Agent skill
trial-eligibility-agent
Parse trial protocols and patient data to produce criterion-level MET/NOT/UNKNOWN determinations with evidence and gaps for clinical trial screening tasks.
Install this agent skill to your Project
npx add-skill https://github.com/majiayu000/claude-skill-registry/tree/main/skills/other/trial-eligibility-agent
SKILL.md
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_summarynarrative and optionalpatient_structuredFHIR bundle.- Declare data sources used (notes, labs, imaging, meds) to show provenance.
Outputs
- Structured table (JSON recommended) listing each criterion id/text with status, evidence snippet, and confidence.
- Overall recommendation (
potentially_eligible,not_eligible,needs_more_information). - Data gap checklist covering missing labs/imaging/biomarkers.
Workflow
- Acquire protocol: Pull eligibility text from ClinicalTrials.gov or sponsor PDF.
- Normalize criteria: Break into atomic checks with AND/OR logic and thresholds.
- Extract patient facts: Map narrative + FHIR data into canonical features (age, labs, ECOG, biomarkers).
- Evaluate: Assign MET/NOT/UNKNOWN with cited evidence for each criterion, flag missing context explicitly.
- 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.mdfor API snippets (FHIR parsing, JSON schema) and dependency versions. - Pair with
Clinical/Trial_Matching/TrialGPTwhen retrieval/ranking is also needed.
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