Profile and optimize Python code using cProfile, memory profilers, and performance best practices.
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-performance-optimizationCompatible with
Supported agents
Covers CPU profiling with cProfile, line-by-line profiling with line_profiler, memory tracking with memory_profiler, and production profiling with py-spy
Includes 20+ optimization patterns: list comprehensions, generators, string concatenation, dictionary lookups, NumPy vectorization, caching, multiprocessing, and async I/O
Provides database optimization techniques including batch opera
AI-first code editor with Composer
Before installing skills in Cursor, ensure your development environment meets these requirements:
node --versionpython-performance-optimizationExecute the skills CLI command in your project's root directory to begin installation:
epx add wshobson/agents --skill python-performance-optimizationFetches python-performance-optimization 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-performance-optimization
Restart Cursor to activate python-performance-optimization. Access via /python-performance-optimizationin 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.
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We added python-performance-optimization from the explainx registry; install was straightforward and the SKILL.md answered most questions upfront.
python-performance-optimization reduced setup friction for our internal harness; good balance of opinion and flexibility.
python-performance-optimization has been reliable in day-to-day use. Documentation quality is above average for community skills.
Solid pick for teams standardizing on skills: python-performance-optimization is focused, and the summary matches what you get after install.
python-performance-optimization reduced setup friction for our internal harness; good balance of opinion and flexibility.
We added python-performance-optimization from the explainx registry; install was straightforward and the SKILL.md answered most questions upfront.
I recommend python-performance-optimization for anyone iterating fast on agent tooling; clear intent and a small, reviewable surface area.
python-performance-optimization is among the better-maintained entries we tried; worth keeping pinned for repeat workflows.
Keeps context tight: python-performance-optimization is the kind of skill you can hand to a new teammate without a long onboarding doc.
python-performance-optimization reduced setup friction for our internal harness; good balance of opinion and flexibility.
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