RAG Pipeline
Details on the Retrieval Augmented Generation pipeline, Ingestion, and Vector Search.
npx skills add majiayu000/claude-skill-registry --skill rag-abdulsamad94-hackhaton-specskitpl-2 --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.
# RAG Pipeline Logic ## Ingestion - **Script**: `backend/ingest.py` - **Process**: 1. Scans `docs/`. 2. Cleans MDX (removes frontmatter/imports). 3. Chunks text (1000 chars, 100 overlap). 4. Embeds using `models/text-embedding-004`. 5. Upserts to Qdrant collection `physical_ai_book`. - **Run**: `python backend/ingest.py` ## Vector Search (Qdrant) - **Client**: `qdrant-client` - **Collection**: `physical_ai_book` - **Vector Size**: 768 (Gecko-004) - **Similarity**: Cosine ## Prompt Engineering - **File**: `backend/utils/helpers.py`. - **RAG Prompt**: Constructs a prompt containing retrieved context chunks. - **Personalization**: `backend/personalization.py` creates system instructions based on `software_background` and `hardware_background` of the user. ## Agentic Flow We use a custom `Agent` class (`backend/agents.py`) that wraps the LLM calls, allowing for future expansion into multi-agent workflows.
- Ingestion
- Vector Search (Qdrant)
- Prompt Engineering
- Agentic Flow
What does the RAG Pipeline skill do?
Details on the Retrieval Augmented Generation pipeline, Ingestion, and Vector Search.
How do I install it?
Run `npx skills add majiayu000/claude-skill-registry --skill rag-abdulsamad94-hackhaton-specskitpl-2 --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 majiayu000/claude-skill-registry, a repository with 534 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.