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Trial eligibility agent

Skill FridrichMethod/awesome-skills/skills/trial-eligibility-agent

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Install
npx -y skills add FridrichMethod/awesome-skills --skill trial-eligibility-agent

Assembled from the repository path, not quoted from the project. Check it against their README if it does not work.

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What its author says it does

Copied from the file, not written here

Parse trial protocols and patient data to produce criterion-level MET/NOT/UNKNOWN determinations with evidence and gaps for clinical trial screening tasks.

SKILL.md

2.4 KB, as published. Nobody here has run it

<!-- # COPYRIGHT NOTICE # This file is part of the "Universal Biomedical Skills" project. # Copyright (c) 2026 MD BABU MIA, PhD <[email protected]> # 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 -->

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 -->

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