Initial commit

.claude-plugin/plugin.json

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+{
+  "name": "docx-smart-extractor",
+  "description": "Extract and analyze Word documents (1MB-50MB+) with minimal token usage. Lossless extraction of all text, tables, formatting, and document structure while achieving 10-50x token reduction through local extraction, semantic chunking by headings, and intelligent caching.",
+  "version": "2.2.0",
+  "author": {
+    "name": "Diego Consolini",
+    "email": "diego@diegocon.nl"
+  },
+  "agents": [
+    "./agents/docx-smart-extractor.md"
+  ]
+}

README.md

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+# docx-smart-extractor
+
+Extract and analyze Word documents (1MB-50MB+) with minimal token usage. Lossless extraction of all text, tables, formatting, and document structure while achieving 10-50x token reduction through local extraction, semantic chunking by headings, and intelligent caching.

agents/docx-smart-extractor.md

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+---
+name: docx-smart-extractor
+description: Extract and analyze Word documents (1MB-50MB+) with minimal token usage through local extraction, semantic chunking by headings, and intelligent caching.
+capabilities: ["word-extraction", "table-extraction", "heading-structure", "token-optimization", "document-analysis", "policy-documents", "contract-analysis", "technical-reports"]
+tools: Read, Bash
+model: inherit
+---
+
+# DOCX Smart Extractor Agent
+
+## Overview
+
+The DOCX Smart Extractor enables efficient analysis of Word documents through local extraction, semantic chunking, and intelligent caching. Extract once, query forever.
+
+## Capabilities
+
+### Document Extraction
+- **Complete text extraction** - All paragraphs with hierarchy preservation
+- **Table extraction** - Full table structure, cells, and content
+- **Formatting preservation** - Bold, italic, fonts, colors, styles
+- **Document metadata** - Author, title, created date, modified date
+- **Heading structure** - H1, H2, H3 hierarchy for navigation
+- **Comments and tracked changes** - Full change history
+- **Headers and footers** - Page-level content
+- **Hyperlinks** - URL extraction and context
+
+### Semantic Chunking
+- **By heading hierarchy** - Chunk at H1, H2, H3 boundaries
+- **By paragraph groups** - 10-20 paragraphs per chunk
+- **By tables** - Each table as separate chunk
+- **Target chunk size** - 500-2000 tokens
+- **No BS metrics** - Honest, verifiable features only
+
+### Query Capabilities
+- **Keyword search** - Fast text search across all chunks
+- **Heading lookup** - Get specific sections by heading
+- **Table access** - Direct table extraction
+- **Document summary** - Metadata and statistics
+
+## When to Use
+
+Use this plugin for:
+- Policy documents (security, privacy, compliance)
+- Technical reports and documentation
+- Large Word documents (>1MB, >50 pages)
+- Documents with clear heading structure
+- Documents with tables and structured data
+- Contract review and analysis
+- Meeting notes and specifications
+
+## Workflow
+
+1. **Extract document**
+   ```bash
+   # Extract to cache (default)
+   python scripts/extract_docx.py /path/to/document.docx
+
+   # Extract and copy to working directory (interactive prompt)
+   python scripts/extract_docx.py /path/to/document.docx
+   # Will prompt: "Copy files? (y/n)"
+   # Will ask: "Keep cache? (y/n)"
+
+   # Extract and copy to specific directory (no prompts)
+   python scripts/extract_docx.py /path/to/document.docx --output-dir ./extracted
+   ```
+   Output: Cache key (e.g., `policy_document_a8f9e2c1`)
+
+2. **Chunk content**
+   ```bash
+   python scripts/semantic_chunker.py policy_document_a8f9e2c1
+   ```
+
+3. **Query content**
+   ```bash
+   # Search for keyword
+   python scripts/query_docx.py search policy_document_a8f9e2c1 "data retention"
+
+   # Get specific heading
+   python scripts/query_docx.py heading policy_document_a8f9e2c1 "Security Controls"
+
+   # Get summary
+   python scripts/query_docx.py summary policy_document_a8f9e2c1
+   ```
+
+## Token Reduction
+
+Typical reductions:
+- Small documents (< 50 paragraphs): 5-10x
+- Medium documents (50-200 paragraphs): 10-30x
+- Large documents (200+ paragraphs): 30-50x
+
+## Persistent Caching (v2.0.0 Unified System)
+
+**⚠️ IMPORTANT: Cache Location**
+
+Extracted content is stored in a **user cache directory**, NOT the working directory:
+
+**Cache locations by platform:**
+- **Linux/Mac:** `~/.claude-cache/docx/{document_name}_{hash}/`
+- **Windows:** `C:\Users\{username}\.claude-cache\docx\{document_name}_{hash}\`
+
+**Why cache directory?**
+1. **Persistent caching:** Extract once, query forever - even across different projects
+2. **Cross-project reuse:** Same document analyzed from different projects uses the same cache
+3. **Performance:** Subsequent queries are instant (no re-extraction needed)
+4. **Token optimization:** 10-50x reduction by loading only relevant sections
+
+**Cache contents:**
+- `full_document.json` - Complete document text with formatting
+- `headings.json` - Document heading structure
+- `tables.json` - Extracted tables
+- `metadata.json` - Document metadata
+- `manifest.json` - Cache manifest
+
+**Accessing cached content:**
+```bash
+# List all cached documents
+python scripts/query_docx.py list
+
+# Query cached content
+python scripts/query_docx.py search {cache_key} "your query"
+
+# Find cache location (shown in extraction output)
+# Example: ~/.claude-cache/docx/policy_document_a1b2c3d4/
+```
+
+**If you need files in working directory:**
+```bash
+# Option 1: Use --output-dir flag during extraction
+python scripts/extract_docx.py document.docx --output-dir ./extracted
+
+# Option 2: Copy from cache manually
+cp -r ~/.claude-cache/docx/{cache_key}/* ./extracted_content/
+```
+
+**Note:** Cache is local and not meant for version control. Keep original Word files in your repo and let each developer extract locally (one-time operation).
+
+## Supported Formats
+
+- ✅ .docx (Word 2007+ XML format)
+- ✅ .docm (Macro-enabled Word documents)
+- ❌ .doc (Legacy Word 97-2003 format - convert to .docx first)
+
+## Limitations
+
+- VBA macros not extracted (design choice)
+- Images extracted as metadata only (position, size, alt text)
+- Charts not extracted (recommend screenshot approach)
+- Password-protected files cannot be opened
+- Embedded objects may not be fully extracted
+
+## Dependencies
+
+- Python >= 3.8
+- python-docx >= 0.8.11
+
+## Example Use Cases
+
+### Policy Document Analysis
+```bash
+# Extract
+python scripts/extract_docx.py InfoSecPolicy.docx
+
+# Chunk
+python scripts/semantic_chunker.py InfoSecPolicy_a8f9e2
+
+# Find password policy section
+python scripts/query_docx.py search InfoSecPolicy_a8f9e2 "password requirements"
+```
+
+### Contract Review
+```bash
+# Extract
+python scripts/extract_docx.py Vendor_Contract.docx
+
+# Get specific section
+python scripts/query_docx.py heading Vendor_Contract_f3a8c1 "Termination Clause"
+```
+
+### Technical Documentation
+```bash
+# Extract large spec document
+python scripts/extract_docx.py API_Specification.docx
+
+# Search for endpoint details
+python scripts/query_docx.py search API_Specification_b9d2e1 "authentication endpoint"
+```
+
+## Performance
+
+- **Extraction speed**: ~1-5 seconds for typical documents (1-10MB)
+- **Chunking speed**: <1 second
+- **Query speed**: <1 second
+- **Cache reuse**: Instant (no re-extraction needed)
+
+## Output Format
+
+All output is JSON with UTF-8 encoding. Structured for easy parsing and analysis.
+
+## No Marketing BS
+
+This plugin does NOT:
+- Claim "100% content preservation" (meaningless metric)
+- Use AI during extraction (all local python-docx)
+- Require internet connection
+- Modify original documents
+- Extract content you don't need
+
+What it DOES:
+- Extract all text, tables, and formatting
+- Chunk by semantic boundaries (headings)
+- Enable fast keyword search
+- Cache for instant reuse
+- Achieve 10-50x token reduction (verified)

plugin.lock.json

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+{
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+    "generatedAt": "2025-11-28T10:16:26.661806Z",
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+    "repoRoot": "/Users/zhongweili/projects/openmind/42plugin-data"
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+    "description": "Extract and analyze Word documents (1MB-50MB+) with minimal token usage. Lossless extraction of all text, tables, formatting, and document structure while achieving 10-50x token reduction through local extraction, semantic chunking by headings, and intelligent caching.",
+    "version": "2.2.0"
+  },
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+}