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Found 2,347 Skills
Python design patterns for CLI scripts and utilities — type-first development, deep modules, complexity management, and red flags. Use when reading, writing, reviewing, or refactoring Python files, especially in .trellis/scripts/ or any CLI/scripting context. Also activate when planning module structure, deciding where to put new code, or doing code review.
Run Python quality checks with ruff, pytest, mypy, and bandit in deterministic order. Use WHEN user requests "quality gate", "lint", "verify code quality", "check python", or "pre-commit check". Use for pre-merge validation, CI/CD gating, or comprehensive code quality reports. Do NOT use for single-tool runs (run tool directly), debugging runtime bugs (use systematic-debugging), refactoring (use systematic-refactoring), or architecture review.
Scaffold a minimal local Deep Agent in Python by following the official quickstart, using provider-native web search instead of Tavily. Use when the user wants to quickly build or try a Deep Agent locally.
CCXT cryptocurrency exchange library for Python developers. Covers both REST API (standard) and WebSocket API (real-time). Helps install CCXT, connect to exchanges, fetch market data, place orders, stream live tickers/orderbooks, handle authentication, and manage errors in Python. Use when working with crypto exchanges in Python projects, trading bots, data analysis, or portfolio management. Supports both sync and async (asyncio) usage.
Sets up Python development environment using UV for fast dependency management. Configures virtual environment, dependencies, testing (pytest), linting/formatting (ruff), and type checking (mypy). ALWAYS use UV - NEVER use pip directly. Use when starting work on Python projects, after cloning Python repositories, setting up CI/CD for Python, or troubleshooting Python environment issues.
Generate Python FastAPI code following project design patterns. Use when creating models, schemas, repositories, services, controllers, database migrations, authentication, or tests. Enforces layered architecture, async patterns, OWASP security, and Alembic migration naming conventions (yyyymmdd_HHmm_feature).
Python code security analysis, performance optimization, and maintainability assessment
Comprehensively reviews Python libraries for quality across project structure, packaging, code quality, testing, security, documentation, API design, and CI/CD. Provides actionable feedback and improvement recommendations. Use when evaluating library health, preparing for major releases, or auditing dependencies.
Designs intuitive Python library APIs following principles of simplicity, consistency, and discoverability. Handles API evolution, deprecation, breaking changes, and error handling. Use when designing new library APIs, reviewing existing APIs for improvements, or managing API versioning and deprecations.
Use when the user wants to build a Python Kafka producer or consumer, add Schema Registry to existing Python code, migrate from raw JSON to schema-backed serialization, or scaffold a confluent-kafka-python project for Confluent Cloud, local Docker, or WarpStream. Also use when user wants to optimize Python Kafka client configuration for WarpStream.
Python resilience patterns including automatic retries, exponential backoff, timeouts, and fault-tolerant decorators. Use when adding retry logic, implementing timeouts, building fault-tolerant services, or handling transient failures.
Use when preparing branches, commits, or PRs for Python changes — scoping work, running validation gates, and ensuring merge readiness. Also use when debugging CI gate failures, resolving lockfile conflicts, or uncertain what checks to run before opening a PR.