sickn33/antigravity-awesome-skills/python-performance-optimization

Python Performance Optimization

Comprehensive guide to profiling, analyzing, and optimizing Python code for better performance, including CPU profiling, memory optimization, and implementation best practices.

Use this skill when

• Identifying performance bottlenecks in Python applications
• Reducing application latency and response times
• Optimizing CPU-intensive operations
• Reducing memory consumption and memory leaks
• Improving database query performance
• Optimizing I/O operations
• Speeding up data processing pipelines
• Implementing high-performance algorithms
• Profiling production applications

Do not use this skill when

• The task is unrelated to python performance optimization
• You need a different domain or tool outside this scope

Instructions

• Clarify goals, constraints, and required inputs.
• Apply relevant best practices and validate outcomes.
• Provide actionable steps and verification.
• If detailed examples are required, open resources/implementation-playbook.md.

Resources

• resources/implementation-playbook.md for detailed patterns and examples.

Limitations

• Use this skill only when the task clearly matches the scope described above.
• Do not treat the output as a substitute for environment-specific validation, testing, or expert review.
• Stop and ask for clarification if required inputs, permissions, safety boundaries, or success criteria are missing.

Measurement and operational limits

The playbook contains standalone demonstrations, not a validated benchmark suite. No timings or speedups are established by this skill. Use representative inputs, equal work, repeated runs and recorded interpreter/platform versions. The Fibonacci comparison measures 1 uncached call against 1,000 mostly warm calls; normalize per call and report cold/warm states separately. lru_cache(maxsize=None) retains entries without a bound; choose eviction and invalidation for changing or sensitive data.

sys.getsizeof is shallow: a regular instance also owns a __dict__ and references shared or separately allocated values. The printed object-size multiplication is not total process memory or a fair complete comparison with slots. Measure representative allocations with tracemalloc or an appropriate process profiler.

Use only approved test endpoints and disposable databases; the query example opens an existing database read-only and requires the shown schema. Production profiling requires authorization and bounded collection: profiles can expose code paths, arguments or other sensitive process data. Store artifacts privately. The examples have not been executed or performance-tested here.