
[TOOL_RESULT id=toolu_0147QysoY6MjksZ21UUzaFLV error=False]
[{'type': 'text', 'text': 'Perfect! Now I have a comprehensive understanding. Let me create a clear explanation document for you:\n\n## LLM Request Capture and Token Analysis Workflow\n\nBased on my analysis of `/Users/psulin/organon/scripts/llm_capture/`, here\'s how the system works:\n\n### Overview: The Complete Workflow\n\n```\nCAPTURE (Live) → EXTRACT (Process) → ANALYZE (Insight)\n```\n\nThe pipeline has three distinct stages:\n\n---\n\n### Stage 1: CAPTURE - Intercept Raw API Requests\n\n**What happens:**\n- `capture-claude-api-request.py` runs as a mitmproxy addon\n- It intercepts HTTPS requests to `api.anthropic.com/v1/messages`\n- Saves the complete raw JSON request to `captured-requests/claude-request-TIMESTAMP.json`\n\n**Why separate from extraction:**\n- Capture is real-time and continuous (happens during your Claude Code session)\n- Extraction is batch processing (happens after you\'ve finished capturing)\n- The raw JSON is HUGE (~100KB+) and contains nested structures that aren\'t human-readable\n- Each capture is immutable - the raw JSON stays untouched\n\n**Capture outputs:**\n- `claude-request-20251020_103738.json` - Complete API request with system, messages, tools, everything\n- `system-message-TIMESTAMP.txt` - Extracted system prompt (preview)\n- `messages-TIMESTAMP.json` - Messages array (preview)\n- `user-message-TIMESTAMP.txt` - Last user message (preview)\n\n---\n\n### Stage 2: EXTRACT - Reconstruct and Decompose\n\n**This is the critical step that makes the raw captures usable.**\n\nThe extraction script (`extract-all-requests.sh`) does the heavy lifting:\n\n1. **Finds all raw captures** in chronological order\n2. **Numbers them sequentially** (request-001, request-002, etc.)\n3. **Extracts and separates** each component:\n   - `01-system-message.txt` - Claude Code system prompt\n   - `02-message-user.txt` - Your first message + CLAUDE.md\n   - `03-message-assistant.txt` - Model\'s response\n   - `04-message-user.txt` - Your second message + tool results\n   - etc.\n4. **Reconstructs the complete model input** using `reconstruct-model-input.py`:\n   - This rebuilds the exact concatenation the model receives\n   - Includes system + tools + all messages in conversation order\n   - Saved as `99-complete-model-input.txt` (the "source of truth")\n5. **Counts tokens** for every file using tiktoken:\n   - System message tokens\n   - Each message\'s tokens\n   - **Total tokens** across the complete input\n\n**Why is extraction necessary as a separate step?**\n\nThis is the critical insight - there are 3 fundamental transformations:\n\n| Transformation | Input | Output | Why Needed |\n|---|---|---|---|\n| **Raw → Decomposed** | Nested JSON structure | Individual text files | JSON is machine-optimized, not human-readable. Need flat files to inspect each part |\n| **Separate → Concatenated** | Individual messages | `99-complete-model-input.txt` | Model processes all parts concatenated into one string. Need to see the exact concatenation to understand token costs |\n| **Text → Token Count** | Raw text files | Token counts in README | Understand size without reading entire files. Identify optimization opportunities |\n\n**Example transformation:**\n\nRaw JSON (40KB):\n```json\n{\n  "model": "claude-sonnet-4-5-20250929",\n  "system": [{"type": "text", "text": "You are Claude Code..."}],\n  "tools": [{...}, {...}],\n  "messages": [\n    {"role": "user", "content": [...]},\n    {"role": "assistant", "content": [...]},\n    {"role": "user", "content": [...]}\n  ]\n}\n```\n\nBecomes extracted files:\n```\nextracted-requests/request-001-20251020_103738/\n├── 00-original-request.json      # Raw JSON (kept for reference)\n├── 01-system-message.txt         # 2,278 tokens\n├── 02-message-user.txt           # 2,445 tokens\n├── 03-message-assistant.txt      # 12,340 tokens\n├── 99-complete-model-input.txt   # 20,148 tokens (complete string)\n└── README.txt                     # Metadata and summary\n```\n\nThe `99-complete-model-input.txt` is what you\'d see if you printed `system + tools + msg1 + msg2 + msg3` end-to-end.\n\n---\n\n### Stage 3: ANALYZE - Find Patterns and Optimization Opportunities\n\n**What happens:**\n- `analyze-token-usage.py` reads the extracted requests\n- Calculates statistics across all requests\n- Identifies optimization opportunities\n\n**Outputs insights like:**\n- Token usage summary\n- Average tokens by message type\n- Context growth over conversation\n- Outlier messages (>2x average size)\n- Specific optimization suggestions (e.g., "large CLAUDE.md overhead", "big tool results")\n\n---\n\n### File Structure\n\n```\ncaptured-requests/                          # Stage 1: Raw captures\n├── claude-request-20251020_103738.json     # Complete API request\n├── claude-request-20251020_103743.json\n└── claude-request-20251020_103749.json\n\nextracted-requests/                         # Stage 2: Processed + decomposed\n├── request-001-20251020_103738/\n│   ├── 00-original-request.json           # Copy of raw JSON\n│   ├── 01-system-message.txt              # Extracted + readable\n│   ├── 02-message-user.txt                # User message 1\n│   ├── 03-message-assistant.txt           # Assistant response\n│   ├── 04-message-user.txt                # User message 2\n│   ├── 99-complete-model-input.txt        # The exact concatenation\n│   └── README.txt                         # Metadata + token counts\n├── request-002-20251020_103743/\n│   └── ...\n└── request-003-20251020_103749/\n    └── ...\n```\n\n**Key insight:** The `99-complete-model-input.txt` file is the golden source - it shows the exact string the model receives.\n\n---\n\n### Why Extraction is a Separate Step\n\n**Problem to solve:** Raw JSON is not actionable\n- 100KB+ nested structures\n- Tool schemas mixed with conversation\n- Token counts unknown\n- Can\'t easily compare requests\n- Hard to see context growth\n\n**Extraction solves this by:**\n\n1. **Separation of concerns** - Each file has ONE purpose (system, message 1, message 2, etc.)\n2. **Readability** - No JSON nesting, just plain text\n3. **Token visibility** - Every file has token counts\n4. **Comparison** - Can diff `request-001/99-complete-model-input.txt` vs `request-002/99-complete-model-input.txt` to see context growth\n5. **Analysis** - Enables `analyze-token-usage.py` to find patterns\n6. **Traceability** - Original JSON kept in `00-original-request.json` for verification\n\n---\n\n### Key Locations\n\n| File | Location | Purpose |\n|---|---|---|\n| Capture script | `/Users/psulin/organon/scripts/llm_capture/capture-claude-api-request.py` | Intercepts API requests |\n| Extract script | `/Users/psulin/organon/scripts/llm_capture/extract-all-requests.sh` | Transforms raw → readable |\n| Reconstruct | `/Users/psulin/organon/scripts/llm_capture/reconstruct-model-input.py` | Rebuilds complete model input |\n| Analyze script | `/Users/psulin/organon/scripts/llm_capture/analyze-token-usage.py` | Finds optimization opportunities |\n| Token counter | `/Users/psulin/organon/scripts/llm_capture/count_tokens.py` | Counts tokens in any text |\n| README | `/Users/psulin/organon/scripts/llm_capture/README.md` | Complete documentation |\n\n---\n\n### Why This Architecture Matters\n\n1. **Immutability** - Raw captures never change, extraction is repeatable\n2. **Transparency** - Can audit exactly what was sent to the model\n3. **Optimization** - Token analysis identifies where context is bloating\n4. **Reproducibility** - Same captures can be re-extracted with improved logic\n5. **Debugging** - Original JSON preserved for when something goes wrong\n\nThe extraction step is essential because it transforms opaque raw data (JSON) into actionable intelligence (decomposed files + token counts + analysis).'}]
