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-lab-data-standardizationgit 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-lab-data-standardization)<a href="https://agentmods.dev/skills/mims-harvard/tooluniverse/tooluniverse-lab-data-standardization"><img src="https://agentmods.dev/badge/skills/mims-harvard/tooluniverse/tooluniverse-lab-data-standardization/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-lab-data-standardization"><img src="https://agentmods.dev/badge/skills/mims-harvard/tooluniverse/tooluniverse-lab-data-standardization.svg" alt="Reviewed on agentmods" width="80" height="20"></a>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 | $0.00000 | $0.01657 |
| Opus 5.5 | $0.00000 | $0.00663 |
| Sonnet 5.5 | $0.00000 | $0.00331 |
| Haiku 4.5 | $0.00000 | $0.00166 |
Grade A, and why
tooluniverse-lab-data-standardization 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 — 118 lines — stays where its author put it; the contents beside it link to each section on GitHub.
Lab Instrument Data Standardization
Convert raw instrument output into a standardized, structured format — distinguishing raw measurements from calculated/derived values with explicit traceability — for LIMS/ELN upload, data lakes, or downstream analysis.
Honesty contract (read first)
- Never fabricate a value. Every number in the output JSON/CSV must come directly from the source file. If a value can't be found or confidently parsed, it is omitted — never guessed.
- Never claim certified ASM output you didn't produce. Tier 1 (native
allotropyparsing) produces genuine Allotrope Simple Model documents. Tier 2 (fallback, this skill's own generic parser) produces a structurally similar but NOT validated document, and is always tagged"tier": "fallback"with an explanatory"note"— never presented as equivalent to a certified conversion. - Traceability or it doesn't count. A "calculated" value with no link back to the raw measurement it came from is not emitted as calculated — the fallback parser only classifies a value as calculated when it can also identify at least one raw column in the same row to point at.
- Say so if a dependency is missing. If
allotropyisn't installed, print thepip install allotropyinstruction and proceed with the fallback tier — do not pretend Tier 1 ran.
When to use vs. not
Use this skill when the user wants to:
- Convert a plate reader / spectrophotometer / qPCR / cell counter / chromatography export (CSV, TSV, XLSX, or a text-extractable PDF table) into a standardized structured format
- Separate raw instrument readings from calculated/derived values with traceability, ahead of a LIMS or data-lake upload
- Get a flattened 2D CSV version of an ASM-style document for spreadsheet import
- Validate that a produced JSON document is structurally sound (has proper traceability, numeric values, no duplicate IDs)
Do NOT use this skill for (route elsewhere):
- General-purpose tabular cleaning/reshaping unrelated to lab instruments
->
tooluniverse-data-wrangling - FASTQ QC ->
tooluniverse-fastq-qc - VCF / variant files ->
tooluniverse-variant-analysis - Proteomics raw files (mzML, RAW) ->
tooluniverse-proteomics-analysis - Single-cell matrices (h5ad, 10X) ->
tooluniverse-single-cell
What ships with it
6 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.
- 10d ago First seen · 118 lines · 0 tokens per session scan A c2be8d971a9a
tooluniverse-lab-data-standardization is a skill published in the GitHub repository mims-harvard/ToolUniverse (1,744 stars, last pushed yesterday), licensed Apache-2.0. It costs nothing until one of its globs matches a file; then it loads 1,657 tokens. 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 2026-10-07.
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