Autonomous biomedical AI agent framework for executing complex research tasks across genomics, drug discovery, molecular biology, and clinical analysis. Use this skill when conducting multi-step biomedical research including CRISPR screening design, single-cell RNA-seq analysis, ADMET prediction, GWAS interpretation, rare disease diagnosis, or lab protocol optimization. Leverages LLM reasoning with code execution and integrated biomedical databases.
Instalar com Codex ou Claude Copie este prompt, cole no Codex, Claude ou outro assistente e deixe que ele revise a página da skill e instale para você.
Um comando direto ignora o prompt de revisão. Verifique a origem antes de executá-lo.
Autonomous biomedical AI agent framework for executing complex research tasks across genomics, drug discovery, molecular biology, and clinical analysis. Use this skill when conducting multi-step biomedical research including CRISPR screening design, single-cell RNA-seq analysis, ADMET prediction, GWAS interpretation, rare disease diagnosis, or lab protocol optimization. Leverages LLM reasoning with code execution and integrated biomedical databases.
Biomni
Overview
Biomni is an open-source biomedical AI agent framework from Stanford's SNAP lab that autonomously executes complex research tasks across biomedical domains. Use this skill when working on multi-step biological reasoning tasks, analyzing biomedical data, or conducting research spanning genomics, drug discovery, molecular biology, and clinical analysis.
Core Capabilities
Biomni excels at:
Multi-step biological reasoning - Autonomous task decomposition and planning for complex biomedical queries
Code generation and execution - Dynamic analysis pipeline creation for data processing
Knowledge retrieval - Access to ~11GB of integrated biomedical databases and literature
Cross-domain problem solving - Unified interface for genomics, proteomics, drug discovery, and clinical tasks
Use scripts/setup_environment.py for interactive setup assistance.
Basic Usage Pattern
from biomni.agent import A1
# Initialize agent with data path and LLM choice
agent = A1(path='./data', llm='claude-sonnet-4-20250514')
# Execute biomedical task autonomously
agent.go("Your biomedical research question or task")
# Save conversation history and results
agent.save_conversation_history("report.pdf")
Working with Biomni
1. Agent Initialization
The A1 class is the primary interface for biomni:
from biomni.agent import A1
from biomni.config import default_config
# Basic initialization
agent = A1(
path='./data', # Path to data lake (~11GB downloaded on first use)
llm='claude-sonnet-4-20250514'# LLM model selection
)
# Advanced configuration
default_config.llm = "gpt-4"
default_config.timeout_seconds = 1200
default_config.max_iterations = 50
Supported LLM Providers:
Anthropic Claude (recommended): claude-sonnet-4-20250514, claude-opus-4-20250514
OpenAI: gpt-4, gpt-4-turbo
Azure OpenAI: via Azure configuration
Google Gemini: gemini-2.0-flash-exp
Groq: llama-3.3-70b-versatile
AWS Bedrock: Various models via Bedrock API
See references/llm_providers.md for detailed LLM configuration instructions.
2. Task Execution Workflow
Biomni follows an autonomous agent workflow:
# Step 1: Initialize agent
agent = A1(path='./data', llm='claude-sonnet-4-20250514')
# Step 2: Execute task with natural language query
result = agent.go("""
Design a CRISPR screen to identify genes regulating autophagy in
HEK293 cells. Prioritize genes based on essentiality and pathway
relevance.
""")
# Step 3: Review generated code and analysis# Agent autonomously:# - Decomposes task into sub-steps# - Retrieves relevant biological knowledge# - Generates and executes analysis code# - Interprets results and provides insights# Step 4: Save results
agent.save_conversation_history("autophagy_screen_report.pdf")
3. Common Task Patterns
CRISPR Screening Design
agent.go("""
Design a genome-wide CRISPR knockout screen for identifying genes
affecting [phenotype] in [cell type]. Include:
1. sgRNA library design
2. Gene prioritization criteria
3. Expected hit genes based on pathway analysis
""")
Single-Cell RNA-seq Analysis
agent.go("""
Analyze this single-cell RNA-seq dataset:
- Perform quality control and filtering
- Identify cell populations via clustering
- Annotate cell types using marker genes
- Conduct differential expression between conditions
File path: [path/to/data.h5ad]
""")
Drug ADMET Prediction
agent.go("""
Predict ADMET properties for these drug candidates:
[SMILES strings or compound IDs]
Focus on:
- Absorption (Caco-2 permeability, HIA)
- Distribution (plasma protein binding, BBB penetration)
- Metabolism (CYP450 interaction)
- Excretion (clearance)
- Toxicity (hERG liability, hepatotoxicity)
""")
See references/use_cases.md for comprehensive task examples across all biomedical domains.
4. Data Integration
Biomni integrates ~11GB of biomedical knowledge sources:
Gene databases - Ensembl, NCBI Gene, UniProt
Protein structures - PDB, AlphaFold
Clinical datasets - ClinVar, OMIM, HPO
Literature indices - PubMed abstracts, biomedical ontologies
Pathway databases - KEGG, Reactome, GO
Data is automatically downloaded to the specified path on first use.
5. MCP Server Integration
Extend biomni with external tools via Model Context Protocol:
# MCP servers can provide:# - FDA drug databases# - Web search for literature# - Custom biomedical APIs# - Laboratory equipment interfaces# Configure MCP servers in .biomni/mcp_config.json
6. Evaluation Framework
Benchmark agent performance on biomedical tasks:
from biomni.evalimport BiomniEval1
evaluator = BiomniEval1()
# Evaluate on specific task types
score = evaluator.evaluate(
task_type='crispr_design',
instance_id='test_001',
answer=agent_output
)
# Access evaluation dataset
dataset = evaluator.load_dataset()
Best Practices
Task Formulation
Be specific - Include biological context, organism, cell type, conditions
Specify outputs - Clearly state desired analysis outputs and formats
Provide data paths - Include file paths for datasets to analyze
Set constraints - Mention time/computational limits if relevant
Security Considerations
⚠️ Important: Biomni executes LLM-generated code with full system privileges. For production use:
Run in isolated environments (Docker, VMs)
Avoid exposing sensitive credentials
Review generated code before execution in sensitive contexts
Use sandboxed execution environments when possible
Performance Optimization
Choose appropriate LLMs - Claude Sonnet 4 recommended for balance of speed/quality
Set reasonable timeouts - Adjust default_config.timeout_seconds for complex tasks
Monitor iterations - Track max_iterations to prevent runaway loops
Cache data - Reuse downloaded data lake across sessions
Result Documentation
# Always save conversation history for reproducibility
agent.save_conversation_history("results/project_name_YYYYMMDD.pdf")
# Include in reports:# - Original task description# - Generated analysis code# - Results and interpretations# - Data sources used
Resources
References
Detailed documentation available in the references/ directory:
api_reference.md - Complete API documentation for A1 class, configuration, and evaluation
