agentsclimarketplace
Sign in

Medea therapeutic discovery

Skill FridrichMethod/awesome-skills/skills/medea-therapeutic-discovery

An AI agent for therapeutic discovery that executes transparent, multi-step omics analyses including research planning, code execution, and literature reasoning.From its SKILL.md

Install
npx -y skills add FridrichMethod/awesome-skills --skill medea-therapeutic-discovery

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

2 things to look at

  • no licenseNo license file was found in the repository. Code published without one is not open source by default, so using it at work is a question for whoever answers licensing questions where you are.
  • 13 stars13 stars. Stars are a popularity signal and not a quality one, but at this level it is likely that nobody has read this closely except its author, and you would be relying on your own review.

What its file declares

Copied from the file, not written here

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

1.6 KB, 271 tokens by cl100k_base, as published. Nobody here has run it

Medea Therapeutic Discovery Agent

Medea is a multi-stage AI agent designed for therapeutic discovery, modeled after 2026 state-of-the-art open source architectures. It executes transparent, multi-step omics analyses.

When to Use This Skill

  • "Run multi-omics therapeutic discovery pipeline"
  • "Analyze omics data for novel drug targets using Medea"
  • "Perform literature reasoning and consensus reconciliation for target X"

Core Capabilities

  1. Research Planning: Formulates step-by-step omics analysis plans.
  2. Code Execution: Generates and executes Python/R scripts for data processing.
  3. Literature Reasoning: Retrieves and synthesizes current literature.
  4. Consensus Stage: Reconciles experimental evidence with literature to propose high-confidence targets.

Workflow

  1. Step 1: Initialize Medea agent with target disease or omics dataset.
  2. Step 2: Execute the multi-stage pipeline across planning, coding, literature review, and consensus validation.

Example Usage

User: "Run Medea analysis on the provided breast cancer multi-omics dataset."

Agent Action:

python3 -m medea.agent --dataset breast_cancer_omics.h5ad --mode full_discovery

What ships with it

Read from the repository

Just SKILL.md. No reference files, no scripts.

Keep looking

Skills are one crate of 325,949. Ordering is by how many stacks a row turns up in, so the top of any crate is what has actually been picked rather than what has the most stars.