docx-to-markdown

name	docx-to-markdown
version	1.0.0
description	[Document Processing] Use when you need to convert Microsoft Word ( DOCX) files to Markdown with GFM support (tables, images, code blocks).
disable-model-invocation	true

Quick Summary

Goal: Convert Microsoft Word (.docx) files to Markdown with GFM support (tables, images, formatting).

Workflow:

1. Install -- Ensure pandoc is available (required dependency)
2. Convert -- Run pandoc with GFM output format and image extraction
3. Clean -- Post-process markdown for consistency

Key Rules:

• Requires pandoc installed on the system
• Extracts images to a media/ directory alongside the markdown
• Preserves tables, formatting, and document structure

Be skeptical. Apply critical thinking, sequential thinking. Every claim needs traced proof, confidence percentages (Idea should be more than 80%).

docx-to-markdown

Convert Microsoft Word (.docx) files to Markdown format with GitHub-Flavored Markdown support.

Installation Required

This skill requires npm dependencies. Run one of the following:

# Option 1: Install via ClaudeKit CLI (recommended)
ck init  # Runs install.sh which handles all skills

# Option 2: Manual installation
cd .claude/skills/docx-to-markdown
npm install

Dependencies: mammoth, turndown, turndown-plugin-gfm

Quick Start

# Basic conversion
node .claude/skills/docx-to-markdown/scripts/convert.cjs --input ./document.docx

# Specify output path
node .claude/skills/docx-to-markdown/scripts/convert.cjs -i ./doc.docx -o ./output.md

# Preserve images to folder
node .claude/skills/docx-to-markdown/scripts/convert.cjs -i ./doc.docx --images ./images/

CLI Options

Option	Short	Description	Default
--input	-i	Input DOCX file path	(required)
--output	-o	Output markdown file path	{input}.md
--images		Directory for extracted images	inline base64
--help	-h	Show help message

Features

• GFM Tables: Properly converts Word tables to markdown tables
• Images: Extracts embedded images (base64 inline or to folder)
• Lists: Ordered and unordered lists preserved
• Code Blocks: Monospace text converted to code blocks
• Links: Hyperlinks preserved
• Headings: Heading levels maintained
• Cross-Platform: Works on Windows, macOS, Linux

Conversion Pipeline

DOCX → mammoth → HTML → turndown → Markdown

The two-stage conversion (DOCX→HTML→MD) follows mammoth's official recommendation for best results.

Output

Returns JSON on success:

{
    "success": true,
    "input": "/path/to/input.docx",
    "output": "/path/to/output.md",
    "stats": {
        "images": 3,
        "tables": 2,
        "headings": 5
    }
}

Limitations

• Complex layouts (columns, text boxes) may not preserve structure
• Merged table cells produce basic markdown tables
• Comments and track changes are stripped
• Some formatting (fonts, colors) lost in conversion

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[IMPORTANT] Use TaskCreate to break ALL work into small tasks BEFORE starting — including tasks for each file read. This prevents context loss from long files. For simple tasks, AI MUST ATTENTION ask user whether to skip.

AI Mistake Prevention — Failure modes to avoid on every task:

Check downstream references before deleting. Deleting components causes documentation and code staleness cascades. Map all referencing files before removal. Verify AI-generated content against actual code. AI hallucinates APIs, class names, and method signatures. Always grep to confirm existence before documenting or referencing. Trace full dependency chain after edits. Changing a definition misses downstream variables and consumers derived from it. Always trace the full chain. Trace ALL code paths when verifying correctness. Confirming code exists is not confirming it executes. Always trace early exits, error branches, and conditional skips — not just happy path. When debugging, ask "whose responsibility?" before fixing. Trace whether bug is in caller (wrong data) or callee (wrong handling). Fix at responsible layer — never patch symptom site. Assume existing values are intentional — ask WHY before changing. Before changing any constant, limit, flag, or pattern: read comments, check git blame, examine surrounding code. Verify ALL affected outputs, not just the first. Changes touching multiple stacks require verifying EVERY output. One green check is not all green checks. Holistic-first debugging — resist nearest-attention trap. When investigating any failure, list EVERY precondition first (config, env vars, DB names, endpoints, DI registrations, data preconditions), then verify each against evidence before forming any code-layer hypothesis. Surgical changes — apply the diff test. Bug fix: every changed line must trace directly to the bug. Don't restyle or improve adjacent code. Enhancement task: implement improvements AND announce them explicitly. Surface ambiguity before coding — don't pick silently. If request has multiple interpretations, present each with effort estimate and ask. Never assume all-records, file-based, or more complex path.

Critical Thinking Mindset — Apply critical thinking, sequential thinking. Every claim needs traced proof, confidence >80% to act. Anti-hallucination: Never present guess as fact — cite sources for every claim, admit uncertainty freely, self-check output for errors, cross-reference independently, stay skeptical of own confidence — certainty without evidence root of all hallucination.

MUST ATTENTION apply critical thinking — every claim needs traced proof, confidence >80% to act. Anti-hallucination: never present guess as fact.

MUST ATTENTION apply AI mistake prevention — holistic-first debugging, fix at responsible layer, surface ambiguity before coding, re-read files after compaction.

Closing Reminders

• MANDATORY IMPORTANT MUST ATTENTION break work into small todo tasks using TaskCreate BEFORE starting
• MANDATORY IMPORTANT MUST ATTENTION search codebase for 3+ similar patterns before creating new code
• MANDATORY IMPORTANT MUST ATTENTION cite file:line evidence for every claim (confidence >80% to act)
• MANDATORY IMPORTANT MUST ATTENTION add a final review todo task to verify work quality

[TASK-PLANNING] Before acting, analyze task scope and systematically break it into small todo tasks and sub-tasks using TaskCreate.