--> --- name: bio-medea-therapeutic-discovery description: An AI agent for therapeutic discovery that executes transparent, multi-step omics analyses including research planning, code execution, and literature reasoning. tool_type: mixed primary_tool: Unknown measurable_outcome: Execute skill workflow successfully with valid output within 15 minutes. allowed-tools: - read_file - run_shell_command --- Medea is a multi-stage AI agent designed for therapeutic discovery, modeled after 2026 state-...
Scanned 9/7/2026
Install to Claude Code
npx -y skills add mdbabumiamssm/LLMs-Universal-Life-Science-and-Clinical-Skills- --skill Medea --agent claude-codeInstalls into .claude/skills of the current project.
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---
name: bio-medea-therapeutic-discovery
description: An AI agent for therapeutic discovery that executes transparent, multi-step
omics analyses including research planning, code execution, and literature reasoning.
tool_type: mixed
primary_tool: Unknown
measurable_outcome: Execute skill workflow successfully with valid output within 15
minutes.
allowed-tools:
- read_file
- run_shell_command
---
# 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**:
```bash
python3 -m medea.agent --dataset breast_cancer_omics.h5ad --mode full_discovery
```
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