Structured logging, metrics, and distributed tracing patterns for Python production systems.
Automate repetitive workflows and reduce manual effort
Example: Generate reports, summarize documents, draft communications
Save 3-5 hours per week on routine tasks
Learn new skills, understand complex topics, get expert guidance
Example: Explain concepts, provide examples, suggest learning resources
Accelerate learning and skill development by 2x
Enhance output quality through reviews, suggestions, and refinements
Example: Review drafts, suggest improvements, catch errors
Improve work quality by 30-40% with less effort
Time estimate: 15-45 minutes depending on use case complexity
Prerequisites
Submit your Claude Code skill and start earning
epx add wshobson/agents --skill python-observabilityCompatible with
Supported agents
Covers four core observability areas: structured JSON logging with structlog, Prometheus metrics for the four golden signals (latency, traffic, errors, saturation), correlation ID propagation across service boundaries, and OpenTelemetry distributed tracing
Includes semantic log level guidance, bounded cardinality rules for metrics to prevent storage explosion, and context manager patterns for co
AI-first code editor with Composer
Before installing skills in Cursor, ensure your development environment meets these requirements:
node --versionpython-observabilityExecute the skills CLI command in your project's root directory to begin installation:
epx add wshobson/agents --skill python-observabilityFetches python-observability from wshobson/agents and configures it for Cursor.
The CLI shows a list of agents. Use arrow keys and space to select Cursor:
Confirm successful installation by checking the skill directory location:
.cursor/skills/python-observability
Restart Cursor to activate python-observability. Access via /python-observabilityin your agent's command palette.
Learn more:View source on GitHubSkills CLI docsAbout CursorWhat are agent skills?
Steps
Common Pitfalls
✓ Do
✗ Don't
💡 Pro Tips
✓ Use when
Use when skill capabilities match your task, clear ROI on time saved, and you can validate outputs. Best for repetitive tasks, learning, and quality improvement.
✗ Avoid when
Avoid when task requires deep expertise you can't validate, involves sensitive decisions, or when learning process is more valuable than speed of completion.
python-code-style
wshobson/agents · Backend · same repo
microservices-patterns
wshobson/agents · Productivity · same repo
grafana-dashboards
wshobson/agents · Productivity · same repo
fastapi-python
mindrally/skills · Backend · tag: python
python-expert-best-practices-code-review
wispbit-ai/skills · Backend · tag: python
python-backend-architecture-review
rknall/claude-skills · Backend · tag: python
python-observability has been reliable in day-to-day use. Documentation quality is above average for community skills.
Useful defaults in python-observability — fewer surprises than typical one-off scripts, and it plays nicely with `npx skills` flows.
I recommend python-observability for anyone iterating fast on agent tooling; clear intent and a small, reviewable surface area.
python-observability fits our agent workflows well — practical, well scoped, and easy to wire into existing repos.
I recommend python-observability for anyone iterating fast on agent tooling; clear intent and a small, reviewable surface area.
Useful defaults in python-observability — fewer surprises than typical one-off scripts, and it plays nicely with `npx skills` flows.
Registry listing for python-observability matched our evaluation — installs cleanly and behaves as described in the markdown.
python-observability reduced setup friction for our internal harness; good balance of opinion and flexibility.
Solid pick for teams standardizing on skills: python-observability is focused, and the summary matches what you get after install.
python-observability reduced setup friction for our internal harness; good balance of opinion and flexibility.
showing 1-10 of 42