parsing-trial-eligibility
Parses free-text clinical-trial eligibility criteria into structured inclusion and exclusion logic, then matches them against patient facts that OpenMed extracted. Use when the user wants to turn a ClinicalTrials.gov eligibility block into machine-readable rules, screen a synthetic patient for trial fit, or explain why a patient does or does not meet criteria. Trigger keywords: eligibility criteria, inclusion, exclusion, trial matching, patient screening, criteria parsing, eligibilityModule, age/sex gates. Pairs after OpenMed and after searching-clinicaltrials: consume the eligibilityModule te
Profile →npx skills add maziyarpanahi/openmed --skill parsing-trial-eligibility --agent claude-code
Same command for any agent — swap --agent for codex, cursor, copilot.
Weekly change comes from our own snapshots, not the repository page — it measures attention, not adoption.
# Parsing trial eligibility & matching patients A ClinicalTrials.gov study exposes its eligibility as a single free-text block (`protocolSection.eligibilityModule.eligibilityCriteria`) plus a few typed fields (`sex`, `minimumAge`, `maximumAge`, `healthyVolunteers`). This skill turns that prose into **structured inclusion / exclusion criteria** and matches each rule against patient facts that **OpenMed** extracted — producing an explainable `eligible | ineligible | unknown` verdict per criterion. This is **decision support, not enrollment**. The output is a candidate list and a rationale for a clinician to review, never an automated eligibility decision. ## When to use - You pulled a study with `searching-clinicaltrials` and need its eligibility as machine-readable rules. - You have a (synthetic) patient profile and want to screen it against one or many trials, with a per-criterion reason. - You want to highlight which patient facts are missing to decide a criterion. ## Quick start The typed gates are deterministic — apply them first. The free-text criteria need parsing into bullet-level inclusion/exclusion items. ```python # Study from ClinicalTrials.gov v2 (see searching-clinicalt
- When to use
- Quick start
- Matching against OpenMed-extracted patient facts
- Workflow
- Hand-off to / from OpenMed
- Edge cases & gotchas
- Standards & references
What does the parsing-trial-eligibility skill do?
Parses free-text clinical-trial eligibility criteria into structured inclusion and exclusion logic, then matches them against patient facts that OpenMed extracted. Use when the user wants to turn a ClinicalTrials.gov eligibility block into machine-readable rules, screen a synthetic patient for trial fit, or explain why a patient does or does not meet criteria. Trigger keywords: eligibility criteria, inclusion, exclusion, trial matching, patient screening, criteria parsing, eligibilityModule, age/sex gates. Pairs after OpenMed and after searching-clinicaltrials: consume the eligibilityModule te
How do I install it?
Run `npx skills add maziyarpanahi/openmed --skill parsing-trial-eligibility --agent claude-code` — it drops the skill into your project so the agent can pick it up. Swap the --agent value for codex, cursor or copilot if you use one of those.
Where does this skill come from?
From maziyarpanahi/openmed, a repository with 4,851 stars. We read it straight from the repository tree rather than a submitted listing, so what you see here is what is actually published.
Is a popular skill a good skill?
Not necessarily. Stars measure attention, not adoption — a repository can trend for a week and be abandoned. That is why we show the weekly change from our own snapshots next to the total, instead of a single flattering number.