liteparse
Use this skill whenever a task involves a document file (PDF, DOCX, PPTX, XLSX, or image) and you need to read it or pull text, tables, or specific values out…
Skill by LlamaIndex
Chalk Labs
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Install
Works with Claude Code, Claude apps, Codex, Cursor, Gemini CLI, GitHub Copilot, VS Code, OpenCode, Goose, Windsurf. Read the repository before you give any tool access to your data.
npx agentmarket install skill/ivanrvpereira-liteparse
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About
Use cases: documents, spreadsheets, presentations, data
Reviews
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Permissions
What this skill can do and what it asks for, from its SKILL.md. Checked Oct 25, 2026.
- Tools
- No tools found
- Secrets
- None asked for
- Install scripts
- None found
- Runs as
- Python package
Network hosts it talks to 1
codeload.github.com· found in install command
Files it touches
~/.claude/skills/liteparse
What's inside
- Skill name
liteparse- Description
- Use this skill whenever a task involves a document file (PDF, DOCX, PPTX, XLSX, or image) and you need to read it or pull text, tables, or specific values out of it — to answer a question about its contents, look up a figure, or extract data. Provides fast, local, model-free extraction via the `lit` CLI with disciplined, low-cost search patterns.
- License
- MIT
- Compatibility
- Requires Node 18+ and `@llamaindex/liteparse` (`npm i -g @llamaindex/liteparse`, verify `lit --version`). LibreOffice for Office files; ImageMagick for images. The bundled search.py helper needs `uv`.
- Metadata
- author: LlamaIndex · version: 1.0.1
- Instructions
- 83 lines in SKILL.md · view on GitHub
Before installing
- Ships executable scripts
Files (2)
- SKILL.md
- search.py · scripts/
SKILL.md (first lines)
# LiteParse
`lit` extracts text from documents locally without a model. Every `lit parse` re-extracts the whole document, and every line you print stays in context for the rest of the session, so the goal is few parses, few turns, small output.
## Parse once to a file, then search the file
```bash
lit parse "/abs/path/doc.pdf" --format text --no-ocr -o /tmp/doc.txt && wc -l /tmp/doc.txt
```
Reuse `/tmp/doc.txt` for every later question about the document. Never re-run `lit parse` to search.
- **Born-digital PDF** (has a text layer, like most reports): pass `--no-ocr`. It is much faster and gives the same text.
- **Scanned PDF or image**: drop `--no-ocr`. If digits look wrong, read the page visually (below).
- `--format json` only when you need bounding boxes or layout. Search it, never print it whole.
## Search in few turns
- Get the context in the same command: `grep -n -i -C4 "total assets" /tmp/doc.txt | head -40`. Use `sed -n 'A,Bp'` only when you need a wider window than `-C` gave.
- Batch independent lookups into one command:
```bash
for q in "carbon intensity" "scope 1" "total revenue"; do \
echo "=== $q ==="; grep -n -i -C3 "$q" /tmp/doc.txt | head -25; done
```
- Bound every output with `head`. Use shell `grep`/`sed` on the saved file rather than the Read and Grep tools.
- If two targeted greps do not find the answer, run the bundled BM25 ranker once instead of trying keyword variants. Give it a natural-language query with several synonyms. `-k` is the number of matches and `-e` the lines of context around each:
```bash
uv run --script <skill-dir>/scripts/search.py /tmp/doc.txt -q "materiality assessment priority topics" -k 8 -e 5
```
## Read a page visually: last resort
Screenshots are the most expensive thing you can put in context. Use one only when text cannot answer the question (dense multi-column tables, figures, charts). Render one page at modest DPI, and re-render higher only if the text is illegible:
```bash
lit screenshot "/abs/path/doc.pdf" --target-pages "13" --dpi 150 -o /tmp/shots/ # then read the PNG
```
The page flag is `--target-pages`, not `--pages`.
## Setup
Go straight to the parse. Skip `lit --version` and `--help` unless something failed. If `lit` is missing: `npm i -g @llamaindex/liteparse`. Office files need LibreOffice and images need ImageMagick; `lit` converts both to PDF.From ivanrvpereira/.agents, MIT.
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Signals
- Stars
- 18
- Forks
- 0
- Open issues
- 0
- Last commit (this folder)
- Jul 13, 2026 (3 months ago)
From GitHub (fetched Oct 24, 2026).
Details
- Version
- 1.0.1
- License
- MIT
- Repository
- github.com/ivanrvpereira/.agents
- First seen
- Oct 24, 2026
- Last checked
- Oct 24, 2026
- Source
- GitHubgithub-graphql
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LlamaParse
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AI parsing tool that turns complex documents, tables and charts into clean markdown at scale.
InfrastructureAFreshness: A★ 65data
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Cite this page
liteparse skill. Agent Mart. https://agentmart.dev/skills/ivanrvpereira-liteparse (last updated 2026-10-24)