OpenClaw-Medical-Skills trial-eligibility-agent

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install
source · Clone the upstream repo
git clone https://github.com/FreedomIntelligence/OpenClaw-Medical-Skills
Claude Code · Install into ~/.claude/skills/
T=$(mktemp -d) && git clone --depth=1 https://github.com/FreedomIntelligence/OpenClaw-Medical-Skills "$T" && mkdir -p ~/.claude/skills && cp -r "$T/skills/trial-eligibility-agent" ~/.claude/skills/freedomintelligence-openclaw-medical-skills-trial-eligibility-agent && rm -rf "$T"
OpenClaw · Install into ~/.openclaw/skills/
T=$(mktemp -d) && git clone --depth=1 https://github.com/FreedomIntelligence/OpenClaw-Medical-Skills "$T" && mkdir -p ~/.openclaw/skills && cp -r "$T/skills/trial-eligibility-agent" ~/.openclaw/skills/freedomintelligence-openclaw-medical-skills-trial-eligibility-agent && rm -rf "$T"
manifest: skills/trial-eligibility-agent/SKILL.md
source content
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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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