Pharmacovigilance (ToolUniverse Claude Skill)

A ToolUniverse agent skill that analyzes drug safety by mining FDA adverse-event reports, computing disproportionality signals, checking label warnings and pharmacogenomic risk, and synthesizing a prioritized safety report.


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-pharmacovigilance/`)
Pricing	Free / OSS (Apache-2.0); reasoning runs locally, database calls go through the ToolUniverse MCP server
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-pharmacovigilance):
```
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-pharmacovigilance ~/.claude/skills/

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

What it does

Runs a multi-phase drug-safety analysis, executing Python (pandas/scipy/statsmodels) for the quantitative steps rather than describing them:

Drug disambiguation — DailyMed_search_spls, ChEMBL_search_drugs resolve the drug to canonical identifiers.
Adverse-event profiling — FAERS_count_reactions_by_drug_event, FAERS_filter_serious_events, FAERS_stratify_by_demographics mine FDA spontaneous reports, with MedDRA British-spelling conventions in queries.
Label warnings — DailyMed_get_spl_by_setid, OpenFDA_search_drug_labels pull black-box warnings and label-change history.
Pharmacogenomics — PharmGKB_search_drugs, CPIC_list_guidelines assess genotype-dependent risk.
Clinical trials & literature — search_clinical_trials, PubMed_search_articles, plus KEGG drug-metabolism queries and OpenAlex citation analysis.
Signal prioritization & report — computes disproportionality measures (PRR, ROR, IC), classifies dose-dependent vs. idiosyncratic reactions, and writes a markdown report plus CSV data files.

Primary use cases: post-marketing safety-signal detection, FAERS disproportionality analysis, label/black-box warning review, pharmacogenomic risk assessment.

Notes

It is a reasoning layer over ToolUniverse; without the MCP server registered, the tool calls fail. Disproportionality measures from spontaneous-report data (FAERS) indicate signals, not confirmed causality, and are subject to reporting bias. Outputs are decision-support reasoning, not clinical advice. Closely related to the tooluniverse-adverse-event-detection skill (also FAERS/PRR/ROR-based). ToolUniverse ships ~68 such skills; the research, repurposing, target-validation, synergy, drug-drug-interaction, and precision-oncology workflows are catalogued separately.

Sources

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