python-observability
Python observability patterns including structured logging, metrics, and distributed tracing. Use when adding logging, implementing metrics collection, setting up tracing, or debugging production systems.
Profile →npx skills add wshobson/agents --skill python-observability --agent claude-code
Same command for any agent — swap --agent for codex, cursor, copilot.
Weekly change comes from our own snapshots, not the repository page — it measures attention, not adoption.
# 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. ### 2. The Four Golden Signals Track latency, traffic, errors, and saturation for every service boundary. ### 3. Correlation IDs Thread a unique ID through all logs and spans for a single request, enabling end-to-end tracing. ### 4. Bounded Cardinality Keep metric label values bounded. Unbounded labels (like user IDs) explode storage costs. ## Quick Start ```python import structlog structlog.configure( processors=[ structlog.processors.TimeStamper(fmt="iso"), structlog.process
- When to Use This Skill
- Core Concepts
- 1. Structured Logging
- 2. The Four Golden Signals
- 3. Correlation IDs
- 4. Bounded Cardinality
- Quick Start
- Fundamental Patterns
- Pattern 1: Structured Logging with Structlog
- Pattern 2: Consistent Log Fields
- Pattern 3: Semantic Log Levels
- Pattern 4: Correlation ID Propagation
- Detailed worked examples and patterns
- Best Practices Summary
What does the python-observability skill do?
Python observability patterns including structured logging, metrics, and distributed tracing. Use when adding logging, implementing metrics collection, setting up tracing, or debugging production systems.
How do I install it?
Run `npx skills add wshobson/agents --skill python-observability --agent claude-code` — it drops the skill into your project so the agent can pick it up. Swap the --agent value for codex, cursor or copilot if you use one of those.
Where does this skill come from?
From wshobson/agents, a repository with 38,479 stars. We read it straight from the repository tree rather than a submitted listing, so what you see here is what is actually published.
Is a popular skill a good skill?
Not necessarily. Stars measure attention, not adoption — a repository can trend for a week and be abandoned. That is why we show the weekly change from our own snapshots next to the total, instead of a single flattering number.