How do I install the Kragen Knowledge Graph skill?

Run npx skillmds@latest add biotender-max/kragen-knowledge-graph in your terminal (requires Node.js), paste this page's agent-chat prompt into Claude, Cursor, or any MCP-connected agent, or download the SKILL.md file and copy it into your agent's skills directory.

What does the Kragen Knowledge Graph skill do?

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Is Kragen Knowledge Graph safe to use?

This skill has not completed SkillMD's automated safety review yet. SkillMD never runs a skill's scripts for you; review the SKILL.md before installing.

Which AI agents work with Kragen Knowledge Graph?

This skill is tagged as working with Claude Code, Claude.ai, OpenAI Codex. SKILL.md is an open format, so most agents that read a skills directory can load it too.

Is Kragen Knowledge Graph free to use?

Yes. Installing skills from SkillMD is free, and the skill stays under its author's original license.

Who published Kragen Knowledge Graph?

BioTender-max (@biotender-max) published this skill. Their other Agent Skills are listed on their SkillMD profile.

Kragen Knowledge Graph by biotender-max

Name: Kragen Knowledge Graph
Author: BioTender-max

File contents

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

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

Workflow

Input: Natural language question.
Retrieval: Fetch relevant sub-graph and similar text chunks.
Reasoning: LLM traverses the graph to find connecting paths.
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"

---name: kragen-knowledge-graphdescription: ---name: kragen-knowledge-graphdescription: Graph-RAG Solverkeywords: - knowledge-graph - RAG - reasoning - graph-of-thoughts - biomedical-qameasurable_outcome: Return a reasoning path and an answer supported by ≥3 knowledge graph nodes for complex biomedical questions with <5s latency.license: MITmetadata: 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 Capabilities1. **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.## Workflow1. **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**:```bashpython -m kragen.solve --question "BRCA1 mutations to ovarian cancer mechanism"```

Frequently asked questions

Kragen Knowledge Graph