Individual skill
complexity-optimizer
Analyze a software codebase for algorithmic complexity and performance hotspots, then propose or implement safe optimizations without breaking behavior. Use when Codex is asked to scan many files, find inefficient loops, nested iteration, repeated scans, costly rendering/recomputation, N+1 queries, avoidable O(n^2) or O(n) operations, or reduce complexity such as O(n^2) to O(n log n) / O(n), while preserving tests, APIs, outputs, and maintainability.
complexity-optimizer/SKILL.md
Source commit 6a1f9674d706a06d462296e53a40f668299e8893
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- Matches the observed repository HEAD
- Yes
- Scanner rules are current
- Yes
- Repository scan complete
- No
- Skill scan complete
- No
- Observed behavior
- Local scripts
- Last checked
- Nov 4, 2026
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- Repository stars (not skill ratings)
- 913
- Repository last updated
- May 15, 2026
- License
- MIT
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- engineering Derived category
- operations Derived category
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SKILL.mdcore · 5,225 bytesSKILL.mdcore · 5,225 bytesagents/openai.yamlasset · 221 bytesreferences/optimization-playbook.mddoc · 2,996 bytesreferences/report-template.mddoc · 797 bytesscripts/analyze_complexity.pyruns · 12,166 bytes
Read excerpt
# Complexity Optimizer
## Core Rule
Optimize only when the current behavior is understood and can be preserved. Prefer a small, proven improvement with tests over a broad rewrite with unclear correctness.
## Default Behavior
When the user asks to analyze, scan, audit, review, or "give me a report" for a codebase, produce the full complexity report automatically. Do not require the user to specify report fields.
Default report contents:
- Scope analyzed and detected stack/test commands.
- Top findings ranked by likely impact.