A Claude Code skill that optimizes Python code generation by selecting the most efficient data structures and number handling strategies based on real-world performance and memory characteristics.

This skill teaches Claude Code to write more efficient Python code by understanding:

• Memory characteristics of Python numbers and collections
• Performance implications of different data structures
• When to use sets vs lists vs dicts
• Memory optimization techniques like __slots__

This skill implements best practices from the article "Python Numbers Every Programmer Should Know" by Michael Kennedy.

• Sets/Dicts for membership testing: 200x faster than lists for x in container checks
• Lists for ordered sequences: Most memory-efficient for iteration
• Proper usage based on access patterns

• Use __slots__ for classes with many instances (30% memory reduction)
• Understanding memory footprint: Lists (36KB) < Sets (59.6KB) < Dicts (90.7KB) per 1000 integers

• O(1) lookups with sets/dicts instead of O(n) with lists
• Efficient counting with Counter or dict
• Fast unique value extraction with sets

To use this skill with Claude Code:

1. Copy python-number-optimization.skill to your Claude Code skills directory:

   • Linux/macOS: ~/.claude/skills/
   • Windows: %USERPROFILE%\.claude\skills\

2. Or reference it in your project-specific .claude/skills/ directory

Once installed, Claude Code will automatically apply these optimizations when:

• You ask for "optimized" or "efficient" Python code
• Working with collections and data structures
• The code involves membership testing or lookups
• Creating classes with many instances

You can also explicitly invoke it:

Use the python-optimization skill to write a function that checks if user IDs are valid

See examples/ directory for before/after code samples demonstrating:

• Membership testing optimization
• Counting and grouping patterns
• Class memory optimization with __slots__
• Container selection for different use cases

To improve this skill:

1. Test it with various Python coding scenarios
2. Submit improvements based on real-world usage
3. Add more patterns and anti-patterns

MIT