| name | ingest |
| description | Ingest and vectorize PDFs or text files for RAG retrieval. Use when the user provides a PDF, document, or text file they want to analyze, study, or generate content from. |
| argument-hint | <file-path> |
| compatibility | Requires Bun 1.0+. First run downloads ~23MB embedding model. |
| allowed-tools | Bash(bash *) Bash(bun *) Bash(node *) Read |
Ingest Documents
Ingest a PDF or text file into the local vector store for retrieval-augmented generation.
Workflow
- Run the ingest script:
bun "${CLAUDE_SKILL_DIR}/scripts/ingest.mjs" $ARGUMENTS
- Verify ingestion:
bun "${CLAUDE_SKILL_DIR}/scripts/list.mjs"
- Confirm to the user: number of chunks and pages. Suggest next actions: report, flashcards, quiz, slides, audio, mind map, infographic, or data table.
Querying ingested sources
To retrieve relevant chunks for any generation task:
bun "${CLAUDE_SKILL_DIR}/scripts/query.mjs" "<question>" --top-k 15
Gotchas
- First run downloads the all-MiniLM-L6-v2 model (~23MB). Subsequent runs use cache.
- Large PDFs (100+ pages) take 2-5 minutes on CPU — embedding is the bottleneck.
- Scanned/image-only PDFs are automatically OCR'd via Tesseract.js (first OCR run downloads ~15MB language data). Use
--no-ocr to skip.
- The store persists at
~/.notebook-plugin/ across sessions. Use clear to reset.
- Multiple files can be ingested into the same store — they're all searchable together.
Additional resources
