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ClaudeWave
Skill2.3k estrellas del repoactualizado 24d ago

bio-database-evidence

Bio-database-evidence performs biological database lookups and evidence gathering across resources like ClinVar, GWAS Catalog, Open Targets, UniProt, and CELLxGENE Census. Use it when you need gene annotations, variant clinical significance, pathway mapping, protein structures, protein interactions, or cross-database ID mapping rather than computational analysis of sequencing data or molecular modeling.

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git clone --depth 1 https://github.com/foryourhealth111-pixel/Vibe-Skills /tmp/bio-database-evidence && cp -r /tmp/bio-database-evidence/bundled/skills/bio-database-evidence ~/.claude/skills/bio-database-evidence
Después abre una sesión nueva de Claude Code; el skill carga automáticamente.

SKILL.md

# Bio Database Evidence

## Use This Skill For

Use this skill when the main task is biological database lookup, annotation, or evidence gathering across one or more biological sources:

- Gene annotation, identifiers, RefSeq, Ensembl IDs, orthologs, VEP, GO, and genomic coordinates.
- Variant clinical significance, VUS interpretation support, ClinVar review status, cancer mutations, and COSMIC evidence.
- GWAS Catalog trait associations, rs IDs, p-values, summary statistics, and genetic epidemiology evidence.
- Pathway mapping, ID conversion, KEGG pathways, Reactome enrichment, disease pathways, and pathway evidence.
- Target-disease association evidence, tractability, safety, known drugs, and Open Targets evidence.
- Protein structure evidence from AlphaFold DB or RCSB PDB, including UniProt IDs, mmCIF/PDB downloads, pLDDT, PAE, and structure metadata.
- Protein-protein interaction evidence, STRING networks, hub proteins, and enrichment evidence.
- Reference single-cell data lookup from CELLxGENE Census when the user asks for census metadata or expression data, not full downstream analysis.
- Cross-database biological ID mapping and evidence tables across multiple resources.

## Do Not Use This Skill For

- Single-cell RNA-seq analysis, clustering, UMAP, marker genes, cell annotation, AnnData/h5ad container editing, or scVI/scANVI batch-correction planning. Use `scanpy`.
- Bulk RNA-seq differential expression. Use `pydeseq2`.
- BAM, SAM, CRAM, VCF, pileup, coverage, or region extraction as a primary file-processing task.
- deepTools signal-track processing and heatmaps.
- Protein language models, embeddings, inverse folding, or protein-design workflows.
- Constraint-based metabolic modeling, FBA, or metabolic-engineering simulation.
- BED/genomic interval embeddings, genomic-region ML, or gene regulatory network inference.
- FCS or flow-cytometry file parsing.

## Workflow

1. Identify the biological entity type: gene, transcript, variant, pathway, target, protein structure, protein interaction, trait association, or reference cell population.
2. Pick the narrowest source that answers the evidence question.
3. Preserve source names, query terms, access dates, identifiers, and API caveats in the result.
4. Return evidence in a table when comparing multiple sources.
5. State when authentication, license, rate limits, or non-public access restricts a source.

## Source Guide

See `references/database-evidence-sources.md` for source-specific boundaries and query notes.
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