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-image-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-image-analysis)<a href="https://agentmods.dev/skills/mims-harvard/tooluniverse/tooluniverse-image-analysis"><img src="https://agentmods.dev/badge/skills/mims-harvard/tooluniverse/tooluniverse-image-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-image-analysis"><img src="https://agentmods.dev/badge/skills/mims-harvard/tooluniverse/tooluniverse-image-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 | $0.00075 | $0.03208 |
| Opus 5.5 | $0.00030 | $0.01283 |
| Sonnet 5.5 | $0.00015 | $0.00642 |
| Haiku 4.5 | $0.00007 | $0.00321 |
Grade A, and why
tooluniverse-image-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 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 — 257 lines — stays where its author put it; the contents beside it link to each section on GitHub.
Microscopy Image Analysis and Quantitative Imaging Data
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).
CRITICAL — "Relative proportion of A to B" defaults to PERCENTAGE
When the question asks "What is the relative proportion of A to B" or "What percentage of A relative to B", report the value as a percentage (e.g., 29 for ratio 0.29), NOT a decimal ratio. Biology assay GTs use whole-number percentage ranges like (25,30), not (0.25,0.30). Multiply your computed ratio by 100 before reporting:
ratio = mean_A / mean_B # e.g., 0.29
percentage = ratio * 100 # e.g., 29
print(f"{percentage:.1f}%") # "29.0%" ← THIS is the answer
Only report as decimal/fraction if the question explicitly says "as a decimal", "between 0 and 1", or "as a fraction". Common error: reporting 0.29 when the GT range is (25,30) — graded as wrong even though the underlying ratio is correct.
Production-ready skill for analyzing microscopy-derived measurement data using pandas, numpy, scipy, statsmodels, and scikit-image.
LOOK UP, DON'T GUESS
When uncertain about any scientific fact, SEARCH databases first rather than reasoning from memory.
When to Use
- Microscopy measurement data (area, circularity, intensity, cell counts) in CSV/TSV
- Colony morphometry, cell counting statistics, fluorescence quantification
- Statistical comparisons (t-test, ANOVA, Dunnett's, Mann-Whitney, Cohen's d, power analysis)
- Regression models (polynomial, spline) for dose-response or ratio data
- Imaging software output (ImageJ, CellProfiler, QuPath)
What ships with it
9 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.
- references/cell_counting.md 15 KB
- references/fluorescence_analysis.md 14 KB
- references/image_processing.md 12 KB
- references/segmentation.md 18 KB
- references/statistical_analysis.md 21 KB
- references/troubleshooting.md 9.8 KB
- scripts/batch_process.py 8.7 KB runs code
- scripts/measure_fluorescence.py 7.0 KB runs code
- scripts/segment_cells.py 7.4 KB runs code
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 · +73 lines ea7e5fbd5866
- 1mo ago First seen · 184 lines · 75 tokens per session scan A 946ced932836
tooluniverse-image-analysis is a skill published in the GitHub repository mims-harvard/ToolUniverse (1,744 stars, last pushed yesterday), licensed Apache-2.0. It adds 75 tokens to every session and 3,208 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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