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PyMuPDF PDF Parser Clawdbot Skill

PyMuPDF PDF Parser Clawdbot Skill

by kesslerio · v1.0.0

Data Analysis
ClawHub
7.9
/ 10
1 evaluations
4.3k Downloads

Overview

Local, fast PDF parsing using PyMuPDF (fitz) to extract content into Markdown or JSON, with optional image and rough table extraction into a per-document output directory.

Key Advantages

1.High-speed PDF text extraction compared to heavier OCR/structured parsers.
2.Runs fully locally, avoiding network latency and data exfiltration risks.
3.Simple CLI interface with clear flags for format, images, tables, language hint, and output directory.
4.Per-document output folder structure (Markdown/JSON/images/tables) keeps artifacts organized.
5.Can serve as a lightweight fallback when heavier, more robust parsers (e.g., OCR-based) are unavailable.

Use Cases

  • Quick local conversion of PDFs to Markdown for downstream processing (e.g., RAG indexing, summarization, code-aware LLM workflows).
  • Batch or ad-hoc ingestion of mostly text-based PDFs where speed is more important than perfect structural fidelity.
  • Fallback parser in a multi-parser pipeline when primary heavy/robust parsers fail or are not installed.
  • Local extraction of embedded images from PDFs for separate analysis or manual review.
  • Rough table extraction where a simple line-based JSON representation is acceptable and full table reconstruction is not required.

Evaluation Scores

7.9
/ 10
Reliability
7.0
Functionality
7.5
Usability
7.5
Safety
9.5
Performance
9.0
Compatibility
7.0

Based on 1 evaluation · Latest: 3/19/2026

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Evaluation History (1)

7.9/103/19/2026
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OS: linux-arm64LLM: anthropic/claude-sonnet-4.6
**Quick judgment:** A fast, lightweight local PDF parser built on PyMuPDF, best suited for mostly text-based PDFs and scenarios where speed and simplicity matter more than perfect structure or robustness. **What it does well** - Converts PDFs to Markdown and/or JSON quickly using local PyMuPDF. - Optionally extracts images and a rough, line-based table representation. - Organizes outputs into per-document directories, which is convenient for pipelines. - Works well as a fallback when heavier OCR/structured parsers are unavailable. **Key risks / limitations** - PyMuPDF is "less robust" for complex PDFs (heavy layout, multi-column, scanned/OCR-needed documents), so content/structure may be incomplete or misaligned. - Table extraction is explicitly rough and line-based; not suitable where accurate tabular reconstruction is critical. - Requires PyMuPDF and compatible system libraries; import/libstdc++ issues are called out in the docs and can block usage if not resolved. **Recommended scenarios** - Use for fast, local extraction from standard, mostly text-based PDFs where minor structural inaccuracies are acceptable. - Use as a backup parser in a multi-tool pipeline when more sophisticated parsers fail or are not installed. - Avoid relying on it as the sole source of truth for complex layouts, high-stakes table data, or scanned PDFs needing OCR; in those cases, prefer a heavier OCR/structured parser and treat this skill as a secondary option.

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