mtaku3/openscience/liteparse

Trust tier 3
  • community
Source check pending

Checking that the SKILL.md file is still in the repository.

Use this skill when the user asks to parse, perform multi-format document conversion or spatially extract text from an unstructured file (PDF, DOCX, PPTX, XLSX, images, etc.) locally without cloud dependencies.

Install

npx vskill@latest install mtaku3/openscience/liteparse

SKILL.md

LiteParse Skill

Parse unstructured documents (PDF, DOCX, PPTX, XLSX, images, and more) locally with LiteParse: fast, lightweight, no cloud dependencies or LLM required.

Initial Setup

When this skill is invoked, respond with:

I'm ready to use LiteParse to parse files locally. Before we begin, please confirm that:
- `@llamaindex/liteparse` is installed globally (`npm i -g @llamaindex/liteparse`)
- The `lit` CLI command is available in your terminal
If both are set, please provide:
1. One or more files to parse (PDF, DOCX, PPTX, XLSX, images, etc.)
2. Any specific options: output format (json/text), page ranges, OCR preferences, DPI, etc.
3. What you'd like to do with the parsed content.
I will produce the appropriate `lit` CLI command or TypeScript script, and once approved, report the results.

Then wait for the user's input.


Step 0 — Install LiteParse (if needed)

If liteparse is not yet installed, install it globally:

bash
npm i -g @llamaindex/liteparse

Verify installation:

bash
lit --version

For Office document support (DOCX, PPTX, XLSX), LibreOffice is required:

bash
# macOS
brew install --cask libreoffice
# Ubuntu/Debian
apt-get install libreoffice

For image parsing, ImageMagick is required:

bash
# macOS
brew install imagemagick
# Ubuntu/Debian
apt-get install imagemagick

Step 1 — Produce the CLI Command or Script

Parse a Single File
bash
# Basic text extraction
lit parse document.pdf
# JSON output saved to a file
lit parse document.pdf --format json -o output.json
# Specific page range
lit parse document.pdf --target-pages "1-5,10,15-20"
# Disable OCR (faster, text-only PDFs)
lit parse document.pdf --no-ocr
# Use an external HTTP OCR server for higher accuracy
lit parse document.pdf --ocr-server-url http://localhost:8828/ocr
# Higher DPI for better quality
lit parse document.pdf --dpi 300
Batch Parse a Directory
bash
lit batch-parse ./input-directory ./output-directory
# Only process PDFs, recursively
lit batch-parse ./input ./output --extension .pdf --recursive
Generate Page Screenshots

Screenshots are useful for LLM agents that need to see visual layout.

bash
# All pages
lit screenshot document.pdf -o ./screenshots
# Specific pages
lit screenshot document.pdf --pages "1,3,5" -o ./screenshots
# High-DPI PNG
lit screenshot document.pdf --dpi 300 --format png -o ./screenshots
# Page range
lit screenshot document.pdf --pages "1-10" -o ./screenshots

Step 3 — Key Options Reference

OCR Options
OptionDescription
(default)Tesseract.js — zero setup, built-in
--ocr-language fraSet OCR language (ISO code)
--ocr-server-url <url>Use external HTTP OCR server (EasyOCR, PaddleOCR, custom)
--no-ocrDisable OCR entirely
Output Options
OptionDescription
--format jsonStructured JSON with bounding boxes
--format textPlain text (default)
-o <file>Save output to file
Performance / Quality Options
OptionDescription
--dpi <n>Rendering DPI (default: 150; use 300 for high quality)
--max-pages <n>Limit pages parsed
--target-pages <pages>Parse specific pages (e.g. "1-5,10")
--no-precise-bboxDisable precise bounding boxes (faster)
--skip-diagonal-textIgnore rotated/diagonal text
--preserve-small-textKeep very small text that would otherwise be dropped

Step 4 — Using a Config File

For repeated use with consistent options, generate a liteparse.config.json:

json
{
"ocrLanguage": "en",
"ocrEnabled": true,
"maxPages": 1000,
"dpi": 150,
"outputFormat": "json",
"preciseBoundingBox": true,
"skipDiagonalText": false,
"preserveVerySmallText": false
}

For an HTTP OCR server:

json
{
"ocrServerUrl": "http://localhost:8828/ocr",
"ocrLanguage": "en",
"outputFormat": "json"
}

Use with:

bash
lit parse document.pdf --config liteparse.config.json

Step 5 — HTTP OCR Server API (Advanced)

If the user wants to plug in a custom OCR backend, the server must implement:

  • Endpoint: POST /ocr
  • Accepts: file (multipart) and language (string) parameters
  • Returns:
json
{
"results": [
{ "text": "Hello", "bbox": [x1, y1, x2, y2], "confidence": 0.98 }
]
}

Ready-to-use wrappers exist for EasyOCR and PaddleOCR in the LiteParse repo.


Supported Input Formats

CategoryFormats
PDF.pdf
Word.doc, .docx, .docm, .odt, .rtf
PowerPoint.ppt, .pptx, .pptm, .odp
Spreadsheets.xls, .xlsx, .xlsm, .ods, .csv, .tsv
Images.jpg, .jpeg, .png, .gif, .bmp, .tiff, .webp, .svg

Office documents require LibreOffice; images require ImageMagick. LiteParse auto-converts these formats to PDF before parsing.

Security

  • Security scanPassed, 90 of 100Pattern checks on the skill's files.
  • Model reviewPassed

Quality evaluation

  • Task evaluationNot runNo evaluation yet. Run one in Skill Studio.

Source

Popularity

7-day trend score
0

Not refreshed yet.

Works with

Any agent vskill installs to: 56 targets in all. These 39 have a page on this site.

  • Claude Code
  • Cursor
  • GitHub Copilot
  • Windsurf
  • Codex
  • Gemini CLI
  • Cline
  • Amp
  • Roo Code
  • Goose
  • Continue
  • Kilo Code
  • Junie
  • Trae
  • OpenHands
  • Qwen Code
  • AI Agent
  • Tabnine Chat
  • Sourcegraph Cody
  • Amazon Q
  • Firebase Genkit
  • Sweep
  • Devin
  • Phind
  • Mentat
  • Claude Desktop
  • Aider
  • GPT Pilot
  • Plandex
  • bolt.new
  • v0
  • Replit Agent
  • Anse
  • TypingMind
  • BetterChatGPT
  • LibreChat
  • Warp AI
  • Pieces
  • Fig AI
Embed badge
vskill VERIFIED badge
[![vskill VERIFIED](https://verified-skill.com/api/v1/skills/mtaku3/openscience/liteparse/badge)](https://verified-skill.com/skills/mtaku3/openscience/liteparse)

Something wrong with this skill or its page? Tell us.