agent_alpha/skills/performant-code/SKILL.md

name, description, tags, version

name	description	tags	version
performant-code	Writing efficient code that handles large data and tight constraints	performance optimization benchmark	1.0.0

Performant Code

How to write code that won't timeout on large inputs.

Think About Scale First

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

I/O Optimization

Large files

• mmap (C: mmap(), Python: mmap.mmap()) — map file into memory, OS handles paging
• Buffered binary reads — fread() in C, open(f, 'rb').read(chunk) in Python
• NEVER read a 500MB file line-by-line with fgets() when you need random access

Writing output

• Buffer writes — don't call write() for every byte
• Use fwrite() or sys.stdout.buffer.write() for binary output
• Flush only when needed

Algorithm Complexity

• O(n) beats O(n log n) beats O(n²) — always
• Nested loops on large data = timeout. Restructure to single pass + hash map
• Sorting is O(n log n) — only sort if you need to
• Use hash maps/sets for lookup instead of linear search
• Pre-compute what you can outside loops

Language-Specific Tips

C

• Use mmap() for large file access
• -O2 or -O3 for compiler optimizations
• Avoid malloc()/free() in tight loops — pre-allocate
• Use memcpy() instead of byte-by-byte copying
• Integer arithmetic > floating point when possible

Python

• Use numpy for numerical work (100x faster than pure Python loops)
• collections.Counter, defaultdict — avoid manual counting
• List comprehensions > explicit loops
• struct.unpack() for binary parsing
• subprocess.run() > os.system()
• For heavy computation: consider writing a small C program instead

General

• Profile before optimizing — find the actual bottleneck
• If a program hangs, it's likely: infinite loop, deadlock, or I/O bound on huge data
• If a program is slow, check: algorithm complexity, I/O pattern, memory allocation

Constraints Awareness

• If the task says "< 5000 bytes" — count your bytes, use wc -c
• If there's a time limit — test with actual data, not toy inputs
• If there's a memory limit — don't load everything into RAM
• Always verify constraints BEFORE declaring done