python-performance

SKILL.md

---
name: python-performance
description: Profiles Python code for performance bottlenecks and memory issues
version: 1.9.8
triggers:
  - python
  - performance
  - profiling
  - optimization
  - cProfile
  - memory
  - Python code is slow or when profiling for optimization before a release
metadata: {"openclaw": {"homepage": "https://github.com/athola/claude-night-market/tree/master/plugins/parseltongue", "emoji": "\u26a1"}}
source: claude-night-market
source_plugin: parseltongue
---

> **Night Market Skill** — ported from [claude-night-market/parseltongue](https://github.com/athola/claude-night-market/tree/master/plugins/parseltongue). For the full experience with agents, hooks, and commands, install the Claude Code plugin.


# Python Performance Optimization

Profiling and optimization patterns for Python code.

## Table of Contents

1. [Quick Start](#quick-start)

## Quick Start

```python
# Basic timing
import timeit
time = timeit.timeit("sum(range(1000000))", number=100)
print(f"Average: {time/100:.6f}s")
```
**Verification:** Run the command with `--help` flag to verify availability.

## When To Use

- Identifying performance bottlenecks
- Reducing application latency
- Optimizing CPU-intensive operations
- Reducing memory consumption
- Profiling production applications
- Improving database query performance

## When NOT To Use

- Async concurrency - use python-async
  instead
- CPU/GPU system monitoring - use conservation:cpu-gpu-performance
- Async concurrency - use python-async
  instead
- CPU/GPU system monitoring - use conservation:cpu-gpu-performance

## Modules

This skill is organized into focused modules for progressive loading:

### [profiling-tools](modules/profiling-tools.md)
CPU profiling with cProfile, line profiling, memory profiling, and production profiling with py-spy. Essential for identifying where your code spends time and memory.

### [optimization-patterns](modules/optimization-patterns.md)
Ten proven optimization patterns including list comprehensions, generators, caching, string concatenation, data structures, NumPy, multiprocessing, and database operations.

### [memory-management](modules/memory-management.md)
Memory optimization techniques including leak tracking with tracemalloc and weak references for caches. Depends on profiling-tools.

### [benchmarking-tools](modules/benchmarking-tools.md)
Benchmarking tools including custom decorators and pytest-benchmark for verifying performance improvements.

### [best-practices](modules/best-practices.md)
Best practices, common pitfalls, and exit criteria for performance optimization work. Synthesizes guidance from profiling-tools and optimization-patterns.

## Exit Criteria

- Profiled code to identify bottlenecks
- Applied appropriate optimization patterns
- Verified improvements with benchmarks
- Memory usage acceptable
- No performance regressions