What problem does it solve? Adding consistent, production-appropriate observability to a service is error-prone: developers often emit unstructured logs, leak secrets into log output, or forget to propagate trace context across service boundaries. This Skill provides concrete rules and code patterns for logging, metrics, and tracing so generated code follows a consistent observability standard. ## Core Features & Use Cases - Structured Logging: Enforces JSON or key-value log formats with contextual fields like request_id, task_id, and service, using the standard logging library or structlog. - Metrics Guidance: Defines counters for task status, queue depth, and processing latency using OpenTelemetry or Prometheus with consistent namespacing. - Distributed Tracing: Propagates trace IDs across gateway, queue, and worker components and correlates logs with trace IDs for debugging. - Use Case: When debugging a production issue in an AI agent platform, use this Skill to add structured logs with task_id context and trace propagation so you can follow a single request across the gateway, queue, and worker. ## Quick Start Add structured logging with request_id and task_id context plus OpenTelemetry tracing to my Python worker service.
