Profile and optimize Python code using cProfile, memory profilers, and performance best practices.
Run in your terminal
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:
Package manager
npx skills add https://github.com/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:
Restart Cursor to activate python-performance-optimization. Access via /python-performance-optimizationin 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.
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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
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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
Package manager
npx skills add https://github.com/wshobson/agents --skill python-performance-optimizationWorks with
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Prerequisites
Time Estimate
15-45 minutes depending on use case complexity
Steps
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💡 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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