medea-therapeutic-discovery
An AI agent for therapeutic discovery that executes transparent, multi-step omics analyses including research planning, code execution, and literature reasoning.
Best use case
medea-therapeutic-discovery is best used when you need a repeatable AI agent workflow instead of a one-off prompt.
An AI agent for therapeutic discovery that executes transparent, multi-step omics analyses including research planning, code execution, and literature reasoning.
Teams using medea-therapeutic-discovery should expect a more consistent output, faster repeated execution, less prompt rewriting.
When to use this skill
- You want a reusable workflow that can be run more than once with consistent structure.
When not to use this skill
- You only need a quick one-off answer and do not need a reusable workflow.
- You cannot install or maintain the underlying files, dependencies, or repository context.
Installation
Claude Code / Cursor / Codex
Manual Installation
- Download SKILL.md from GitHub
- Place it in
.claude/skills/medea-therapeutic-discovery/SKILL.mdinside your project - Restart your AI agent — it will auto-discover the skill
How medea-therapeutic-discovery Compares
| Feature / Agent | medea-therapeutic-discovery | Standard Approach |
|---|---|---|
| Platform Support | Not specified | Limited / Varies |
| Context Awareness | High | Baseline |
| Installation Complexity | Unknown | N/A |
Frequently Asked Questions
What does this skill do?
An AI agent for therapeutic discovery that executes transparent, multi-step omics analyses including research planning, code execution, and literature reasoning.
Where can I find the source code?
You can find the source code on GitHub using the link provided at the top of the page.
Related Guides
Best AI Skills for Claude
Explore the best AI skills for Claude and Claude Code across coding, research, workflow automation, documentation, and agent operations.
AI Agent for Product Research
Browse AI agent skills for product research, competitive analysis, customer discovery, and structured product decision support.
Best AI Skills for ChatGPT
Find the best AI skills to adapt into ChatGPT workflows for research, writing, summarization, planning, and repeatable assistant tasks.
SKILL.md Source
# 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 ```
Related Skills
tooluniverse-protein-therapeutic-design
Design novel protein therapeutics (binders, enzymes, scaffolds) using AI-guided de novo design. Uses RFdiffusion for backbone generation, ProteinMPNN for sequence design, ESMFold/AlphaFold2 for validation. Use when asked to design protein binders, therapeutic proteins, or engineer protein function.
tooluniverse-gwas-drug-discovery
Transform GWAS signals into actionable drug targets and repurposing opportunities. Performs locus-to-gene mapping, target druggability assessment, existing drug identification, safety profile evaluation, and clinical trial matching. Use when discovering drug targets from GWAS data, finding drug repurposing opportunities from genetic associations, or translating GWAS findings into therapeutic leads.
tooluniverse-binder-discovery
Discover novel small molecule binders for protein targets using structure-based and ligand-based approaches. Creates actionable reports with candidate compounds, ADMET profiles, and synthesis feasibility. Use when users ask to find small molecules for a target, identify novel binders, perform virtual screening, or need hit-to-lead compound identification.
drug-discovery-search
End-to-end drug discovery platform combining ChEMBL compounds, DrugBank, targets, and FDA labels. Natural language powered by Valyu.
zinc-database
Access ZINC (230M+ purchasable compounds). Search by ZINC ID/SMILES, similarity searches, 3D-ready structures for docking, analog discovery, for virtual screening and drug discovery.
zarr-python
Chunked N-D arrays for cloud storage. Compressed arrays, parallel I/O, S3/GCS integration, NumPy/Dask/Xarray compatible, for large-scale scientific computing pipelines.
xlsx
Use this skill any time a spreadsheet file is the primary input or output. This means any task where the user wants to: open, read, edit, or fix an existing .xlsx, .xlsm, .csv, or .tsv file (e.g., adding columns, computing formulas, formatting, charting, cleaning messy data); create a new spreadsheet from scratch or from other data sources; or convert between tabular file formats. Trigger especially when the user references a spreadsheet file by name or path — even casually (like "the xlsx in my downloads") — and wants something done to it or produced from it. Also trigger for cleaning or restructuring messy tabular data files (malformed rows, misplaced headers, junk data) into proper spreadsheets. The deliverable must be a spreadsheet file. Do NOT trigger when the primary deliverable is a Word document, HTML report, standalone Python script, database pipeline, or Google Sheets API integration, even if tabular data is involved.
writing-skills
Use when creating new skills, editing existing skills, or verifying skills work before deployment
writing-plans
Use when you have a spec or requirements for a multi-step task, before touching code
wikipedia-search
Search and fetch structured content from Wikipedia using the MediaWiki API for reliable, encyclopedic information
wellally-tech
Integrate digital health data sources (Apple Health, Fitbit, Oura Ring) and connect to WellAlly.tech knowledge base. Import external health device data, standardize to local format, and recommend relevant WellAlly.tech knowledge base articles based on health data. Support generic CSV/JSON import, provide intelligent article recommendations, and help users better manage personal health data.
weightloss-analyzer
分析减肥数据、计算代谢率、追踪能量缺口、管理减肥阶段