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check-perf

Analyze code diffs for performance issues and optimization opportunities.

36|12|Updated Nov 7, 2018
Search Tags:#performance#checklist#optimization#bottlenecks#code-review#analyze#diffs
One-click install
npx skills add https://github.com/covoiturage-gouv-fr/mono --skill check-perf-covoiturage-gouv-fr
Or copy as Structured Prompt for Agent▼
Please help me install this Agent Skill.
Skill: check-perf
Source: https://github.com/covoiturage-gouv-fr/mono/tree/main/.claude/skills/check-perf
Command: npx skills add https://github.com/covoiturage-gouv-fr/mono --skill check-perf-covoiturage-gouv-fr

SYSTEM DOCUMENTATION & REQUIREMENTS

What problem does it solve?

Analyzes code changes for performance implications and optimization opportunities.

Core Features & Use Cases

  • Conducts targeted checks for common performance risks in diffs, such as inefficient database access, memory-heavy operations, and slow API calls.
  • Provides actionable recommendations to optimize queries, data handling, and algorithms.
  • Supports integration into code-review workflows to flag potential performance regressions.

Quick Start

Run the performance analysis on your latest diff to surface bottlenecks.

Frequently Asked Questions about check-perf

High-intent search queries and answers about installing and using this skill.

FAQPage Schema
How do I spot performance bottlenecks in my code changes during a pull request review?▼

To spot performance bottlenecks in code changes, analyze your pull request diffs for inefficient database access, memory-heavy operations, and slow API calls. This surfaces specific optimization opportunities like N+1 queries and missing indexes directly within your code review workflow.

What are common database performance issues found in code diffs?▼

Common database performance issues in code diffs include N+1 queries, missing indexes, large result sets, and improper transaction scope. Analyzing diffs identifies these specific bottlenecks and provides actionable recommendations to optimize queries and data handling.

How can I optimize API response times when reviewing new code?▼

Optimize API response times in new code by analyzing diffs for slow API calls and memory-heavy operations. The analysis checks API response optimization and data handling, providing actionable recommendations to improve overall algorithmic efficiency.

Can I analyze memory usage and connection pooling limits in my latest diff?▼

Yes, you can analyze memory usage and connection pooling limits in your latest diff. Running a performance analysis on code changes identifies these specific bottlenecks and surfaces optimization opportunities across repositories.

What is the best way to prevent performance regressions across multiple repositories?▼

The best way to prevent performance regressions across repositories is integrating targeted performance checks into your code review workflows. Analyzing diffs for N+1 queries, missing indexes, and memory usage flags potential regressions before merging.

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