Skill: athola/nm-parseltongue-python-performance (version 1.0.2)

--- name: python-performance description: | Python performance profiling and optimization: bottleneck detection, memory tuning, benchmarking version: 1.9.5 triggers: - python - performance - profiling - optimization - cProfile - memory 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 ## Troubleshooting ### Common Issues **Command not found** Ensure all dependencies are installed and in PATH **Permission errors** Check file permissions and run with appropriate privileges **Unexpected behavior** Enable verbose logging with `--verbose` flag


Security scan verdicts — ClawScan: clean | static analysis: clean | VirusTotal: clean | SkillSpector: clean (LOW)