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tooluniverse-epidemiological-analysis

End-to-end observational epidemiology analysis — from research question (PECO Population/Exposure/Comparator/Outcome) to publication-ready statistical report. Covers cohort/case-control/cross-sectional design, regression with confounders, propensity scoring, sensitivity analysis. Writes Python code for every step. Use for epidemiology study analysis, NHANES/UK-Biobank-style analyses.

Quality

85%

Does it follow best practices?

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SecuritybySnyk

Low

Low-risk findings worth noting

The canonical home for this skill is tooluniverse-epidemiological-analysis in mims-harvard/ToolUniverse

SKILL.md
Quality
Evals
Security

Quality

Content

70%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 well-sequenced, highly actionable analysis workflow with strong validation checkpoints, but it is a ~300-line monolith: the ToolUniverse data-source catalog buried inside Steps 2–3 belongs in a one-level-deep reference file, and repeated 'verified live' padding inflates token cost.

Suggestions

Move the data-source catalog (euhealth, Disease.sh, WHO GHO, WAQI/OpenMeteo, health_disparities) into a references/ file (e.g. references/data-sources.md), keeping a one-line pointer per source family in Steps 2–3 so the main workflow stays lean.

Compress the euhealth block: enumerate the ~20 tool names in the reference file and keep only the shared parameter shape, response shape, and setup warning inline.

Trim the repeated 'verified live' qualifiers — state once per section that behavior was verified, rather than after each tool.

DimensionReasoningScore

Conciseness

The workflow itself is lean and code-first with no explanation of concepts Claude already knows, but the inline data-source catalog (euhealth with ~20 tool names enumerated, Disease.sh, WHO GHO, WAQI/OpenMeteo, SVI — roughly lines 105–140) is a large always-loaded block that could be tightened or moved, and 'verified live' qualifiers repeat to the point of padding.

3 / 5

Actionability

Mostly executable, concrete code throughout (download helper, power analysis, Table 1, sequential logit models, VIF, sensitivity analyses) with explicit 'adapt to your data' signposting; placeholders like url1/id_col/threshold/api_url keep it from being copy-paste ready.

4 / 5

Workflow Clarity

Nine clearly sequenced steps with explicit checkpoints — power analysis before dataset commitment, dataset-fitness evaluation, missing-data decision thresholds, model selection by outcome type, VIF checks, the datastore-warning check, sensitivity analyses — capped by a completeness checklist.

5 / 5

Progressive Disclosure

No bundle files exist, and content that clearly belongs in a separate reference file (the multi-paragraph data-source/tool catalog) is inlined in the main body; section structure itself is good, so this sits at 'some structure but could be better organized'.

3 / 5

Total

15

/

20

Passed

Description

92%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: concrete, comprehensive, third-person, with an explicit 'Use for' trigger clause naming distinctive data sources. The only weakness is keyword coverage that omits a few natural synonyms users would say.

DimensionReasoningScore

Specificity

Lists multiple specific concrete actions covering the workflow end to end — research question (PECO), cohort/case-control/cross-sectional design, regression with confounders, propensity scoring, sensitivity analysis, and writing Python code — with no meaningful coverage gaps.

5 / 5

Completeness

Explicitly answers both: the 'what' is concretely enumerated (PECO to publication-ready report with named methods), and the 'when' is an explicit 'Use for epidemiology study analysis, NHANES/UK-Biobank-style analyses' clause with concrete triggers.

5 / 5

Trigger Term Quality

Good natural keywords — 'epidemiology study analysis', 'NHANES/UK-Biobank-style analyses', cohort/case-control/cross-sectional — but misses common variations a user might say such as 'observational study', 'health survey analysis', or 'risk factor analysis'.

4 / 5

Distinctiveness Conflict Risk

Carves a clear niche (observational epidemiology, PECO, NHANES/UK-Biobank) with distinct triggers and minimal conflict risk; only slight adjacency to a generic statistics skill.

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

plugins/tooluniverse/skills/tooluniverse-epidemiological-analysis/SKILL.md

Reviewed

Table of Contents

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