--> --- name: trial-eligibility-agent description: Parse trial protocols and patient data to produce criterion-level MET/NOT/UNKNOWN determinations with evidence and gaps for clinical trial screening tasks. allowed-tools: - read_file - run_shell_command ---
Scanned 9/4/2026
Install to Claude Code
npx -y skills add gabrielmoreira/agent-skills-mirror --skill trial-eligibility-agent --agent claude-codeInstalls into .claude/skills of the current project.
Are you the author of Trial Eligibility Agent?
Add the live security badge to your README — it updates automatically with every re-scan.
[](https://www.skillsdirectory.com/skills/gabrielmoreira-trial-eligibility-agent)More formats (shields.io, HTML) on the badges page.
<!--
# COPYRIGHT NOTICE
# This file is part of the "Universal Biomedical Skills" project.
# Copyright (c) 2026 MD BABU MIA, PhD <md.babu.mia@mssm.edu>
# All Rights Reserved.
#
# This code is proprietary and confidential.
# Unauthorized copying of this file, via any medium is strictly prohibited.
#
# Provenance: Authenticated by MD BABU MIA
-->
---
name: trial-eligibility-agent
description: Parse trial protocols and patient data to produce criterion-level MET/NOT/UNKNOWN determinations with evidence and gaps for clinical trial screening tasks.
allowed-tools:
- read_file
- run_shell_command
---
## At-a-Glance
- **description (10-20 chars):** Trial triage hub
- **keywords:** eligibility, ClinicalTrials, FHIR, evidence, gaps
- **measurable_outcome:** Produce a MET/NOT/UNKNOWN matrix with supporting citations for ≥90% of inclusion/exclusion criteria within 5 minutes per trial request.
## Inputs
- `trial_id` (NCT or sponsor ID) plus protocol text if not public.
- `patient_summary` narrative and optional `patient_structured` FHIR bundle.
- Declare data sources used (notes, labs, imaging, meds) to show provenance.
## Outputs
1. Structured table (JSON recommended) listing each criterion id/text with status, evidence snippet, and confidence.
2. Overall recommendation (`potentially_eligible`, `not_eligible`, `needs_more_information`).
3. Data gap checklist covering missing labs/imaging/biomarkers.
## Workflow
1. **Acquire protocol:** Pull eligibility text from ClinicalTrials.gov or sponsor PDF.
2. **Normalize criteria:** Break into atomic checks with AND/OR logic and thresholds.
3. **Extract patient facts:** Map narrative + FHIR data into canonical features (age, labs, ECOG, biomarkers).
4. **Evaluate:** Assign MET/NOT/UNKNOWN with cited evidence for each criterion, flag missing context explicitly.
5. **Summarize:** Present recommendation and highlight gating unknowns plus next-best actions.
## Guardrails
- Never claim enrollment decisions; mark outputs as advisory.
- Cite direct patient evidence for every MET/NOT call; default to UNKNOWN rather than guessing.
- Respect PHI handling expectations—avoid storing raw notes outside secure paths.
## Tooling & References
- Use `README.md` for API snippets (FHIR parsing, JSON schema) and dependency versions.
- Pair with `Clinical/Trial_Matching/TrialGPT` when retrieval/ranking is also needed.
<!-- AUTHOR_SIGNATURE: 9a7f3c2e-MD-BABU-MIA-2026-MSSM-SECURE -->Is this your skill, or is something wrong with this listing? Request removal or report an issue. Author removals are honored within 72 hours.
No comments yet. Be the first to comment!