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.
Install
With your agent
Install OMGSkills skill "jeffallan/claude-skills:rag-architect" into this agent's normal skills directory: call the OMGSkills MCP get_install_plan tool for your agent and run the script it returns (it installs only the pinned commit and verifies SKILL.md), and stop if MCP or the pin is unavailable. Do not overwrite or change existing skills without asking me.
Manual command
git clone https://github.com/Jeffallan/claude-skills /tmp/claude-skills && ln -s /tmp/claude-skills/skills/rag-architect ~/.claude/skills/rag-architectFrom README
RAG Architect Core Workflow Requirements Analysis — Identify retrieval needs, latency constraints, accuracy requirements, and scale Vector Store Design — Select database, schema design, indexing strategy, sharding approach Chunking Strategy — Document splitting, overlap, semantic boundaries, metadata enrichment Retrieval Pipeline — Embedding selection, query transformation, hybrid search, reranking Evaluation & Iteration — Metrics tracking, retrieval debugging, continuous optimization For each step, validate before moving on (see checkpoints below).
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