When should I use the codereview-performance skill?

Invoke this skill during code reviews involving heavy data transformations, database interactions, file I/O, or parallel operations.

Can it help prevent database connection exhaustion?

Yes, it specifically checks for proper resource cleanup, ensuring that database connections and file handles are released back to the pool.

Does it provide specific code suggestions?

Yes, it provides a detailed table with the issue location, the performance impact, and a recommended fix or pattern to follow.

What kind of performance issues does this skill detect?

It identifies algorithmic complexity (O(n²)), N+1 database queries, memory leaks, resource cleanup failures, and concurrency race conditions.

Performance Code Reviewer

Name: Performance Code Reviewer
Author: xinbenlv

byxinbenlv

Collaboration Tools

Identifies algorithmic inefficiencies, memory leaks, and database bottlenecks to ensure code remains performant at scale.

About

The Performance Code Reviewer skill acts as a senior performance engineer within your development workflow, specifically targeting issues that arise when code is executed at high volume or scale. It systematically audits loops, database queries, resource management, and concurrency logic to prevent production failures like OOM errors, race conditions, and N+1 query overhead. By providing detailed impact analysis and clear remediation steps, it helps developers transition from 'happy path' coding to production-hardened engineering.

Key Features

Identifies O(n²) algorithmic complexity and redundant iterations
Analyzes concurrency for potential race conditions and deadlocks
Detects N+1 database query patterns and suggests batching strategies
Provides impact tables comparing current performance vs. optimized states
0 GitHub stars
Scans for memory leaks in closures, event listeners, and global scopes

Use Cases

Debugging memory usage in long-running Node.js or Python processes
Refactoring backend service layers to reduce database latency
Auditing data processing pipelines for high-volume record handling

About

Key Features

Identifies O(n²) algorithmic complexity and redundant iterations
Analyzes concurrency for potential race conditions and deadlocks
Detects N+1 database query patterns and suggests batching strategies
Provides impact tables comparing current performance vs. optimized states
0 GitHub stars
Scans for memory leaks in closures, event listeners, and global scopes

Use Cases

Debugging memory usage in long-running Node.js or Python processes
Refactoring backend service layers to reduce database latency
Auditing data processing pipelines for high-volume record handling