liteparse

run-llama/llamaparse-agent-skills · updated Apr 8, 2026

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$npx skills add https://github.com/run-llama/llamaparse-agent-skills --skill liteparse
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summary

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

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:

npm i -g @llamaindex/liteparse

Verify installation:

lit --version

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

# macOS
brew install --cask libreoffice

# Ubuntu/Debian
apt-get install libreoffice

For image parsing, ImageMagick is required:

# macOS
brew install imagemagick

# Ubuntu/Debian
apt-get install imagemagick

Step 1 — Produce the CLI Command or Script

Parse a Single File

# 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

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.

# 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

Option Description
(default) Tesseract.js — zero setup, built-in
--ocr-language fra Set OCR language (ISO code)
--ocr-server-url <url> Use external HTTP OCR server (EasyOCR, PaddleOCR, custom)
--no-ocr Disable OCR entirely

Output Options

Option Description
--format json Structured JSON with bounding boxes
--format text Plain text (default)
-o <file> Save output to file

Performance / Quality Options

Option Description
--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-bbox Disable precise bounding boxes (faster)
--skip-diagonal-text Ignore rotated/diagonal text
--preserve-small-text Keep 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:

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

For an HTTP OCR server:

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

Use with:

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:
{
  "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

Category Formats
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.

how to use liteparse

How to use liteparse on Cursor

AI-first code editor with Composer

1

Prerequisites

Before installing skills in Cursor, ensure your development environment meets these requirements:

  • Cursor installed and configured on your development machine
  • Node.js version 16.0+ with npm package manager (verify with node --version)
  • Active project directory or workspace where you want to add liteparse
2

Execute installation command

Execute the skills CLI command in your project's root directory to begin installation:

$npx skills add https://github.com/run-llama/llamaparse-agent-skills --skill liteparse

The skills CLI fetches liteparse from GitHub repository run-llama/llamaparse-agent-skills and configures it for Cursor.

3

Select Cursor when prompted

The CLI will show a list of available agents. Use arrow keys to navigate and space to select Cursor:

◆ Which agents do you want to install to?
│ ── Universal (.agents/skills) ── always included ────
│ • Amp
│ • Antigravity
│ • Cline
│ • Codex
│ ●Cursor(selected)
│ • Cursor
│ • Windsurf
4

Verify installation

Confirm successful installation by checking the skill directory location:

.cursor/skills/liteparse

Reload or restart Cursor to activate liteparse. Access the skill through slash commands (e.g., /liteparse) or your agent's skill management interface.

Security & Verification Notice

We perform automated surface-level scans (Gen AI Scanner, Socket, Snyk) during installation. These checks detect common vulnerabilities but do not guarantee complete security. Always review skill source code and verify the publisher's reputation before production use.

Skills execute code in your development environment. Always verify the publisher's identity, review recent commits, and test in isolated environments before production deployment.

List & Monetize Your Skill

Submit your Claude Code skill and start earning

GET_STARTED →

Use Cases

User Story & Requirements Generation

Create detailed user stories, acceptance criteria, and feature specs

Example

Generate user stories for 'password reset feature' with acceptance criteria, edge cases, and test scenarios

Reduce spec writing time by 50%, ensure comprehensive coverage

Competitive Analysis

Research competitors, compare features, identify gaps

Example

Analyze 5 competitor products, create feature comparison matrix, suggest differentiation opportunities

Complete competitive research in 2 hours instead of 2 days

Roadmap Prioritization

Evaluate features using frameworks (RICE, ICE, Kano) and create prioritized backlogs

Example

Score 20 feature ideas using RICE framework, generate prioritized roadmap with rationale

Make data-driven prioritization decisions faster

Stakeholder Communication

Draft PRDs, status updates, and stakeholder presentations

Example

Create executive summary of Q3 roadmap, monthly progress report, feature launch announcement

Save 3-5 hours/week on communication overhead

Implementation Guide

Prerequisites

  • Claude Desktop or compatible AI client
  • Access to product documentation and roadmap tools (Jira, Notion, etc.)
  • Understanding of product management frameworks (RICE, Jobs-to-be-Done, etc.)
  • Stakeholder contact information and communication channels

Time Estimate

30-60 minutes to see productivity improvements

Installation Steps

  1. 1.Install product management skill
  2. 2.Start with user story generation for known feature
  3. 3.Progress to competitive analysis: research 2-3 competitors
  4. 4.Use for roadmap prioritization: apply RICE/ICE scoring
  5. 5.Draft stakeholder communications and refine based on feedback
  6. 6.Build template library for recurring PM tasks
  7. 7.Share effective prompts with product team

Common Pitfalls

  • Not validating competitive research—verify facts before sharing
  • Accepting user stories without involving engineering team
  • Over-relying on frameworks without qualitative judgment
  • Not customizing outputs to company culture and communication style
  • Skipping stakeholder validation of generated requirements

Best Practices

✓ Do

  • +Validate research and competitive analysis with real data
  • +Collaborate with engineering when generating technical requirements
  • +Customize frameworks and templates to your company context
  • +Use skill for first drafts, refine with stakeholder input
  • +Document successful prompt patterns for PM tasks
  • +Combine AI efficiency with human judgment and intuition

✗ Don't

  • Don't publish competitive analysis without fact-checking
  • Don't finalize user stories without engineering review
  • Don't make prioritization decisions solely on AI scoring
  • Don't skip customer validation of generated requirements
  • Don't ignore company-specific context and culture

💡 Pro Tips

  • Provide context: company goals, constraints, customer feedback
  • Ask for alternatives: 'Show 3 ways to prioritize this roadmap'
  • Request stakeholder-specific formatting: 'Executive summary vs. engineering spec'
  • Use skill for 70% generation + 30% customization to company needs

When to Use This

✓ Use When

Use for user story writing, competitive research, roadmap prioritization, stakeholder communication, and PRD drafting. Best for reducing repetitive documentation and research work.

✗ Avoid When

Avoid for strategic product vision (requires deep customer empathy), pricing decisions (needs market and financial expertise), or when face-to-face customer discovery is more valuable than speed.

Learning Path

  1. 1Basic: user stories, feature specs, status updates
  2. 2Intermediate: competitive analysis, prioritization frameworks, PRDs
  3. 3Advanced: product strategy, go-to-market planning, OKR setting
  4. 4Expert: product vision, market positioning, business model innovation

Discussion

Product Hunt–style comments (not star reviews)
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general reviews

Ratings

4.649 reviews
  • Liam Okafor· Dec 4, 2024

    liteparse reduced setup friction for our internal harness; good balance of opinion and flexibility.

  • Yuki Smith· Dec 4, 2024

    Useful defaults in liteparse — fewer surprises than typical one-off scripts, and it plays nicely with `npx skills` flows.

  • Arya Bhatia· Nov 23, 2024

    Registry listing for liteparse matched our evaluation — installs cleanly and behaves as described in the markdown.

  • Li Tandon· Oct 14, 2024

    Keeps context tight: liteparse is the kind of skill you can hand to a new teammate without a long onboarding doc.

  • Benjamin Sethi· Oct 6, 2024

    We added liteparse from the explainx registry; install was straightforward and the SKILL.md answered most questions upfront.

  • Li Srinivasan· Sep 25, 2024

    Keeps context tight: liteparse is the kind of skill you can hand to a new teammate without a long onboarding doc.

  • Chen Agarwal· Sep 9, 2024

    liteparse fits our agent workflows well — practical, well scoped, and easy to wire into existing repos.

  • Yash Thakker· Sep 5, 2024

    Useful defaults in liteparse — fewer surprises than typical one-off scripts, and it plays nicely with `npx skills` flows.

  • Sofia Abbas· Sep 5, 2024

    liteparse is among the better-maintained entries we tried; worth keeping pinned for repeat workflows.

  • Rahul Santra· Sep 1, 2024

    liteparse fits our agent workflows well — practical, well scoped, and easy to wire into existing repos.

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