Multi-Format Document Optimizer

Unified document optimization skill that chains docling-converter, imagemagick-expert, and markdown-to-pdf. Auto-detects input format, applies the appropriate conversion pipeline, optimizes embedded images, and produces web/print-ready output with configurable quality presets.

docling + ImageMagick + markdown-to-pdf

Download Skill Package (.skill) View Source on GitHub

Table of Contents

1. Overview

Detects format and routes through pipelines (pdf_optimize / docx_to_pdf / pptx_to_pdf etc.) with 4 quality presets — web (80% / 96dpi / WebP), print (95% / 300dpi), archive (90% / 150dpi), minimal (70% / 72dpi). Supports CLI commands analyze/convert/batch/optimize-images/verify, PDF image extraction & re-embedding via PyMuPDF, and parallel batch workers.

2. Prerequisites

  • Python 3.9+
  • docling CLI (pip install docling)
  • ImageMagick 7+
  • fpdf2 / Playwright + chromium
  • PyMuPDF (optional)

3. Quick Start 

# Install the skill locally
make install SKILL=multi-format-document-optimizer

# Or fetch the .skill package
curl -L -o multi-format-document-optimizer.skill https://github.com/takusaotome/claude-skills-library/raw/main/skill-packages/multi-format-document-optimizer.skill

Then trigger the skill in Claude Code by describing what you want — see the Usage Examples section below for trigger phrases.

4. How It Works

The skill follows the workflow documented in its SKILL.md. Key stages:

  1. Input parsing — interprets the user request and any provided source files.
  2. Core processing — applies the skill’s domain logic (see Reference section).
  3. Output generation — produces structured artifacts (markdown / JSON / templates) ready for downstream use.

For the authoritative step-by-step procedure, open skills/multi-format-document-optimizer/SKILL.md.

5. Usage Examples

  • You need to convert PPTX/DOCX to PDF and shrink embedded images for web
  • You’re batch-processing a directory of mixed-format documents
  • You want web vs. print vs. archive quality presets as a single CLI flag
  • You need to verify output quality after optimization

6. Understanding the Output

The skill produces structured output following the conventions in its templates and reference docs (see Section 10). Outputs are:

  • Reproducible — identical input + same templates → same output structure.
  • Reviewable — each section is labeled and ordered consistently.
  • Composable — outputs of this skill can feed adjacent skills (see Section 8).

7. Tips & Best Practices

  • Start with a small, realistic input to validate the workflow before scaling.
  • Keep skills/multi-format-document-optimizer/SKILL.md open alongside this guide; it remains the authoritative source.
  • Read the most relevant reference file first (see Section 10) instead of trying to absorb all of them.
  • Run scripts on test data before applying to production-bound inputs.
  • Preserve intermediate outputs so you can explain assumptions and trace decisions.

8. Combining with Other Skills

  • Pair with adjacent skills in the same category to cover the planning → execution → review arc.
  • Browse the Operations & Docs category for neighboring workflows: category index.
  • See the full English skill catalog: skill catalog.

9. Troubleshooting

  • Re-check prerequisites first; missing runtime dependencies are the most common failure mode.
  • Run helper scripts on a minimal input before applying them to a full dataset.
  • Compare your input shape against the reference files to confirm expected fields, sections, or metadata.
  • Confirm Python version (3.9+) and required packages are installed in the active environment.
  • When output looks incomplete, re-read the relevant reference file to verify the input contract.

10. Reference

References:

  • skills/multi-format-document-optimizer/references/image_optimization_guide.md
  • skills/multi-format-document-optimizer/references/pipeline_guide.md

Scripts:

  • skills/multi-format-document-optimizer/scripts/document_optimizer.py

Assets:

(none)