rag-dev
Use when building knowledge bases, ingesting documents, running semantic search, or adding LLM-synthesized Q&A over private content with Butterbase RAG
Profile →npx skills add butterbase-ai/butterbase-skills --skill rag-dev --agent claude-code
Same command for any agent — swap --agent for codex, cursor, copilot.
Weekly change comes from our own snapshots, not the repository page — it measures attention, not adoption.
# Butterbase RAG (Retrieval-Augmented Generation) Two tools cover the entire RAG surface: - **`manage_rag_content`** — collections, document ingestion, status polling, deletion - **`rag_query`** — semantic search, optional LLM synthesis Documents are ingested asynchronously: text or files become embeddings stored in pgvector, and queries do a similarity search at runtime. --- ## 1. The mental model ``` Collection Documents Chunks ────────── ────────── ────── "product-faq" ──────────────► doc_1 (PDF) ───────────► chunk 1, 2, 3... doc_2 (text) ──────────► chunk 4, 5... doc_3 (markdown) ──────► chunk 6... ``` A **collection** holds documents; a **document** is split into **chunks** and embedded; **`rag_query`** searches by cosine similarity across chunks within a collection. `chunk_size` and `chunk_overlap` are set **once at collection creation** and immutable — to change them, delete and recreate the collection. --- ## 2. End-to-end workflow ``` ┌────────────────────────────────────────────┐ │ 1. create_collection (once per knowledge) │ ├────────────────────────────────────────────┤ │ 2. ingest_document (text OR storage_object)│ ├────────────────────────────────────────────┤ │ 3. pol
- 1. The mental model
- 2. End-to-end workflow
- Step 1 — create the collection
- Step 2a — ingest raw text
- Step 2b — ingest an uploaded file
- Step 3 — poll until ready
- Step 4 — query
- 3. Listing and cleanup
- 4. Choosing chunk size and overlap
- 5. Metadata-driven filtering
- 6. Common patterns
- Customer-support bot
- Per-tenant knowledge base
- Versioned docs
What does the rag-dev skill do?
Use when building knowledge bases, ingesting documents, running semantic search, or adding LLM-synthesized Q&A over private content with Butterbase RAG
How do I install it?
Run `npx skills add butterbase-ai/butterbase-skills --skill rag-dev --agent claude-code` — it drops the skill into your project so the agent can pick it up. Swap the --agent value for codex, cursor or copilot if you use one of those.
Where does this skill come from?
From butterbase-ai/butterbase-skills, a repository with 532 stars. We read it straight from the repository tree rather than a submitted listing, so what you see here is what is actually published.
Is a popular skill a good skill?
Not necessarily. Stars measure attention, not adoption — a repository can trend for a week and be abandoned. That is why we show the weekly change from our own snapshots next to the total, instead of a single flattering number.