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tooluniverse-dataset-discovery

Find and evaluate research datasets for any scientific question. Maps research questions to required study designs (longitudinal vs cross-sectional, observational vs experimental, single-cohort vs multi-cohort). Use when the user asks 'find data about X', 'where can I get data on Y', or needs a specific cohort/survey/repository. Covers GEO, ArrayExpress, dbGaP, NHANES, UK Biobank, ClinicalTrials.gov, GWAS Catalog, and 30+ scientific repositories.

Quality

90%

Does it follow best practices?

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SecuritybySnyk

Low

Low-risk findings worth noting

SKILL.md
Quality
Evals
Security

Quality

Content

82%Weight 40%Scale 1-5

Reviews the quality of instructions and guidance provided to agents. Good implementation is clear, handles edge cases, and produces reliable results.

A high-quality, actionable skill body: concrete tool names, executable code, a clear five-step workflow, and unusually strong honesty/verification rules. The main improvements are trimming already-known Python basics from the cookbook, adding explicit error-recovery loops, and externalizing the reference-style repository catalog.

Suggestions

Trim the Data Loading Cookbook to the non-obvious formats (XPT/sas7bdat, fwf, URL download with format detection, API pagination) and drop the basic loaders (`pd.read_csv`, `pd.read_excel`, `pd.read_stata`) and the generic matplotlib scatter example — Claude already knows these.

Add explicit error-recovery feedback loops to the workflow, e.g., 'if a repository search returns nothing, fall back to the literature strategy in Layer 3' and 'if a download fails or is access-restricted, report the access tier and try the next-ranked candidate'.

Move the Layer-2 per-tool repository details (re3data, DDBJ, Synapse annotations) into a references/ file, keeping SKILL.md as the decision-rule overview with one-line pointers per repository.

DimensionReasoningScore

Conciseness

The body is dense with non-obvious domain knowledge (design-to-question mapping, repository tiers, fitness criteria, honesty rules) and avoids concept-explaining prose. It slips below a 5 because the Data Loading Cookbook includes basics Claude already knows — `pd.read_csv("data.csv")`, `pd.read_excel`, and the generic matplotlib scatter block — that could be trimmed to the non-obvious formats (XPT, fwf, URL format-detection).

4 / 5

Actionability

Fully executable throughout: named MCP tools (`re3data_search_repositories`, `DHSProgram_search_indicators`, `Synapse_search_entities`, `DbGaP_search_studies`), copy-paste-ready Python for downloads/pagination/analysis, and concrete heuristics ('~800 subjects at 80% power', '~10 events per predictor'). This matches the anchor for fully executable, copy-paste-ready guidance covering common cases.

5 / 5

Workflow Clarity

A clear five-step sequence (Understand → Search → Evaluate → Download/Analyze → Report) with a Step-3 checklist and real checkpoints ('download a small sample first and inspect', 'verify the codebook', 'Always run the code and report actual numbers'). It misses a 5 because explicit error-recovery feedback loops (e.g., what to do when a download fails or returns nothing) are absent.

4 / 5

Progressive Disclosure

The single file is well-sectioned with clear headers and every section is plausibly needed at decision time, and no bundle files exist to organize. It stays at 4 rather than 5 because the ~45-line loader cookbook and the per-tool Layer-2 repository catalog (e.g., the long re3data paragraph) are reference-style content that could live in a references/ file for a skill this broad.

4 / 5

Total

17

/

20

Passed

Description

95%Weight 40%Scale 1-5

Based on the skill's description, can an agent find and select it at the right time? Clear, specific descriptions lead to better discovery.

A strong description that explicitly states what the skill does, when to use it with verbatim user trigger phrases, and which repositories it covers. The only weaknesses are the slightly generic verb 'evaluate' and the mild over-claim 'for any scientific question'.

DimensionReasoningScore

Specificity

The description lists several concrete actions — 'Find and evaluate research datasets', 'Maps research questions to required study designs (longitudinal vs cross-sectional, observational vs experimental, single-cohort vs multi-cohort)' — and enumerates named repositories (GEO, ArrayExpress, dbGaP, NHANES, UK Biobank, ClinicalTrials.gov, GWAS Catalog). It falls short of a 5 because 'evaluate' is generic and 'for any scientific question' is a mild over-claim.

4 / 5

Completeness

Both questions are answered explicitly: what ('Find and evaluate research datasets... Maps research questions to required study designs') and when ("Use when the user asks 'find data about X', 'where can I get data on Y', or needs a specific cohort/survey/repository"). This is a direct match to the anchor-5 example pattern of explicit what-then-when with concrete trigger phrases.

5 / 5

Trigger Term Quality

It directly quotes natural user phrasing — "Use when the user asks 'find data about X', 'where can I get data on Y', or needs a specific cohort/survey/repository" — and includes concrete search terms users would actually type (NHANES, UK Biobank, dbGaP). This matches or exceeds the anchor-5 example's coverage of natural terms and synonyms.

5 / 5

Distinctiveness Conflict Risk

It occupies a clear niche — scientific research dataset discovery — with triggers ('find data about X', 'specific cohort/survey/repository') that are distinct from general search or data-analysis skills. The named repository coverage further distinguishes it; minimal conflict risk.

5 / 5

Total

19

/

20

Passed

Validation

100%

Checks the skill against the spec for correct structure and formatting. All validation checks must pass before discovery and implementation can be scored.

Validation — 16 / 16 Passed

Validation for skill structure

No warnings or errors.

Repository

mims-harvard/ToolUniverse

Path

plugin/skills/tooluniverse-dataset-discovery/SKILL.md

Reviewed

Table of Contents

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