DOCX Official

DOCX creation, editing, and analysis

Overview

A user may ask you to create, edit, or analyze the contents of a .docx file. A .docx file is essentially a ZIP archive containing XML files and other resources that you can read or edit. You have different tools and workflows available for different tasks.

Workflow Decision Tree

Reading/Analyzing Content

Use "Text extraction" or "Raw XML access" sections below

Creating New Document

Use "Creating a new Word document" workflow

Editing Existing Document

• Your own document + simple changes Use "Basic OOXML editing" workflow

• Someone else's document Use "Redlining workflow" (recommended default)

• Legal, academic, business, or government docs Use "Redlining workflow" (required)

Reading and analyzing content

Text extraction

If you just need to read the text contents of a document, you should convert the document to markdown using pandoc. Pandoc provides excellent support for preserving document structure and can show tracked changes:

# Convert document to markdown with tracked changes
pandoc --track-changes=all path-to-file.docx -o output.md
# Options: --track-changes=accept/reject/all

Raw XML access

You need raw XML access for: comments, complex formatting, document structure, embedded media, and metadata. For any of these features, you'll need to unpack a document and read its raw XML contents.

Unpacking a file

python ooxml/scripts/unpack.py <office_file> <output_directory>

Key file structures

• word/document.xml - Main document contents
• word/comments.xml - Comments referenced in document.xml
• word/media/ - Embedded images and media files
• Tracked changes use <w:ins> (insertions) and <w:del> (deletions) tags

Creating a new Word document

When creating a new Word document from scratch, use docx-js, which allows you to create Word documents using JavaScript/TypeScript.

Workflow

1. MANDATORY - READ ENTIRE FILE: Read docx-js.md (~500 lines) completely from start to finish. NEVER set any range limits when reading this file. Read the full file content for detailed syntax, critical formatting rules, and best practices before proceeding with document creation.
2. Create a JavaScript/TypeScript file using Document, Paragraph, TextRun components (You can assume all dependencies are installed, but if not, refer to the dependencies section below)
3. Export as .docx using Packer.toBuffer()

Editing an existing Word document

When editing an existing Word document, use the Document library (a Python library for OOXML manipulation). The library automatically handles infrastructure setup and provides methods for document manipulation. For complex scenarios, you can access the underlying DOM directly through the library.

Workflow

1. MANDATORY - READ ENTIRE FILE: Read ooxml.md (~600 lines) completely from start to finish. NEVER set any range limits when reading this file. Read the full file content for the Document library API and XML patterns for directly editing document files.
2. Unpack the document: python ooxml/scripts/unpack.py <office_file> <output_directory>
3. Create and run a Python script using the Document library (see "Document Library" section in ooxml.md)
4. Pack the final document: python ooxml/scripts/pack.py <input_directory> <office_file>

The Document library provides both high-level methods for common operations and direct DOM access for complex scenarios.

Redlining workflow for document review

This workflow allows you to plan comprehensive tracked changes using markdown before implementing them in OOXML. CRITICAL: For complete tracked changes, you must implement ALL changes systematically.

Batching Strategy: Group related changes into batches of 3-10 changes. This makes debugging manageable while maintaining efficiency. Test each batch before moving to the next.

Principle: Minimal, Precise Edits When implementing tracked changes, only mark text that actually changes. Repeating unchanged text makes edits harder to review and appears unprofessional. Break replacements into: [unchanged text] + [deletion] + [insertion] + [unchanged text]. Preserve the original run's RSID for unchanged text by extracting the <w:r> element from the original and reusing it.

Example - Changing "30 days" to "60 days" in a sentence:

# BAD - Replaces entire sentence
'<w:del><w:r><w:delText>The term is 30 days.</w:delText></w:r></w:del><w:ins><w:r><w:t>The term is 60 days.</w:t></w:r></w:ins>'

# GOOD - Only marks what changed, preserves original <w:r> for unchanged text
'<w:r w:rsidR="00AB12CD"><w:t>The term is </w:t></w:r><w:del><w:r><w:delText>30</w:delText></w:r></w:del><w:ins><w:r><w:t>60</w:t></w:r></w:ins><w:r w:rsidR="00AB12CD"><w:t> days.</w:t></w:r>'

Tracked changes workflow

1. Get markdown representation: Convert document to markdown with tracked changes preserved:

   pandoc --track-changes=all path-to-file.docx -o current.md
   

2. Identify and group changes: Review the document and identify ALL changes needed, organizing them into logical batches:

   Location methods (for finding changes in XML):

   • Section/heading numbers (e.g., "Section 3.2", "Article IV")
   • Paragraph identifiers if numbered
   • Grep patterns with unique surrounding text
   • Document structure (e.g., "first paragraph", "signature block")
   • DO NOT use markdown line numbers - they don't map to XML structure

   Batch organization (group 3-10 related changes per batch):

   • By section: "Batch 1: Section 2 amendments", "Batch 2: Section 5 updates"
   • By type: "Batch 1: Date corrections", "Batch 2: Party name changes"
   • By complexity: Start with simple text replacements, then tackle complex structural changes
   • Sequential: "Batch 1: Pages 1-3", "Batch 2: Pages 4-6"

3. Read documentation and unpack:

   • MANDATORY - READ ENTIRE FILE: Read ooxml.md (~600 lines) completely from start to finish. NEVER set any range limits when reading this file. Pay special attention to the "Document Library" and "Tracked Change Patterns" sections.
   • Unpack the document: python ooxml/scripts/unpack.py <file.docx> <dir>
   • Note the suggested RSID: The unpack script will suggest an RSID to use for your tracked changes. Copy this RSID for use in step 4b.

4. Implement changes in batches: Group changes logically (by section, by type, or by proximity) and implement them together in a single script. This approach:

   • Makes debugging easier (smaller batch = easier to isolate errors)
   • Allows incremental progress
   • Maintains efficiency (batch size of 3-10 changes works well)

   Suggested batch groupings:

   • By document section (e.g., "Section 3 changes", "Definitions", "Termination clause")
   • By change type (e.g., "Date changes", "Party name updates", "Legal term replacements")
   • By proximity (e.g., "Changes on pages 1-3", "Changes in first half of document")

   For each batch of related changes:

   a. Map text to XML: Grep for text in word/document.xml to verify how text is split across <w:r> elements.

   b. Create and run script: Use get_node to find nodes, implement changes, then doc.save(). See "Document Library" section in ooxml.md for patterns.

   Note: Always grep word/document.xml immediately before writing a script to get current line numbers and verify text content. Line numbers change after each script run.

5. Pack the document: After all batches are complete, convert the unpacked directory back to .docx:

   python ooxml/scripts/pack.py unpacked reviewed-document.docx
   

6. Final verification: Do a comprehensive check of the complete document:

   • Convert final document to markdown:

     pandoc --track-changes=all reviewed-document.docx -o verification.md
     

   • Verify ALL changes were applied correctly:

     grep "original phrase" verification.md  # Should NOT find it
     grep "replacement phrase" verification.md  # Should find it
     

   • Check that no unintended changes were introduced

Converting Documents to Images

To visually analyze Word documents, convert them to images using a two-step process:

1. Convert DOCX to PDF:

   soffice --headless --convert-to pdf document.docx
   

2. Convert PDF pages to JPEG images:

   pdftoppm -jpeg -r 150 document.pdf page
   

   This creates files like page-1.jpg, page-2.jpg, etc.

Options:

• -r 150: Sets resolution to 150 DPI (adjust for quality/size balance)
• -jpeg: Output JPEG format (use -png for PNG if preferred)
• -f N: First page to convert (e.g., -f 2 starts from page 2)
• -l N: Last page to convert (e.g., -l 5 stops at page 5)
• page: Prefix for output files

Example for specific range:

pdftoppm -jpeg -r 150 -f 2 -l 5 document.pdf page  # Converts only pages 2-5

Code Style Guidelines

IMPORTANT: When generating code for DOCX operations:

• Write concise code
• Avoid verbose variable names and redundant operations
• Avoid unnecessary print statements

Dependencies

Required dependencies (install if not available):

• pandoc: sudo apt-get install pandoc (for text extraction)
• docx: npm install -g docx (for creating new documents)
• LibreOffice: sudo apt-get install libreoffice (for PDF conversion)
• Poppler: sudo apt-get install poppler-utils (for pdftoppm to convert PDF to images)
• defusedxml: pip install defusedxml (for secure XML parsing)

When to Use

This skill is applicable to execute the workflow or actions described in the overview.

🧠 AGI Framework Integration

Adapted for @techwavedev/agi-agent-kit Original source: antigravity-awesome-skills

Qdrant Memory Integration

Before executing complex tasks with this skill:

python3 execution/memory_manager.py auto --query "<task summary>"

• Cache hit? Use cached response directly — no need to re-process.
• Memory match? Inject context_chunks into your reasoning.
• No match? Proceed normally, then store results:

python3 execution/memory_manager.py store \\
  --content "Description of what was decided/solved" \\
  --type decision \\
  --tags docx <relevant-tags>

Agent Team Collaboration

• This skill can be invoked by the orchestrator agent via intelligent routing.
• In Agent Teams mode, results are shared via Qdrant shared memory for cross-agent context.
• In Subagent mode, this skill runs in isolation with its own memory namespace.

Local LLM Support

When available, use local Ollama models for embedding and lightweight inference:

• Embeddings: nomic-embed-text via Qdrant memory system
• Lightweight analysis: Local models reduce API costs for repetitive patterns