Individual skill
tooluniverse-variant-analysis
VCF and variant analysis — parsing, annotation, classification (synonymous, missense, frameshift, stop_gained), VAF filtering, coding vs non-coding categorization, multi-condition variant comparison, variant-notation conversion (SPDI/HGVS/VCF/rsID interconversion, ALFA per-ancestry allele frequencies), European Variation Archive (EVA) lookups (per-study cohort variant data, RS-accession resolution), HGVS validation/normalization (Mutalyzer), gene-specific curated variants (LOVD), canonical cross-database allele IDs (ClinGen Allele Registry), automated ACMG/AMP classification (GeneBe), principal-transcript selection (APPRIS), Ensembl linkage disequilibrium (LD) for GWAS/fine-mapping, GRCh37/GRCh38 coordinate liftover and protein/cDNA-to-genomic mapping, and Ensembl ID utilities (stable-ID version history, cross-references, region/gene feature overlap, assembly/species metadata). Use for VCF parsing, variant fraction calculations (denominator = coding subset only, NOT all variants)...
plugins/tooluniverse/skills/tooluniverse-variant-analysis/SKILL.md
Source commit 5598a9fc97e32964fe9b703818e7d0747142f179
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- Matches the observed repository HEAD
- Yes
- Scanner rules are current
- Yes
- Repository scan complete
- No
- Skill scan complete
- No
- Observed behavior
- Network or broad shell
- Last checked
- Nov 9, 2026
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- Repository stars (not skill ratings)
- 1,779
- Repository last updated
- Nov 8, 2026
- License
- Apache-2.0
Observed agent integration: No specific integration evidence. Standard SKILL.md installation does not imply verified compatibility.
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Read excerpt
# Variant Analysis and Annotation
## RULE ZERO — Check for pre-computed results FIRST
Before following any instruction below, scan the data folder for:
- `*_executed.ipynb` → read with `tu run read_executed_notebook '{"data_folder":"<path>","search":"<keyword>"}'` and cite its cell outputs as the authoritative answer
- Pre-computed result files (CSV/TSV with names like `*results*`, `*deseq*`, `*enrich*`, `*stats*`, `*_simplified.csv`) → read directly and report the requested value
- Canonical analysis scripts (`analysis.R`, `run_*.py`, `find_*.R`, `*.Rmd`) → execute as-is and read the output
Only follow this skill's re-analysis recipe below if **none** of the above exist. Re-running from raw data produces different numbers than the published answer and is much slower (often 5-10× turn count).
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## PRIMARY SCRIPTS — use these FIRST