python-observability-patterns
Observability patterns for Python applications. Triggers on: logging, metrics, tracing, opentelemetry, prometheus, observability, monitoring, structlog, correlation id.
Install with Tessl CLI
npx tessl i github:NeverSight/skills_feed --skill python-observability-patterns83
Does it follow best practices?
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npx tessl skill review --optimize ./path/to/skillValidation for skill structure
Discovery
72%Based on the skill's description, can an agent find and select it at the right time? Clear, specific descriptions lead to better discovery.
The description has strong trigger term coverage and clear distinctiveness for Python observability, but lacks specificity about what concrete actions the skill enables. The 'Triggers on:' format effectively communicates when to use the skill, but the capability description is too abstract to help Claude understand what the skill actually does.
Suggestions
Replace 'Observability patterns' with specific actions like 'Configure structured logging with structlog, implement distributed tracing with OpenTelemetry, set up Prometheus metrics endpoints'
Add concrete use cases such as 'adding correlation IDs to requests', 'instrumenting FastAPI/Flask applications', or 'creating custom metrics'
| Dimension | Reasoning | Score |
|---|---|---|
Specificity | Names the domain (observability, Python applications) and mentions general categories (logging, metrics, tracing) but doesn't list concrete actions like 'configure structured logging', 'set up distributed tracing', or 'create Prometheus metrics endpoints'. | 2 / 3 |
Completeness | Has a 'Triggers on:' clause which serves as explicit trigger guidance, but the 'what' portion is weak - 'Observability patterns' doesn't clearly explain what actions the skill performs. Missing explicit 'Use when...' format but has equivalent trigger list. | 2 / 3 |
Trigger Term Quality | Excellent coverage of natural terms users would say: 'logging', 'metrics', 'tracing', 'opentelemetry', 'prometheus', 'observability', 'monitoring', 'structlog', 'correlation id' - these are all terms developers naturally use when seeking help with observability. | 3 / 3 |
Distinctiveness Conflict Risk | Clear niche with Python-specific observability focus and distinct triggers like 'opentelemetry', 'prometheus', 'structlog', 'correlation id' that are unlikely to conflict with general logging or monitoring skills. | 3 / 3 |
Total | 10 / 12 Passed |
Implementation
87%Reviews the quality of instructions and guidance provided to agents. Good implementation is clear, handles edge cases, and produces reliable results.
This is a well-crafted skill that provides comprehensive, executable observability patterns for Python applications. The code examples are production-ready and the progressive disclosure is excellent with clear references to detailed documentation. The main weakness is the lack of workflow guidance on how to verify the observability stack is working correctly and how the components integrate together.
Suggestions
Add a brief 'Verification' section showing how to confirm each component is working (e.g., curl /metrics endpoint, check trace export logs)
Consider adding a short integration note explaining how request_id flows from logging through to tracing for correlation
| Dimension | Reasoning | Score |
|---|---|---|
Conciseness | The content is lean and efficient, jumping directly into executable code examples without explaining what logging, metrics, or tracing are. Every section provides actionable code without unnecessary preamble. | 3 / 3 |
Actionability | All code examples are fully executable and copy-paste ready with proper imports, configuration, and usage patterns. The middleware examples, metric definitions, and tracing setup are complete and production-ready. | 3 / 3 |
Workflow Clarity | While individual patterns are clear, there's no guidance on the order of implementation, how these components work together, or validation steps to verify observability is working correctly. Missing verification checkpoints for confirming metrics are being collected or traces are being exported. | 2 / 3 |
Progressive Disclosure | Excellent structure with a concise overview, quick reference tables, and clear one-level-deep references to detailed documentation in ./references/ directory. Content is appropriately split between the main skill and supporting files. | 3 / 3 |
Total | 11 / 12 Passed |
Validation
90%Checks the skill against the spec for correct structure and formatting. All validation checks must pass before discovery and implementation can be scored.
Validation — 10 / 11 Passed
Validation for skill structure
| Criteria | Description | Result |
|---|---|---|
frontmatter_unknown_keys | Unknown frontmatter key(s) found; consider removing or moving to metadata | Warning |
Total | 10 / 11 Passed | |
- Reviewed
Table of Contents
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