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algorithmic-complexity-review

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Algorithmic complexity (Big-O) review — finding nested loops, N+1 queries, exponential recursion, quadratic string builds, and other accidental complexity blowups. Covers Python, JavaScript/TypeScript, Java, Go, and similar languages. Use whenever writing, reviewing, or refactoring code where Big-O matters. Trigger even when the user doesn't mention "Big-O" explicitly — if they're reviewing code for performance, refactoring a hot path, asking "why is this slow," or working with data that scales (loops, recursions, collections, ORM access), apply this skill to classify the time/space complexity and suggest the fix. Especially trigger on tasks like "review for performance," "find slow code," "make this faster," "this code is O(n²)," or when reading code that processes collections.

skills/.experimental/algorithmic-complexity-review/SKILL.md

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Nov 11, 2026

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Aug 15, 2026
License
MIT

Observed agent integration: codex

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# dot-skills Algorithmic Complexity (Big-O) Best Practices

Find, classify, and fix algorithmic complexity (Big-O) problems in code — language-agnostic. The 39 rules across 8 categories cover the patterns responsible for the vast majority of accidental quadratic, exponential, and N+1 blowups in production code: nested iteration, loop-invariant I/O, data-structure mismatch, recursion explosions, redundant computation, collection-building anti-patterns, search/sort selection, and space traps.

## When to Apply

Use this skill when:

- Reviewing a pull request or function for performance regressions
- Asked "why is this slow?" or "can we make this faster?"
- Refactoring a hot path or a function that handles user-scaled input
- Reading code that contains: nested loops, `.includes`/`.find`/`x in list` inside iteration, ORM access in a loop, recursion without memoization, string/array building via `+=` or spread, file/database I/O inside iteration
- Reviewing code that processes lists, trees, or streams whose size will grow

## Workflow: Find, Classify, Fix
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