ToolUniverse is a collection of tools, interfaces, and supporting components for building AI systems that perform scientific work. It is for developers creating AI scientist agents that use APIs, databases, machine-learning tools, and domain-specific utilities. The catalogue includes skills, commands, an MCP server, an agent, and a hook for working with the ecosystem.
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 mims-harvard/ToolUniverse --skill tooluniverse-variant-functional-annotationgit clone --depth 1 https://github.com/mims-harvard/ToolUniverseWrote 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/mims-harvard/tooluniverse/tooluniverse-variant-functional-annotation)<a href="https://agentmods.dev/skills/mims-harvard/tooluniverse/tooluniverse-variant-functional-annotation"><img src="https://agentmods.dev/badge/skills/mims-harvard/tooluniverse/tooluniverse-variant-functional-annotation/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/mims-harvard/tooluniverse/tooluniverse-variant-functional-annotation"><img src="https://agentmods.dev/badge/skills/mims-harvard/tooluniverse/tooluniverse-variant-functional-annotation.svg" alt="Reviewed on agentmods" width="80" height="20"></a>- NVIDIA SkillSpector pass
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 | $0.00077 | $0.03375 |
| Opus 5.5 | $0.00031 | $0.01350 |
| Sonnet 5.5 | $0.00015 | $0.00675 |
| Haiku 4.5 | $0.00008 | $0.00337 |
Grade A, and why
tooluniverse-variant-functional-annotation 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 10d 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 — 237 lines — stays where its author put it; the contents beside it link to each section on GitHub.
Protein Variant Functional Annotation
Comprehensive functional annotation of protein variants by combining ProtVar structural/functional context, ClinVar clinical classifications, gnomAD population frequencies, CADD deleteriousness scoring, and ClinGen gene-disease validity.
Differentiation from tooluniverse-variant-interpretation: This skill focuses specifically on
protein-level functional evidence — structural mapping, residue context, protein domain impact,
and population allele frequencies. It does NOT produce full ACMG classifications or treatment
recommendations. Use tooluniverse-variant-interpretation for complete ACMG clinical classification.
LOOK UP, DON'T GUESS
When uncertain about any scientific fact, SEARCH databases first (PubMed, UniProt, ChEMBL, ClinVar, etc.) rather than reasoning from memory. A database-verified answer is always more reliable than a guess.
COMPUTE, DON'T DESCRIBE
When analysis requires computation (statistics, data processing, scoring, enrichment), write and run Python code via Bash. Don't describe what you would do — execute it and report actual results. Use ToolUniverse tools to retrieve data, then Python (pandas, scipy, statsmodels, matplotlib) to analyze it.
When to Use This Skill
Triggers:
- "Annotate variant [GENE]:[protein_change]" (e.g., "TP53:p.R175H")
- "What is the functional impact of [variant]?"
- "ProtVar annotation for [HGVS or rsID]"
- "Population frequency of [variant]"
- "Is [variant] in a conserved domain?"
- "Structural context of [amino acid change]"
Pathogenicity Reasoning Framework
Assessing variant pathogenicity requires building a converging case from four independent evidence dimensions. No single metric is sufficient — concordance across dimensions defines confidence.
1. Conservation: Is the position evolutionarily constrained? If the residue has been maintained across vertebrates or all eukaryotes, mutation is likely to disrupt function. ProtVar's conservation score and GERP/PhastCons from OpenCRAVAT quantify this. High conservation at a position raises prior probability of pathogenicity, especially for missense variants. Low conservation suggests the position tolerates variation.
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.
- 10d ago Changed b5a3aca25c01
- 1mo ago First seen · 237 lines · 77 tokens per session scan A 548196ce4e64
tooluniverse-variant-functional-annotation is a skill published in the GitHub repository mims-harvard/ToolUniverse (1,744 stars, last pushed yesterday), licensed Apache-2.0. It adds 77 tokens to every session and 3,375 once invoked, about $0.0003 per session on Opus 5.5. 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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