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-proteomics-analysisgit 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-proteomics-analysis)<a href="https://agentmods.dev/skills/mims-harvard/tooluniverse/tooluniverse-proteomics-analysis"><img src="https://agentmods.dev/badge/skills/mims-harvard/tooluniverse/tooluniverse-proteomics-analysis/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-proteomics-analysis"><img src="https://agentmods.dev/badge/skills/mims-harvard/tooluniverse/tooluniverse-proteomics-analysis.svg" alt="Reviewed on agentmods" width="80" height="20"></a>- Socket pass
- Snyk warn
- 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 | $0.00076 | $0.02976 |
| Opus 5.5 | $0.00030 | $0.01190 |
| Sonnet 5.5 | $0.00015 | $0.00595 |
| Haiku 4.5 | $0.00008 | $0.00298 |
Grade A, and why
tooluniverse-proteomics-analysis 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 19d 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 — 171 lines — stays where its author put it; the contents beside it link to each section on GitHub.
Proteomics Analysis
RULE ZERO — Check for pre-computed results FIRST
Before following any instruction below, scan the data folder for:
*_executed.ipynb→ read withtu run read_executed_notebook '{"data_folder":"<path>","search":"<keyword>"}'and cite its cell outputs as the authoritative answer- Pre-computed result files (CSV/TSV with names like
*results*,*deseq*,*enrich*,*stats*,*_simplified.csv) → read directly and report the requested value - Canonical analysis scripts (
analysis.R,run_*.py,find_*.R,*.Rmd) → execute as-is and read the output
Only follow this skill's re-analysis recipe below if none of the above exist. Re-running from raw data produces different numbers than the published answer and is much slower (often 5-10× turn count).
Comprehensive analysis of mass spectrometry-based proteomics data from protein identification through quantification, differential expression, post-translational modifications, and systems-level interpretation.
When to Use This Skill
Triggers: User has proteomics MS output files, asks about protein abundance/expression, differential protein expression, PTM analysis, protein-RNA correlation, multi-omics integration involving proteomics, protein complex/interaction analysis, or proteomics biomarker discovery.
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.
Core Capabilities
- Data Import: MaxQuant, Spectronaut, DIA-NN, Proteome Discoverer, FragPipe outputs
- Quality Control: Missing value analysis, intensity distributions, sample clustering
- Normalization: Median, quantile, TMM, VSN — choice depends on experimental design (see Interpretation Framework)
- Imputation: MinProb (MNAR), KNN (MAR), QRILC for missing values
- Differential Expression: Limma, DEP, MSstats for statistical testing
- PTM Analysis: Phospho-site localization, PTM enrichment, kinase prediction
- Protein-RNA Integration: Correlation analysis, translation efficiency
- Pathway Enrichment: Over-representation and GSEA for protein sets
- PPI Analysis: Protein complex detection, interaction networks via STRING/IntAct
What ships with it
3 files beside SKILL.md in the same directory: the scripts, references and assets a skill reads on demand. Not counted in the per-session cost; read them before you install if any of them is executable.
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.
- 19d ago Changed · +22 lines 79527d270c07
- 1mo ago First seen · 149 lines · 76 tokens per session scan A 21a5133c1b90
tooluniverse-proteomics-analysis is a skill published in the GitHub repository mims-harvard/ToolUniverse (1,757 stars, last pushed today), licensed Apache-2.0. It adds 76 tokens to every session and 2,976 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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