python-observability
Python Observability
Instrument Python applications with structured logs, metrics, and traces. When something breaks in production, you need to answer "what, where, and why" without deploying new code.
When to Use This Skill
- Adding structured logging to applications
- Implementing metrics collection with Prometheus
- Setting up distributed tracing across services
- Propagating correlation IDs through request chains
- Debugging production issues
- Building observability dashboards
Core Concepts
1. Structured Logging
Emit logs as JSON with consistent fields for production environments. Machine-readable logs enable powerful queries and alerts. For local development, consider human-readable formats.
More from ericgrill/agents-skills-plugins
debugging-strategies
Master systematic debugging techniques, profiling tools, and root cause analysis to efficiently track down bugs across any codebase or technology stack. Use when investigating bugs, performance issues, or unexpected behavior.
10test-driven-development
Use when implementing any feature or bugfix, before writing implementation code
9subagent-driven-development
Use when executing implementation plans with independent tasks in the current session
9systematic-debugging
Use when encountering any bug, test failure, or unexpected behavior, before proposing fixes
9nodejs-backend-patterns
Build production-ready Node.js backend services with Express/Fastify, implementing middleware patterns, error handling, authentication, database integration, and API design best practices. Use when creating Node.js servers, REST APIs, GraphQL backends, or microservices architectures.
9openapi-spec-generation
Generate and maintain OpenAPI 3.1 specifications from code, design-first specs, and validation patterns. Use when creating API documentation, generating SDKs, or ensuring API contract compliance.
8