Python Observability

What it does

This skill allows AI agents to generate Python code for observability, specifically focusing on integrating metrics, logs, and traces into applications. It leverages popular libraries like Prometheus client, logging frameworks, and OpenTelemetry to provide structured data for monitoring and debugging. The goal is to enable automated instrumentation of Python applications for improved operational visibility.

When to use it

• Automated Metric Generation: When you need to quickly generate code to expose key application metrics (e.g., request latency, error rates) in a Prometheus-compatible format.
• Structured Logging Implementation: To automatically add structured logging (JSON format) to existing Python applications for easier analysis and correlation.
• OpenTelemetry Integration: When you want to instrument your code with OpenTelemetry to collect distributed traces and metrics for performance monitoring.
• Debugging Complex Systems: To generate code snippets that can aid in debugging by providing detailed logs and tracing information within a Python application.

Key capabilities

• Prometheus client integration
• Structured logging (JSON format)
• OpenTelemetry instrumentation
• Code generation for metrics, logs, and traces

Example prompts

• "Generate code to expose the number of active users as a Prometheus metric."
• "Add structured logging to this Python function that records request latency and status code."
• "Instrument this FastAPI endpoint with OpenTelemetry for distributed tracing."

Tips & gotchas

This skill requires familiarity with basic Python concepts. The generated code might need adjustments based on the specific application context and existing infrastructure.