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python-parallelization

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Transform sequential Python code into parallel/concurrent implementations. Use when asked to parallelize Python code, improve code performance through concurrency, convert loops to parallel execution, or identify parallelization opportunities. Handles CPU-bound (multiprocessing), I/O-bound (asyncio, threading), and data-parallel (vectorization) scenarios.

tasks/parallel-tfidf-search/environment/skills/python-parallelization/SKILL.md

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# Python Parallelization Skill

Transform sequential Python code to leverage parallel and concurrent execution patterns.

## Workflow

1. **Analyze** the code to identify parallelization candidates
2. **Classify** the workload type (CPU-bound, I/O-bound, or data-parallel)
3. **Select** the appropriate parallelization strategy
4. **Transform** the code with proper synchronization and error handling
5. **Verify** correctness and measure expected speedup

## Parallelization Decision Tree
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