←Leaderboard/Science/OpenClaw-Medical-Skills
Last commit on March 8, 2026·Created on March 8, 2026

FreedomIntelligence/OpenClaw-Medical-Skills

“A specialized library of AI capabilities for medical research, protein design, and drug discovery.”
Combined rank
#158
across all skills
In Science
#3
category rank
Stars
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Forks
419
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Watchers
20
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TL;DR

This collection provides a suite of technical skills tailored for the life sciences and healthcare domains. It enables AI agents to interface with cloud laboratory platforms for protein validation, automate AAV vector design, and streamline the drug discovery pipeline.

By integrating specific tools for binding assays, thermostability measurements, and sequence optimization, the repository helps researchers move from computational design to experimental validation with greater structural precision.

WHO IT'S FOR
Computational biologists
designing proteins and validating via cloud labs
Drug discovery researchers
automating protein structure prediction and design
Bioinformatics data scientists
analyzing temporal medical and biological data
Gene therapy engineers
designing AAV vectors for medical delivery
Compatible AgentsThe repository documents support for these agents. The skills may also work with other agents that can load SKILL.md files, but they may need some setup or small changes.

Use this skill collection

Follow the documented setup, then try a first task.

I’m using

OpenClaw workspace installation

Installs: Full OpenClaw Medical Skills collection, available in one workspace · OpenClaw

Before you start
  • OpenClaw installed and running
  • Git
  • A workspace skills directory at <your-workspace>/skills/

Platform: OpenClaw

  1. In a terminal, clone the repository using sparse checkout and enter the cloned repository.

    # Clone this repository (skills only — skips large data files)
    git clone --depth=1 --no-checkout https://github.com/FreedomIntelligence/OpenClaw-Medical-Skills.git
    cd OpenClaw-Medical-Skills
    git sparse-checkout init --cone
    git sparse-checkout set skills
    git checkout main
  2. In the terminal, copy the collection into the workspace skills directory, replacing <your-workspace> with the intended workspace path.

    # Install to your workspace skills directory
    cp -r skills/* <your-workspace>/skills/
README.md ↗ · Checked Oct 18, 2026

Give it something to do.

Suggested first task

Query TP53 protein interactions

Uses mcpmed-bioinformatics-server

  • Gene symbol: TP53
  • Python 3.10+
  • Access to the MCPmed-integrated STRING web service
Use the mcpmed-bioinformatics-server skill: "Query the STRING database for interactions with TP53."
Suggested first task

Summarize molecular properties for one SMILES

Uses chemical-property-lookup

  • A SMILES string substituted for <SMILES_STRING>
  • An RDKit-capable Python runtime
  • The skill's molecular_tools.py helper module
Use the chemical-property-lookup skill to validate <SMILES_STRING> and return MW, logP, TPSA, HBD/HBA, QED, and a Lipinski pass/fail verdict with violations and calculation warnings. State that these are screening heuristics, not definitive ADMET outcomes.
Suggested first task

Check Warfarin and Aspirin for interactions

Uses drug-interaction-checker

  • Medication names: Warfarin and Aspirin
  • Access to the skill's internal interaction database
Use the drug-interaction-checker skill: "Check interactions for Warfarin and Aspirin."