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optimize-performance

Skill0 starsChanged 9 months agoMITView on GitHub

Analyze the current code for performance bottlenecks and provide optimization recommendation...; keywords: api, performance. Use only on explicit request; skip in routine implementation discussions.

We split this skill into the rules it gives an agent

1 rule. 0 are common, 1 is distinctive, and 0 are very specific to this project.

  • 0

    Common

    Most of the 7 edited versions we read keep these.

  • 1

    Distinctive

    This author's own choices. Worth a look.

  • 0

    Very specific

    Tied to their project's names, files or tools. Probably skip.

  • Common
  • Distinctive
  • Very specific
  1. Distinctive:

    Consider trade-offs between performance and maintainability

    line 28

“Common” is measured: kept by at least 6 in 10 of the 7 edited versions of this file we could read. “Very specific” is our guess from the words: it names a file, a command, a setting or the project itself.

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Files
SKILL.md

Optimize Performance

Overview

Analyze the current code for performance bottlenecks and provide optimization recommendations, focusing on measurable improvements while maintaining code quality and readability.

Steps

  1. Performance Analysis
    • Identify slow algorithms and inefficient data structures
    • Find memory leaks and excessive allocations
    • Detect unnecessary computations and redundant operations
    • Analyze database queries and API calls
  2. Optimization Strategies
    • Suggest algorithm improvements and better data structures
    • Recommend caching strategies where appropriate
    • Propose lazy loading and pagination solutions
    • Identify opportunities for parallel processing
  3. Implementation
    • Provide optimized code with explanations
    • Include performance impact estimates
    • Suggest profiling and monitoring approaches
    • Consider trade-offs between performance and maintainability

Optimize Performance Checklist

  • Identified slow algorithms and inefficient data structures
  • Found memory leaks and excessive allocations
  • Detected unnecessary computations and redundant operations
  • Analyzed database queries and API calls
  • Suggested algorithm improvements and better data structures
  • Recommended caching strategies where appropriate
  • Provided optimized code with explanations
  • Included performance impact estimates
  • Considered trade-offs between performance and maintainability

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