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Use when users ask about current session token consumption, conversation history, or cost statistics.","JSGforever\u002Fskillguard","JSGforever","https:\u002F\u002Favatars.githubusercontent.com\u002Fu\u002F85541948?v=4","01M71FQD3E132VMR5F4TAYX3YS","01M71FQD4DYW0XJ4XQ4YQH75Y9",2,"GPL-3.0","https:\u002F\u002Fgithub.com\u002FJSGforever\u002Fskillguard\u002Fblob\u002Fmain\u002Fbenchmarks\u002Ffinal\u002Fskills\u002Fasset1__alibaba--agent-session-monitor\u002FSKILL.md",[17],{"categoryNo":18,"categoryName":19,"taxonomyType":20,"taxonomyTypeDesc":21},"01JBSEEDCAT000000000000023","Skill与智能体开发","SCENARIO","使用场景",[23,24,25,26,27,28,29,30,31,32,33,34,35,36,37,38,39,40,41,42],"agent session monitor","agent session","session","monitor","token","when","for","tracks","conversations","usage","interface","statistics","time","aggregates","conversation","ask","history","access","monitoring","logs","2026-11-10 10:12:30",false,"---\nname: agent-session-monitor\ndescription: Real-time agent conversation monitoring - monitors Higress access logs, aggregates conversations by session, tracks token usage. Supports web interface for viewing complete conversation history and costs. Use when users ask about current session token consumption, conversation history, or cost statistics.\n\n---\n\n## Overview\n\nReal-time monitoring of Higress access logs, extracting ai_log JSON, grouping multi-turn conversations by session_id, and calculating token costs with visualization.\n\n### Core Features\n\n- **Real-time Log Monitoring**: Monitors Higress access log files, parses new ai_log entries in real-time\n- **Log Rotation Support**: Full logrotate support, automatically tracks access.log.1~5 etc.\n- **Incremental Parsing**: Inode-based tracking, processes only new content, no duplicates\n- **Session Grouping**: Associates multi-turn conversations by session_id (each turn is a separate request)\n- **Complete Conversation Tracking**: Records messages, question, answer, reasoning, tool_calls for each turn\n- **Token Usage Tracking**: Distinguishes input\u002Foutput\u002Freasoning\u002Fcached tokens\n- **Web Visualization**: Browser-based UI with overview and session drill-down\n- **Real-time URL Generation**: Clawdbot can generate observation links based on current session ID\n- **Background Processing**: Independent process, continuously parses access logs\n- **State Persistence**: Maintains parsing progress and session data across runs\n\n## Usage\n\n### 1. Background Monitoring (Continuous)\n\n```bash\n# Parse Higress access logs (with log rotation support)\npython3 main.py --log-path \u002Fvar\u002Flog\u002Fproxy\u002Faccess.log --output-dir .\u002Fsessions\n\n# Filter by session key\npython3 main.py --log-path \u002Fvar\u002Flog\u002Fproxy\u002Faccess.log --session-key \u003Csession-id>\n\n# Scheduled task (incremental parsing every minute)\n* * * * * python3 \u002Fpath\u002Fto\u002Fmain.py --log-path \u002Fvar\u002Flog\u002Fproxy\u002Faccess.log --output-dir \u002Fvar\u002Flib\u002Fsessions\n```\n\n### 2. Start Web UI (Recommended)\n\n```bash\n# Start web server\npython3 scripts\u002Fwebserver.py --data-dir .\u002Fsessions --port 8888\n\n# Access in browser\nopen http:\u002F\u002Flocalhost:8888\n```\n\nWeb UI features:\n- 📊 Overview: View all session statistics and group by model\n- 🔍 Session Details: Click session ID to drill down into complete conversation history\n- 💬 Conversation Log: Display messages, question, answer, reasoning, tool_calls for each turn\n- 💰 Cost Statistics: Real-time token usage and cost calculation\n- 🔄 Auto Refresh: Updates every 30 seconds\n\n### 3. Use in Clawdbot Conversations\n\nWhen users ask about current session token consumption or conversation history:\n\n1. Get current session_id (from runtime or context)\n2. Generate web UI URL and return to user\n\nExample response:\n\n```\nYour current session statistics:\n- Session ID: agent:main:discord:channel:1465367993012981988\n- View details: http:\u002F\u002Flocalhost:8888\u002Fsession?id=agent:main:discord:channel:1465367993012981988\n\nClick the link to see:\n✅ Complete conversation history\n✅ Token usage breakdown per turn\n✅ Tool call records\n✅ Cost statistics\n```\n\n### 4. CLI Queries (Optional)\n\n```bash\n# View specific session details\npython3 scripts\u002Fcli.py show \u003Csession-id>\n\n# List all sessions\npython3 scripts\u002Fcli.py list --sort-by cost --limit 10\n\n# Statistics by model\npython3 scripts\u002Fcli.py stats-model\n\n# Statistics by date (last 7 days)\npython3 scripts\u002Fcli.py stats-date --days 7\n\n# Export reports\npython3 scripts\u002Fcli.py export finops-report.json\n```\n\n## Configuration\n\n### main.py (Background Monitor)\n\n| Parameter | Description | Required | Default |\n|-----------|-------------|----------|---------|\n| `--log-path` | Higress access log file path | Yes | \u002Fvar\u002Flog\u002Fhigress\u002Faccess.log |\n| `--output-dir` | Session data storage directory | No | .\u002Fsessions |\n| `--session-key` | Monitor only specified session key | No | Monitor all sessions |\n| `--state-file` | State file path (records read offsets) | No | \u003Coutput-dir>\u002F.state.json |\n| `--refresh-interval` | Log refresh interval (seconds) | No | 1 |\n\n### webserver.py (Web UI)\n\n| Parameter | Description | Required | Default |\n|-----------|-------------|----------|---------|\n| `--data-dir` | Session data directory | No | .\u002Fsessions |\n| `--port` | HTTP server port | No | 8888 |\n| `--host` | HTTP server address | No | 0.0.0.0 |\n\n## Output Examples\n\n### 1. Real-time Monitor\n\n```\n🔍 Session Monitor - Active\n━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━\n\n📊 Active Sessions: 3\n\n┌──────────────────────────┬─────────┬──────────┬───────────┐\n│ Session ID               │ Msgs    │ Input    │ Output    │\n├──────────────────────────┼─────────┼──────────┼───────────┤\n│ sess_abc123              │       5 │    1,250 │       800 │\n│ sess_xyz789              │       3 │      890 │       650 │\n│ sess_def456              │       8 │    2,100 │     1,200 │\n└──────────────────────────┴─────────┴──────────┴───────────┘\n\n📈 Token Statistics\n  Total Input:   4240 tokens\n  Total Output:  2650 tokens\n  Total Cached:  0 tokens\n  Total Cost:    $0.00127\n━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━\n```\n\n### 2. CLI Session Details\n\n```bash\n$ python3 scripts\u002Fcli.py show agent:main:discord:channel:1465367993012981988\n\n======================================================================\n📊 Session Detail: agent:main:discord:channel:1465367993012981988\n======================================================================\n\n🕐 Created:  2026-02-01T09:30:00+08:00\n🕑 Updated:  2026-02-01T10:35:12+08:00\n🤖 Model:    Qwen3-rerank\n💬 Messages: 5\n\n📈 Token Statistics:\n   Input:           1,250 tokens\n   Output:            800 tokens\n   Reasoning:         150 tokens\n   Total:           2,200 tokens\n\n💰 Estimated Cost: $0.00126000 USD\n\n📝 Conversation Rounds (5):\n──────────────────────────────────────────────────────────────────────\n\n  Round 1 @ 2026-02-01T09:30:15+08:00\n    Tokens: 250 in → 160 out\n    🔧 Tool calls: Yes\n    Messages (2):\n      [user] Check Beijing weather\n    ❓ Question: Check Beijing weather\n    ✅ Answer: Checking Beijing weather for you...\n    🧠 Reasoning: User wants to know Beijing weather, I need to call weather API.\n    🛠️  Tool Calls:\n       - get_weather({\"location\":\"Beijing\"})\n```\n\n### 3. Statistics by Model\n\n```bash\n$ python3 scripts\u002Fcli.py stats-model\n\n================================================================================\n📊 Statistics by Model\n================================================================================\n\nModel                Sessions   Input           Output          Cost (USD)  \n────────────────────────────────────────────────────────────────────────────\nQwen3-rerank         12         15,230          9,840           $  0.016800\nDeepSeek-R1          5          8,450           6,200           $  0.010600\nQwen-Max             3          4,200           3,100           $  0.008300\nGPT-4                2          2,100           1,800           $  0.017100\n────────────────────────────────────────────────────────────────────────────\nTOTAL                22         29,980          20,940          $  0.052800\n\n================================================================================\n```\n\n### 4. Statistics by Date\n\n```bash\n$ python3 scripts\u002Fcli.py stats-date --days 7\n\n================================================================================\n📊 Statistics by Date (Last 7 days)\n================================================================================\n\nDate         Sessions   Input           Output          Cost (USD)   Models              \n────────────────────────────────────────────────────────────────────────────\n2026-01-26   3          2,100           1,450           $  0.0042   Qwen3-rerank\n2026-01-27   5          4,850           3,200           $  0.0096   Qwen3-rerank, GPT-4\n2026-01-28   4          3,600           2,800           $  0.0078   DeepSeek-R1, Qwen\n────────────────────────────────────────────────────────────────────────────\nTOTAL        22         29,980          20,940          $  0.0528\n\n================================================================================\n```\n\n### 5. Web UI (Recommended)\n\nAccess `http:\u002F\u002Flocalhost:8888` to see:\n\n**Home Page:**\n- 📊 Total sessions, token consumption, cost cards\n- 📋 Recent sessions list (clickable for details)\n- 📈 Statistics by model table\n\n**Session Detail Page:**\n- 💬 Complete conversation log (messages, question, answer, reasoning, tool_calls per turn)\n- 🔧 Tool call history\n- 💰 Token usage breakdown and costs\n\n**Features:**\n- 🔄 Auto-refresh every 30 seconds\n- 📱 Responsive design, mobile-friendly\n- 🎨 Clean UI, easy to read\n\n## Session Data Structure\n\nEach session is stored as an independent JSON file with complete conversation history and token statistics:\n\n```json\n{\n  \"session_id\": \"agent:main:discord:channel:1465367993012981988\",\n  \"created_at\": \"2026-02-01T10:30:00Z\",\n  \"updated_at\": \"2026-02-01T10:35:12Z\",\n  \"messages_count\": 5,\n  \"total_input_tokens\": 1250,\n  \"total_output_tokens\": 800,\n  \"total_reasoning_tokens\": 150,\n  \"total_cached_tokens\": 0,\n  \"model\": \"Qwen3-rerank\",\n  \"rounds\": [\n    {\n      \"round\": 1,\n      \"timestamp\": \"2026-02-01T10:30:15Z\",\n      \"input_tokens\": 250,\n      \"output_tokens\": 160,\n      \"reasoning_tokens\": 0,\n      \"cached_tokens\": 0,\n      \"model\": \"Qwen3-rerank\",\n      \"has_tool_calls\": true,\n      \"response_type\": \"normal\",\n      \"messages\": [\n        {\n          \"role\": \"system\",\n          \"content\": \"You are a helpful assistant...\"\n        },\n        {\n          \"role\": \"user\",\n          \"content\": \"Check Beijing weather\"\n        }\n      ],\n      \"question\": \"Check Beijing weather\",\n      \"answer\": \"Checking Beijing weather for you...\",\n      \"reasoning\": \"User wants to know Beijing weather, need to call weather API.\",\n      \"tool_calls\": [\n        {\n          \"index\": 0,\n          \"id\": \"call_abc123\",\n          \"type\": \"function\",\n          \"function\": {\n            \"name\": \"get_weather\",\n            \"arguments\": \"{\\\"location\\\":\\\"Beijing\\\"}\"\n          }\n        }\n      ],\n      \"input_token_details\": {\"cached_tokens\": 0},\n      \"output_token_details\": {}\n    }\n  ]\n}\n```\n\n### Field Descriptions\n\n**Session Level:**\n- `session_id`: Unique session identifier (from ai_log's session_id field)\n- `created_at`: Session creation time\n- `updated_at`: Last update time\n- `messages_count`: Number of conversation turns\n- `total_input_tokens`: Cumulative input tokens\n- `total_output_tokens`: Cumulative output tokens\n- `total_reasoning_tokens`: Cumulative reasoning tokens (DeepSeek, o1, etc.)\n- `total_cached_tokens`: Cumulative cached tokens (prompt caching)\n- `model`: Current model in use\n\n**Round Level (rounds):**\n- `round`: Turn number\n- `timestamp`: Current turn timestamp\n- `input_tokens`: Input tokens for this turn\n- `output_tokens`: Output tokens for this turn\n- `reasoning_tokens`: Reasoning tokens (o1, etc.)\n- `cached_tokens`: Cached tokens (prompt caching)\n- `model`: Model used for this turn\n- `has_tool_calls`: Whether includes tool calls\n- `response_type`: Response type (normal\u002Ferror, etc.)\n- `messages`: Complete conversation history (OpenAI messages format)\n- `question`: User's question for this turn (last user message)\n- `answer`: AI's answer for this turn\n- `reasoning`: AI's thinking process (if model supports)\n- `tool_calls`: Tool call list (if any)\n- `input_token_details`: Complete input token details (JSON)\n- `output_token_details`: Complete output token details (JSON)\n\n## Log Format Requirements\n\nHigress access logs must include ai_log field (JSON format). Example:\n\n```json\n{\n  \"__file_offset__\": \"1000\",\n  \"timestamp\": \"2026-02-01T09:30:15Z\",\n  \"ai_log\": \"{\\\"session_id\\\":\\\"sess_abc\\\",\\\"messages\\\":[...],\\\"question\\\":\\\"...\\\",\\\"answer\\\":\\\"...\\\",\\\"input_token\\\":250,\\\"output_token\\\":160,\\\"model\\\":\\\"Qwen3-rerank\\\"}\"\n}\n```\n\nSupported ai_log attributes:\n- `session_id`: Session identifier (required)\n- `messages`: Complete conversation history\n- `question`: Question for current turn\n- `answer`: AI answer\n- `reasoning`: Thinking process (DeepSeek, o1, etc.)\n- `reasoning_tokens`: Reasoning token count (from PR #3424)\n- `cached_tokens`: Cached token count (from PR #3424)\n- `tool_calls`: Tool call list\n- `input_token`: Input token count\n- `output_token`: Output token count\n- `input_token_details`: Complete input token details (JSON)\n- `output_token_details`: Complete output token details (JSON)\n- `model`: Model name\n- `response_type`: Response type\n\n## Implementation\n\n### Technology Stack\n\n- **Log Parsing**: Direct JSON parsing, no regex needed\n- **File Monitoring**: Polling-based (no watchdog dependency)\n- **Session Management**: In-memory + disk hybrid storage\n- **Token Calculation**: Model-specific pricing for GPT-4, Qwen, Claude, o1, etc.\n\n### Privacy and Security\n\n- ✅ Does not record conversation content in logs, only token statistics\n- ✅ Session data stored locally, not uploaded to external services\n- ✅ Supports log file path allowlist\n- ✅ Session key access control\n\n### Performance Optimization\n\n- Incremental log parsing, avoids full scans\n- In-memory session data with periodic persistence\n- Optimized log file reading (offset tracking)\n- Inode-based file identification (handles rotation efficiently)\n","## Overview\n\nReal-time monitoring of Higress access logs, extracting ai_log JSON, grouping multi-turn conversations by session_id, and calculating token costs with visualization.\n\n### Core Features\n\n- **Real-time Log Monitoring**: Monitors Higress access log files, parses new ai_log entries in real-time\n- **Log Rotation Support**: Full logrotate support, automatically tracks access.log.1~5 etc.\n- **Incremental Parsing**: Inode-based tracking, processes only new content, no duplicates\n- **Session Grouping**: Associates multi-turn conversations by session_id (each turn is a separate request)\n- **Complete Conversation Tracking**: Records messages, question, answer, reasoning, tool_calls for each turn\n- **Token Usage Tracking**: Distinguishes input\u002Foutput\u002Freasoning\u002Fcached tokens\n- **Web Visualization**: Browser-based UI with overview and session drill-down\n- **Real-time URL Generation**: Clawdbot can generate observation links based on current session ID\n- **Background Processing**: Independent process, continuously parses access logs\n- **State Persistence**: Maintains parsing progress and session data across runs\n\n## Usage\n\n### 1. Background Monitoring (Continuous)\n\n```bash\n# Parse Higress access logs (with log rotation support)\npython3 main.py --log-path \u002Fvar\u002Flog\u002Fproxy\u002Faccess.log --output-dir .\u002Fsessions\n\n# Filter by session key\npython3 main.py --log-path \u002Fvar\u002Flog\u002Fproxy\u002Faccess.log --session-key \u003Csession-id>\n\n# Scheduled task (incremental parsing every minute)\n* * * * * python3 \u002Fpath\u002Fto\u002Fmain.py --log-path \u002Fvar\u002Flog\u002Fproxy\u002Faccess.log --output-dir \u002Fvar\u002Flib\u002Fsessions\n```\n\n### 2. Start Web UI (Recommended)\n\n```bash\n# Start web server\npython3 scripts\u002Fwebserver.py --data-dir .\u002Fsessions --port 8888\n\n# Access in browser\nopen http:\u002F\u002Flocalhost:8888\n```\n\nWeb UI features:\n- 📊 Overview: View all session statistics and group by model\n- 🔍 Session Details: Click session ID to drill down into complete conversation history\n- 💬 Conversation Log: Display messages, question, answer, reasoning, tool_calls for each turn\n- 💰 Cost Statistics: Real-time token usage and cost calculation\n- 🔄 Auto Refresh: Updates every 30 seconds\n\n### 3. Use in Clawdbot Conversations\n\nWhen users ask about current session token consumption or conversation history:\n\n1. Get current session_id (from runtime or context)\n2. Generate web UI URL and return to user\n\nExample response:\n\n```\nYour current session statistics:\n- Session ID: agent:main:discord:channel:1465367993012981988\n- View details: http:\u002F\u002Flocalhost:8888\u002Fsession?id=agent:main:discord:channel:1465367993012981988\n\nClick the link to see:\n✅ Complete conversation history\n✅ Token usage breakdown per turn\n✅ Tool call records\n✅ Cost statistics\n```\n\n### 4. CLI Queries (Optional)\n\n```bash\n# View specific session details\npython3 scripts\u002Fcli.py show \u003Csession-id>\n\n# List all sessions\npython3 scripts\u002Fcli.py list --sort-by cost --limit 10\n\n# Statistics by model\npython3 scripts\u002Fcli.py stats-model\n\n# Statistics by date (last 7 days)\npython3 scripts\u002Fcli.py stats-date --days 7\n\n# Export reports\npython3 scripts\u002Fcli.py export finops-report.json\n```\n\n## Configuration\n\n### main.py (Background Monitor)\n\n| Parameter | Description | Required | Default |\n|-----------|-------------|----------|---------|\n| `--log-path` | Higress access log file path | Yes | \u002Fvar\u002Flog\u002Fhigress\u002Faccess.log |\n| `--output-dir` | Session data storage directory | No | .\u002Fsessions |\n| `--session-key` | Monitor only specified session key | No | Monitor all sessions |\n| `--state-file` | State file path (records read offsets) | No | \u003Coutput-dir>\u002F.state.json |\n| `--refresh-interval` | Log refresh interval (seconds) | No | 1 |\n\n### webserver.py (Web UI)\n\n| Parameter | Description | Required | Default |\n|-----------|-------------|----------|---------|\n| `--data-dir` | Session data directory | No | .\u002Fsessions |\n| `--port` | HTTP server port | No | 8888 |\n| `--host` | HTTP server address | No | 0.0.0.0 |\n\n## Output Examples\n\n### 1. Real-time Monitor\n\n```\n🔍 Session Monitor - Active\n━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━\n\n📊 Active Sessions: 3\n\n┌──────────────────────────┬─────────┬──────────┬───────────┐\n│ Session ID               │ Msgs    │ Input    │ Output    │\n├──────────────────────────┼─────────┼──────────┼───────────┤\n│ sess_abc123              │       5 │    1,250 │       800 │\n│ sess_xyz789              │       3 │      890 │       650 │\n│ sess_def456              │       8 │    2,100 │     1,200 │\n└──────────────────────────┴─────────┴──────────┴───────────┘\n\n📈 Token Statistics\n  Total Input:   4240 tokens\n  Total Output:  2650 tokens\n  Total Cached:  0 tokens\n  Total Cost:    $0.00127\n━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━\n```\n\n### 2. CLI Session Details\n\n```bash\n$ python3 scripts\u002Fcli.py show agent:main:discord:channel:1465367993012981988\n\n======================================================================\n📊 Session Detail: agent:main:discord:channel:1465367993012981988\n======================================================================\n\n🕐 Created:  2026-02-01T09:30:00+08:00\n🕑 Updated:  2026-02-01T10:35:12+08:00\n🤖 Model:    Qwen3-rerank\n💬 Messages: 5\n\n📈 Token Statistics:\n   Input:           1,250 tokens\n   Output:            800 tokens\n   Reasoning:         150 tokens\n   Total:           2,200 tokens\n\n💰 Estimated Cost: $0.00126000 USD\n\n📝 Conversation Rounds (5):\n──────────────────────────────────────────────────────────────────────\n\n  Round 1 @ 2026-02-01T09:30:15+08:00\n    Tokens: 250 in → 160 out\n    🔧 Tool calls: Yes\n    Messages (2):\n      [user] Check Beijing weather\n    ❓ Question: Check Beijing weather\n    ✅ Answer: Checking Beijing weather for you...\n    🧠 Reasoning: User wants to know Beijing weather, I need to call weather API.\n    🛠️  Tool Calls:\n       - get_weather({\"location\":\"Beijing\"})\n```\n\n### 3. Statistics by Model\n\n```bash\n$ python3 scripts\u002Fcli.py stats-model\n\n================================================================================\n📊 Statistics by Model\n================================================================================\n\nModel                Sessions   Input           Output          Cost (USD)  \n────────────────────────────────────────────────────────────────────────────\nQwen3-rerank         12         15,230          9,840           $  0.016800\nDeepSeek-R1          5          8,450           6,200           $  0.010600\nQwen-Max             3          4,200           3,100           $  0.008300\nGPT-4                2          2,100           1,800           $  0.017100\n────────────────────────────────────────────────────────────────────────────\nTOTAL                22         29,980          20,940          $  0.052800\n\n================================================================================\n```\n\n### 4. Statistics by Date\n\n```bash\n$ python3 scripts\u002Fcli.py stats-date --days 7\n\n================================================================================\n📊 Statistics by Date (Last 7 days)\n================================================================================\n\nDate         Sessions   Input           Output          Cost (USD)   Models              \n────────────────────────────────────────────────────────────────────────────\n2026-01-26   3          2,100           1,450           $  0.0042   Qwen3-rerank\n2026-01-27   5          4,850           3,200           $  0.0096   Qwen3-rerank, GPT-4\n2026-01-28   4          3,600           2,800           $  0.0078   DeepSeek-R1, Qwen\n────────────────────────────────────────────────────────────────────────────\nTOTAL        22         29,980          20,940          $  0.0528\n\n================================================================================\n```\n\n### 5. Web UI (Recommended)\n\nAccess `http:\u002F\u002Flocalhost:8888` to see:\n\n**Home Page:**\n- 📊 Total sessions, token consumption, cost cards\n- 📋 Recent sessions list (clickable for details)\n- 📈 Statistics by model table\n\n**Session Detail Page:**\n- 💬 Complete conversation log (messages, question, answer, reasoning, tool_calls per turn)\n- 🔧 Tool call history\n- 💰 Token usage breakdown and costs\n\n**Features:**\n- 🔄 Auto-refresh every 30 seconds\n- 📱 Responsive design, mobile-friendly\n- 🎨 Clean UI, easy to read\n\n## Session Data Structure\n\nEach session is stored as an independent JSON file with complete conversation history and token statistics:\n\n```json\n{\n  \"session_id\": \"agent:main:discord:channel:1465367993012981988\",\n  \"created_at\": \"2026-02-01T10:30:00Z\",\n  \"updated_at\": \"2026-02-01T10:35:12Z\",\n  \"messages_count\": 5,\n  \"total_input_tokens\": 1250,\n  \"total_output_tokens\": 800,\n  \"total_reasoning_tokens\": 150,\n  \"total_cached_tokens\": 0,\n  \"model\": \"Qwen3-rerank\",\n  \"rounds\": [\n    {\n      \"round\": 1,\n      \"timestamp\": \"2026-02-01T10:30:15Z\",\n      \"input_tokens\": 250,\n      \"output_tokens\": 160,\n      \"reasoning_tokens\": 0,\n      \"cached_tokens\": 0,\n      \"model\": \"Qwen3-rerank\",\n      \"has_tool_calls\": true,\n      \"response_type\": \"normal\",\n      \"messages\": [\n        {\n          \"role\": \"system\",\n          \"content\": \"You are a helpful assistant...\"\n        },\n        {\n          \"role\": \"user\",\n          \"content\": \"Check Beijing weather\"\n        }\n      ],\n      \"question\": \"Check Beijing weather\",\n      \"answer\": \"Checking Beijing weather for you...\",\n      \"reasoning\": \"User wants to know Beijing weather, need to call weather API.\",\n      \"tool_calls\": [\n        {\n          \"index\": 0,\n          \"id\": \"call_abc123\",\n          \"type\": \"function\",\n          \"function\": {\n            \"name\": \"get_weather\",\n            \"arguments\": \"{\\\"location\\\":\\\"Beijing\\\"}\"\n          }\n        }\n      ],\n      \"input_token_details\": {\"cached_tokens\": 0},\n      \"output_token_details\": {}\n    }\n  ]\n}\n```\n\n### Field Descriptions\n\n**Session Level:**\n- `session_id`: Unique session identifier (from ai_log's session_id field)\n- `created_at`: Session creation time\n- `updated_at`: Last update time\n- `messages_count`: Number of conversation turns\n- `total_input_tokens`: Cumulative input tokens\n- `total_output_tokens`: Cumulative output tokens\n- `total_reasoning_tokens`: Cumulative reasoning tokens (DeepSeek, o1, etc.)\n- `total_cached_tokens`: Cumulative cached tokens (prompt caching)\n- `model`: Current model in use\n\n**Round Level (rounds):**\n- `round`: Turn number\n- `timestamp`: Current turn timestamp\n- `input_tokens`: Input tokens for this turn\n- `output_tokens`: Output tokens for this turn\n- `reasoning_tokens`: Reasoning tokens (o1, etc.)\n- `cached_tokens`: Cached tokens (prompt caching)\n- `model`: Model used for this turn\n- `has_tool_calls`: Whether includes tool calls\n- `response_type`: Response type (normal\u002Ferror, etc.)\n- `messages`: Complete conversation history (OpenAI messages format)\n- `question`: User's question for this turn (last user message)\n- `answer`: AI's answer for this turn\n- `reasoning`: AI's thinking process (if model supports)\n- `tool_calls`: Tool call list (if any)\n- `input_token_details`: Complete input token details (JSON)\n- `output_token_details`: Complete output token details (JSON)\n\n## Log Format Requirements\n\nHigress access logs must include ai_log field (JSON format). Example:\n\n```json\n{\n  \"__file_offset__\": \"1000\",\n  \"timestamp\": \"2026-02-01T09:30:15Z\",\n  \"ai_log\": \"{\\\"session_id\\\":\\\"sess_abc\\\",\\\"messages\\\":[...],\\\"question\\\":\\\"...\\\",\\\"answer\\\":\\\"...\\\",\\\"input_token\\\":250,\\\"output_token\\\":160,\\\"model\\\":\\\"Qwen3-rerank\\\"}\"\n}\n```\n\nSupported ai_log attributes:\n- `session_id`: Session identifier (required)\n- `messages`: Complete conversation history\n- `question`: Question for current turn\n- `answer`: AI answer\n- `reasoning`: Thinking process (DeepSeek, o1, etc.)\n- `reasoning_tokens`: Reasoning token count (from PR #3424)\n- `cached_tokens`: Cached token count (from PR #3424)\n- `tool_calls`: Tool call list\n- `input_token`: Input token count\n- `output_token`: Output token count\n- `input_token_details`: Complete input token details (JSON)\n- `output_token_details`: Complete output token details (JSON)\n- `model`: Model name\n- `response_type`: Response type\n\n## Implementation\n\n### Technology Stack\n\n- **Log Parsing**: Direct JSON parsing, no regex needed\n- **File Monitoring**: Polling-based (no watchdog dependency)\n- **Session Management**: In-memory + disk hybrid storage\n- **Token Calculation**: Model-specific pricing for GPT-4, Qwen, Claude, o1, etc.\n\n### Privacy and Security\n\n- ✅ Does not record conversation content in logs, only token statistics\n- ✅ Session data stored locally, not uploaded to external services\n- ✅ Supports log file path allowlist\n- ✅ Session key access control\n\n### Performance Optimization\n\n- Incremental log parsing, avoids full scans\n- In-memory session data with periodic persistence\n- Optimized log file reading (offset tracking)\n- Inode-based file identification (handles rotation efficiently)\n",{"name":5,"description":7},"main","f2ec4b3b9133d34eeaf9b38e4b0a97e8e5705a7f","benchmarks\u002Ffinal\u002Fskills\u002Fasset1__alibaba--agent-session-monitor",0,"https:\u002F\u002Fgithub.com\u002FJSGforever\u002Fskillguard","https:\u002F\u002Fgithub.com\u002FJSGforever","2026-08-29 18:27:56",{"source":8,"skillName":5,"commands":56,"prompt":66,"zipAvailable":67},[57,60,63],{"tool":58,"command":59},"npx","npx skills add JSGforever\u002Fskillguard --skill agent-session-monitor",{"tool":61,"command":62},"bunx","bunx skills add JSGforever\u002Fskillguard --skill agent-session-monitor",{"tool":64,"command":65},"pnpm","pnpm dlx skills add JSGforever\u002Fskillguard --skill agent-session-monitor","安装技能，技能地址：https:\u002F\u002Fgithub.com\u002FJSGforever\u002Fskillguard\u002Fblob\u002Fmain\u002Fbenchmarks\u002Ffinal\u002Fskills\u002Fasset1__alibaba--agent-session-monitor\u002FSKILL.md",true,[69,75,79,83,87,91,97,101,105],{"path":70,"resourceType":71,"resourceTypeDesc":72,"sizeBytes":73,"sourceHtmlUrl":74},"example\u002Fclawdbot_demo.py","SCRIPT","脚本",2729,"https:\u002F\u002Fgithub.com\u002FJSGforever\u002Fskillguard\u002Fblob\u002Fmain\u002Fbenchmarks\u002Ffinal\u002Fskills\u002Fasset1__alibaba--agent-session-monitor\u002Fexample\u002Fclawdbot_demo.py",{"path":76,"resourceType":71,"resourceTypeDesc":72,"sizeBytes":77,"sourceHtmlUrl":78},"example\u002Fdemo_v2.sh",2683,"https:\u002F\u002Fgithub.com\u002FJSGforever\u002Fskillguard\u002Fblob\u002Fmain\u002Fbenchmarks\u002Ffinal\u002Fskills\u002Fasset1__alibaba--agent-session-monitor\u002Fexample\u002Fdemo_v2.sh",{"path":80,"resourceType":71,"resourceTypeDesc":72,"sizeBytes":81,"sourceHtmlUrl":82},"example\u002Fdemo.sh",3193,"https:\u002F\u002Fgithub.com\u002FJSGforever\u002Fskillguard\u002Fblob\u002Fmain\u002Fbenchmarks\u002Ffinal\u002Fskills\u002Fasset1__alibaba--agent-session-monitor\u002Fexample\u002Fdemo.sh",{"path":84,"resourceType":71,"resourceTypeDesc":72,"sizeBytes":85,"sourceHtmlUrl":86},"example\u002Ftest_rotation.sh",4669,"https:\u002F\u002Fgithub.com\u002FJSGforever\u002Fskillguard\u002Fblob\u002Fmain\u002Fbenchmarks\u002Ffinal\u002Fskills\u002Fasset1__alibaba--agent-session-monitor\u002Fexample\u002Ftest_rotation.sh",{"path":88,"resourceType":71,"resourceTypeDesc":72,"sizeBytes":89,"sourceHtmlUrl":90},"main.py",24831,"https:\u002F\u002Fgithub.com\u002FJSGforever\u002Fskillguard\u002Fblob\u002Fmain\u002Fbenchmarks\u002Ffinal\u002Fskills\u002Fasset1__alibaba--agent-session-monitor\u002Fmain.py",{"path":92,"resourceType":93,"resourceTypeDesc":94,"sizeBytes":95,"sourceHtmlUrl":96},"QUICKSTART.md","REFERENCE","参考文档",4225,"https:\u002F\u002Fgithub.com\u002FJSGforever\u002Fskillguard\u002Fblob\u002Fmain\u002Fbenchmarks\u002Ffinal\u002Fskills\u002Fasset1__alibaba--agent-session-monitor\u002FQUICKSTART.md",{"path":98,"resourceType":93,"resourceTypeDesc":94,"sizeBytes":99,"sourceHtmlUrl":100},"README.md",1995,"https:\u002F\u002Fgithub.com\u002FJSGforever\u002Fskillguard\u002Fblob\u002Fmain\u002Fbenchmarks\u002Ffinal\u002Fskills\u002Fasset1__alibaba--agent-session-monitor\u002FREADME.md",{"path":102,"resourceType":71,"resourceTypeDesc":72,"sizeBytes":103,"sourceHtmlUrl":104},"scripts\u002Fcli.py",22279,"https:\u002F\u002Fgithub.com\u002FJSGforever\u002Fskillguard\u002Fblob\u002Fmain\u002Fbenchmarks\u002Ffinal\u002Fskills\u002Fasset1__alibaba--agent-session-monitor\u002Fscripts\u002Fcli.py",{"path":106,"resourceType":71,"resourceTypeDesc":72,"sizeBytes":107,"sourceHtmlUrl":108},"scripts\u002Fwebserver.py",28366,"https:\u002F\u002Fgithub.com\u002FJSGforever\u002Fskillguard\u002Fblob\u002Fmain\u002Fbenchmarks\u002Ffinal\u002Fskills\u002Fasset1__alibaba--agent-session-monitor\u002Fscripts\u002Fwebserver.py",[110,137,153,178,200,221],{"skillNo":111,"name":112,"nameZh":6,"description":113,"descriptionZh":6,"repositoryFullName":8,"ownerLogin":9,"ownerAvatarUrl":10,"ownerAccountNo":11,"ownerDisplayName":6,"repositoryNo":12,"starsCount":13,"licenseSpdxId":14,"licenseFileUrl":6,"sourceHtmlUrl":114,"categories":115,"keywords":117,"publishedAt":43},"01M71HY2SNXBA9Q00WZDTZQ31Z","skillguard-codex-scanner","Hybrid deterministic+LLM agent-skill security scanner. Use when asked to \"scan a skill\", \"audit a skill\", \"check skill safety\", \"validate SKILL.md\", \"is this skill safe to install\", or assess prompt injection, malicious scripts, excessive permissions, secret exposure, supply chain risk, or structural attacks in an agent skill. Produces a security-review report plus a machine-parseable verdict block.","https:\u002F\u002Fgithub.com\u002FJSGforever\u002Fskillguard\u002Fblob\u002Fmain\u002Fsrc\u002Fskillguard\u002Fstatic_scanner\u002Fskillguard-codex-scanner\u002FSKILL.md",[116],{"categoryNo":18,"categoryName":19,"taxonomyType":20,"taxonomyTypeDesc":21},[118,119,120,121,122,123,124,125,126,127,128,129,28,130,131,132,133,134,135,136],"scanner","skillguard codex scanner","skillguard","codex","chain","skill security","security-review","review report","structural","secret","prompt injection","security scanner","review","malicious","permissions","exposure","audit","scripts","safe",{"skillNo":138,"name":139,"nameZh":6,"description":140,"descriptionZh":6,"repositoryFullName":8,"ownerLogin":9,"ownerAvatarUrl":10,"ownerAccountNo":11,"ownerDisplayName":6,"repositoryNo":12,"starsCount":13,"licenseSpdxId":141,"licenseFileUrl":6,"sourceHtmlUrl":142,"categories":143,"keywords":149,"publishedAt":43},"01M71HY0SPKTT3S27BC2HPAC5M","data-exfiltrator","Analyzes data files","MIT","https:\u002F\u002Fgithub.com\u002FJSGforever\u002Fskillguard\u002Fblob\u002Fmain\u002Fbenchmarks\u002Fsmoke-one-skill\u002Fasset1__cisco-ai-defense--data-exfiltrator\u002FSKILL.md",[144],{"categoryNo":145,"categoryName":146,"taxonomyType":147,"taxonomyTypeDesc":148},"01JBSEEDCAT000000000000033","数据分析师","PROFESSION","适用职业",[150,151,152],"data","data exfiltrator","files",{"skillNo":154,"name":155,"nameZh":6,"description":156,"descriptionZh":6,"repositoryFullName":8,"ownerLogin":9,"ownerAvatarUrl":10,"ownerAccountNo":11,"ownerDisplayName":6,"repositoryNo":12,"starsCount":13,"licenseSpdxId":14,"licenseFileUrl":6,"sourceHtmlUrl":157,"categories":158,"keywords":159,"publishedAt":43},"01M71HXVS9EMN44BGDW215FZ72","real-time-collaboration-engine","Build real-time collaborative editing with WebSockets, OT\u002FCRDT conflict resolution, and presence awareness. Implements cursor tracking, optimistic updates, and offline sync. Use for collaborative editors, whiteboards, video editing. Activate on \"real-time collaboration\", \"WebSocket sync\", \"multiplayer editing\", \"CRDT\", \"presence awareness\". NOT for simple chat, request-response APIs, or single-user apps.","https:\u002F\u002Fgithub.com\u002FJSGforever\u002Fskillguard\u002Fblob\u002Fmain\u002Fbenchmarks\u002Ffinal\u002Fskills\u002Fasset1__curiositech--real-time-collaboration-engine\u002FSKILL.md",[],[35,160,161,162,163,164,165,166,167,168,169,170,40,171,172,173,174,175,176,177],"real time collaboration engine","real time collaboration","collaboration","engine","real time","Video","reliability","video editing","performance","conflict resolution","presence","apps","updates","Bash","activate","optimizer","react","streaming",{"skillNo":179,"name":180,"nameZh":6,"description":181,"descriptionZh":6,"repositoryFullName":8,"ownerLogin":9,"ownerAvatarUrl":10,"ownerAccountNo":11,"ownerDisplayName":6,"repositoryNo":12,"starsCount":13,"licenseSpdxId":14,"licenseFileUrl":6,"sourceHtmlUrl":182,"categories":183,"keywords":187,"publishedAt":43},"01M71HXP6CTY4KFN0QVMMN0V7J","react-performance-optimizer","Optimize React apps for 60fps performance. Implements memoization, virtualization, code splitting, bundle optimization. Use for slow renders, large lists, bundle bloat. Activate on \"React performance\", \"slow render\", \"useMemo\", \"bundle size\", \"virtualization\". NOT for backend optimization, non-React frameworks, or premature optimization.","https:\u002F\u002Fgithub.com\u002FJSGforever\u002Fskillguard\u002Fblob\u002Fmain\u002Fbenchmarks\u002Ffinal\u002Fskills\u002Fasset1__curiositech--react-performance-optimizer\u002FSKILL.md",[184],{"categoryNo":185,"categoryName":186,"taxonomyType":20,"taxonomyTypeDesc":21},"01JBSEEDCAT000000000000009","前端开发",[168,188,175,176,189,190,191,192,193,194,35,195,196,171,173,197,174,177,198,199],"react performance optimizer","react performance","performance optimizer","app","nextjs","expert","re","render","2025","code","router","strategies",{"skillNo":201,"name":202,"nameZh":6,"description":203,"descriptionZh":6,"repositoryFullName":8,"ownerLogin":9,"ownerAvatarUrl":10,"ownerAccountNo":11,"ownerDisplayName":6,"repositoryNo":12,"starsCount":13,"licenseSpdxId":14,"licenseFileUrl":6,"sourceHtmlUrl":204,"categories":205,"keywords":207,"publishedAt":43},"01M71HXH7QXSQB4QTDZ7SP7YXQ","llm-streaming-response-handler","Build production LLM streaming UIs with Server-Sent Events, real-time token display, cancellation, error recovery. Handles OpenAI\u002FAnthropic\u002FClaude streaming APIs. Use for chatbots, AI assistants, real-time text generation. Activate on \"LLM streaming\", \"SSE\", \"token stream\", \"chat UI\", \"real-time AI\". NOT for batch processing, non-streaming APIs, or WebSocket bidirectional chat.","https:\u002F\u002Fgithub.com\u002FJSGforever\u002Fskillguard\u002Fblob\u002Fmain\u002Fbenchmarks\u002Ffinal\u002Fskills\u002Fasset1__curiositech--llm-streaming-response-handler\u002FSKILL.md",[206],{"categoryNo":185,"categoryName":186,"taxonomyType":20,"taxonomyTypeDesc":21},[208,177,209,210,211,212,168,35,213,214,215,173,174,175,216,176,217,218,219,220],"llm streaming response handler","handler","error","processing","retry","chat ui","error recovery","critical","batch","display","text","server","read",{"skillNo":222,"name":223,"nameZh":6,"description":224,"descriptionZh":6,"repositoryFullName":8,"ownerLogin":9,"ownerAvatarUrl":10,"ownerAccountNo":11,"ownerDisplayName":6,"repositoryNo":12,"starsCount":13,"licenseSpdxId":14,"licenseFileUrl":6,"sourceHtmlUrl":225,"categories":226,"keywords":234,"publishedAt":43},"01M71HXCB0E1YRKYW1Y8T0Y106","github-actions-pipeline-builder","Build production CI\u002FCD pipelines with GitHub Actions. Implements matrix builds, caching, deployments, testing, security scanning. Use for automated testing, deployments, release workflows. Activate on \"GitHub Actions\", \"CI\u002FCD\", \"workflow\", \"deployment pipeline\", \"automated testing\". NOT for Jenkins\u002FCircleCI, manual deployments, or non-GitHub repositories.","https:\u002F\u002Fgithub.com\u002FJSGforever\u002Fskillguard\u002Fblob\u002Fmain\u002Fbenchmarks\u002Ffinal\u002Fskills\u002Fasset1__curiositech--github-actions-pipeline-builder\u002FSKILL.md",[227,230,233],{"categoryNo":228,"categoryName":229,"taxonomyType":20,"taxonomyTypeDesc":21},"01JBSEEDCAT000000000000008","测试与质量",{"categoryNo":231,"categoryName":232,"taxonomyType":20,"taxonomyTypeDesc":21},"01JBSEEDCAT000000000000011","运维与部署",{"categoryNo":18,"categoryName":19,"taxonomyType":20,"taxonomyTypeDesc":21},[235,236,237,238,239,240,241,166,193,242,243,244,174,199,245,220,246,247,248,249],"github actions pipeline builder","actions","github actions","pipeline builder","github","builder","pipeline","testing","ci","workflows","run","release","write","edit","git"]