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    Enables deterministic, auditable retrieval over a document corpus by decomposing queries into subqueries, allocating recall budgets, and merging ranked results that are bit-reproducible from a recorded seed.
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    Enables zero-hallucination retrieval over local documents using hybrid semantic and keyword search, ensuring answers are based solely on provided PDFs.
    3
    MIT
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    Enables LLM agents to query a contract data room using structured metadata filters and BM25 full-text search over contract articles, returning relevant contracts, justifying clauses, and explicitly excluded unknowns. It supports exhaustive list answers and citation-ready JSON for MCP clients.
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  • A
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    Provides AI assistants with intelligent access to ML textbook content for creating accurate, source-grounded documentation using local models for privacy and cost efficiency.
    2
    MIT
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    Enables semantic search over the CCI Black Book, a scanned grow manual, retrieving both text and visual content with cited evidence packs for AI assistants.
    MIT
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    Provides AI assistants with intelligent access to ML textbook content through RAG-powered search, chapter retrieval, and documentation generation capabilities. Uses local models for privacy-focused, source-grounded responses from indexed technical books.
    MIT
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    Automatically generates typed React hooks for Next.js projects by crawling API routes, GraphQL queries, and components. Analyzes pages to suggest optimal render modes (SSR/CSR/ISR) and produces comprehensive documentation with AI-powered guidance.
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    Documentation crawler MCP server that crawls and indexes documentation sites so that any MCP-compatible AI can search, read, and expand on the content.
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