Boof 🍑
Local-first document processing: PDF → markdown → RAG index → token-efficient analysis.
Documents stay local. Only relevant chunks go to the LLM. Maximum knowledge absorption, minimum token burn.
Powered by opendataloader-pdf — #1 in PDF parsing benchmarks (0.90 overall, 0.93 table accuracy). CPU-only, no GPU required.
Quick Reference
Convert + index a document
bash {SKILL_DIR}/scripts/boof.sh /path/to/document.pdf
Convert with custom collection name
bash {SKILL_DIR}/scripts/boof.sh /path/to/document.pdf --collection my-project
Query indexed content
qmd query "your question" -c collection-name
Core Workflow
-
Boof it: Run
boof.shon a PDF. This converts it to markdown via opendataloader-pdf (local Java engine, no API, no GPU) and indexes it into QMD for semantic search. -
Query it: Use
qmd queryto retrieve only the relevant chunks. Send those chunks to the LLM — not the entire document. -
Analyze it: The LLM sees focused, relevant excerpts. No wasted tokens, no lost-in-the-middle problems.
When to Use Each Approach
"Analyze this specific aspect of the paper" → Boof + query (cheapest, most focused)
"Summarize this entire document" → Boof, then read the markdown section by section. Summarize each section individually, then merge summaries. See advanced-usage.md.
"Compare findings across multiple papers" → Boof all papers into one collection, then query across them.
"Find where the paper discusses X" → qmd search "X" -c collection for exact match, qmd query "X" -c collection for semantic match.
Output Location
Converted markdown files are saved to knowledge/boofed/ by default (override with --output-dir).
Setup
If boof.sh reports missing dependencies, see setup-guide.md for installation instructions (Java + opendataloader-pdf + QMD).
Environment
ODL_ENV— Path to opendataloader-pdf Python venv (default:~/.openclaw/tools/odl-env)QMD_BIN— Path to qmd binary (default:~/.bun/bin/qmd)BOOF_OUTPUT_DIR— Default output directory (default:~/.openclaw/workspace/knowledge/boofed)