What types of performance issues can this skill detect?

It identifies N+1 queries, nested loops, missing pagination, large memory file loading, inefficient database filtering, and synchronous I/O within loops.

Can I customize the performance thresholds?

Yes, you can configure strict mode and set custom environment variables for maximum query times, loop sizes, and file operations to match your project's needs.

Does this skill run actual benchmarks on my code?

No, it focuses on identifying common architectural anti-patterns and providing static analysis-based fixes rather than running live profiling or load tests.

Does it support languages other than Python?

While the examples are in Python, the logic for detecting loops, I/O patterns, and database query structures can be applied to many modern programming languages.

Performance Budget Checker

Name: Performance Budget Checker
Author: MacroMan5

byMacroMan5

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Analíticas y Monitorización

Analyzes code for performance bottlenecks and anti-patterns like N+1 queries and nested loops to ensure high-performance production releases.

Acerca de

The Performance Budget Checker is an essential skill for developers using Claude Code to maintain high-efficiency codebases. It automatically identifies common performance killers—such as inefficient database queries, O(n²) algorithmic complexity, and memory-heavy file operations—providing actionable reports and code fixes. By integrating performance budgets directly into the development workflow, it helps catch latency issues before they reach production, ensuring applications remain scalable and responsive while adhering to strict execution time and memory limits.

Características Principales

Identifies O(n²) nested loops and recommends O(n) hash-map or set alternatives.
Generates standardized performance reports with status indicators and estimated speed improvements.
Detects N+1 database query problems and suggests batch fetching or joins.
Enforces pagination and caching best practices for API endpoints and expensive computations.
2 GitHub stars
Flags large file operations and suggests streaming methods to reduce memory overhead.

Casos de Uso

Reducing memory footprints of data processing pipelines by switching from full loads to streaming.
Optimizing backend API response times by identifying and fixing database bottlenecks.
Refactoring legacy code to eliminate redundant computations and inefficient linear searches.

Acerca de

Características Principales

Identifies O(n²) nested loops and recommends O(n) hash-map or set alternatives.
Generates standardized performance reports with status indicators and estimated speed improvements.
Detects N+1 database query problems and suggests batch fetching or joins.
Enforces pagination and caching best practices for API endpoints and expensive computations.
2 GitHub stars
Flags large file operations and suggests streaming methods to reduce memory overhead.

Casos de Uso

Reducing memory footprints of data processing pipelines by switching from full loads to streaming.
Optimizing backend API response times by identifying and fixing database bottlenecks.
Refactoring legacy code to eliminate redundant computations and inefficient linear searches.