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SKILL verified MIT Self-run

Bioresearch Disease Case Study

skill-alim430-bioresearch-agent-disease-case-study · by Alim430

Run a reproducible, real-public-data disease case study that composes the literature → biomarker → causal workflows end-to-end and emits a blind benchmark evaluation (known-gene recovery, pathway sanity, reproducibility hash). Use when the user wants to prove the framework on a real disease (e.g. Parkinson's GSE7621) or asks for a "case study", "validation run", or "benchmark" of the biomedical w…

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Install

$ agentstack add skill-alim430-bioresearch-agent-disease-case-study

✓ scanned · ✓ verified — works with Claude Code, Cursor, and more.

Security review

✓ Passed

No issues found. Passed automated security review. · v0.1.0 How review works →

  • ✓ Prompt-injection patterns
  • ✓ Secret / credential exfiltration
  • ✓ Dangerous shell & filesystem operations
  • ✓ Untrusted network calls
  • ✓ Known-malicious package signatures

What it can access

  • ✓ Network access No
  • ✓ Filesystem access No
  • ✓ Shell / process execution No
  • ✓ Environment & secrets No
  • ✓ Dynamic code execution No

From automated source analysis of v0.1.0. “Used” means the capability is present in the source — more access means more to trust, not that it’s unsafe.

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About

BioResearch Agent — Disease Case Study Skill

Capability

A workflow-composition + validation skill. It runs a real, public GEO dataset through the framework's analysis engine (differential expression → pathway enrichment → candidate ranking, with optional literature and causal-inference stages) and produces a blind benchmark evaluation:

  • Known-gene recovery — how many established disease genes surface in the top-ranked

candidates (e.g. SNCA / LRRK2 / PARK7 / PINK1 / PRKN / GBA for Parkinson's).

  • Pathway sanity — whether disease-relevant pathways (dopamine / mitochondrial / oxidative /

immune) are enriched.

  • Reproducibility — commit hash, environment, and data sha256 recorded in an Evidence

Package.

Case Study 1 ships with the suite: Parkinson's disease / GSE7621 (GPL570, 25 samples). See bio-research-os/eval/case_study_pd.py and the Biomedical Workflow Validation Suite README.

Run

python bio-research-os/eval/case_study_pd.py \
    --matrix-path downloads/gse7621_matrix.txt.gz \
    --annot-path  downloads/gpl570.annot.gz \
    --output-dir  docs/case-study

(If paths are omitted the runner downloads the real GSE7621 matrix + GPL570 annotation itself.)

Outputs (in docs/case-study/)

  • GSE7621_deg.csv — differential expression results
  • GSE7621_top_candidates.csv — ranked biomarker candidates
  • GSE7621_pathway_enrichment.csv — enriched KEGG / GO terms
  • GSE7621_volcano.png — volcano plot
  • GSE7621_report.txt — structured report incl. blind benchmark
  • GSE7621_evidence_package.json — provenance + benchmark + limitations

Note

This skill is a composition & validation interface. It adds no statistics of its own — every number comes from the framework's workflow modules. It is the honest way to demonstrate the framework works on real data (not synthetic injection). Requires no LLM key.

Source & license

This open-source skill is cataloged on AgentStack and links to its original source — we do not rehost the code.

Install and usage instructions live in the source repository linked above.

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Versions

  • v0.1.0 Imported from the upstream source.

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