0db870eb11
Add a maintainers script to safely promote high-confidence legacy risk labels from unknown to concrete values, cover it with tests, and regenerate the canonical skill artifacts and plugin copies. This reduces the legacy unknown backlog without forcing noisy classifications that still need manual review.
40 lines
1.4 KiB
Markdown
40 lines
1.4 KiB
Markdown
---
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name: python-performance-optimization
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description: "Profile and optimize Python code using cProfile, memory profilers, and performance best practices. Use when debugging slow Python code, optimizing bottlenecks, or improving application performance."
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risk: safe
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source: community
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date_added: "2026-02-27"
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---
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# Python Performance Optimization
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Comprehensive guide to profiling, analyzing, and optimizing Python code for better performance, including CPU profiling, memory optimization, and implementation best practices.
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## Use this skill when
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- Identifying performance bottlenecks in Python applications
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- Reducing application latency and response times
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- Optimizing CPU-intensive operations
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- Reducing memory consumption and memory leaks
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- Improving database query performance
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- Optimizing I/O operations
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- Speeding up data processing pipelines
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- Implementing high-performance algorithms
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- Profiling production applications
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## Do not use this skill when
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- The task is unrelated to python performance optimization
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- You need a different domain or tool outside this scope
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## Instructions
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- Clarify goals, constraints, and required inputs.
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- Apply relevant best practices and validate outcomes.
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- Provide actionable steps and verification.
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- If detailed examples are required, open `resources/implementation-playbook.md`.
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## Resources
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- `resources/implementation-playbook.md` for detailed patterns and examples.
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