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Found 1,374 Skills
Python performance optimization patterns using profiling, algorithmic improvements, and acceleration techniques. Use when optimizing slow Python code, reducing memory usage, or improving application throughput and latency.
Principal backend engineering intelligence for Python AI/ML systems. Actions: plan, design, build, implement, review, fix, optimize, refactor, debug, secure, scale ML services and pipelines. Focus: data quality, reproducibility, reliability, performance, security, observability, model evaluation, MLOps.
Principal backend engineering intelligence for Python services and data systems. Actions: plan, design, build, implement, review, fix, optimize, refactor, debug, secure, scale backend code and architectures. Focus: correctness, reliability, performance, security, observability, scalability, operability, cost.
Python Coding Standards, including type hints, logging specifications, naming conventions, code structure, etc. Applicable to all Python code files.
Quick reference mapping global architecture concepts to Python/FastAPI/SQLAlchemy syntax. For concepts, see the global skills.
Databricks development guidance including Python SDK, Databricks Connect, CLI, and REST API. Use when working with databricks-sdk, databricks-connect, or Databricks APIs.
Modern Python development with uv (10-100x faster package manager) and ruff (extremely fast linter/formatter). Use when managing Python projects, dependencies, virtual environments, installing packages, linting code, or formatting Python files. Triggers on phrases like "uv install", "ruff check", "python package manager", "format python code", or working with pyproject.toml files.
Python virtual environment management, dependency handling, and project setup automation.
Python scripting with uv and PEP 723 inline dependencies. Use when creating standalone Python scripts with automatic dependency management.
Production Python engineering patterns covering architecture, observability, testing, performance/concurrency, and core practices. Use when designing Python systems, implementing async/sync APIs, setting up monitoring, structuring tests, optimizing performance, or following Python best practices.
Use when writing, fixing, editing, reviewing, or refactoring any Python code. Enforces Robert Martin's complete Clean Code catalog—naming, functions, comments, DRY, and boundary conditions.
Structure Python so LLMs can understand it in 50 lines.