OpenClaw-Medical-Skills precision-oncology-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 "$T/skills/precision-oncology-agent" ~/.claude/skills/freedomintelligence-openclaw-medical-skills-precision-oncology-agent && rm -rf "$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 "$T/skills/precision-oncology-agent" ~/.openclaw/skills/freedomintelligence-openclaw-medical-skills-precision-oncology-agent && rm -rf "$T"
manifest: skills/precision-oncology-agent/SKILL.md
source content
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name: precision-oncology-agent description: Fuse genomic variants, pathology findings, and clinical context to draft evidence-linked therapy options for tumor board review. allowed-tools:

  • read_file
  • run_shell_command

At-a-Glance

  • description (10-20 chars): Tumor board copilot
  • keywords: oncology, genomics, OncoKB, therapy-ranking, evidence
  • measurable_outcome: Deliver a ranked therapy list with OncoKB/NCCN citations plus data-gap checklist for every case within 10 minutes of receiving inputs.

Inputs

  • vcf_path
    (hg38 preferred) plus optional CNV/fusion summaries.
  • pathology_report
    text for histology/grade/biomarkers.
  • clinical_context
    dict capturing tumor type, stage, prior lines, ECOG.

Outputs

  1. Ranked treatment options (approved, off-label, clinical trials) with evidence strength + contraindications.
  2. Variant interpretation table (pathogenicity, tier, therapy linkage).
  3. Biomarker summary (TMB, MSI, PD-L1 if provided) and missing-test checklist.

Workflow

  1. Ingest & normalize: Harmonize gene symbols, genome build, and variant effects.
  2. Annotate: Query OncoKB/NCCN + internal knowledge for actionability tiers.
  3. Contextualize: Blend pathology + prior therapy info to filter contraindicated options.
  4. Recommend: Present therapies ordered by evidence + patient fit; cite sources.
  5. Gaps: Highlight assays or confirmations still required before treatment.

Guardrails

  • No autonomous treatment decisions—flag outputs as advisory.
  • Cite evidence rigorously (guideline version, publication).
  • Highlight resistance mechanisms and prior exposure conflicts.

References

  • See
    README.md
    for detailed workflow plus cited Nature Cancer study.
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