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 "$T/skills/biomedical-data-analysis" ~/.claude/skills/freedomintelligence-openclaw-medical-skills-biomedical-data-analysis && rm -rf "$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 "$T/skills/biomedical-data-analysis" ~/.openclaw/skills/freedomintelligence-openclaw-medical-skills-biomedical-data-analysis && rm -rf "$T"
manifest:
skills/biomedical-data-analysis/SKILL.mdsource content
<!--
# COPYRIGHT NOTICE
# This file is part of the "Universal Biomedical Skills" project.
# Copyright (c) 2026 MD BABU MIA, PhD <md.babu.mia@mssm.edu>
# 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
- Scope request: Identify analysis_type (
,exploratory
,statistical
,predictive
) and required language/tooling.visualization - Acquire data: Load from CSV/Parquet/SQL using pandas, tidyverse, or connectors described in
.README.md - Process: Apply wrangling, descriptive stats, modeling, or SQL aggregations as listed in the capability tables.
- Visualize: Choose Matplotlib/Seaborn/Plotly for inline plots or emit Tableau/Power BI specs per need.
- 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
(plusREADME.md
for step-by-step lessons).tutorials/README.md