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Ingests PDF datasheets or reference manuals into the embedded docs search index via ingest_docs tool. Reports chunks ingested and tables found.
How this skill is triggered — by the user, by Claude, or both
Slash command
/bitwise-embedded-docs:ingest-docsThe summary Claude sees in its skill listing — used to decide when to auto-load this skill
Help the user ingest a PDF documentation file into the embedded docs search index. Use the `ingest_docs` MCP tool with the path to the PDF file.
Help the user ingest a PDF documentation file into the embedded docs search index. Use the ingest_docs MCP tool with the path to the PDF file.
Steps:
ingest_docs with the pathNote: Ingestion may take several minutes for large documents (1000+ pages).
npx claudepluginhub michaelayles/bitwise-mcp --plugin bitwise-embedded-docsIndexes PDF documents with LightRAG, extracts text via PyMuPDF, builds embeddings and knowledge graphs, enables hybrid semantic searches with citations for document Q&A.
Bulk imports knowledge from files, directories, or URLs into structured backlogs, or captures a single document with a 5-section template (claims, worth-keeping, contested, action, reaction).
Implements Google Gemini File Search for managed RAG on 100+ file formats including PDF, code, Markdown. Use for document Q&A, knowledge bases, immutability errors, quotas, polling failures.