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Python performance optimization

Skill newmindsgroup/ai-agent-skills-library/dist/skills/python-performance-optimization

Profile and optimize Python code using cProfile, memory profilers, and performance best practices. Use when debugging slow Python code, optimizing bottlenecks, or improving application performance.From its SKILL.md

Install
npx -y skills add newmindsgroup/ai-agent-skills-library --skill python-performance-optimization

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  • 1 stars1 stars. Stars are a popularity signal and not a quality one, but at this level it is likely that nobody has read this closely except its author, and you would be relying on your own review.

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SKILL.md

1.9 KB, 265 tokens by cl100k_base, as published. Nobody here has run it

Python Performance Optimization

Comprehensive guide to profiling, analyzing, and optimizing Python code for better performance, including CPU profiling, memory optimization, and implementation best practices.

Use this skill when

  • Identifying performance bottlenecks in Python applications
  • Reducing application latency and response times
  • Optimizing CPU-intensive operations
  • Reducing memory consumption and memory leaks
  • Improving database query performance
  • Optimizing I/O operations
  • Speeding up data processing pipelines
  • Implementing high-performance algorithms
  • Profiling production applications

Do not use this skill when

  • The task is unrelated to python performance optimization
  • You need a different domain or tool outside this scope

Instructions

  • Clarify goals, constraints, and required inputs.
  • Apply relevant best practices and validate outcomes.
  • Provide actionable steps and verification.
  • If detailed examples are required, open resources/implementation-playbook.md.

Resources

  • resources/implementation-playbook.md for detailed patterns and examples.

Limitations

  • Use this skill only when the task clearly matches the scope described above.
  • Do not treat the output as a substitute for environment-specific validation, testing, or expert review.
  • Stop and ask for clarification if required inputs, permissions, safety boundaries, or success criteria are missing.

What ships with it: 1 file

20.7 KB alongside SKILL.md

resources/

Gives 0 of the 12 instructions most performance cost skills give in 265 tokens

Counted across 803 of the 1,058 authors here whose files we hold, read 2026-08-07

  • Keep skill files under 500 lines or tokensin 82 of 803, across 16 files
  • Use imperative form in instructionsin 80 of 803, across 9 files
  • Draft assertions while test runs are in progressin 75 of 803, across 9 files
  • Create two to three realistic test promptsin 74 of 803, across 9 files
  • Write skill descriptions to be pushyin 72 of 803, across 7 files
  • Save test cases to evals JSONin 72 of 803, across 6 files
  • Ask questions about edge cases and input formatsin 72 of 803, across 7 files
  • Save timing data immediately when runs completein 70 of 803, across 5 files
  • Include all trigger conditions in the skill descriptionin 69 of 803, across 3 files
  • Launch all test runs in a single turn or simultaneouslyin 69 of 803, across 3 files
  • Capture intent before writing a skillin 67 of 803, across 1 file
  • Import directly instead of barrel filesin 52 of 803, across 15 files

Said here and by no other author read

  • validate outcomes
  • open the implementation playbook for detailed examples

Grouped from the skills themselves: near-identical wordings counted once, and counted by distinct author, so one author publishing three of these counts once. Length counted with cl100k_base; the agent that loads this file may tokenize it differently.

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