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-predictor-dms-validationgit 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-predictor-dms-validation)<a href="https://agentmods.dev/skills/mims-harvard/tooluniverse/tooluniverse-variant-predictor-dms-validation"><img src="https://agentmods.dev/badge/skills/mims-harvard/tooluniverse/tooluniverse-variant-predictor-dms-validation/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-predictor-dms-validation"><img src="https://agentmods.dev/badge/skills/mims-harvard/tooluniverse/tooluniverse-variant-predictor-dms-validation.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 | $0.00119 | $0.05359 |
| Opus 5.5 | $0.00048 | $0.02144 |
| Sonnet 5.5 | $0.00024 | $0.01072 |
| Haiku 4.5 | $0.00012 | $0.00536 |
Grade A, and why
tooluniverse-variant-predictor-dms-validation 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 — 447 lines — stays where its author put it; the contents beside it link to each section on GitHub.
Variant-effect predictor benchmarking against DMS
The core user question: "I have a variant-effect predictor — does it actually correlate with experimental DMS measurements on this protein?"
The predictor can be anything that assigns a numeric score to single missense variants:
- ESM-C 6B Sparse Autoencoder (SAE) feature drops at the mutation site
- AlphaMissense pathogenicity scores
- ESM logits-based variant scoring (
ESM_score_sequence) - EVE / EVE++ scores
- Conservation scores (ConSurf, Rate4Site)
- DynaMut2 ΔΔG predictions
- A custom in-house model
This skill validates ALL of these against a DMS dataset with the same statistical framework. SAE is shown as the worked example because the surrounding skills in this collection are SAE-themed, but the procedure is predictor-agnostic.
When to use this skill
- You picked a variant-effect predictor and want to know if it's worth trusting on your protein of interest
- You're comparing two predictors on the same DMS dataset (run this skill twice, compare the resulting per-K Mann-Whitney U p-values + effect sizes)
- Reviewers want robustness evidence — the parameter sweep (K, neutral band, disruptive quantile) is exactly that
- You're publishing a new variant-effect method and need a benchmark figure
Not for:
- Per-position / per-feature interpretation — use
tooluniverse-residue-functional-mechanism-interpretation - Single-variant interpretation (you have one variant, no DMS) — use
tooluniverse-protein-sae-variant-interpretationortooluniverse-protein-lof-mechanism - Building a predictor from scratch — out of scope
Required inputs
| Input | Format | Example |
|---|---|---|
| DMS effect matrix | (20 amino acids × n_positions) np.array, NaN for unmeasured |
from MaveDB_get_effect_matrix |
| Disruptive tail convention | "top" (ΔΔG positive = destabilizing) or "bottom" (fitness low = LoF) |
metadata from DMS retrieval step |
| Per-variant predictor scores | (20 × n_positions) np.array matching DMS layout |
computed from your chosen predictor — see Step 2 |
| Aggregation K | int, mean of top-K when the predictor outputs many sub-scores per variant |
only relevant for multi-feature predictors like SAE; ignore otherwise |
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 · 447 lines · 119 tokens per session scan A 1a33bde1fd2a
tooluniverse-variant-predictor-dms-validation is a skill published in the GitHub repository mims-harvard/ToolUniverse (1,744 stars, last pushed yesterday), licensed Apache-2.0. It adds 119 tokens to every session and 5,359 once invoked, about $0.0005 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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