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Precision oncology agent

Skill BioTender-max/awesome-bio-agent-skills/skills/openclaw/precision-oncology-agent

A curated collection of AI agent skills for biomedical research, covering genomics, proteomics, single-cell analysis, clinical AI, and protein design.

Install
npx -y skills add BioTender-max/awesome-bio-agent-skills --skill precision-oncology-agent

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

One thing to look at

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SKILL.md

2.1 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 -->

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.
<!-- AUTHOR_SIGNATURE: 9a7f3c2e-MD-BABU-MIA-2026-MSSM-SECURE -->

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