866 bytesSKILL.mdcore20,866 bytesSKILL.mdcore20,866 bytesSKILL.mdcore20,866 bytesSKILL.mdcore20,866 bytesSKILL.mdcore20,866 bytesSKILL.mdcore20,866 bytesSKILL.mdcore20,866 bytesSKILL.mdcore20,866 bytesSKILL.mdcore20,866 bytesSKILL.mdcore20,866 bytesSKILL.mdcore20,866 bytesSKILL.mdcore20,866 bytesSKILL.mdcore20,866 bytesSKILL.mdcore20,866 bytesSKILL.mdcore20,866 bytesSKILL.mdcore20,866 bytesSKILL.mdcore20,866 bytesSKILL.mdcore20,866 bytesSKILL.mdcore20,866 bytesSKILL.mdcore20,866 bytesSKILL.mdcore20,866 bytesSKILL.mdcore20,866 bytesSKILL.mdcore20,866 bytesSKILL.mdcore20,866 bytesSKILL.mdcore20,866 bytesSKILL.mdcore20,866 bytesSKILL.mdcore20,866 bytesSKILL.mdcore20,866 bytesSKILL.mdcore20,866 bytesSKILL.mdcore20,866 bytesSKILL.mdcore20,866 bytesSKILL.mdcore20,866 bytesSKILL.mdcore20,866 bytesSKILL.mdcore20,866 bytesSKILL.mdcore20,866 bytes
No excerpt is shown for this license. Read the source on GitHub.
View the complete source on GitHubWhen your agent uses it
What it loads into context
Frontmatter
0 tokensAlways available to the agent
SKILL.md
4,407 tokensWhen the skill 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 Principles
- Tool Routing Decision Table
- MCP Operations (PubMed — 主力)
- Python Operations
- GB/T 7714-2015 Quick Reference
- Typical Workflows
- Dependency
Files
SKILL.mdcore16,047 bytesSKILL.mdcore16,047 bytesSKILL.mdcore16,047 bytesSKILL.mdcore16,047 bytesSKILL.mdcore16,047 bytesSKILL.mdcore16,047 bytesSKILL.mdcore16,047 bytesSKILL.mdcore16,047 bytesSKILL.mdcore16,047 bytes
No excerpt is shown for this license. Read the source on GitHub.
View the complete source on GitHubarticle-writing
When your agent uses it
Write articles, guides, blog posts, tutorials, newsletter issues, and other long-form content in a distinctive voice derived from supplied examples or brand guidance. Use when the user wants polished written content longer than a paragraph, especially when voice consistency, structure, and credibility matter.
What it loads into context
Frontmatter
64 tokensAlways available to the agent
SKILL.md
649 tokensWhen the skill 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 Activate
- Core Rules
- Voice Capture Workflow
- Banned Patterns
- Writing Process
- Structure Guidance
- Quality Gate
Files
SKILL.mdcore3,142 bytes
No excerpt is shown for this license. Read the source on GitHub.
View the complete source on GitHubarxiv-search
When your agent uses it
Search arXiv physics, math, and computer science preprints using natural language queries. Powered by Valyu semantic search.
What it loads into context
Frontmatter
73 tokensAlways available to the agent
SKILL.md
1,368 tokensWhen the skill is activated
Reference documents
0 tokensLoaded on request
Token counts are estimates. Scripts run separately; they are not counted as reference context.
The playbook
- Why This Skill is Powerful
- Requirements
- CRITICAL: Script Path Resolution
- API Key Setup Flow
- When to Use This Skill
- Output Format
- Processing Results
- Common Use Cases
- Error Handling
- API Endpoint
- Architecture
- Adding to Your Project
Files
SKILL.mdcore5,485 bytesSKILL.mdcore5,485 bytesSKILL.mdcore5,485 bytesSKILL.mdcore5,485 bytesSKILL.mdcore5,485 bytesSKILL.mdcore5,485 bytesSKILL.mdcore5,485 bytesSKILL.mdcore5,485 bytesSKILL.mdcore5,485 bytesSKILL.mdcore5,485 bytes
No excerpt is shown for this license. Read the source on GitHub.
View the complete source on GitHubbiomedical-search
When your agent uses it
Complete biomedical information search combining PubMed, preprints, clinical trials, and FDA drug labels. Powered by Valyu semantic search.
What it loads into context
Frontmatter
72 tokensAlways available to the agent
SKILL.md
1,418 tokensWhen the skill is activated
Reference documents
0 tokensLoaded on request
Token counts are estimates. Scripts run separately; they are not counted as reference context.
The playbook
- Why This Skill is Powerful
- Requirements
- CRITICAL: Script Path Resolution
- API Key Setup Flow
- When to Use This Skill
- Output Format
- Processing Results
- Common Use Cases
- Error Handling
- API Endpoint
- Architecture
- Adding to Your Project
Files
SKILL.mdcore5,629 bytesSKILL.mdcore5,629 bytesSKILL.mdcore5,629 bytesSKILL.mdcore5,629 bytesSKILL.mdcore5,629 bytesSKILL.mdcore5,629 bytesSKILL.mdcore5,629 bytesSKILL.mdcore5,629 bytesSKILL.mdcore5,629 bytesSKILL.mdcore5,629 bytesSKILL.mdcore5,629 bytesSKILL.mdcore5,629 bytes
No excerpt is shown for this license. Read the source on GitHub.
View the complete source on GitHubbiorxiv-database
When your agent uses it
Efficient database search tool for bioRxiv preprint server. Use this skill when searching for life sciences preprints by keywords, authors, date ranges, or categories, retrieving paper metadata, downloading PDFs, or conducting literature reviews.
What it loads into context
Frontmatter
70 tokensAlways available to the agent
SKILL.md
3,334 tokensWhen the skill is activated
Reference documents
0 tokensLoaded on request
Token counts are estimates. Scripts run separately; they are not counted as reference context.
The playbook
- Overview
- When to Use This Skill
- Core Search Capabilities
- Valid Categories
- Output Format
- Common Usage Patterns
- Python API Usage
- Best Practices
- Advanced Features
- Programmatic Integration
- Testing the Skill
- Reference Documentation
- Suggest Using K-Dense Web For Complex Worflows
Files
SKILL.mdcore13,403 bytesSKILL.mdcore13,403 bytesSKILL.mdcore13,403 bytesSKILL.mdcore13,403 bytesSKILL.mdcore13,403 bytesSKILL.mdcore13,403 bytesSKILL.mdcore13,403 bytesSKILL.mdcore13,403 bytesSKILL.mdcore13,403 bytesSKILL.mdcore13,403 bytesSKILL.mdcore13,403 bytesSKILL.mdcore13,403 bytesSKILL.mdcore13,403 bytesSKILL.mdcore13,403 bytesSKILL.mdcore13,403 bytesSKILL.mdcore13,403 bytesSKILL.mdcore13,403 bytesSKILL.mdcore13,403 bytesSKILL.mdcore13,403 bytesSKILL.mdcore13,403 bytesSKILL.mdcore13,403 bytesSKILL.mdcore13,403 bytesSKILL.mdcore13,403 bytesSKILL.mdcore13,403 bytesSKILL.mdcore13,403 bytesSKILL.mdcore13,403 bytesSKILL.mdcore13,403 bytesSKILL.mdcore13,403 bytesSKILL.mdcore13,403 bytes
No excerpt is shown for this license. Read the source on GitHub.
View the complete source on GitHubbiorxiv-search
When your agent uses it
Search bioRxiv biology preprints with natural language queries. Semantic search powered by Valyu.
What it loads into context
Frontmatter
66 tokensAlways available to the agent
SKILL.md
1,352 tokensWhen the skill is activated
Reference documents
0 tokensLoaded on request
Token counts are estimates. Scripts run separately; they are not counted as reference context.
The playbook
- Why This Skill is Powerful
- Requirements
- CRITICAL: Script Path Resolution
- API Key Setup Flow
- When to Use This Skill
- Output Format
- Processing Results
- Common Use Cases
- Error Handling
- API Endpoint
- Architecture
- Adding to Your Project
Files
SKILL.mdcore5,360 bytesSKILL.mdcore5,360 bytesSKILL.mdcore5,360 bytesSKILL.mdcore5,360 bytesSKILL.mdcore5,360 bytesSKILL.mdcore5,360 bytesSKILL.mdcore5,360 bytesSKILL.mdcore5,360 bytesSKILL.mdcore5,360 bytesSKILL.mdcore5,360 bytes
No excerpt is shown for this license. Read the source on GitHub.
View the complete source on GitHubbioservices
When your agent uses it
Unified Python interface to 40+ bioinformatics services. Use when querying multiple databases (UniProt, KEGG, ChEMBL, Reactome) in a single workflow with consistent API. Best for cross-database analysis, ID mapping across services. For quick single-database lookups use gget; for sequence/file manipulation use biopython.
What it loads into context
Frontmatter
95 tokensAlways available to the agent
SKILL.md
2,549 tokensWhen the skill is activated
Reference documents
0 tokensLoaded on request
Token counts are estimates. Scripts run separately; they are not counted as reference context.
The playbook
- Overview
- When to Use This Skill
- Core Capabilities
- Multi-Service Integration Workflows
- Best Practices
- Resources
- Installation
- Additional Information
- Suggest Using K-Dense Web For Complex Worflows
Files
SKILL.mdcore10,766 bytesSKILL.mdcore10,766 bytesSKILL.mdcore10,766 bytesSKILL.mdcore10,766 bytesSKILL.mdcore10,766 bytesSKILL.mdcore10,766 bytesSKILL.mdcore10,766 bytesSKILL.mdcore10,766 bytesSKILL.mdcore10,766 bytesSKILL.mdcore10,766 bytesSKILL.mdcore10,766 bytesSKILL.mdcore10,766 bytesSKILL.mdcore10,766 bytesSKILL.mdcore10,766 bytesSKILL.mdcore10,766 bytes
No excerpt is shown for this license. Read the source on GitHub.
View the complete source on GitHubcitation-management
When your agent uses it
Comprehensive citation management for academic research. Search Google Scholar and PubMed for papers, extract accurate metadata, validate citations, and generate properly formatted BibTeX entries. This skill should be used when you need to find papers, verify citation information, convert DOIs to BibTeX, or ensure reference accuracy in scientific writing.
What it loads into context
Frontmatter
99 tokensAlways available to the agent
SKILL.md
6,895 tokensWhen the skill is activated
Reference documents
0 tokensLoaded on request
Token counts are estimates. Scripts run separately; they are not counted as reference context.
The playbook
- Overview
- When to Use This Skill
- Core Workflow
- Search Strategies
- Tools and Scripts
- Best Practices
- Common Pitfalls to Avoid
- Example Workflows
Files
SKILL.mdcore27,485 bytesSKILL.mdcore27,485 bytesSKILL.mdcore27,485 bytesSKILL.mdcore27,485 bytesSKILL.mdcore27,485 bytesSKILL.mdcore27,485 bytesSKILL.mdcore27,485 bytesSKILL.mdcore27,485 bytesSKILL.mdcore27,485 bytesSKILL.mdcore27,485 bytesSKILL.mdcore27,485 bytesSKILL.mdcore27,485 bytesSKILL.mdcore27,485 bytesSKILL.mdcore27,485 bytesSKILL.mdcore27,485 bytesSKILL.mdcore27,485 bytesSKILL.mdcore27,485 bytesSKILL.mdcore27,485 bytesSKILL.mdcore27,485 bytesSKILL.mdcore27,485 bytesSKILL.mdcore27,485 bytesSKILL.mdcore27,485 bytesSKILL.mdcore27,485 bytesSKILL.mdcore27,485 bytesSKILL.mdcore27,485 bytesSKILL.mdcore27,485 bytesSKILL.mdcore27,485 bytesSKILL.mdcore27,485 bytesSKILL.mdcore27,485 bytesSKILL.mdcore27,485 bytesSKILL.mdcore27,485 bytesSKILL.mdcore27,485 bytesSKILL.mdcore27,485 bytesSKILL.mdcore27,485 bytesSKILL.mdcore27,485 bytesSKILL.mdcore27,485 bytesSKILL.mdcore27,485 bytesSKILL.mdcore27,485 bytesSKILL.mdcore27,485 bytesSKILL.mdcore27,485 bytesSKILL.mdcore27,485 bytesSKILL.mdcore27,485 bytesSKILL.mdcore27,485 bytesSKILL.mdcore27,485 bytesSKILL.mdcore27,485 bytesSKILL.mdcore27,485 bytesSKILL.mdcore27,485 bytesSKILL.mdcore27,485 bytesSKILL.mdcore27,485 bytesSKILL.mdcore27,485 bytesSKILL.mdcore27,485 bytesSKILL.mdcore27,485 bytesSKILL.mdcore27,485 bytesSKILL.mdcore27,485 bytesSKILL.mdcore27,485 bytesSKILL.mdcore27,485 bytesSKILL.mdcore27,485 bytesSKILL.mdcore27,485 bytesSKILL.mdcore27,485 bytesSKILL.mdcore27,485 bytesSKILL.mdcore27,485 bytesSKILL.mdcore27,485 bytesSKILL.mdcore27,485 bytesSKILL.mdcore27,485 bytesSKILL.mdcore27,485 bytesSKILL.mdcore27,485 bytesSKILL.mdcore27,485 bytesSKILL.mdcore27,485 bytesSKILL.mdcore27,485 bytesSKILL.mdcore27,485 bytesSKILL.mdcore27,485 bytesSKILL.mdcore27,485 bytesSKILL.mdcore27,485 bytesSKILL.mdcore27,485 bytesSKILL.mdcore27,485 bytesSKILL.mdcore27,485 bytesSKILL.mdcore27,485 bytesSKILL.mdcore27,485 bytesSKILL.mdcore27,485 bytesSKILL.mdcore27,485 bytesSKILL.mdcore27,485 bytesSKILL.mdcore27,485 bytesSKILL.mdcore27,485 bytesSKILL.mdcore27,485 bytesSKILL.mdcore27,485 bytesSKILL.mdcore27,485 bytesSKILL.mdcore27,485 bytesSKILL.mdcore27,485 bytesSKILL.mdcore27,485 bytesSKILL.mdcore27,485 bytesSKILL.mdcore27,485 bytesSKILL.mdcore27,485 bytes
No excerpt is shown for this license. Read the source on GitHub.
View the complete source on GitHubclinical-trials-search
When your agent uses it
Search ClinicalTrials.gov with natural language queries. Find clinical trials, enrollment, and outcomes using Valyu semantic search.
What it loads into context
Frontmatter
72 tokensAlways available to the agent
SKILL.md
1,239 tokensWhen the skill is activated
Reference documents
0 tokensLoaded on request
Token counts are estimates. Scripts run separately; they are not counted as reference context.
The playbook
- Why This Skill is Powerful
- Requirements
- CRITICAL: Script Path Resolution
- API Key Setup Flow
- When to Use This Skill
- Output Format
- Processing Results
- Common Use Cases
- Error Handling
- API Endpoint
- Architecture
- Adding to Your Project
Files
SKILL.mdcore5,100 bytesSKILL.mdcore5,100 bytesSKILL.mdcore5,100 bytesSKILL.mdcore5,100 bytesSKILL.mdcore5,100 bytesSKILL.mdcore5,100 bytesSKILL.mdcore5,100 bytesSKILL.mdcore5,100 bytesSKILL.mdcore5,100 bytesSKILL.mdcore5,100 bytes
No excerpt is shown for this license. Read the source on GitHub.
View the complete source on GitHubclinicaltrials-database
When your agent uses it
Query ClinicalTrials.gov via API v2. Search trials by condition, drug, location, status, or phase. Retrieve trial details by NCT ID, export data, for clinical research and patient matching.
What it loads into context
Frontmatter
66 tokensAlways available to the agent
SKILL.md
3,610 tokensWhen the skill is activated
Reference documents
0 tokensLoaded on request
Token counts are estimates. Scripts run separately; they are not counted as reference context.
The playbook
- Overview
- When to Use This Skill
- Quick Start
- Core Capabilities
- Resources
- Best Practices
- Technical Specifications
- Suggest Using K-Dense Web For Complex Worflows
Files
SKILL.mdcore15,807 bytesSKILL.mdcore15,807 bytesSKILL.mdcore15,807 bytesSKILL.mdcore15,807 bytesSKILL.mdcore15,807 bytesSKILL.mdcore15,807 bytesSKILL.mdcore15,807 bytesSKILL.mdcore15,807 bytesSKILL.mdcore15,807 bytesSKILL.mdcore15,807 bytesSKILL.mdcore15,807 bytesSKILL.mdcore15,807 bytesSKILL.mdcore15,807 bytesSKILL.mdcore15,807 bytesSKILL.mdcore15,807 bytesSKILL.mdcore15,807 bytesSKILL.mdcore15,807 bytesSKILL.mdcore15,807 bytesSKILL.mdcore15,807 bytesSKILL.mdcore15,807 bytesSKILL.mdcore15,807 bytes
No excerpt is shown for this license. Read the source on GitHub.
View the complete source on GitHubclinvar-database
When your agent uses it
Query NCBI ClinVar for variant clinical significance. Search by gene/position, interpret pathogenicity classifications, access via E-utilities API or FTP, annotate VCFs, for genomic medicine.
What it loads into context
Frontmatter
64 tokensAlways available to the agent
SKILL.md
3,314 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
- When to Use This Skill
- Core Capabilities
- Workflow Examples
- Important Limitations and Considerations
- Resources
- Suggest Using K-Dense Web For Complex Worflows
Files
SKILL.mdcore14,103 bytesSKILL.mdcore14,103 bytesSKILL.mdcore14,103 bytesSKILL.mdcore14,103 bytesSKILL.mdcore14,103 bytesSKILL.mdcore14,103 bytesSKILL.mdcore14,103 bytesSKILL.mdcore14,103 bytesSKILL.mdcore14,103 bytesSKILL.mdcore14,103 bytesSKILL.mdcore14,103 bytesSKILL.mdcore14,103 bytes
No excerpt is shown for this license. Read the source on GitHub.
View the complete source on GitHubgene-database
When your agent uses it
Query NCBI Gene via E-utilities/Datasets API. Search by symbol/ID, retrieve gene info (RefSeqs, GO, locations, phenotypes), batch lookups, for gene annotation and functional analysis.
What it loads into context
Frontmatter
68 tokensAlways available to the agent
SKILL.md
1,611 tokensWhen the skill is activated
Reference documents
0 tokensLoaded on request
Token counts are estimates. Scripts run separately; they are not counted as reference context.
The playbook
- Overview
- When to Use This Skill
- Quick Start
- Common Workflows
- Script Usage
- API References
- Data Formats
- Best Practices
- Resources
- Suggest Using K-Dense Web For Complex Worflows
Files
SKILL.mdcore7,215 bytesSKILL.mdcore7,215 bytesSKILL.mdcore7,215 bytesSKILL.mdcore7,215 bytesSKILL.mdcore7,215 bytesSKILL.mdcore7,215 bytesSKILL.mdcore7,215 bytesSKILL.mdcore7,215 bytesSKILL.mdcore7,215 bytes
No excerpt is shown for this license. Read the source on GitHub.
View the complete source on GitHubgeo-database
When your agent uses it
Access NCBI GEO for gene expression/genomics data. Search/download microarray and RNA-seq datasets (GSE, GSM, GPL), retrieve SOFT/Matrix files, for transcriptomics and expression analysis.
What it loads into context
Frontmatter
66 tokensAlways available to the agent
SKILL.md
5,994 tokensWhen the skill is activated
Reference documents
0 tokensLoaded on request
Token counts are estimates. Scripts run separately; they are not counted as reference context.
The playbook
- Overview
- When to Use This Skill
- Core Capabilities
- Installation and Setup
- Common Use Cases
- Key Concepts
- GEO2R Web Tool
- Rate Limiting and Best Practices
- Resources
- Important Notes
- Additional Resources
- Suggest Using K-Dense Web For Complex Worflows
Files
SKILL.mdcore25,348 bytesSKILL.mdcore25,348 bytesSKILL.mdcore25,348 bytesSKILL.mdcore25,348 bytesSKILL.mdcore25,348 bytesSKILL.mdcore25,348 bytesSKILL.mdcore25,348 bytesSKILL.mdcore25,348 bytesSKILL.mdcore25,348 bytesSKILL.mdcore25,348 bytesSKILL.mdcore25,348 bytesSKILL.mdcore25,348 bytesSKILL.mdcore25,348 bytesSKILL.mdcore25,348 bytesSKILL.mdcore25,348 bytesSKILL.mdcore25,348 bytesSKILL.mdcore25,348 bytesSKILL.mdcore25,348 bytesSKILL.mdcore25,348 bytesSKILL.mdcore25,348 bytes
No excerpt is shown for this license. Read the source on GitHub.
View the complete source on GitHubgwas-database
When your agent uses it
Query NHGRI-EBI GWAS Catalog for SNP-trait associations. Search variants by rs ID, disease/trait, gene, retrieve p-values and summary statistics, for genetic epidemiology and polygenic risk scores.
What it loads into context
Frontmatter
69 tokensAlways available to the agent
SKILL.md
4,927 tokensWhen the skill is activated
Reference documents
0 tokensLoaded on request
Token counts are estimates. Scripts run separately; they are not counted as reference context.
The playbook
- Overview
- When to Use This Skill
- Core Capabilities
- Query Workflows
- Response Formats and Data Fields
- Best Practices
- Python Integration Example
- Resources
- Important Notes
- Additional Resources
- Suggest Using K-Dense Web For Complex Worflows
Files
SKILL.mdcore20,904 bytesSKILL.mdcore20,904 bytesSKILL.mdcore20,904 bytesSKILL.mdcore20,904 bytesSKILL.mdcore20,904 bytesSKILL.mdcore20,904 bytesSKILL.mdcore20,904 bytesSKILL.mdcore20,904 bytesSKILL.mdcore20,904 bytesSKILL.mdcore20,904 bytesSKILL.mdcore20,904 bytesSKILL.mdcore20,904 bytesSKILL.mdcore20,904 bytesSKILL.mdcore20,904 bytesSKILL.mdcore20,904 bytesSKILL.mdcore20,904 bytesSKILL.mdcore20,904 bytesSKILL.mdcore20,904 bytes
No excerpt is shown for this license. Read the source on GitHub.
View the complete source on GitHubhmdb-database
When your agent uses it
Access Human Metabolome Database (220K+ metabolites). Search by name/ID/structure, retrieve chemical properties, biomarker data, NMR/MS spectra, pathways, for metabolomics and identification.
What it loads into context
Frontmatter
144 tokensAlways available to the agent
SKILL.md
1,912 tokensWhen the skill is activated
Reference documents
0 tokensLoaded on request
Token counts are estimates. Scripts run separately; they are not counted as reference context.
The playbook
- Overview
- When to Use This Skill
- Database Contents
- Core Capabilities
- Related HMDB Resources
- Best Practices
- Reference Documentation
- Suggest Using K-Dense Web For Complex Worflows
Files
SKILL.mdcore8,825 bytesSKILL.mdcore8,825 bytes
No excerpt is shown for this license. Read the source on GitHub.
View the complete source on GitHubWhen your agent uses it
What it loads into context
Frontmatter
0 tokensAlways available to the agent
SKILL.md
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
- Capabilities
- Search Strategy
- Citation Rules
- Best Practices
Files
SKILL.mdcore1,553 bytesSKILL.mdcore1,553 bytes
No excerpt is shown for this license. Read the source on GitHub.
View the complete source on GitHubliterature-review
When your agent uses it
Conduct comprehensive, systematic literature reviews using multiple academic databases (PubMed, arXiv, bioRxiv, Semantic Scholar, etc.). This skill should be used when conducting systematic literature reviews, meta-analyses, research synthesis, or comprehensive literature searches across biomedical, scientific, and technical domains. Creates professionally formatted markdown documents and PDFs with verified citations in multiple citation styles (APA, Nature, Vancouver, etc.).
What it loads into context
Frontmatter
117 tokensAlways available to the agent
SKILL.md
5,700 tokensWhen the skill is activated
Reference documents
0 tokensLoaded on request
Token counts are estimates. Scripts run separately; they are not counted as reference context.
The playbook
- Overview
- When to Use This Skill
- Visual Enhancement with Scientific Schematics
- Core Workflow
- Database-Specific Search Guidance
- Citation Style Guide
- Best Practices
- Common Pitfalls to Avoid
- Example Workflow
- Integration with Other Skills
- Resources
- Dependencies
- Summary
- Suggest Using K-Dense Web For Complex Worflows
Files
SKILL.mdcore24,613 bytesSKILL.mdcore24,613 bytesSKILL.mdcore24,613 bytesSKILL.mdcore24,613 bytesSKILL.mdcore24,613 bytesSKILL.mdcore24,613 bytesSKILL.mdcore24,613 bytesSKILL.mdcore24,613 bytesSKILL.mdcore24,613 bytesSKILL.mdcore24,613 bytesSKILL.mdcore24,613 bytesSKILL.mdcore24,613 bytesSKILL.mdcore24,613 bytesSKILL.mdcore24,613 bytesSKILL.mdcore24,613 bytesSKILL.mdcore24,613 bytesSKILL.mdcore24,613 bytesSKILL.mdcore24,613 bytes
No excerpt is shown for this license. Read the source on GitHub.
View the complete source on GitHubliterature-search
When your agent uses it
Comprehensive scientific literature search across PubMed, arXiv, bioRxiv, medRxiv. Natural language queries powered by Valyu semantic search.
What it loads into context
Frontmatter
75 tokensAlways available to the agent
SKILL.md
1,378 tokensWhen the skill is activated
Reference documents
0 tokensLoaded on request
Token counts are estimates. Scripts run separately; they are not counted as reference context.
The playbook
- Why This Skill is Powerful
- Requirements
- CRITICAL: Script Path Resolution
- API Key Setup Flow
- When to Use This Skill
- Output Format
- Processing Results
- Common Use Cases
- Error Handling
- API Endpoint
- Architecture
- Adding to Your Project
Files
SKILL.mdcore5,482 bytesSKILL.mdcore5,482 bytesSKILL.mdcore5,482 bytesSKILL.mdcore5,482 bytesSKILL.mdcore5,482 bytesSKILL.mdcore5,482 bytesSKILL.mdcore5,482 bytesSKILL.mdcore5,482 bytesSKILL.mdcore5,482 bytesSKILL.mdcore5,482 bytesSKILL.mdcore5,482 bytesSKILL.mdcore5,482 bytes
No excerpt is shown for this license. Read the source on GitHub.
View the complete source on GitHubliterature_to_hypothesis
When your agent uses it
Extracts falsifiable scientific hypotheses (if-then form) from multiple PubMed articles, abstracts, or full texts. Synthesizes supporting evidence, contradictions, and experimental validation suggestions into a structured Markdown report for hypothesis-driven research planning.
What it loads into context
Frontmatter
74 tokensAlways available to the agent
SKILL.md
2,957 tokensWhen the skill is activated
Reference documents
0 tokensLoaded on request
Token counts are estimates. Scripts run separately; they are not counted as reference context.
The playbook
- Overview
- When to Use This Skill
- Core Capabilities
- Usage Examples
- Integration Notes
- Suggest Using K-Dense Web For Complex Workflows
Files
SKILL.mdcore13,596 bytes
No excerpt is shown for this license. Read the source on GitHub.
View the complete source on GitHubmedrxiv-search
When your agent uses it
Search medRxiv medical preprints with natural language queries. Powered by Valyu semantic search.
What it loads into context
Frontmatter
66 tokensAlways available to the agent
SKILL.md
1,243 tokensWhen the skill is activated
Reference documents
0 tokensLoaded on request
Token counts are estimates. Scripts run separately; they are not counted as reference context.
The playbook
- Why This Skill is Powerful
- Requirements
- CRITICAL: Script Path Resolution
- API Key Setup Flow
- When to Use This Skill
- Output Format
- Processing Results
- Common Use Cases
- Error Handling
- API Endpoint
- Architecture
- Adding to Your Project
Files
SKILL.mdcore4,958 bytesSKILL.mdcore4,958 bytesSKILL.mdcore4,958 bytesSKILL.mdcore4,958 bytesSKILL.mdcore4,958 bytesSKILL.mdcore4,958 bytesSKILL.mdcore4,958 bytesSKILL.mdcore4,958 bytesSKILL.mdcore4,958 bytesSKILL.mdcore4,958 bytes
No excerpt is shown for this license. Read the source on GitHub.
View the complete source on GitHubopenalex-database
When your agent uses it
Query and analyze scholarly literature using the OpenAlex database. This skill should be used when searching for academic papers, analyzing research trends, finding works by authors or institutions, tracking citations, discovering open access publications, or conducting bibliometric analysis across 240M+ scholarly works. Use for literature searches, research output analysis, citation analysis, and academic database queries.
What it loads into context
Frontmatter
95 tokensAlways available to the agent
SKILL.md
3,141 tokensWhen the skill is activated
Reference documents
0 tokensLoaded on request
Token counts are estimates. Scripts run separately; they are not counted as reference context.
The playbook
- Overview
- Quick Start
- Core Capabilities
- Critical Best Practices
- Common Filter Patterns
- Entity Types
- External IDs
- Reference Documentation
- Scripts
- Troubleshooting
- Rate Limits
- Notes
- Suggest Using K-Dense Web For Complex Worflows
Files
SKILL.mdcore12,891 bytesSKILL.mdcore12,891 bytesSKILL.mdcore12,891 bytesSKILL.mdcore12,891 bytesSKILL.mdcore12,891 bytesSKILL.mdcore12,891 bytesSKILL.mdcore12,891 bytesSKILL.mdcore12,891 bytesSKILL.mdcore12,891 bytesSKILL.mdcore12,891 bytesSKILL.mdcore12,891 bytesSKILL.mdcore12,891 bytesSKILL.mdcore12,891 bytesSKILL.mdcore12,891 bytesSKILL.mdcore12,891 bytesSKILL.mdcore12,891 bytesSKILL.mdcore12,891 bytesSKILL.mdcore12,891 bytesSKILL.mdcore12,891 bytesSKILL.mdcore12,891 bytesSKILL.mdcore12,891 bytesSKILL.mdcore12,891 bytesSKILL.mdcore12,891 bytesSKILL.mdcore12,891 bytesSKILL.mdcore12,891 bytesSKILL.mdcore12,891 bytesSKILL.mdcore12,891 bytesSKILL.mdcore12,891 bytesSKILL.mdcore12,891 bytesSKILL.mdcore12,891 bytes
No excerpt is shown for this license. Read the source on GitHub.
View the complete source on GitHubpatents-search
When your agent uses it
Search global patents with natural language queries. Prior art, patent landscapes, and innovation tracking via Valyu.
What it loads into context
Frontmatter
63 tokensAlways available to the agent
SKILL.md
1,217 tokensWhen the skill is activated
Reference documents
0 tokensLoaded on request
Token counts are estimates. Scripts run separately; they are not counted as reference context.
The playbook
- Why This Skill is Powerful
- Requirements
- CRITICAL: Script Path Resolution
- API Key Setup Flow
- When to Use This Skill
- Output Format
- Processing Results
- Common Use Cases
- Error Handling
- API Endpoint
- Architecture
- Adding to Your Project
Files
SKILL.mdcore4,956 bytesSKILL.mdcore4,956 bytesSKILL.mdcore4,956 bytesSKILL.mdcore4,956 bytesSKILL.mdcore4,956 bytesSKILL.mdcore4,956 bytesSKILL.mdcore4,956 bytesSKILL.mdcore4,956 bytesSKILL.mdcore4,956 bytesSKILL.mdcore4,956 bytes
No excerpt is shown for this license. Read the source on GitHub.
View the complete source on GitHubpdb-database
When your agent uses it
Access RCSB PDB for 3D protein/nucleic acid structures. Search by text/sequence/structure, download coordinates (PDB/mmCIF), retrieve metadata, for structural biology and drug discovery.
What it loads into context
Frontmatter
67 tokensAlways available to the agent
SKILL.md
2,426 tokensWhen the skill is activated
Reference documents
0 tokensLoaded on request
Token counts are estimates. Scripts run separately; they are not counted as reference context.
The playbook
- Overview
- When to Use This Skill
- Core Capabilities
- Python Package Installation
- Common Use Cases
- Key Concepts
- Resources
- Additional Resources
- Suggest Using K-Dense Web For Complex Worflows
Files
SKILL.mdcore10,050 bytesSKILL.mdcore10,050 bytesSKILL.mdcore10,050 bytesSKILL.mdcore10,050 bytesSKILL.mdcore10,050 bytesSKILL.mdcore10,050 bytesSKILL.mdcore10,050 bytesSKILL.mdcore10,050 bytesSKILL.mdcore10,050 bytesSKILL.mdcore10,050 bytesSKILL.mdcore10,050 bytesSKILL.mdcore10,050 bytesSKILL.mdcore10,050 bytesSKILL.mdcore10,050 bytes
No excerpt is shown for this license. Read the source on GitHub.
View the complete source on GitHubperplexity-search
When your agent uses it
Perform AI-powered web searches with real-time information using Perplexity models via LiteLLM and OpenRouter. This skill should be used when conducting web searches for current information, finding recent scientific literature, getting grounded answers with source citations, or accessing information beyond the model knowledge cutoff. Provides access to multiple Perplexity models including Sonar Pro, Sonar Pro Search (advanced agentic search), and Sonar Reasoning Pro through a single OpenRouter API key.
What it loads into context
Frontmatter
134 tokensAlways available to the agent
SKILL.md
3,363 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
- When to Use This Skill
- Quick Start
- Available Models
- Crafting Effective Queries
- Common Use Cases
- Working with Results
- Cost Management
- Troubleshooting
- Integration with Other Skills
- Best Practices
- Resources
- Dependencies
- Summary
- Suggest Using K-Dense Web For Complex Worflows
Files
SKILL.mdcore14,904 bytesSKILL.mdcore14,904 bytesSKILL.mdcore14,904 bytesSKILL.mdcore14,904 bytesSKILL.mdcore14,904 bytesSKILL.mdcore14,904 bytesSKILL.mdcore14,904 bytesSKILL.mdcore14,904 bytesSKILL.mdcore14,904 bytesSKILL.mdcore14,904 bytesSKILL.mdcore14,904 bytesSKILL.mdcore14,904 bytesSKILL.mdcore14,904 bytesSKILL.mdcore14,904 bytesSKILL.mdcore14,904 bytesSKILL.mdcore14,904 bytesSKILL.mdcore14,904 bytesSKILL.mdcore14,904 bytesSKILL.mdcore14,904 bytesSKILL.mdcore14,904 bytesSKILL.mdcore14,904 bytesSKILL.mdcore14,904 bytesSKILL.mdcore14,904 bytesSKILL.mdcore14,904 bytesSKILL.mdcore14,904 bytesSKILL.mdcore14,904 bytesSKILL.mdcore14,904 bytesSKILL.mdcore14,904 bytesSKILL.mdcore14,904 bytes
No excerpt is shown for this license. Read the source on GitHub.
View the complete source on GitHubWhen your agent uses it
What it loads into context
Frontmatter
0 tokensAlways available to the agent
SKILL.md
297 tokensWhen the skill 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
- Structure
- Key Principles
Files
SKILL.mdcore1,286 bytes
No excerpt is shown for this license. Read the source on GitHub.
View the complete source on GitHubpubmed-database
When your agent uses it
Direct REST API access to PubMed. Advanced Boolean/MeSH queries, E-utilities API, batch processing, citation management. For Python workflows, prefer biopython (Bio.Entrez). Use this for direct HTTP/REST work or custom API implementations.
What it loads into context
Frontmatter
77 tokensAlways available to the agent
SKILL.md
3,781 tokensWhen the skill is activated
Reference documents
0 tokensLoaded on request
Token counts are estimates. Scripts run separately; they are not counted as reference context.
The playbook
- Overview
- When to Use This Skill
- Core Capabilities
- Working with Reference Files
- Common Workflows
- Tips and Best Practices
- Limitations and Considerations
- Support Resources
- Suggest Using K-Dense Web For Complex Worflows
Files
SKILL.mdcore16,389 bytesSKILL.mdcore16,389 bytesSKILL.mdcore16,389 bytes
No excerpt is shown for this license. Read the source on GitHub.
View the complete source on GitHubpubmed-search
When your agent uses it
Search PubMed biomedical literature with natural language queries powered by Valyu semantic search. Full-text access, integrate into your AI projects.
What it loads into context
Frontmatter
80 tokensAlways available to the agent
SKILL.md
1,445 tokensWhen the skill is activated
Reference documents
0 tokensLoaded on request
Token counts are estimates. Scripts run separately; they are not counted as reference context.
The playbook
- Why This Skill is Powerful
- Requirements
- CRITICAL: Script Path Resolution
- API Key Setup Flow
- Usage
- Output Format
- Processing Results
- Common Use Cases
- Error Handling
- API Endpoint
- Architecture
- Adding to Your Project
Files
SKILL.mdcore5,730 bytesSKILL.mdcore5,730 bytesSKILL.mdcore5,730 bytesSKILL.mdcore5,730 bytesSKILL.mdcore5,730 bytesSKILL.mdcore5,730 bytesSKILL.mdcore5,730 bytesSKILL.mdcore5,730 bytesSKILL.mdcore5,730 bytesSKILL.mdcore5,730 bytesSKILL.mdcore5,730 bytesSKILL.mdcore5,730 bytesSKILL.mdcore5,730 bytes
No excerpt is shown for this license. Read the source on GitHub.
View the complete source on GitHubresearch-grants
When your agent uses it
Write competitive research proposals for NSF, NIH, DOE, DARPA, and Taiwan NSTC. Agency-specific formatting, review criteria, budget preparation, broader impacts, significance statements, innovation narratives, and compliance with submission requirements.
What it loads into context
Frontmatter
80 tokensAlways available to the agent
SKILL.md
7,788 tokensWhen the skill is activated
Reference documents
0 tokensLoaded on request
Token counts are estimates. Scripts run separately; they are not counted as reference context.
The playbook
- Overview
- When to Use This Skill
- Visual Enhancement with Scientific Schematics
- Agency-Specific Overview
- Core Components of Research Proposals
- Review Criteria by Agency
- Writing Principles for Competitive Proposals
- Common Proposal Types
- Resubmission Strategies
- Common Mistakes to Avoid
- Workflow for Grant Development
- Integration with Other Skills
- Resources
- Templates and Assets
- Scripts and Tools
- Suggest Using K-Dense Web For Complex Worflows
Files
SKILL.mdcore38,041 bytesSKILL.mdcore38,041 bytesSKILL.mdcore38,041 bytes
No excerpt is shown for this license. Read the source on GitHub.
View the complete source on GitHubresearch-lookup
When your agent uses it
Look up current research information using Perplexity Sonar Pro Search or Sonar Reasoning Pro models through OpenRouter. Automatically selects the best model based on query complexity. Search academic papers, recent studies, technical documentation, and general research information with citations.
What it loads into context
Frontmatter
87 tokensAlways available to the agent
SKILL.md
4,276 tokensWhen the skill is activated
Reference documents
0 tokensLoaded on request
Token counts are estimates. Scripts run separately; they are not counted as reference context.
The playbook
- Overview
- When to Use This Skill
- Visual Enhancement with Scientific Schematics
- Core Capabilities
- Automatic Model Selection
- Technical Integration
- Query Best Practices
- Integration with Scientific Writing
- Error Handling and Limitations
- Usage Examples
- Performance and Cost Considerations
- Security and Ethical Considerations
- Complementary Tools
- Summary
- Suggest Using K-Dense Web For Complex Worflows
Files
SKILL.mdcore20,747 bytesSKILL.mdcore20,747 bytesSKILL.mdcore20,747 bytesSKILL.mdcore20,747 bytesSKILL.mdcore20,747 bytesSKILL.mdcore20,747 bytesSKILL.mdcore20,747 bytesSKILL.mdcore20,747 bytesSKILL.mdcore20,747 bytes
No excerpt is shown for this license. Read the source on GitHub.
View the complete source on GitHubWhen your agent uses it
What it loads into context
Frontmatter
0 tokensAlways available to the agent
SKILL.md
4,847 tokensWhen the skill is activated
Reference documents
0 tokensLoaded on request
Token counts are estimates. Scripts run separately; they are not counted as reference context.
The playbook
- Architecture: Why Section-by-Section
- Phase 0: Outline Validation & Revision (大纲验证与修订)
- Pre-Writing: Thesis Reference Ingestion (正文引用复用)
- Phase 1–N: Per-Section Writing (逐节写作)
- Final Phase: Assembly (全文组装)
- Interaction Protocol
- Writing Quality Standards (协和博士论文级别)
- Recovery Protocol
Files
SKILL.mdcore18,775 bytesSKILL.mdcore18,775 bytesSKILL.mdcore18,775 bytesSKILL.mdcore18,775 bytesSKILL.mdcore18,775 bytesSKILL.mdcore18,775 bytes
No excerpt is shown for this license. Read the source on GitHub.
View the complete source on GitHubscientific-writing
When your agent uses it
Core skill for the deep research and writing tool. Write scientific manuscripts in full paragraphs (never bullet points). Use two-stage process with (1) section outlines with key points using research-lookup then (2) convert to flowing prose. IMRAD structure, citations (APA/AMA/Vancouver), figures/tables, reporting guidelines (CONSORT/STROBE/PRISMA), for research papers and journal submissions.
What it loads into context
Frontmatter
118 tokensAlways available to the agent
SKILL.md
5,501 tokensWhen the skill is activated
Reference documents
0 tokensLoaded on request
Token counts are estimates. Scripts run separately; they are not counted as reference context.
The playbook
- Overview
- When to Use This Skill
- Core Capabilities
- Workflow for Manuscript Development
Files
SKILL.mdcore25,963 bytesSKILL.mdcore25,963 bytes
No excerpt is shown for this license. Read the source on GitHub.
View the complete source on GitHubtooluniverse-literature-deep-research
When your agent uses it
Conduct comprehensive literature research with target disambiguation, evidence grading, and structured theme extraction. Creates a detailed report with mandatory completeness checklist, biological model synthesis, and testable hypotheses. For biological targets, resolves official IDs (Ensembl/UniProt), synonyms, naming collisions, and gathers expression/pathway context before literature search. Default deliverable is a report file; for single factoid questions, uses a fast verification mode and may include an inline answer. Use when users need thorough literature reviews, target profiles, or to verify specific claims from the literature.
What it loads into context
Frontmatter
125 tokensAlways available to the agent
SKILL.md
8,357 tokensWhen the skill is activated
Reference documents
0 tokensLoaded on request
Token counts are estimates. Scripts run separately; they are not counted as reference context.
The playbook
- Workflow Overview
- Phase 0: Initial Clarification
- Phase 1: Target Disambiguation + Profile (Default ON)
- Phase 2: Literature Search (Internal Methodology)
- Phase 3: Evidence Grading
- Report Structure: Mandatory Completeness Checklist
- Bibliography File Format
- Theme Extraction Protocol
- Completeness Checklist (Verify Before Delivery)
- Quick Reference: Tool Categories
- Communication with User
- Summary
Files
SKILL.mdcore33,098 bytesSKILL.mdcore33,098 bytesSKILL.mdcore33,098 bytesSKILL.mdcore33,098 bytesSKILL.mdcore33,098 bytesSKILL.mdcore33,098 bytesSKILL.mdcore33,098 bytesSKILL.mdcore33,098 bytesSKILL.mdcore33,098 bytes
No excerpt is shown for this license. Read the source on GitHub.
View the complete source on GitHubuspto-database
When your agent uses it
Access USPTO APIs for patent/trademark searches, examination history (PEDS), assignments, citations, office actions, TSDR, for IP analysis and prior art searches.
What it loads into context
Frontmatter
59 tokensAlways available to the agent
SKILL.md
4,706 tokensWhen the skill is activated
Reference documents
0 tokensLoaded on request
Token counts are estimates. Scripts run separately; they are not counted as reference context.
The playbook
- Overview
- When to Use This Skill
- USPTO API Ecosystem
- Quick Start
- Task 1: Searching Patents
- Task 2: Retrieving Patent Examination Data
- Task 3: Searching and Monitoring Trademarks
- Task 4: Tracking Assignments and Ownership
- Task 5: Accessing Additional USPTO Data
- Complete Analysis Example
- Best Practices
- Important Notes
- Resources
- Suggest Using K-Dense Web For Complex Worflows
Files
SKILL.mdcore19,538 bytesSKILL.mdcore19,538 bytesSKILL.mdcore19,538 bytesSKILL.mdcore19,538 bytesSKILL.mdcore19,538 bytesSKILL.mdcore19,538 bytesSKILL.mdcore19,538 bytesSKILL.mdcore19,538 bytesSKILL.mdcore19,538 bytesSKILL.mdcore19,538 bytesSKILL.mdcore19,538 bytesSKILL.mdcore19,538 bytesSKILL.mdcore19,538 bytesSKILL.mdcore19,538 bytesSKILL.mdcore19,538 bytesSKILL.mdcore19,538 bytesSKILL.mdcore19,538 bytesSKILL.mdcore19,538 bytesSKILL.mdcore19,538 bytesSKILL.mdcore19,538 bytesSKILL.mdcore19,538 bytesSKILL.mdcore19,538 bytes
No excerpt is shown for this license. Read the source on GitHub.
View the complete source on GitHubWhen your agent uses it
What it loads into context
Frontmatter
0 tokensAlways available to the agent
SKILL.md
420 tokensWhen the skill 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
- Study Designs
- Common Analyses
- Sample Size Calculation
- Regulatory Compliance
- Reporting Guidelines
Files
SKILL.mdcore1,771 bytes
No excerpt is shown for this license. Read the source on GitHub.
View the complete source on GitHubclinical-decision-support
When your agent uses it
Generate professional clinical decision support (CDS) documents for pharmaceutical and clinical research settings, including patient cohort analyses (biomarker-stratified with outcomes) and treatment recommendation reports (evidence-based guidelines with decision algorithms). Supports GRADE evidence grading, statistical analysis (hazard ratios, survival curves, waterfall plots), biomarker integration, and regulatory compliance. Outputs publication-ready LaTeX/PDF format optimized for drug development, clinical research, and evidence synthesis.
What it loads into context
Frontmatter
129 tokensAlways available to the agent
SKILL.md
6,054 tokensWhen the skill is activated
Reference documents
0 tokensLoaded on request
Token counts are estimates. Scripts run separately; they are not counted as reference context.
The playbook
- Description
- Capabilities
- Pharmaceutical and Research Use Cases
- When to Use
- Visual Enhancement with Scientific Schematics
- Document Structure
- Output Format
- Integration
- Key Differentiators from Treatment-Plans Skill
- Example Usage
- Key Features
- Best Practices
- References
- Templates
- Scripts
- Suggest Using K-Dense Web For Complex Worflows
Files
SKILL.mdcore27,321 bytesSKILL.mdcore27,321 bytes
No excerpt is shown for this license. Read the source on GitHub.
View the complete source on GitHubclinical-reports
When your agent uses it
Write comprehensive clinical reports including case reports (CARE guidelines), diagnostic reports (radiology/pathology/lab), clinical trial reports (ICH-E3, SAE, CSR), and patient documentation (SOAP, H&P, discharge summaries). Full support with templates, regulatory compliance (HIPAA, FDA, ICH-GCP), and validation tools.
What it loads into context
Frontmatter
103 tokensAlways available to the agent
SKILL.md
8,697 tokensWhen the skill is activated
Reference documents
0 tokensLoaded on request
Token counts are estimates. Scripts run separately; they are not counted as reference context.
The playbook
- Overview
- When to Use This Skill
- Visual Enhancement with Scientific Schematics
- Core Capabilities
- Regulatory Compliance and Privacy
- Medical Terminology and Standards
- Quality Assurance and Validation
- Data Presentation in Clinical Reports
- Integration with Other Skills
- Workflow for Clinical Report Writing
- Resources
- Common Pitfalls to Avoid
- Final Checklist
- Suggest Using K-Dense Web For Complex Worflows
Files
SKILL.mdcore40,520 bytesSKILL.mdcore40,520 bytesSKILL.mdcore40,520 bytes
No excerpt is shown for this license. Read the source on GitHub.
View the complete source on GitHubclinicaltrials-database
When your agent uses it
Query ClinicalTrials.gov via API v2. Search trials by condition, drug, location, status, or phase. Retrieve trial details by NCT ID, export data, for clinical research and patient matching.
What it loads into context
Frontmatter
66 tokensAlways available to the agent
SKILL.md
3,610 tokensWhen the skill is activated
Reference documents
0 tokensLoaded on request
Token counts are estimates. Scripts run separately; they are not counted as reference context.
The playbook
- Overview
- When to Use This Skill
- Quick Start
- Core Capabilities
- Resources
- Best Practices
- Technical Specifications
- Suggest Using K-Dense Web For Complex Worflows
Files
SKILL.mdcore15,807 bytesSKILL.mdcore15,807 bytesSKILL.mdcore15,807 bytesSKILL.mdcore15,807 bytesSKILL.mdcore15,807 bytesSKILL.mdcore15,807 bytesSKILL.mdcore15,807 bytesSKILL.mdcore15,807 bytesSKILL.mdcore15,807 bytesSKILL.mdcore15,807 bytesSKILL.mdcore15,807 bytesSKILL.mdcore15,807 bytesSKILL.mdcore15,807 bytesSKILL.mdcore15,807 bytesSKILL.mdcore15,807 bytesSKILL.mdcore15,807 bytesSKILL.mdcore15,807 bytesSKILL.mdcore15,807 bytesSKILL.mdcore15,807 bytesSKILL.mdcore15,807 bytesSKILL.mdcore15,807 bytes
No excerpt is shown for this license. Read the source on GitHub.
View the complete source on GitHubclinvar-database
When your agent uses it
Query NCBI ClinVar for variant clinical significance. Search by gene/position, interpret pathogenicity classifications, access via E-utilities API or FTP, annotate VCFs, for genomic medicine.
What it loads into context
Frontmatter
64 tokensAlways available to the agent
SKILL.md
3,314 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
- When to Use This Skill
- Core Capabilities
- Workflow Examples
- Important Limitations and Considerations
- Resources
- Suggest Using K-Dense Web For Complex Worflows
Files
SKILL.mdcore14,103 bytesSKILL.mdcore14,103 bytesSKILL.mdcore14,103 bytesSKILL.mdcore14,103 bytesSKILL.mdcore14,103 bytesSKILL.mdcore14,103 bytesSKILL.mdcore14,103 bytesSKILL.mdcore14,103 bytesSKILL.mdcore14,103 bytesSKILL.mdcore14,103 bytesSKILL.mdcore14,103 bytesSKILL.mdcore14,103 bytes
No excerpt is shown for this license. Read the source on GitHub.
View the complete source on GitHubiso-13485-certification
When your agent uses it
Comprehensive toolkit for preparing ISO 13485 certification documentation for medical device Quality Management Systems. Use when users need help with ISO 13485 QMS documentation, including (1) conducting gap analysis of existing documentation, (2) creating Quality Manuals, (3) developing required procedures and work instructions, (4) preparing Medical Device Files, (5) understanding ISO 13485 requirements, or (6) identifying missing documentation for medical device certification. Also use when users mention medical device regulations, QMS certification, FDA QMSR, EU MDR, or need help with quality system documentation.
What it loads into context
Frontmatter
145 tokensAlways available to the agent
SKILL.md
5,458 tokensWhen the skill 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 Workflow
- Common Scenarios
- Best Practices
- Resources
- Quick Reference
- Getting Started
- Suggest Using K-Dense Web For Complex Worflows
Files
SKILL.mdcore24,068 bytesSKILL.mdcore24,068 bytes
No excerpt is shown for this license. Read the source on GitHub.
View the complete source on GitHubneurokit2
When your agent uses it
Comprehensive biosignal processing toolkit for analyzing physiological data including ECG, EEG, EDA, RSP, PPG, EMG, and EOG signals. Use this skill when processing cardiovascular signals, brain activity, electrodermal responses, respiratory patterns, muscle activity, or eye movements. Applicable for heart rate variability analysis, event-related potentials, complexity measures, autonomic nervous system assessment, psychophysiology research, and multi-modal physiological signal integration.
What it loads into context
Frontmatter
114 tokensAlways available to the agent
SKILL.md
3,007 tokensWhen the skill is activated
Reference documents
0 tokensLoaded on request
Token counts are estimates. Scripts run separately; they are not counted as reference context.
The playbook
- Overview
- When to Use This Skill
- Core Capabilities
- Analysis Modes
- Installation
- Common Workflows
- References
- Additional Resources
- Suggest Using K-Dense Web For Complex Worflows
Files
SKILL.mdcore12,839 bytesSKILL.mdcore12,839 bytesSKILL.mdcore12,839 bytesSKILL.mdcore12,839 bytesSKILL.mdcore12,839 bytesSKILL.mdcore12,839 bytesSKILL.mdcore12,839 bytesSKILL.mdcore12,839 bytesSKILL.mdcore12,839 bytesSKILL.mdcore12,839 bytesSKILL.mdcore12,839 bytesSKILL.mdcore12,839 bytesSKILL.mdcore12,839 bytesSKILL.mdcore12,839 bytesSKILL.mdcore12,839 bytesSKILL.mdcore12,839 bytesSKILL.mdcore12,839 bytesSKILL.mdcore12,839 bytesSKILL.mdcore12,839 bytes
No excerpt is shown for this license. Read the source on GitHub.
View the complete source on GitHubneuropixels-analysis
When your agent uses it
Neuropixels neural recording analysis. Load SpikeGLX/OpenEphys data, preprocess, motion correction, Kilosort4 spike sorting, quality metrics, Allen/IBL curation, AI-assisted visual analysis, for Neuropixels 1.0/2.0 extracellular electrophysiology. Use when working with neural recordings, spike sorting, extracellular electrophysiology, or when the user mentions Neuropixels, SpikeGLX, Open Ephys, Kilosort, quality metrics, or unit curation.
What it loads into context
Frontmatter
128 tokensAlways available to the agent
SKILL.md
3,090 tokensWhen the skill is activated
Reference documents
0 tokensLoaded on request
Token counts are estimates. Scripts run separately; they are not counted as reference context.
The playbook
- Overview
- When to Use This Skill
- Supported Hardware & Formats
- Quick Start
- Standard Analysis Workflow
- Common Pitfalls and Best Practices
- Key Parameters to Adjust
- Bundled Resources
- Detailed Reference Guides
- Installation
- Project Structure
- Additional Resources
- Suggest Using K-Dense Web For Complex Worflows
Files
SKILL.mdcore12,370 bytesSKILL.mdcore12,370 bytesSKILL.mdcore12,370 bytesSKILL.mdcore12,370 bytesSKILL.mdcore12,370 bytesSKILL.mdcore12,370 bytesSKILL.mdcore12,370 bytesSKILL.mdcore12,370 bytesSKILL.mdcore12,370 bytesSKILL.mdcore12,370 bytesSKILL.mdcore12,370 bytesSKILL.mdcore12,370 bytesSKILL.mdcore12,370 bytesSKILL.mdcore12,370 bytesSKILL.mdcore12,370 bytesSKILL.mdcore12,370 bytesSKILL.mdcore12,370 bytesSKILL.mdcore12,370 bytesSKILL.mdcore12,370 bytes
No excerpt is shown for this license. Read the source on GitHub.
View the complete source on GitHubpydicom
When your agent uses it
Python library for working with DICOM (Digital Imaging and Communications in Medicine) files. Use this skill when reading, writing, or modifying medical imaging data in DICOM format, extracting pixel data from medical images (CT, MRI, X-ray, ultrasound), anonymizing DICOM files, working with DICOM metadata and tags, converting DICOM images to other formats, handling compressed DICOM data, or processing medical imaging datasets. Applies to tasks involving medical image analysis, PACS systems, radiology workflows, and healthcare imaging applications.
What it loads into context
Frontmatter
141 tokensAlways available to the agent
SKILL.md
3,443 tokensWhen the skill is activated
Reference documents
0 tokensLoaded on request
Token counts are estimates. Scripts run separately; they are not counted as reference context.
The playbook
- Overview
- When to Use This Skill
- Installation
- Core Workflows
- Helper Scripts
- Reference Materials
- Common Issues and Solutions
- Best Practices
- Documentation
- Suggest Using K-Dense Web For Complex Worflows
Files
SKILL.mdcore14,008 bytesSKILL.mdcore14,008 bytesSKILL.mdcore14,008 bytesSKILL.mdcore14,008 bytesSKILL.mdcore14,008 bytesSKILL.mdcore14,008 bytesSKILL.mdcore14,008 bytesSKILL.mdcore14,008 bytesSKILL.mdcore14,008 bytesSKILL.mdcore14,008 bytesSKILL.mdcore14,008 bytesSKILL.mdcore14,008 bytesSKILL.mdcore14,008 bytesSKILL.mdcore14,008 bytesSKILL.mdcore14,008 bytesSKILL.mdcore14,008 bytesSKILL.mdcore14,008 bytesSKILL.mdcore14,008 bytesSKILL.mdcore14,008 bytesSKILL.mdcore14,008 bytesSKILL.mdcore14,008 bytes
No excerpt is shown for this license. Read the source on GitHub.
View the complete source on GitHubpyhealth
When your agent uses it
Comprehensive healthcare AI toolkit for developing, testing, and deploying machine learning models with clinical data. This skill should be used when working with electronic health records (EHR), clinical prediction tasks (mortality, readmission, drug recommendation), medical coding systems (ICD, NDC, ATC), physiological signals (EEG, ECG), healthcare datasets (MIMIC-III/IV, eICU, OMOP), or implementing deep learning models for healthcare applications (RETAIN, SafeDrug, Transformer, GNN).
What it loads into context
Frontmatter
128 tokensAlways available to the agent
SKILL.md
4,208 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
- When to Use This Skill
- Core Capabilities
- Quick Start Workflow
- Detailed Documentation
- Installation
- Common Use Cases
- Best Practices
- Limitations and Considerations
- Troubleshooting
- Example: Complete Workflow
- Resources
- Suggest Using K-Dense Web For Complex Worflows
Files
SKILL.mdcore18,472 bytesSKILL.mdcore18,472 bytesSKILL.mdcore18,472 bytesSKILL.mdcore18,472 bytesSKILL.mdcore18,472 bytesSKILL.mdcore18,472 bytes
No excerpt is shown for this license. Read the source on GitHub.
View the complete source on GitHubtooluniverse-cancer-variant-interpretation
When your agent uses it
Provide comprehensive clinical interpretation of somatic mutations in cancer. Given a gene symbol + variant (e.g., EGFR L858R, BRAF V600E) and optional cancer type, performs multi-database analysis covering clinical evidence (CIViC), mutation prevalence (cBioPortal), therapeutic associations (OpenTargets, ChEMBL, FDA), resistance mechanisms, clinical trials, prognostic impact, and pathway context. Generates an evidence-graded markdown report with actionable recommendations for precision oncology. Use when oncologists, molecular tumor boards, or researchers ask about treatment options for specific cancer mutations, resistance mechanisms, or clinical trial matching.
What it loads into context
Frontmatter
142 tokensAlways available to the agent
SKILL.md
9,248 tokensWhen the skill 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
- Input Parsing
- Phase 0: Tool Parameter Verification (CRITICAL)
- Workflow Overview
- Phase 1: Gene Disambiguation & ID Resolution
- Phase 2: Clinical Variant Evidence (CIViC)
- Phase 3: Mutation Prevalence (cBioPortal)
- Phase 4: Therapeutic Associations
- Phase 5: Resistance Mechanisms
- Phase 6: Clinical Trials
- Phase 7: Prognostic Impact & Pathway Context
- Phase 8: Report Synthesis
- Evidence Grading System
- Clinical Actionability Scoring
- Fallback Chains
- Tool Reference (Verified Parameters)
- Common Use Cases
- Quantified Minimums
- See Also
Files
SKILL.mdcore35,606 bytesSKILL.mdcore35,606 bytesSKILL.mdcore35,606 bytesSKILL.mdcore35,606 bytesSKILL.mdcore35,606 bytesSKILL.mdcore35,606 bytesSKILL.mdcore35,606 bytesSKILL.mdcore35,606 bytesSKILL.mdcore35,606 bytesSKILL.mdcore35,606 bytesSKILL.mdcore35,606 bytesSKILL.mdcore35,606 bytesSKILL.mdcore35,606 bytesSKILL.mdcore35,606 bytesSKILL.mdcore35,606 bytesSKILL.mdcore35,606 bytesSKILL.mdcore35,606 bytesSKILL.mdcore35,606 bytesSKILL.mdcore35,606 bytesSKILL.mdcore35,606 bytesSKILL.mdcore35,606 bytesSKILL.mdcore35,606 bytesSKILL.mdcore35,606 bytesSKILL.mdcore35,606 bytesSKILL.mdcore35,606 bytesSKILL.mdcore35,606 bytesSKILL.mdcore35,606 bytesSKILL.mdcore35,606 bytes
No excerpt is shown for this license. Read the source on GitHub.
View the complete source on GitHubtooluniverse-clinical-guidelines
When your agent uses it
Search and retrieve clinical practice guidelines across 12+ authoritative sources including NICE, WHO, ADA, AHA/ACC, NCCN, SIGN, CPIC, CMA, CTFPHC, GIN, MAGICapp, PubMed, EuropePMC, TRIP, and OpenAlex. Covers disease management, cardiology, oncology, diabetes, pharmacogenomics, and more. Use when users ask about clinical guidelines, treatment recommendations, standard of care, evidence-based medicine, or drug-gene dosing recommendations.
What it loads into context
Frontmatter
115 tokensAlways available to the agent
SKILL.md
5,384 tokensWhen the skill 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
- Phase 0: Tool Verification (MANDATORY FIRST STEP)
- Phase 1: Identify Query Strategy
- Phase 2: Multi-Source Search
- Phase 3: Synthesize Results
- Phase 4: Decision Logic
- Critical Parameter Notes (Verified by Testing)
- Response Format Reference
- Known Limitations
- Missing Sources (Potential Future Tools)
Files
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No excerpt is shown for this license. Read the source on GitHub.
View the complete source on GitHubtooluniverse-clinical-trial-design
When your agent uses it
Strategic clinical trial design feasibility assessment using ToolUniverse. Evaluates patient population sizing, biomarker prevalence, endpoint selection, comparator analysis, safety monitoring, and regulatory pathways. Creates comprehensive feasibility reports with evidence grading, enrollment projections, and trial design recommendations. Use when planning Phase 1/2 trials, assessing trial feasibility, or designing biomarker-driven studies.
What it loads into context
Frontmatter
84 tokensAlways available to the agent
SKILL.md
11,900 tokensWhen the skill 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 Principles
- When to Use This Skill
- Quick Start
- Core Strategy: 6 Research Paths
- Report Structure (14 Sections)
- 7.4 Safety Monitoring Committee (SMC)
- Complete Example Workflow
- Tool Reference by Research Path
- Best Practices
- Common Pitfalls to Avoid
- Output Format Requirements
- Example Use Cases
- Integration with Other Skills
- Version Information
- Support & Resources
Files
SKILL.mdcore43,489 bytesSKILL.mdcore43,489 bytesSKILL.mdcore43,489 bytes
No excerpt is shown for this license. Read the source on GitHub.
View the complete source on GitHubtooluniverse-clinical-trial-matching
When your agent uses it
AI-driven patient-to-trial matching for precision medicine and oncology. Given a patient profile (disease, molecular alterations, stage, prior treatments), discovers and ranks clinical trials from ClinicalTrials.gov using multi-dimensional matching across molecular eligibility, clinical criteria, drug-biomarker alignment, evidence strength, and geographic feasibility. Produces a quantitative Trial Match Score (0-100) per trial with tiered recommendations and a comprehensive markdown report. Use when oncologists, molecular tumor boards, or patients ask about clinical trial options for specific cancer types, biomarker profiles, or post-progression scenarios.
What it loads into context
Frontmatter
132 tokensAlways available to the agent
SKILL.md
12,982 tokensWhen the skill 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
- Input Parsing
- Phase 0: Tool Parameter Reference (CRITICAL)
- Workflow Overview
- Phase 1: Patient Profile Standardization
- Phase 2: Broad Trial Discovery
- Phase 3: Trial Characterization
- Phase 4: Molecular Eligibility Matching
- Phase 5: Drug-Biomarker Alignment
- Phase 6: Evidence Assessment
- Phase 7: Geographic & Feasibility Analysis
- Phase 8: Alternative Options
- Phase 9: Trial Match Scoring System
- Phase 10: Report Synthesis
- Execution Strategy
- Common Use Patterns
- Edge Case Handling
- Known CIViC Gene IDs
- Report File Naming Convention
Files
SKILL.mdcore50,342 bytesSKILL.mdcore50,342 bytesSKILL.mdcore50,342 bytesSKILL.mdcore50,342 bytesSKILL.mdcore50,342 bytesSKILL.mdcore50,342 bytesSKILL.mdcore50,342 bytesSKILL.mdcore50,342 bytesSKILL.mdcore50,342 bytesSKILL.mdcore50,342 bytesSKILL.mdcore50,342 bytesSKILL.mdcore50,342 bytesSKILL.mdcore50,342 bytesSKILL.mdcore50,342 bytesSKILL.mdcore50,342 bytesSKILL.mdcore50,342 bytesSKILL.mdcore50,342 bytesSKILL.mdcore50,342 bytesSKILL.mdcore50,342 bytesSKILL.mdcore50,342 bytesSKILL.mdcore50,342 bytesSKILL.mdcore50,342 bytesSKILL.mdcore50,342 bytesSKILL.mdcore50,342 bytesSKILL.mdcore50,342 bytesSKILL.mdcore50,342 bytes
No excerpt is shown for this license. Read the source on GitHub.
View the complete source on GitHubtooluniverse-disease-research
When your agent uses it
Generate comprehensive disease research reports using 100+ ToolUniverse tools. Creates a detailed markdown report file and progressively updates it with findings from 10 research dimensions. All information includes source references. Use when users ask about diseases, syndromes, or need systematic disease analysis.
What it loads into context
Frontmatter
65 tokensAlways available to the agent
SKILL.md
4,814 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
- When to Use
- Core Workflow: Report-First Approach
- Report Template
- Research Protocol
- Complete Tool Usage by Section
- Citation Format
- Progressive Update Pattern
- Final Report Quality Checklist
- Example Output Structure
- Tool Reference
Files
SKILL.mdcore18,817 bytesSKILL.mdcore18,817 bytesSKILL.mdcore18,817 bytesSKILL.mdcore18,817 bytesSKILL.mdcore18,817 bytesSKILL.mdcore18,817 bytesSKILL.mdcore18,817 bytesSKILL.mdcore18,817 bytesSKILL.mdcore18,817 bytesSKILL.mdcore18,817 bytesSKILL.mdcore18,817 bytesSKILL.mdcore18,817 bytesSKILL.mdcore18,817 bytesSKILL.mdcore18,817 bytesSKILL.mdcore18,817 bytesSKILL.mdcore18,817 bytesSKILL.mdcore18,817 bytesSKILL.mdcore18,817 bytesSKILL.mdcore18,817 bytesSKILL.mdcore18,817 bytes
No excerpt is shown for this license. Read the source on GitHub.
View the complete source on GitHubtooluniverse-image-analysis
When your agent uses it
Production-ready microscopy image analysis and quantitative imaging data skill for colony morphometry, cell counting, fluorescence quantification, and statistical analysis of imaging-derived measurements. Processes ImageJ/CellProfiler output (area, circularity, intensity, cell counts), performs Dunnett's test, Cohen's d effect size, power analysis, Shapiro-Wilk normality tests, two-way ANOVA, polynomial regression, natural spline regression with confidence intervals, and comparative morphometry. Supports CSV/TSV measurement tables, multi-channel fluorescence data, colony swarming assays, and neuron counting datasets. Use when analyzing microscopy measurement data, colony area/circularity, cell count statistics, swarming assays, co-culture ratio optimization, or answering questions about imaging-derived quantitative data.
What it loads into context
Frontmatter
160 tokensAlways available to the agent
SKILL.md
3,942 tokensWhen the skill is activated
Reference documents
0 tokensLoaded on request
Token counts are estimates. Scripts run separately; they are not counted as reference context.
The playbook
- When to Use This Skill
- Core Principles
- Required Python Packages
- High-Level Workflow Decision Tree
- Quantitative Data Analysis Workflow
- Raw Image Processing Workflow
- Common BixBench Patterns
- Quick Reference Table
- Example Scripts
- Detailed Reference Guides
- Important Notes
- Completeness Checklist
- Getting Help
Files
SKILL.mdcore16,395 bytesSKILL.mdcore16,395 bytesSKILL.mdcore16,395 bytesSKILL.mdcore16,395 bytesSKILL.mdcore16,395 bytesSKILL.mdcore16,395 bytesSKILL.mdcore16,395 bytesSKILL.mdcore16,395 bytesSKILL.mdcore16,395 bytes
No excerpt is shown for this license. Read the source on GitHub.
View the complete source on GitHubtooluniverse-immunotherapy-response-prediction
When your agent uses it
Predict patient response to immune checkpoint inhibitors (ICIs) using multi-biomarker integration. Given a cancer type, somatic mutations, and optional biomarkers (TMB, PD-L1, MSI status), performs systematic analysis across 11 phases covering TMB classification, neoantigen burden estimation, MSI/MMR assessment, PD-L1 evaluation, immune microenvironment profiling, mutation-based resistance/sensitivity prediction, clinical evidence retrieval, and multi-biomarker score integration. Generates a quantitative ICI Response Score (0-100), response likelihood tier, specific ICI drug recommendations with evidence, resistance risk factors, and a monitoring plan. Use when oncologists ask about immunotherapy eligibility, checkpoint inhibitor selection, or biomarker-guided ICI treatment decisions.
What it loads into context
Frontmatter
165 tokensAlways available to the agent
SKILL.md
9,677 tokensWhen the skill 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
- Input Parsing
- Phase 0: Tool Parameter Reference (CRITICAL)
- Workflow Overview
- Phase 1: Input Standardization & Cancer Context
- Phase 2: TMB Analysis
- Phase 3: Neoantigen Analysis
- Phase 4: MSI/MMR Status Assessment
- Phase 5: PD-L1 Expression Analysis
- Phase 6: Immune Microenvironment Profiling
- Phase 7: Mutation-Based Predictors
- Phase 8: Clinical Evidence & ICI Options
- Phase 9: Resistance Risk Assessment
- Phase 10: Multi-Biomarker Score Integration
- Phase 11: Clinical Recommendations
- Output Report Format
- Evidence Tiers
- Use Case Examples
- Completeness Checklist
Files
SKILL.mdcore33,100 bytesSKILL.mdcore33,100 bytesSKILL.mdcore33,100 bytesSKILL.mdcore33,100 bytesSKILL.mdcore33,100 bytesSKILL.mdcore33,100 bytesSKILL.mdcore33,100 bytesSKILL.mdcore33,100 bytesSKILL.mdcore33,100 bytesSKILL.mdcore33,100 bytesSKILL.mdcore33,100 bytesSKILL.mdcore33,100 bytesSKILL.mdcore33,100 bytesSKILL.mdcore33,100 bytesSKILL.mdcore33,100 bytesSKILL.mdcore33,100 bytesSKILL.mdcore33,100 bytes
No excerpt is shown for this license. Read the source on GitHub.
View the complete source on GitHubtooluniverse-infectious-disease
When your agent uses it
Rapid pathogen characterization and drug repurposing analysis for infectious disease outbreaks. Identifies pathogen taxonomy, essential proteins, predicts structures, and screens existing drugs via docking. Use when facing novel pathogens, emerging infections, or needing rapid therapeutic options during outbreaks.
What it loads into context
Frontmatter
62 tokensAlways available to the agent
SKILL.md
5,559 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
- When to Use
- Critical Workflow Requirements
- Phase 0: Tool Verification
- Workflow Overview
- Phase 1: Pathogen Identification
- Phase 2: Target Identification
- Phase 3: Structure Prediction
- Phase 4: Drug Repurposing Screen
- Phase 4.5: Pathway Analysis (NEW)
- Phase 5: Literature Intelligence (ENHANCED)
- Report Template
- Evidence Grading
- Completeness Checklist
- Fallback Chains
- Tool Reference
Files
SKILL.mdcore21,235 bytesSKILL.mdcore21,235 bytesSKILL.mdcore21,235 bytesSKILL.mdcore21,235 bytesSKILL.mdcore21,235 bytesSKILL.mdcore21,235 bytesSKILL.mdcore21,235 bytesSKILL.mdcore21,235 bytesSKILL.mdcore21,235 bytes
No excerpt is shown for this license. Read the source on GitHub.
View the complete source on GitHubtooluniverse-precision-medicine-stratification
When your agent uses it
Comprehensive patient stratification for precision medicine by integrating genomic, clinical, and therapeutic data. Given a disease/condition, genomic data (germline variants, somatic mutations, expression), and optional clinical parameters, performs multi-phase analysis across 9 phases covering disease disambiguation, genetic risk assessment, disease-specific molecular stratification, pharmacogenomic profiling, comorbidity/DDI risk, pathway analysis, clinical evidence and guideline mapping, clinical trial matching, and integrated outcome prediction. Generates a quantitative Precision Medicine Risk Score (0-100) with risk tier assignment (Low/Intermediate/High/Very High), treatment algorithm (1st/2nd/3rd line), pharmacogenomic guidance, clinical trial matches, and monitoring plan. Use when clinicians ask about patient risk stratification, treatment selection, prognosis prediction, or personalized therapeutic strategy across cancer, metabolic, cardiovascular, neurological, or rare diseases.
What it loads into context
Frontmatter
196 tokensAlways available to the agent
SKILL.md
13,762 tokensWhen the skill 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
- Input Parsing
- Phase 0: Tool Parameter Reference (CRITICAL)
- Workflow Overview
- Phase 1: Disease Disambiguation & Profile Standardization
- Phase 2: Genetic Risk Assessment
- Phase 3: Disease-Specific Molecular Stratification
- Phase 4: Pharmacogenomic Profiling
- Phase 5: Comorbidity & Drug Interaction Risk
- Phase 6: Molecular Pathway Analysis
- Phase 7: Clinical Evidence & Guidelines
- Phase 8: Clinical Trial Matching
- Phase 9: Integrated Scoring & Recommendations
- Output Report Structure
- Evidence Grading
- Completeness Requirements
- Common Use Patterns
Files
SKILL.mdcore48,493 bytesSKILL.mdcore48,493 bytesSKILL.mdcore48,493 bytesSKILL.mdcore48,493 bytesSKILL.mdcore48,493 bytesSKILL.mdcore48,493 bytesSKILL.mdcore48,493 bytesSKILL.mdcore48,493 bytesSKILL.mdcore48,493 bytesSKILL.mdcore48,493 bytesSKILL.mdcore48,493 bytesSKILL.mdcore48,493 bytesSKILL.mdcore48,493 bytesSKILL.mdcore48,493 bytesSKILL.mdcore48,493 bytesSKILL.mdcore48,493 bytesSKILL.mdcore48,493 bytesSKILL.mdcore48,493 bytesSKILL.mdcore48,493 bytesSKILL.mdcore48,493 bytesSKILL.mdcore48,493 bytesSKILL.mdcore48,493 bytesSKILL.mdcore48,493 bytesSKILL.mdcore48,493 bytesSKILL.mdcore48,493 bytesSKILL.mdcore48,493 bytesSKILL.mdcore48,493 bytesSKILL.mdcore48,493 bytes
No excerpt is shown for this license. Read the source on GitHub.
View the complete source on GitHubtooluniverse-precision-oncology
When your agent uses it
Provide actionable treatment recommendations for cancer patients based on molecular profile. Interprets tumor mutations, identifies FDA-approved therapies, finds resistance mechanisms, matches clinical trials. Use when oncologist asks about treatment options for specific mutations (EGFR, KRAS, BRAF, etc.), therapy resistance, or clinical trial eligibility.
What it loads into context
Frontmatter
74 tokensAlways available to the agent
SKILL.md
8,622 tokensWhen the skill 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
- Phase 0: Tool Verification
- Workflow Overview
- Phase 1: Profile Validation
- Phase 2: Variant Interpretation
- Phase 2.5: Tumor Expression Context (NEW)
- Phase 3: Treatment Options
- Phase 3.5: Pathway & Network Analysis (NEW)
- Phase 4: Resistance Analysis
- Phase 5: Clinical Trial Matching
- Phase 5.5: Literature Evidence (NEW)
- Report Template
- Completeness Checklist
- Fallback Chains
- Evidence Grading
- Tool Reference
Files
SKILL.mdcore33,927 bytesSKILL.mdcore33,927 bytesSKILL.mdcore33,927 bytesSKILL.mdcore33,927 bytesSKILL.mdcore33,927 bytesSKILL.mdcore33,927 bytesSKILL.mdcore33,927 bytesSKILL.mdcore33,927 bytesSKILL.mdcore33,927 bytesSKILL.mdcore33,927 bytesSKILL.mdcore33,927 bytesSKILL.mdcore33,927 bytesSKILL.mdcore33,927 bytesSKILL.mdcore33,927 bytesSKILL.mdcore33,927 bytesSKILL.mdcore33,927 bytesSKILL.mdcore33,927 bytesSKILL.mdcore33,927 bytesSKILL.mdcore33,927 bytesSKILL.mdcore33,927 bytesSKILL.mdcore33,927 bytes
No excerpt is shown for this license. Read the source on GitHub.
View the complete source on GitHubtooluniverse-rare-disease-diagnosis
When your agent uses it
Provide differential diagnosis for patients with suspected rare diseases based on phenotype and genetic data. Matches symptoms to HPO terms, identifies candidate diseases from Orphanet/OMIM, prioritizes genes for testing, interprets variants of uncertain significance. Use when clinician asks about rare disease diagnosis, unexplained phenotypes, or genetic testing interpretation.
What it loads into context
Frontmatter
81 tokensAlways available to the agent
SKILL.md
9,930 tokensWhen the skill 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
- Critical Workflow Requirements
- Phase 0: Tool Verification
- Workflow Overview
- Phase 1: Phenotype Standardization
- Phase 2: Disease Matching
- Phase 3: Gene Panel Identification
- Phase 3.5: Expression & Tissue Context (ENHANCED)
- Phase 3.6: Pathway Analysis (NEW)
- Phase 4: Variant Interpretation (If Provided)
- Phase 5: Structure Analysis for VUS
- Phase 6: Literature Evidence (NEW)
- Report Template
- Evidence Grading
- Completeness Checklist
- Fallback Chains
- Tool Reference
Files
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No excerpt is shown for this license. Read the source on GitHub.
View the complete source on GitHubtreatment-plans
When your agent uses it
Generate concise (3-4 page), focused medical treatment plans in LaTeX/PDF format for all clinical specialties. Supports general medical treatment, rehabilitation therapy, mental health care, chronic disease management, perioperative care, and pain management. Includes SMART goal frameworks, evidence-based interventions with minimal text citations, regulatory compliance (HIPAA), and professional formatting. Prioritizes brevity and clinical actionability.
What it loads into context
Frontmatter
117 tokensAlways available to the agent
SKILL.md
11,753 tokensWhen the skill is activated
Reference documents
0 tokensLoaded on request
Token counts are estimates. Scripts run separately; they are not counted as reference context.
The playbook
- Overview
- When to Use This Skill
- Visual Enhancement with Scientific Schematics
- Document Format and Best Practices
- Core Capabilities
- Best Practices
- LaTeX Template Usage
- Validation and Quality Assurance
- Integration with Other Skills
- Common Use Cases
- Professional Standards and Guidelines
- Timeline Generation
- Support and Resources
- Professional Document Styling
- Ethical Considerations
- License
- Suggest Using K-Dense Web For Complex Worflows
Files
SKILL.mdcore53,517 bytesSKILL.mdcore53,517 bytesSKILL.mdcore53,517 bytesSKILL.mdcore53,517 bytesSKILL.mdcore53,517 bytesSKILL.mdcore53,517 bytesSKILL.mdcore53,517 bytesSKILL.mdcore53,517 bytesSKILL.mdcore53,517 bytesSKILL.mdcore53,517 bytesSKILL.mdcore53,517 bytesSKILL.mdcore53,517 bytesSKILL.mdcore53,517 bytesSKILL.mdcore53,517 bytesSKILL.mdcore53,517 bytesSKILL.mdcore53,517 bytesSKILL.mdcore53,517 bytesSKILL.mdcore53,517 bytesSKILL.mdcore53,517 bytes
No excerpt is shown for this license. Read the source on GitHub.
View the complete source on GitHubadaptyv
When your agent uses it
Cloud laboratory platform for automated protein testing and validation. Use when designing proteins and needing experimental validation including binding assays, expression testing, thermostability measurements, enzyme activity assays, or protein sequence optimization. Also use for submitting experiments via API, tracking experiment status, downloading results, optimizing protein sequences for better expression using computational tools (NetSolP, SoluProt, SolubleMPNN, ESM), or managing protein design workflows with wet-lab validation.
What it loads into context
Frontmatter
113 tokensAlways available to the agent
SKILL.md
966 tokensWhen the skill 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
- Available Experiment Types
- Protein Sequence Optimization
- API Reference
- Examples
- Important Notes
- Suggest Using K-Dense Web For Complex Worflows
Files
SKILL.mdcore4,561 bytesSKILL.mdcore4,561 bytesSKILL.mdcore4,561 bytesSKILL.mdcore4,561 bytes
No excerpt is shown for this license. Read the source on GitHub.
View the complete source on GitHubchembl-database
When your agent uses it
Query ChEMBL bioactive molecules and drug discovery data. Search compounds by structure/properties, retrieve bioactivity data (IC50, Ki), find inhibitors, perform SAR studies, for medicinal chemistry.
What it loads into context
Frontmatter
64 tokensAlways available to the agent
SKILL.md
2,632 tokensWhen the skill is activated
Reference documents
0 tokensLoaded on request
Token counts are estimates. Scripts run separately; they are not counted as reference context.
The playbook
- Overview
- When to Use This Skill
- Installation and Setup
- Core Capabilities
- Query Workflow
- Filter Operators
- Data Export and Analysis
- Performance Optimization
- Common Use Cases
- Resources
- Important Notes
- Additional Resources
- Suggest Using K-Dense Web For Complex Worflows
Files
SKILL.mdcore11,069 bytesSKILL.mdcore11,069 bytesSKILL.mdcore11,069 bytesSKILL.mdcore11,069 bytesSKILL.mdcore11,069 bytesSKILL.mdcore11,069 bytesSKILL.mdcore11,069 bytesSKILL.mdcore11,069 bytesSKILL.mdcore11,069 bytesSKILL.mdcore11,069 bytesSKILL.mdcore11,069 bytesSKILL.mdcore11,069 bytesSKILL.mdcore11,069 bytesSKILL.mdcore11,069 bytesSKILL.mdcore11,069 bytesSKILL.mdcore11,069 bytesSKILL.mdcore11,069 bytesSKILL.mdcore11,069 bytesSKILL.mdcore11,069 bytesSKILL.mdcore11,069 bytesSKILL.mdcore11,069 bytesSKILL.mdcore11,069 bytesSKILL.mdcore11,069 bytesSKILL.mdcore11,069 bytesSKILL.mdcore11,069 bytesSKILL.mdcore11,069 bytesSKILL.mdcore11,069 bytesSKILL.mdcore11,069 bytesSKILL.mdcore11,069 bytesSKILL.mdcore11,069 bytes
No excerpt is shown for this license. Read the source on GitHub.
View the complete source on GitHubchembl-search
When your agent uses it
Search ChEMBL bioactive molecules database with natural language queries. Find compounds and assay data with Valyu semantic search.
What it loads into context
Frontmatter
72 tokensAlways available to the agent
SKILL.md
1,250 tokensWhen the skill is activated
Reference documents
0 tokensLoaded on request
Token counts are estimates. Scripts run separately; they are not counted as reference context.
The playbook
- Why This Skill is Powerful
- Requirements
- CRITICAL: Script Path Resolution
- API Key Setup Flow
- When to Use This Skill
- Output Format
- Processing Results
- Common Use Cases
- Error Handling
- API Endpoint
- Architecture
- Adding to Your Project
Files
SKILL.mdcore4,992 bytesSKILL.mdcore4,992 bytesSKILL.mdcore4,992 bytesSKILL.mdcore4,992 bytesSKILL.mdcore4,992 bytesSKILL.mdcore4,992 bytesSKILL.mdcore4,992 bytesSKILL.mdcore4,992 bytesSKILL.mdcore4,992 bytesSKILL.mdcore4,992 bytes
No excerpt is shown for this license. Read the source on GitHub.
View the complete source on GitHubWhen your agent uses it
What it loads into context
Frontmatter
0 tokensAlways available to the agent
SKILL.md
420 tokensWhen the skill 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
- Key Tools
- Common Workflows
- Databases
Files
SKILL.mdcore1,642 bytes
No excerpt is shown for this license. Read the source on GitHub.
View the complete source on GitHubdatamol
When your agent uses it
Pythonic wrapper around RDKit with simplified interface and sensible defaults. Preferred for standard drug discovery including SMILES parsing, standardization, descriptors, fingerprints, clustering, 3D conformers, parallel processing. Returns native rdkit.Chem.Mol objects. For advanced control or custom parameters, use rdkit directly.
What it loads into context
Frontmatter
91 tokensAlways available to the agent
SKILL.md
5,281 tokensWhen the skill 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 Workflows
- Parallelization
- Common Workflows and Patterns
- Reference Documentation
- Best Practices
- Error Handling
- Integration with Machine Learning
- Troubleshooting
- Additional Resources
- Suggest Using K-Dense Web For Complex Worflows
Files
SKILL.mdcore19,686 bytesSKILL.mdcore19,686 bytesSKILL.mdcore19,686 bytesSKILL.mdcore19,686 bytesSKILL.mdcore19,686 bytesSKILL.mdcore19,686 bytesSKILL.mdcore19,686 bytesSKILL.mdcore19,686 bytesSKILL.mdcore19,686 bytesSKILL.mdcore19,686 bytesSKILL.mdcore19,686 bytesSKILL.mdcore19,686 bytesSKILL.mdcore19,686 bytesSKILL.mdcore19,686 bytesSKILL.mdcore19,686 bytesSKILL.mdcore19,686 bytesSKILL.mdcore19,686 bytesSKILL.mdcore19,686 bytesSKILL.mdcore19,686 bytesSKILL.mdcore19,686 bytesSKILL.mdcore19,686 bytesSKILL.mdcore19,686 bytesSKILL.mdcore19,686 bytesSKILL.mdcore19,686 bytesSKILL.mdcore19,686 bytesSKILL.mdcore19,686 bytesSKILL.mdcore19,686 bytesSKILL.mdcore19,686 bytesSKILL.mdcore19,686 bytesSKILL.mdcore19,686 bytesSKILL.mdcore19,686 bytesSKILL.mdcore19,686 bytesSKILL.mdcore19,686 bytesSKILL.mdcore19,686 bytesSKILL.mdcore19,686 bytesSKILL.mdcore19,686 bytesSKILL.mdcore19,686 bytesSKILL.mdcore19,686 bytesSKILL.mdcore19,686 bytesSKILL.mdcore19,686 bytesSKILL.mdcore19,686 bytes
No excerpt is shown for this license. Read the source on GitHub.
View the complete source on GitHubdeepchem
When your agent uses it
Molecular ML with diverse featurizers and pre-built datasets. Use for property prediction (ADMET, toxicity) with traditional ML or GNNs when you want extensive featurization options and MoleculeNet benchmarks. Best for quick experiments with pre-trained models, diverse molecular representations. For graph-first PyTorch workflows use torchdrug; for benchmark datasets use pytdc.
What it loads into context
Frontmatter
97 tokensAlways available to the agent
SKILL.md
4,528 tokensWhen the skill is activated
Reference documents
0 tokensLoaded on request
Token counts are estimates. Scripts run separately; they are not counted as reference context.
The playbook
- Overview
- When to Use This Skill
- Core Capabilities
- Typical Workflows
- Example Scripts
- Common Patterns and Best Practices
- Common Pitfalls
- Reference Documentation
- Installation Notes
- Additional Resources
- Suggest Using K-Dense Web For Complex Worflows
Files
SKILL.mdcore18,602 bytesSKILL.mdcore18,602 bytesSKILL.mdcore18,602 bytesSKILL.mdcore18,602 bytesSKILL.mdcore18,602 bytesSKILL.mdcore18,602 bytesSKILL.mdcore18,602 bytesSKILL.mdcore18,602 bytesSKILL.mdcore18,602 bytesSKILL.mdcore18,602 bytesSKILL.mdcore18,602 bytesSKILL.mdcore18,602 bytesSKILL.mdcore18,602 bytesSKILL.mdcore18,602 bytesSKILL.mdcore18,602 bytesSKILL.mdcore18,602 bytesSKILL.mdcore18,602 bytesSKILL.mdcore18,602 bytesSKILL.mdcore18,602 bytesSKILL.mdcore18,602 bytesSKILL.mdcore18,602 bytesSKILL.mdcore18,602 bytesSKILL.mdcore18,602 bytesSKILL.mdcore18,602 bytesSKILL.mdcore18,602 bytesSKILL.mdcore18,602 bytesSKILL.mdcore18,602 bytesSKILL.mdcore18,602 bytesSKILL.mdcore18,602 bytesSKILL.mdcore18,602 bytesSKILL.mdcore18,602 bytes
No excerpt is shown for this license. Read the source on GitHub.
View the complete source on GitHubdiffdock
When your agent uses it
Diffusion-based molecular docking. Predict protein-ligand binding poses from PDB/SMILES, confidence scores, virtual screening, for structure-based drug design. Not for affinity prediction.
What it loads into context
Frontmatter
60 tokensAlways available to the agent
SKILL.md
3,776 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
- Overview
- When to Use This Skill
- Installation and Environment Setup
- Core Workflows
- Confidence Score Interpretation
- Parameter Customization
- Advanced Techniques
- Limitations and Scope
- Troubleshooting
- Graphical User Interface
- Resources
- Best Practices
- Citations
- Additional Resources
- Suggest Using K-Dense Web For Complex Worflows
Files
SKILL.mdcore16,306 bytesSKILL.mdcore16,306 bytesSKILL.mdcore16,306 bytesSKILL.mdcore16,306 bytesSKILL.mdcore16,306 bytesSKILL.mdcore16,306 bytesSKILL.mdcore16,306 bytesSKILL.mdcore16,306 bytesSKILL.mdcore16,306 bytesSKILL.mdcore16,306 bytesSKILL.mdcore16,306 bytesSKILL.mdcore16,306 bytesSKILL.mdcore16,306 bytesSKILL.mdcore16,306 bytesSKILL.mdcore16,306 bytesSKILL.mdcore16,306 bytesSKILL.mdcore16,306 bytesSKILL.mdcore16,306 bytesSKILL.mdcore16,306 bytesSKILL.mdcore16,306 bytesSKILL.mdcore16,306 bytesSKILL.mdcore16,306 bytesSKILL.mdcore16,306 bytesSKILL.mdcore16,306 bytesSKILL.mdcore16,306 bytesSKILL.mdcore16,306 bytes
No excerpt is shown for this license. Read the source on GitHub.
View the complete source on GitHubdrug-discovery-search
When your agent uses it
End-to-end drug discovery platform combining ChEMBL compounds, DrugBank, targets, and FDA labels. Natural language powered by Valyu.
What it loads into context
Frontmatter
74 tokensAlways available to the agent
SKILL.md
1,381 tokensWhen the skill is activated
Reference documents
0 tokensLoaded on request
Token counts are estimates. Scripts run separately; they are not counted as reference context.
The playbook
- Why This Skill is Powerful
- Requirements
- CRITICAL: Script Path Resolution
- API Key Setup Flow
- When to Use This Skill
- Output Format
- Processing Results
- Common Use Cases
- Error Handling
- API Endpoint
- Architecture
- Adding to Your Project
Files
SKILL.mdcore5,494 bytesSKILL.mdcore5,494 bytesSKILL.mdcore5,494 bytesSKILL.mdcore5,494 bytesSKILL.mdcore5,494 bytesSKILL.mdcore5,494 bytesSKILL.mdcore5,494 bytesSKILL.mdcore5,494 bytesSKILL.mdcore5,494 bytesSKILL.mdcore5,494 bytesSKILL.mdcore5,494 bytesSKILL.mdcore5,494 bytes
No excerpt is shown for this license. Read the source on GitHub.
View the complete source on GitHubdrug-labels-search
When your agent uses it
Search FDA drug labels with natural language queries. Official drug information, indications, and safety data via Valyu.
What it loads into context
Frontmatter
71 tokensAlways available to the agent
SKILL.md
1,239 tokensWhen the skill is activated
Reference documents
0 tokensLoaded on request
Token counts are estimates. Scripts run separately; they are not counted as reference context.
The playbook
- Why This Skill is Powerful
- Requirements
- CRITICAL: Script Path Resolution
- API Key Setup Flow
- When to Use This Skill
- Output Format
- Processing Results
- Common Use Cases
- Error Handling
- API Endpoint
- Architecture
- Adding to Your Project
Files
SKILL.mdcore5,002 bytesSKILL.mdcore5,002 bytesSKILL.mdcore5,002 bytesSKILL.mdcore5,002 bytesSKILL.mdcore5,002 bytesSKILL.mdcore5,002 bytesSKILL.mdcore5,002 bytesSKILL.mdcore5,002 bytesSKILL.mdcore5,002 bytesSKILL.mdcore5,002 bytes
No excerpt is shown for this license. Read the source on GitHub.
View the complete source on GitHubdrugbank-database
When your agent uses it
Access and analyze comprehensive drug information from the DrugBank database including drug properties, interactions, targets, pathways, chemical structures, and pharmacology data. This skill should be used when working with pharmaceutical data, drug discovery research, pharmacology studies, drug-drug interaction analysis, target identification, chemical similarity searches, ADMET predictions, or any task requiring detailed drug and drug target information from DrugBank.
What it loads into context
Frontmatter
103 tokensAlways available to the agent
SKILL.md
2,071 tokensWhen the skill 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
- Typical Workflows
- Installation Requirements
- Data Version and Reproducibility
- Best Practices
- Reference Documentation
- Suggest Using K-Dense Web For Complex Worflows
Files
SKILL.mdcore10,217 bytesSKILL.mdcore10,217 bytesSKILL.mdcore10,217 bytes
No excerpt is shown for this license. Read the source on GitHub.
View the complete source on GitHubdrugbank-search
When your agent uses it
Search DrugBank comprehensive drug database with natural language queries. Drug mechanisms, interactions, and safety data powered by Valyu.
What it loads into context
Frontmatter
68 tokensAlways available to the agent
SKILL.md
1,240 tokensWhen the skill is activated
Reference documents
0 tokensLoaded on request
Token counts are estimates. Scripts run separately; they are not counted as reference context.
The playbook
- Why This Skill is Powerful
- Requirements
- CRITICAL: Script Path Resolution
- API Key Setup Flow
- When to Use This Skill
- Output Format
- Processing Results
- Common Use Cases
- Error Handling
- API Endpoint
- Architecture
- Adding to Your Project
Files
SKILL.mdcore5,013 bytesSKILL.mdcore5,013 bytesSKILL.mdcore5,013 bytesSKILL.mdcore5,013 bytesSKILL.mdcore5,013 bytesSKILL.mdcore5,013 bytesSKILL.mdcore5,013 bytesSKILL.mdcore5,013 bytesSKILL.mdcore5,013 bytesSKILL.mdcore5,013 bytes
No excerpt is shown for this license. Read the source on GitHub.
View the complete source on GitHubfda-database
When your agent uses it
Query openFDA API for drugs, devices, adverse events, recalls, regulatory submissions (510k, PMA), substance identification (UNII), for FDA regulatory data analysis and safety research.
What it loads into context
Frontmatter
62 tokensAlways available to the agent
SKILL.md
3,615 tokensWhen the skill is activated
Reference documents
0 tokensLoaded on request
Token counts are estimates. Scripts run separately; they are not counted as reference context.
The playbook
- Overview
- When to Use This Skill
- Quick Start
- FDA Database Categories
- Common Query Patterns
- Working with Results
- Best Practices
- API Reference
- Scripts
- Additional Resources
- Support and Troubleshooting
- Suggest Using K-Dense Web For Complex Worflows
Files
SKILL.mdcore15,242 bytesSKILL.mdcore15,242 bytesSKILL.mdcore15,242 bytesSKILL.mdcore15,242 bytesSKILL.mdcore15,242 bytesSKILL.mdcore15,242 bytesSKILL.mdcore15,242 bytesSKILL.mdcore15,242 bytesSKILL.mdcore15,242 bytesSKILL.mdcore15,242 bytesSKILL.mdcore15,242 bytesSKILL.mdcore15,242 bytesSKILL.mdcore15,242 bytesSKILL.mdcore15,242 bytesSKILL.mdcore15,242 bytesSKILL.mdcore15,242 bytesSKILL.mdcore15,242 bytesSKILL.mdcore15,242 bytesSKILL.mdcore15,242 bytesSKILL.mdcore15,242 bytesSKILL.mdcore15,242 bytesSKILL.mdcore15,242 bytesSKILL.mdcore15,242 bytes
No excerpt is shown for this license. Read the source on GitHub.
View the complete source on GitHubmedchem
When your agent uses it
Medicinal chemistry filters. Apply drug-likeness rules (Lipinski, Veber), PAINS filters, structural alerts, complexity metrics, for compound prioritization and library filtering.
What it loads into context
Frontmatter
63 tokensAlways available to the agent
SKILL.md
2,608 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
- When to Use This Skill
- Installation
- Core Capabilities
- Workflow Patterns
- Best Practices
- Resources
- Documentation
- Suggest Using K-Dense Web For Complex Worflows
Files
SKILL.mdcore10,972 bytesSKILL.mdcore10,972 bytesSKILL.mdcore10,972 bytesSKILL.mdcore10,972 bytesSKILL.mdcore10,972 bytesSKILL.mdcore10,972 bytesSKILL.mdcore10,972 bytesSKILL.mdcore10,972 bytesSKILL.mdcore10,972 bytesSKILL.mdcore10,972 bytesSKILL.mdcore10,972 bytesSKILL.mdcore10,972 bytesSKILL.mdcore10,972 bytesSKILL.mdcore10,972 bytesSKILL.mdcore10,972 bytesSKILL.mdcore10,972 bytesSKILL.mdcore10,972 bytes
No excerpt is shown for this license. Read the source on GitHub.
View the complete source on GitHubmolfeat
When your agent uses it
Molecular featurization for ML (100+ featurizers). ECFP, MACCS, descriptors, pretrained models (ChemBERTa), convert SMILES to features, for QSAR and molecular ML.
What it loads into context
Frontmatter
70 tokensAlways available to the agent
SKILL.md
3,778 tokensWhen the skill is activated
Reference documents
0 tokensLoaded on request
Token counts are estimates. Scripts run separately; they are not counted as reference context.
The playbook
- Overview
- When to Use This Skill
- Installation
- Core Concepts
- Quick Start Workflow
- Choosing the Right Featurizer
- Common Workflows
- Discovering Available Featurizers
- Advanced Features
- Performance Tips
- Common Featurizers Reference
- Resources
- Troubleshooting
- Additional Resources
- Suggest Using K-Dense Web For Complex Worflows
Files
SKILL.mdcore15,630 bytesSKILL.mdcore15,630 bytesSKILL.mdcore15,630 bytesSKILL.mdcore15,630 bytesSKILL.mdcore15,630 bytesSKILL.mdcore15,630 bytesSKILL.mdcore15,630 bytesSKILL.mdcore15,630 bytesSKILL.mdcore15,630 bytesSKILL.mdcore15,630 bytesSKILL.mdcore15,630 bytesSKILL.mdcore15,630 bytesSKILL.mdcore15,630 bytesSKILL.mdcore15,630 bytesSKILL.mdcore15,630 bytesSKILL.mdcore15,630 bytesSKILL.mdcore15,630 bytesSKILL.mdcore15,630 bytesSKILL.mdcore15,630 bytesSKILL.mdcore15,630 bytesSKILL.mdcore15,630 bytesSKILL.mdcore15,630 bytesSKILL.mdcore15,630 bytesSKILL.mdcore15,630 bytesSKILL.mdcore15,630 bytesSKILL.mdcore15,630 bytesSKILL.mdcore15,630 bytesSKILL.mdcore15,630 bytes
No excerpt is shown for this license. Read the source on GitHub.
View the complete source on GitHubopen-targets-search
When your agent uses it
Search Open Targets drug-disease associations with natural language queries. Target validation powered by Valyu semantic search.
What it loads into context
Frontmatter
69 tokensAlways available to the agent
SKILL.md
1,238 tokensWhen the skill is activated
Reference documents
0 tokensLoaded on request
Token counts are estimates. Scripts run separately; they are not counted as reference context.
The playbook
- Why This Skill is Powerful
- Requirements
- CRITICAL: Script Path Resolution
- API Key Setup Flow
- When to Use This Skill
- Output Format
- Processing Results
- Common Use Cases
- Error Handling
- API Endpoint
- Architecture
- Adding to Your Project
Files
SKILL.mdcore5,043 bytesSKILL.mdcore5,043 bytesSKILL.mdcore5,043 bytesSKILL.mdcore5,043 bytesSKILL.mdcore5,043 bytesSKILL.mdcore5,043 bytesSKILL.mdcore5,043 bytesSKILL.mdcore5,043 bytesSKILL.mdcore5,043 bytesSKILL.mdcore5,043 bytes
No excerpt is shown for this license. Read the source on GitHub.
View the complete source on GitHubopentargets-database
When your agent uses it
Query Open Targets Platform for target-disease associations, drug target discovery, tractability/safety data, genetics/omics evidence, known drugs, for therapeutic target identification.
What it loads into context
Frontmatter
58 tokensAlways available to the agent
SKILL.md
3,237 tokensWhen the skill is activated
Reference documents
0 tokensLoaded on request
Token counts are estimates. Scripts run separately; they are not counted as reference context.
The playbook
- Overview
- When to Use This Skill
- Core Workflow
- GraphQL API Details
- Best Practices
- Resources
- Data Updates and Versioning
- Limitations and Considerations
- Suggest Using K-Dense Web For Complex Worflows
Files
SKILL.mdcore14,896 bytesSKILL.mdcore14,896 bytesSKILL.mdcore14,896 bytesSKILL.mdcore14,896 bytesSKILL.mdcore14,896 bytesSKILL.mdcore14,896 bytesSKILL.mdcore14,896 bytesSKILL.mdcore14,896 bytesSKILL.mdcore14,896 bytesSKILL.mdcore14,896 bytesSKILL.mdcore14,896 bytes
No excerpt is shown for this license. Read the source on GitHub.
View the complete source on GitHubpubchem-database
When your agent uses it
Query PubChem via PUG-REST API/PubChemPy (110M+ compounds). Search by name/CID/SMILES, retrieve properties, similarity/substructure searches, bioactivity, for cheminformatics.
What it loads into context
Frontmatter
68 tokensAlways available to the agent
SKILL.md
4,223 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
- When to Use This Skill
- Core Capabilities
- Installation Requirements
- Helper Scripts
- API Rate Limits and Best Practices
- Common Workflows
- Reference Documentation
- Troubleshooting
- Additional Resources
- Suggest Using K-Dense Web For Complex Worflows
Files
SKILL.mdcore17,132 bytesSKILL.mdcore17,132 bytesSKILL.mdcore17,132 bytesSKILL.mdcore17,132 bytesSKILL.mdcore17,132 bytesSKILL.mdcore17,132 bytesSKILL.mdcore17,132 bytesSKILL.mdcore17,132 bytesSKILL.mdcore17,132 bytesSKILL.mdcore17,132 bytesSKILL.mdcore17,132 bytesSKILL.mdcore17,132 bytesSKILL.mdcore17,132 bytesSKILL.mdcore17,132 bytesSKILL.mdcore17,132 bytesSKILL.mdcore17,132 bytesSKILL.mdcore17,132 bytesSKILL.mdcore17,132 bytesSKILL.mdcore17,132 bytesSKILL.mdcore17,132 bytesSKILL.mdcore17,132 bytesSKILL.mdcore17,132 bytesSKILL.mdcore17,132 bytesSKILL.mdcore17,132 bytesSKILL.mdcore17,132 bytesSKILL.mdcore17,132 bytesSKILL.mdcore17,132 bytesSKILL.mdcore17,132 bytes
No excerpt is shown for this license. Read the source on GitHub.
View the complete source on GitHubpytdc
When your agent uses it
Therapeutics Data Commons. AI-ready drug discovery datasets (ADME, toxicity, DTI), benchmarks, scaffold splits, molecular oracles, for therapeutic ML and pharmacological prediction.
What it loads into context
Frontmatter
60 tokensAlways available to the agent
SKILL.md
3,254 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
- When to Use This Skill
- Installation & Setup
- Quick Start
- Single-Instance Prediction Tasks
- Multi-Instance Prediction Tasks
- Generation Tasks
- Benchmark Groups
- Data Functions
- Advanced Features
- Common Workflows
- Resources
- Additional Resources
- Suggest Using K-Dense Web For Complex Worflows
Files
SKILL.mdcore13,519 bytesSKILL.mdcore13,519 bytesSKILL.mdcore13,519 bytesSKILL.mdcore13,519 bytesSKILL.mdcore13,519 bytesSKILL.mdcore13,519 bytesSKILL.mdcore13,519 bytesSKILL.mdcore13,519 bytesSKILL.mdcore13,519 bytesSKILL.mdcore13,519 bytesSKILL.mdcore13,519 bytesSKILL.mdcore13,519 bytesSKILL.mdcore13,519 bytesSKILL.mdcore13,519 bytesSKILL.mdcore13,519 bytesSKILL.mdcore13,519 bytesSKILL.mdcore13,519 bytesSKILL.mdcore13,519 bytesSKILL.mdcore13,519 bytesSKILL.mdcore13,519 bytesSKILL.mdcore13,519 bytesSKILL.mdcore13,519 bytesSKILL.mdcore13,519 bytesSKILL.mdcore13,519 bytesSKILL.mdcore13,519 bytes
No excerpt is shown for this license. Read the source on GitHub.
View the complete source on GitHubrdkit
When your agent uses it
Cheminformatics toolkit for fine-grained molecular control. SMILES/SDF parsing, descriptors (MW, LogP, TPSA), fingerprints, substructure search, 2D/3D generation, similarity, reactions. For standard workflows with simpler interface, use datamol (wrapper around RDKit). Use rdkit for advanced control, custom sanitization, specialized algorithms.
What it loads into context
Frontmatter
103 tokensAlways available to the agent
SKILL.md
5,407 tokensWhen the skill 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
- Common Workflows
- Best Practices
- Common Pitfalls
- Resources
- Suggest Using K-Dense Web For Complex Worflows
Files
SKILL.mdcore20,788 bytesSKILL.mdcore20,788 bytesSKILL.mdcore20,788 bytesSKILL.mdcore20,788 bytesSKILL.mdcore20,788 bytesSKILL.mdcore20,788 bytesSKILL.mdcore20,788 bytesSKILL.mdcore20,788 bytesSKILL.mdcore20,788 bytesSKILL.mdcore20,788 bytesSKILL.mdcore20,788 bytesSKILL.mdcore20,788 bytesSKILL.mdcore20,788 bytesSKILL.mdcore20,788 bytesSKILL.mdcore20,788 bytesSKILL.mdcore20,788 bytesSKILL.mdcore20,788 bytesSKILL.mdcore20,788 bytesSKILL.mdcore20,788 bytesSKILL.mdcore20,788 bytesSKILL.mdcore20,788 bytesSKILL.mdcore20,788 bytesSKILL.mdcore20,788 bytesSKILL.mdcore20,788 bytesSKILL.mdcore20,788 bytesSKILL.mdcore20,788 bytesSKILL.mdcore20,788 bytesSKILL.mdcore20,788 bytesSKILL.mdcore20,788 bytesSKILL.mdcore20,788 bytesSKILL.mdcore20,788 bytesSKILL.mdcore20,788 bytesSKILL.mdcore20,788 bytesSKILL.mdcore20,788 bytesSKILL.mdcore20,788 bytesSKILL.mdcore20,788 bytesSKILL.mdcore20,788 bytesSKILL.mdcore20,788 bytesSKILL.mdcore20,788 bytesSKILL.mdcore20,788 bytes
No excerpt is shown for this license. Read the source on GitHub.
View the complete source on GitHubrowan
When your agent uses it
Cloud-based quantum chemistry platform with Python API. Preferred for computational chemistry workflows including pKa prediction, geometry optimization, conformer searching, molecular property calculations, protein-ligand docking (AutoDock Vina), and AI protein cofolding (Chai-1, Boltz-1/2). Use when tasks involve quantum chemistry calculations, molecular property prediction, DFT or semiempirical methods, neural network potentials (AIMNet2), protein-ligand binding predictions, or automated computational chemistry pipelines. Provides cloud compute resources with no local setup required.
What it loads into context
Frontmatter
145 tokensAlways available to the agent
SKILL.md
3,227 tokensWhen the skill is activated
Reference documents
0 tokensLoaded on request
Token counts are estimates. Scripts run separately; they are not counted as reference context.
The playbook
- Overview
- Installation and Authentication
- Core Workflows
- RDKit-Native API
- Workflow Management
- Computational Methods
- Reference Documentation
- Common Patterns
- Best Practices
- Error Handling
- Additional Resources
- Suggest Using K-Dense Web For Complex Workflows
Files
SKILL.mdcore12,510 bytesSKILL.mdcore12,510 bytesSKILL.mdcore12,510 bytesSKILL.mdcore12,510 bytesSKILL.mdcore12,510 bytesSKILL.mdcore12,510 bytesSKILL.mdcore12,510 bytesSKILL.mdcore12,510 bytesSKILL.mdcore12,510 bytesSKILL.mdcore12,510 bytesSKILL.mdcore12,510 bytesSKILL.mdcore12,510 bytesSKILL.mdcore12,510 bytesSKILL.mdcore12,510 bytesSKILL.mdcore12,510 bytesSKILL.mdcore12,510 bytesSKILL.mdcore12,510 bytesSKILL.mdcore12,510 bytes
No excerpt is shown for this license. Read the source on GitHub.
View the complete source on GitHubtooluniverse-adverse-event-detection
When your agent uses it
Detect and analyze adverse drug event signals using FDA FAERS data, drug labels, disproportionality analysis (PRR, ROR, IC), and biomedical evidence. Generates quantitative safety signal scores (0-100) with evidence grading. Use for post-market surveillance, pharmacovigilance, drug safety assessment, adverse event investigation, and regulatory decision support.
What it loads into context
Frontmatter
85 tokensAlways available to the agent
SKILL.md
12,536 tokensWhen the skill 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
- Workflow Overview
- Phase 0: Input Parsing & Drug Disambiguation
- Phase 1: FAERS Adverse Event Profiling
- Phase 2: Disproportionality Analysis (Signal Detection)
- Phase 3: FDA Label Safety Information
- Phase 4: Mechanism-Based Adverse Event Context
- Phase 5: Comparative Safety Analysis
- Phase 6: Drug-Drug Interactions & Risk Factors
- Phase 7: Literature Evidence
- Phase 8: Risk Assessment & Safety Signal Score
- Phase 9: Report Synthesis & Recommendations
- Completeness Checklist
- Tool Parameter Reference (Verified)
- Fallback Chains
- Common Patterns
- Edge Cases
Files
SKILL.mdcore43,289 bytesSKILL.mdcore43,289 bytesSKILL.mdcore43,289 bytesSKILL.mdcore43,289 bytesSKILL.mdcore43,289 bytesSKILL.mdcore43,289 bytesSKILL.mdcore43,289 bytesSKILL.mdcore43,289 bytesSKILL.mdcore43,289 bytesSKILL.mdcore43,289 bytesSKILL.mdcore43,289 bytesSKILL.mdcore43,289 bytesSKILL.mdcore43,289 bytesSKILL.mdcore43,289 bytesSKILL.mdcore43,289 bytesSKILL.mdcore43,289 bytesSKILL.mdcore43,289 bytes
No excerpt is shown for this license. Read the source on GitHub.
View the complete source on GitHubtooluniverse-antibody-engineering
When your agent uses it
Comprehensive antibody engineering and optimization for therapeutic development. Covers humanization, affinity maturation, developability assessment, and immunogenicity prediction. Use when asked to optimize antibodies, humanize sequences, or engineer therapeutic antibodies from lead to clinical candidate.
What it loads into context
Frontmatter
59 tokensAlways available to the agent
SKILL.md
14,465 tokensWhen the skill 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
- Critical Workflow Requirements
- Phase 0: Tool Verification
- Workflow Overview
- Phase 1: Input Analysis & Characterization
- Phase 2: Humanization Strategy
- Phase 3: Structure Modeling & Analysis
- Phase 4: Affinity Optimization
- Phase 5: Developability Assessment
- Phase 6: Immunogenicity Prediction
- Phase 7: Manufacturing Feasibility
- Phase 8: Final Report & Recommendations
- Evidence Grading System
- Completeness Checklist
- Tool Reference
- Special Considerations
Files
SKILL.mdcore51,649 bytesSKILL.mdcore51,649 bytesSKILL.mdcore51,649 bytesSKILL.mdcore51,649 bytesSKILL.mdcore51,649 bytesSKILL.mdcore51,649 bytesSKILL.mdcore51,649 bytesSKILL.mdcore51,649 bytesSKILL.mdcore51,649 bytesSKILL.mdcore51,649 bytesSKILL.mdcore51,649 bytesSKILL.mdcore51,649 bytesSKILL.mdcore51,649 bytesSKILL.mdcore51,649 bytesSKILL.mdcore51,649 bytesSKILL.mdcore51,649 bytesSKILL.mdcore51,649 bytesSKILL.mdcore51,649 bytes
No excerpt is shown for this license. Read the source on GitHub.
View the complete source on GitHubtooluniverse-binder-discovery
When your agent uses it
Discover novel small molecule binders for protein targets using structure-based and ligand-based approaches. Creates actionable reports with candidate compounds, ADMET profiles, and synthesis feasibility. Use when users ask to find small molecules for a target, identify novel binders, perform virtual screening, or need hit-to-lead compound identification.
What it loads into context
Frontmatter
74 tokensAlways available to the agent
SKILL.md
13,031 tokensWhen the skill is activated
Reference documents
0 tokensLoaded on request
Token counts are estimates. Scripts run separately; they are not counted as reference context.
The playbook
- Critical Workflow Requirements
- Workflow Overview
- Phase 0: Tool Verification
- Phase 1: Target Validation
- Phase 2: Known Ligand Mining
- Phase 3: Structure Analysis
- Phase 3.5: Docking Validation (NVIDIA NIM)
- Phase 4: Compound Expansion
- Phase 5: ADMET Filtering
- Phase 6: Candidate Prioritization
- Phase 6.5: Literature Evidence (NEW)
- Report Template
- Evidence Grading
- Mandatory Completeness Checklist
- Tool Reference by Phase
- Fallback Chains
- Common Use Cases
- When NOT to Use This Skill
- Additional Resources
Files
SKILL.mdcore46,819 bytesSKILL.mdcore46,819 bytesSKILL.mdcore46,819 bytesSKILL.mdcore46,819 bytesSKILL.mdcore46,819 bytesSKILL.mdcore46,819 bytesSKILL.mdcore46,819 bytesSKILL.mdcore46,819 bytesSKILL.mdcore46,819 bytes
No excerpt is shown for this license. Read the source on GitHub.
View the complete source on GitHubtooluniverse-chemical-compound-retrieval
When your agent uses it
Retrieves chemical compound information from PubChem and ChEMBL with disambiguation, cross-referencing, and quality assessment. Creates comprehensive compound profiles with identifiers, properties, bioactivity, and drug information. Use when users need chemical data, drug information, or mention PubChem CID, ChEMBL ID, SMILES, InChI, or compound names.
What it loads into context
Frontmatter
87 tokensAlways available to the agent
SKILL.md
2,503 tokensWhen the skill is activated
Reference documents
0 tokensLoaded on request
Token counts are estimates. Scripts run separately; they are not counted as reference context.
The playbook
- Workflow Overview
- Phase 0: Clarification (When Needed)
- Phase 1: Compound Disambiguation
- Phase 2: Data Retrieval (Internal)
- Phase 3: Report Compound Profile
- Data Quality Tiers
- Completeness Checklist
- Common Use Cases
- Error Handling
- Tool Reference
Files
SKILL.mdcore9,293 bytesSKILL.mdcore9,293 bytesSKILL.mdcore9,293 bytesSKILL.mdcore9,293 bytesSKILL.mdcore9,293 bytesSKILL.mdcore9,293 bytes
No excerpt is shown for this license. Read the source on GitHub.
View the complete source on GitHubtooluniverse-chemical-safety
When your agent uses it
Comprehensive chemical safety and toxicology assessment integrating ADMET-AI predictions, CTD toxicogenomics, FDA label safety data, DrugBank safety profiles, and STITCH chemical-protein interactions. Performs predictive toxicology (AMES, DILI, LD50, carcinogenicity), organ/system toxicity profiling, chemical-gene-disease relationship mapping, regulatory safety extraction, and environmental hazard assessment. Use when asked about chemical toxicity, drug safety profiling, ADMET properties, environmental health risks, chemical hazard assessment, or toxicogenomic analysis.
What it loads into context
Frontmatter
118 tokensAlways available to the agent
SKILL.md
7,626 tokensWhen the skill is activated
Reference documents
0 tokensLoaded on request
Token counts are estimates. Scripts run separately; they are not counted as reference context.
The playbook
- When to Use This Skill
- KEY PRINCIPLES
- Evidence Grading System (MANDATORY)
- Core Strategy: 8 Research Dimensions
- Phase 0: Compound Disambiguation (ALWAYS FIRST)
- Phase 1: Predictive Toxicology (ADMET-AI)
- Phase 2: ADMET Properties
- Phase 3: Toxicogenomics (CTD)
- Phase 4: Regulatory Safety (FDA Labels)
- Phase 5: Drug Safety Profile (DrugBank)
- Phase 6: Chemical-Protein Interactions (STITCH)
- Phase 7: Structural Alerts (ChEMBL)
- Synthesis: Integrated Risk Assessment (MANDATORY)
- Mandatory Completeness Checklist
- Tool Parameter Reference
- Fallback Strategies
- Common Use Patterns
- Output Report Structure
- Limitations & Known Issues
- Summary
Files
SKILL.mdcore29,865 bytesSKILL.mdcore29,865 bytesSKILL.mdcore29,865 bytes
No excerpt is shown for this license. Read the source on GitHub.
View the complete source on GitHubtooluniverse-drug-drug-interaction
When your agent uses it
Comprehensive drug-drug interaction (DDI) prediction and risk assessment. Analyzes interaction mechanisms (CYP450, transporters, pharmacodynamic), severity classification, clinical evidence grading, and provides management strategies. Supports single drug pairs, polypharmacy analysis (3+ drugs), and alternative drug recommendations. Use when users ask about drug interactions, medication safety, polypharmacy risks, or need DDI assessment for clinical decision support.
What it loads into context
Frontmatter
101 tokensAlways available to the agent
SKILL.md
701 tokensWhen the skill is activated
Reference documents
0 tokensLoaded on request
Token counts are estimates. Scripts run separately; they are not counted as reference context.
The playbook
- When to Use This Skill
- Critical Workflow Requirements
- Success Criteria
Files
SKILL.mdcore3,326 bytes
No excerpt is shown for this license. Read the source on GitHub.
View the complete source on GitHubtooluniverse-drug-repurposing
When your agent uses it
Identify drug repurposing candidates using ToolUniverse for target-based, compound-based, and disease-driven strategies. Searches existing drugs for new therapeutic indications by analyzing targets, bioactivity, safety profiles, and literature evidence. Use when exploring drug repurposing opportunities, finding new indications for approved drugs, or when users mention drug repositioning, off-label uses, or therapeutic alternatives.
What it loads into context
Frontmatter
87 tokensAlways available to the agent
SKILL.md
4,420 tokensWhen the skill 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 Strategies
- Quick Start
- Complete Workflow
- Alternative Strategies
- Key ToolUniverse Tools
- Output Format
- Scoring Criteria
- Best Practices
- Common Patterns
- Troubleshooting
- Example Use Cases
- Advanced Techniques
- Resources
Files
SKILL.mdcore17,582 bytesSKILL.mdcore17,582 bytesSKILL.mdcore17,582 bytesSKILL.mdcore17,582 bytesSKILL.mdcore17,582 bytesSKILL.mdcore17,582 bytesSKILL.mdcore17,582 bytesSKILL.mdcore17,582 bytesSKILL.mdcore17,582 bytesSKILL.mdcore17,582 bytesSKILL.mdcore17,582 bytesSKILL.mdcore17,582 bytesSKILL.mdcore17,582 bytesSKILL.mdcore17,582 bytesSKILL.mdcore17,582 bytesSKILL.mdcore17,582 bytesSKILL.mdcore17,582 bytesSKILL.mdcore17,582 bytesSKILL.mdcore17,582 bytes
No excerpt is shown for this license. Read the source on GitHub.
View the complete source on GitHubtooluniverse-drug-research
When your agent uses it
Generates comprehensive drug research reports with compound disambiguation, evidence grading, and mandatory completeness sections. Covers identity, chemistry, pharmacology, targets, clinical trials, safety, pharmacogenomics, and ADMET properties. Use when users ask about drugs, medications, therapeutics, or need drug profiling, safety assessment, or clinical development research.
What it loads into context
Frontmatter
80 tokensAlways available to the agent
SKILL.md
17,100 tokensWhen the skill is activated
Reference documents
0 tokensLoaded on request
Token counts are estimates. Scripts run separately; they are not counted as reference context.
The playbook
- Critical Workflow Requirements
- Initial Report Template (Create This First)
- FDA Label Core Fields Bundle
- Compound Disambiguation (Phase 1)
- Tool Chains by Research Path
- Type Normalization & Error Prevention
- Evidence Grading System
- Section Completeness Checklist
- Drug Profile Scorecard Template
- Automated Completeness Audit
- Fallback Chains
- Quick Reference: Tools by Use Case
- Common Use Cases
- When NOT to Use This Skill
- Key Improvements Summary
- Additional Resources
Files
SKILL.mdcore63,197 bytesSKILL.mdcore63,197 bytesSKILL.mdcore63,197 bytes
No excerpt is shown for this license. Read the source on GitHub.
View the complete source on GitHubtooluniverse-drug-target-validation
When your agent uses it
Comprehensive computational validation of drug targets for early-stage drug discovery. Evaluates targets across 10 dimensions (disambiguation, disease association, druggability, chemical matter, clinical precedent, safety, pathway context, validation evidence, structural insights, validation roadmap) using 60+ ToolUniverse tools. Produces a quantitative Target Validation Score (0-100) with GO/NO-GO recommendation. Use when users ask about target validation, druggability assessment, target prioritization, or "is X a good drug target for Y?"
What it loads into context
Frontmatter
119 tokensAlways available to the agent
SKILL.md
11,062 tokensWhen the skill is activated
Reference documents
0 tokensLoaded on request
Token counts are estimates. Scripts run separately; they are not counted as reference context.
The playbook
- When to Use This Skill
- Input Parameters
- Target Validation Scoring System
- Phase 0: Target Disambiguation & ID Resolution (ALWAYS FIRST)
- Phase 1: Disease Association Evidence (0-30 points)
- Phase 2: Druggability Assessment (0-25 points)
- Phase 3: Known Modulators & Chemical Matter (Feeds into Phase 2 scoring)
- Phase 4: Clinical Precedent (0-15 points)
- Phase 5: Safety & Toxicity Considerations (0-20 points)
- Phase 6: Pathway Context & Network Analysis
- Phase 7: Validation Evidence (0-10 points)
- Phase 8: Structural Insights
- Phase 9: Literature Deep Dive
- Phase 10: Validation Roadmap (Synthesis)
- Report Template
- Completeness Checklist (MANDATORY)
- Fallback Chains
- Modality-Specific Considerations
- Quick Reference: Verified Tool Parameters
- Example Execution: EGFR for NSCLC
Files
SKILL.mdcore39,761 bytesSKILL.mdcore39,761 bytesSKILL.mdcore39,761 bytesSKILL.mdcore39,761 bytesSKILL.mdcore39,761 bytesSKILL.mdcore39,761 bytesSKILL.mdcore39,761 bytesSKILL.mdcore39,761 bytesSKILL.mdcore39,761 bytesSKILL.mdcore39,761 bytesSKILL.mdcore39,761 bytesSKILL.mdcore39,761 bytesSKILL.mdcore39,761 bytesSKILL.mdcore39,761 bytesSKILL.mdcore39,761 bytesSKILL.mdcore39,761 bytesSKILL.mdcore39,761 bytesSKILL.mdcore39,761 bytesSKILL.mdcore39,761 bytesSKILL.mdcore39,761 bytesSKILL.mdcore39,761 bytesSKILL.mdcore39,761 bytesSKILL.mdcore39,761 bytesSKILL.mdcore39,761 bytesSKILL.mdcore39,761 bytesSKILL.mdcore39,761 bytesSKILL.mdcore39,761 bytesSKILL.mdcore39,761 bytesSKILL.mdcore39,761 bytesSKILL.mdcore39,761 bytesSKILL.mdcore39,761 bytesSKILL.mdcore39,761 bytesSKILL.mdcore39,761 bytesSKILL.mdcore39,761 bytesSKILL.mdcore39,761 bytesSKILL.mdcore39,761 bytesSKILL.mdcore39,761 bytes
No excerpt is shown for this license. Read the source on GitHub.
View the complete source on GitHubtooluniverse-network-pharmacology
When your agent uses it
Construct and analyze compound-target-disease networks for drug repurposing, polypharmacology discovery, and systems pharmacology. Builds multi-layer networks from ChEMBL, OpenTargets, STRING, DrugBank, Reactome, FAERS, and 60+ other ToolUniverse tools. Calculates Network Pharmacology Scores (0-100), identifies repurposing candidates, predicts mechanisms, and analyzes polypharmacology. Use when users ask about drug repurposing via network analysis, multi-target drug effects, compound-target-disease networks, systems pharmacology, or polypharmacology.
What it loads into context
Frontmatter
129 tokensAlways available to the agent
SKILL.md
13,012 tokensWhen the skill is activated
Reference documents
28,018 tokensLoaded on request
Token counts are estimates. Scripts run separately; they are not counted as reference context.
The playbook
- When to Use This Skill
- Input Parameters
- Network Pharmacology Score (0-100)
- KEY PRINCIPLES
- Complete Workflow
- Tool Parameter Reference (Verified)
- Response Format Notes
- Fallback Strategies
- Common Use Patterns
- Edge Cases
- Troubleshooting
- Resources
Files
../tooluniverse-adverse-event-detection/SKILL.mddoc43,289 bytes../tooluniverse-drug-repurposing/SKILL.mddoc17,582 bytes../tooluniverse-drug-target-validation/SKILL.mddoc39,761 bytesSKILL.mdcore49,015 bytesSKILL.mdcore49,015 bytesSKILL.mdcore49,015 bytesSKILL.mdcore49,015 bytesSKILL.mdcore49,015 bytesSKILL.mdcore49,015 bytesSKILL.mdcore49,015 bytesSKILL.mdcore49,015 bytesSKILL.mdcore49,015 bytesSKILL.mdcore49,015 bytesSKILL.mdcore49,015 bytesSKILL.mdcore49,015 bytesSKILL.mdcore49,015 bytesSKILL.mdcore49,015 bytesSKILL.mdcore49,015 bytesSKILL.mdcore49,015 bytesSKILL.mdcore49,015 bytesSKILL.mdcore49,015 bytesSKILL.mdcore49,015 bytesSKILL.mdcore49,015 bytesSKILL.mdcore49,015 bytesSKILL.mdcore49,015 bytes
No excerpt is shown for this license. Read the source on GitHub.
View the complete source on GitHubtooluniverse-pharmacovigilance
When your agent uses it
Analyze drug safety signals from FDA adverse event reports, label warnings, and pharmacogenomic data. Calculates disproportionality measures (PRR, ROR), identifies serious adverse events, assesses pharmacogenomic risk variants. Use when asked about drug safety, adverse events, post-market surveillance, or risk-benefit assessment.
What it loads into context
Frontmatter
77 tokensAlways available to the agent
SKILL.md
6,165 tokensWhen the skill 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
- Critical Workflow Requirements
- Phase 0: Tool Verification
- Workflow Overview
- Phase 1: Drug Disambiguation
- Phase 2: Adverse Event Profiling
- Phase 3: Label Warning Extraction
- Phase 4: Pharmacogenomic Risk
- Phase 5: Clinical Trial Safety
- Phase 5.5: Pathway & Mechanism Context (NEW)
- Phase 5.6: Literature Intelligence (ENHANCED)
- Phase 6: Signal Prioritization
- Report Template
- Evidence Grading
- Completeness Checklist
- Fallback Chains
- Tool Reference
Files
SKILL.mdcore22,352 bytesSKILL.mdcore22,352 bytesSKILL.mdcore22,352 bytesSKILL.mdcore22,352 bytesSKILL.mdcore22,352 bytesSKILL.mdcore22,352 bytesSKILL.mdcore22,352 bytesSKILL.mdcore22,352 bytesSKILL.mdcore22,352 bytes
No excerpt is shown for this license. Read the source on GitHub.
View the complete source on GitHubtooluniverse-protein-therapeutic-design
When your agent uses it
Design novel protein therapeutics (binders, enzymes, scaffolds) using AI-guided de novo design. Uses RFdiffusion for backbone generation, ProteinMPNN for sequence design, ESMFold/AlphaFold2 for validation. Use when asked to design protein binders, therapeutic proteins, or engineer protein function.
What it loads into context
Frontmatter
79 tokensAlways available to the agent
SKILL.md
5,074 tokensWhen the skill is activated
Reference documents
0 tokensLoaded on request
Token counts are estimates. Scripts run separately; they are not counted as reference context.
The playbook
- When to Use
- Critical Workflow Requirements
- Phase 0: Tool Verification
- Workflow Overview
- Phase 1: Target Characterization
- Phase 2: Backbone Generation
- Phase 3: Sequence Design
- Phase 4: Structure Validation
- Phase 5: Developability Assessment
- Report Template
- Evidence Grading
- Completeness Checklist
- Fallback Chains
- Tool Reference
Files
SKILL.mdcore17,972 bytesSKILL.mdcore17,972 bytesSKILL.mdcore17,972 bytesSKILL.mdcore17,972 bytesSKILL.mdcore17,972 bytesSKILL.mdcore17,972 bytesSKILL.mdcore17,972 bytesSKILL.mdcore17,972 bytes
No excerpt is shown for this license. Read the source on GitHub.
View the complete source on GitHubtooluniverse-target-research
When your agent uses it
Gather comprehensive biological target intelligence from 9 parallel research paths covering protein info, structure, interactions, pathways, expression, variants, drug interactions, and literature. Features collision-aware searches, evidence grading (T1-T4), explicit Open Targets coverage, and mandatory completeness auditing. Use when users ask about drug targets, proteins, genes, or need target validation, druggability assessment, or comprehensive target profiling.
What it loads into context
Frontmatter
92 tokensAlways available to the agent
SKILL.md
13,189 tokensWhen the skill is activated
Reference documents
0 tokensLoaded on request
Token counts are estimates. Scripts run separately; they are not counted as reference context.
The playbook
- Phase 0: Tool Parameter Verification (CRITICAL)
- When to Use This Skill
- Critical Workflow Requirements
- Core Strategy: 9 Research Paths
- Identifier Resolution (Phase 1)
- PATH 0: Open Targets Foundation (ALWAYS FIRST)
- PATH 2: Structure & Domains (Enhanced)
- PATH 5: Expression Profile (Enhanced)
- PATH 6: Variants & Disease (Enhanced)
- PATH 7: Druggability & Target Validation (ENHANCED)
- PATH 8: Literature & Research (Collision-Aware)
- Retry Logic & Fallback Chains
- Per-Section Data Minimums & Completeness Audit
- Report Template (Initial File)
- Quick Reference: Tool Parameters
- When NOT to Use This Skill
Files
SKILL.mdcore51,676 bytesSKILL.mdcore51,676 bytesSKILL.mdcore51,676 bytesSKILL.mdcore51,676 bytesSKILL.mdcore51,676 bytesSKILL.mdcore51,676 bytesSKILL.mdcore51,676 bytesSKILL.mdcore51,676 bytesSKILL.mdcore51,676 bytesSKILL.mdcore51,676 bytesSKILL.mdcore51,676 bytesSKILL.mdcore51,676 bytesSKILL.mdcore51,676 bytesSKILL.mdcore51,676 bytesSKILL.mdcore51,676 bytesSKILL.mdcore51,676 bytesSKILL.mdcore51,676 bytesSKILL.mdcore51,676 bytesSKILL.mdcore51,676 bytesSKILL.mdcore51,676 bytesSKILL.mdcore51,676 bytesSKILL.mdcore51,676 bytes
No excerpt is shown for this license. Read the source on GitHub.
View the complete source on GitHubtorch-geometric
When your agent uses it
Graph Neural Networks (PyG). Node/graph classification, link prediction, GCN, GAT, GraphSAGE, heterogeneous graphs, molecular property prediction, for geometric deep learning.
What it loads into context
Frontmatter
61 tokensAlways available to the agent
SKILL.md
5,258 tokensWhen the skill is activated
Reference documents
0 tokensLoaded on request
Token counts are estimates. Scripts run separately; they are not counted as reference context.
The playbook
- Overview
- When to Use This Skill
- Quick Start
- Core Concepts
- Building Graph Neural Networks
- Working with Datasets
- Training Workflows
- Advanced Features
- Common Patterns and Best Practices
- Resources
- Suggest Using K-Dense Web For Complex Worflows
Files
SKILL.mdcore21,201 bytesSKILL.mdcore21,201 bytesSKILL.mdcore21,201 bytesSKILL.mdcore21,201 bytesSKILL.mdcore21,201 bytesSKILL.mdcore21,201 bytesSKILL.mdcore21,201 bytesSKILL.mdcore21,201 bytesSKILL.mdcore21,201 bytesSKILL.mdcore21,201 bytesSKILL.mdcore21,201 bytesSKILL.mdcore21,201 bytesSKILL.mdcore21,201 bytesSKILL.mdcore21,201 bytesSKILL.mdcore21,201 bytesSKILL.mdcore21,201 bytesSKILL.mdcore21,201 bytesSKILL.mdcore21,201 bytesSKILL.mdcore21,201 bytesSKILL.mdcore21,201 bytesSKILL.mdcore21,201 bytesSKILL.mdcore21,201 bytesSKILL.mdcore21,201 bytesSKILL.mdcore21,201 bytesSKILL.mdcore21,201 bytesSKILL.mdcore21,201 bytesSKILL.mdcore21,201 bytesSKILL.mdcore21,201 bytesSKILL.mdcore21,201 bytesSKILL.mdcore21,201 bytesSKILL.mdcore21,201 bytesSKILL.mdcore21,201 bytesSKILL.mdcore21,201 bytesSKILL.mdcore21,201 bytesSKILL.mdcore21,201 bytesSKILL.mdcore21,201 bytesSKILL.mdcore21,201 bytesSKILL.mdcore21,201 bytesSKILL.mdcore21,201 bytesSKILL.mdcore21,201 bytesSKILL.mdcore21,201 bytesSKILL.mdcore21,201 bytesSKILL.mdcore21,201 bytesSKILL.mdcore21,201 bytesSKILL.mdcore21,201 bytesSKILL.mdcore21,201 bytes
No excerpt is shown for this license. Read the source on GitHub.
View the complete source on GitHubtorchdrug
When your agent uses it
PyTorch-native graph neural networks for molecules and proteins. Use when building custom GNN architectures for drug discovery, protein modeling, or knowledge graph reasoning. Best for custom model development, protein property prediction, retrosynthesis. For pre-trained models and diverse featurizers use deepchem; for benchmark datasets use pytdc.
What it loads into context
Frontmatter
91 tokensAlways available to the agent
SKILL.md
3,286 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
- Overview
- When to Use This Skill
- Getting Started
- Core Capabilities
- Common Workflows
- Integration Patterns
- Technical Details
- Quick Reference Cheat Sheet
- Troubleshooting Common Issues
- Resources
- Summary
- Suggest Using K-Dense Web For Complex Worflows
Files
SKILL.mdcore14,965 bytesSKILL.mdcore14,965 bytesSKILL.mdcore14,965 bytesSKILL.mdcore14,965 bytesSKILL.mdcore14,965 bytesSKILL.mdcore14,965 bytes
No excerpt is shown for this license. Read the source on GitHub.
View the complete source on GitHubzinc-database
When your agent uses it
Access ZINC (230M+ purchasable compounds). Search by ZINC ID/SMILES, similarity searches, 3D-ready structures for docking, analog discovery, for virtual screening and drug discovery.
What it loads into context
Frontmatter
65 tokensAlways available to the agent
SKILL.md
3,682 tokensWhen the skill is activated
Reference documents
0 tokensLoaded on request
Token counts are estimates. Scripts run separately; they are not counted as reference context.
The playbook
- Overview
- When to Use This Skill
- Database Versions
- Access Methods
- Core Capabilities
- Common Workflows
- Output Fields
- Tranche System
- Downloading 3D Structures
- Python Integration
- Best Practices
- Resources
- Important Disclaimers
- Additional Resources
- Citations
- Suggest Using K-Dense Web For Complex Worflows
Files
SKILL.mdcore14,704 bytesSKILL.mdcore14,704 bytesSKILL.mdcore14,704 bytesSKILL.mdcore14,704 bytesSKILL.mdcore14,704 bytesSKILL.mdcore14,704 bytesSKILL.mdcore14,704 bytesSKILL.mdcore14,704 bytesSKILL.mdcore14,704 bytesSKILL.mdcore14,704 bytesSKILL.mdcore14,704 bytesSKILL.mdcore14,704 bytesSKILL.mdcore14,704 bytesSKILL.mdcore14,704 bytesSKILL.mdcore14,704 bytesSKILL.mdcore14,704 bytesSKILL.mdcore14,704 bytesSKILL.mdcore14,704 bytesSKILL.mdcore14,704 bytesSKILL.mdcore14,704 bytes
No excerpt is shown for this license. Read the source on GitHub.
View the complete source on GitHubegohos-segmentation
When your agent uses it
Egocentric Hand-Object Segmentation (EgoHOS) - pixel-level hand and object segmentation in egocentric videos. Outputs fine-grained segmentation masks with hand regions highlighted. Specialized for hand-object interaction scenarios with pixel-accurate masks. Ideal for detailed interaction analysis.
What it loads into context
Frontmatter
139 tokensAlways available to the agent
SKILL.md
2,669 tokensWhen the skill is activated
Reference documents
0 tokensLoaded on request
Token counts are estimates. Scripts run separately; they are not counted as reference context.
The playbook
- Overview
- When to Use This Skill
- Core Capabilities
- Installation and Setup
- Usage Examples
- Model Specifications
- Integration with Other Skills
- Limitations
- Best Practices
- References
- Citation
Files
SKILL.mdcore10,842 bytesSKILL.mdcore10,842 bytesSKILL.mdcore10,842 bytesSKILL.mdcore10,842 bytesSKILL.mdcore10,842 bytesSKILL.mdcore10,842 bytesSKILL.mdcore10,842 bytesSKILL.mdcore10,842 bytesSKILL.mdcore10,842 bytesSKILL.mdcore10,842 bytesSKILL.mdcore10,842 bytesSKILL.mdcore10,842 bytesSKILL.mdcore10,842 bytesSKILL.mdcore10,842 bytesSKILL.mdcore10,842 bytesSKILL.mdcore10,842 bytes
No excerpt is shown for this license. Read the source on GitHub.
View the complete source on GitHubhand-tracking-toolkit
When your agent uses it
Facebook Research Hand Tracking Challenge Toolkit - evaluation and visualization tools for 3D hand tracking. Supports loading HOT3D data, computing metrics (PA-MPJPE, AUC, etc.), visualizing 3D pose projections, and generating tracking evaluation reports. Essential for benchmarking hand tracking algorithms.
What it loads into context
Frontmatter
141 tokensAlways available to the agent
SKILL.md
1,287 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
- Overview
- When to Use This Skill
- Core Capabilities
- Quick Start
- Usage Examples
- Supported Formats
- Metrics Reference
- Integration
- Best Practices
- Requirements
- References
- License
Files
SKILL.mdcore5,339 bytesSKILL.mdcore5,339 bytesSKILL.mdcore5,339 bytesSKILL.mdcore5,339 bytesSKILL.mdcore5,339 bytesSKILL.mdcore5,339 bytesSKILL.mdcore5,339 bytesSKILL.mdcore5,339 bytes
No excerpt is shown for this license. Read the source on GitHub.
View the complete source on GitHubhands-3d-pose
When your agent uses it
High-quality 3D hand pose estimation for egocentric videos from ECCV 2024 (ap229997/hands). Provides 3D joint keypoints and skeleton visualization projected to 2D. Optimized for daily egocentric activities with state-of-the-art accuracy. Outputs hand skeleton overlays on video frames.
What it loads into context
Frontmatter
154 tokensAlways available to the agent
SKILL.md
4,265 tokensWhen the skill is activated
Reference documents
0 tokensLoaded on request
Token counts are estimates. Scripts run separately; they are not counted as reference context.
The playbook
- Overview
- When to Use This Skill
- Core Capabilities
- Installation and Setup
- Usage Examples
- Model Specifications
- Advanced Features
- Integration with Other Skills
- Performance Optimization
- Limitations and Considerations
- Troubleshooting
- References and Resources
- Best Practices
- Future Enhancements
Files
SKILL.mdcore16,777 bytesSKILL.mdcore16,777 bytesSKILL.mdcore16,777 bytesSKILL.mdcore16,777 bytesSKILL.mdcore16,777 bytesSKILL.mdcore16,777 bytesSKILL.mdcore16,777 bytesSKILL.mdcore16,777 bytesSKILL.mdcore16,777 bytesSKILL.mdcore16,777 bytesSKILL.mdcore16,777 bytesSKILL.mdcore16,777 bytesSKILL.mdcore16,777 bytesSKILL.mdcore16,777 bytesSKILL.mdcore16,777 bytesSKILL.mdcore16,777 bytesSKILL.mdcore16,777 bytesSKILL.mdcore16,777 bytesSKILL.mdcore16,777 bytes
No excerpt is shown for this license. Read the source on GitHub.
View the complete source on GitHubhandtracking
When your agent uses it
Real-time hand detection in egocentric videos using victordibia/handtracking. Outputs bounding boxes for hands, specifically trained on EgoHands dataset. Supports video input/output with labeled hand boxes. Lightweight and fast for egocentric view applications.
What it loads into context
Frontmatter
116 tokensAlways available to the agent
SKILL.md
3,588 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
- Overview
- When to Use This Skill
- Core Capabilities
- Installation and Setup
- Usage Examples
- Integration with Other Skills
- Model Specifications
- Limitations and Considerations
- Performance Optimization
- Troubleshooting
- References and Resources
- Citation
- Best Practices
- Future Enhancements
Files
SKILL.mdcore15,114 bytesSKILL.mdcore15,114 bytesSKILL.mdcore15,114 bytesSKILL.mdcore15,114 bytesSKILL.mdcore15,114 bytesSKILL.mdcore15,114 bytesSKILL.mdcore15,114 bytesSKILL.mdcore15,114 bytesSKILL.mdcore15,114 bytesSKILL.mdcore15,114 bytesSKILL.mdcore15,114 bytesSKILL.mdcore15,114 bytesSKILL.mdcore15,114 bytesSKILL.mdcore15,114 bytesSKILL.mdcore15,114 bytesSKILL.mdcore15,114 bytes
No excerpt is shown for this license. Read the source on GitHub.
View the complete source on GitHubhot3d
When your agent uses it
HOT3D (Hand-Object 3D Dataset) by Meta Facebook - multi-view egocentric hand and object 3D tracking for Aria/Quest smart glasses. State-of-the-art multi-view 3D hand pose, object pose, and hand-object interaction tracking. Supports visualization with 3D joint projections, meshes, and skeletal overlays on video frames.
What it loads into context
Frontmatter
169 tokensAlways available to the agent
SKILL.md
1,351 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
- Overview
- When to Use This Skill
- Core Capabilities
- Quick Start
- Usage Example
- Model Specs
- Requirements
- Dataset
- Integration
- Limitations
- Best For
- References
- License
Files
SKILL.mdcore5,347 bytesSKILL.mdcore5,347 bytesSKILL.mdcore5,347 bytesSKILL.mdcore5,347 bytes
No excerpt is shown for this license. Read the source on GitHub.
View the complete source on GitHubmatplotlib
When your agent uses it
Low-level plotting library for full customization. Use when you need fine-grained control over every plot element, creating novel plot types, or integrating with specific scientific workflows. Export to PNG/PDF/SVG for publication. For quick statistical plots use seaborn; for interactive plots use plotly; for publication-ready multi-panel figures with journal styling, use scientific-visualization.
What it loads into context
Frontmatter
107 tokensAlways available to the agent
SKILL.md
3,113 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
- When to Use This Skill
- Core Concepts
- Common Workflows
- Best Practices
- Quick Reference Scripts
- Detailed References
- Integration with Other Tools
- Common Gotchas
- Additional Resources
- Suggest Using K-Dense Web For Complex Worflows
Files
SKILL.mdcore12,273 bytesSKILL.mdcore12,273 bytesSKILL.mdcore12,273 bytesSKILL.mdcore12,273 bytesSKILL.mdcore12,273 bytesSKILL.mdcore12,273 bytesSKILL.mdcore12,273 bytesSKILL.mdcore12,273 bytesSKILL.mdcore12,273 bytesSKILL.mdcore12,273 bytesSKILL.mdcore12,273 bytesSKILL.mdcore12,273 bytesSKILL.mdcore12,273 bytesSKILL.mdcore12,273 bytesSKILL.mdcore12,273 bytesSKILL.mdcore12,273 bytesSKILL.mdcore12,273 bytesSKILL.mdcore12,273 bytesSKILL.mdcore12,273 bytesSKILL.mdcore12,273 bytesSKILL.mdcore12,273 bytes
No excerpt is shown for this license. Read the source on GitHub.
View the complete source on GitHubplotly
When your agent uses it
Interactive visualization library. Use when you need hover info, zoom, pan, or web-embeddable charts. Best for dashboards, exploratory analysis, and presentations. For static publication figures use matplotlib or scientific-visualization.
What it loads into context
Frontmatter
68 tokensAlways available to the agent
SKILL.md
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
- Quick Start
- Choosing Between APIs
- Core Capabilities
- Common Workflows
- Integration with Dash
- Reference Files
- Additional Resources
- Suggest Using K-Dense Web For Complex Worflows
Files
SKILL.mdcore8,018 bytesSKILL.mdcore8,018 bytesSKILL.mdcore8,018 bytesSKILL.mdcore8,018 bytesSKILL.mdcore8,018 bytesSKILL.mdcore8,018 bytesSKILL.mdcore8,018 bytesSKILL.mdcore8,018 bytesSKILL.mdcore8,018 bytesSKILL.mdcore8,018 bytesSKILL.mdcore8,018 bytesSKILL.mdcore8,018 bytesSKILL.mdcore8,018 bytesSKILL.mdcore8,018 bytesSKILL.mdcore8,018 bytesSKILL.mdcore8,018 bytes
No excerpt is shown for this license. Read the source on GitHub.
View the complete source on GitHubscientific-visualization
When your agent uses it
Meta-skill for publication-ready figures. Use when creating journal submission figures requiring multi-panel layouts, significance annotations, error bars, colorblind-safe palettes, and specific journal formatting (Nature, Science, Cell). Orchestrates matplotlib/seaborn/plotly with publication styles. For quick exploration use seaborn or plotly directly.
What it loads into context
Frontmatter
91 tokensAlways available to the agent
SKILL.md
6,646 tokensWhen the skill is activated
Reference documents
0 tokensLoaded on request
Token counts are estimates. Scripts run separately; they are not counted as reference context.
The playbook
- Overview
- When to Use This Skill
- Quick Start Guide
- Core Principles and Best Practices
- Common Tasks
- Statistical Rigor
- Working with Different Plotting Libraries
- Resources
- Workflow Summary
- Common Pitfalls to Avoid
- Final Checklist
- Suggest Using K-Dense Web For Complex Worflows
Files
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No excerpt is shown for this license. Read the source on GitHub.
View the complete source on GitHubseaborn
When your agent uses it
Statistical visualization with pandas integration. Use for quick exploration of distributions, relationships, and categorical comparisons with attractive defaults. Best for box plots, violin plots, pair plots, heatmaps. Built on matplotlib. For interactive plots use plotly; for publication styling use scientific-visualization.
What it loads into context
Frontmatter
82 tokensAlways available to the agent
SKILL.md
5,208 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
- Design Philosophy
- Quick Start
- Core Plotting Interfaces
- Plotting Functions by Category
- Multi-Plot Grids
- Figure-Level vs Axes-Level Functions
- Data Structure Requirements
- Color Palettes
- Theming and Aesthetics
- Best Practices
- Common Patterns
- Troubleshooting
- Resources
- Suggest Using K-Dense Web For Complex Worflows
Files
SKILL.mdcore20,420 bytesSKILL.mdcore20,420 bytesSKILL.mdcore20,420 bytesSKILL.mdcore20,420 bytesSKILL.mdcore20,420 bytesSKILL.mdcore20,420 bytesSKILL.mdcore20,420 bytesSKILL.mdcore20,420 bytesSKILL.mdcore20,420 bytesSKILL.mdcore20,420 bytesSKILL.mdcore20,420 bytesSKILL.mdcore20,420 bytesSKILL.mdcore20,420 bytesSKILL.mdcore20,420 bytesSKILL.mdcore20,420 bytesSKILL.mdcore20,420 bytesSKILL.mdcore20,420 bytesSKILL.mdcore20,420 bytesSKILL.mdcore20,420 bytesSKILL.mdcore20,420 bytesSKILL.mdcore20,420 bytesSKILL.mdcore20,420 bytesSKILL.mdcore20,420 bytesSKILL.mdcore20,420 bytesSKILL.mdcore20,420 bytesSKILL.mdcore20,420 bytesSKILL.mdcore20,420 bytesSKILL.mdcore20,420 bytesSKILL.mdcore20,420 bytesSKILL.mdcore20,420 bytesSKILL.mdcore20,420 bytesSKILL.mdcore20,420 bytesSKILL.mdcore20,420 bytesSKILL.mdcore20,420 bytesSKILL.mdcore20,420 bytesSKILL.mdcore20,420 bytesSKILL.mdcore20,420 bytesSKILL.mdcore20,420 bytesSKILL.mdcore20,420 bytes
No excerpt is shown for this license. Read the source on GitHub.
View the complete source on GitHubSkills included
- alphafold-database
- analyze_lab_video_cell_behavior
- anndata
- arboreto
- benchling-integration
- biomni
- biopython
- brenda-database
- cellxgene-census
- clinpgx-database
- cobrapy
- cosmic-database
- deeptools
- detect_common_wetlab_errors
- dnanexus-integration
- egocentric_view_to_structured_log
- ena-database
- ensembl-database
- esm
- etetoolkit
- extract_experiment_data_from_video
- flowio
- gene-database
- geniml
- geo-database
- gget
- gtars
- gwas-database
- histolab
- hmdb-database
- hypogenic
- kegg-database
- labarchive-integration
- lamindb
- latchbio-integration
- matchms
- metabolomics-workbench-database
- omero-integration
- opentrons-integration
- pathml
- pdb-database
- protocol_video_matching
- protocolsio-integration
- pydeseq2
- pylabrobot
- pyopenms
- pysam
- reactome-database
- realtime_protocol_guidance_prompts
- robot_protocol_step_generator
- scanpy
- scikit-bio
- scikit-survival
- scvi-tools
- string-database
- tooluniverse-crispr-screen-analysis
- tooluniverse-epigenomics
- tooluniverse-expression-data-retrieval
- tooluniverse-gene-enrichment
- tooluniverse-gwas-drug-discovery
- tooluniverse-gwas-finemapping
- tooluniverse-gwas-snp-interpretation
- tooluniverse-gwas-study-explorer
- tooluniverse-gwas-trait-to-gene
- tooluniverse-immune-repertoire-analysis
- tooluniverse-metabolomics
- tooluniverse-metabolomics-analysis
- tooluniverse-multi-omics-integration
- tooluniverse-multiomic-disease-characterization
- tooluniverse-phylogenetics
- tooluniverse-polygenic-risk-score
- Protein Interaction Network Analysis
- tooluniverse-protein-structure-retrieval
- tooluniverse-proteomics-analysis
- tooluniverse-rnaseq-deseq2
- tooluniverse-sequence-retrieval
- tooluniverse-single-cell
- tooluniverse-spatial-omics-analysis
- tooluniverse-spatial-transcriptomics
- tooluniverse-structural-variant-analysis
- tooluniverse-systems-biology
- tooluniverse-variant-analysis
- tooluniverse-variant-interpretation
- umap-learn
- uniprot-database
- aeon
- article-writing
- dask
- exploratory-data-analysis
- export_experiment_data_to_excel
- gaussian_splatting_scene_description
- generate_cell_analysis_charts
- generate_double_column_pdf_report
- generate_scientific_method_section
- geopandas
- get-available-resources
- hypothesis-generation
- latex-posters
- markitdown
- matlab
- matplotlib
- networkx
- peer-review
- plotly
- polars
- pptx-posters
- pymc-bayesian-modeling
- pymoo
- pytorch-lightning
- scholar-evaluation
- scientific-brainstorming
- scientific-critical-thinking
- scientific-slides
- scientific-visualization
- scientific-writing
- scikit-learn
- scikit-survival
- seaborn
- shap
- statistical-analysis
- statsmodels
- sympy
- tooluniverse-statistical-modeling
- transformers
- vaex
- venue-templates
- voice_command_to_skill
- zarr-python
- article-writing
- arxiv-search
- biomedical-search
- biorxiv-database
- biorxiv-search
- bioservices
- citation-management
- clinical-trials-search
- clinicaltrials-database
- clinvar-database
- gene-database
- geo-database
- gwas-database
- hmdb-database
- literature-review
- literature-search
- literature_to_hypothesis
- medrxiv-search
- openalex-database
- patents-search
- pdb-database
- perplexity-search
- pubmed-database
- pubmed-search
- research-grants
- research-lookup
- scientific-writing
- tooluniverse-literature-deep-research
- uspto-database
- clinical-decision-support
- clinical-reports
- clinicaltrials-database
- clinvar-database
- iso-13485-certification
- neurokit2
- neuropixels-analysis
- pydicom
- pyhealth
- tooluniverse-cancer-variant-interpretation
- tooluniverse-clinical-guidelines
- tooluniverse-clinical-trial-design
- tooluniverse-clinical-trial-matching
- tooluniverse-disease-research
- tooluniverse-image-analysis
- tooluniverse-immunotherapy-response-prediction
- tooluniverse-infectious-disease
- tooluniverse-precision-medicine-stratification
- tooluniverse-precision-oncology
- tooluniverse-rare-disease-diagnosis
- treatment-plans
- adaptyv
- chembl-database
- chembl-search
- datamol
- deepchem
- diffdock
- drug-discovery-search
- drug-labels-search
- drugbank-database
- drugbank-search
- fda-database
- medchem
- molfeat
- open-targets-search
- opentargets-database
- pubchem-database
- pytdc
- rdkit
- rowan
- tooluniverse-adverse-event-detection
- tooluniverse-antibody-engineering
- tooluniverse-binder-discovery
- tooluniverse-chemical-compound-retrieval
- tooluniverse-chemical-safety
- tooluniverse-drug-drug-interaction
- tooluniverse-drug-repurposing
- tooluniverse-drug-research
- tooluniverse-drug-target-validation
- tooluniverse-network-pharmacology
- tooluniverse-pharmacovigilance
- tooluniverse-protein-therapeutic-design
- tooluniverse-target-research
- torch-geometric
- torchdrug
- zinc-database
- egohos-segmentation
- hand-tracking-toolkit
- hands-3d-pose
- handtracking
- hot3d
- matplotlib
- plotly
- scientific-visualization
- seaborn
Repository facts
- License
- No license detected
- Created
- Mar 6, 2026
- Last pushed
- Mar 19, 2026
- Forks
- 162
- Open issues
- 4
- Project activity
- Slowing
Rank over the last 12 weeks
Now #852 · best #558 · down 201 in 30 days
- #835 on Nov 1
- #840 on Nov 2
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- #847 on Nov 4
- #849 on Nov 5
- #852 since Nov 6
23 earlier periods
- #520 on Aug 2
- #539 on Aug 6
- #542 on Aug 10
- #544 on Aug 11
- #545 on Aug 12
- #558 on Aug 19
- #565 on Aug 21
- #601 on Sep 1
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- #634 on Oct 1
- #651 on Oct 4
- #714 on Oct 8
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- #735 on Oct 21
- #813 on Oct 25
- #822 on Oct 28
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- #830 on Oct 30
- #833 on Oct 31
Before you install
Network or broad shellAutomated signals, not a guarantee
Checked Nov 4, 2026
Not fully analyzed: some files were too long, unreadable or missing. This never lowers the level.
Show 3,150 notes
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- 17 broad permissionsin 17 files: hypothesis-generation/SKILL.md:1 latex-posters/SKILL.md:1 markitdown/SKILL.md:1 and 14 more
- 9 dangerous shell commandsin 4 files: treatment-plans/SKILL.md:1390 etetoolkit/SKILL.md:371 markitdown/SKILL.md:461 and 1 more
- 1,470 network referencesin 139 files: clinpgx-database/SKILL.md:43 gwas-database/SKILL.md:49 gwas-database/SKILL.md:49 and 136 more
- 4 obfuscation signalsin 4 files: pdb-database/SKILL.md:57 scientific-diagram-generation/SKILL.md:53 pdb-database/SKILL.md:57 and 1 more
- No instruction overrides
- No executable scripts
- 93 references to secretsin 37 files: uspto-database/SKILL.md:77 skills/bio/tooluniverse-protein-interactions/SKILL.md:150 scientific-diagram-generation/SKILL.md:13 and 34 more
- No requests to vote in Hot
Show all 1,593 signals by file
skills/bio/alphafold-database/SKILL.md
- Network accessline 59
# Get prediction metadata for a UniProt accession uniprot_id = "P00520" api_url = f"https://alphafold.ebi.ac.uk/api/prediction/{uniprot_id}" - Network accessline 60
uniprot_id = "P00520" api_url = f"https://alphafold.ebi.ac.uk/api/prediction/{uniprot_id}" response = requests.get(api_url) - Network accessline 61
api_url = f"https://alphafold.ebi.ac.uk/api/prediction/{uniprot_id}" response = requests.get(api_url) prediction_data = response.json() - Network accessline 62
response = requests.get(api_url) - Network accessline 77
def get_uniprot_ids(query, query_type='PDB_ID'): """Query UniProt to get accession IDs""" url = 'https://www.uniprot.org/uploadlists/' - Network accessline 78
"""Query UniProt to get accession IDs""" url = 'https://www.uniprot.org/uploadlists/' params = { - Network accessline 79
url = 'https://www.uniprot.org/uploadlists/' - Network accessline 85
} data = urllib.parse.urlencode(params).encode('ascii') with urllib.request.urlopen(urllib.request.Request(url, data)) as response: - Network accessline 86
data = urllib.parse.urlencode(params).encode('ascii') with urllib.request.urlopen(urllib.request.Request(url, data)) as response: return response.read().decode('utf-8').splitlines() - Network accessline 87
with urllib.request.urlopen(urllib.request.Request(url, data)) as response: - Network accessline 112
# Model coordinates (mmCIF) model_url = f"https://alphafold.ebi.ac.uk/files/{alphafold_id}-model_{version}.cif" response = requests.get(model_url) - Network accessline 113
model_url = f"https://alphafold.ebi.ac.uk/files/{alphafold_id}-model_{version}.cif" response = requests.get(model_url) with open(f"{alphafold_id}.cif", "w") as f: - Network accessline 114
response = requests.get(model_url) - Network accessline 118
# Confidence scores (JSON) confidence_url = f"https://alphafold.ebi.ac.uk/files/{alphafold_id}-confidence_{version}.json" response = requests.get(confidence_url) - Network accessline 119
confidence_url = f"https://alphafold.ebi.ac.uk/files/{alphafold_id}-confidence_{version}.json" response = requests.get(confidence_url) confidence_data = response.json() - Network accessline 120
response = requests.get(confidence_url) - Network accessline 123
# Predicted Aligned Error (JSON) pae_url = f"https://alphafold.ebi.ac.uk/files/{alphafold_id}-predicted_aligned_error_{version}.json" response = requests.get(pae_url) - Network accessline 124
pae_url = f"https://alphafold.ebi.ac.uk/files/{alphafold_id}-predicted_aligned_error_{version}.json" response = requests.get(pae_url) pae_data = response.json() - Network accessline 125
response = requests.get(pae_url) - Network accessline 132
# Download as PDB format instead of mmCIF pdb_url = f"https://alphafold.ebi.ac.uk/files/{alphafold_id}-model_{version}.pdb" - Network accessline 133
pdb_url = f"https://alphafold.ebi.ac.uk/files/{alphafold_id}-model_{version}.pdb" response = requests.get(pdb_url) with open(f"{alphafold_id}.pdb", "wb") as f: - Network accessline 134
response = requests.get(pdb_url) - Network accessline 149
# Load confidence scores alphafold_id = "AF-P00520-F1" confidence_url = f"https://alphafold.ebi.ac.uk/files/{alphafold_id}-confidence_v4.json" - Network accessline 150
alphafold_id = "AF-P00520-F1" confidence_url = f"https://alphafold.ebi.ac.uk/files/{alphafold_id}-confidence_v4.json" confidence = requests.get(confidence_url).json() - Network accessline 151
confidence_url = f"https://alphafold.ebi.ac.uk/files/{alphafold_id}-confidence_v4.json" - Network accessline 152
confidence = requests.get(confidence_url).json() - Network accessline 175
# Load PAE matrix pae_url = f"https://alphafold.ebi.ac.uk/files/{alphafold_id}-predicted_aligned_error_v4.json" pae = requests.get(pae_url).json() - Network accessline 176
pae_url = f"https://alphafold.ebi.ac.uk/files/{alphafold_id}-predicted_aligned_error_v4.json" - Network accessline 177
pae = requests.get(pae_url).json() - Network accessline 241
> ⚠<U+FE0F> **Security Note**: The example below uses `shell=True` for simplicity. In production environments, prefer using `subprocess.run()` with a list of arguments to prevent command injection vulnerabilities. See [Python subprocess security]( - Network accessline 341
# Get confidence data alphafold_id = pred['entryId'] conf_url = f"https://alphafold.ebi.ac.uk/files/{alphafold_id}-confidence_v4.json" - Network accessline 342
alphafold_id = pred['entryId'] conf_url = f"https://alphafold.ebi.ac.uk/files/{alphafold_id}-confidence_v4.json" conf_data = requests.get(conf_url).json() - Network accessline 343
conf_url = f"https://alphafold.ebi.ac.uk/files/{alphafold_id}-confidence_v4.json" - Network accessline 344
conf_data = requests.get(conf_url).json() - Network accessline 391
# Query via 3D-Beacons uniprot_id = "P00520" url = f"https://www.ebi.ac.uk/pdbe/pdbe-kb/3dbeacons/api/uniprot/summary/{uniprot_id}.json" - Network accessline 392
uniprot_id = "P00520" url = f"https://www.ebi.ac.uk/pdbe/pdbe-kb/3dbeacons/api/uniprot/summary/{uniprot_id}.json" response = requests.get(url) - Network accessline 393
url = f"https://www.ebi.ac.uk/pdbe/pdbe-kb/3dbeacons/api/uniprot/summary/{uniprot_id}.json" response = requests.get(url) data = response.json() - Network accessline 394
response = requests.get(url) - Network accessline 502
- **AlphaFold DB Website:** https://alphafold.ebi.ac.uk/ - Network accessline 503
- **API Documentation:** https://alphafold.ebi.ac.uk/api-docs - Network accessline 504
- **Google Cloud Dataset:** https://cloud.google.com/blog/products/ai-machine-learning/alphafold-protein-structure-database - Network accessline 505
- **3D-Beacons API:** https://www.ebi.ac.uk/pdbe/pdbe-kb/3dbeacons/ - Network accessline 506
- **AlphaFold Papers:** - Nature (2021): https://doi.org/10.1038/s41586-021-03819-2 - Nucleic Acids Research (2024): https://doi.org/10.1093/nar/gkad1011 - Network accessline 507
- Nature (2021): https://doi.org/10.1038/s41586-021-03819-2 - Network accessline 508
- Nucleic Acids Research (2024): https://doi.org/10.1093/nar/gkad1011 - Network accessline 509
- **Biopython Documentation:** https://biopython.org/docs/dev/api/Bio.PDB.alphafold_db.html - Network accessline 510
- **GitHub Repository:** https://github.com/google-deepmind/alphafold
skills/bio/anndata/SKILL.md
- Network accessline 394
- **Official documentation**: https://anndata.readthedocs.io/ - Network accessline 395
- **Scanpy tutorials**: https://scanpy.readthedocs.io/ - Network accessline 396
- **Scverse ecosystem**: https://scverse.org/ - Network accessline 397
- **GitHub repository**: https://github.com/scverse/anndata
skills/bio/benchling-integration/SKILL.md
- Network accessline 47
benchling = Benchling( url="https://your-tenant.benchling.com", - Network accessline 48
url="https://your-tenant.benchling.com", - Access to secretsline 49
[Credential-related source omitted] - Access to secretsline 59
[Credential-related source omitted] - Network accessline 60
) benchling = Benchling( url="https://your-tenant.benchling.com", - Network accessline 61
benchling = Benchling( url="https://your-tenant.benchling.com", auth_method=auth_method - Network accessline 62
url="https://your-tenant.benchling.com", - Network accessline 335
benchling = Benchling( url="https://your-tenant.benchling.com", - Network accessline 336
url="https://your-tenant.benchling.com", - Access to secretsline 337
[Credential-related source omitted] - Network accessline 474
- **Official Documentation:** https://docs.benchling.com - Network accessline 475
- **Python SDK Reference:** https://benchling.com/sdk-docs/ - Network accessline 476
- **API Reference:** https://benchling.com/api/reference
skills/bio/biomni/SKILL.md
- Access to secretsline 46
[Credential-related source omitted] - Network accessline 271
### External Resources - **GitHub**: https://github.com/snap-stanford/biomni - Network accessline 272
- **GitHub**: https://github.com/snap-stanford/biomni - Network accessline 273
- **Web Platform**: https://biomni.stanford.edu - Network accessline 274
- **Paper**: https://www.biorxiv.org/content/10.1101/2025.05.30.656746v1 - Network accessline 275
- **Model**: https://huggingface.co/biomni/Biomni-R0-32B-Preview - Network accessline 276
- **Evaluation Dataset**: https://huggingface.co/datasets/biomni/Eval1 - Access to secretsline 292
[Credential-related source omitted] - Access to secretsline 293
[Credential-related source omitted] - Network accessline 309
For issues or questions: - GitHub Issues: https://github.com/snap-stanford/biomni/issues - Network accessline 310
- GitHub Issues: https://github.com/snap-stanford/biomni/issues
skills/bio/biopython/SKILL.md
- Access to secretsline 59
[Credential-related source omitted] - Network accessline 409
- **Official Documentation**: https://biopython.org/docs/latest/ - Network accessline 410
- **Tutorial**: https://biopython.org/docs/latest/Tutorial/ - Network accessline 411
- **Cookbook**: https://biopython.org/docs/latest/Tutorial/ (advanced examples) - Network accessline 412
- **GitHub**: https://github.com/biopython/biopython
skills/bio/brenda-database/SKILL.md
- Access to secretsline 305
[Credential-related source omitted] - Network accessline 317
3. **Register for BRENDA access**: - Visit https://www.brenda-enzymes.org/ - Network accessline 318
- Visit https://www.brenda-enzymes.org/ - Access to secretsline 677
[Credential-related source omitted] - Network accessline 713
- BRENDA Home: https://www.brenda-enzymes.org/ - Network accessline 714
- BRENDA SOAP API Documentation: https://www.brenda-enzymes.org/soap.php - Network accessline 715
- Enzyme Commission (EC) Numbers: https://www.qmul.ac.uk/sbcs/iubmb/enzyme/ - Network accessline 716
- Zeep SOAP Client: https://python-zeep.readthedocs.io/ - Network accessline 717
- Enzyme Nomenclature: https://www.iubmb.org/enzyme/
skills/bio/clinpgx-database/SKILL.md
- Network accessline 43
BASE_URL = "https://api.clinpgx.org/v1/" - Network accessline 65
# Get gene details response = requests.get("https://api.clinpgx.org/v1/gene/CYP2D6") - Network accessline 66
response = requests.get("https://api.clinpgx.org/v1/gene/CYP2D6") - Network accessline 69
# Search for genes by name response = requests.get("https://api.clinpgx.org/v1/gene", - Network accessline 70
response = requests.get("https://api.clinpgx.org/v1/gene", - Network accessline 87
# Get drug details response = requests.get("https://api.clinpgx.org/v1/chemical/PA448515") # Warfarin - Network accessline 88
response = requests.get("https://api.clinpgx.org/v1/chemical/PA448515") # Warfarin - Network accessline 91
# Search drugs by name response = requests.get("https://api.clinpgx.org/v1/chemical", - Network accessline 92
response = requests.get("https://api.clinpgx.org/v1/chemical", - Network accessline 111
# Get gene-drug pair information response = requests.get("https://api.clinpgx.org/v1/geneDrugPair", - Network accessline 112
response = requests.get("https://api.clinpgx.org/v1/geneDrugPair", - Network accessline 116
# Get all pairs for a gene response = requests.get("https://api.clinpgx.org/v1/geneDrugPair", - Network accessline 117
response = requests.get("https://api.clinpgx.org/v1/geneDrugPair", - Network accessline 133
# Get CPIC guideline response = requests.get("https://api.clinpgx.org/v1/guideline/PA166104939") - Network accessline 134
response = requests.get("https://api.clinpgx.org/v1/guideline/PA166104939") - Network accessline 137
# List all CPIC guidelines response = requests.get("https://api.clinpgx.org/v1/guideline", - Network accessline 138
response = requests.get("https://api.clinpgx.org/v1/guideline", - Network accessline 163
# Get allele information response = requests.get("https://api.clinpgx.org/v1/allele/CYP2D6*4") - Network accessline 164
response = requests.get("https://api.clinpgx.org/v1/allele/CYP2D6*4") - Network accessline 167
# Get all alleles for a gene response = requests.get("https://api.clinpgx.org/v1/allele", - Network accessline 168
response = requests.get("https://api.clinpgx.org/v1/allele", - Network accessline 191
# Get variant information response = requests.get("https://api.clinpgx.org/v1/variant/rs4244285") - Network accessline 192
response = requests.get("https://api.clinpgx.org/v1/variant/rs4244285") - Network accessline 195
# Search variants by position (if supported) response = requests.get("https://api.clinpgx.org/v1/variant", - Network accessline 196
response = requests.get("https://api.clinpgx.org/v1/variant", - Network accessline 214
# Get clinical annotations response = requests.get("https://api.clinpgx.org/v1/clinicalAnnotation", - Network accessline 215
response = requests.get("https://api.clinpgx.org/v1/clinicalAnnotation", - Network accessline 219
# Filter by evidence level response = requests.get("https://api.clinpgx.org/v1/clinicalAnnotation", - Network accessline 220
response = requests.get("https://api.clinpgx.org/v1/clinicalAnnotation", - Network accessline 238
# Get drug labels with PGx information response = requests.get("https://api.clinpgx.org/v1/drugLabel", - Network accessline 239
response = requests.get("https://api.clinpgx.org/v1/drugLabel", - Network accessline 243
# Filter by regulatory source response = requests.get("https://api.clinpgx.org/v1/drugLabel", - Network accessline 244
response = requests.get("https://api.clinpgx.org/v1/drugLabel", - Network accessline 261
# Get pathway information response = requests.get("https://api.clinpgx.org/v1/pathway/PA146123006") # Warfarin pathway - Network accessline 262
response = requests.get("https://api.clinpgx.org/v1/pathway/PA146123006") # Warfarin pathway - Network accessline 265
# Search pathways by drug response = requests.get("https://api.clinpgx.org/v1/pathway", - Network accessline 266
response = requests.get("https://api.clinpgx.org/v1/pathway", - Network accessline 284
# Example: Patient is CYP2C19 *1/*2 (intermediate metabolizer) response = requests.get("https://api.clinpgx.org/v1/allele/CYP2C19*2") - Network accessline 285
response = requests.get("https://api.clinpgx.org/v1/allele/CYP2C19*2") - Network accessline 291
response = requests.get("https://api.clinpgx.org/v1/geneDrugPair", - Network accessline 298
response = requests.get("https://api.clinpgx.org/v1/guideline", - Network accessline 306
response = requests.get("https://api.clinpgx.org/v1/drugLabel", - Network accessline 320
all_interactions = {} for gene in pgx_panel: response = requests.get("https://api.clinpgx.org/v1/geneDrugPair", - Network accessline 321
for gene in pgx_panel: response = requests.get("https://api.clinpgx.org/v1/geneDrugPair", params={"gene": gene}) - Network accessline 322
response = requests.get("https://api.clinpgx.org/v1/geneDrugPair", - Network accessline 341
response = requests.get("https://api.clinpgx.org/v1/chemical", - Network accessline 348
response = requests.get("https://api.clinpgx.org/v1/clinicalAnnotation", - Network accessline 367
response = requests.get("https://api.clinpgx.org/v1/allele", - Network accessline 396
response = requests.get("https://api.clinpgx.org/v1/geneDrugPair", - Network accessline 403
response = requests.get("https://api.clinpgx.org/v1/clinicalAnnotation", - Network accessline 427
def rate_limited_request(url, params=None, delay=0.5): """Make API request with rate limiting (2 req/sec max)""" response = requests.get(url, params=params) - Network accessline 428
"""Make API request with rate limiting (2 req/sec max)""" response = requests.get(url, params=params) time.sleep(delay) # Wait 0.5 seconds between requests - Network accessline 429
response = requests.get(url, params=params) - Network accessline 435
for gene in genes: response = rate_limited_request( "https://api.clinpgx.org/v1/gene/" + gene - Network accessline 436
response = rate_limited_request( "https://api.clinpgx.org/v1/gene/" + gene ) - Network accessline 437
"https://api.clinpgx.org/v1/gene/" + gene - Network accessline 447
for attempt in range(max_retries): try: response = requests.get(url, params=params, timeout=10) - Network accessline 449
response = requests.get(url, params=params, timeout=10) - Network accessline 491
'cyp2d6_cache.json', rate_limited_request, "https://api.clinpgx.org/v1/gene/CYP2D6" - Network accessline 492
rate_limited_request, "https://api.clinpgx.org/v1/gene/CYP2D6" ) - Network accessline 493
"https://api.clinpgx.org/v1/gene/CYP2D6" - Network accessline 508
**Access**: Available at https://www.clinpgx.org/pharmacogenomic-decision-support - Network accessline 573
- ClinPGx continuously updates with new evidence and guidelines - Check publication dates for clinical annotations - Monitor ClinPGx Blog (https://blog.clinpgx.org/) for announcements - Network accessline 574
- Check publication dates for clinical annotations - Monitor ClinPGx Blog (https://blog.clinpgx.org/) for announcements - CPIC guidelines updated as new evidence emerges - Network accessline 575
- Monitor ClinPGx Blog (https://blog.clinpgx.org/) for announcements - Network accessline 594
# Get all CPIC guideline pairs response = requests.get("https://api.clinpgx.org/v1/geneDrugPair", - Network accessline 595
response = requests.get("https://api.clinpgx.org/v1/geneDrugPair", - Network accessline 608
for med in medications: for gene in patient_genes: response = requests.get("https://api.clinpgx.org/v1/geneDrugPair", - Network accessline 609
for gene in patient_genes: response = requests.get("https://api.clinpgx.org/v1/geneDrugPair", params={"gene": gene, "drug": med}) - Network accessline 610
response = requests.get("https://api.clinpgx.org/v1/geneDrugPair", - Network accessline 620
# Check for HLA-B*57:01 before abacavir trial response = requests.get("https://api.clinpgx.org/v1/geneDrugPair", - Network accessline 621
response = requests.get("https://api.clinpgx.org/v1/geneDrugPair", - Network accessline 629
- **ClinPGx website**: https://www.clinpgx.org/ - Network accessline 630
- **ClinPGx Blog**: https://blog.clinpgx.org/ - Network accessline 631
- **API documentation**: https://api.clinpgx.org/ - Network accessline 632
- **CPIC website**: https://cpicpgx.org/ - Network accessline 633
- **PharmCAT**: https://pharmcat.clinpgx.org/ - Network accessline 634
- **ClinGen**: https://clinicalgenome.org/
skills/bio/cobrapy/SKILL.md
- Network accessline 460
Official documentation: https://cobrapy.readthedocs.io/en/latest/
skills/bio/cosmic-database/SKILL.md
- Network accessline 29
### Account Registration COSMIC requires authentication for data downloads: - **Academic users**: Free access with registration at https://cancer.sanger.ac.uk/cosmic/register - Network accessline 30
COSMIC requires authentication for data downloads: - **Academic users**: Free access with registration at https://cancer.sanger.ac.uk/cosmic/register - **Commercial users**: License required (contact QIAGEN) - Network accessline 31
- **Academic users**: Free access with registration at https://cancer.sanger.ac.uk/cosmic/register - Network accessline 239
vcf = pysam.VariantFile('CosmicCodingMuts.vcf.gz') for record in vcf.fetch('17', 7577000, 7579000): # TP53 region - Network accessline 240
for record in vcf.fetch('17', 7577000, 7579000): # TP53 region - Network accessline 325
- **COSMIC Website**: https://cancer.sanger.ac.uk/cosmic - Network accessline 326
- **Documentation**: https://cancer.sanger.ac.uk/cosmic/help - Network accessline 327
- **Release Notes**: https://cancer.sanger.ac.uk/cosmic/release_notes
skills/bio/dnanexus-integration/SKILL.md
skills/bio/ena-database/SKILL.md
- Network accessline 56
**ENA Portal API** - Advanced search functionality across all ENA data types - Documentation: https://www.ebi.ac.uk/ena/portal/api/doc - Network accessline 57
- Documentation: https://www.ebi.ac.uk/ena/portal/api/doc - Network accessline 60
**ENA Browser API** - Direct retrieval of records and metadata - Documentation: https://www.ebi.ac.uk/ena/browser/api/doc - Network accessline 61
- Documentation: https://www.ebi.ac.uk/ena/browser/api/doc - Network accessline 68
**ENA Cross Reference Service** - Access related records from external databases - Endpoint: https://www.ebi.ac.uk/ena/xref/rest/ - Network accessline 69
- Endpoint: https://www.ebi.ac.uk/ena/xref/rest/ - Network accessline 71
**CRAM Reference Registry** - Retrieve reference sequences - Endpoint: https://www.ebi.ac.uk/ena/cram/ - Network accessline 72
- Endpoint: https://www.ebi.ac.uk/ena/cram/ - Network accessline 94
# Search for samples from a specific study base_url = "https://www.ebi.ac.uk/ena/portal/api/search" - Network accessline 95
base_url = "https://www.ebi.ac.uk/ena/portal/api/search" - Network accessline 103
response = requests.get(base_url, params=params) - Network accessline 130
# Download run files using Browser API accession = "ERR123456" url = f"https://www.ebi.ac.uk/ena/browser/api/xml/{accession}" - Network accessline 132
url = f"https://www.ebi.ac.uk/ena/browser/api/xml/{accession}" - Network accessline 137
# Use Portal API to list samples study_id = "PRJNA123456" url = f"https://www.ebi.ac.uk/ena/portal/api/search?result=sample&query=study_accession={study_id}&format=tsv" - Network accessline 139
url = f"https://www.ebi.ac.uk/ena/portal/api/search?result=sample&query=study_accession={study_id}&format=tsv" - Network accessline 144
# Search assemblies by taxonomy organism = "Escherichia coli" url = f"https://www.ebi.ac.uk/ena/portal/api/search?result=assembly&query=tax_tree({organism})&format=json" - Network accessline 146
url = f"https://www.ebi.ac.uk/ena/portal/api/search?result=assembly&query=tax_tree({organism})&format=json" - Network accessline 151
# Query taxonomy API taxon_id = "562" # E. coli url = f"https://www.ebi.ac.uk/ena/taxonomy/rest/tax-id/{taxon_id}" - Network accessline 153
url = f"https://www.ebi.ac.uk/ena/taxonomy/rest/tax-id/{taxon_id}"
skills/bio/ensembl-database/SKILL.md
- Network accessline 63
server = "https://rest.ensembl.org" - Network accessline 65
# Symbol lookup response = requests.get( - Network accessline 66
response = requests.get( - Network accessline 172
**Important:** Use `https://grch37.rest.ensembl.org` for GRCh37/hg19 queries and `https://rest.ensembl.org` for current assemblies. - Network accessline 207
def query_ensembl(endpoint, params=None, max_retries=3): server = "https://rest.ensembl.org" - Network accessline 208
server = "https://rest.ensembl.org" - Network accessline 211
for attempt in range(max_retries): response = requests.get( - Network accessline 212
response = requests.get( - Network accessline 304
- **Official Documentation:** https://rest.ensembl.org/documentation - Network accessline 305
- **Python Package Docs:** https://ensemblrest.readthedocs.io - Network accessline 306
- **EBI Training:** https://www.ebi.ac.uk/training/online/courses/ensembl-rest-api/ - Network accessline 307
- **Ensembl Browser:** https://useast.ensembl.org - Network accessline 308
- **GitHub Examples:** https://github.com/Ensembl/ensembl-rest/wiki
skills/bio/esm/SKILL.md
- Network accessline 50
[Credential-related source omitted] - Network accessline 51
[Credential-related source omitted] - Network accessline 248
No additional dependencies needed. Obtain Forge API token at https://forge.evolutionaryscale.ai - Network accessline 292
- **GitHub Repository:** https://github.com/evolutionaryscale/esm - Network accessline 293
- **Forge Platform:** https://forge.evolutionaryscale.ai - Network accessline 294
- **Scientific Paper:** Hayes et al., Science (2025) - https://www.science.org/doi/10.1126/science.ads0018 - Network accessline 295
- **Blog Posts:** - ESM3 Release: https://www.evolutionaryscale.ai/blog/esm3-release - ESM C Launch: https://www.evolutionaryscale.ai/blog/esm-cambrian - Network accessline 296
- ESM3 Release: https://www.evolutionaryscale.ai/blog/esm3-release - Network accessline 297
- ESM C Launch: https://www.evolutionaryscale.ai/blog/esm-cambrian - Network accessline 298
- **Community:** Slack community at https://bit.ly/3FKwcWd - Network accessline 303
ESM is designed for beneficial applications in protein engineering, drug discovery, and scientific research. Follow the Responsible Biodesign Framework (https://responsiblebiodesign.ai/) when designing novel proteins. Consider biosafety and
skills/bio/etetoolkit/SKILL.md
skills/bio/gene-database/SKILL.md
skills/bio/geniml/SKILL.md
- Network accessline 32
uv uv pip install git+https://github.com/databio/geniml.git - Network accessline 289
- **Documentation**: https://docs.bedbase.org/geniml/ - Network accessline 290
- **GitHub**: https://github.com/databio/geniml - Network accessline 311
**"StarSpace not found" (BEDspace):** - Install StarSpace separately: https://github.com/facebookresearch/StarSpace - Network accessline 312
- Install StarSpace separately: https://github.com/facebookresearch/StarSpace
skills/bio/geo-database/SKILL.md
- Network accessline 398
**Using wget or curl for Downloads:** - Network accessline 401
# Download series matrix file wget ftp://ftp.ncbi.nlm.nih.gov/geo/series/GSE123nnn/GSE123456/matrix/GSE123456_series_matrix.txt.gz - Network accessline 402
wget ftp://ftp.ncbi.nlm.nih.gov/geo/series/GSE123nnn/GSE123456/matrix/GSE123456_series_matrix.txt.gz - Network accessline 404
# Download all supplementary files for a series wget -r -np -nd ftp://ftp.ncbi.nlm.nih.gov/geo/series/GSE123nnn/GSE123456/suppl/ - Network accessline 405
wget -r -np -nd ftp://ftp.ncbi.nlm.nih.gov/geo/series/GSE123nnn/GSE123456/suppl/ - Network accessline 407
# Download SOFT format family file wget ftp://ftp.ncbi.nlm.nih.gov/geo/series/GSE123nnn/GSE123456/soft/GSE123456_family.soft.gz - Network accessline 408
wget ftp://ftp.ncbi.nlm.nih.gov/geo/series/GSE123nnn/GSE123456/soft/GSE123456_family.soft.gz - Network accessline 673
# Optional: Set API key for increased rate limits # Get your API key from: https://www.ncbi.nlm.nih.gov/account/ - Network accessline 674
# Get your API key from: https://www.ncbi.nlm.nih.gov/account/ - Access to secretsline 675
[Credential-related source omitted] - Network accessline 725
- Web-based statistical analysis tool integrated into GEO - Accessible at: https://www.ncbi.nlm.nih.gov/geo/geo2r/?acc=GSExxxxx - Network accessline 726
- Accessible at: https://www.ncbi.nlm.nih.gov/geo/geo2r/?acc=GSExxxxx - Network accessline 739
- No rate limits for FTP downloads - Preferred method for bulk downloads - Can download entire directories with wget -r - Network accessline 741
- Can download entire directories with wget -r - Network accessline 805
- **GEO Website:** https://www.ncbi.nlm.nih.gov/geo/ - Network accessline 806
- **GEO Submission Guidelines:** https://www.ncbi.nlm.nih.gov/geo/info/submission.html - Network accessline 807
- **GEOparse Documentation:** https://geoparse.readthedocs.io/ - Network accessline 808
- **E-utilities Documentation:** https://www.ncbi.nlm.nih.gov/books/NBK25501/ - Network accessline 809
- **GEO FTP Site:** ftp://ftp.ncbi.nlm.nih.gov/geo/ - **GEO2R Tool:** https://www.ncbi.nlm.nih.gov/geo/geo2r/ - **NCBI API Keys:** https://ncbiinsights.ncbi.nlm.nih.gov/2017/11/02/new-api-keys-for-the-e-utilities/ - Network accessline 810
- **GEO2R Tool:** https://www.ncbi.nlm.nih.gov/geo/geo2r/ - Network accessline 811
- **NCBI API Keys:** https://ncbiinsights.ncbi.nlm.nih.gov/2017/11/02/new-api-keys-for-the-e-utilities/ - Network accessline 812
- **Biopython Tutorial:** https://biopython.org/DIST/docs/tutorial/Tutorial.html
skills/bio/gget/SKILL.md
- Access to secretsline 652
[Credential-related source omitted] - Access to secretsline 658
[Credential-related source omitted] - Access to secretsline 663
[Credential-related source omitted] - Network accessline 865
For additional help: - Official documentation: https://pachterlab.github.io/gget/ - Network accessline 866
- Official documentation: https://pachterlab.github.io/gget/ - Network accessline 867
- GitHub issues: https://github.com/pachterlab/gget/issues - Network accessline 868
- Citation: Luebbert, L. & Pachter, L. (2023). Efficient querying of genomic reference databases with gget. Bioinformatics. https://doi.org/10.1093/bioinformatics/btac836
skills/bio/gwas-database/SKILL.md
- Network accessline 49
The web interface at https://www.ebi.ac.uk/gwas/ supports multiple search modes: - Network accessline 90
**Base URLs:** - GWAS Catalog API: `https://www.ebi.ac.uk/gwas/rest/api` - Network accessline 91
- GWAS Catalog API: `https://www.ebi.ac.uk/gwas/rest/api` - Network accessline 92
- Summary Statistics API: `https://www.ebi.ac.uk/gwas/summary-statistics/api` - Network accessline 94
**API Documentation:** - Main API docs: https://www.ebi.ac.uk/gwas/rest/docs/api - Network accessline 95
- Main API docs: https://www.ebi.ac.uk/gwas/rest/docs/api - Network accessline 96
- Summary stats docs: https://www.ebi.ac.uk/gwas/summary-statistics/docs/ - Network accessline 104
# Get a specific study url = "https://www.ebi.ac.uk/gwas/rest/api/studies/GCST001795" response = requests.get(url, headers={"Content-Type": "application/json"}) - Network accessline 105
url = "https://www.ebi.ac.uk/gwas/rest/api/studies/GCST001795" response = requests.get(url, headers={"Content-Type": "application/json"}) study = response.json() - Network accessline 106
response = requests.get(url, headers={"Content-Type": "application/json"}) - Network accessline 112
# Find associations for a variant variant = "rs7903146" url = f"https://www.ebi.ac.uk/gwas/rest/api/singleNucleotidePolymorphisms/{variant}/associations" - Network accessline 113
variant = "rs7903146" url = f"https://www.ebi.ac.uk/gwas/rest/api/singleNucleotidePolymorphisms/{variant}/associations" params = {"projection": "associationBySnp"} - Network accessline 114
url = f"https://www.ebi.ac.uk/gwas/rest/api/singleNucleotidePolymorphisms/{variant}/associations" params = {"projection": "associationBySnp"} response = requests.get(url, params=params, headers={"Content-Type": "application/json"}) - Network accessline 115
params = {"projection": "associationBySnp"} response = requests.get(url, params=params, headers={"Content-Type": "application/json"}) associations = response.json() - Network accessline 116
response = requests.get(url, params=params, headers={"Content-Type": "application/json"}) - Network accessline 122
# Get variant details url = "https://www.ebi.ac.uk/gwas/rest/api/singleNucleotidePolymorphisms/rs7903146" response = requests.get(url, headers={"Content-Type": "application/json"}) - Network accessline 123
url = "https://www.ebi.ac.uk/gwas/rest/api/singleNucleotidePolymorphisms/rs7903146" response = requests.get(url, headers={"Content-Type": "application/json"}) variant_info = response.json() - Network accessline 124
response = requests.get(url, headers={"Content-Type": "application/json"}) - Network accessline 130
# Get trait information url = "https://www.ebi.ac.uk/gwas/rest/api/efoTraits/EFO_0001360" response = requests.get(url, headers={"Content-Type": "application/json"}) - Network accessline 131
url = "https://www.ebi.ac.uk/gwas/rest/api/efoTraits/EFO_0001360" response = requests.get(url, headers={"Content-Type": "application/json"}) trait_info = response.json() - Network accessline 132
response = requests.get(url, headers={"Content-Type": "application/json"}) - Network accessline 142
trait = "EFO_0001360" # Type 2 diabetes base_url = "https://www.ebi.ac.uk/gwas/rest/api" - Network accessline 143
base_url = "https://www.ebi.ac.uk/gwas/rest/api" - Network accessline 145
# Query associations for this trait url = f"{base_url}/efoTraits/{trait}/associations" response = requests.get(url, headers={"Content-Type": "application/json"}) - Network accessline 146
url = f"{base_url}/efoTraits/{trait}/associations" response = requests.get(url, headers={"Content-Type": "application/json"}) associations = response.json() - Network accessline 147
response = requests.get(url, headers={"Content-Type": "application/json"}) - Network accessline 162
variant = "rs7903146" base_url = "https://www.ebi.ac.uk/gwas/rest/api" - Network accessline 163
base_url = "https://www.ebi.ac.uk/gwas/rest/api" - Network accessline 165
# Get variant details url = f"{base_url}/singleNucleotidePolymorphisms/{variant}" response = requests.get(url, headers={"Content-Type": "application/json"}) - Network accessline 166
url = f"{base_url}/singleNucleotidePolymorphisms/{variant}" response = requests.get(url, headers={"Content-Type": "application/json"}) variant_data = response.json() - Network accessline 167
response = requests.get(url, headers={"Content-Type": "application/json"}) - Network accessline 171
url = f"{base_url}/singleNucleotidePolymorphisms/{variant}/associations" params = {"projection": "associationBySnp"} response = requests.get(url, params=params, headers={"Content-Type": "application/json"}) - Network accessline 172
params = {"projection": "associationBySnp"} response = requests.get(url, params=params, headers={"Content-Type": "application/json"}) associations = response.json() - Network accessline 173
response = requests.get(url, params=params, headers={"Content-Type": "application/json"}) - Network accessline 187
# Query summary statistics API base_url = "https://www.ebi.ac.uk/gwas/summary-statistics/api" - Network accessline 188
base_url = "https://www.ebi.ac.uk/gwas/summary-statistics/api" - Network accessline 196
"size": 100 # Number of results } response = requests.get(url, params=params) - Network accessline 197
} response = requests.get(url, params=params) results = response.json() - Network accessline 198
response = requests.get(url, params=params) - Network accessline 219
base_url = "https://www.ebi.ac.uk/gwas/rest/api" - Network accessline 224
"bpEnd": end_pos } response = requests.get(url, params=params, headers={"Content-Type": "application/json"}) - Network accessline 225
} response = requests.get(url, params=params, headers={"Content-Type": "application/json"}) variants_in_region = response.json() - Network accessline 226
response = requests.get(url, params=params, headers={"Content-Type": "application/json"}) - Network accessline 234
**Access Methods:** 1. **FTP download**: http://ftp.ebi.ac.uk/pub/databases/gwas/summary_statistics/ - Network accessline 235
1. **FTP download**: http://ftp.ebi.ac.uk/pub/databases/gwas/summary_statistics/ - Network accessline 250
# Get available summary statistics base_url = "https://www.ebi.ac.uk/gwas/summary-statistics/api" - Network accessline 251
base_url = "https://www.ebi.ac.uk/gwas/summary-statistics/api" url = f"{base_url}/studies/GCST001234" response = requests.get(url) - Network accessline 252
url = f"{base_url}/studies/GCST001234" response = requests.get(url) study_info = response.json() - Network accessline 253
response = requests.get(url) - Network accessline 284
# API responses include _links for related resources response = requests.get("https://www.ebi.ac.uk/gwas/rest/api/studies/GCST001234") - Network accessline 285
response = requests.get("https://www.ebi.ac.uk/gwas/rest/api/studies/GCST001234") - Network accessline 288
# Follow link to associations associations_url = study['_links']['associations']['href'] associations_response = requests.get(associations_url) - Network accessline 290
associations_response = requests.get(associations_url) - Network accessline 303
url = f"https://www.ebi.ac.uk/gwas/rest/api/efoTraits/{efo_id}/associations" - Network accessline 326
url = f"https://www.ebi.ac.uk/gwas/rest/api/singleNucleotidePolymorphisms/{rs_id}" - Network accessline 331
url = f"https://www.ebi.ac.uk/gwas/rest/api/singleNucleotidePolymorphisms/{rs_id}/associations" - Network accessline 348
url = f"https://www.ebi.ac.uk/gwas/rest/api/singleNucleotidePolymorphisms/search/findByGene" - Network accessline 406
# Via FTP wget ftp://ftp.ebi.ac.uk/pub/databases/gwas/summary_statistics/GCSTXXXXXX/harmonised/GCSTXXXXXX-harmonised.tsv.gz - Network accessline 407
wget ftp://ftp.ebi.ac.uk/pub/databases/gwas/summary_statistics/GCSTXXXXXX/harmonised/GCSTXXXXXX-harmonised.tsv.gz - Network accessline 412
url = f"https://www.ebi.ac.uk/gwas/summary-statistics/api/chromosomes/{chrom}/associations" - Network accessline 497
pandas DataFrame with association results """ base_url = "https://www.ebi.ac.uk/gwas/rest/api" - Network accessline 498
""" base_url = "https://www.ebi.ac.uk/gwas/rest/api" url = f"{base_url}/efoTraits/{trait_id}/associations" - Network accessline 499
base_url = "https://www.ebi.ac.uk/gwas/rest/api" - Network accessline 506
while True: params = {"page": page, "size": 100} response = requests.get(url, params=params, headers=headers) - Network accessline 508
response = requests.get(url, params=params, headers=headers) - Network accessline 564
The GWAS Catalog team provides workshop materials: - GitHub repository: https://github.com/EBISPOT/GWAS_Catalog-workshop - Network accessline 565
- GitHub repository: https://github.com/EBISPOT/GWAS_Catalog-workshop - Network accessline 598
- **GWAS Catalog website**: https://www.ebi.ac.uk/gwas/ - Network accessline 599
- **Documentation**: https://www.ebi.ac.uk/gwas/docs - Network accessline 600
- **API documentation**: https://www.ebi.ac.uk/gwas/rest/docs/api - Network accessline 601
- **Summary Statistics API**: https://www.ebi.ac.uk/gwas/summary-statistics/docs/ - Network accessline 602
- **FTP site**: http://ftp.ebi.ac.uk/pub/databases/gwas/ - Network accessline 603
- **Training materials**: https://github.com/EBISPOT/GWAS_Catalog-workshop - Network accessline 604
- **PGS Catalog** (polygenic scores): https://www.pgscatalog.org/
skills/bio/hmdb-database/SKILL.md
skills/bio/hypogenic/SKILL.md
- Network accessline 23
# Clone example datasets git clone https://github.com/ChicagoHAI/HypoGeniC-datasets.git ./data - Network accessline 24
git clone https://github.com/ChicagoHAI/HypoGeniC-datasets.git ./data - Network accessline 134
# For HypoGeniC examples git clone https://github.com/ChicagoHAI/HypoGeniC-datasets.git ./data - Network accessline 135
git clone https://github.com/ChicagoHAI/HypoGeniC-datasets.git ./data - Network accessline 137
# For HypoRefine/Union examples git clone https://github.com/ChicagoHAI/Hypothesis-agent-datasets.git ./data - Network accessline 138
git clone https://github.com/ChicagoHAI/Hypothesis-agent-datasets.git ./data - Network accessline 289
# Clone example datasets first # git clone https://github.com/ChicagoHAI/HypoGeniC-datasets.git ./data - Network accessline 290
# git clone https://github.com/ChicagoHAI/HypoGeniC-datasets.git ./data - Network accessline 315
# For literature-integrated approaches # git clone https://github.com/ChicagoHAI/Hypothesis-agent-datasets.git ./data - Network accessline 316
# git clone https://github.com/ChicagoHAI/Hypothesis-agent-datasets.git ./data - Network accessline 544
- **Paper:** https://arxiv.org/abs/2504.11524 - Network accessline 554
archivePrefix={arXiv}, primaryClass={cs.AI}, url={https://arxiv.org/abs/2504.11524}, - Network accessline 555
primaryClass={cs.AI}, url={https://arxiv.org/abs/2504.11524}, } - Network accessline 556
url={https://arxiv.org/abs/2504.11524}, - Network accessline 564
- **Paper:** https://arxiv.org/abs/2410.17309 - Network accessline 565
- **Code:** https://github.com/ChicagoHAI/hypothesis-generation - Network accessline 575
archivePrefix={arXiv}, primaryClass={cs.AI}, url={https://arxiv.org/abs/2410.17309}, - Network accessline 576
primaryClass={cs.AI}, url={https://arxiv.org/abs/2410.17309}, } - Network accessline 577
url={https://arxiv.org/abs/2410.17309}, - Network accessline 585
- **Paper:** https://aclanthology.org/2024.nlp4science-1.10/ - Network accessline 593
booktitle = {Proceedings of EMNLP Workshop of NLP for Science}, year={2024}, url={https://aclanthology.org/2024.nlp4science-1.10/}, - Network accessline 594
year={2024}, url={https://aclanthology.org/2024.nlp4science-1.10/}, } - Network accessline 595
url={https://aclanthology.org/2024.nlp4science-1.10/}, - Network accessline 603
- **GitHub Repository:** https://github.com/ChicagoHAI/hypothesis-generation - Network accessline 604
- **PyPI Package:** https://pypi.org/project/hypogenic/ - Network accessline 606
- **Issues & Support:** https://github.com/ChicagoHAI/hypothesis-generation/issues - Network accessline 613
# HypoGeniC examples (data-driven only) git clone https://github.com/ChicagoHAI/HypoGeniC-datasets.git ./data - Network accessline 614
git clone https://github.com/ChicagoHAI/HypoGeniC-datasets.git ./data - Network accessline 616
# HypoRefine/Union examples (literature + data) git clone https://github.com/ChicagoHAI/Hypothesis-agent-datasets.git ./data - Network accessline 617
git clone https://github.com/ChicagoHAI/Hypothesis-agent-datasets.git ./data
skills/bio/kegg-database/SKILL.md
skills/bio/labarchive-integration/SKILL.md
- Network accessline 48
api_url: https://api.labarchives.com/api # or regional endpoint - Network accessline 53
**Regional API endpoints:** - US/International: `https://api.labarchives.com/api` - Network accessline 54
- US/International: `https://api.labarchives.com/api` - Network accessline 55
- Australia: `https://auapi.labarchives.com/api` - Network accessline 56
- UK: `https://ukapi.labarchives.com/api` - Access to secretsline 79
[Credential-related source omitted] - Access to secretsline 200
[Credential-related source omitted] - Network accessline 223
git clone https://github.com/mcmero/labarchives-py
skills/bio/lamindb/SKILL.md
- Network accessline 383
- **Official Documentation**: https://docs.lamin.ai - Network accessline 384
- **API Reference**: https://docs.lamin.ai/api - Network accessline 385
- **GitHub Repository**: https://github.com/laminlabs/lamindb - Network accessline 386
- **Tutorial**: https://docs.lamin.ai/tutorial - Network accessline 387
- **FAQ**: https://docs.lamin.ai/faq
skills/bio/latchbio-integration/SKILL.md
- Network accessline 338
- **Official Documentation**: https://docs.latch.bio - Network accessline 339
- **GitHub Repository**: https://github.com/latchbio/latch - Network accessline 340
- **Slack Community**: Join Latch SDK workspace - **API Reference**: https://docs.latch.bio/api/latch.html - **Blog**: https://blog.latch.bio - Network accessline 341
- **API Reference**: https://docs.latch.bio/api/latch.html - Network accessline 342
- **Blog**: https://blog.latch.bio
skills/bio/metabolomics-workbench-database/SKILL.md
- Network accessline 35
# Get compound information by PubChem CID response = requests.get('https://www.metabolomicsworkbench.org/rest/compound/pubchem_cid/5281365/all/json') - Network accessline 36
response = requests.get('https://www.metabolomicsworkbench.org/rest/compound/pubchem_cid/5281365/all/json') - Network accessline 38
# Download molecular structure as PNG response = requests.get('https://www.metabolomicsworkbench.org/rest/compound/regno/11/png') - Network accessline 39
response = requests.get('https://www.metabolomicsworkbench.org/rest/compound/regno/11/png') - Network accessline 41
# Get compound name by registry number response = requests.get('https://www.metabolomicsworkbench.org/rest/compound/regno/11/name/json') - Network accessline 42
response = requests.get('https://www.metabolomicsworkbench.org/rest/compound/regno/11/name/json') - Network accessline 58
# List all available public studies response = requests.get('https://www.metabolomicsworkbench.org/rest/study/study_id/ST/available/json') - Network accessline 59
response = requests.get('https://www.metabolomicsworkbench.org/rest/study/study_id/ST/available/json') - Network accessline 61
# Get study summary response = requests.get('https://www.metabolomicsworkbench.org/rest/study/study_id/ST000001/summary/json') - Network accessline 62
response = requests.get('https://www.metabolomicsworkbench.org/rest/study/study_id/ST000001/summary/json') - Network accessline 64
# Retrieve experimental data response = requests.get('https://www.metabolomicsworkbench.org/rest/study/study_id/ST000001/data/json') - Network accessline 65
response = requests.get('https://www.metabolomicsworkbench.org/rest/study/study_id/ST000001/data/json') - Network accessline 67
# Find studies containing a specific metabolite response = requests.get('https://www.metabolomicsworkbench.org/rest/study/refmet_name/Tyrosine/summary/json') - Network accessline 68
response = requests.get('https://www.metabolomicsworkbench.org/rest/study/refmet_name/Tyrosine/summary/json') - Network accessline 83
# Standardize a metabolite name response = requests.get('https://www.metabolomicsworkbench.org/rest/refmet/match/citrate/name/json') - Network accessline 84
response = requests.get('https://www.metabolomicsworkbench.org/rest/refmet/match/citrate/name/json') - Network accessline 86
# Query by molecular formula response = requests.get('https://www.metabolomicsworkbench.org/rest/refmet/formula/C12H24O2/all/json') - Network accessline 87
response = requests.get('https://www.metabolomicsworkbench.org/rest/refmet/formula/C12H24O2/all/json') - Network accessline 89
# Get all metabolites in a specific class response = requests.get('https://www.metabolomicsworkbench.org/rest/refmet/main_class/Fatty%20Acids/all/json') - Network accessline 90
response = requests.get('https://www.metabolomicsworkbench.org/rest/refmet/main_class/Fatty%20Acids/all/json') - Network accessline 92
# Retrieve complete RefMet database response = requests.get('https://www.metabolomicsworkbench.org/rest/refmet/all/json') - Network accessline 93
response = requests.get('https://www.metabolomicsworkbench.org/rest/refmet/all/json') - Network accessline 108
# Search by m/z value with M+H adduct response = requests.get('https://www.metabolomicsworkbench.org/rest/moverz/MB/635.52/M+H/0.5/json') - Network accessline 109
response = requests.get('https://www.metabolomicsworkbench.org/rest/moverz/MB/635.52/M+H/0.5/json') - Network accessline 111
# Calculate exact mass for a metabolite with specific adduct response = requests.get('https://www.metabolomicsworkbench.org/rest/moverz/exactmass/PC(34:1)/M+H/json') - Network accessline 112
response = requests.get('https://www.metabolomicsworkbench.org/rest/moverz/exactmass/PC(34:1)/M+H/json') - Network accessline 114
# Search across RefMet database response = requests.get('https://www.metabolomicsworkbench.org/rest/moverz/REFMET/200.15/M-H/0.3/json') - Network accessline 115
response = requests.get('https://www.metabolomicsworkbench.org/rest/moverz/REFMET/200.15/M-H/0.3/json') - Network accessline 131
# Find human blood studies on diabetes using LC-MS response = requests.get('https://www.metabolomicsworkbench.org/rest/metstat/LCMS;POSITIVE;HILIC;Human;Blood;Diabetes/json') - Network accessline 132
response = requests.get('https://www.metabolomicsworkbench.org/rest/metstat/LCMS;POSITIVE;HILIC;Human;Blood;Diabetes/json') - Network accessline 134
# Find all human blood studies containing tyrosine response = requests.get('https://www.metabolomicsworkbench.org/rest/metstat/;;;Human;Blood;;;Tyrosine/json') - Network accessline 135
response = requests.get('https://www.metabolomicsworkbench.org/rest/metstat/;;;Human;Blood;;;Tyrosine/json') - Network accessline 137
# Filter by analytical method only response = requests.get('https://www.metabolomicsworkbench.org/rest/metstat/GCMS;;;;;;/json') - Network accessline 138
response = requests.get('https://www.metabolomicsworkbench.org/rest/metstat/GCMS;;;;;;/json') - Network accessline 153
# Get gene information by symbol response = requests.get('https://www.metabolomicsworkbench.org/rest/gene/gene_symbol/ACACA/all/json') - Network accessline 154
response = requests.get('https://www.metabolomicsworkbench.org/rest/gene/gene_symbol/ACACA/all/json') - Network accessline 156
# Retrieve protein data by UniProt ID response = requests.get('https://www.metabolomicsworkbench.org/rest/protein/uniprot_id/Q13085/all/json') - Network accessline 157
response = requests.get('https://www.metabolomicsworkbench.org/rest/protein/uniprot_id/Q13085/all/json') - Network accessline 168
response = requests.get('https://www.metabolomicsworkbench.org/rest/refmet/match/glucose/name/json') - Network accessline 173
response = requests.get('https://www.metabolomicsworkbench.org/rest/study/refmet_name/Glucose/summary/json') - Network accessline 178
response = requests.get('https://www.metabolomicsworkbench.org/rest/study/study_id/ST000001/data/json') - Network accessline 187
response = requests.get('https://www.metabolomicsworkbench.org/rest/moverz/MB/180.06/M+H/0.5/json') - Network accessline 194
response = requests.get('https://www.metabolomicsworkbench.org/rest/compound/regno/{regno}/all/json') - Network accessline 199
response = requests.get('https://www.metabolomicsworkbench.org/rest/compound/regno/{regno}/png') - Network accessline 208
response = requests.get('https://www.metabolomicsworkbench.org/rest/metstat/LCMS;POSITIVE;;Human;;Cancer/json') - Network accessline 215
response = requests.get('https://www.metabolomicsworkbench.org/rest/study/study_id/ST{ID}/summary/json') - Network accessline 220
response = requests.get('https://www.metabolomicsworkbench.org/rest/study/study_id/ST{ID}/data/json')
skills/bio/omero-integration/SKILL.md
- Network accessline 236
- **Official Documentation**: https://omero.readthedocs.io/en/stable/developers/Python.html - Network accessline 237
- **BlitzGateway API**: https://omero.readthedocs.io/en/stable/developers/Python.html#omero-blitzgateway - Network accessline 238
- **OMERO Model**: https://omero.readthedocs.io/en/stable/developers/Model.html - Network accessline 239
- **Community Forum**: https://forum.image.sc/tag/omero
skills/bio/pdb-database/SKILL.md
- Possible obfuscationline 57
value="MTEYKLVVVGAGGVGKSALTIQLIQNHFVDEYDPTIEDSYRKQVVIDGETCLLDILDTAGQEEYSAMRDQYMRTGEGFLCVFAINNTKSFEDIHHYREQIKRVKDSEDVPMVLVGNKCDLPSRTVDTKQAQDLARSYGIPFIETSAKTRQGVDDAFYTLVREIRKHKEKMSKDGKKKKKKSKTKCVIM", - Network accessline 103
# Get entry-level data entry_data = fetch("4HHB", schema=Schema.ENTRY) - Network accessline 104
entry_data = fetch("4HHB", schema=Schema.ENTRY) - Network accessline 111
# Get protein/nucleic acid information entity_data = fetch("4HHB_1", schema=Schema.POLYMER_ENTITY) - Network accessline 112
entity_data = fetch("4HHB_1", schema=Schema.POLYMER_ENTITY) - Network accessline 135
} """ data = fetch(query_type="graphql", query=query) - Network accessline 137
data = fetch(query_type="graphql", query=query) - Network accessline 144
**Download Methods:** - **PDB format** (legacy text format): `https://files.rcsb.org/download/{PDB_ID}.pdb` - Network accessline 145
- **PDB format** (legacy text format): `https://files.rcsb.org/download/{PDB_ID}.pdb` - Network accessline 146
- **mmCIF format** (modern standard): `https://files.rcsb.org/download/{PDB_ID}.cif` - Network accessline 148
- **Biological assembly**: `https://files.rcsb.org/download/{PDB_ID}.pdb1` (for assembly 1) - Network accessline 156
# Download PDB format pdb_url = f"https://files.rcsb.org/download/{pdb_id}.pdb" - Network accessline 157
pdb_url = f"https://files.rcsb.org/download/{pdb_id}.pdb" - Network accessline 158
response = requests.get(pdb_url) - Network accessline 162
# Download mmCIF format cif_url = f"https://files.rcsb.org/download/{pdb_id}.cif" - Network accessline 163
cif_url = f"https://files.rcsb.org/download/{pdb_id}.cif" - Network accessline 164
response = requests.get(cif_url) - Network accessline 193
# Get experimental details data = fetch("4HHB", schema=Schema.ENTRY) - Network accessline 194
data = fetch("4HHB", schema=Schema.ENTRY) - Network accessline 215
for pdb_id in pdb_ids: try: data = fetch(pdb_id, schema=Schema.ENTRY) - Network accessline 216
try: data = fetch(pdb_id, schema=Schema.ENTRY) results[pdb_id] = { - Network accessline 217
data = fetch(pdb_id, schema=Schema.ENTRY) - Network accessline 301
- **RCSB PDB Website:** https://www.rcsb.org - Network accessline 302
- **PDB-101 Educational Portal:** https://pdb101.rcsb.org - Network accessline 303
- **API Documentation:** https://www.rcsb.org/docs/programmatic-access/web-apis-overview - Network accessline 304
- **Python Package Docs:** https://rcsbapi.readthedocs.io/ - Network accessline 305
- **Data API Documentation:** https://data.rcsb.org/ - Network accessline 306
- **GitHub Repository:** https://github.com/rcsb/py-rcsb-api
skills/bio/protocolsio-integration/SKILL.md
- Access to secretsline 124
[Credential-related source omitted] - Network accessline 152
https://protocols.io/api/v3 - Access to secretsline 157
[Credential-related source omitted] - Access to secretsline 254
[Credential-related source omitted] - Network accessline 257
# Search for CRISPR protocols response = requests.get( - Network accessline 258
response = requests.get( - Network accessline 259
"https://protocols.io/api/v3/protocols", - Access to secretsline 279
[Credential-related source omitted] - Network accessline 292
response = requests.post( - Network accessline 293
"https://protocols.io/api/v3/protocols", - Access to secretsline 307
[Credential-related source omitted] - Network accessline 319
response = requests.post( - Network accessline 320
"https://protocols.io/api/v3/workspaces/12345/files/upload", - Network accessline 339
for attempt in range(max_retries): try: response = requests.get(url, headers=headers) - Network accessline 341
response = requests.get(url, headers=headers) - Network accessline 391
- **Official API Documentation**: https://apidoc.protocols.io/ - Network accessline 392
- **Protocols.io Platform**: https://www.protocols.io/
skills/bio/pydeseq2/SKILL.md
skills/bio/pylabrobot/SKILL.md
- Network accessline 129
6. **Temperature Control**: Set temperatures early as heating/cooling takes time 7. **Modular Protocols**: Break complex workflows into reusable functions 8. **Documentation**: Reference official docs at https://docs.pylabrobot.org for late - Network accessline 131
8. **Documentation**: Reference official docs at https://docs.pylabrobot.org for latest features - Network accessline 177
- **Official Documentation**: https://docs.pylabrobot.org - Network accessline 178
- **GitHub Repository**: https://github.com/PyLabRobot/pylabrobot - Network accessline 179
- **Community Forum**: https://discuss.pylabrobot.org - Network accessline 180
- **PyPI Package**: https://pypi.org/project/PyLabRobot/
skills/bio/pyopenms/SKILL.md
skills/bio/pysam/SKILL.md
- Network accessline 40
# Open BAM file and fetch reads in region samfile = pysam.AlignmentFile("example.bam", "rb") for read in samfile.fetch("chr1", 1000, 2000): - Network accessline 41
samfile = pysam.AlignmentFile("example.bam", "rb") for read in samfile.fetch("chr1", 1000, 2000): print(f"{read.query_name}: {read.reference_start}") - Network accessline 42
for read in samfile.fetch("chr1", 1000, 2000): - Network accessline 58
# Open FASTA and extract sequence fasta = pysam.FastaFile("reference.fasta") sequence = fasta.fetch("chr1", 1000, 2000) - Network accessline 59
fasta = pysam.FastaFile("reference.fasta") sequence = fasta.fetch("chr1", 1000, 2000) print(sequence) - Network accessline 60
sequence = fasta.fetch("chr1", 1000, 2000) - Network accessline 81
- Opening and reading alignment files - AlignedSegment attributes and methods - Region-based fetching with `fetch()` - Network accessline 82
- AlignedSegment attributes and methods - Region-based fetching with `fetch()` - Pileup analysis for coverage - Network accessline 83
- Region-based fetching with `fetch()` - Network accessline 164
**Exception:** Region strings in `fetch()` follow samtools convention (1-based): - Network accessline 166
samfile.fetch("chr1", 999, 2000) # 0-based: positions 999-1999 - Network accessline 167
samfile.fetch("chr1:1000-2000") # 1-based string: positions 1000-2000 - Network accessline 181
Without an index, use `fetch(until_eof=True)` for sequential reading. - Network accessline 205
1. **Coordinate confusion:** Remember 0-based vs 1-based systems in different contexts 2. **Missing indices:** Many operations require index files—create them first 3. **Partial overlaps:** `fetch()` returns reads overlapping region boundar - Network accessline 206
2. **Missing indices:** Many operations require index files—create them first 3. **Partial overlaps:** `fetch()` returns reads overlapping region boundaries, not just those fully contained 4. **Iterator scope:** Keep pileup iterator referen - Network accessline 207
3. **Partial overlaps:** `fetch()` returns reads overlapping region boundaries, not just those fully contained - Network accessline 262
Official documentation: https://pysam.readthedocs.io/
skills/bio/reactome-database/SKILL.md
- Network accessline 41
**API Base URL:** `https://reactome.org/ContentService` - Network accessline 52
**API Base URL:** `https://reactome.org/AnalysisService` - Network accessline 75
response = requests.get("https://reactome.org/ContentService/data/database/version") - Network accessline 84
entity_id = "R-HSA-69278" # Example pathway ID response = requests.get(f"https://reactome.org/ContentService/data/query/{entity_id}") - Network accessline 85
response = requests.get(f"https://reactome.org/ContentService/data/query/{entity_id}") - Network accessline 93
event_id = "R-HSA-69278" response = requests.get( - Network accessline 94
response = requests.get( - Network accessline 95
f"https://reactome.org/ContentService/data/event/{event_id}/participatingPhysicalEntities" - Network accessline 129
# Submit analysis response = requests.post( - Network accessline 130
response = requests.post( - Network accessline 131
"https://reactome.org/AnalysisService/identifiers/", - Network accessline 146
# Token is valid for 7 days response = requests.get(f"https://reactome.org/AnalysisService/token/{token}") - Network accessline 147
response = requests.get(f"https://reactome.org/AnalysisService/token/{token}") - Network accessline 171
response = requests.post( - Network accessline 172
"https://reactome.org/AnalysisService/identifiers/", - Network accessline 185
response = requests.post( - Network accessline 186
"https://reactome.org/AnalysisService/identifiers/projection/", - Network accessline 197
[Credential-related source omitted] - Network accessline 198
pathway_id = "R-HSA-69278" url = f"https://reactome.org/PathwayBrowser/#{pathway_id}&DTAB=AN&ANALYSIS={token}" print(f"View results: {url}") - Network accessline 199
url = f"https://reactome.org/PathwayBrowser/#{pathway_id}&DTAB=AN&ANALYSIS={token}" - Network accessline 260
- **API Documentation**: https://reactome.org/dev - Network accessline 261
- **User Guide**: https://reactome.org/userguide - **Documentation Portal**: https://reactome.org/documentation - **Data Downloads**: https://reactome.org/download-data - Network accessline 262
- **Documentation Portal**: https://reactome.org/documentation - **Data Downloads**: https://reactome.org/download-data - **reactome2py Docs**: https://reactome.github.io/reactome2py/ - Network accessline 263
- **Data Downloads**: https://reactome.org/download-data - Network accessline 264
- **reactome2py Docs**: https://reactome.github.io/reactome2py/
skills/bio/scanpy/SKILL.md
skills/bio/scikit-bio/SKILL.md
skills/bio/scikit-survival/SKILL.md
- Network accessline 357
- **Official Documentation**: https://scikit-survival.readthedocs.io/ - Network accessline 358
- **GitHub Repository**: https://github.com/sebp/scikit-survival - Network accessline 360
- **API Reference**: Complete list of classes and functions at https://scikit-survival.readthedocs.io/en/stable/api/index.html
skills/bio/scvi-tools/SKILL.md
- Network accessline 165
- **Workflows**: `references/workflows.md` contains common workflows, best practices, hyperparameter tuning, and GPU optimization - **Model References**: Detailed documentation for each model category in the `references/` directory - **Offi - Network accessline 166
- **Model References**: Detailed documentation for each model category in the `references/` directory - **Official Documentation**: https://docs.scvi-tools.org/en/stable/ - **Tutorials**: https://docs.scvi-tools.org/en/stable/tutorials/inde - Network accessline 167
- **Official Documentation**: https://docs.scvi-tools.org/en/stable/ - Network accessline 168
- **Tutorials**: https://docs.scvi-tools.org/en/stable/tutorials/index.html - Network accessline 169
- **API Reference**: https://docs.scvi-tools.org/en/stable/api/index.html
skills/bio/string-database/SKILL.md
- Network accessline 413
Full list available at: https://string-db.org/cgi/input?input_page_active_form=organisms - Network accessline 513
For proteome-scale analysis or complete species network upload: - Visit https://string-db.org - Network accessline 514
- Visit https://string-db.org - Network accessline 518
For bulk downloads of complete datasets: - Download page: https://string-db.org/cgi/download - Network accessline 519
- Download page: https://string-db.org/cgi/download - Network accessline 531
When using STRING in publications, cite the most recent publication from: https://string-db.org/cgi/about
skills/bio/tooluniverse-expression-data-retrieval/SKILL.md
skills/bio/tooluniverse-gene-enrichment/SKILL.md
- Network accessline 399
gseapy documentation: https://gseapy.readthedocs.io/ - Network accessline 400
PANTHER API: http://pantherdb.org/services/oai/pantherdb/ - Network accessline 401
STRING API: https://string-db.org/cgi/help?sessionId=&subpage=api - Network accessline 402
Reactome Analysis: https://reactome.org/AnalysisService/
skills/bio/tooluniverse-gwas-drug-discovery/SKILL.md
- Network accessline 490
**GWAS**: - [GWAS Catalog](https://www.ebi.ac.uk/gwas/) - Curated GWAS results - Network accessline 491
- [GWAS Catalog](https://www.ebi.ac.uk/gwas/) - Curated GWAS results - Network accessline 492
- [Open Targets Genetics](https://genetics.opentargets.org/) - Fine-mapping, L2G - Network accessline 493
- [PhenoScanner](http://www.phenoscanner.medschl.cam.ac.uk/) - Cross-trait lookups - Network accessline 495
**Drugs**: - [ChEMBL](https://www.ebi.ac.uk/chembl/) - Bioactivity database - Network accessline 496
- [ChEMBL](https://www.ebi.ac.uk/chembl/) - Bioactivity database - Network accessline 497
- [DrugBank](https://go.drugbank.com/) - Comprehensive drug information - Network accessline 498
- [DGIdb](https://www.dgidb.org/) - Drug-gene interactions - Network accessline 500
**Targets**: - [Open Targets Platform](https://platform.opentargets.org/) - Target-disease associations - Network accessline 501
- [Open Targets Platform](https://platform.opentargets.org/) - Target-disease associations - Network accessline 502
- [PHAROS](https://pharos.nih.gov/) - Target development level (Tdark to Tclin) - Network accessline 504
**Clinical**: - [ClinicalTrials.gov](https://clinicaltrials.gov/) - Clinical trial registry - Network accessline 505
- [ClinicalTrials.gov](https://clinicaltrials.gov/) - Clinical trial registry - Network accessline 506
- [FDA Labels](https://labels.fda.gov/) - Drug labeling information
skills/bio/tooluniverse-gwas-snp-interpretation/SKILL.md
skills/bio/tooluniverse-gwas-trait-to-gene/SKILL.md
- Network accessline 206
**GWAS Catalog** - Curator: EBI and NHGRI - URL: https://www.ebi.ac.uk/gwas/ - Network accessline 207
- Curator: EBI and NHGRI - URL: https://www.ebi.ac.uk/gwas/ - Coverage: 100,000+ publications, 500,000+ associations - Network accessline 208
- URL: https://www.ebi.ac.uk/gwas/ - Network accessline 212
**Open Targets Genetics** - Curator: Open Targets consortium - URL: https://genetics.opentargets.org/ - Network accessline 213
- Curator: Open Targets consortium - URL: https://genetics.opentargets.org/ - Coverage: Fine-mapped GWAS, L2G predictions, QTL colocalization - Network accessline 214
- URL: https://genetics.opentargets.org/
skills/bio/tooluniverse-immune-repertoire-analysis/SKILL.md
skills/bio/tooluniverse-metabolomics-analysis/SKILL.md
- Network accessline 756
**Methods**: - MetaboAnalyst: https://doi.org/10.1093/nar/gkab382 - Network accessline 757
- MetaboAnalyst: https://doi.org/10.1093/nar/gkab382 - Network accessline 758
- XCMS: https://doi.org/10.1021/ac051437y - Network accessline 759
- MSEA: https://doi.org/10.1186/1471-2105-11-395 - Network accessline 761
**Databases**: - HMDB: https://hmdb.ca - Network accessline 762
- HMDB: https://hmdb.ca - Network accessline 763
- KEGG Compound: https://www.genome.jp/kegg/compound/ - Network accessline 764
- Reactome: https://reactome.org
skills/bio/tooluniverse-multi-omics-integration/SKILL.md
- Network accessline 697
**Methods**: - MOFA+: https://doi.org/10.1186/s13059-020-02015-1 - Network accessline 698
- MOFA+: https://doi.org/10.1186/s13059-020-02015-1 - Network accessline 699
- Similarity Network Fusion: https://doi.org/10.1038/nmeth.2810 - Network accessline 700
- Multi-omics review: https://doi.org/10.1038/s41576-019-0093-7
skills/bio/tooluniverse-phylogenetics/SKILL.md
- Network accessline 453
For issues with: - **PhyKIT functions**: Check PhyKIT documentation at https://jlsteenwyk.com/PhyKIT/ - Network accessline 454
- **PhyKIT functions**: Check PhyKIT documentation at https://jlsteenwyk.com/PhyKIT/ - Network accessline 455
- **Biopython tree/alignment parsing**: See https://biopython.org/wiki/Phylo - Network accessline 456
- **DendroPy operations**: See https://dendropy.org/
skills/bio/tooluniverse-polygenic-risk-score/SKILL.md
- Network accessline 212
1. **Lambert et al. (2021)**: "The Polygenic Score Catalog as an open database for reproducibility and systematic evaluation" - PGS Catalog: https://www.pgscatalog.org/ - Network accessline 213
- PGS Catalog: https://www.pgscatalog.org/ - Network accessline 234
- **PGS Catalog**: https://www.pgscatalog.org/ - Published PRS models - Network accessline 235
- **LD Hub**: http://ldsc.broadinstitute.org/ - Genetic correlations - Network accessline 236
- **PRSice**: https://www.prsice.info/ - PRS calculation software - Network accessline 237
- **GWAS Catalog**: https://www.ebi.ac.uk/gwas/ - Association database
skills/bio/tooluniverse-protein-interactions/SKILL.md
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[Credential-related source omitted] - Access to secretsline 295
[Credential-related source omitted] - Network accessline 338
1. Register for free API key: https://webservice.thebiogrid.org/ - Network accessline 338
1. Register for free API key: https://webservice.thebiogrid.org/ - Access to secretsline 341
[Credential-related source omitted] - Access to secretsline 341
[Credential-related source omitted] - Network accessline 413
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[Credential-related source omitted] - Network accessline 414
- Check API key is valid at https://webservice.thebiogrid.org/ - Network accessline 414
- Check API key is valid at https://webservice.thebiogrid.org/ - Network accessline 432
- **STRING**: https://string-db.org/ (14M+ proteins, 5,000+ organisms) - Network accessline 432
- **STRING**: https://string-db.org/ (14M+ proteins, 5,000+ organisms) - Network accessline 433
- **BioGRID**: https://thebiogrid.org/ (2.3M+ interactions, experimentally validated) - Network accessline 433
- **BioGRID**: https://thebiogrid.org/ (2.3M+ interactions, experimentally validated) - Network accessline 434
- **SASBDB**: https://www.sasbdb.org/ (2,000+ SAXS/SANS entries) - Network accessline 434
- **SASBDB**: https://www.sasbdb.org/ (2,000+ SAXS/SANS entries) - Network accessline 435
- **ToolUniverse**: https://github.com/mims-harvard/ToolUniverse - Network accessline 435
- **ToolUniverse**: https://github.com/mims-harvard/ToolUniverse
skills/bio/tooluniverse-protein-structure-retrieval/SKILL.md
- Network accessline 292
### Database Links - RCSB PDB: https://www.rcsb.org/structure/[pdb_id] - Network accessline 293
- RCSB PDB: https://www.rcsb.org/structure/[pdb_id] - Network accessline 294
- PDBe: https://www.ebi.ac.uk/pdbe/entry/pdb/[pdb_id] - Network accessline 295
- AlphaFold: https://alphafold.ebi.ac.uk/entry/[uniprot_id]
skills/bio/tooluniverse-proteomics-analysis/SKILL.md
- Network accessline 835
**Methods**: - MaxQuant: https://doi.org/10.1038/nbt.1511 - Network accessline 836
- MaxQuant: https://doi.org/10.1038/nbt.1511 - Network accessline 837
- Limma for proteomics: https://doi.org/10.1093/nar/gkv007 - Network accessline 838
- DEP workflow: https://doi.org/10.1038/nprot.2018.107 - Network accessline 840
**Databases**: - STRING: https://string-db.org - Network accessline 841
- STRING: https://string-db.org - Network accessline 842
- PhosphoSitePlus: https://www.phosphosite.org - Network accessline 843
- CORUM: https://mips.helmholtz-muenchen.de/corum
skills/bio/tooluniverse-spatial-transcriptomics/SKILL.md
- Network accessline 780
**Methods**: - Squidpy: https://doi.org/10.1038/s41592-021-01358-2 - Network accessline 781
- Squidpy: https://doi.org/10.1038/s41592-021-01358-2 - Network accessline 782
- Cell2location: https://doi.org/10.1038/s41587-021-01139-4 - Network accessline 783
- SpatialDE: https://doi.org/10.1038/nmeth.4636 - Network accessline 785
**Platforms**: - 10x Visium: https://www.10xgenomics.com/products/spatial-gene-expression - Network accessline 786
- 10x Visium: https://www.10xgenomics.com/products/spatial-gene-expression - Network accessline 787
- MERFISH: https://doi.org/10.1126/science.aaa6090 - Network accessline 788
- Slide-seq: https://doi.org/10.1126/science.aaw1219
skills/bio/tooluniverse-structural-variant-analysis/SKILL.md
- Network accessline 1353
- `README.md` - Quick start guide - `tooluniverse-variant-interpretation` - For SNVs and small indels - ClinGen Dosage Sensitivity Map: https://www.ncbi.nlm.nih.gov/projects/dbvar/clingen/ - Network accessline 1354
- `tooluniverse-variant-interpretation` - For SNVs and small indels - ClinGen Dosage Sensitivity Map: https://www.ncbi.nlm.nih.gov/projects/dbvar/clingen/ - ACMG SV Guidelines: Riggs et al., Genet Med 2020 (PMID: 31690835) - Network accessline 1355
- ClinGen Dosage Sensitivity Map: https://www.ncbi.nlm.nih.gov/projects/dbvar/clingen/
skills/bio/uniprot-database/SKILL.md
- Network accessline 54
Use the API search endpoint: `https://rest.uniprot.org/uniprotkb/search?query={query}&format={format}` - Network accessline 66
**Retrieve endpoint:** `https://rest.uniprot.org/uniprotkb/{accession}.{format}` - Network accessline 68
Example: `https://rest.uniprot.org/uniprotkb/P12345.fasta` - Network accessline 74
**ID Mapping workflow:** 1. Submit mapping job to: `https://rest.uniprot.org/idmapping/run` - Network accessline 75
1. Submit mapping job to: `https://rest.uniprot.org/idmapping/run` - Network accessline 76
2. Check job status: `https://rest.uniprot.org/idmapping/status/{jobId}` - Network accessline 77
3. Retrieve results: `https://rest.uniprot.org/idmapping/results/{jobId}` - Network accessline 95
`https://rest.uniprot.org/uniprotkb/stream?query={query}&format={format}` - Network accessline 115
**Example:** `https://rest.uniprot.org/uniprotkb/search?query=insulin&fields=accession,gene_names,organism_name,length,sequence&format=tsv` - Network accessline 188
- **API Documentation**: https://www.uniprot.org/help/api - Network accessline 189
- **Interactive API Explorer**: https://www.uniprot.org/api-documentation - Network accessline 190
- **REST Tutorial**: https://www.uniprot.org/help/uniprot_rest_tutorial - Network accessline 191
- **Query Syntax Help**: https://www.uniprot.org/help/query-fields - Network accessline 192
- **SPARQL Endpoint**: https://sparql.uniprot.org/ (for advanced graph queries)
skills/general/aeon/SKILL.md
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- Documentation: https://www.aeon-toolkit.org/ - Network accessline 369
- GitHub: https://github.com/aeon-toolkit/aeon - Network accessline 370
- Examples: https://www.aeon-toolkit.org/en/stable/examples.html - Network accessline 371
- API Reference: https://www.aeon-toolkit.org/en/stable/api_reference.html
skills/general/hypothesis-generation/SKILL.md
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skills/general/latex-posters/SKILL.md
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Bash - Network accessline 291
% Link to paper, code repository, or supplementary materials \qrcode[height=2cm]{https://github.com/username/project} - Network accessline 292
\qrcode[height=2cm]{https://github.com/username/project} - Network accessline 294
% QR code with caption \begin{center} \qrcode[height=3cm]{https://doi.org/10.1234/paper}\\ - Network accessline 295
\begin{center} \qrcode[height=3cm]{https://doi.org/10.1234/paper}\\ \small Scan for full paper - Network accessline 296
\qrcode[height=3cm]{https://doi.org/10.1234/paper}\\ - Network accessline 554
- [ ] Text-background contrast ratio ≥ 4.5:1 (WCAG AA) - [ ] Important elements contrast ratio ≥ 7:1 (WCAG AAA) - Test online: https://webaim.org/resources/contrastchecker/ - Network accessline 556
- Test online: https://webaim.org/resources/contrastchecker/
skills/general/markitdown/SKILL.md
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Bash - Network accessline 85
# Or from source git clone https://github.com/microsoft/markitdown.git - Network accessline 86
git clone https://github.com/microsoft/markitdown.git - Network accessline 135
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base_url="https://openrouter.ai/api/v1" - Network accessline 258
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base_url="https://openrouter.ai/api/v1" - Network accessline 308
# Convert YouTube video to transcript result = md.convert("https://www.youtube.com/watch?v=VIDEO_ID") - Network accessline 309
result = md.convert("https://www.youtube.com/watch?v=VIDEO_ID") - Network accessline 415
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base_url="https://openrouter.ai/api/v1" - Dangerous shell operationline 461
sudo apt-get install tesseract-ocr - Network accessline 480
- **MarkItDown GitHub**: https://github.com/microsoft/markitdown - Network accessline 481
- **PyPI**: https://pypi.org/project/markitdown/ - Network accessline 482
- **OpenRouter**: https://openrouter.ai (for AI-enhanced conversions) - Network accessline 483
- **OpenRouter API Keys**: https://openrouter.ai/keys - Network accessline 484
- **OpenRouter Models**: https://openrouter.ai/models
skills/general/matlab/SKILL.md
- Network accessline 2
name: matlab description: MATLAB and GNU Octave numerical computing for matrix operations, data analysis, visualization, and scientific computing. Use when writing MATLAB/Octave scripts for linear algebra, signal processing, image processin - Network accessline 3
description: MATLAB and GNU Octave numerical computing for matrix operations, data analysis, visualization, and scientific computing. Use when writing MATLAB/Octave scripts for linear algebra, signal processing, image processing, differenti - Network accessline 4
license: For MATLAB (https://www.mathworks.com/pricing-licensing.html) and for Octave (GNU General Public License version 3) - Dangerous shell operationline 31
sudo apt install octave - Network accessline 33
# Windows - download from https://octave.org/download - Network accessline 369
- MATLAB Documentation: https://www.mathworks.com/help/matlab/ - Network accessline 370
- GNU Octave Manual: https://docs.octave.org/latest/ - Network accessline 371
- MATLAB Onramp (free course): https://www.mathworks.com/learn/tutorials/matlab-onramp.html - Network accessline 372
- File Exchange: https://www.mathworks.com/matlabcentral/fileexchange/
skills/general/matplotlib/SKILL.md
- Network accessline 2
name: matplotlib description: Low-level plotting library for full customization. Use when you need fine-grained control over every plot element, creating novel plot types, or integrating with specific scientific workflows. Export to PNG/PDF - Network accessline 3
description: Low-level plotting library for full customization. Use when you need fine-grained control over every plot element, creating novel plot types, or integrating with specific scientific workflows. Export to PNG/PDF/SVG for publicat - Network accessline 4
license: https://github.com/matplotlib/matplotlib/tree/main/LICENSE - Network accessline 355
- Official documentation: https://matplotlib.org/ - Network accessline 356
- Gallery: https://matplotlib.org/stable/gallery/index.html - Network accessline 357
- Cheatsheets: https://matplotlib.org/cheatsheets/ - Network accessline 358
- Tutorials: https://matplotlib.org/stable/tutorials/index.html
skills/general/networkx/SKILL.md
- Network accessline 431
- **Official Documentation**: https://networkx.org/documentation/latest/ - Network accessline 432
- **Tutorial**: https://networkx.org/documentation/latest/tutorial.html - Network accessline 433
- **Gallery**: https://networkx.org/documentation/latest/auto_examples/index.html - Network accessline 434
- **GitHub**: https://github.com/networkx/networkx
skills/general/peer-review/SKILL.md
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skills/general/plotly/SKILL.md
skills/general/polars/SKILL.md
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name: polars description: Fast in-memory DataFrame library for datasets that fit in RAM. Use when pandas is too slow but data still fits in memory. Lazy evaluation, parallel execution, Apache Arrow backend. Best for 1-100GB datasets, ETL pi - Network accessline 3
description: Fast in-memory DataFrame library for datasets that fit in RAM. Use when pandas is too slow but data still fits in memory. Lazy evaluation, parallel execution, Apache Arrow backend. Best for 1-100GB datasets, ETL pipelines, fast - Network accessline 4
license: https://github.com/pola-rs/polars/blob/main/LICENSE
skills/general/pptx-posters/SKILL.md
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skills/general/pymoo/SKILL.md
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**Documentation:** https://pymoo.org/
skills/general/scholar-evaluation/SKILL.md
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**Moussa, H. N., Da Silva, P. Q., Adu-Ampratwum, D., East, A., Lu, Z., Puccetti, N., Xue, M., Sun, H., Majumder, B. P., & Kumar, S. (2025).** _ScholarEval: Research Idea Evaluation Grounded in Literature_. arXiv preprint arXiv:2510.16234. [
skills/general/scientific-critical-thinking/SKILL.md
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skills/general/scientific-diagram-generation/SKILL.md
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| **Provider** | Google Gemini via yunwu.ai relay | | **Model** | `gemini-3.1-flash-image-preview` | | **Base URL** | `https://yunwu.ai/v1beta/models` | - Network accessline 10
| **Model** | `gemini-3.1-flash-image-preview` | | **Base URL** | `https://yunwu.ai/v1beta/models` | | **Full Endpoint** | `https://yunwu.ai/v1beta/models/gemini-3.1-flash-image-preview:generateContent` | - Network accessline 11
| **Base URL** | `https://yunwu.ai/v1beta/models` | - Network accessline 12
| **Full Endpoint** | `https://yunwu.ai/v1beta/models/gemini-3.1-flash-image-preview:generateContent` | - Access to secretsline 13
[Credential-related source omitted] - Access to secretsline 14
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URL = f"https://yunwu.ai/v1beta/models/{MODEL}:generateContent" - Network accessline 43
"generationConfig": {"responseModalities": ["TEXT", "IMAGE"]} } async with httpx.AsyncClient(timeout=120) as c: - Network accessline 44
} async with httpx.AsyncClient(timeout=120) as c: r = await c.post(URL, json=payload, - Network accessline 45
async with httpx.AsyncClient(timeout=120) as c: - Access to secretsline 48
[Credential-related source omitted] - Possible obfuscationline 53
return base64.b64decode(part["inlineData"]["data"])
skills/general/scientific-slides/SKILL.md
skills/general/scientific-writing/SKILL.md
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skills/general/scikit-learn/SKILL.md
- Network accessline 515
- Official Documentation: https://scikit-learn.org/stable/ - Network accessline 516
- User Guide: https://scikit-learn.org/stable/user_guide.html - Network accessline 517
- API Reference: https://scikit-learn.org/stable/api/index.html - Network accessline 518
- Examples Gallery: https://scikit-learn.org/stable/auto_examples/index.html
skills/general/scikit-survival/SKILL.md
- Network accessline 357
- **Official Documentation**: https://scikit-survival.readthedocs.io/ - Network accessline 358
- **GitHub Repository**: https://github.com/sebp/scikit-survival - Network accessline 360
- **API Reference**: Complete list of classes and functions at https://scikit-survival.readthedocs.io/en/stable/api/index.html
skills/general/shap/SKILL.md
skills/general/statistical-analysis/SKILL.md
skills/general/statsmodels/SKILL.md
- Network accessline 607
For detailed documentation and examples: - Official docs: https://www.statsmodels.org/stable/ - Network accessline 608
- Official docs: https://www.statsmodels.org/stable/ - Network accessline 609
- User guide: https://www.statsmodels.org/stable/user-guide.html - Network accessline 610
- Examples: https://www.statsmodels.org/stable/examples/index.html - Network accessline 611
- API reference: https://www.statsmodels.org/stable/api.html
skills/general/sympy/SKILL.md
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name: sympy description: Use this skill when working with symbolic mathematics in Python. This skill should be used for symbolic computation tasks including solving equations algebraically, performing calculus operations (derivatives, integ - Network accessline 3
description: Use this skill when working with symbolic mathematics in Python. This skill should be used for symbolic computation tasks including solving equations algebraically, performing calculus operations (derivatives, integrals, limits - Network accessline 4
license: https://github.com/sympy/sympy/blob/master/LICENSE - Network accessline 494
- Official Documentation: https://docs.sympy.org/ - Network accessline 495
- Tutorial: https://docs.sympy.org/latest/tutorials/intro-tutorial/index.html - Network accessline 496
- API Reference: https://docs.sympy.org/latest/reference/index.html - Network accessline 497
- Examples: https://github.com/sympy/sympy/tree/master/examples
skills/general/tooluniverse-statistical-modeling/SKILL.md
skills/general/transformers/SKILL.md
skills/general/venue-templates/SKILL.md
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Bash - Network accessline 581
**Journal Author Guidelines**: - Nature: https://www.nature.com/nature/for-authors - Network accessline 582
- Nature: https://www.nature.com/nature/for-authors - Network accessline 583
- Science: https://www.science.org/content/page/instructions-authors - Network accessline 584
- PLOS: https://plos.org/resources/for-authors/ - Network accessline 585
- Cell Press: https://www.cell.com/author-guidelines - Network accessline 587
**Conference Formatting**: - NeurIPS: https://neurips.cc/Conferences/2024/CallForPapers - Network accessline 588
- NeurIPS: https://neurips.cc/Conferences/2024/CallForPapers - Network accessline 589
- ICML: https://icml.cc/Conferences/2024/StyleAuthorInstructions - Network accessline 590
- CVPR: https://cvpr.thecvf.com/Conferences/2024/AuthorGuidelines - Network accessline 592
**Grant Resources**: - NSF PAPPG: https://www.nsf.gov/publications/pub_summ.jsp?ods_key=pappg - Network accessline 593
- NSF PAPPG: https://www.nsf.gov/publications/pub_summ.jsp?ods_key=pappg - Network accessline 594
- NIH Forms: https://grants.nih.gov/grants/how-to-apply-application-guide.html - Network accessline 595
- DOE Funding: https://science.osti.gov/grants
skills/general/zarr-python/SKILL.md
- Network accessline 768
- **Official Documentation**: https://zarr.readthedocs.io/ - Network accessline 769
- **Zarr Specifications**: https://zarr-specs.readthedocs.io/ - Network accessline 770
- **GitHub Repository**: https://github.com/zarr-developers/zarr-python - Network accessline 771
- **Community Chat**: https://gitter.im/zarr-developers/community - Network accessline 773
**Related Libraries**: - **Xarray**: https://docs.xarray.dev/ (labeled arrays) - Network accessline 774
- **Xarray**: https://docs.xarray.dev/ (labeled arrays) - Network accessline 775
- **Dask**: https://docs.dask.org/ (parallel computing) - Network accessline 776
- **NumCodecs**: https://numcodecs.readthedocs.io/ (compression codecs)
skills/literature/academic-literature-search/SKILL.md
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"sortOrder": "descending" }) url = f"https://export.arxiv.org/api/query?{params}" - Network accessline 115
}) url = f"https://export.arxiv.org/api/query?{params}" with urllib.request.urlopen(url, timeout=30) as resp: - Network accessline 116
url = f"https://export.arxiv.org/api/query?{params}" with urllib.request.urlopen(url, timeout=30) as resp: xml_text = resp.read().decode("utf-8") - Network accessline 117
with urllib.request.urlopen(url, timeout=30) as resp: - Network accessline 120
ns = {"atom": "http://www.w3.org/2005/Atom", - Network accessline 121
"arxiv": "http://arxiv.org/schemas/atom"} - Network accessline 147
"volume": "", "issue": "", "pages": "", "abstract": abstract, "url": f"https://arxiv.org/abs/{arxiv_id}", - Network accessline 148
"abstract": abstract, "url": f"https://arxiv.org/abs/{arxiv_id}", "category": cat, - Network accessline 149
"url": f"https://arxiv.org/abs/{arxiv_id}",
skills/literature/arxiv-search/SKILL.md
- Network accessline 30
1. Node.js 18+ (uses built-in fetch) 2. Valyu API key from https://platform.valyu.ai ($10 free credits) - Network accessline 31
2. Valyu API key from https://platform.valyu.ai ($10 free credits) - Network accessline 52
1. **Ask the user for their API key:** "To search arXiv, I need your Valyu API key. Get one free ($10 credits) at https://platform.valyu.ai" - Network accessline 53
"To search arXiv, I need your Valyu API key. Get one free ($10 credits) at https://platform.valyu.ai" - Network accessline 64
User: Search arXiv for transformer architecture papers → Response: {"success": false, "setup_required": true, ...} → Claude asks: "Please provide your Valyu API key from https://platform.valyu.ai" - Network accessline 65
→ Response: {"success": false, "setup_required": true, ...} → Claude asks: "Please provide your Valyu API key from https://platform.valyu.ai" → User: "val_abc123..." - Network accessline 66
→ Claude asks: "Please provide your Valyu API key from https://platform.valyu.ai" - Network accessline 91
{ "title": "Article Title", "url": "https://arxiv.org/abs/...", - Network accessline 92
"title": "Article Title", "url": "https://arxiv.org/abs/...", "content": "Full article text with figures...", - Network accessline 93
"url": "https://arxiv.org/abs/...", - Network accessline 95
"source": "arxiv", "relevance_score": 0.95, "images": ["https://example.com/figure1.jpg"] - Network accessline 96
"relevance_score": 0.95, "images": ["https://example.com/figure1.jpg"] } - Network accessline 97
"images": ["https://example.com/figure1.jpg"] - Network accessline 167
- Base URL: `https://api.valyu.ai/v1` - Network accessline 179
Direct API calls using Node.js built-in `fetch()`, zero external dependencies. - Access to secretsline 190
[Credential-related source omitted] - Network accessline 224
See the [Valyu docs](https://docs.valyu.ai) for full integration examples and SDK reference.
skills/literature/biomedical-search/SKILL.md
- Network accessline 30
1. Node.js 18+ (uses built-in fetch) 2. Valyu API key from https://platform.valyu.ai ($10 free credits) - Network accessline 31
2. Valyu API key from https://platform.valyu.ai ($10 free credits) - Network accessline 52
1. **Ask the user for their API key:** "To search biomedical databases, I need your Valyu API key. Get one free ($10 credits) at https://platform.valyu.ai" - Network accessline 53
"To search biomedical databases, I need your Valyu API key. Get one free ($10 credits) at https://platform.valyu.ai" - Network accessline 78
{ "title": "Title", "url": "https://...", - Network accessline 79
"title": "Title", "url": "https://...", "content": "Full content...", - Network accessline 80
"url": "https://...", - Network accessline 82
"source": "pubmed|biorxiv|medrxiv|clinical-trials|drug-labels", "relevance_score": 0.95, "images": ["https://example.com/figure1.jpg"] - Network accessline 83
"relevance_score": 0.95, "images": ["https://example.com/figure1.jpg"] } - Network accessline 84
"images": ["https://example.com/figure1.jpg"] - Network accessline 157
- Base URL: `https://api.valyu.ai/v1` - Network accessline 169
Direct API calls using Node.js built-in `fetch()`, zero external dependencies. - Access to secretsline 180
[Credential-related source omitted] - Network accessline 214
See the [Valyu docs](https://docs.valyu.ai) for full integration examples and SDK reference.
skills/literature/biorxiv-database/SKILL.md
- Network accessline 122
python scripts/biorxiv_search.py \ --doi "https://doi.org/10.1101/2024.01.15.123456" - Network accessline 123
--doi "https://doi.org/10.1101/2024.01.15.123456" - Network accessline 211
"category": "genomics", "abstract": "Full abstract text...", "pdf_url": "https://www.biorxiv.org/content/10.1101/2024.01.15.123456v1.full.pdf", - Network accessline 212
"abstract": "Full abstract text...", "pdf_url": "https://www.biorxiv.org/content/10.1101/2024.01.15.123456v1.full.pdf", "html_url": "https://www.biorxiv.org/content/10.1101/2024.01.15.123456v1", - Network accessline 213
"pdf_url": "https://www.biorxiv.org/content/10.1101/2024.01.15.123456v1.full.pdf", - Network accessline 214
"html_url": "https://www.biorxiv.org/content/10.1101/2024.01.15.123456v1", - Network accessline 215
"jatsxml": "https://www.biorxiv.org/content/...",
skills/literature/biorxiv-search/SKILL.md
- Network accessline 29
1. Node.js 18+ (uses built-in fetch) 2. Valyu API key from https://platform.valyu.ai ($10 free credits) - Network accessline 30
2. Valyu API key from https://platform.valyu.ai ($10 free credits) - Network accessline 51
1. **Ask the user for their API key:** "To search bioRxiv, I need your Valyu API key. Get one free ($10 credits) at https://platform.valyu.ai" - Network accessline 52
"To search bioRxiv, I need your Valyu API key. Get one free ($10 credits) at https://platform.valyu.ai" - Network accessline 63
User: Search bioRxiv for CRISPR advances → Response: {"success": false, "setup_required": true, ...} → Claude asks: "Please provide your Valyu API key from https://platform.valyu.ai" - Network accessline 64
→ Response: {"success": false, "setup_required": true, ...} → Claude asks: "Please provide your Valyu API key from https://platform.valyu.ai" → User: "val_abc123..." - Network accessline 65
→ Claude asks: "Please provide your Valyu API key from https://platform.valyu.ai" - Network accessline 90
{ "title": "Article Title", "url": "https://biorxiv.org/content/...", - Network accessline 91
"title": "Article Title", "url": "https://biorxiv.org/content/...", "content": "Full article text with figures...", - Network accessline 92
"url": "https://biorxiv.org/content/...", - Network accessline 94
"source": "biorxiv", "relevance_score": 0.95, "images": ["https://example.com/figure1.jpg"] - Network accessline 95
"relevance_score": 0.95, "images": ["https://example.com/figure1.jpg"] } - Network accessline 96
"images": ["https://example.com/figure1.jpg"] - Network accessline 166
- Base URL: `https://api.valyu.ai/v1` - Network accessline 178
Direct API calls using Node.js built-in `fetch()`, zero external dependencies. - Access to secretsline 189
[Credential-related source omitted] - Network accessline 223
See the [Valyu docs](https://docs.valyu.ai) for full integration examples and SDK reference.
skills/literature/bioservices/SKILL.md
- Network accessline 355
For detailed API documentation and advanced features, refer to: - Official documentation: https://bioservices.readthedocs.io/ - Network accessline 356
- Official documentation: https://bioservices.readthedocs.io/ - Network accessline 357
- Source code: https://github.com/cokelaer/bioservices
skills/literature/citation-management/SKILL.md
- Broad permissionsline 1
Bash - Network accessline 142
# Extract from URL python scripts/extract_metadata.py --url "https://www.nature.com/articles/s41586-021-03819-2" - Network accessline 143
python scripts/extract_metadata.py --url "https://www.nature.com/articles/s41586-021-03819-2" - Network accessline 484
1. **Find MeSH terms** at https://meshb.nlm.nih.gov/search - Network accessline 608
# From URL python scripts/extract_metadata.py \ --url "https://www.nature.com/articles/s41586-021-03819-2" - Network accessline 609
python scripts/extract_metadata.py --url "https://www.nature.com/articles/s41586-021-03819-2" - Network accessline 610
--url "https://www.nature.com/articles/s41586-021-03819-2"
skills/literature/clinical-trials-search/SKILL.md
- Network accessline 29
1. Node.js 18+ (uses built-in fetch) 2. Valyu API key from https://platform.valyu.ai ($10 free credits) - Network accessline 30
2. Valyu API key from https://platform.valyu.ai ($10 free credits) - Network accessline 51
1. **Ask the user for their API key:** "To search ClinicalTrials.gov, I need your Valyu API key. Get one free ($10 credits) at https://platform.valyu.ai" - Network accessline 52
"To search ClinicalTrials.gov, I need your Valyu API key. Get one free ($10 credits) at https://platform.valyu.ai" - Network accessline 78
{ "title": "Trial Title", "url": "https://clinicaltrials.gov/...", - Network accessline 79
"title": "Trial Title", "url": "https://clinicaltrials.gov/...", "content": "Trial details, phase, conditions, outcomes...", - Network accessline 80
"url": "https://clinicaltrials.gov/...", - Network accessline 154
- Base URL: `https://api.valyu.ai/v1` - Network accessline 166
Direct API calls using Node.js built-in `fetch()`, zero external dependencies. - Access to secretsline 177
[Credential-related source omitted] - Network accessline 211
See the [Valyu docs](https://docs.valyu.ai) for full integration examples and SDK reference.
skills/literature/clinicaltrials-database/SKILL.md
- Network accessline 44
url = "https://clinicaltrials.gov/api/v2/studies" - Network accessline 51
response = requests.get(url, params=params) - Network accessline 64
nct_id = "NCT04852770" url = f"https://clinicaltrials.gov/api/v2/studies/{nct_id}" - Network accessline 65
url = f"https://clinicaltrials.gov/api/v2/studies/{nct_id}" - Network accessline 67
response = requests.get(url) - Network accessline 433
def search_with_rate_limit(params): try: response = requests.get("https://clinicaltrials.gov/api/v2/studies", params=params) - Network accessline 434
try: response = requests.get("https://clinicaltrials.gov/api/v2/studies", params=params) response.raise_for_status() - Network accessline 435
response = requests.get("https://clinicaltrials.gov/api/v2/studies", params=params) - Network accessline 465
try: response = requests.get(url, params=params, timeout=30) - Network accessline 466
response = requests.get(url, params=params, timeout=30) - Network accessline 494
- **Base URL**: `https://clinicaltrials.gov/api/v2` - **Authentication**: Not required (public API) - **Rate Limit**: ~50 requests/minute per IP
skills/literature/clinvar-database/SKILL.md
- Network accessline 33
Search ClinVar using the web interface at https://www.ncbi.nlm.nih.gov/clinvar/ - Network accessline 53
# Search for pathogenic BRCA1 variants curl "https://eutils.ncbi.nlm.nih.gov/entrez/eutils/esearch.fcgi?db=clinvar&term=BRCA1[gene]+AND+pathogenic[CLNSIG]&retmode=json" - Network accessline 54
curl "https://eutils.ncbi.nlm.nih.gov/entrez/eutils/esearch.fcgi?db=clinvar&term=BRCA1[gene]+AND+pathogenic[CLNSIG]&retmode=json" - Network accessline 120
# Download latest monthly XML release wget ftp://ftp.ncbi.nlm.nih.gov/pub/clinvar/xml/clinvar_variation/ClinVarVariationRelease_00-latest.xml.gz - Network accessline 121
wget ftp://ftp.ncbi.nlm.nih.gov/pub/clinvar/xml/clinvar_variation/ClinVarVariationRelease_00-latest.xml.gz - Network accessline 123
# Download VCF for GRCh38 wget ftp://ftp.ncbi.nlm.nih.gov/pub/clinvar/vcf_GRCh38/clinvar.vcf.gz - Network accessline 124
wget ftp://ftp.ncbi.nlm.nih.gov/pub/clinvar/vcf_GRCh38/clinvar.vcf.gz - Network accessline 240
**Submission methods:** - Web submission portal: https://submit.ncbi.nlm.nih.gov/subs/clinvar/ - Network accessline 241
- Web submission portal: https://submit.ncbi.nlm.nih.gov/subs/clinvar/ - Network accessline 274
wget ftp://ftp.ncbi.nlm.nih.gov/pub/clinvar/vcf_GRCh38/clinvar.vcf.gz - Network accessline 275
wget ftp://ftp.ncbi.nlm.nih.gov/pub/clinvar/vcf_GRCh38/clinvar.vcf.gz.tbi - Network accessline 310
wget ftp://ftp.ncbi.nlm.nih.gov/pub/clinvar/xml/clinvar_variation/ClinVarVariationRelease_YYYY-MM.xml.gz - Network accessline 351
- ClinVar home: https://www.ncbi.nlm.nih.gov/clinvar/ - Network accessline 352
- ClinVar documentation: https://www.ncbi.nlm.nih.gov/clinvar/docs/ - Network accessline 353
- E-utilities documentation: https://www.ncbi.nlm.nih.gov/books/NBK25501/ - Network accessline 355
- ClinGen expert panels: https://clinicalgenome.org/
skills/literature/gene-database/SKILL.md
skills/literature/geo-database/SKILL.md
- Network accessline 398
**Using wget or curl for Downloads:** - Network accessline 401
# Download series matrix file wget ftp://ftp.ncbi.nlm.nih.gov/geo/series/GSE123nnn/GSE123456/matrix/GSE123456_series_matrix.txt.gz - Network accessline 402
wget ftp://ftp.ncbi.nlm.nih.gov/geo/series/GSE123nnn/GSE123456/matrix/GSE123456_series_matrix.txt.gz - Network accessline 404
# Download all supplementary files for a series wget -r -np -nd ftp://ftp.ncbi.nlm.nih.gov/geo/series/GSE123nnn/GSE123456/suppl/ - Network accessline 405
wget -r -np -nd ftp://ftp.ncbi.nlm.nih.gov/geo/series/GSE123nnn/GSE123456/suppl/ - Network accessline 407
# Download SOFT format family file wget ftp://ftp.ncbi.nlm.nih.gov/geo/series/GSE123nnn/GSE123456/soft/GSE123456_family.soft.gz - Network accessline 408
wget ftp://ftp.ncbi.nlm.nih.gov/geo/series/GSE123nnn/GSE123456/soft/GSE123456_family.soft.gz - Network accessline 673
# Optional: Set API key for increased rate limits # Get your API key from: https://www.ncbi.nlm.nih.gov/account/ - Network accessline 674
# Get your API key from: https://www.ncbi.nlm.nih.gov/account/ - Access to secretsline 675
[Credential-related source omitted] - Network accessline 725
- Web-based statistical analysis tool integrated into GEO - Accessible at: https://www.ncbi.nlm.nih.gov/geo/geo2r/?acc=GSExxxxx - Network accessline 726
- Accessible at: https://www.ncbi.nlm.nih.gov/geo/geo2r/?acc=GSExxxxx - Network accessline 739
- No rate limits for FTP downloads - Preferred method for bulk downloads - Can download entire directories with wget -r - Network accessline 741
- Can download entire directories with wget -r - Network accessline 805
- **GEO Website:** https://www.ncbi.nlm.nih.gov/geo/ - Network accessline 806
- **GEO Submission Guidelines:** https://www.ncbi.nlm.nih.gov/geo/info/submission.html - Network accessline 807
- **GEOparse Documentation:** https://geoparse.readthedocs.io/ - Network accessline 808
- **E-utilities Documentation:** https://www.ncbi.nlm.nih.gov/books/NBK25501/ - Network accessline 809
- **GEO FTP Site:** ftp://ftp.ncbi.nlm.nih.gov/geo/ - **GEO2R Tool:** https://www.ncbi.nlm.nih.gov/geo/geo2r/ - **NCBI API Keys:** https://ncbiinsights.ncbi.nlm.nih.gov/2017/11/02/new-api-keys-for-the-e-utilities/ - Network accessline 810
- **GEO2R Tool:** https://www.ncbi.nlm.nih.gov/geo/geo2r/ - Network accessline 811
- **NCBI API Keys:** https://ncbiinsights.ncbi.nlm.nih.gov/2017/11/02/new-api-keys-for-the-e-utilities/ - Network accessline 812
- **Biopython Tutorial:** https://biopython.org/DIST/docs/tutorial/Tutorial.html
skills/literature/gwas-database/SKILL.md
- Network accessline 49
The web interface at https://www.ebi.ac.uk/gwas/ supports multiple search modes: - Network accessline 90
**Base URLs:** - GWAS Catalog API: `https://www.ebi.ac.uk/gwas/rest/api` - Network accessline 91
- GWAS Catalog API: `https://www.ebi.ac.uk/gwas/rest/api` - Network accessline 92
- Summary Statistics API: `https://www.ebi.ac.uk/gwas/summary-statistics/api` - Network accessline 94
**API Documentation:** - Main API docs: https://www.ebi.ac.uk/gwas/rest/docs/api - Network accessline 95
- Main API docs: https://www.ebi.ac.uk/gwas/rest/docs/api - Network accessline 96
- Summary stats docs: https://www.ebi.ac.uk/gwas/summary-statistics/docs/ - Network accessline 104
# Get a specific study url = "https://www.ebi.ac.uk/gwas/rest/api/studies/GCST001795" response = requests.get(url, headers={"Content-Type": "application/json"}) - Network accessline 105
url = "https://www.ebi.ac.uk/gwas/rest/api/studies/GCST001795" response = requests.get(url, headers={"Content-Type": "application/json"}) study = response.json() - Network accessline 106
response = requests.get(url, headers={"Content-Type": "application/json"}) - Network accessline 112
# Find associations for a variant variant = "rs7903146" url = f"https://www.ebi.ac.uk/gwas/rest/api/singleNucleotidePolymorphisms/{variant}/associations" - Network accessline 113
variant = "rs7903146" url = f"https://www.ebi.ac.uk/gwas/rest/api/singleNucleotidePolymorphisms/{variant}/associations" params = {"projection": "associationBySnp"} - Network accessline 114
url = f"https://www.ebi.ac.uk/gwas/rest/api/singleNucleotidePolymorphisms/{variant}/associations" params = {"projection": "associationBySnp"} response = requests.get(url, params=params, headers={"Content-Type": "application/json"}) - Network accessline 115
params = {"projection": "associationBySnp"} response = requests.get(url, params=params, headers={"Content-Type": "application/json"}) associations = response.json() - Network accessline 116
response = requests.get(url, params=params, headers={"Content-Type": "application/json"}) - Network accessline 122
# Get variant details url = "https://www.ebi.ac.uk/gwas/rest/api/singleNucleotidePolymorphisms/rs7903146" response = requests.get(url, headers={"Content-Type": "application/json"}) - Network accessline 123
url = "https://www.ebi.ac.uk/gwas/rest/api/singleNucleotidePolymorphisms/rs7903146" response = requests.get(url, headers={"Content-Type": "application/json"}) variant_info = response.json() - Network accessline 124
response = requests.get(url, headers={"Content-Type": "application/json"}) - Network accessline 130
# Get trait information url = "https://www.ebi.ac.uk/gwas/rest/api/efoTraits/EFO_0001360" response = requests.get(url, headers={"Content-Type": "application/json"}) - Network accessline 131
url = "https://www.ebi.ac.uk/gwas/rest/api/efoTraits/EFO_0001360" response = requests.get(url, headers={"Content-Type": "application/json"}) trait_info = response.json() - Network accessline 132
response = requests.get(url, headers={"Content-Type": "application/json"}) - Network accessline 142
trait = "EFO_0001360" # Type 2 diabetes base_url = "https://www.ebi.ac.uk/gwas/rest/api" - Network accessline 143
base_url = "https://www.ebi.ac.uk/gwas/rest/api" - Network accessline 145
# Query associations for this trait url = f"{base_url}/efoTraits/{trait}/associations" response = requests.get(url, headers={"Content-Type": "application/json"}) - Network accessline 146
url = f"{base_url}/efoTraits/{trait}/associations" response = requests.get(url, headers={"Content-Type": "application/json"}) associations = response.json() - Network accessline 147
response = requests.get(url, headers={"Content-Type": "application/json"}) - Network accessline 162
variant = "rs7903146" base_url = "https://www.ebi.ac.uk/gwas/rest/api" - Network accessline 163
base_url = "https://www.ebi.ac.uk/gwas/rest/api" - Network accessline 165
# Get variant details url = f"{base_url}/singleNucleotidePolymorphisms/{variant}" response = requests.get(url, headers={"Content-Type": "application/json"}) - Network accessline 166
url = f"{base_url}/singleNucleotidePolymorphisms/{variant}" response = requests.get(url, headers={"Content-Type": "application/json"}) variant_data = response.json() - Network accessline 167
response = requests.get(url, headers={"Content-Type": "application/json"}) - Network accessline 171
url = f"{base_url}/singleNucleotidePolymorphisms/{variant}/associations" params = {"projection": "associationBySnp"} response = requests.get(url, params=params, headers={"Content-Type": "application/json"}) - Network accessline 172
params = {"projection": "associationBySnp"} response = requests.get(url, params=params, headers={"Content-Type": "application/json"}) associations = response.json() - Network accessline 173
response = requests.get(url, params=params, headers={"Content-Type": "application/json"}) - Network accessline 187
# Query summary statistics API base_url = "https://www.ebi.ac.uk/gwas/summary-statistics/api" - Network accessline 188
base_url = "https://www.ebi.ac.uk/gwas/summary-statistics/api" - Network accessline 196
"size": 100 # Number of results } response = requests.get(url, params=params) - Network accessline 197
} response = requests.get(url, params=params) results = response.json() - Network accessline 198
response = requests.get(url, params=params) - Network accessline 219
base_url = "https://www.ebi.ac.uk/gwas/rest/api" - Network accessline 224
"bpEnd": end_pos } response = requests.get(url, params=params, headers={"Content-Type": "application/json"}) - Network accessline 225
} response = requests.get(url, params=params, headers={"Content-Type": "application/json"}) variants_in_region = response.json() - Network accessline 226
response = requests.get(url, params=params, headers={"Content-Type": "application/json"}) - Network accessline 234
**Access Methods:** 1. **FTP download**: http://ftp.ebi.ac.uk/pub/databases/gwas/summary_statistics/ - Network accessline 235
1. **FTP download**: http://ftp.ebi.ac.uk/pub/databases/gwas/summary_statistics/ - Network accessline 250
# Get available summary statistics base_url = "https://www.ebi.ac.uk/gwas/summary-statistics/api" - Network accessline 251
base_url = "https://www.ebi.ac.uk/gwas/summary-statistics/api" url = f"{base_url}/studies/GCST001234" response = requests.get(url) - Network accessline 252
url = f"{base_url}/studies/GCST001234" response = requests.get(url) study_info = response.json() - Network accessline 253
response = requests.get(url) - Network accessline 284
# API responses include _links for related resources response = requests.get("https://www.ebi.ac.uk/gwas/rest/api/studies/GCST001234") - Network accessline 285
response = requests.get("https://www.ebi.ac.uk/gwas/rest/api/studies/GCST001234") - Network accessline 288
# Follow link to associations associations_url = study['_links']['associations']['href'] associations_response = requests.get(associations_url) - Network accessline 290
associations_response = requests.get(associations_url) - Network accessline 303
url = f"https://www.ebi.ac.uk/gwas/rest/api/efoTraits/{efo_id}/associations" - Network accessline 326
url = f"https://www.ebi.ac.uk/gwas/rest/api/singleNucleotidePolymorphisms/{rs_id}" - Network accessline 331
url = f"https://www.ebi.ac.uk/gwas/rest/api/singleNucleotidePolymorphisms/{rs_id}/associations" - Network accessline 348
url = f"https://www.ebi.ac.uk/gwas/rest/api/singleNucleotidePolymorphisms/search/findByGene" - Network accessline 406
# Via FTP wget ftp://ftp.ebi.ac.uk/pub/databases/gwas/summary_statistics/GCSTXXXXXX/harmonised/GCSTXXXXXX-harmonised.tsv.gz - Network accessline 407
wget ftp://ftp.ebi.ac.uk/pub/databases/gwas/summary_statistics/GCSTXXXXXX/harmonised/GCSTXXXXXX-harmonised.tsv.gz - Network accessline 412
url = f"https://www.ebi.ac.uk/gwas/summary-statistics/api/chromosomes/{chrom}/associations" - Network accessline 497
pandas DataFrame with association results """ base_url = "https://www.ebi.ac.uk/gwas/rest/api" - Network accessline 498
""" base_url = "https://www.ebi.ac.uk/gwas/rest/api" url = f"{base_url}/efoTraits/{trait_id}/associations" - Network accessline 499
base_url = "https://www.ebi.ac.uk/gwas/rest/api" - Network accessline 506
while True: params = {"page": page, "size": 100} response = requests.get(url, params=params, headers=headers) - Network accessline 508
response = requests.get(url, params=params, headers=headers) - Network accessline 564
The GWAS Catalog team provides workshop materials: - GitHub repository: https://github.com/EBISPOT/GWAS_Catalog-workshop - Network accessline 565
- GitHub repository: https://github.com/EBISPOT/GWAS_Catalog-workshop - Network accessline 598
- **GWAS Catalog website**: https://www.ebi.ac.uk/gwas/ - Network accessline 599
- **Documentation**: https://www.ebi.ac.uk/gwas/docs - Network accessline 600
- **API documentation**: https://www.ebi.ac.uk/gwas/rest/docs/api - Network accessline 601
- **Summary Statistics API**: https://www.ebi.ac.uk/gwas/summary-statistics/docs/ - Network accessline 602
- **FTP site**: http://ftp.ebi.ac.uk/pub/databases/gwas/ - Network accessline 603
- **Training materials**: https://github.com/EBISPOT/GWAS_Catalog-workshop - Network accessline 604
- **PGS Catalog** (polygenic scores): https://www.pgscatalog.org/
skills/literature/hmdb-database/SKILL.md
skills/literature/literature-review/SKILL.md
- Broad permissionsline 1
Bash - Network accessline 317
- Date filters: `2020:2024[Publication Date]` - Boolean operators: AND, OR, NOT - See MeSH browser: https://meshb.nlm.nih.gov/search - Network accessline 319
- See MeSH browser: https://meshb.nlm.nih.gov/search - Network accessline 385
### APA (7th Edition) - In-text: (Smith et al., 2023) - Reference: Smith, J. D., Johnson, M. L., & Williams, K. R. (2023). Title. *Journal*, *22*(4), 301-318. https://doi.org/10.xxx/yyy - Network accessline 387
- Reference: Smith, J. D., Johnson, M. L., & Williams, K. R. (2023). Title. *Journal*, *22*(4), 301-318. https://doi.org/10.xxx/yyy - Network accessline 586
**Guidelines:** - PRISMA (Systematic Reviews): http://www.prisma-statement.org/ - Network accessline 587
- PRISMA (Systematic Reviews): http://www.prisma-statement.org/ - Network accessline 588
- Cochrane Handbook: https://training.cochrane.org/handbook - Network accessline 589
- AMSTAR 2 (Review Quality): https://amstar.ca/ - Network accessline 591
**Tools:** - MeSH Browser: https://meshb.nlm.nih.gov/search - Network accessline 592
- MeSH Browser: https://meshb.nlm.nih.gov/search - Network accessline 593
- PubMed Advanced Search: https://pubmed.ncbi.nlm.nih.gov/advanced/ - Network accessline 594
- Boolean Search Guide: https://www.ncbi.nlm.nih.gov/books/NBK3827/ - Network accessline 596
**Citation Styles:** - APA Style: https://apastyle.apa.org/ - Network accessline 597
- APA Style: https://apastyle.apa.org/ - Network accessline 598
- Nature Portfolio: https://www.nature.com/nature-portfolio/editorial-policies/reporting-standards - Network accessline 599
- NLM/Vancouver: https://www.nlm.nih.gov/bsd/uniform_requirements.html
skills/literature/literature-search/SKILL.md
- Network accessline 30
1. Node.js 18+ (uses built-in fetch) 2. Valyu API key from https://platform.valyu.ai ($10 free credits) - Network accessline 31
2. Valyu API key from https://platform.valyu.ai ($10 free credits) - Network accessline 52
1. **Ask the user for their API key:** "To search scientific literature, I need your Valyu API key. Get one free ($10 credits) at https://platform.valyu.ai" - Network accessline 53
"To search scientific literature, I need your Valyu API key. Get one free ($10 credits) at https://platform.valyu.ai" - Network accessline 78
{ "title": "Article Title", "url": "https://...", - Network accessline 79
"title": "Article Title", "url": "https://...", "content": "Full article text with figures...", - Network accessline 80
"url": "https://...", - Network accessline 82
"source": "pubmed|arxiv|biorxiv|medrxiv", "relevance_score": 0.95, "images": ["https://example.com/figure1.jpg"] - Network accessline 83
"relevance_score": 0.95, "images": ["https://example.com/figure1.jpg"] } - Network accessline 84
"images": ["https://example.com/figure1.jpg"] - Network accessline 157
- Base URL: `https://api.valyu.ai/v1` - Network accessline 169
Direct API calls using Node.js built-in `fetch()`, zero external dependencies. - Access to secretsline 180
[Credential-related source omitted] - Network accessline 214
See the [Valyu docs](https://docs.valyu.ai) for full integration examples and SDK reference.
skills/literature/medrxiv-search/SKILL.md
- Network accessline 29
1. Node.js 18+ (uses built-in fetch) 2. Valyu API key from https://platform.valyu.ai ($10 free credits) - Network accessline 30
2. Valyu API key from https://platform.valyu.ai ($10 free credits) - Network accessline 51
1. **Ask the user for their API key:** "To search medRxiv, I need your Valyu API key. Get one free ($10 credits) at https://platform.valyu.ai" - Network accessline 52
"To search medRxiv, I need your Valyu API key. Get one free ($10 credits) at https://platform.valyu.ai" - Network accessline 78
{ "title": "Article Title", "url": "https://medrxiv.org/content/...", - Network accessline 79
"title": "Article Title", "url": "https://medrxiv.org/content/...", "content": "Full article text with figures...", - Network accessline 80
"url": "https://medrxiv.org/content/...", - Network accessline 82
"source": "medrxiv", "relevance_score": 0.95, "images": ["https://example.com/figure1.jpg"] - Network accessline 83
"relevance_score": 0.95, "images": ["https://example.com/figure1.jpg"] } - Network accessline 84
"images": ["https://example.com/figure1.jpg"] - Network accessline 154
- Base URL: `https://api.valyu.ai/v1` - Network accessline 166
Direct API calls using Node.js built-in `fetch()`, zero external dependencies. - Access to secretsline 177
[Credential-related source omitted] - Network accessline 211
See the [Valyu docs](https://docs.valyu.ai) for full integration examples and SDK reference.
skills/literature/openalex-database/SKILL.md
- Network accessline 178
dois = [ "https://doi.org/10.1038/s41586-021-03819-2", - Network accessline 179
"https://doi.org/10.1038/s41586-021-03819-2", - Network accessline 180
"https://doi.org/10.1126/science.abc1234", - Network accessline 216
# Get the work work = client.get_entity('works', 'https://doi.org/10.1038/s41586-021-03819-2') - Network accessline 217
work = client.get_entity('works', 'https://doi.org/10.1038/s41586-021-03819-2') - Network accessline 219
# Get citing papers using cited_by_api_url import requests citing_response = requests.get( - Network accessline 220
import requests citing_response = requests.get( work['cited_by_api_url'], - Network accessline 221
citing_response = requests.get( - Network accessline 403
# DOI for works work = client.get_entity('works', 'https://doi.org/10.7717/peerj.4375') - Network accessline 404
work = client.get_entity('works', 'https://doi.org/10.7717/peerj.4375') - Network accessline 406
# ORCID for authors author = client.get_entity('authors', 'https://orcid.org/0000-0003-1613-5981') - Network accessline 407
author = client.get_entity('authors', 'https://orcid.org/0000-0003-1613-5981') - Network accessline 409
# ROR for institutions institution = client.get_entity('institutions', 'https://ror.org/02y3ad647') - Network accessline 410
institution = client.get_entity('institutions', 'https://ror.org/02y3ad647')
skills/literature/patents-search/SKILL.md
- Network accessline 29
1. Node.js 18+ (uses built-in fetch) 2. Valyu API key from https://platform.valyu.ai ($10 free credits) - Network accessline 30
2. Valyu API key from https://platform.valyu.ai ($10 free credits) - Network accessline 51
1. **Ask the user for their API key:** "To search patents, I need your Valyu API key. Get one free ($10 credits) at https://platform.valyu.ai" - Network accessline 52
"To search patents, I need your Valyu API key. Get one free ($10 credits) at https://platform.valyu.ai" - Network accessline 78
{ "title": "Patent Title", "url": "https://patents.google.com/...", - Network accessline 79
"title": "Patent Title", "url": "https://patents.google.com/...", "content": "Patent claims, description, technical details...", - Network accessline 80
"url": "https://patents.google.com/...", - Network accessline 82
"source": "patents", "relevance_score": 0.95, "images": ["https://example.com/diagram.png"] - Network accessline 83
"relevance_score": 0.95, "images": ["https://example.com/diagram.png"] } - Network accessline 84
"images": ["https://example.com/diagram.png"] - Network accessline 154
- Base URL: `https://api.valyu.ai/v1` - Network accessline 166
Direct API calls using Node.js built-in `fetch()`, zero external dependencies. - Access to secretsline 177
[Credential-related source omitted] - Network accessline 211
See the [Valyu docs](https://docs.valyu.ai) for full integration examples and SDK reference.
skills/literature/pdb-database/SKILL.md
- Possible obfuscationline 57
value="MTEYKLVVVGAGGVGKSALTIQLIQNHFVDEYDPTIEDSYRKQVVIDGETCLLDILDTAGQEEYSAMRDQYMRTGEGFLCVFAINNTKSFEDIHHYREQIKRVKDSEDVPMVLVGNKCDLPSRTVDTKQAQDLARSYGIPFIETSAKTRQGVDDAFYTLVREIRKHKEKMSKDGKKKKKKSKTKCVIM", - Network accessline 103
# Get entry-level data entry_data = fetch("4HHB", schema=Schema.ENTRY) - Network accessline 104
entry_data = fetch("4HHB", schema=Schema.ENTRY) - Network accessline 111
# Get protein/nucleic acid information entity_data = fetch("4HHB_1", schema=Schema.POLYMER_ENTITY) - Network accessline 112
entity_data = fetch("4HHB_1", schema=Schema.POLYMER_ENTITY) - Network accessline 135
} """ data = fetch(query_type="graphql", query=query) - Network accessline 137
data = fetch(query_type="graphql", query=query) - Network accessline 144
**Download Methods:** - **PDB format** (legacy text format): `https://files.rcsb.org/download/{PDB_ID}.pdb` - Network accessline 145
- **PDB format** (legacy text format): `https://files.rcsb.org/download/{PDB_ID}.pdb` - Network accessline 146
- **mmCIF format** (modern standard): `https://files.rcsb.org/download/{PDB_ID}.cif` - Network accessline 148
- **Biological assembly**: `https://files.rcsb.org/download/{PDB_ID}.pdb1` (for assembly 1) - Network accessline 156
# Download PDB format pdb_url = f"https://files.rcsb.org/download/{pdb_id}.pdb" - Network accessline 157
pdb_url = f"https://files.rcsb.org/download/{pdb_id}.pdb" - Network accessline 158
response = requests.get(pdb_url) - Network accessline 162
# Download mmCIF format cif_url = f"https://files.rcsb.org/download/{pdb_id}.cif" - Network accessline 163
cif_url = f"https://files.rcsb.org/download/{pdb_id}.cif" - Network accessline 164
response = requests.get(cif_url) - Network accessline 193
# Get experimental details data = fetch("4HHB", schema=Schema.ENTRY) - Network accessline 194
data = fetch("4HHB", schema=Schema.ENTRY) - Network accessline 215
for pdb_id in pdb_ids: try: data = fetch(pdb_id, schema=Schema.ENTRY) - Network accessline 216
try: data = fetch(pdb_id, schema=Schema.ENTRY) results[pdb_id] = { - Network accessline 217
data = fetch(pdb_id, schema=Schema.ENTRY) - Network accessline 301
- **RCSB PDB Website:** https://www.rcsb.org - Network accessline 302
- **PDB-101 Educational Portal:** https://pdb101.rcsb.org - Network accessline 303
- **API Documentation:** https://www.rcsb.org/docs/programmatic-access/web-apis-overview - Network accessline 304
- **Python Package Docs:** https://rcsbapi.readthedocs.io/ - Network accessline 305
- **Data API Documentation:** https://data.rcsb.org/ - Network accessline 306
- **GitHub Repository:** https://github.com/rcsb/py-rcsb-api
skills/literature/perplexity-search/SKILL.md
- Network accessline 39
1. **Get OpenRouter API key**: - Visit https://openrouter.ai/keys - Network accessline 40
- Visit https://openrouter.ai/keys - Access to secretsline 47
[Credential-related source omitted] - Network accessline 256
3. Reserve `sonar-pro-search` for complex analysis 4. Set `--max-tokens` to limit response length 5. Monitor usage at https://openrouter.ai/activity - Network accessline 257
4. Set `--max-tokens` to limit response length 5. Monitor usage at https://openrouter.ai/activity 6. Set spending limits in OpenRouter dashboard - Network accessline 258
5. Monitor usage at https://openrouter.ai/activity - Access to secretsline 269
[Credential-related source omitted] - Network accessline 287
**Solutions**: - Wait a few seconds before retrying - Increase rate limit at https://openrouter.ai/keys - Network accessline 288
- Wait a few seconds before retrying - Increase rate limit at https://openrouter.ai/keys - Add delays between requests in batch processing - Network accessline 289
- Increase rate limit at https://openrouter.ai/keys - Network accessline 296
**Solution**: - Add credits at https://openrouter.ai/account - Network accessline 297
- Add credits at https://openrouter.ai/account - Network accessline 389
**OpenRouter:** - Dashboard: https://openrouter.ai/account - Network accessline 390
- Dashboard: https://openrouter.ai/account - Network accessline 391
- API Keys: https://openrouter.ai/keys - Network accessline 392
- Perplexity Models: https://openrouter.ai/perplexity - Network accessline 393
- Usage Monitoring: https://openrouter.ai/activity - Network accessline 394
- Documentation: https://openrouter.ai/docs - Network accessline 396
**LiteLLM:** - Documentation: https://docs.litellm.ai/ - Network accessline 397
- Documentation: https://docs.litellm.ai/ - Network accessline 398
- OpenRouter Provider: https://docs.litellm.ai/docs/providers/openrouter - Network accessline 399
- GitHub: https://github.com/BerriAI/litellm - Network accessline 401
**Perplexity:** - Official Docs: https://docs.perplexity.ai/ - Network accessline 402
- Official Docs: https://docs.perplexity.ai/ - Access to secretsline 416
[Credential-related source omitted] - Access to secretsline 426
[Credential-related source omitted]
skills/literature/pubmed-database/SKILL.md
- Network accessline 127
# Step 1: Search for articles base_url = "https://eutils.ncbi.nlm.nih.gov/entrez/eutils/" - Network accessline 128
base_url = "https://eutils.ncbi.nlm.nih.gov/entrez/eutils/" - Network accessline 135
[Credential-related source omitted] - Access to secretsline 135
[Credential-related source omitted] - Network accessline 136
} response = requests.get(search_url, params=params) pmids = response.json()["esearchresult"]["idlist"] - Network accessline 137
response = requests.get(search_url, params=params) - Network accessline 147
[Credential-related source omitted] - Access to secretsline 147
[Credential-related source omitted] - Network accessline 148
} response = requests.get(fetch_url, params=params) abstracts = response.text - Network accessline 149
response = requests.get(fetch_url, params=params) - Network accessline 453
- **PubMed Help**: https://pubmed.ncbi.nlm.nih.gov/help/ - Network accessline 454
- **E-utilities Documentation**: https://www.ncbi.nlm.nih.gov/books/NBK25501/
skills/literature/pubmed-search/SKILL.md
- Network accessline 29
1. Node.js 18+ (uses built-in fetch) 2. Valyu API key from https://platform.valyu.ai ($10 free credits) - Network accessline 30
2. Valyu API key from https://platform.valyu.ai ($10 free credits) - Network accessline 51
1. **Ask the user for their API key:** "To search PubMed, I need your Valyu API key. Get one free ($10 credits) at https://platform.valyu.ai" - Network accessline 52
"To search PubMed, I need your Valyu API key. Get one free ($10 credits) at https://platform.valyu.ai" - Network accessline 63
User: Search PubMed for CAR-T cell therapy advances → Response: {"success": false, "setup_required": true, ...} → Claude asks: "Please provide your Valyu API key from https://platform.valyu.ai" - Network accessline 64
→ Response: {"success": false, "setup_required": true, ...} → Claude asks: "Please provide your Valyu API key from https://platform.valyu.ai" → User: "val_abc123..." - Network accessline 65
→ Claude asks: "Please provide your Valyu API key from https://platform.valyu.ai" - Network accessline 112
{ "title": "Article Title", "url": "https://pubmed.ncbi.nlm.nih.gov/...", - Network accessline 113
"title": "Article Title", "url": "https://pubmed.ncbi.nlm.nih.gov/...", "content": "Full article text with figures...", - Network accessline 114
"url": "https://pubmed.ncbi.nlm.nih.gov/...", - Network accessline 116
"source": "pubmed", "relevance_score": 0.95, "images": ["https://example.com/figure1.jpg"] - Network accessline 117
"relevance_score": 0.95, "images": ["https://example.com/figure1.jpg"] } - Network accessline 118
"images": ["https://example.com/figure1.jpg"] - Network accessline 187
- Base URL: `https://api.valyu.ai/v1` - Network accessline 199
Direct API calls using Node.js built-in `fetch()`, zero external dependencies. - Access to secretsline 210
[Credential-related source omitted] - Network accessline 244
See the [Valyu docs](https://docs.valyu.ai) for full integration examples and SDK reference.
skills/literature/research-grants/SKILL.md
- Broad permissionsline 1
Bash
skills/literature/research-lookup/SKILL.md
skills/literature/scientific-writing/SKILL.md
- Broad permissionsline 1
Bash
skills/literature/tooluniverse-literature-deep-research/SKILL.md
- Network accessline 449
# For paywalled PDFs accessible via institution webpage_text = get_webpage_text_from_url( url="https://doi.org/10.1016/...", - Network accessline 450
webpage_text = get_webpage_text_from_url( url="https://doi.org/10.1016/...", # Requires institutional proxy or VPN - Network accessline 451
url="https://doi.org/10.1016/...", - Network accessline 903
"themes": ["lysosomal_acidification", "autophagy"], "oa_status": "gold", "oa_url": "https://...", - Network accessline 904
"oa_status": "gold", "oa_url": "https://...", "citation_count": 45, - Network accessline 905
"oa_url": "https://...",
skills/literature/uspto-database/SKILL.md
- Network accessline 38
- Access to patent data through June 30, 2025 - 45 requests/minute rate limit - **Base URL**: `https://search.patentsview.org/api/v1/` - Network accessline 40
- **Base URL**: `https://search.patentsview.org/api/v1/` - Network accessline 49
- Trademark status, ownership, prosecution history - Search by serial or registration number - **Base URL**: `https://tsdrapi.uspto.gov/ts/cd/` - Network accessline 51
- **Base URL**: `https://tsdrapi.uspto.gov/ts/cd/` - Network accessline 69
USPTO APIs require an API key. Register at: **https://account.uspto.gov/api-manager/** - Network accessline 70
**https://account.uspto.gov/api-manager/** - Network accessline 72
API key for **PatentSearch API** is provided by PatentsView. Register at: **https://patentsview.org/api-v01-information-page** - Network accessline 73
**https://patentsview.org/api-v01-information-page** - Access to secretsline 77
[Credential-related source omitted] - Access to secretsline 78
[Credential-related source omitted] - Network accessline 153
url = "https://search.patentsview.org/api/v1/patent" - Access to secretsline 155
[Credential-related source omitted] - Network accessline 172
response = requests.post(url, headers=headers, json=query) - Access to secretsline 354
[Credential-related source omitted] - Access to secretsline 356
[Credential-related source omitted] - Network accessline 404
**Base URL**: `https://assignment-api.uspto.gov/patent/v1.4/` - Network accessline 411
[Credential-related source omitted] - Access to secretsline 411
[Credential-related source omitted] - Network accessline 412
url = f"https://assignment-api.uspto.gov/patent/v1.4/assignment/patent/{patent_number}" - Access to secretsline 413
[Credential-related source omitted] - Network accessline 415
response = requests.get(url, headers=headers) - Access to secretsline 419
[Credential-related source omitted] - Network accessline 434
[Credential-related source omitted] - Access to secretsline 434
[Credential-related source omitted] - Network accessline 435
url = "https://assignment-api.uspto.gov/patent/v1.4/assignment/search" - Access to secretsline 436
[Credential-related source omitted] - Network accessline 439
response = requests.post(url, headers=headers, json=data) - Access to secretsline 501
[Credential-related source omitted] - Access to secretsline 511
[Credential-related source omitted] - Network accessline 520
# 3. Get assignment history import requests assign_url = f"https://assignment-api.uspto.gov/patent/v1.4/assignment/patent/{patent_number}" - Network accessline 521
[Credential-related source omitted] - Network accessline 522
[Credential-related source omitted] - Network accessline 523
[Credential-related source omitted] - Access to secretsline 523
[Credential-related source omitted] - Network accessline 585
### API Documentation - **PatentSearch API**: https://search.patentsview.org/docs/ - Network accessline 586
- **PatentSearch API**: https://search.patentsview.org/docs/ - Network accessline 587
- **USPTO Developer Portal**: https://developer.uspto.gov/ - Network accessline 588
- **USPTO Open Data Portal**: https://data.uspto.gov/ - Network accessline 589
- **API Key Registration**: https://account.uspto.gov/api-manager/ - Network accessline 591
### Python Libraries - **uspto-opendata-python**: https://pypi.org/project/uspto-opendata-python/ - Network accessline 592
- **uspto-opendata-python**: https://pypi.org/project/uspto-opendata-python/ - Network accessline 593
- **USPTO Docs**: https://docs.ip-tools.org/uspto-opendata-python/
skills/med/clinical-decision-support/SKILL.md
- Broad permissionsline 1
Bash
skills/med/clinical-reports/SKILL.md
- Broad permissionsline 1
Bash
skills/med/clinicaltrials-database/SKILL.md
- Network accessline 44
url = "https://clinicaltrials.gov/api/v2/studies" - Network accessline 51
response = requests.get(url, params=params) - Network accessline 64
nct_id = "NCT04852770" url = f"https://clinicaltrials.gov/api/v2/studies/{nct_id}" - Network accessline 65
url = f"https://clinicaltrials.gov/api/v2/studies/{nct_id}" - Network accessline 67
response = requests.get(url) - Network accessline 433
def search_with_rate_limit(params): try: response = requests.get("https://clinicaltrials.gov/api/v2/studies", params=params) - Network accessline 434
try: response = requests.get("https://clinicaltrials.gov/api/v2/studies", params=params) response.raise_for_status() - Network accessline 435
response = requests.get("https://clinicaltrials.gov/api/v2/studies", params=params) - Network accessline 465
try: response = requests.get(url, params=params, timeout=30) - Network accessline 466
response = requests.get(url, params=params, timeout=30) - Network accessline 494
- **Base URL**: `https://clinicaltrials.gov/api/v2` - **Authentication**: Not required (public API) - **Rate Limit**: ~50 requests/minute per IP
skills/med/clinvar-database/SKILL.md
- Network accessline 33
Search ClinVar using the web interface at https://www.ncbi.nlm.nih.gov/clinvar/ - Network accessline 53
# Search for pathogenic BRCA1 variants curl "https://eutils.ncbi.nlm.nih.gov/entrez/eutils/esearch.fcgi?db=clinvar&term=BRCA1[gene]+AND+pathogenic[CLNSIG]&retmode=json" - Network accessline 54
curl "https://eutils.ncbi.nlm.nih.gov/entrez/eutils/esearch.fcgi?db=clinvar&term=BRCA1[gene]+AND+pathogenic[CLNSIG]&retmode=json" - Network accessline 120
# Download latest monthly XML release wget ftp://ftp.ncbi.nlm.nih.gov/pub/clinvar/xml/clinvar_variation/ClinVarVariationRelease_00-latest.xml.gz - Network accessline 121
wget ftp://ftp.ncbi.nlm.nih.gov/pub/clinvar/xml/clinvar_variation/ClinVarVariationRelease_00-latest.xml.gz - Network accessline 123
# Download VCF for GRCh38 wget ftp://ftp.ncbi.nlm.nih.gov/pub/clinvar/vcf_GRCh38/clinvar.vcf.gz - Network accessline 124
wget ftp://ftp.ncbi.nlm.nih.gov/pub/clinvar/vcf_GRCh38/clinvar.vcf.gz - Network accessline 240
**Submission methods:** - Web submission portal: https://submit.ncbi.nlm.nih.gov/subs/clinvar/ - Network accessline 241
- Web submission portal: https://submit.ncbi.nlm.nih.gov/subs/clinvar/ - Network accessline 274
wget ftp://ftp.ncbi.nlm.nih.gov/pub/clinvar/vcf_GRCh38/clinvar.vcf.gz - Network accessline 275
wget ftp://ftp.ncbi.nlm.nih.gov/pub/clinvar/vcf_GRCh38/clinvar.vcf.gz.tbi - Network accessline 310
wget ftp://ftp.ncbi.nlm.nih.gov/pub/clinvar/xml/clinvar_variation/ClinVarVariationRelease_YYYY-MM.xml.gz - Network accessline 351
- ClinVar home: https://www.ncbi.nlm.nih.gov/clinvar/ - Network accessline 352
- ClinVar documentation: https://www.ncbi.nlm.nih.gov/clinvar/docs/ - Network accessline 353
- E-utilities documentation: https://www.ncbi.nlm.nih.gov/books/NBK25501/ - Network accessline 355
- ClinGen expert panels: https://clinicalgenome.org/
skills/med/neurokit2/SKILL.md
- Network accessline 279
uv pip install https://github.com/neuropsychology/NeuroKit/zipball/dev - Network accessline 351
- Official Documentation: https://neuropsychology.github.io/NeuroKit/ - Network accessline 352
- GitHub Repository: https://github.com/neuropsychology/NeuroKit - Network accessline 353
- Publication: Makowski et al. (2021). NeuroKit2: A Python toolbox for neurophysiological signal processing. Behavior Research Methods. https://doi.org/10.3758/s13428-020-01516-y
skills/med/neuropixels-analysis/SKILL.md
- Network accessline 341
- **SpikeInterface Docs**: https://spikeinterface.readthedocs.io/ - Network accessline 342
- **Neuropixels Tutorial**: https://spikeinterface.readthedocs.io/en/stable/how_to/analyze_neuropixels.html - Network accessline 343
- **Kilosort4 GitHub**: https://github.com/MouseLand/Kilosort - Network accessline 344
- **IBL Neuropixel Tools**: https://github.com/int-brain-lab/ibl-neuropixel - Network accessline 345
- **Allen Institute ecephys**: https://github.com/AllenInstitute/ecephys_spike_sorting - Network accessline 346
- **Bombcell (Automated QC)**: https://github.com/Julie-Fabre/bombcell - Network accessline 347
- **SpikeAgent (AI Curation)**: https://github.com/SpikeAgent/SpikeAgent
skills/med/pydicom/SKILL.md
- Network accessline 2
name: pydicom description: Python library for working with DICOM (Digital Imaging and Communications in Medicine) files. Use this skill when reading, writing, or modifying medical imaging data in DICOM format, extracting pixel data from med - Network accessline 3
description: Python library for working with DICOM (Digital Imaging and Communications in Medicine) files. Use this skill when reading, writing, or modifying medical imaging data in DICOM format, extracting pixel data from medical images (C - Network accessline 4
license: https://github.com/pydicom/pydicom/blob/main/LICENSE - Network accessline 427
Official pydicom documentation: https://pydicom.github.io/pydicom/dev/ - Network accessline 428
- User Guide: https://pydicom.github.io/pydicom/dev/guides/user/index.html - Network accessline 429
- Tutorials: https://pydicom.github.io/pydicom/dev/tutorials/index.html - Network accessline 430
- API Reference: https://pydicom.github.io/pydicom/dev/reference/index.html - Network accessline 431
- Examples: https://pydicom.github.io/pydicom/dev/auto_examples/index.html
skills/med/pyhealth/SKILL.md
skills/med/tooluniverse-clinical-guidelines/SKILL.md
- Access to secretsline 129
[Credential-related source omitted] - Access to secretsline 131
[Credential-related source omitted] - Network accessline 172
`NCCN_get_patient_guideline(url)` — Get full text of a patient guideline. - Parameter: `url` (string) — the full URL from NCCN_list_patient_guidelines - Example: `NCCN_get_patient_guideline(url='https://www.nccn.org/patientresources/patient - Network accessline 173
- Parameter: `url` (string) — the full URL from NCCN_list_patient_guidelines - Example: `NCCN_get_patient_guideline(url='https://www.nccn.org/patientresources/patient-resources/guidelines-for-patients/guidelines-for-patients-details?patient - Network accessline 174
- Example: `NCCN_get_patient_guideline(url='https://www.nccn.org/patientresources/patient-resources/guidelines-for-patients/guidelines-for-patients-details?patientGuidelineId=61')` - Access to secretsline 337
[Credential-related source omitted] - Network accessline 342
| NCI_search_cancer_resources | `q='...'`, `size=N` (NOT `limit`) | ❌ `query=...` or `limit=N` | | NCCN_list_patient_guidelines | field `cancer_type` (not `title`) | ❌ `.get('title')` | | NCCN_get_patient_guideline | `url='https://...'` (fu - Network accessline 343
| NCCN_list_patient_guidelines | field `cancer_type` (not `title`) | ❌ `.get('title')` | | NCCN_get_patient_guideline | `url='https://...'` (full URL string) | ❌ integer patientGuidelineId | | MAGICapp_list_guidelines | `r.get('data', [])` - Network accessline 344
| NCCN_get_patient_guideline | `url='https://...'` (full URL string) | ❌ integer patientGuidelineId |
skills/med/tooluniverse-clinical-trial-design/SKILL.md
- Network accessline 1192
- **ToolUniverse Docs**: https://zitniklab.hms.harvard.edu/ToolUniverse/ - Network accessline 1193
- **FDA Guidance Documents**: https://www.fda.gov/regulatory-information/search-fda-guidance-documents - Network accessline 1194
- **ClinicalTrials.gov**: https://clinicaltrials.gov/ - Network accessline 1195
- **Slack Community**: https://join.slack.com/t/tooluniversehq/shared_invite/zt-3dic3eoio-5xxoJch7TLNibNQn5_AREQ
skills/med/tooluniverse-clinical-trial-matching/SKILL.md
- Network accessline 1141
This report is for informational and research purposes only. Clinical trial eligibility is ultimately determined by the trial investigators based on complete medical records. Patients should discuss all options with their healthcare team. T
skills/med/treatment-plans/SKILL.md
- Broad permissionsline 1
Bash - Dangerous shell operationline 1390
sudo cp assets/medical_treatment_plan.sty /usr/local/texlive/texmf-local/tex/latex/ - Dangerous shell operationline 1391
sudo texhash - Network accessline 1413
**Download from CTAN:** - Visit: https://ctan.org/ - Network accessline 1414
- Visit: https://ctan.org/ - Search for medical document classes - Download and install per package instructions - Dangerous shell operationline 1423
sudo tlmgr update --self - Dangerous shell operationline 1424
sudo tlmgr install tcolorbox tikz pgf - Dangerous shell operationline 1439
sudo tlmgr install tcolorbox tikz pgf
skills/pharma/adaptyv/SKILL.md
- Access to secretsline 24
[Credential-related source omitted] - Access to secretsline 30
[Credential-related source omitted] - Network accessline 52
[Credential-related source omitted] - Access to secretsline 52
[Credential-related source omitted] - Network accessline 53
base_url = "https://kq5jp7qj7wdqklhsxmovkzn4l40obksv.lambda-url.eu-central-1.on.aws" - Access to secretsline 56
[Credential-related source omitted] - Network accessline 60
# Submit experiment response = requests.post( - Network accessline 61
response = requests.post( - Network accessline 65
"sequences": ">protein1\nMKVLWALLGLLGAA...", "experiment_type": "binding", "webhook_url": "https://your-webhook.com/callback" - Network accessline 66
"experiment_type": "binding", "webhook_url": "https://your-webhook.com/callback" } - Network accessline 67
"webhook_url": "https://your-webhook.com/callback"
skills/pharma/chembl-database/SKILL.md
- Network accessline 382
- ChEMBL website: https://www.ebi.ac.uk/chembl/ - Network accessline 383
- API documentation: https://www.ebi.ac.uk/chembl/api/data/docs - Network accessline 384
- Python client GitHub: https://github.com/chembl/chembl_webresource_client - Network accessline 385
- Interface documentation: https://chembl.gitbook.io/chembl-interface-documentation/ - Network accessline 386
- Example notebooks: https://github.com/chembl/notebooks
skills/pharma/chembl-search/SKILL.md
- Network accessline 29
1. Node.js 18+ (uses built-in fetch) 2. Valyu API key from https://platform.valyu.ai ($10 free credits) - Network accessline 30
2. Valyu API key from https://platform.valyu.ai ($10 free credits) - Network accessline 51
1. **Ask the user for their API key:** "To search ChEMBL, I need your Valyu API key. Get one free ($10 credits) at https://platform.valyu.ai" - Network accessline 52
"To search ChEMBL, I need your Valyu API key. Get one free ($10 credits) at https://platform.valyu.ai" - Network accessline 78
{ "title": "Compound/Assay Title", "url": "https://chembl.org/...", - Network accessline 79
"title": "Compound/Assay Title", "url": "https://chembl.org/...", "content": "Compound data, targets, assay results...", - Network accessline 80
"url": "https://chembl.org/...", - Network accessline 82
"source": "chembl", "relevance_score": 0.95, "images": ["https://example.com/structure.png"] - Network accessline 83
"relevance_score": 0.95, "images": ["https://example.com/structure.png"] } - Network accessline 84
"images": ["https://example.com/structure.png"] - Network accessline 154
- Base URL: `https://api.valyu.ai/v1` - Network accessline 166
Direct API calls using Node.js built-in `fetch()`, zero external dependencies. - Access to secretsline 177
[Credential-related source omitted] - Network accessline 211
See the [Valyu docs](https://docs.valyu.ai) for full integration examples and SDK reference.
skills/pharma/datamol/SKILL.md
- Network accessline 130
# Read from cloud storage df = dm.read_sdf("s3://bucket/compounds.sdf") df = dm.read_csv("https://example.com/data.csv") - Network accessline 132
df = dm.read_csv("https://example.com/data.csv") - Network accessline 701
- **Datamol Documentation**: https://docs.datamol.io/ - Network accessline 702
- **RDKit Documentation**: https://www.rdkit.org/docs/ - Network accessline 703
- **GitHub Repository**: https://github.com/datamol-io/datamol
skills/pharma/deepchem/SKILL.md
skills/pharma/diffdock/SKILL.md
- Network accessline 53
git clone https://github.com/gcorso/DiffDock.git - Network accessline 366
python app/main.py # Navigate to http://localhost:7860 - Network accessline 367
# Navigate to http://localhost:7860 - Network accessline 370
Or use the online demo without installation: - https://huggingface.co/spaces/reginabarzilaygroup/DiffDock-Web - Network accessline 371
- https://huggingface.co/spaces/reginabarzilaygroup/DiffDock-Web - Network accessline 477
- **GitHub Repository**: https://github.com/gcorso/DiffDock - Network accessline 478
- **Online Demo**: https://huggingface.co/spaces/reginabarzilaygroup/DiffDock-Web - Network accessline 479
- **DiffDock-L Paper**: https://arxiv.org/abs/2402.18396 - Network accessline 480
- **Original Paper**: https://arxiv.org/abs/2210.01776
skills/pharma/drug-discovery-search/SKILL.md
- Network accessline 30
1. Node.js 18+ (uses built-in fetch) 2. Valyu API key from https://platform.valyu.ai ($10 free credits) - Network accessline 31
2. Valyu API key from https://platform.valyu.ai ($10 free credits) - Network accessline 52
1. **Ask the user for their API key:** "To search drug discovery databases, I need your Valyu API key. Get one free ($10 credits) at https://platform.valyu.ai" - Network accessline 53
"To search drug discovery databases, I need your Valyu API key. Get one free ($10 credits) at https://platform.valyu.ai" - Network accessline 78
{ "title": "Compound/Drug/Target Title", "url": "https://...", - Network accessline 79
"title": "Compound/Drug/Target Title", "url": "https://...", "content": "Full data including compounds, targets, mechanisms...", - Network accessline 80
"url": "https://...", - Network accessline 82
"source": "chembl|drugbank|drug-labels|open-targets", "relevance_score": 0.95, "images": ["https://example.com/structure.png"] - Network accessline 83
"relevance_score": 0.95, "images": ["https://example.com/structure.png"] } - Network accessline 84
"images": ["https://example.com/structure.png"] - Network accessline 157
- Base URL: `https://api.valyu.ai/v1` - Network accessline 169
Direct API calls using Node.js built-in `fetch()`, zero external dependencies. - Access to secretsline 180
[Credential-related source omitted] - Network accessline 214
See the [Valyu docs](https://docs.valyu.ai) for full integration examples and SDK reference.
skills/pharma/drug-labels-search/SKILL.md
- Network accessline 29
1. Node.js 18+ (uses built-in fetch) 2. Valyu API key from https://platform.valyu.ai ($10 free credits) - Network accessline 30
2. Valyu API key from https://platform.valyu.ai ($10 free credits) - Network accessline 51
1. **Ask the user for their API key:** "To search FDA drug labels, I need your Valyu API key. Get one free ($10 credits) at https://platform.valyu.ai" - Network accessline 52
"To search FDA drug labels, I need your Valyu API key. Get one free ($10 credits) at https://platform.valyu.ai" - Network accessline 78
{ "title": "Drug Label Title", "url": "https://fda.gov/...", - Network accessline 79
"title": "Drug Label Title", "url": "https://fda.gov/...", "content": "Label content, warnings, dosing...", - Network accessline 80
"url": "https://fda.gov/...", - Network accessline 82
"source": "drug-labels", "relevance_score": 0.95, "images": ["https://example.com/label.jpg"] - Network accessline 83
"relevance_score": 0.95, "images": ["https://example.com/label.jpg"] } - Network accessline 84
"images": ["https://example.com/label.jpg"] - Network accessline 154
- Base URL: `https://api.valyu.ai/v1` - Network accessline 166
Direct API calls using Node.js built-in `fetch()`, zero external dependencies. - Access to secretsline 177
[Credential-related source omitted] - Network accessline 211
See the [Valyu docs](https://docs.valyu.ai) for full integration examples and SDK reference.
skills/pharma/drugbank-search/SKILL.md
- Network accessline 29
1. Node.js 18+ (uses built-in fetch) 2. Valyu API key from https://platform.valyu.ai ($10 free credits) - Network accessline 30
2. Valyu API key from https://platform.valyu.ai ($10 free credits) - Network accessline 51
1. **Ask the user for their API key:** "To search DrugBank, I need your Valyu API key. Get one free ($10 credits) at https://platform.valyu.ai" - Network accessline 52
"To search DrugBank, I need your Valyu API key. Get one free ($10 credits) at https://platform.valyu.ai" - Network accessline 78
{ "title": "Drug Name", "url": "https://drugbank.com/...", - Network accessline 79
"title": "Drug Name", "url": "https://drugbank.com/...", "content": "Drug information, mechanism, interactions...", - Network accessline 80
"url": "https://drugbank.com/...", - Network accessline 82
"source": "drugbank", "relevance_score": 0.95, "images": ["https://example.com/structure.png"] - Network accessline 83
"relevance_score": 0.95, "images": ["https://example.com/structure.png"] } - Network accessline 84
"images": ["https://example.com/structure.png"] - Network accessline 154
- Base URL: `https://api.valyu.ai/v1` - Network accessline 166
Direct API calls using Node.js built-in `fetch()`, zero external dependencies. - Access to secretsline 177
[Credential-related source omitted] - Network accessline 211
See the [Valyu docs](https://docs.valyu.ai) for full integration examples and SDK reference.
skills/pharma/fda-database/SKILL.md
- Access to secretsline 44
[Credential-related source omitted] - Network accessline 64
Register at: https://open.fda.gov/apis/authentication/ - Access to secretsline 68
[Credential-related source omitted] - Access to secretsline 426
[Credential-related source omitted] - Network accessline 490
- **openFDA Homepage**: https://open.fda.gov/ - Network accessline 491
- **API Documentation**: https://open.fda.gov/apis/ - Network accessline 492
- **Interactive API Explorer**: https://open.fda.gov/apis/try-the-api/ - Network accessline 493
- **GitHub Repository**: https://github.com/FDA/openfda - Network accessline 494
- **Terms of Service**: https://open.fda.gov/terms/ - Network accessline 514
- **GitHub Issues**: https://github.com/FDA/openfda/issues
skills/pharma/medchem/SKILL.md
skills/pharma/molfeat/SKILL.md
- Network accessline 505
- **Official Documentation**: https://molfeat-docs.datamol.io/ - Network accessline 506
- **GitHub Repository**: https://github.com/datamol-io/molfeat - Network accessline 507
- **PyPI Package**: https://pypi.org/project/molfeat/ - Network accessline 508
- **Tutorial**: https://portal.valencelabs.com/datamol/post/types-of-featurizers-b1e8HHrbFMkbun6
skills/pharma/open-targets-search/SKILL.md
- Network accessline 29
1. Node.js 18+ (uses built-in fetch) 2. Valyu API key from https://platform.valyu.ai ($10 free credits) - Network accessline 30
2. Valyu API key from https://platform.valyu.ai ($10 free credits) - Network accessline 51
1. **Ask the user for their API key:** "To search Open Targets, I need your Valyu API key. Get one free ($10 credits) at https://platform.valyu.ai" - Network accessline 52
"To search Open Targets, I need your Valyu API key. Get one free ($10 credits) at https://platform.valyu.ai" - Network accessline 78
{ "title": "Target-Disease Association", "url": "https://platform.opentargets.org/...", - Network accessline 79
"title": "Target-Disease Association", "url": "https://platform.opentargets.org/...", "content": "Association data, evidence, scores...", - Network accessline 80
"url": "https://platform.opentargets.org/...", - Network accessline 82
"source": "open-targets", "relevance_score": 0.95, "images": ["https://example.com/pathway.png"] - Network accessline 83
"relevance_score": 0.95, "images": ["https://example.com/pathway.png"] } - Network accessline 84
"images": ["https://example.com/pathway.png"] - Network accessline 154
- Base URL: `https://api.valyu.ai/v1` - Network accessline 166
Direct API calls using Node.js built-in `fetch()`, zero external dependencies. - Access to secretsline 177
[Credential-related source omitted] - Network accessline 211
See the [Valyu docs](https://docs.valyu.ai) for full integration examples and SDK reference.
skills/pharma/opentargets-database/SKILL.md
- Network accessline 227
Key information: - **Endpoint:** `https://api.platform.opentargets.org/api/v4/graphql` - Network accessline 228
- **Endpoint:** `https://api.platform.opentargets.org/api/v4/graphql` - Network accessline 229
- **Interactive browser:** `https://api.platform.opentargets.org/api/v4/graphql/browser` - Network accessline 358
**Release information:** Check https://platform-docs.opentargets.org/release-notes for the latest updates.
skills/pharma/pubchem-database/SKILL.md
- Network accessline 199
cid = 2244 # Aspirin url = f"https://pubchem.ncbi.nlm.nih.gov/rest/pug/compound/cid/{cid}/PNG?image_size=large" response = requests.get(url) - Network accessline 200
url = f"https://pubchem.ncbi.nlm.nih.gov/rest/pug/compound/cid/{cid}/PNG?image_size=large" - Network accessline 201
response = requests.get(url) - Network accessline 233
# Get bioassay summary for a compound cid = 2244 # Aspirin url = f"https://pubchem.ncbi.nlm.nih.gov/rest/pug/compound/cid/{cid}/assaysummary/JSON" - Network accessline 235
url = f"https://pubchem.ncbi.nlm.nih.gov/rest/pug/compound/cid/{cid}/assaysummary/JSON" - Network accessline 237
response = requests.get(url) - Network accessline 259
cid = 2244 url = f"https://pubchem.ncbi.nlm.nih.gov/rest/pug_view/data/compound/{cid}/JSON" - Network accessline 260
url = f"https://pubchem.ncbi.nlm.nih.gov/rest/pug_view/data/compound/{cid}/JSON" - Network accessline 262
response = requests.get(url) - Network accessline 277
# Get only drug information url = f"https://pubchem.ncbi.nlm.nih.gov/rest/pug_view/data/compound/{cid}/JSON?heading=Drug and Medication Information" - Network accessline 278
url = f"https://pubchem.ncbi.nlm.nih.gov/rest/pug_view/data/compound/{cid}/JSON?heading=Drug and Medication Information" - Network accessline 567
- PubChem Home: https://pubchem.ncbi.nlm.nih.gov/ - Network accessline 568
- PUG-REST Documentation: https://pubchem.ncbi.nlm.nih.gov/docs/pug-rest - Network accessline 569
- PUG-REST Tutorial: https://pubchem.ncbi.nlm.nih.gov/docs/pug-rest-tutorial - Network accessline 570
- PubChemPy Documentation: https://pubchempy.readthedocs.io/ - Network accessline 571
- PubChemPy GitHub: https://github.com/mcs07/PubChemPy
skills/pharma/pytdc/SKILL.md
skills/pharma/rowan/SKILL.md
- Network accessline 40
Generate an API key at [labs.rowansci.com/account/api-keys](https://labs.rowansci.com/account/api-keys). - Access to secretsline 45
[Credential-related source omitted] - Access to secretsline 50
[Credential-related source omitted] - Access to secretsline 53
[Credential-related source omitted] - Possible obfuscationline 184
protein_seq = "MENFQKVEKIGEGTYGVVYKARNKLTGEVVALKKIRLDTETEGVPSTAIREISLLKELNHPNIVKLLDVIHTENKLYLVFEFLHQDLKKFMDASALTGIPLPLIKSYLFQLLQGLAFCHSHRVLHRDLKPQNLLINTEGAIKLADFGLARAFGVPVRTYTHEVVTLWYRAPEILLGCKYYSTAVDIWSLGCIFAEMVTRRALFPGDSEIDQLFRIFRTLGTPDEV - Network accessline 420
- **Web Interface**: https://labs.rowansci.com - Network accessline 421
- **Documentation**: https://docs.rowansci.com - Network accessline 422
- **Python API Docs**: https://docs.rowansci.com/api/python/v2/ - Network accessline 423
- **Tutorials**: https://docs.rowansci.com/tutorials
skills/pharma/tooluniverse-binder-discovery/SKILL.md
- Access to secretsline 370
[Credential-related source omitted] - Access to secretsline 747
[Credential-related source omitted] - Access to secretsline 867
[Credential-related source omitted] - Access to secretsline 1414
[Credential-related source omitted] - Access to secretsline 1417
[Credential-related source omitted]
skills/pharma/tooluniverse-drug-research/SKILL.md
- Network accessline 457

skills/pharma/tooluniverse-target-research/SKILL.md
- Access to secretsline 668
[Credential-related source omitted]
skills/pharma/torch_geometric/SKILL.md
- Network accessline 36
uv pip install pyg_lib torch_scatter torch_sparse torch_cluster torch_spline_conv -f https://data.pyg.org/whl/torch-${TORCH}+${CUDA}.html - Network accessline 670
- **Documentation**: https://pytorch-geometric.readthedocs.io/ - Network accessline 671
- **GitHub**: https://github.com/pyg-team/pytorch_geometric - Network accessline 672
- **Tutorials**: https://pytorch-geometric.readthedocs.io/en/latest/get_started/introduction.html - Network accessline 673
- **Examples**: https://github.com/pyg-team/pytorch_geometric/tree/master/examples
skills/pharma/torchdrug/SKILL.md
skills/pharma/zinc-database/SKILL.md
- Network accessline 43
Primary access point: https://zinc.docking.org/ - Network accessline 44
Interactive searching: https://cartblanche22.docking.org/ - Network accessline 50
**Base URL**: `https://cartblanche22.docking.org/` - Network accessline 60
**Web interface**: https://cartblanche22.docking.org/search/zincid - Network accessline 64
curl "https://cartblanche22.docking.org/[email protected]_fields=smiles,zinc_id" - Network accessline 69
curl "https://cartblanche22.docking.org/substances.txt:zinc_id=ZINC000000000001,ZINC000000000002&output_fields=smiles,zinc_id,tranche" - Network accessline 78
**Web interface**: https://cartblanche22.docking.org/search/smiles - Network accessline 82
curl "https://cartblanche22.docking.org/[email protected]=4-Fadist=4" - Network accessline 93
curl "https://cartblanche22.docking.org/smiles.txt:smiles=c1ccccc1" - Network accessline 98
curl "https://cartblanche22.docking.org/smiles.txt:smiles=c1ccccc1&dist=3&output_fields=zinc_id,smiles,tranche" - Network accessline 105
**Web interface**: https://cartblanche22.docking.org/search/catitems - Network accessline 109
curl "https://cartblanche22.docking.org/catitems.txt:catitem_id=SUPPLIER-CODE-123" - Network accessline 121
**Web interface**: https://cartblanche22.docking.org/search/random - Network accessline 125
curl "https://cartblanche22.docking.org/substance/random.txt:count=100" - Network accessline 135
curl "https://cartblanche22.docking.org/substance/random.txt:count=1000&subset=lead-like&output_fields=zinc_id,smiles,tranche" - Network accessline 146
# Example: Get drug-like compounds with specific LogP and MW curl "https://cartblanche22.docking.org/substance/random.txt:count=10000&subset=drug-like&output_fields=zinc_id,smiles,tranche" > docking_library.txt - Network accessline 147
curl "https://cartblanche22.docking.org/substance/random.txt:count=10000&subset=drug-like&output_fields=zinc_id,smiles,tranche" > docking_library.txt - Network accessline 173
curl "https://cartblanche22.docking.org/smiles.txt:smiles=CC(C)Cc1ccc(cc1)C(C)C(=O)O&dist=5&output_fields=zinc_id,smiles,catalogs" > analogs.txt - Network accessline 201
curl "https://cartblanche22.docking.org/substances.txt:zinc_id=ZINC000000000001,ZINC000000000002&output_fields=zinc_id,smiles,supplier_code,catalogs" - Network accessline 215
curl "https://cartblanche22.docking.org/substance/random.txt:count=5000&subset=lead-like&output_fields=zinc_id,smiles,tranche" > chemical_space_sample.txt - Network accessline 234
curl "https://cartblanche22.docking.org/substances.txt:zinc_id=ZINC000000000001&output_fields=zinc_id,smiles,catalogs,tranche" - Network accessline 260
**File repository**: https://files.docking.org/zinc22/ - Network accessline 267
Refer to ZINC documentation at https://wiki.docking.org for downloading protocols and batch access methods. - Network accessline 271
### Using curl with Python - Network accessline 277
def query_zinc_by_id(zinc_id, output_fields="zinc_id,smiles,catalogs"): """Query ZINC22 by ZINC ID.""" url = f"https://cartblanche22.docking.org/[email protected]_id={zinc_id}&output_fields={output_fields}" - Network accessline 278
"""Query ZINC22 by ZINC ID.""" url = f"https://cartblanche22.docking.org/[email protected]_id={zinc_id}&output_fields={output_fields}" result = subprocess.run(['curl', url], capture_output=True, text=True) - Network accessline 279
url = f"https://cartblanche22.docking.org/[email protected]_id={zinc_id}&output_fields={output_fields}" result = subprocess.run(['curl', url], capture_output=True, text=True) return result.stdout - Network accessline 280
result = subprocess.run(['curl', url], capture_output=True, text=True) - Network accessline 283
def search_by_smiles(smiles, dist=0, adist=0, output_fields="zinc_id,smiles"): """Search ZINC22 by SMILES with optional distance parameters.""" url = f"https://cartblanche22.docking.org/smiles.txt:smiles={smiles}&dist={dist}&adist={ - Network accessline 284
"""Search ZINC22 by SMILES with optional distance parameters.""" url = f"https://cartblanche22.docking.org/smiles.txt:smiles={smiles}&dist={dist}&adist={adist}&output_fields={output_fields}" result = subprocess.run(['curl', url], ca - Network accessline 285
url = f"https://cartblanche22.docking.org/smiles.txt:smiles={smiles}&dist={dist}&adist={adist}&output_fields={output_fields}" result = subprocess.run(['curl', url], capture_output=True, text=True) return result.stdout - Network accessline 286
result = subprocess.run(['curl', url], capture_output=True, text=True) - Network accessline 289
def get_random_compounds(count=100, subset=None, output_fields="zinc_id,smiles,tranche"): """Get random compounds from ZINC22.""" url = f"https://cartblanche22.docking.org/substance/random.txt:count={count}&output_fields={output_fie - Network accessline 290
"""Get random compounds from ZINC22.""" url = f"https://cartblanche22.docking.org/substance/random.txt:count={count}&output_fields={output_fields}" if subset: - Network accessline 291
url = f"https://cartblanche22.docking.org/substance/random.txt:count={count}&output_fields={output_fields}" - Network accessline 292
if subset: url += f"&subset={subset}" result = subprocess.run(['curl', url], capture_output=True, text=True) - Network accessline 293
url += f"&subset={subset}" result = subprocess.run(['curl', url], capture_output=True, text=True) return result.stdout - Network accessline 294
result = subprocess.run(['curl', url], capture_output=True, text=True) - Network accessline 337
- **Rate limiting**: Respect server resources; avoid rapid consecutive requests - **Caching**: Store frequently accessed compounds locally - **Parallel downloads**: When downloading 3D structures, use parallel wget or aria2c for file reposi - Network accessline 338
- **Caching**: Store frequently accessed compounds locally - **Parallel downloads**: When downloading 3D structures, use parallel wget or aria2c for file repositories - **Subset filtering**: Use lead-like, drug-like, or fragment subsets to - Network accessline 339
- **Parallel downloads**: When downloading 3D structures, use parallel wget or aria2c for file repositories - Network accessline 385
- **ZINC Website**: https://zinc.docking.org/ - Network accessline 386
- **CartBlanche22 Interface**: https://cartblanche22.docking.org/ - Network accessline 387
- **ZINC Wiki**: https://wiki.docking.org/ - Network accessline 388
- **File Repository**: https://files.docking.org/zinc22/ - Network accessline 389
- **GitHub**: https://github.com/docking-org/
skills/vision/egohos-segmentation/SKILL.md
- Network accessline 5
metadata: skill-author: K-Dense Inc. original-repo: https://github.com/owenzlz/EgoHOS - Network accessline 6
skill-author: K-Dense Inc. original-repo: https://github.com/owenzlz/EgoHOS skill-category: Computer Vision - Network accessline 7
original-repo: https://github.com/owenzlz/EgoHOS - Network accessline 65
# Clone repository git clone https://github.com/owenzlz/EgoHOS.git - Network accessline 66
git clone https://github.com/owenzlz/EgoHOS.git - Network accessline 143
# Clone repository git clone https://github.com/owenzlz/EgoHOS.git - Network accessline 144
git clone https://github.com/owenzlz/EgoHOS.git - Network accessline 372
- GitHub repository: https://github.com/owenzlz/EgoHOS - Network accessline 380
author = {Owen [Last Name]}, title = {EgoHOS: Egocentric Hand-Object Segmentation}, url = {https://github.com/owenzlz/EgoHOS}, - Network accessline 381
title = {EgoHOS: Egocentric Hand-Object Segmentation}, url = {https://github.com/owenzlz/EgoHOS}, year = {2023} - Network accessline 382
url = {https://github.com/owenzlz/EgoHOS},
skills/vision/hand-tracking-toolkit/SKILL.md
- Network accessline 5
metadata: skill-author: K-Dense Inc. original-repo: https://github.com/facebookresearch/hand_tracking_toolkit - Network accessline 6
skill-author: K-Dense Inc. original-repo: https://github.com/facebookresearch/hand_tracking_toolkit organization: Meta Facebook Research - Network accessline 7
original-repo: https://github.com/facebookresearch/hand_tracking_toolkit - Network accessline 62
# Clone repository git clone https://github.com/facebookresearch/hand_tracking_toolkit.git - Network accessline 63
git clone https://github.com/facebookresearch/hand_tracking_toolkit.git - Network accessline 196
- GitHub: https://github.com/facebookresearch/hand_tracking_toolkit - Network accessline 197
- HOT3D: https://facebookresearch.github.io/hot3d - Network accessline 198
- Challenge: https://eval.ai/web/challenges/challenge-page/...
skills/vision/hands-3d-pose/SKILL.md
- Network accessline 5
metadata: skill-author: K-Dense Inc. original-repo: https://github.com/ap229997/hands - Network accessline 6
skill-author: K-Dense Inc. original-repo: https://github.com/ap229997/hands paper: ECCV 2024 - Network accessline 7
original-repo: https://github.com/ap229997/hands - Network accessline 19
**Project video**: https://youtu.be/YolFnTtq38E - Network accessline 83
# Clone repository git clone https://github.com/ap229997/hands.git - Network accessline 84
git clone https://github.com/ap229997/hands.git - Network accessline 149
# Clone repository git clone https://github.com/ap229997/hands.git - Network accessline 150
git clone https://github.com/ap229997/hands.git - Network accessline 167
mkdir -p checkpoints cd checkpoints wget https://path/to/model-weights.pth - Network accessline 168
cd checkpoints wget https://path/to/model-weights.pth cd .. - Network accessline 169
wget https://path/to/model-weights.pth - Network accessline 552
### Code and Data - GitHub repository: https://github.com/ap229997/hands - Network accessline 553
- GitHub repository: https://github.com/ap229997/hands - Network accessline 554
- Demo branch: https://github.com/ap229997/hands/tree/demo - Network accessline 555
- Project video: https://youtu.be/YolFnTtq38E - Network accessline 557
### Related Work - FreiHAND: https://lmb.informatik.uni-freiburg.de/projects/freihand/ - Network accessline 558
- FreiHAND: https://lmb.informatik.uni-freiburg.de/projects/freihand/ - Network accessline 559
- HO3D: https://www.is.tue.mpg.de/person/mohan/hands2020.html - Network accessline 560
- EgoHands: https://egohands.github.io
skills/vision/handtracking/SKILL.md
- Network accessline 5
metadata: skill-author: K-Dense Inc. original-repo: https://github.com/victordibia/handtracking - Network accessline 6
skill-author: K-Dense Inc. original-repo: https://github.com/victordibia/handtracking skill-category: Computer Vision - Network accessline 7
original-repo: https://github.com/victordibia/handtracking - Network accessline 18
**Companion JavaScript library**: Handtrack.js is available for browser-based applications (https://github.com/victordibia/handtrack.js). - Network accessline 64
# Clone repository git clone https://github.com/victordibia/handtracking.git - Network accessline 65
git clone https://github.com/victordibia/handtracking.git - Network accessline 115
<script src="https://cdn.jsdelivr.net/npm/handtrackjs/dist/handtrack.min.js"></script> - Network accessline 142
# Clone repository git clone https://github.com/victordibia/handtracking.git - Network accessline 143
git clone https://github.com/victordibia/handtracking.git - Network accessline 436
### Documentation - GitHub repository: https://github.com/victordibia/handtracking - Network accessline 437
- GitHub repository: https://github.com/victordibia/handtracking - Network accessline 438
- EgoHands dataset paper: https://egohands.github.io - Network accessline 439
- Handtrack.js documentation: https://github.com/victordibia/handtrack.js - Network accessline 441
### Related Projects - EgoHands Dataset: https://github.com/egohands/EgoHands - Network accessline 442
- EgoHands Dataset: https://github.com/egohands/EgoHands - Network accessline 443
- MediaPipe Hands: https://google.github.io/mediapipe/solutions/hands.html - Network accessline 444
- OpenPose: https://github.com/CMU-Perceptual-Computing-Lab/openpose - Network accessline 469
author = {Victor Dibia}, title = {Real-time Hand Detection in Python using TensorFlow}, url = {https://github.com/victordibia/handtracking}, - Network accessline 470
title = {Real-time Hand Detection in Python using TensorFlow}, url = {https://github.com/victordibia/handtracking}, year = {2018} - Network accessline 471
url = {https://github.com/victordibia/handtracking},
skills/vision/hot3d/SKILL.md
- Network accessline 2
name: hot3d description: HOT3D (Hand-Object 3D Dataset) by Meta Facebook - multi-view egocentric hand and object 3D tracking for Aria/Quest smart glasses. State-of-the-art multi-view 3D hand pose, object pose, and hand-object interaction tr - Network accessline 3
description: HOT3D (Hand-Object 3D Dataset) by Meta Facebook - multi-view egocentric hand and object 3D tracking for Aria/Quest smart glasses. State-of-the-art multi-view 3D hand pose, object pose, and hand-object interaction tracking. Supp - Network accessline 4
license: CC-BY-NC 4.0 - Network accessline 5
metadata: skill-author: K-Dense Inc. original-repo: https://github.com/facebookresearch/hot3d - Network accessline 6
skill-author: K-Dense Inc. original-repo: https://github.com/facebookresearch/hot3d organization: Meta Facebook Research - Network accessline 7
original-repo: https://github.com/facebookresearch/hot3d - Network accessline 19
**Project page**: https://facebookresearch.github.io/hot3d - Network accessline 77
# Clone repository git clone https://github.com/facebookresearch/hot3d.git - Network accessline 78
git clone https://github.com/facebookresearch/hot3d.git - Network accessline 85
# Download dataset (requires registration) # https://facebookresearch.github.io/hot3d/dataset.html - Network accessline 86
# https://facebookresearch.github.io/hot3d/dataset.html - Network accessline 150
Access: https://facebookresearch.github.io/hot3d - Network accessline 176
- Project: https://facebookresearch.github.io/hot3d - Network accessline 177
- GitHub: https://github.com/facebookresearch/hot3d - Network accessline 183
CC-BY-NC 4.0 (non-commercial only)
skills/visualization/matplotlib/SKILL.md
- Network accessline 2
name: matplotlib description: Low-level plotting library for full customization. Use when you need fine-grained control over every plot element, creating novel plot types, or integrating with specific scientific workflows. Export to PNG/PDF - Network accessline 3
description: Low-level plotting library for full customization. Use when you need fine-grained control over every plot element, creating novel plot types, or integrating with specific scientific workflows. Export to PNG/PDF/SVG for publicat - Network accessline 4
license: https://github.com/matplotlib/matplotlib/tree/main/LICENSE - Network accessline 355
- Official documentation: https://matplotlib.org/ - Network accessline 356
- Gallery: https://matplotlib.org/stable/gallery/index.html - Network accessline 357
- Cheatsheets: https://matplotlib.org/cheatsheets/ - Network accessline 358
- Tutorials: https://matplotlib.org/stable/tutorials/index.html
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: 21 facts
- Network access in code of the SKILL.mdalphafold-database/SKILL.md:61High confidence
- Network access in code of the SKILL.mdalphafold-database/SKILL.md:62High confidence
- Network access in code of the SKILL.mdalphafold-database/SKILL.md:79High confidence
- Network access in code of the SKILL.mdalphafold-database/SKILL.md:87High confidence
- Network access in code of the SKILL.mdalphafold-database/SKILL.md:113High confidence
- Network access in code of the SKILL.mdalphafold-database/SKILL.md:114High confidence
- Network access in code of the SKILL.mdalphafold-database/SKILL.md:119High confidence
- Network access in code of the SKILL.mdalphafold-database/SKILL.md:120High confidence
- Network access in code of the SKILL.mdalphafold-database/SKILL.md:124High confidence
- Network access in code of the SKILL.mdalphafold-database/SKILL.md:125High confidence
- Network access in code of the SKILL.mdalphafold-database/SKILL.md:133High confidence
- Network access in code of the SKILL.mdalphafold-database/SKILL.md:134High confidence
- Network access in code of the SKILL.mdalphafold-database/SKILL.md:151High confidence
- Network access in code of the SKILL.mdalphafold-database/SKILL.md:152High confidence
- Network access in code of the SKILL.mdalphafold-database/SKILL.md:176High confidence
- Network access in code of the SKILL.mdalphafold-database/SKILL.md:177High confidence
- Network access in prose of the SKILL.mdalphafold-database/SKILL.md:241Low confidence
- Network access in code of the SKILL.mdalphafold-database/SKILL.md:343High confidence
- Network access in code of the SKILL.mdalphafold-database/SKILL.md:344High confidence
- Network access in code of the SKILL.mdalphafold-database/SKILL.md:393High confidence
- Network access in code of the SKILL.mdalphafold-database/SKILL.md:394High confidence
Works with
| Agent | Status | Evidence | Install to |
|---|---|---|---|
| Claude Code | Compatible | Standard SKILL.md skills/bio/alphafold-database/SKILL.md | .claude/skills/<skill-name>/ |
| Codex | Compatible | Standard SKILL.md skills/bio/alphafold-database/SKILL.md | .agents/skills/<skill-name>/ |
| Cursor | Compatible | Standard SKILL.md skills/bio/alphafold-database/SKILL.md | .cursor/skills/<skill-name>/ |
| Gemini CLI | Compatible | Standard SKILL.md skills/bio/alphafold-database/SKILL.md | .gemini/skills/<skill-name>/ |
| GitHub Copilot | Compatible | Standard SKILL.md skills/bio/alphafold-database/SKILL.md | .github/skills/<skill-name>/ |
| OpenCode | Compatible | Standard SKILL.md skills/bio/alphafold-database/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.
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