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Biomedical data analysis

Skill BioTender-max/awesome-bio-agent-skills/skills/openclaw/biomedical-data-analysis

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 biomedical-data-analysis

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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: biomedical-data-analysis description: Omics data forge keywords:

  • pandas
  • R-tidyverse
  • SQL
  • visualization
  • reproducible measurable_outcome: Deliver a cleaned dataset + statistical summary + at least one visualization or dashboard spec for each request within 1 working session (≤30 minutes). license: MIT metadata: author: BioSkills Team version: "1.0.0" compatibility:
  • system: Python 3.9+ / R 4.0+ allowed-tools:
  • run_shell_command
  • read_file
  • python_repl

Biomedical Data Analysis

Run the cross-language data analysis workflows (Python, R, SQL, Tableau/Power BI) described in this module to clean, analyze, and visualize biomedical datasets end-to-end.

Workflow

  1. Scope request: Identify analysis_type (exploratory, statistical, predictive, visualization) and required language/tooling.
  2. Acquire data: Load from CSV/Parquet/SQL using pandas, tidyverse, or connectors described in README.md.
  3. Process: Apply wrangling, descriptive stats, modeling, or SQL aggregations as listed in the capability tables.
  4. Visualize: Choose Matplotlib/Seaborn/Plotly for inline plots or emit Tableau/Power BI specs per need.
  5. Document: Provide code snippets + outputs, noting package versions and any assumptions.

Guardrails

  • Use reproducible scripts or notebooks—avoid manual spreadsheet edits.
  • Keep PHI secure; when touching EHR-level SQL list filters minimizing data exposure.
  • Clearly separate exploratory findings from validated statistical conclusions.

References

  • Capability tables, code samples, and parameter definitions live in README.md (plus tutorials/README.md for step-by-step lessons).
<!-- AUTHOR_SIGNATURE: 9a7f3c2e-MD-BABU-MIA-2026-MSSM-SECURE -->

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