Accelint Ts Performance

This is a systematic performance auditing tool that catches common JavaScript/TypeScript anti-patterns like chaining array methods, using Array.includes in loops instead of Sets, and placing try/catch blocks in hot paths. It's opinionated about auditing everything, not just obvious bottlenecks, since utilities and formatters often end up in hot paths despite looking simple. The workflow combines V8 profiling knowledge with practical patterns, showing before/after code with expected gains. The strongest aspect is its focus on algorithmic complexity first (O(n²) to O(n) beats any micro-optimization), though the extensive "never do this" list might feel heavy handed if you just want quick wins. Best used when you need to understand why code is slow, not just make it faster blindly.

skills.sh installs: 347
GitHub stars: 25
last updated: Aug 21, 2026

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Install

$ npx -y skills add gohypergiant/agent-skills --skill accelint-ts-performance --agent claude-code

Installs into .claude/skills of the current project.

Files

TypeScript Performance Optimization

Systematic performance optimization for JavaScript/TypeScript codebases. Combines audit workflow with expert-level optimization patterns for runtime performance.

NEVER Do When Optimizing Performance

Note: For general best practices (type safety with any/enum, avoiding null, not mutating parameters), use the accelint-ts-best-practices skill instead. This section focuses exclusively on performance-specific anti-patterns.

NEVER assume code is cold path - Utility functions, formatters, parsers, and validators appear simple but are frequently called in loops, rendering pipelines, or real-time systems. Always audit ALL code for performance anti-patterns. Do not make assumptions about usage frequency or skip auditing based on perceived simplicity.
NEVER apply all optimizations blindly - Performance patterns have trade-offs. Balance optimization gains against code complexity. When conducting audits, identify ALL anti-patterns through systematic analysis and report them with expected gains. Let users decide which optimizations to apply based on their specific context.

SKILL.md on GitHub