claude

rag-plugin

RAG plugin: the 7 rag-architect skills (chunking, hybrid retrieval, GraphRAG, agentic and corrective RAG, evaluation, production checklist) + 3 commands. MCP server: Pinecone (needs your API key).

6 on GitHubby latestaiagentsv2.1.0MITupdated 9 days agoAI/ML Engineering
/

Plugin rag-plugin from marketplace latestaiagents in latestaiagents/agent-skills. Commands read from the repo's manifest — 8 days ago.

This plugin runs code on your machine — it starts local MCP servers. The exact commands are listed below; read them before installing.

What it installs

Skills (7)

  • agentic-rag

    Build agentic RAG systems with autonomous planning, reflection, and tool use. Use this skill when implementing agent-driven retrieval, query decomposition, iterative refinement, or multi-source RAG orchestration. Activate when: agentic RAG, A-RAG, autonomous retrieval, query planning, iterative RAG,

  • chunking-strategies

    Optimize document chunking for RAG performance and retrieval quality. Use this skill when splitting documents, choosing chunk sizes, implementing semantic chunking, or improving RAG retrieval accuracy. Activate when: chunking, split documents, chunk size, text splitting, document processing, RAG per

  • corrective-rag

    Implement Corrective RAG (CRAG) and Self-RAG for reliable, self-healing retrieval systems. Use this skill when building reliable RAG, preventing hallucinations, implementing retrieval evaluation, or adding self-correction to RAG pipelines. Activate when: corrective RAG, CRAG, self-RAG, hallucination

  • graphrag-patterns

    Implement GraphRAG combining knowledge graphs with RAG for multi-hop reasoning. Use this skill when building knowledge graph RAG, implementing multi-hop queries, using Neo4j with RAG, or connecting entities across documents. Activate when: GraphRAG, knowledge graph, multi-hop reasoning, Neo4j RAG, e

  • hybrid-retrieval

    Implement hybrid search combining vector and keyword retrieval for RAG systems. Use this skill when building RAG retrieval, combining semantic search with BM25, implementing reciprocal rank fusion (RRF), or optimizing retrieval accuracy. Activate when: vector search, keyword search, BM25, semantic s

  • production-rag-checklist

    Deploy RAG systems to production with proper architecture, monitoring, and operations. Use this skill when deploying RAG to production, scaling retrieval systems, implementing RAG monitoring, or building enterprise RAG infrastructure. Activate when: production RAG, RAG deployment, enterprise RAG, RA

  • rag-evaluation

    Test and benchmark RAG systems with proper metrics and evaluation frameworks. Use this skill when evaluating RAG quality, measuring retrieval performance, using RAGAS metrics, or building RAG test suites. Activate when: RAG evaluation, RAG testing, RAGAS, retrieval metrics, faithfulness, relevance,

Slash commands (3)

  • /rag-plugin:build-rag

    Step-by-step guidance to build a RAG pipeline from scratch

  • /rag-plugin:eval-rag

    Evaluate RAG pipeline quality with comprehensive metrics

  • /rag-plugin:rag-debug

    Debug RAG pipeline issues with systematic retrieval and generation analysis

MCP servers (1)

  • pinecone

    runs: npx -y @pinecone-database/mcp

View source on GitHub latestaiagents.com

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