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tooluniverse-adverse-event-detection

Detect and analyze adverse drug event signals using FDA FAERS reports, drug labels, and disproportionality statistics (PRR, ROR, IC). Generates quantitative safety signal scores (0-100) with evidence grading. Use for post-market surveillance, pharmacovigilance, drug safety assessment, regulatory submissions, and detecting rare AE signals not visible in clinical trials.

70

Quality

85%

Does it follow best practices?

Run evals on this skill

Adds up to 20 points to the overall score

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SecuritybySnyk

Low

Low-risk findings worth noting

The canonical home for this skill is tooluniverse-adverse-event-detection in mims-harvard/ToolUniverse

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.

The body is well-structured and actionable with a clear phased workflow and concrete tooling, scoring well on conciseness and actionability. Its main weakness is progressive disclosure: four reference files are named but absent from the bundle.

Suggestions

Add the referenced bundle files (PHASE_DETAILS.md, REPORT_TEMPLATE.md, TOOL_REFERENCE.md, QUICK_START.md) to the skill directory so the signaled navigation actually resolves.

Introduce explicit validation checkpoints in Phase 2 and Phase 8 (e.g., 'verify all top-20 AEs have CIs before scoring') to strengthen workflow clarity for the batch analysis steps.

Trim the Naranjo algorithm walkthrough and KEY PRINCIPLES list to essentials, moving the full scoring rubric and criteria tables into PHASE_DETAILS.md to improve token efficiency.

DimensionReasoningScore

Conciseness

Largely lean with terse phase summaries and bullet lists, but the seven-item KEY PRINCIPLES and detailed Naranjo algorithm walkthrough include some explanation that could be trimmed; efficient yet slightly above the leanest anchor.

4 / 5

Actionability

Provides concrete tool names, explicit signal criteria (PRR >= 2.0, lower CI > 1.0, N >= 3), strength thresholds, and scoring components, but defers most executable code to PHASE_DETAILS.md rather than including copy-paste examples inline.

4 / 5

Workflow Clarity

A clear ten-phase sequence is laid out with an ASCII diagram, Phase 2 flagged as CRITICAL, and an Edge Cases section, but validation checkpoints are mostly implicit (report-first principle, completeness checklist) rather than explicit validate-then-proceed loops.

4 / 5

Progressive Disclosure

The body signals one-level-deep references to PHASE_DETAILS.md, REPORT_TEMPLATE.md, TOOL_REFERENCE.md, and QUICK_START.md, but none of these bundle files actually exist in the directory, breaking the navigation it advertises.

3 / 5

Total

15

/

20

Passed

Description

100%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.

The description is concrete, trigger-rich, and explicitly pairs capability statements with 'Use for' guidance, hitting the top anchor across all four dimensions. It avoids vague fluff and third-person voice is maintained throughout.

DimensionReasoningScore

Specificity

Lists multiple concrete actions ('Detect and analyze adverse drug event signals', 'Generates quantitative safety signal scores (0-100) with evidence grading') and names specific methods (PRR, ROR, IC), giving comprehensive coverage of capabilities.

5 / 5

Completeness

Explicitly answers both 'what' (detect, analyze, score, grade signals) and 'when' via a concrete 'Use for ...' trigger clause listing multiple applicable scenarios.

5 / 5

Trigger Term Quality

Covers comprehensive natural terms users would say — 'post-market surveillance', 'pharmacovigilance', 'drug safety assessment', 'regulatory submissions', 'FAERS reports', plus synonyms (adverse drug event / AE) — matching the top anchor.

5 / 5

Distinctiveness Conflict Risk

Occupies a clear niche (FAERS disproportionality-based adverse-event signal detection with a 0-100 Safety Signal Score) with domain-specific triggers and minimal overlap with unrelated skills.

5 / 5

Total

20

/

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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