RAG Knowledge Builder
SkillDocs & knowledgeBuild and evaluate RAG corpora - ingest, chunk, embed, index, and spot-check retrieval quality. Use when creating knowledge bases for agents.
Available today. Use it from your connected AI after setup.
No other account needed.
Connect ahel once, and every AI you use reads what you have installed.
Then ask your AI: use the RAG Knowledge Builder skill
What this skill tells your AI
The instructions your AI receives, as published by navinspire-ia/navin in navin/skills/rag-knowledge-builder/SKILL.md and read by ahel’s review.
Overview
Garbage in, garbage out. Clean sources beat clever chunkers.
Workflow
- Define the question types the RAG must answer.
- Ingest sources (docs,
web-extractoroutput, PDFs). - Chunk with structure awareness (headings > fixed blind windows).
- Embed/index with the project’s vector store (note model + dims).
- Evaluate with 10-20 gold questions; measure hit rate / faithfulness.
- Fix gaps (missing docs, bad chunking) before tuning prompts.
Rules
- Track provenance (source URL/path) on every chunk.
- Exclude secrets and credentials from the corpus.
Signals
- GitHub stars
- 22
- Forks
- 4
- Last commit
- Sep 2026
Advanced
- Catalog kind
- skill
- Gateway key
rag-knowledge-builder- Source
- github.com/navinspire-ia/navin