<!-- 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:
Install with the open skills CLI (global, non-interactive — available in every Claude Code session):
npx skills add FreedomIntelligence/OpenClaw-Medical-Skills --skill "trial-eligibility-agent" -g -a claude-code -yOr manually — clone and copy the skill directory (SKILL.md + companion files):
git clone --depth 1 https://github.com/FreedomIntelligence/OpenClaw-Medical-Skills /tmp/OpenClaw-Medical-Skills && cp -r /tmp/OpenClaw-Medical-Skills/skills/trial-eligibility-agent ~/.claude/skills/trial-eligibility-agentThis skill is a directory: SKILL.md is the entry point; the files below ship with it.
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# 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.
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# Provenance: Authenticated by MD BABU MIA
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---
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.
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