liteparse
Local document and PDF parsing that returns spatial text with bounding boxes. Use for extracting text from PDFs, DOCX, Office files, and images; running OCR on scans; producing layout-preserved JSON for RAG; batch-ingesting folders of papers; or rendering pages to PNG for multimodal agents. Distinguishing capabilities are per-token bounding boxes, page raster output, and fully local processing with no cloud API.
npx skills add K-Dense-AI/scientific-agent-skills --skill liteparse --agent claude-code
Same command for any agent — swap --agent for codex, cursor, copilot.
Weekly change comes from our own snapshots, not the repository page — it measures attention, not adoption.
# LiteParse — Local Document Parsing ## Overview LiteParse is a fast, open-source document parser (Rust core, Python/Node bindings) focused on **local, layout-aware text extraction** with bounding boxes. It does not produce Markdown and does not call cloud LLMs. Outputs are **plain text** (layout-preserved) or **structured JSON** with per-page `text_items` (position, font metadata, optional confidence). **Version note:** Examples target **liteparse 2.0.0** (PyPI, May 2026). The upstream V1 branch is legacy; this skill documents **V2 / main** only. For parser selection vs MarkItDown, the `pdf` skill, or LlamaParse, see `references/choosing_a_parser.md`. ## When to Use This Skill Use LiteParse when you need: - **Fast local parsing** of PDFs or converted Office/image files without cloud dependencies - **Spatial text** with bounding boxes for layout-aware RAG, citation grounding, or figure/table region logic - **OCR** on scanned PDFs or images (bundled Tesseract, or a user-run HTTP OCR server) - **Page screenshots** (PNG) for multimodal agents that must see charts, figures, or handwriting - **Batch ingestion** of literature folders, supplementary PDFs, or protocol libraries - **Page su
- Overview
- When to Use This Skill
- When Not to Use
- Installation
- Quick Start
- Python
- CLI
- Core Workflows
- 1. Parse to layout-preserved text
- 2. Parse to structured JSON (bounding boxes)
- 3. Parse specific pages
- 4. Parse from bytes or stdin
- 5. Page screenshots for multimodal agents
- 6. Batch-parse a directory
uv pip install "liteparse==2.0.0" lit --help python -c "import liteparse; print(liteparse.__version__)" Layout-preserved text (default) lit parse paper.pdf Structured JSON with bounding boxes lit parse paper.pdf --format json -o paper.json Disable OCR on text-native PDFs (faster) lit parse paper.pdf --no-ocr lit parse document.pdf -o output.txt
What does the liteparse skill do?
Local document and PDF parsing that returns spatial text with bounding boxes. Use for extracting text from PDFs, DOCX, Office files, and images; running OCR on scans; producing layout-preserved JSON for RAG; batch-ingesting folders of papers; or rendering pages to PNG for multimodal agents. Distinguishing capabilities are per-token bounding boxes, page raster output, and fully local processing with no cloud API.
How do I install it?
Run `npx skills add K-Dense-AI/scientific-agent-skills --skill liteparse --agent claude-code` — it drops the skill into your project so the agent can pick it up. Swap the --agent value for codex, cursor or copilot if you use one of those.
Where does this skill come from?
From K-Dense-AI/scientific-agent-skills, a repository with 32,619 stars. We read it straight from the repository tree rather than a submitted listing, so what you see here is what is actually published.
Is a popular skill a good skill?
Not necessarily. Stars measure attention, not adoption — a repository can trend for a week and be abandoned. That is why we show the weekly change from our own snapshots next to the total, instead of a single flattering number.