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OpenClaw-Medical-Skills kragen-knowledge-graph

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install
source · Clone the upstream repo
git clone https://github.com/FreedomIntelligence/OpenClaw-Medical-Skills
Claude Code · Install into ~/.claude/skills/
T=$(mktemp -d) && git clone --depth=1 https://github.com/FreedomIntelligence/OpenClaw-Medical-Skills "$T" && mkdir -p ~/.claude/skills && cp -r "$T/skills/kragen-knowledge-graph" ~/.claude/skills/freedomintelligence-openclaw-medical-skills-kragen-knowledge-graph && rm -rf "$T"
OpenClaw · Install into ~/.openclaw/skills/
T=$(mktemp -d) && git clone --depth=1 https://github.com/FreedomIntelligence/OpenClaw-Medical-Skills "$T" && mkdir -p ~/.openclaw/skills && cp -r "$T/skills/kragen-knowledge-graph" ~/.openclaw/skills/freedomintelligence-openclaw-medical-skills-kragen-knowledge-graph && rm -rf "$T"
manifest: skills/kragen-knowledge-graph/SKILL.md
tags
#knowledge-graph#rag#biomedical-qa#graph-reasoning#graph-of-thoughts
source content
<!-- # 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. # # Provenance: Authenticated by MD BABU MIA -->

name: kragen-knowledge-graph description: Graph-RAG Solver keywords:

  • knowledge-graph
  • RAG
  • reasoning
  • graph-of-thoughts
  • biomedical-qa measurable_outcome: Return a reasoning path and an answer supported by ≥3 knowledge graph nodes for complex biomedical questions with <5s latency. license: MIT metadata: author: Bioinformatics Oxford version: "1.0.0" compatibility:
  • system: Python 3.9+ allowed-tools:
  • run_shell_command
  • web_fetch

KRAGEN (Knowledge Graph Enhanced RAG)

A knowledge graph-enhanced Retrieval-Augmented Generation system for biomedical problem solving, using Graph-of-Thoughts (GoT) reasoning.

When to Use

  • Complex Reasoning: Questions requiring multi-hop deduction (e.g., "How does gene A influence disease B via protein C?").
  • Hypothesis Verification: Checking if a proposed mechanism is supported by existing knowledge graphs.
  • Literature Synthesis: Combining facts from structured DBs and unstructured text.

Core Capabilities

  1. Graph Retrieval: Query biomedical knowledge graphs (e.g., PrimeKG, SPOKE).
  2. Graph-of-Thoughts: structured reasoning over retrieved nodes.
  3. Vector DB Integration: Combines graph data with vector embeddings for hybrid search.

Workflow

  1. Input: Natural language question.
  2. Retrieval: Fetch relevant sub-graph and similar text chunks.
  3. Reasoning: LLM traverses the graph to find connecting paths.
  4. Answer: Generate response with citation of graph nodes.

Example Usage

User: "Explain the mechanism connecting BRCA1 mutations to ovarian cancer."

Agent Action:

python -m kragen.solve --question "BRCA1 mutations to ovarian cancer mechanism"
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