annotating-variants

annotating-variants is a skill for Claude Code from maziyarpanahi/openmed. It costs 179 tokens per session (2,293 once invoked), scanned A, original, Apache-2.0.

A tool for adding information to genetic variants and standardizing their HGVS names, which describe DNA and protein changes. It can connect variants with population frequencies and clinical findings extracted from medical text.

In plain words
What is it for?
Use it to predict variant effects, identify affected transcripts and protein changes, convert variants between genomic descriptions, check whether variants are common or rare, and relate them to clinical or cancer findings.
Why use it?
It helps turn raw VCF rows, rsIDs, or inconsistent HGVS strings into records that are easier to compare and interpret. It uses open resources such as Ensembl, gnomAD, and ClinVar.

Skill for Claude Code

Written for Claude Code: shipped in a Claude Code plugin.

Part of the openmed-skills plugin — 74 skills shipped together

Good fit Use it to predict variant effects, identify affected transcripts and protein changes, convert variants between genomic descriptions, check whether variants are common or rare, and relate them to clinical or cancer findings.

Compare 6 skills from other repositories ↓
Install with agentmods
npx agentmods add skills/maziyarpanahi/openmed/annotating-variants
About the project

OpenMed is local-first healthcare AI software that extracts clinical information and removes personally identifying details from clinical text on hardware controlled by the user. Healthcare developers use its Python runtime, Apple Silicon and mobile SDKs, and browser support for on-device clinical NER and PII de-identification.

maziyarpanahi/openmed · 5,700 stars · on GitHub · openmed.life

Install

Getting it into your agent

One page per mod, every tool's command on it. A separate URL per tool would split the same page into five that compete with each other.

Any agent
npx skills add maziyarpanahi/openmed --skill annotating-variants
Clone the repo
git clone --depth 1 https://github.com/maziyarpanahi/openmed

Made for: Claude Code.

Or install openmed-skills, the plugin that ships this one along with the rest of its 74 skills.

Wrote this? Show the measurements

A badge with what this costs and how it scanned, read live from this page, so it follows the numbers instead of freezing them. Markdown for a README, HTML for a documentation site or a project page.

agentmods badge for annotating-variants

README.md
[![agentmods](https://agentmods.dev/badge/skills/maziyarpanahi/openmed/annotating-variants/github.svg)](https://agentmods.dev/skills/maziyarpanahi/openmed/annotating-variants)
Your own site
<a href="https://agentmods.dev/skills/maziyarpanahi/openmed/annotating-variants"><img src="https://agentmods.dev/badge/skills/maziyarpanahi/openmed/annotating-variants/github.svg" alt="Measured on agentmods" height="20"></a>

Or the 80×15 button, for a site that already has a row of RSS and ATOM ones. Only the verdict fits; the numbers stay here.

agentmods 80×15 button for annotating-variants

Your own site · 80×15
<a href="https://agentmods.dev/skills/maziyarpanahi/openmed/annotating-variants"><img src="https://agentmods.dev/badge/skills/maziyarpanahi/openmed/annotating-variants.svg" alt="Reviewed on agentmods" width="80" height="20"></a>
Per session 179 Skills are progressive disclosure: only the name and description are preloaded; the body loads when the skill is used.
When invoked 2,293 The whole file, excluding the scripts and references it only reads on demand.
Security scan A 1 finding. A grade says what 26 rules found in the file — not that it is safe. Third-party audits
  • NVIDIA SkillSpector warn 7 Sept 2026
SkillSpector: 2 findings, up to medium

These are SkillSpector’s own severities. On a checked sample its high-severity flags on skills were ~96% false positives — a documented command, a public API, a “never do X” rule — so we show them as a caution to read, not a verdict. Why →

  • medium Data Exfiltration · line 66
    Data is being sent to an external URL. This could be legitimate telemetry or data exfiltration. Manual review is recommended.
    Fix: Verify the destination URL is trusted and necessary. Remove or replace with documented APIs. Ensure no secrets, tokens, or PII are transmitted.
  • medium Data Exfiltration · line 105
    Data is being sent to an external URL. This could be legitimate telemetry or data exfiltration. Manual review is recommended.
    Fix: Verify the destination URL is trusted and necessary. Remove or replace with documented APIs. Ensure no secrets, tokens, or PII are transmitted.
How audits are shown
Origin original No closer match found in the catalogue.
Token cost

What it costs to keep this loaded

Counted locally with the o200k_base tokenizer, which is exact for GPT models; Claude uses its own tokenizer and its counts differ. Treat this as one consistent yardstick across the catalogue rather than a bill. Prices are per million input tokens.

ModelPer sessionOnce invoked
Fable 5.1 $0.00179 $0.02293
Opus 5.5 $0.00072 $0.00917
Sonnet 5.5 $0.00036 $0.00459
Haiku 4.5 $0.00018 $0.00229

Measured 1mo ago against content hash b5f6c5fe74e1, method: parsed. Prices are Anthropic first-party input rates as of 2026-10-07, from the pricing page.

Security

Grade A, and why

annotating-variants scanned grade A with 1 finding against 26 rules in 11 categories — prompt injection, anti-refusal, data exfiltration, privilege escalation, supply chain, agent snooping, system-prompt leakage, SSRF and excessive agency — measured 1mo ago.

A static scan of the body, not an audit. Every finding is printed with the line that produced it so you can judge whether it matters here. A mod is markdown that instructs an agent; that is exactly why what it instructs is worth reading.

Makes network callslowCapability

Not a fault in itself. Listed so you know the mod talks to something, and to what.

r = requests.get(f"{REST}/vep/human/hgvs/{hgvs}", headers=HEADERS, timeout=30)
skills/annotating-variants/SKILL.md · 180 lines

How it starts

The opening of the file, as written. The whole thing — 180 lines — stays where its author put it; the contents beside it link to each section on GitHub.

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). For GRCh37 use https://grch37.rest.ensembl.org. Default species is human/homo_sapiens.

import requests

REST = "https://rest.ensembl.org"
HEADERS = {"Content-Type": "application/json", "Accept": "application/json"}

def vep_hgvs(hgvs: str) -> list[dict]:
    """Annotate a single HGVS variant (GET)."""
    r = requests.get(f"{REST}/vep/human/hgvs/{hgvs}", headers=HEADERS, timeout=30)
    r.raise_for_status()
    return r.json()

# Transcript-level HGVS (coding) — note the build-aware default transcript set
ann = vep_hgvs("ENST00000269305.9:c.215C>G")   # TP53 example
v = ann[0]
print(v["most_severe_consequence"])            # e.g. "missense_variant"
for tc in v.get("transcript_consequences", []):
    print(tc["gene_symbol"], tc.get("hgvsp"), tc.get("sift_prediction"),
          tc.get("polyphen_prediction"))

Read the full file on GitHub · 180 lines

Changes

What this file has done since we first saw it

Hashed on every crawl. A supply-chain change to an agent config is a question of when, not whether, so the history is kept rather than the latest state alone.

  1. 1mo ago First seen · 180 lines · 179 tokens per session scan A b5f6c5fe74e1

Subscribe to this mod's changes

annotating-variants is a skill published in the GitHub repository maziyarpanahi/openmed (5,700 stars, last pushed today), licensed Apache-2.0. It adds 179 tokens to every session and 2,293 once invoked, about $0.0007 per session on Opus 5.5. A static security scan graded it A with 1 finding (makes network calls). No closer match exists in the catalogue, so it is treated as the original; first seen 2026-08-30.

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