Can it help with frontend performance?

Yes, it detects issues like missing cleanup for event listeners, uncleared timers, and sequential fetch calls that should be parallelized.

How do I get a more detailed analysis after a hint?

The skill will suggest specific commands such as /deep-review or /quick-check to trigger a comprehensive performance analysis of your files.

What kind of database issues can this skill find?

It specifically looks for N+1 query problems (queries inside loops), missing eager loading, and lack of pagination on large datasets.

Does Performance Hint perform full code benchmarking?

No, Performance Hint is designed for lightweight awareness. it flags obvious bottlenecks and complexity issues during conversation rather than performing deep execution profiling.

Performance Hint

Name: Performance Hint
Author: AlexeyTripleA

byAlexeyTripleA

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Collaboration Tools

Identifies critical performance bottlenecks like nested loops and N+1 queries during real-time code discussions.

About

Performance Hint is a lightweight diagnostic skill for Claude Code designed to maintain high software efficiency by proactively flagging obvious performance pitfalls. It monitors code for O(n²) complexity, inefficient database patterns, and sequential network requests, providing immediate, brief feedback. By acting as an early-warning system rather than a heavy profiler, it ensures performance remains a top-of-mind priority during the development process and guides users toward deeper analysis when necessary.

Key Features

Identifies N+1 query patterns and missing database pagination
Detects algorithmic complexity issues like nested loops and inefficient sorting
Flags sequential API calls that could be optimized with parallel execution
Monitors for memory risks including unmanaged event listeners and timers
2 GitHub stars
Suggests context-aware commands for deep-dive performance reviews

Use Cases

Reviewing new code submissions for common efficiency anti-patterns
Identifying frontend memory leaks and responsiveness bottlenecks
Debugging slow API responses or database interactions in real-time

About

Key Features

Identifies N+1 query patterns and missing database pagination
Detects algorithmic complexity issues like nested loops and inefficient sorting
Flags sequential API calls that could be optimized with parallel execution
Monitors for memory risks including unmanaged event listeners and timers
2 GitHub stars
Suggests context-aware commands for deep-dive performance reviews

Use Cases

Reviewing new code submissions for common efficiency anti-patterns
Identifying frontend memory leaks and responsiveness bottlenecks
Debugging slow API responses or database interactions in real-time