Skills liteparse

Name: liteparse
Author: openclaw

Parse, extract text from, and screenshot PDF and document files locally using the LiteParse CLI (`lit`). Use when asked to extract text from a PDF, parse a Word/Excel/PowerPoint file, batch-process a folder of documents, or generate page screenshots for LLM vision workflows. Runs entirely offline — no cloud, no API key. Supports PDF, DOCX, XLSX, PPTX, images (jpg/png/webp), and more. Triggers on phrases like "extract text from this PDF", "parse this document", "get the text out of", "screenshot this PDF page", or any request to read/extract content from a file.

install

source · Clone the upstream repo

git clone https://github.com/openclaw/skills

Claude Code · Install into ~/.claude/skills/

T=$(mktemp -d) && git clone --depth=1 https://github.com/openclaw/skills "$T" && mkdir -p ~/.claude/skills && cp -r "$T/skills/alfred-intel-handler-source/liteparse" ~/.claude/skills/openclaw-skills-liteparse && rm -rf "$T"

OpenClaw · Install into ~/.openclaw/skills/

T=$(mktemp -d) && git clone --depth=1 https://github.com/openclaw/skills "$T" && mkdir -p ~/.openclaw/skills && cp -r "$T/skills/alfred-intel-handler-source/liteparse" ~/.openclaw/skills/openclaw-skills-liteparse && rm -rf "$T"

manifest: skills/alfred-intel-handler-source/liteparse/SKILL.md

LiteParse

Local document parser built on PDF.js + Tesseract.js. Zero cloud dependencies.

Binary:

lit

(installed globally via npm) Docs: https://developers.llamaindex.ai/liteparse/

Quick Reference

# Parse a PDF to text (stdout)
lit parse document.pdf

# Parse to file
lit parse document.pdf -o output.txt

# Parse to JSON (includes bounding boxes)
lit parse document.pdf --format json -o output.json

# Specific pages only
lit parse document.pdf --target-pages "1-5,10,15-20"

# No OCR (faster, text-layer PDFs only)
lit parse document.pdf --no-ocr

# Batch parse a directory
lit batch-parse ./input-dir ./output-dir

# Screenshot pages (for vision model input)
lit screenshot document.pdf -o ./screenshots
lit screenshot document.pdf --target-pages "1,3,5" --dpi 300 -o ./screenshots

Output Formats

Format	Use case
`text` (default)	Plain text extraction, feeding into prompts
`json`	Structured output with bounding boxes, useful for layout-aware tasks

OCR Behavior

OCR is on by default via Tesseract.js (downloads ~10MB English data on first run)
First run will be slow; subsequent runs use cached data
```
--no-ocr
```
for pure text-layer PDFs (faster, no network needed)
For multi-language:
```
--ocr-language fra+eng
```

Supported File Types

Works natively: PDF

Requires LibreOffice (

brew install --cask libreoffice

): .docx, .doc, .xlsx, .xls, .pptx, .ppt, .odt, .csv

Requires ImageMagick (

brew install imagemagick

): .jpg, .png, .gif, .bmp, .tiff, .webp

Installation Notes

Installed via npm:
```
npm install -g @llamaindex/liteparse
```
Brew formula exists (
```
brew tap run-llama/liteparse
```
) but requires current macOS CLT — use npm as primary install path on this machine
Binary path:
```
/opt/homebrew/bin/lit
```

Workflow Tips

For VA forms, job description PDFs, military docs:
```
lit parse file.pdf -o /tmp/output.txt
```
then read into context
For scanned PDFs (no text layer): OCR is required; complex layouts may degrade — consider LlamaParse cloud for critical docs
For vision model workflows: use
```
lit screenshot
```
to generate page images, then pass to
```
image
```
tool or similar
For batch jobs: use
```
lit batch-parse
```
— it reuses the PDF engine across files for efficiency

Limitations

Complex tables, multi-column layouts, and scanned government forms may produce imperfect output
LlamaParse (cloud) handles the hard cases: https://cloud.llamaindex.ai
Max recommended DPI for screenshots: 300 (higher = slower, larger files)

Reference

See

references/output-examples.md

for sample JSON/text output structure.