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biomni-research-agent

biomni-research-agent,来自 FreedomIntelligence/OpenClaw-Medical-Skills 的 agent 技能。

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技能内容

<!--

COPYRIGHT NOTICE

This file is part of the "Universal Biomedical Skills" project.

Copyright (c) 2026 MD BABU MIA, PhD <md.babu.mia@mssm.edu>

All Rights Reserved.

#

This code is proprietary and confidential.

Unauthorized copying of this file, via any medium is strictly prohibited.

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Provenance: Authenticated by MD BABU MIA

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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.
  1. 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.

<!-- AUTHOR_SIGNATURE: 9a7f3c2e-MD-BABU-MIA-2026-MSSM-SECURE -->

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它属于哪个仓库

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本站分层T1
该仓技能数897
原文件路径skills/biomni-research-agent/SKILL.md

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