Pydantic-deepagents performant-code

Writing efficient code that handles large data and tight constraints

install
source · Clone the upstream repo
git clone https://github.com/vstorm-co/pydantic-deepagents
Claude Code · Install into ~/.claude/skills/
T=$(mktemp -d) && git clone --depth=1 https://github.com/vstorm-co/pydantic-deepagents "$T" && mkdir -p ~/.claude/skills && cp -r "$T/pydantic_deep/bundled_skills/performant-code" ~/.claude/skills/vstorm-co-pydantic-deepagents-performant-code-e6b38b && rm -rf "$T"
manifest: pydantic_deep/bundled_skills/performant-code/SKILL.md
source content

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 sizeApproach
< 1 MBLoad into memory, any approach works
1-100 MBLoad into memory, but use efficient algorithms
100 MB - 1 GBStream/mmap, avoid loading entirely into memory
> 1 GBStreaming 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