rag-architect
Designs and implements production-grade RAG systems by chunking documents, generating embeddings, configuring vector stores, building hybrid search pipelines, applying reranking, and evaluating retrieval quality. Use when building RAG systems, vector databases, or knowledge-grounded AI applications requiring semantic search, document retrieval, context augmentation, similarity search, or embedding-based indexing.
npx skills add Jeffallan/claude-skills --skill rag-architect --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 Architect ## Core Workflow 1. **Requirements Analysis** — Identify retrieval needs, latency constraints, accuracy requirements, and scale 2. **Vector Store Design** — Select database, schema design, indexing strategy, sharding approach 3. **Chunking Strategy** — Document splitting, overlap, semantic boundaries, metadata enrichment 4. **Retrieval Pipeline** — Embedding selection, query transformation, hybrid search, reranking 5. **Evaluation & Iteration** — Metrics tracking, retrieval debugging, continuous optimization For each step, validate before moving on (see checkpoints below). ## Reference Guide Load detailed guidance based on context: | Topic | Reference | Load When | |-------|-----------|-----------| | Vector Databases | `references/vector-databases.md` | Comparing Pinecone, Weaviate, Chroma, pgvector, Qdrant | | Embedding Models | `references/embedding-models.md` | Selecting embeddings, fine-tuning, dimension trade-offs | | Chunking Strategies | `references/chunking-strategies.md` | Document splitting, overlap, semantic chunking | | Retrieval Optimization | `references/retrieval-optimization.md` | Hybrid search, reranking, query expansion, filtering | | RAG Evaluatio
- Core Workflow
- Reference Guide
- Implementation Examples
- 1. Chunking Documents
- 2. Generating Embeddings & Indexing
- 3. Hybrid Search (Vector + BM25)
- 4. Reranking Top-K Results
- 5. Retrieval Evaluation
- Constraints
- MUST DO
- MUST NOT DO
- Output Templates
What does the rag-architect skill do?
Designs and implements production-grade RAG systems by chunking documents, generating embeddings, configuring vector stores, building hybrid search pipelines, applying reranking, and evaluating retrieval quality. Use when building RAG systems, vector databases, or knowledge-grounded AI applications requiring semantic search, document retrieval, context augmentation, similarity search, or embedding-based indexing.
How do I install it?
Run `npx skills add Jeffallan/claude-skills --skill rag-architect --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 Jeffallan/claude-skills, a repository with 10,871 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.