←Leaderboard/Knowledge/carrel
Last commit on October 8, 2026·Created on September 28, 2026

FengZhenfei/carrel

“Ontology-Augmented Generation for AI agents.”
Combined rank
#662
across all skills
In Knowledge
#28
category rank
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312
+593.3% in last 7d
Forks
3
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2
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TL;DR

Ontology-Augmented Generation for AI agents.

WHO IT'S FOR
AI agent / automation builders
integrate ontology-augmented retrieval into agents
Knowledge graph engineers
implement RAG workflows using structured ontologies
Backend / infrastructure engineers
deploy and configure local knowledge base APIs
Technical leads / architects
design evidence-based AI answering systems
Repository contents

1 skill file

Compatible AgentsThe repository documents support for these agents. The skills may also work with other agents that can load SKILL.md files, but they may need some setup or small changes.

Use this skill

Follow the documented setup, then try a first task.

See the repository for installation instructions.

Open repository instructions ↗

Give it something to do.

Suggested first task

Answer a question from your knowledge base

Uses carrel-search

  • Replace <your question> with a concrete question about your stored documents.
  • A running Carrel search service with documents processed into an enabled knowledge base.
  • Python 3.9+ for the skill's standard-library client.
  • If the service is remote, configure its address and any required token through the documented environment variables or configuration file.

Make sure carrel-search is installed in your agent before trying this task.

Use carrel-search to answer this question from my stored documents: <your question>. Give a concise answer grounded in the retrieved evidence, list the reference files, and identify any gaps in the evidence.
skills/carrel-search/SKILL.md ↗ · Checked Oct 19, 2026README.md ↗ · Checked Oct 19, 2026