performance-review

Identify performance degradation risks in pull request code reviews.

1|Updated Apr 24, 2026

Search Tags:#performance #scalability #optimization #concurrency #profiling #code-review #benchmarks

Ready to run

npx skills add https://github.com/NoSugarCoffee/dotai --skill performance-review-nosugarcoffee

💬 Prompt for ChatGPT / Claude Chat▼

Please help me install this Agent Skill.
Skill: performance-review
Source: https://github.com/NoSugarCoffee/dotai/tree/main/skills/performance-review
Command: npx skills add https://github.com/NoSugarCoffee/dotai --skill performance-review-nosugarcoffee

SYSTEM DOCUMENTATION & REQUIREMENTS

What problem does it solve?

Identify changes that will cause measurable performance degradation in code reviews, enabling teams to catch bottlenecks before they reach production.

Core Features & Use Cases

Algorithmic complexity checks for hot paths (N+1 queries, O(n^2) patterns) and expensive loops.
Resource management and lifecycle concerns, including memory leaks and unclosed handles.
Database query optimization, including missing indices, unpaginated results, and expensive joins.
Caching and concurrency considerations to prevent cache stampedes and race conditions.
Real-world use cases: assessing PRs for performance regressions on services with profiling data or load tests.

Quick Start

Review a PR with profiling data and generate a performance impact report.

Frequently Asked Questions about performance-review

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

FAQPage Schema

How do I catch performance regressions during code reviews?▼

To catch performance regressions during code reviews, analyze pull requests using profiling or benchmarking data to enforce checks on algorithmic complexity, database queries, and resource management. This generates actionable, severity-scored findings for bottlenecks.

What is an N+1 query and how can I identify it in a pull request?▼

An N+1 query is an inefficient database access pattern occurring in loops. You can identify it in a pull request by running algorithmic complexity checks on hot paths to detect expensive loops and missing query optimizations before they reach production.

How do I check for cache stampedes and race conditions in my code changes?▼

To check for cache stampedes and race conditions in code changes, apply concurrency and caching behavior analysis during pull request reviews. This evaluates resource lifecycle management to prevent race conditions and ensure safe concurrency.

Does code review performance analysis work without existing profiling data?▼

Code review performance analysis is most effective when applied to pull requests with available profiling, monitoring, or benchmarking data. While it can assess algorithmic complexity independently, real-world use cases rely on load test data for measurable impact reports.

What is the best way to detect memory leaks and unclosed handles in backend services?▼

The best way to detect memory leaks and unclosed handles in backend services is to enforce resource management and lifecycle checks during pull request reviews. This proactively identifies resource bottlenecks introduced by new code changes before production deployment.