Python performance
Skill athola/claude-night-market/plugins/parseltongue/skills/python-performance
23 Claude Code plugins: TDD enforcement hooks, git/PR workflows, spec-driven development, code review, project lifecycle, fix-from-error, maintenance automation, context optimization, research, and multi-LLM delegation. 186 skills, 128 commands, 54 agents.
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Profiles Python code for performance bottlenecks and memory issues. Use when Python code is slow or when profiling for optimization before a release.
SKILL.md
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Python Performance Optimization
Profiling and optimization patterns for Python code.
Table of Contents
Quick Start
# 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
Eleven proven optimization patterns including list comprehensions, generators, caching, string concatenation, data structures, NumPy, multiprocessing, database operations, and loop transformations (what works in Python vs the compiler).
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