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performance-analyzer

Skill0 starsChanged 7 months agoMITView on GitHub

Detect performance anti-patterns and estimate Big-O complexity in Python code

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    1. Synter-Media-AI/mcp-server★ 21same name

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  • In this repository7 other files it gives its agents.

The file

We split this skill into the rules it gives an agent

2 rules. About 0 are common, 2 are distinctive, and 0 are very specific to this project.

  • ≈0

    Common · estimate

    Many files like this have a rule of this kind.

  • ≈2

    Distinctive · estimate

    This author's own choices. Worth a look.

  • 0

    Very specific

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  • Common
  • Distinctive
  • Very specific
  1. Distinctive:

    Identify target code: Use Glob and Grep to find the files to analyze.

    line 18
  2. Distinctive:

    Offer to optimize: Ask if the user wants you to apply the suggested optimizations.

    line 40

≈ “Common” is an estimate: at least 1 in 5 of the 350,495 skill files we read have a rule of the same kind, not the same words. “Very specific” is our guess from the words: it names a file, a command, a setting or the project itself.

Keep what fits and drop the rest. Your version, ready for your agents.
Files
SKILL.md

Performance Analyzer Skill

You are a performance engineering specialist. When the user asks about performance, follow this workflow:

Workflow

  1. Identify target code: Use Glob and Grep to find the files to analyze.
  2. Run anti-pattern detection:
    python3 {skill_path}/scripts/analyzer.py <file_path>
    Detects: nested loops, string concat in loops, repeated computation, N+1 patterns, unnecessary copies, inefficient data structures.
  3. Estimate complexity:
    python3 {skill_path}/scripts/complexity_calc.py <file_path>
    Estimates Big-O time and space complexity per function.
  4. Review optimization tips: Reference {skill_path}/references/optimization_tips.md for specific advice.
  5. Present findings:
    • List anti-patterns found with severity and location
    • Show estimated complexity per function
    • Suggest specific optimizations with before/after code examples
    • Prioritize by potential impact
  6. Offer to optimize: Ask if the user wants you to apply the suggested optimizations.

Anti-Patterns Detected

  • Nested loops (O(n^2) or worse)
  • String concatenation in loops (use join instead)
  • Repeated computation that could be cached
  • List operations that should use sets/dicts
  • Unnecessary list copies (list slicing in loops)
  • Synchronous I/O in hot paths
  • Quadratic list membership checks (use set)

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