Agent skill · Backend & API

annotating-variants

Annotates VCF variants and normalizes HGVS nomenclature with public, license-free annotators (Ensembl VEP REST, VEP/SnpEff/ANNOVAR offline) and links variants to gnomAD population frequencies and the clinical context OpenMed extracts. Use when the user wants to predict variant consequences, map HGVS to genomic coordinates, annotate a VCF, attach allele frequencies, or pair variants with phenotype/oncology context. Trigger keywords: VCF, HGVS, variant annotation, VEP, SnpEff, ANNOVAR, consequence, missense, gnomAD, allele frequency, GRCh38, rsID, transcript. Pairs adjacent to OpenMed: combine a

maziyarpanahigithub.com/maziyarpanahiGitHub ↗
claude-codeApache-2.0
Install
npx skills add maziyarpanahi/openmed --skill annotating-variants --agent claude-code

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

Facts
Files in the skill folder: 1
SKILL.md size: 8 KB
Bundled scripts: none
Version: 1.0
Path: skills/annotating-variants/SKILL.md
Open the folder on GitHub →
Where it comes from
Stars: 4,851
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

# Annotating variants & normalizing HGVS Turn raw genomic variants — VCF rows, rsIDs, or **HGVS** strings — into annotated, consequence-predicted records, and link them to the **clinical context** OpenMed extracts from text (genes, variants, oncology findings, phenotype). The workhorse for a quick, no-install annotation is the **Ensembl VEP REST API**; for scale, run **VEP**, **SnpEff**, or **ANNOVAR** offline. These annotators are **free and license-permissive**. Restricted clinical interpretation databases (e.g. licensed HGMD) are **user-supplied** — this skill sticks to open resources (Ensembl, gnomAD, ClinVar). ## When to use - You have a VCF / HGVS / rsID and need consequence predictions (missense, stop-gain, splice), affected transcripts, and protein change. - You need to **normalize HGVS** to genomic coordinates (and back) on a known build (GRCh38 by default; GRCh37 via the dedicated endpoint). - You want **gnomAD** population allele frequencies to flag common vs rare. - You are pairing molecular findings with the phenotype/oncology context that OpenMed pulls from notes or literature. ## Quick start (real Ensembl VEP REST call) Base URL: `https://rest.ensembl.org` (GRCh38).

What's inside
Steps it walks through
  1. When to use
  2. Quick start (real Ensembl VEP REST call)
  3. Population frequencies via gnomAD (GraphQL)
  4. Offline annotation at scale
  5. Workflow
  6. Hand-off to / from OpenMed
  7. Edge cases & gotchas
  8. Standards & references
Commands it runs
curl 'https://rest.ensembl.org/vep/human/hgvs/ENST00000269305.9:c.215C>G' \
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About this skill
What does the annotating-variants skill do?

Annotates VCF variants and normalizes HGVS nomenclature with public, license-free annotators (Ensembl VEP REST, VEP/SnpEff/ANNOVAR offline) and links variants to gnomAD population frequencies and the clinical context OpenMed extracts. Use when the user wants to predict variant consequences, map HGVS to genomic coordinates, annotate a VCF, attach allele frequencies, or pair variants with phenotype/oncology context. Trigger keywords: VCF, HGVS, variant annotation, VEP, SnpEff, ANNOVAR, consequence, missense, gnomAD, allele frequency, GRCh38, rsID, transcript. Pairs adjacent to OpenMed: combine a

How do I install it?

Run `npx skills add maziyarpanahi/openmed --skill annotating-variants --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 maziyarpanahi/openmed, a repository with 4,851 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.

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