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Trialgpt matching

Skill FridrichMethod/awesome-skills/skills/trialgpt-matching

Trial shortlistFrom its SKILL.md

Install
npx -y skills add FridrichMethod/awesome-skills --skill trialgpt-matching

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

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  • no licenseNo license file was found in the repository. Code published without one is not open source by default, so using it at work is a question for whoever answers licensing questions where you are.
  • 13 stars13 stars. Stars are a popularity signal and not a quality one, but at this level it is likely that nobody has read this closely except its author, and you would be relying on your own review.

What its file declares

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The file declares its own license as MIT. That is the author’s claim about this one file, and it is not the same thing as the license GitHub reports for the repository, which is listed with the other numbers below.

SKILL.md

2.3 KB, 439 tokens by cl100k_base, 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 -->

TrialGPT Matching

Run the locally checked-out TrialGPT pipeline to retrieve, rank, and explain candidate trials for a patient before deeper eligibility review.

Inputs

  • Patient summary (structured JSON or free text) with condition keywords.
  • Optional filters: geography, phase, intervention, biomarker.
  • Up-to-date ClinicalTrials.gov dump or API access.

Outputs

  • Ranked trial table with NCT ID, title, score, and short justification.
  • Parsed inclusion/exclusion text ready for downstream eligibility agents.
  • Missing data checklist (e.g., "ECOG not provided").

Workflow

  1. Setup: cd repo && pip install -r requirements.txt (or reuse env).
  2. Trial retrieval: Run TrialGPT retriever to pull candidate trials for the indication.
  3. Criteria parsing: Convert eligibility blocks to structured criteria JSON.
  4. Patient profiling: Summarize patient facts (labs, prior therapies, biomarkers).
  5. Ranking: Execute TrialGPT ranking script to score each trial and emit explanations.
  6. Handoff: Export ranked list + structured criteria for trial-eligibility-agent.

Guardrails

  • Refresh ClinicalTrials.gov metadata regularly to avoid stale trials.
  • Label scores as AI-generated suggestions pending clinician validation.
  • Retain prompt/config metadata for audit trails.

References

  • Detailed usage instructions and repo layout live in README.md.
  • Coordinate with Skills/Clinical/Trial_Eligibility_Agent for criterion-level review.
<!-- AUTHOR_SIGNATURE: 9a7f3c2e-MD-BABU-MIA-2026-MSSM-SECURE -->

What ships with it: 12 files

41.7 KB alongside SKILL.md, 8 of them executable

repo/

Keep looking

Skills are one crate of 325,949. Ordering is by how many stacks a row turns up in, so the top of any crate is what has actually been picked rather than what has the most stars.