Menu

Individual skill

adverse-event-detection

0upvotes

ToolUniverse workflow — Adverse Event Detection

skills/adverse-event-detection/SKILL.md

Read pinned source

Source commit 894598cd3a90891a077c254566276bd379038bad

What was checked

Matches the observed repository HEAD
Yes
Scanner rules are current
No
Repository scan complete
No
Skill scan complete
No
Observed behavior
Not checked yet
Last checked
Nov 5, 2026

These are observations at the recorded commit, not a guarantee of safety. Review the source before running it.

Install this skill

Choose an agent in your Stack or copy a command below. Commands fetch the pinned commit and copy only this skill directory. Existing destinations are refused. Copying a command does not install anything.

An install command is unavailable: current rules, complete repository and skill scans, an eligible behavior level and a permitted license are required.

Repository context

Repository stars (not skill ratings)
251
Repository last updated
Oct 30, 2026
License
Apache-2.0

Observed agent integration: No specific integration evidence. Standard SKILL.md installation does not imply verified compatibility.

Files and excerpt

File metadata and permitted, redacted excerpts come from the recorded scan. Source links are pinned to that commit.

Browse files
Read excerpt

---
name: tooluniverse-adverse-event-detection
description: Detect and analyze adverse drug event signals using FDA FAERS data, drug labels, disproportionality analysis (PRR, ROR, IC), and biomedical evidence. Generates quantitative safety signal scores (0-100) with evidence grading. Use for post-market surveillance, pharmacovigilance, drug safety assessment, adverse event investigation, and regulatory decision support.
---

# Adverse Drug Event Signal Detection & Analysis

Automated pipeline for detecting, quantifying, and contextualizing adverse drug event signals using FAERS disproportionality analysis, FDA label mining, mechanism-based prediction, and literature evidence. Produces a quantitative Safety Signal Score (0-100) for regulatory and clinical decision-making.

**KEY PRINCIPLES**:
1. **Signal quantification first** - Every adverse event must have PRR/ROR/IC with confidence intervals
2. **Serious events priority** - Deaths, hospitalizations, life-threatening events always analyzed first
3. **Multi-source triangulation** - FAERS + FDA labels + OpenTargets + DrugBank + literature
4. **Context-aware assessment** - Distinguish drug-specific vs class-wide vs confounding signals