Agent skill

biomni-research-agent

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Forks 275

Install this agent skill to your Project

npx add-skill https://github.com/FreedomIntelligence/OpenClaw-Medical-Skills/tree/main/skills/biomni-research-agent

SKILL.md


name: biomni-research-agent description: Bio-Research Generalist license: MIT metadata: author: Stanford (Snap Lab) source: "https://github.com/snap-stanford/Biomni" version: "1.0.0" compatibility:

  • system: Python 3.9+ allowed-tools:
  • run_shell_command
  • web_fetch
  • python_repl keywords:
  • biomni
  • automation
  • biomedical
  • reasoning
  • tools measurable_outcome: Execute complex research tasks with >95% success rate and validated tool usage.

Biomni (General Biomedical Agent)

A general-purpose biomedical AI agent capable of executing complex research workflows using over 150 tools and databases.

Biomni is a "General-Purpose" biomedical agent. Unlike specialized skills (e.g., just for folding proteins), Biomni acts as a high-level orchestrator that can break down complex open-ended research questions into solvable sub-tasks using a vast library of tools.

When to Use This Skill

  • Complex Questions: "What are the potential drug targets for Alzheimer's related to mitochondrial dysfunction?"
  • Multi-Step Research: Tasks requiring literature search, data retrieval, and analysis in sequence.
  • Exploratory Analysis: When the exact path to the answer isn't known and requires "reasoning."

Core Capabilities

  1. Tool Use: Access to 150+ tools, 105 software packages, and 59 databases.
  2. Hypothesis Generation: Can formulate scientific hypotheses and plan experiments to test them.
  3. Plan & Execute: Breaks down queries into a dependency graph of tasks.
  4. Database QA: High accuracy (74.4%) in querying biomedical databases.

Workflow

  1. Query Parsing: The agent analyzes the user's natural language request.
  2. Tool Selection: It selects relevant tools (e.g., "Search GWAS Catalog", "Run GO Enrichment", "Fetch Uniprot Data").
  3. Execution Loop:
    • Step 1: Get data.
    • Step 2: Analyze data.
    • Step 3: Refine plan based on intermediate results.
  4. Synthesis: Combines all findings into a comprehensive answer.

Example Usage

User: "Identify genes associated with Type 2 Diabetes that are also expressed in the pancreas and have approved drugs."

Agent Action:

  1. Tool: OpenTargets -> Get T2D associated genes.
  2. Tool: GTEx -> Filter for high expression in Pancreas.
  3. Tool: DrugBank -> Intersect with drug targets.
  4. Result: Returns a list of genes (e.g., GLP1R, DPP4) and their drugs.

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