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AMG-RAG

Skill Development Poor
Works inClaude Code
Poor Scanned — metadata only

Reduces manual knowledge graph maintenance and improves QA accuracy in regulated medical domains by automating graph construction, integrating reasoning, and…

AMG-RAG (Agentic Medical Graph-RAG) is a comprehensive framework that automates the construction and continuous updating of Medical Knowledge Graphs (MKGs), integrates reasoning, and retrieves current external evidence for medical Question Answering (QA).

43 starsFreeQuick setup
Below standard — Significant caveats apply. Not recommended without careful review of the security and evaluation sections.

Reduces manual knowledge graph maintenance and improves QA accuracy in regulated medical domains by automating graph construction, integrating reasoning, and surfacing current evidence—critical for compliance and clinical decision support.

Healthcare software teams and clinical informatics consultants building QA systems requiring current, traceable medical evidence.

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https://github.com/MrRezaeiUofT/AMG-RAG

By MrRezaeiUofT

How to Get It

Option 1: Claude Desktop App (Code Mode)Click the + button next to the prompt box → Plugins → Add plugin. Search and click Install. Skills work in Claude Code only.
Option 2: Paste into Claude CodeCopy the command below and paste it into your conversation. Claude will install it.
Command
claude plugins install MrRezaeiUofT/AMG-RAG

Tip: Paste this into a Claude Code conversation. Verify command matches your Claude Code version.

Auto-generated from the tool's public listing — not hands-on verified. Cross-check against the source repo's README before running.

First thing to try

After installing, paste this into Claude:

Help me generate medical knowledge graphs automatically from clinical literature and research documents
CostFree

Trust Signals Auto-scanned

Stars43Contributors2Last updated2026-10-07Known CVEsNone foundSources: GitHub Advisory Database + OSV.dev · Scanned 2026-10-24 · scanner v1

Reviewer notes

Auto-scanned review. These are observations, not a security certification.

Scored from trust signals (evidence-eval-v1): 42 GitHub stars; contributors unknown; last commit 8d ago; license no license.

Things to check

  • Scanned, not hands-on tested — this entry was auto-scanned from public metadata (GitHub metrics, license, security flags). No reviewer has run it, and no tool-specific limitations have been documented yet.

How to evaluate tools before deploying →

Data shown here comes from public APIs and automated scanning. Reviewer notes reflect one person's experience. This is not a security certification or legal recommendation. Always evaluate tools according to your own organization's policies.

Evaluation

Ease of Use
1/5
Versatility
2/5
Reliability
3/5
Security
3/5
Overall score2.05 / 5.00 PoorEvaluatedOct 2026
Scored from trust signals (evidence-eval-v1): 42 GitHub stars; contributors unknown; last commit 8d ago; license no license.

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