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.
npx skills add AndyZhuang/Opentest --skill tooluniverse-variant-interpretationgit clone --depth 1 https://github.com/AndyZhuang/OpentestWrote 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.
[](https://agentmods.dev/skills/andyzhuang/opentest/tooluniverse-variant-interpretation)<a href="https://agentmods.dev/skills/andyzhuang/opentest/tooluniverse-variant-interpretation"><img src="https://agentmods.dev/badge/skills/andyzhuang/opentest/tooluniverse-variant-interpretation/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.
<a href="https://agentmods.dev/skills/andyzhuang/opentest/tooluniverse-variant-interpretation"><img src="https://agentmods.dev/badge/skills/andyzhuang/opentest/tooluniverse-variant-interpretation.svg" alt="Reviewed on agentmods" width="80" height="20"></a>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.
| Model | Per session | Once invoked |
|---|---|---|
| Fable 5.1 | $0.00103 | $0.09188 |
| Opus 5 | $0.00051 | $0.04594 |
| Sonnet 5 | $0.00021 | $0.01838 |
| Haiku 4.5 | $0.00010 | $0.00919 |
Grade A, and why
tooluniverse-variant-interpretation scanned grade A with 0 findings 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 8d 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.
Nothing flagged
None of the 26 patterns this scan looks for appear in this file: no shell pipes, no recursive deletes, no credential paths, no hidden text, no instruction-override or anti-refusal phrasing, no agent-config snooping. That is not a guarantee, it is the absence of the things that are checkable.
How it starts
The opening of the file, as written. The whole thing — 1,119 lines — stays where its author put it; the contents beside it link to each section on GitHub.
name: tooluniverse-variant-interpretation description: Systematic clinical variant interpretation from raw variant calls to ACMG-classified recommendations with structural impact analysis. Aggregates evidence from ClinVar, gnomAD, CIViC, UniProt, and PDB across ACMG criteria. Produces pathogenicity scores (0-100), clinical recommendations, and treatment implications. Use when interpreting genetic variants, classifying variants of uncertain significance (VUS), performing ACMG variant classification, or translating variant calls to clinical actionability.
Clinical Variant Interpreter
Systematic variant interpretation skill using ToolUniverse - from raw variant calls to ACMG-classified clinical recommendations with structural impact analysis.
Problem This Skill Solves
Clinical labs and researchers face critical challenges in variant interpretation:
- Variant classification uncertainty - VUS (Variants of Uncertain Significance) comprise 40-60% of clinical variants
- Evidence aggregation burden - Must integrate data from 10+ databases per variant
- Structural context missing - Traditional annotation ignores 3D protein impact
- Clinical actionability unclear - How does classification translate to patient care?
This skill provides: A systematic workflow that combines population databases, functional predictions, structural analysis (via AlphaFold2), and literature evidence into ACMG-compliant interpretations with clear clinical recommendations.
Key Principles
- ACMG-Guided Classification - Follow ACMG/AMP 2015 guidelines with explicit evidence codes
- Structural Evidence Integration - Use AlphaFold2 for novel structural impact analysis
- Population Context - gnomAD frequencies with ancestry-specific data
- Gene-Disease Validity - ClinGen curation status for clinical relevance
- Actionable Output - Clear recommendations, not just classifications
- English-first queries - Always use English terms in tool calls (gene names, variant descriptions, disease names), even if the user writes in another language. Only try original-language terms as a fallback. Respond in the user's language
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.
- 8d ago First seen · 1,119 lines · 103 tokens per session scan A e2b81c1eb285
tooluniverse-variant-interpretation is a skill published in the GitHub repository AndyZhuang/Opentest (22 stars, last pushed 6mo ago), licensed MIT. It adds 103 tokens to every session and 9,188 once invoked, about $0.0005 per session on Opus 5. A static security scan graded it A with 0 findings. No closer match exists in the catalogue, so it is treated as the original; first seen 2026-09-03.
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