Local document and PDF parsing with spatial text and bounding boxes. Use for extracting text from PDFs, DOCX, Office files, and images; OCR on scans; layout-preserved JSON for RAG; batch-ingesting paper folders; or page screenshots for multimodal agents — even when the user does
Installs just this skill. Get the whole plugin for auto-invocation.
⚡ How it fires
How this skill gets triggered: by you, by Claude, or both.
Fires itselfClaude auto-loads it when your prompt matches the work.
You can call itInvoke it directly when you want it.
Slash command/liteparse
👁️ Context preview
The summary Claude sees to decide when to auto-load this skill.
Local document and PDF parsing with spatial text and bounding boxes. Use for extracting text from PDFs, DOCX, Office files, and images; OCR on scans; layout-preserved JSON for RAG; batch-ingesting paper folders; or page screenshots for multimodal agents — even when the user does
📊 Stats
Stars31,545
Forks3,146
LanguagePython
LicenseMIT
📦 Ships with scientific-agent-skills
</> SKILL.md
liteparse.SKILL.md
---name: liteparse
description: Local document and PDF parsing with spatial text and bounding boxes. Use for extracting text from PDFs, DOCX, Office files, and images; OCR on scans; layout-preserved JSON for RAG; batch-ingesting paper folders; or page screenshots for multimodal agents — even when the user does not name liteparse. Prefer over MarkItDown when you need bboxes, fast local parsing, or PNG page renders; prefer over the pdf skill for merge/split/forms.
license: Apache-2.0
allowed-tools: Read Write Edit Bash
compatibility: Python 3.10+. Optional LibreOffice (Office formats) and ImageMagick (images). Bundled Tesseract for OCR. All processing is local — no cloud API required.
metadata: {"version": "1.0", "skill-author": "K-Dense Inc."}
---# 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:
**Offline / air-gapped:** set `TESSDATA_PREFIX` to a directory of `.traineddata` files, or pass `--tessdata-path`. Details: `references/ocr_and_formats.md`.
### 8. Encrypted PDFs
```python
parser = LiteParse(password="secret", quiet=True)
result = parser.parse("protected.pdf")
```
```bash
lit parse protected.pdf --password secret
```
### 9. Search text items by phrase
Merge adjacent items and return combined bounding boxes for a phrase (e.g. section titles).
```python
from liteparse import search_items
page = result.get_page(1)
matches = search_items(page.text_items, "Materials and Methods", case_sensitive=False)
Files are converted to PDF internally, then parsed. If conversion tools are missing, parsing fails with an actionable error — install the dependency and retry.
---
## Performance Tips
- **`--no-ocr`** on born-digital PDFs — largest speedup
- **`target_pages`** — parse only methods/supplement sections
- **`num_workers`** — scale OCR across CPU cores
- **`max_pages`** — cap very large files (default 1000)
- **`lit batch-parse`** — directory-scale jobs with `--recursive` and `--extension`
- Lower **`dpi`** (e.g. 100) when OCR quality is already sufficient
---
## Reference Files
| File | Read when |
|------|-----------|
| `references/choosing_a_parser.md` | Unsure whether to use LiteParse, MarkItDown, pdf, or LlamaParse |
