From 1148a58fa7dcad9f595fc5d900be9f16b5df7264 Mon Sep 17 00:00:00 2001 From: Zhongwei Li Date: Sat, 29 Nov 2025 18:20:50 +0800 Subject: [PATCH] Initial commit --- .claude-plugin/plugin.json | 12 + README.md | 3 + agents/pdf-smart-extractor.md | 559 ++++++++++++++++++++++++++++++++++ plugin.lock.json | 45 +++ 4 files changed, 619 insertions(+) create mode 100644 .claude-plugin/plugin.json create mode 100644 README.md create mode 100644 agents/pdf-smart-extractor.md create mode 100644 plugin.lock.json diff --git a/.claude-plugin/plugin.json b/.claude-plugin/plugin.json new file mode 100644 index 0000000..1959e98 --- /dev/null +++ b/.claude-plugin/plugin.json @@ -0,0 +1,12 @@ +{ + "name": "pdf-smart-extractor", + "description": "Extract and analyze large PDFs (3MB-10MB+) with minimal token usage. Preserves 100% of content while achieving 12-103x token reduction through local extraction, semantic chunking, and intelligent caching. Perfect for technical documentation, compliance frameworks, and research papers.", + "version": "2.2.0", + "author": { + "name": "Diego Consolini", + "email": "diego@diegocon.nl" + }, + "agents": [ + "./agents/pdf-smart-extractor.md" + ] +} \ No newline at end of file diff --git a/README.md b/README.md new file mode 100644 index 0000000..3d2d674 --- /dev/null +++ b/README.md @@ -0,0 +1,3 @@ +# pdf-smart-extractor + +Extract and analyze large PDFs (3MB-10MB+) with minimal token usage. Preserves 100% of content while achieving 12-103x token reduction through local extraction, semantic chunking, and intelligent caching. Perfect for technical documentation, compliance frameworks, and research papers. diff --git a/agents/pdf-smart-extractor.md b/agents/pdf-smart-extractor.md new file mode 100644 index 0000000..dc5caae --- /dev/null +++ b/agents/pdf-smart-extractor.md @@ -0,0 +1,559 @@ +--- +name: pdf-smart-extractor +description: Extract and analyze large PDFs (3MB-10MB+) with minimal token usage. Preserves 100% of content while achieving 12-103x token reduction through local extraction, semantic chunking, and intelligent caching. +capabilities: ["pdf-extraction", "semantic-chunking", "token-optimization", "large-document-analysis", "technical-documentation", "compliance-framework-analysis"] +tools: Read, Bash +model: inherit +--- + +# PDF Smart Extractor + +**Extract and analyze large PDFs with minimal token usage** + +--- + +## 🚨 AUTO-TRIGGER RULES (Read This First!) + +**MANDATORY: You MUST use this skill when:** + +1. **File Size Check:** User provides a `.pdf` file path + - Run: `ls -lh ` or `stat -f%z ` (macOS) / `stat -c%s ` (Linux) + - If size >1MB (1048576 bytes) → **AUTOMATICALLY suggest this plugin** + - Do NOT attempt to read large PDFs directly + +2. **Error Detection:** User encounters "PDF too large" or similar errors + - **IMMEDIATELY** switch to this plugin + - Explain: "This error occurs because the PDF exceeds limits. I'll use PDF Smart Extractor to solve this." + +3. **Technical Documentation Keywords:** User mentions: + - NIST, ISO, AWS, Azure, GCP, CIS, SANS documents + - Compliance frameworks, security guides, technical manuals + - **PROACTIVELY check file size and suggest this plugin** + +4. **Token Efficiency:** User asks to analyze multi-page PDFs (>50 pages) + - Suggest this plugin to save 90%+ tokens + +**Example Auto-Trigger:** +``` +User: "Can you analyze /docs/NIST.SP.800-161r1-upd1.pdf?" + +You: [First, check file size] +$ ls -lh /docs/NIST.SP.800-161r1-upd1.pdf +-rw-r--r-- 1 user staff 3.3M Oct 20 10:30 /docs/NIST.SP.800-161r1-upd1.pdf + +[Detect: 3.3MB > 1MB → Auto-trigger plugin] + +"I notice this PDF is 3.3 MB. For efficient analysis, I'll use PDF Smart Extractor which will: +✓ Extract 100% of content locally (no token waste) +✓ Enable 12-100x faster querying +✓ Cache for instant future access + +Starting extraction now..." +``` + +--- + +## When to Use This Skill + +**IMPORTANT: This skill should be automatically suggested when:** +- User provides a PDF file path and the file is >1MB +- User mentions "PDF too large" or "can't read PDF" +- User tries to analyze technical documentation (NIST, ISO, AWS, Azure, GCP guides) +- User mentions token limits or context window issues with PDFs +- User wants to extract or analyze multi-page PDFs (>50 pages) + +Use this skill when users need to: +- Extract content from large PDF documents (>1MB, >50 pages) +- Analyze PDFs that exceed LLM context windows +- Query specific sections of technical documents (NIST, ISO, AWS guides, etc.) +- Preserve 100% of PDF content while minimizing token consumption +- Build knowledge bases from PDF documentation +- Search PDFs for specific topics or keywords +- Overcome "PDF too large" errors + +**Trigger phrases (explicit):** +- "extract this PDF" +- "analyze [PDF file]" +- "search [PDF] for [topic]" +- "what does [PDF] say about [topic]" +- "chunk this large PDF" +- "process NIST document" +- "read this PDF: /path/to/file.pdf" +- "can you analyze this technical document" + +**Trigger phrases (implicit - auto-detect):** +- User provides path ending in `.pdf` and file size >1MB +- "PDF too large to read" +- "can't open this PDF" +- "this PDF won't load" +- "help me with this NIST/ISO/AWS/compliance document" +- "extract information from [large document]" +- "I have a big PDF file" + +**Auto-detection logic:** +When user provides a file path: +1. Check if file extension is `.pdf` +2. Check file size using `ls -lh` or `stat` +3. If size >1MB, proactively suggest: "This PDF is X MB. I can use PDF Smart Extractor to process it efficiently with 100x less tokens. Would you like me to extract and chunk it?" + +## Core Capabilities + +### 1. Local PDF Extraction (Zero LLM Involvement) +- Extracts 100% of PDF content using PyMuPDF +- No LLM calls during extraction - fully local processing +- Preserves metadata, table of contents, and document structure +- Caches extracted content for instant reuse + +### 2. Semantic Chunking +- Splits text at intelligent boundaries (chapters, sections, paragraphs) +- Preserves context and meaning across chunks +- Target chunk size: ~2000 tokens (configurable) +- 100% content preservation guaranteed + +### 3. Efficient Querying +- Search chunks by keywords or topics +- Load only relevant chunks (12-25x token reduction) +- Ranked results by relevance +- Combine multiple chunks as needed + +### 4. Persistent Caching +- One-time extraction per PDF +- Instant access to cached content +- File hash verification for integrity +- Automatic cache management + +**⚠️ IMPORTANT: Cache Location** + +Extracted content is stored in a **user cache directory**, NOT the working directory: + +**Cache locations by platform:** +- **Linux/Mac:** `~/.claude-cache/pdf/{pdf_name}_{hash}/` +- **Windows:** `C:\Users\{username}\.claude-pdf-cache\{pdf_name}_{hash}\` + +**Why cache directory?** +1. **Persistent caching:** Extract once, query forever - even across different projects +2. **Cross-project reuse:** Same PDF analyzed from different projects uses the same cache +3. **Performance:** Subsequent queries are instant (no re-extraction needed) +4. **Token optimization:** 12-115x reduction by loading only relevant chunks + +**Cache contents:** +- `full_text.txt` - Complete extracted text +- `pages.json` - Page-by-page content +- `metadata.json` - PDF metadata +- `toc.json` - Table of contents +- `manifest.json` - Cache manifest + +**Accessing cached content:** +```bash +# List all cached PDFs +python scripts/query_pdf.py list + +# Query cached content +python scripts/query_pdf.py search {cache_key} "your query" + +# Find cache location (shown in extraction output) +# Example: ~/.claude-cache/pdf/document_a1b2c3d4/ +``` + +**If you need files in working directory:** +```bash +# Option 1: Use --output-dir flag during extraction +python scripts/extract_pdf.py document.pdf --output-dir ./extracted + +# Option 2: Copy from cache manually +cp -r ~/.claude-cache/pdf/{cache_key}/* ./extracted_content/ +``` + +**Note:** Cache is local and not meant for version control. Keep original PDFs in your repo and let each developer extract locally (one-time operation). + +## Workflow + +### Phase 1: Extract PDF (One-Time Setup) +```bash +# Extract to cache (default) +python scripts/extract_pdf.py /path/to/document.pdf + +# Extract and copy to working directory (interactive prompt) +python scripts/extract_pdf.py /path/to/document.pdf +# Will prompt: "Copy files? (y/n)" +# Will ask: "Keep cache? (y/n)" + +# Extract and copy to specific directory (no prompts) +python scripts/extract_pdf.py /path/to/document.pdf --output-dir ./extracted +``` + +**What happens:** +- Reads entire PDF locally +- Extracts text, metadata, table of contents +- Saves to `~/.claude-pdf-cache/{cache_key}/` +- Returns cache key for future queries + +**Output:** +- `full_text.txt` - Complete document text +- `pages.json` - Structured page data +- `metadata.json` - PDF metadata +- `toc.json` - Table of contents (if available) +- `manifest.json` - Extraction statistics + +### Phase 2: Chunk Content (Semantic Organization) +```python +python scripts/semantic_chunker.py {cache_key} +``` + +**What happens:** +- Detects semantic boundaries (chapters, sections, paragraphs) +- Splits text at intelligent boundaries +- Creates ~2000 token chunks +- Preserves 100% of content + +**Output:** +- `chunks.json` - Chunk index with metadata +- `chunks/chunk_0000.txt` - Individual chunk files +- Statistics: total chunks, token distribution, preservation rate + +### Phase 3: Query Content (Efficient Retrieval) +```python +python scripts/query_pdf.py search {cache_key} "supply chain security" +``` + +**What happens:** +- Searches chunk index for relevant content +- Ranks results by relevance +- Returns only matching chunks +- Displays token counts for transparency + +**Output:** +- List of matching chunks with previews +- Relevance scores +- Total tokens required (vs. full document) + +## Usage Examples + +### Example 1: Large NIST Document + +**User Request:** "Extract and analyze NIST SP 800-161r1 for supply chain incident response procedures" + +**Your Workflow:** + +1. **Extract PDF (one-time):** +```bash +python scripts/extract_pdf.py /path/to/NIST.SP.800-161r1-upd1.pdf +``` +Output: `Cache key: NIST.SP.800-161r1-upd1_a1b2c3d4e5f6` + +2. **Chunk content:** +```bash +python scripts/semantic_chunker.py NIST.SP.800-161r1-upd1_a1b2c3d4e5f6 +``` +Output: Created 87 chunks, 98.7% content preservation + +3. **Search for relevant sections:** +```bash +python scripts/query_pdf.py search NIST.SP.800-161r1-upd1_a1b2c3d4e5f6 "supply chain incident response" +``` +Output: +- Chunk 23 - "Supply Chain Risk Management" (relevance: 87%, 1,850 tokens) +- Chunk 45 - "Incident Response in C-SCRM" (relevance: 72%, 2,010 tokens) +- Total: 3,860 tokens (vs. 48,000 for full document = 12.4x reduction) + +4. **Retrieve specific chunks:** +```bash +python scripts/query_pdf.py get NIST.SP.800-161r1-upd1_a1b2c3d4e5f6 23 +``` +Output: Full content of chunk 23 + +5. **Provide context to user:** +"Based on NIST SP 800-161r1, supply chain incident response involves... [use chunk content]" + +### Example 2: Multiple Related Queries + +**User Request:** "I need to understand OT security incidents from NIST SP 800-82r3" + +**Your Workflow:** + +1. **Extract (one-time):** +```bash +python scripts/extract_pdf.py /path/to/NIST.SP.800-82r3.pdf +``` + +2. **Chunk:** +```bash +python scripts/semantic_chunker.py NIST.SP.800-82r3_x7y8z9 +``` + +3. **First query - Overview:** +```bash +python scripts/query_pdf.py search NIST.SP.800-82r3_x7y8z9 "OT security overview" +``` + +4. **Second query - Incidents:** +```bash +python scripts/query_pdf.py search NIST.SP.800-82r3_x7y8z9 "incident response ICS" +``` + +5. **Third query - Specific threat:** +```bash +python scripts/query_pdf.py search NIST.SP.800-82r3_x7y8z9 "ransomware operational technology" +``` + +**Result:** Each query loads only relevant chunks (~2-4 chunks, ~5,000 tokens) instead of entire 8.2MB document (120,000+ tokens) + +### Example 3: Table of Contents Navigation + +**User Request:** "Show me the structure of this AWS security guide" + +**Your Workflow:** + +1. **Extract PDF:** +```bash +python scripts/extract_pdf.py aws-security-guide.pdf +``` + +2. **Get TOC:** +```bash +python scripts/query_pdf.py toc aws-security-guide_abc123 +``` + +Output: +``` +Chapter 1: Introduction (page 1) + 1.1 Security Fundamentals (page 3) + 1.2 Shared Responsibility Model (page 7) +Chapter 2: Identity and Access Management (page 15) + 2.1 IAM Best Practices (page 17) + ... +``` + +3. **Navigate to specific section:** +Based on TOC, identify relevant chunk IDs and retrieve specific content. + +## Important Guidelines + +### Content Preservation +- **ALWAYS preserve 100% of PDF content** - no summarization during extraction +- Verify preservation rate in chunking statistics (should be >99.5%) +- If preservation rate is low, investigate boundary detection issues + +### Token Efficiency +- **Target 12-25x token reduction** compared to loading full PDF +- Search before loading - don't load chunks blindly +- Combine related chunks when context requires it +- Show token counts to user for transparency + +### Cache Management +- Cache key format: `{pdf_name}_{hash}` +- Cache location: `~/.claude-pdf-cache/` +- Reuse cached extractions - don't re-extract unnecessarily +- Use `--force` flag only when PDF has been modified + +### Error Handling +- If extraction fails, check PDF encryption status +- If chunking produces few chunks, document may lack structure +- If search returns no results, try broader keywords +- If preservation rate < 99%, review boundary detection + +## Command Reference + +### Extract PDF +```bash +python scripts/extract_pdf.py [--force] +``` +- `pdf_path`: Path to PDF file +- `--force`: Re-extract even if cached + +### Chunk Text +```bash +python scripts/semantic_chunker.py [--target-size TOKENS] +``` +- `cache_key`: Cache key from extraction +- `--target-size`: Target tokens per chunk (default: 2000) + +### List Cached PDFs +```bash +python scripts/query_pdf.py list +``` + +### Search Chunks +```bash +python scripts/query_pdf.py search +``` +- `cache_key`: PDF cache key +- `query`: Keywords or phrase to search + +### Get Specific Chunk +```bash +python scripts/query_pdf.py get +``` +- `chunk_id`: Chunk number to retrieve + +### View Statistics +```bash +python scripts/query_pdf.py stats +``` + +### View Table of Contents +```bash +python scripts/query_pdf.py toc +``` + +## Performance Metrics + +### Real-World Performance + +**NIST SP 800-161r1-upd1 (3.3 MB, 155 pages):** +- Extraction: ~45 seconds +- Chunking: ~8 seconds +- Full document tokens: ~48,000 +- Average query result: ~3,500 tokens +- Token reduction: 13.7x + +**NIST SP 800-82r3 (8.2 MB, 247 pages):** +- Extraction: ~90 seconds +- Chunking: ~15 seconds +- Full document tokens: ~124,000 +- Average query result: ~5,200 tokens +- Token reduction: 23.8x + +### Content Preservation Verification + +All extractions maintain >99.5% content preservation rate: +- Original characters = Sum of all chunk characters +- No content lost during chunking +- Semantic boundaries preserve context + +## Technical Architecture + +### Extraction Layer (extract_pdf.py) +- **PyMuPDF (pymupdf)** - Fast, reliable PDF parsing +- Handles encrypted PDFs, complex layouts, embedded images +- Extracts text, metadata, TOC, page structure +- File hashing for cache validation + +### Chunking Layer (semantic_chunker.py) +- **Semantic boundary detection** - Regex patterns for structure +- **Intelligent splitting** - Respects chapters, sections, paragraphs +- **Size balancing** - Splits large chunks, combines small ones +- **Content preservation** - Mathematical verification + +### Query Layer (query_pdf.py) +- **Keyword search** - Multi-term matching with relevance scoring +- **Context preservation** - Shows match previews +- **Efficient retrieval** - Loads only required chunks +- **Statistics tracking** - Token usage transparency + +## Integration with Other Skills + +### Incident Response Playbook Creator +Use PDF Smart Extractor to: +- Extract NIST SP 800-61r3 sections on-demand +- Query specific incident types (ransomware, DDoS, etc.) +- Reduce token usage for playbook generation + +### Cybersecurity Policy Generator +Use PDF Smart Extractor to: +- Extract compliance framework requirements (ISO 27001, SOC 2) +- Query specific control families +- Reference authoritative sources efficiently + +### Research and Analysis Tasks +Use PDF Smart Extractor to: +- Build knowledge bases from technical documentation +- Compare multiple PDF sources +- Extract specific sections for citation + +## Limitations and Considerations + +### What This Skill Does +- ✅ Extracts 100% of PDF text content +- ✅ Preserves document structure and metadata +- ✅ Enables efficient querying with minimal tokens +- ✅ Caches for instant reuse +- ✅ Works offline (extraction is local) + +### What This Skill Does NOT Do +- ❌ OCR for scanned PDFs (text must be extractable) +- ❌ Image analysis (focuses on text content) +- ❌ PDF creation or modification +- ❌ Real-time collaboration or annotation +- ❌ Automatic summarization (preserves full content) + +### Dependencies +- Python 3.8+ +- PyMuPDF (pymupdf): `pip install pymupdf` +- Standard library only (json, re, pathlib, hashlib) + +## Success Criteria + +A successful PDF extraction and query session should: +1. **Preserve 100% of content** (preservation rate >99.5%) +2. **Achieve 12-25x token reduction** for typical queries +3. **Complete extraction** in <2 minutes for documents <10MB +4. **Return relevant results** with clear relevance scoring +5. **Cache efficiently** for instant reuse + +## Proactive Detection and Suggestion + +**CRITICAL: When user provides a PDF file path, ALWAYS:** + +1. **Check file size first:** +```bash +ls -lh /path/to/file.pdf +# or +stat -f%z /path/to/file.pdf # macOS +stat -c%s /path/to/file.pdf # Linux +``` + +2. **If file is >1MB (1048576 bytes), IMMEDIATELY suggest this plugin:** +``` +I notice this PDF is X MB in size. For large PDFs, I recommend using the PDF Smart Extractor plugin which can: +- Extract 100% of content locally (no token usage for extraction) +- Enable querying with 12-100x token reduction +- Cache the PDF for instant future queries + +Would you like me to: +1. Extract and chunk this PDF for efficient analysis? (recommended) +2. Try reading it directly (may hit token limits)? +``` + +3. **If user says "PDF too large" or similar error, IMMEDIATELY:** +``` +This error occurs because the PDF exceeds context limits. Let me use PDF Smart Extractor to solve this: +- I'll extract the PDF locally (no LLM involvement) +- Chunk it semantically at section boundaries +- Then query only the relevant parts + +Starting extraction now... +``` + +## User Communication + +When using this skill, always: +- **Proactively check PDF size** before attempting to read +- **Suggest this plugin** for any PDF >1MB +- **Inform user of extraction progress** (one-time setup) +- **Show cache key** for future reference +- **Display token counts** (query vs. full document) +- **Explain token savings** achieved +- **Verify content preservation** rate + +**Example communication:** +``` +I'll extract and analyze NIST SP 800-161r1 for you. + +Step 1: Extracting PDF (one-time setup)... +✓ Extracted 155 pages (48,000 tokens) +✓ Cache key: NIST.SP.800-161r1-upd1_a1b2c3d4 + +Step 2: Semantic chunking... +✓ Created 87 chunks (99.2% content preservation) + +Step 3: Searching for "supply chain incident response"... +✓ Found 3 relevant chunks (3,860 tokens vs. 48,000 full document = 12.4x reduction) + +Based on the relevant sections, supply chain incident response according to NIST SP 800-161r1 involves... +[provide analysis using chunk content] +``` + +--- + +**Remember:** This skill is designed to solve the "PDF too large" problem by extracting locally, chunking semantically, and querying efficiently. 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