Night Market Skill — ported from claude-night-market/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#

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#

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#

Ten proven optimization patterns including list comprehensions, generators, caching, string concatenation, data structures, NumPy, multiprocessing, and database operations.

memory-management#

Memory optimization techniques including leak tracking with tracemalloc and weak references for caches. Depends on profiling-tools.

benchmarking-tools#

Benchmarking tools including custom decorators and pytest-benchmark for verifying performance improvements.

best-practices#

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