Getting it into your agent
One page per mod, every tool's command on it. A separate URL per tool would split the same page into five that compete with each other.
npx skills add silverstein/claude-scientific-skills-desktop --skill biomnigit clone --depth 1 https://github.com/silverstein/claude-scientific-skills-desktopWrote this? Show the measurements
A badge with what this costs and how it scanned, read live from this page, so it follows the numbers instead of freezing them. Markdown for a README, HTML for a documentation site or a project page.
[](https://agentmods.dev/skills/silverstein/claude-scientific-skills-desktop/biomni)<a href="https://agentmods.dev/skills/silverstein/claude-scientific-skills-desktop/biomni"><img src="https://agentmods.dev/badge/skills/silverstein/claude-scientific-skills-desktop/biomni/github.svg" alt="Measured on agentmods" height="20"></a>Or the 80×15 button, for a site that already has a row of RSS and ATOM ones. Only the verdict fits; the numbers stay here.
<a href="https://agentmods.dev/skills/silverstein/claude-scientific-skills-desktop/biomni"><img src="https://agentmods.dev/badge/skills/silverstein/claude-scientific-skills-desktop/biomni.svg" alt="Reviewed on agentmods" width="80" height="20"></a>What it costs to keep this loaded
Counted locally with the o200k_base tokenizer, which is exact for GPT models; Claude uses its own tokenizer and its counts differ. Treat this as one consistent yardstick across the catalogue rather than a bill. Prices are per million input tokens.
| Model | Per session | Once invoked |
|---|---|---|
| Fable 5.1 | $0.00082 | $0.02265 |
| Opus 5 | $0.00041 | $0.01132 |
| Sonnet 5 | $0.00016 | $0.00453 |
| Haiku 4.5 | $0.00008 | $0.00227 |
Grade A, and why
biomni scanned grade A with 0 findings against 26 rules in 11 categories — prompt injection, anti-refusal, data exfiltration, privilege escalation, supply chain, agent snooping, system-prompt leakage, SSRF and excessive agency — measured 12d ago.
A static scan of the body, not an audit. Every finding is printed with the line that produced it so you can judge whether it matters here. A mod is markdown that instructs an agent; that is exactly why what it instructs is worth reading.
Nothing flagged
None of the 26 patterns this scan looks for appear in this file: no shell pipes, no recursive deletes, no credential paths, no hidden text, no instruction-override or anti-refusal phrasing, no agent-config snooping. That is not a guarantee, it is the absence of the things that are checkable.
This is a copy
91% identical to biomni — 5 lines differ, which has more behind it and is treated as the original. This page carries a canonical link to it rather than competing with it.
How it starts
The opening of the file, as written. The whole thing — 310 lines — stays where its author put it; the contents beside it link to each section on GitHub.
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
When to Use This Skill
Use biomni for:
- CRISPR screening - Design screens, prioritize genes, analyze knockout effects
- Single-cell RNA-seq - Cell type annotation, differential expression, trajectory analysis
- Drug discovery - ADMET prediction, target identification, compound optimization
- GWAS analysis - Variant interpretation, causal gene identification, pathway enrichment
- Clinical genomics - Rare disease diagnosis, variant pathogenicity, phenotype-genotype mapping
- Lab protocols - Protocol optimization, literature synthesis, experimental design
Quick Start
Installation and Setup
Install Biomni and configure API keys for LLM providers:
uv pip install biomni --upgrade
Configure API keys (store in .env file or environment variables):
export ANTHROPIC_API_KEY="your-key-here"
# Optional: OpenAI, Azure, Google, Groq, AWS Bedrock keys
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")
What ships with it
5 files beside SKILL.md in the same directory: the scripts, references and assets a skill reads on demand. Not counted in the per-session cost; read them before you install if any of them is executable.
What this file has done since we first saw it
Hashed on every crawl. A supply-chain change to an agent config is a question of when, not whether, so the history is kept rather than the latest state alone.
- 12d ago First seen · 310 lines · 82 tokens per session scan A 4e04b64bf45c
biomni is a skill published in the GitHub repository silverstein/claude-scientific-skills-desktop (22 stars, last pushed 5mo ago), licensed MIT. It adds 82 tokens to every session and 2,265 once invoked, about $0.0004 per session on Opus 5. A static security scan graded it A with 0 findings. It is 91% identical to biomni, differing in 5 lines, and is treated as a copy.
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