python-logging-best-practices

What it does

This skill enables AI agents to implement robust logging patterns in Python applications, ensuring logs are structured, filtered by severity, and formatted for easy consumption. It covers configuring handlers, setting log levels, and integrating with external services like the Serilog sink for centralized monitoring.

When to use it

• Debugging complex backend issues where traceability is critical.
• Setting up production-ready applications that require audit trails.
• Integrating Python services with observability platforms like ELK or Splunk.
• Creating custom log formats for specific compliance or security requirements.

Key capabilities

• Configuring multiple logging handlers (console, file, network).
• Defining and managing log levels (DEBUG, INFO, WARNING, ERROR, CRITICAL).
• Implementing structured logging with JSON formatting.
• Filtering logs based on context and severity thresholds.
• Integrating with third-party sinks like Serilog for advanced routing.

Example prompts

• "Set up a Python logger that outputs structured JSON to a file and sends errors to Slack."
• "Create a logging configuration that filters out DEBUG messages in production but keeps them in development."
• "Implement a custom log handler that rotates files every 10MB and archives old logs."

Tips & gotchas

Ensure your application runs with appropriate permissions to write to log files or connect to external services. Avoid logging sensitive data like passwords or tokens, even when using structured formats.