claude-code-skills-reference/markdown-tools/references/heavy-mode-guide.md

Heavy Mode Guide

Detailed documentation for markdown-tools Heavy Mode conversion.

Overview

Heavy Mode runs multiple conversion tools in parallel and intelligently merges their outputs to produce the highest quality markdown possible.

When to Use Heavy Mode

Use Heavy Mode when:

• Document has complex tables that need precise formatting
• Images must be preserved with proper references
• Structure hierarchy (headings, lists) must be accurate
• Output quality is more important than conversion speed
• Document will be used for LLM processing

Use Quick Mode when:

• Speed is priority
• Document is simple (mostly text)
• Output is for draft/review purposes

Tool Capabilities

PyMuPDF4LLM (Best for PDFs)

Strengths:

• Native table detection with multiple strategies
• Image extraction with position metadata
• LLM-optimized output format
• Preserves reading order

Usage:

import pymupdf4llm

md_text = pymupdf4llm.to_markdown(
    "document.pdf",
    write_images=True,
    table_strategy="lines_strict",
    image_path="./assets",
    dpi=150
)

markitdown (Universal Converter)

Strengths:

• Supports many formats (PDF, DOCX, PPTX, XLSX)
• Good text extraction
• Simple API

Limitations:

• May miss complex tables
• No native image extraction

pandoc (Best for Office Docs)

Strengths:

• Excellent DOCX/PPTX structure preservation
• Proper heading hierarchy
• List formatting

Limitations:

• Requires system installation
• PDF support limited

Merge Strategy

Segment-Level Selection

Heavy Mode doesn't just pick one tool's output. It:

1. Parses each output into segments
2. Scores each segment independently
3. Selects the best version of each segment

Segment Types

Type	Detection Pattern	Scoring Criteria
Table	|.*| rows	Row count, column count, header separator
Heading	^#{1-6}	Proper level, reasonable length
Image	!\[.*\]\(.*\)	Alt text present, local path
List	^[-*+\d.]	Item count, nesting depth
Code	Triple backticks	Line count, language specified
Paragraph	Default	Word count, completeness

Scoring Example

Table from pymupdf4llm:
  - 10 rows × 5 columns = 5.0 points
  - Header separator present = 1.0 points
  - Total: 6.0 points

Table from markitdown:
  - 8 rows × 5 columns = 4.0 points
  - No header separator = 0.0 points
  - Total: 4.0 points

→ Select pymupdf4llm version

Advanced Usage

Force Specific Tool

# Use only pandoc
uv run scripts/convert.py document.docx -o output.md --tool pandoc

Custom Assets Directory

# Heavy mode with custom image output
uv run scripts/convert.py document.pdf -o output.md --heavy --assets-dir ./images

Validate After Conversion

# Convert then validate
uv run scripts/convert.py document.pdf -o output.md --heavy
uv run scripts/validate_output.py document.pdf output.md --report quality.html

Troubleshooting

Low Text Retention Score

Causes:

• PDF has scanned images (not searchable text)
• Encoding issues in source document
• Complex layouts confusing the parser

Solutions:

• Use OCR preprocessing for scanned PDFs
• Try different tool with --tool flag
• Manual cleanup may be needed

Missing Tables

Causes:

• Tables without visible borders
• Tables spanning multiple pages
• Merged cells

Solutions:

• Use Heavy Mode for better detection
• Try pymupdf4llm with different table_strategy
• Manual table reconstruction

Image References Broken

Causes:

• Assets directory not created
• Relative path issues
• Image extraction failed

Solutions:

• Ensure --assets-dir points to correct location
• Check images_metadata.json for extraction status
• Use extract_pdf_images.py separately