python-pro

# Python Pro - Advanced Python Patterns ## When to Use This Skill **Invoke python-pro for:** - CatBoost model training (ml/train_models.py, feature_builder.py) - Analytics tools optimization (async batching, caching) - Performance profiling (bottleneck identification) - Advanced Python patterns (decorators, generators, context managers) - Heavy data processing on campaign datasets - Executing ML designs from @sama-2.0 **Use fastapi-production-patterns instead for:** - API endpoints, routing, middleware - Pydantic validation, request/response models - CORS configuration, authentication middleware - FastAPI-specific patterns (dependency injection at API layer) **Clear Boundary:** | fastapi-production-patterns | python-pro | |-----------------------------|------------| | API layer (HTTP, routing) | Business logic (ML, analytics) | | Pydantic, middleware, CORS | Decorators, generators, profiling | | FastAPI endpoints | Core Python optimization | --- ## Executable Scripts Run these scripts directly for profiling and debugging: ### Profile a Function ```bash python scripts/profile_function.py app.ml.feature_builder build_features python scripts/profile_function.py app.agents.analyst gather_evidence_async --args '{"candidate": {"list_id": "GM_30D"}}' ``` ### Compare Two Implementations ```bash python scripts/benchmark_compare.py app.tools.v1:analyze app.tools.v2:analyze_async --runs 10 ``` ### Check Memory Usage ```bash python scripts/memory_check.py app.ml.feature_builder build_all_features --args '{"n_campaigns": 1000}' ``` --- ## {{PROJECT_NAME}} ML System Use `references/mission_inbox_ml.md` for ML-specific documentation: - Model locations (`ml/models/*.cbm`) - Training commands (`python ml/train_models.py`) - Feature list (110 features from `FeatureBuilder`) - How `MLPredictor` serves predictions to Analyst Agent - EPC lookup (historical, NOT ML) - Database tables used for training --- ## Core Patterns and Examples Use `references/patterns.md` for detailed code patterns and examples across: - Decorators (caching, timing, retries, validation) - Generators (lazy feature building, chunking, async generators) - Async/concurrency (batching, sync-to-async, semaphores) - Profiling (cProfile, line_profiler, memory_profiler, benchmarking) - Type hints and static analysis (TypedDict, Protocol, Generic, mypy/ruff/black) - Testing (fixtures, parametrization, async tests, mocking) - Design patterns (strategy/factory for ML and tool creation) - Quick reference cheat sheet --- ## Usage Guidance - Prefer clear, typed interfaces for analytics and ML modules. - Favor async batching when tool calls are independent. - Profile before optimizing; keep hotspots visible. --- > Converted and distributed by [TomeVault](https://tomevault.io/claim/ak-eyther) — claim your Tome and manage your conversions. <!-- tomevault:4.0:skill_md:2026-04-15 -->