log_activity
Track AI tool activity by logging detailed metrics like token usage, costs, and operation duration into daily markdown files for structured work history analysis.
Instructions
Log AI tool activity to a daily worklog file with comprehensive metrics
Input Schema
| Name | Required | Description | Default |
|---|---|---|---|
| ai_model | No | AI model used (e.g., 'gemini-2.5-pro', 'claude-3-sonnet', 'gpt-4') | |
| context_length | No | Context window length used (optional) | |
| cost_usd | No | Estimated cost in USD (optional) | |
| duration_ms | No | Duration of the operation in milliseconds (optional) | |
| error_message | No | Error message if operation failed (optional) | |
| input_tokens | No | Input tokens used (optional) | |
| log_message | Yes | Detailed log message describing what was accomplished | |
| output_tokens | No | Output tokens generated (optional) | |
| success | No | Whether the operation was successful (optional, defaults to true) | |
| tags | No | Tags to categorize the activity (e.g., ['coding', 'debugging', 'refactoring']) (optional) | |
| tokens_used | No | Total tokens consumed in the request (optional) | |
| tool_name | Yes | Name of the AI tool that performed the activity (e.g., 'Warp', 'Claude Code', 'GitHub Copilot') |