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updated:2026년 2월 23일 22:30
SKILL.md
Building, compiling, and resolving dependency issues across languages
Systematic code review for bugs, security, style, and performance
Working with diverse data formats: binary, text, structured, and custom
Systematic exploration of unknown environments before starting work
Git operations: commits, branches, PRs, and conflict resolution
Refactor code to improve structure and maintainability
| name | performant-code |
| description | Writing efficient code that handles large data and tight constraints |
| tags | ["performance","optimization","benchmark"] |
| version | 1.0.0 |
How to write code that won't timeout on large inputs.
Before writing code, ask: how big is the data?
| Data size | Approach |
|---|---|
| < 1 MB | Load into memory, any approach works |
| 1-100 MB | Load into memory, but use efficient algorithms |
| 100 MB - 1 GB | Stream/mmap, avoid loading entirely into memory |
| > 1 GB | Streaming only, chunk-based processing |
mmap(), Python: mmap.mmap()) — map file into memory, OS handles pagingfread() in C, open(f, 'rb').read(chunk) in Pythonfgets() when you need random accesswrite() for every bytefwrite() or sys.stdout.buffer.write() for binary outputmmap() for large file access-O2 or -O3 for compiler optimizationsmalloc()/free() in tight loops — pre-allocatememcpy() instead of byte-by-byte copyingnumpy for numerical work (100x faster than pure Python loops)collections.Counter, defaultdict — avoid manual countingstruct.unpack() for binary parsingsubprocess.run() > os.system()wc -c