Agent skill / wshobson
Structured logging, metrics, and distributed tracing patterns for Python production systems.
Core file
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:
Package manager
npx skills add https://github.com/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:
Restart Cursor to activate python-observability. Access via /python-observabilityin your agent's command palette.
We perform automated surface-level scans (Gen AI Scanner, Socket, Snyk) during installation. These checks detect common vulnerabilities but do not guarantee complete security. Always review skill source code and verify the publisher's reputation before production use.
Skills execute code in your environment. Always review source, verify the publisher, and test in isolation before production.
Submit your Claude Code skill and start earning
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
Copy the command for your terminal
Package manager
npx skills add https://github.com/wshobson/agents --skill python-observabilityWorks with
Prerequisites
Time Estimate
15-45 minutes depending on use case complexity
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.
wshobson/agents
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shubhamsaboo/awesome-llm-apps
mindrally/skills
jwynia/agent-skills
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.
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