Agent skill

bio-variant-calling-clinical-interpretation

Clinical variant interpretation using ClinVar, ACMG guidelines, and pathogenicity predictors. Prioritize variants for diagnostic and research applications. Use when interpreting clinical significance of variants.

FreedomIntelligencegithub.com/FreedomIntelligenceGitHub ↗
claude-codeships scripts
Install
npx skills add FreedomIntelligence/OpenClaw-Medical-Skills --skill bio-variant-calling-clinical-interpretation --agent claude-code

Same command for any agent — swap --agent for codex, cursor, copilot.

Facts
Files in the skill folder: 3
SKILL.md size: 11 KB
Bundled scripts: yes
Path: skills/bio-variant-calling-clinical-interpretation/SKILL.md
Open the folder on GitHub →
Where it comes from
Stars: 2,909
Language: Python
Read our review of the source →

Weekly change comes from our own snapshots, not the repository page — it measures attention, not adoption.

From the SKILL.md

## Version Compatibility Reference examples tested with: Entrez Direct 21.0+, bcftools 1.19+ Before using code patterns, verify installed versions match. If versions differ: - Python: `pip show <package>` then `help(module.function)` to check signatures - CLI: `<tool> --version` then `<tool> --help` to confirm flags If code throws ImportError, AttributeError, or TypeError, introspect the installed package and adapt the example to match the actual API rather than retrying. # Clinical Variant Interpretation Prioritize and interpret variants for clinical significance using databases and ACMG/AMP guidelines. ## Interpretation Framework ``` Annotated VCF │ ├── Database Lookup │ ├── ClinVar (clinical assertions) │ ├── OMIM (disease associations) │ └── gnomAD (population frequency) │ ├── Computational Predictions │ ├── SIFT, PolyPhen-2 │ ├── CADD, REVEL │ └── SpliceAI │ ├── ACMG Classification │ └── Pathogenic → Likely Pathogenic → VUS → Likely Benign → Benign │ └── Prioritized Variant List ``` ## ClinVar Annotation **Goal:** Annotate variants with ClinVar clinical significance and filter by pathogenicity. **Approach:** Download the ClinVar VCF, add CLNSIG/CLNDN/CLNREVSTAT fields with bcf

What's inside
Steps it walks through
  1. Version Compatibility
  2. Interpretation Framework
  3. ClinVar Annotation
  4. Download ClinVar
  5. Annotate with bcftools
  6. Filter Pathogenic Variants
  7. ClinVar Significance Levels
  8. ClinVar Review Status
  9. InterVar (ACMG Classification)
  10. Installation
  11. Run InterVar
  12. From VCF
  13. ACMG/AMP Criteria
  14. Pathogenic Criteria
Ships with 2 files
  • examples/clinical_filter.py
  • usage-guide.md
Commands it runs
wget https://ftp.ncbi.nlm.nih.gov/pub/clinvar/vcf_GRCh38/clinvar.vcf.gz
wget https://ftp.ncbi.nlm.nih.gov/pub/clinvar/vcf_GRCh38/clinvar.vcf.gz.tbi
bcftools annotate \
input.vcf.gz -Oz -o with_clinvar.vcf.gz
Pathogenic or Likely pathogenic
bcftools view -i 'INFO/CLNSIG~"Pathogenic" || INFO/CLNSIG~"Likely_pathogenic"' \
with_clinvar.vcf.gz -Oz -o pathogenic.vcf.gz
Exclude benign
bcftools view -e 'INFO/CLNSIG~"Benign" || INFO/CLNSIG~"Likely_benign"' \
with_clinvar.vcf.gz -Oz -o not_benign.vcf.gz
More from OpenClaw-Medical-Skills
All skills →
About this skill
What does the bio-variant-calling-clinical-interpretation skill do?

Clinical variant interpretation using ClinVar, ACMG guidelines, and pathogenicity predictors. Prioritize variants for diagnostic and research applications. Use when interpreting clinical significance of variants.

How do I install it?

Run `npx skills add FreedomIntelligence/OpenClaw-Medical-Skills --skill bio-variant-calling-clinical-interpretation --agent claude-code` — it drops the skill into your project so the agent can pick it up. Swap the --agent value for codex, cursor or copilot if you use one of those.

Where does this skill come from?

From FreedomIntelligence/OpenClaw-Medical-Skills, a repository with 2,909 stars. We read it straight from the repository tree rather than a submitted listing, so what you see here is what is actually published.

Is a popular skill a good skill?

Not necessarily. Stars measure attention, not adoption — a repository can trend for a week and be abandoned. That is why we show the weekly change from our own snapshots next to the total, instead of a single flattering number.

Keep going