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-residue-functional-mechanism-interpretationgit 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-residue-functional-mechanism-interpretation)<a href="https://agentmods.dev/skills/mims-harvard/tooluniverse/tooluniverse-residue-functional-mechanism-interpretation"><img src="https://agentmods.dev/badge/skills/mims-harvard/tooluniverse/tooluniverse-residue-functional-mechanism-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/mims-harvard/tooluniverse/tooluniverse-residue-functional-mechanism-interpretation"><img src="https://agentmods.dev/badge/skills/mims-harvard/tooluniverse/tooluniverse-residue-functional-mechanism-interpretation.svg" alt="Reviewed on agentmods" width="80" height="20"></a>- NVIDIA SkillSpector warn
SkillSpector: 1 finding, 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 analysis-evasion · line 1 Suspicious Unicode normalization or mixed-script contentFix: Review the flagged content for security risks. Ensure no credentials, secrets, or sensitive data are exposed.
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 | $0.00138 | $0.05889 |
| Opus 5.5 | $0.00055 | $0.02356 |
| Sonnet 5.5 | $0.00028 | $0.01178 |
| Haiku 4.5 | $0.00014 | $0.00589 |
Grade A, and why
tooluniverse-residue-functional-mechanism-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 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.
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 — 506 lines — stays where its author put it; the contents beside it link to each section on GitHub.
Residue functional mechanism interpretation
The core user question: "Why are these residues functionally critical?"
Answering needs more than one source: a residue in the ligand pocket means something different from one in a catalytic triad, an interface, the hydrophobic core, or a PTM sequon. This skill synthesizes evidence from multiple TU tools to call a mechanism for each residue (or cluster of adjacent residues).
The residues can come from any source — this skill is agnostic to where they came from:
| Residue source | Typical call pattern |
|---|---|
| DMS map hotspots | Use Step 1 (optional) to detect top-K by max effect, then continue |
| ClinVar recurrent variants | Pull recurrent positions from ClinVar, pass directly as user_provided_positions |
| Literature hot regions | Paste positions from a paper's Fig 1, pass directly |
| Evolutionarily conserved residues | Filter by conservation score, pass top-N positions |
| Druggable site residues | From a binding-site predictor, pass directly |
| Clinician's question | "Why does mutation at R175 keep showing up in tumors?" — pass [175] |
When to use this skill
- You have a list of residues (from anywhere) and need to explain why they matter biologically
- You're writing the methods/discussion section of a paper and need mechanistic claims per residue or cluster
- You're comparing residue sets across orthologs and need a per-residue category to align by
Not for:
- Validating a predictor against DMS — use
tooluniverse-variant-predictor-dms-validation - Single-variant SAE feature decomposition when you want to see which
ESMC features changed — use
tooluniverse-protein-sae-variant-interpretation - Single-variant LoF mechanism synthesis for one variant in isolation
— use
tooluniverse-protein-lof-mechanism
The single-variant skills focus on one mutation's signature; this skill focuses on which residues matter and why.
Required inputs (choose one entry path)
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.
- 1mo ago First seen · 506 lines · 138 tokens per session scan A e78672ba0249
tooluniverse-residue-functional-mechanism-interpretation is a skill published in the GitHub repository mims-harvard/ToolUniverse (1,744 stars, last pushed yesterday), licensed Apache-2.0. It adds 138 tokens to every session and 5,889 once invoked, about $0.0006 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.
Other skills, from other repositories
ai-agent-tool-routing
Use when designing or reviewing an AI agent tool registry or MCP router: document parameter schemas, caller authorization, execution limits, replay-safe retries and verification evidence.
nanoresearch-writing
Draft a LaTeX research paper from all previous stage outputs.
construct-toy-examples
Generate and analyze simpler examples that satisfy both the assumptions and the conclusion of a theorem statement or subgoal. Use when you are stuck in reasoning and need simpler examples to regain traction, or when you want to see where the assumptions take effect and gain intuition.
obtain-immediate-conclusions
Derive immediate mathematical consequences from a theorem statement or subgoal. Use when starting a new problem, branch, or subgoal, or when cheap progress or a cleaner reformulation is needed before deeper proof search.
clinical-research-analysis-framework
Guided workflow for statistical and ML analysis of clinical data. Use when planning or executing research analyses on MIMIC, eICU, or similar EHR data. Ensures methodological rigor through structured consultation, assumption checking, and stepwise execution with audit trails.
apache-iv-score
Calculate APACHE IV (Acute Physiology and Chronic Health Evaluation IV) score for ICU mortality prediction. Use for severity assessment, hospital mortality prediction, ICU benchmarking, or case-mix adjustment. eICU has pre-computed scores; MIMIC-IV requires custom implementation with diagnosis mapping challenges.