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tooluniverse-clinical-data-integration

End-to-end drug safety review integrating FDA labels, FAERS adverse event reports, PRR/ROR disproportionality, pharmacogenomic biomarkers, clinical trial data, and published literature. Use for regulatory drug safety reviews, comprehensive pharmacovigilance reports, label-vs-real-world AE comparison, and clinical decision support for drug safety.

68

Quality

81%

Does it follow best practices?

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SecuritybySnyk

Low

Low-risk findings worth noting

SKILL.md
Quality
Evals
Security

Quality

Content

71%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 dense, well-structured workflow with highly actionable tool-specific guidance and clear sequencing. Weakest on progressive disclosure, since all reference-grade detail is inlined in a single long file with no external references.

Suggestions

Move the detailed per-tool input/output reference and the PRR/ROR/IC interpretation tables into a separate references/ file, keeping SKILL.md as an overview with one-level-deep links.

Add a concrete executable Python snippet (pandas/scipy) for computing or validating disproportionality metrics to back the COMPUTE, DON'T DESCRIBE directive.

Trim the general pharmacovigilance concept explanations (reporting biases, signal-credibility primer) to the specific thresholds and tool gotchas Claude would not already know.

DimensionReasoningScore

Conciseness

Mostly efficient and tool-specific (param gotchas like 'setid NOT set_id', 'limit=1000', NOT_FOUND handling), though the bias explanations (channeling, notoriety, protopathic) and the PRR interpretation table edge toward over-explanation Claude largely already knows.

4 / 5

Actionability

Concrete tool names with explicit inputs, outputs, and decision thresholds (PRR >= 2.0, lower CI > 1.0, N >= 3), but the COMPUTE section directs Python use without an executable code example for the disproportionality math.

4 / 5

Workflow Clarity

Clear 7-phase sequence (0-6) with per-phase objectives, tools, and numbered steps plus signal criteria as decision checkpoints; read-only pipeline so destructive-validation caps do not apply, but no explicit error-recovery feedback loop is present.

4 / 5

Progressive Disclosure

Well-organized into sections but monolithic (~290 lines, no bundle files) with detailed tool I/O reference and signal-interpretation tables inlined that could live in separate reference files; no one-level-deep references are signaled.

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, specific description that clearly states both the capability and trigger conditions in third-person voice with concrete data sources. Minor room for additional synonyms in the trigger terms.

DimensionReasoningScore

Specificity

Lists multiple concrete data sources and actions ('integrating FDA labels, FAERS adverse event reports, PRR/ROR disproportionality, pharmacogenomic biomarkers, clinical trial data, and published literature'), giving comprehensive coverage of capabilities.

5 / 5

Completeness

Explicitly answers both what ('End-to-end drug safety review integrating...') and when ('Use for regulatory drug safety reviews...') with concrete trigger phrases.

5 / 5

Trigger Term Quality

Good natural trigger coverage ('regulatory drug safety reviews, comprehensive pharmacovigilance reports, label-vs-real-world AE comparison, clinical decision support'), though a few common synonyms (e.g. 'side effects') are absent.

4 / 5

Distinctiveness Conflict Risk

Clear niche (regulatory-grade multi-source drug safety integration) with specific data sources (FAERS, PRR/ROR, CPIC PGx) that minimize overlap with generic pharmacovigilance skills.

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.

Validation16 / 16 Passed

Validation for skill structure

No warnings or errors.

Repository
mims-harvard/ToolUniverse
Reviewed

Table of Contents

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