Adverse Event Detection (ToolUniverse Claude Skill)

A ToolUniverse agent skill for pharmacovigilance signal detection — it mines FDA FAERS reports, drug labels, and disproportionality statistics to produce a quantitative safety-signal score with evidence grading.


Type	Claude Skill (one of ToolUniverse’s pre-built agent skills)
Supplier	Zitnik Lab, Harvard Medical School
Availability	GA — part of the ToolUniverse skills collection (`skills/tooluniverse-adverse-event-detection/`)
Pricing	Free / OSS (Apache-2.0); wraps public APIs (openFDA FAERS + labels, OpenTargets, ChEMBL, DrugBank, PubMed/OpenAlex/EuropePMC)
Capabilities	Read-only — drives ToolUniverse tool calls; no data writes
Verified	works · 2026-07-20
Security	cleared · 2026-07-20 — provenance matches Zitnik Lab, Apache-2.0, skill dir confirmed, no OSV advisories

How to install

This skill calls ToolUniverse tools, so the ToolUniverse MCP server must be installed first (see the ToolUniverse page). Simplest registration:

claude mcp add --transport stdio tooluniverse -- uvx tooluniverse

Then add the skills:

Claude Code — install the whole skill collection (the skill resolves as tooluniverse-adverse-event-detection):
```
npx skills add mims-harvard/ToolUniverse
```
Manual / other agents — copy just this skill directory into your skills folder:
```
git clone https://github.com/mims-harvard/ToolUniverse
cp -r ToolUniverse/skills/tooluniverse-adverse-event-detection ~/.claude/skills/
```
(replace ~/.claude/skills/ with your agent’s skills directory if you are not using Claude Code/Desktop.)

The skill sets disable-model-invocation: true upstream, so invoke it explicitly (e.g. ask Claude to “use the adverse-event-detection skill”) rather than relying on automatic dispatch.

What it does

Executes a nine-phase pharmacovigilance pipeline:

Drug disambiguation — ChEMBL/DrugBank IDs, mechanism of action, approved indications.
FAERS profiling — frequency, seriousness, demographics, outcomes.
Disproportionality analysis — PRR, ROR, IC with 95% CIs; a signal is flagged when PRR >= 2.0 AND lower CI > 1.0 AND N >= 3.
FDA label extraction — boxed warnings, contraindications, interactions, special populations.
Mechanism-based context — target safety profiles, ADMET predictions, off-target effects.
Comparative safety — analysis across drug classes.
Interactions — drug-drug interactions and pharmacogenomic risk factors.
Literature synthesis — PubMed, OpenAlex, EuropePMC.
Safety Signal Score — 0–100 with evidence grading (T1–T4) and a report.

Primary use cases: post-market drug safety surveillance, disproportionality signal triage, comparative safety review of a drug class.

Notes

It is a reasoning layer over ToolUniverse; without the MCP server registered, the tool calls fail. FAERS disproportionality statistics are hypothesis-generating signals, not confirmed causal associations. ToolUniverse ships ~150 such skills; other drug-discovery and pharmacovigilance workflows (e.g. Pharmacovigilance) are catalogued separately.

Sources

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