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Biomedical data analysis

Skill FridrichMethod/awesome-skills/skills/biomedical-data-analysis

Curated, auto-synced collection of 2,000+ Claude Code & Codex skills for AI4Protein, bioinformatics, AI development, and academic paper writing. One curl command installs them all.

Install
npx -y skills add FridrichMethod/awesome-skills --skill biomedical-data-analysis

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

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What its author says it does

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Omics data forge

The file declares its own license as MIT. That is the author’s claim about this one file, and it is not the same thing as the license GitHub reports for the repository, which is listed with the other numbers below.

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 -->

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 -->

Keep looking

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