biomni
Automate biomedical research with AI agents
Also available from: davila7
Biomni transforms complex biomedical research by autonomously executing multi-step analysis tasks. Researchers can focus on scientific questions while AI handles data processing, literature review, and computational analysis across genomics, drug discovery, and clinical domains.
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Using "biomni". Design a CRISPR screen for autophagy regulators
Expected outcome:
- Generated sgRNA library with 76,230 guides targeting 19,057 genes
- Designed 4 sgRNAs per gene with on-target scores above 0.7
- Included positive controls: ATG5, BECN1, ULK1, mTOR
- Prioritized 347 candidate genes based on pathway analysis
- Provided Python code for screen analysis pipeline
Using "biomni". Analyze single-cell RNA-seq from tumor samples
Expected outcome:
- Identified 12 distinct cell populations via clustering
- Annotated major immune cell types: T cells, B cells, macrophages
- Found 3 novel cell clusters with unknown markers
- Differential expression revealed 234 upregulated genes in tumor region
Security Audit
Low RiskThe static analysis flagged 415 patterns, but 95% are FALSE POSITIVES from markdown documentation. The backtick patterns are markdown code delimiters, not shell execution. The API key patterns show example environment variable names in documentation, not actual secrets. The skill is a legitimate Stanford SNAP lab biomedical research framework. The code execution + network + credential combination is the intended design for an AI agent that generates bioinformatics analysis code. Proper security warnings are documented recommending sandboxed execution.
Risk Factors
⚙️ External commands (3)
🔑 Env variables (2)
📁 Filesystem access (2)
🌐 Network access (1)
Quality Score
What You Can Build
Design genome-wide CRISPR screens
Automate sgRNA library design, gene prioritization, and knockout effect analysis for functional genomics studies
Process single-cell sequencing data
Perform quality control, clustering, cell type annotation, and differential expression analysis
Predict compound ADMET properties
Evaluate absorption, distribution, metabolism, excretion, and toxicity for drug candidates
Try These Prompts
Design a CRISPR knockout screen to identify genes regulating autophagy in HEK293 cells. Include sgRNA library design, positive/negative controls, and gene prioritization based on pathway relevance.
Analyze this single-cell RNA-seq dataset: perform QC, identify cell populations via clustering, annotate cell types using marker genes, and conduct differential expression. File: path/to/data.h5ad
Interpret GWAS results for Type 2 Diabetes: identify genome-wide significant variants, map to causal genes, perform pathway enrichment, and predict functional consequences
Predict ADMET properties for these compounds: [SMILES strings]. Focus on Caco-2 permeability, plasma protein binding, CYP450 interactions, clearance, and hERG toxicity
Best Practices
- Specify biological context including organism, cell type, and experimental conditions
- Provide data file paths when analyzing datasets
- Set computational constraints for complex analyses
- Save conversation history for reproducibility
Avoid
- Running without reviewing generated code in production environments
- Sharing API keys or credentials in shared environments
- Processing sensitive clinical data without proper authorization
- Ignoring timeout settings for long-running analyses
Frequently Asked Questions
Is biomni safe to use?
What LLM providers does biomni support?
How much data does biomni download?
Can biomni analyze my experimental data?
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Developer Details
Author
K-Dense-AILicense
Apache-2.0 license
Repository
https://github.com/K-Dense-AI/claude-scientific-skills/tree/main/scientific-skills/biomniRef
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