Liteparse logo

Liteparse

OrganizationPopular
Prismer-AI
liteparse

Parse documents into LLM-ready content entirely on the local machine — PDF / DOCX / XLSX / PPTX / images → Markdown, structured JSON (with bounding boxes), or page screenshots, via the `lit` CLI. No cloud, no LLM, works offline. Use whenever the user attaches or points to a document that must be read before reasoning, or asks to extract text / tables / page images from a file.

Overview

PublisherPrismer-AI
RepositoryPrismerCloud
Skill nameliteparse
Stars
1.6K
Forks
14
Bundled files
Instructions only
LicenseMIT
Links
  • Markdown instructions

    A SKILL.md file the model loads on demand, so it only costs tokens when a request actually matches.

  • Works with any LLM

    AI skills are plain Markdown, not provider-specific code, so this works with GPT, Claude, Gemini, Grok, or a local model.

  • Self-contained

    Everything the model needs lives in the instructions — no extra files to sync.

  • Open source

    Published by Prismer-AI on GitHub. Read the source before you install it.

Installation

Install the Liteparse AI skill in TypingMind to use it with any LLM, or drop it into another agent that reads SKILL.md.

1

Install in TypingMind

TypingMind installs a skill straight from its GitHub folder — it reads SKILL.md, bundles the resource files, and stores the result locally.

  1. Open the app and go to Plugins → Skills.
  2. Choose "Install from GitHub".
  3. Paste the skill folder URL below and confirm.
  4. Enable the skill in any chat where you want it available.
Plugins → Skills → Add skill → From GitHub URL, then paste the folder URL and press Continue.
2

Install in another agent

Any agent that reads the Agent Skills format can use this skill — copy the folder into that agent's skills directory.

Claude Code — .claude/skills
git clone --depth 1 https://github.com/Prismer-AI/PrismerCloud.git /tmp/PrismerCloud
mkdir -p .claude/skills
cp -r /tmp/PrismerCloud/sdk/prismer-cloud/built-in-skills/liteparse .claude/skills/liteparse
Restart Claude Code after copying so it picks up the new skill.

Use it in TypingMind

Enable Liteparse in any TypingMind chat and the model takes it from there. Its name and description sit in the system prompt, and the moment a request matches, the model loads the full instructions itself — you never invoke it by hand, and it costs no tokens until it is actually used.

The model loads Liteparse on its own as soon as a request matches it.

Works with any AI model

AI skills are plain Markdown instructions rather than provider-specific code, so Liteparse is not tied to the model it was written for. Install it once in TypingMind and use it with GPT-5, Claude, Gemini, Grok, DeepSeek, Mistral, Llama, or a local model you run yourself — all on your own API keys.

  • Loaded only when it is needed

    The system prompt carries just the name and description. The instructions are fetched on the first matching request, so an idle skill costs nothing.

  • Switch models mid-chat

    Because the skill is instructions rather than code, changing model does not break it — the next model reads the same SKILL.md.

Skill instructions

This is the SKILL.md content the model loads. Read it before installing — a skill is instructions your model will follow.

LiteParse (local-first document parsing)

LiteParse is a standalone OSS parser (Rust core, PDFium + Tesseract + LibreOffice) that turns documents into text the model can read — fully local, zero cloud dependency, works with no network. It is the daemon's local-first document-parsing path.

Upstream: run-llama/liteparse

  • skill run-llama/llamaparse-agent-skills. Apache-2.0 (core) / MIT (skill), © LlamaIndex. Vendored as a Prismer built-in; the CLI (lit) self-installs on first use (see below).

Scope — liteparse vs ingest

Two document paths coexist; pick by network + fidelity needs:

  • liteparse (this skill) — local-first. Local document files → Markdown / JSON / screenshots, fully offline, zero cost, zero cloud. Covers PDF, Word, PowerPoint, spreadsheets, images. Built-in Tesseract OCR for clean scans; LibreOffice for Office formats. Default for: files already on disk, offline / cloud-unreachable situations, digital PDFs, quick extraction, bounding boxes, page screenshots.
  • ingest (sibling skill) — cloud-backed. cloud load / cloud search for web URLs (HQCC compression) and cloud parse for hi-res OCR of scans / handwriting / dense tables. Use when: the source is a web page (liteparse can't fetch URLs), or local parsing quality is insufficient (dense tables, multi-column, handwriting, low-quality scans).

Decision rule: local file → try liteparse first (free, offline, fast). Escalate to ingest's cloud parse -m hires only when local fidelity falls short. Web URL / search → ingest (liteparse parses local files and stdin, not remote pages — if you only have a URL, curl -sL <url> -o file then parse the local copy, or route to ingest).

When local parsing is low-confidence (empty pages, garbled OCR), flag the region and consider escalating to ingest; never invent content.

First-use install (self-bootstrapping)

The lit CLI ships via npm/pip/cargo (same CLI). Check, then install if missing. Do this once per environment, quietly.

bash
# 1. Is it already available? (sandbox image may bake it)
command -v lit && lit --version && echo "lit ready" || {
  # 2. Install the CLI
  npm i -g @llamaindex/liteparse && lit --version
}

System dependencies for Office conversion + image handling (only needed for non-PDF inputs):

bash
# LibreOffice — required for DOCX/XLSX/PPTX/ODT/RTF conversion
brew install --cask libreoffice        # macOS
apt-get install -y libreoffice         # Debian/Ubuntu (daemon image)

# ImageMagick — required for some image formats
brew install imagemagick               # macOS
apt-get install -y imagemagick         # Debian/Ubuntu

If install fails (no network, no package manager), do not fabricate parsed content — report lit unavailable (<reason>) and stop.

CLI reference

Parse a file

bash
lit parse document.pdf                                  # → text on stdout
lit parse document.pdf --format json -o out.json        # structured JSON + bounding boxes
lit parse document.pdf --target-pages "1-5,10,15-20"    # page subset
lit parse document.pdf --no-ocr                          # skip OCR (digital PDFs only)
lit parse document.pdf --dpi 300                         # higher render DPI
lit parse scan.pdf --ocr-language eng+chi_sim            # Tesseract lang codes
lit parse secured.pdf --password '****'                  # encrypted docs
curl -sL https://example.com/report.pdf | lit parse -    # stdin (download-then-parse)

Key lit parse options: -o/--output, --format json|text (default text), --no-ocr, --ocr-language <lang>, --ocr-server-url <url> (plug an external OCR HTTP server), --tessdata-path, --target-pages "1-5,10", --max-pages <n> (default 1000), --dpi (default 150), --preserve-small-text, --password, --num-workers, -q/--quiet.

Batch a directory

bash
lit batch-parse ./input-dir ./output-dir
lit batch-parse ./input-dir ./output-dir --recursive --extension .pdf --format json

Page screenshots (for visual content text can't capture)

bash
lit screenshot document.pdf -o ./screenshots                  # all pages → PNG
lit screenshot document.pdf --target-pages "1,3,5" -o ./shots
lit screenshot document.pdf --dpi 300 -o ./shots              # high-res

Supported formats: PDF · Word (DOC/DOCX/DOCM/ODT/RTF) · PowerPoint (PPT/PPTX/PPTM/ODP) · Spreadsheets (XLS/XLSX/ODS/CSV/TSV) · Images (JPG/PNG/GIF/BMP/TIFF/WEBP/SVG).

Workflow

  1. Locate the file. If it's a workspace asset, resolve its local path (see assets skill). If it's a URL, curl -sL <url> -o <name> first — lit parses local files (and stdin), not remote pages.
  2. Pick mode.
    • Digital PDF / clean doc → lit parse <f> (add --no-ocr to skip OCR and go faster when the PDF has a real text layer).
    • Scan / image / handwriting → leave OCR on; set --ocr-language and bump --dpi 300 if text is small.
    • Need spatial structure / tables with coordinates → --format json (gives bounding boxes).
    • Need to see a chart/figure/signature → lit screenshot, then read the PNG.
  3. Read the result as source of truth. Base extraction/summary only on what lit returned. If a page is empty or low-confidence, say so; don't guess.
  4. Deliver products, if the parse output itself is the deliverable. Write final files into $PRISMER_ARTIFACTS_DIR (the dispatch's artifacts/ dir — auto-archived as IMAssets by the daemon's artifacts-watcher and attached to your reply). See office-artifacts SKILL.md §Delivery contract for the full delivery rules.

Output reporting

  • After a parse: Parsed <filename>: <N> pages, format=<text|json>, ocr=<on|off> — then proceed with the user's actual question.
  • Don't dump the entire parsed body into chat unless asked; summarize and cite page numbers.
  • After screenshots: Rendered <N> page screenshot(s) → <dir> and read the relevant ones.

HARD RULE — never claim a parse you didn't run

Forbidden unless lit actually ran with exit 0 and produced output: "parsed the document", "the PDF says…", "extracted the table". If lit is unavailable or the parse failed, completion text must start with 无法解析文档 (reason) / Cannot parse document (reason) — do not substitute guessed content.

Frequently asked questions

What does the Liteparse AI skill do?

Parse documents into LLM-ready content entirely on the local machine — PDF / DOCX / XLSX / PPTX / images → Markdown, structured JSON (with bounding boxes), or page screenshots, via the `lit` CLI. No cloud, no LLM, works offline. Use whenever the user attaches or points to a document that must be read before reasoning, or asks to extract text / tables / page images from a file.

Why use Liteparse on TypingMind?

Because you install it once and use it with any model. Liteparse is plain Markdown rather than provider-specific code, so the same skill runs on GPT-5, Claude, Gemini, Grok, or a local model — and you can switch model mid-chat without it breaking. TypingMind runs on your own API keys, so you pay providers directly instead of a per-seat subscription, and your skills and chats stay in your own storage.

How do I install Liteparse in TypingMind?

Open Plugins → Skills → Install from GitHub in TypingMind and paste https://github.com/Prismer-AI/PrismerCloud/tree/main/sdk/prismer-cloud/built-in-skills/liteparse. TypingMind reads its SKILL.md and installs it as a skill you can enable per chat.

Which AI models can use Liteparse?

Any model you connect in TypingMind. AI skills are plain Markdown instructions rather than provider-specific code, so GPT, Claude, Gemini, Grok, and local models can all load this skill when a request matches it.

How many AI models can I use with Liteparse?

As many as you like. As long as a model supports skills, you can use Liteparse with it — GPT, Claude, Gemini, Grok, DeepSeek, Mistral, Llama and more — all on TypingMind with your own API keys.

Is the Liteparse AI skill free?

Yes. It is published on GitHub by Prismer-AI under the MIT license. You only pay your own AI provider for the tokens you use.

What are AI skills?

An AI skill is a reusable instruction bundle that teaches an AI model how to do one specific task. It follows the open Agent Skills format: a SKILL.md file with a name and description, plus any scripts, templates or reference files the model may need. The model reads the instructions only when your request matches the skill, so an installed skill costs nothing until it is used.

How are AI skills different from plugins or MCP servers?

A plugin or MCP server gives a model new tools to call — code that runs somewhere and returns a result. An AI skill gives the model knowledge and process instead: how to approach a task, which steps to follow, what good output looks like. Skills are plain Markdown, so they need no server, no API key and no runtime, and they work with any model.

View all

Set up your own AI workspace now

Get notified about new features and future giveaways by subscribing to our newsletter 👇