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Found 40 Skills
Set up formatting, linting, import sorting, type checking, and pre-commit hooks when scaffolding or starting a new project. Use this skill whenever creating a new project, initializing a repo, scaffolding an app, or when the user asks to add linting/formatting to an existing project. Triggers on: "new project", "scaffold", "init", "set up linting", "add formatter", "add pre-commit hooks", "configure biome", "configure ruff". The goal is to establish code quality tooling from day one so issues are caught incrementally, not in a painful bulk-fix later.
Comprehensive Python engineering guidelines for writing production-quality Python code. This skill should be used when writing Python code, performing Python code reviews, working with Python tools (uv, ruff, mypy, pytest), or answering questions about Python best practices and patterns. Applies to CLI tools, AI agents (langgraph), and general Python development.
Develop Python applications using modern patterns, uv, functional-first design, and production-first practices. Use this whenever working with .py files, pyproject.toml, uv commands, pip/pip3, poetry, virtualenv/venv, inline script metadata, or Python tooling like pytest, mypy, ruff, asyncio, itertools, functools, or dataclasses. If the task involves running Python, managing Python dependencies, creating environments, or building Python packages, load this skill and prefer uv-oriented workflows.
Python patterns for CLI tools, async concurrency, and backend services. Use when working with Python code, building CLI apps, FastAPI services, async with asyncio, background jobs, or configuring uv, ruff, ty, pytest, or pyproject.toml.
Master Python 3.12+ with modern features, async programming, performance optimization, and production-ready practices. Expert in the latest Python ecosystem including uv, ruff, pydantic, and FastAPI. Use PROACTIVELY for Python development, optimization, or advanced Python patterns.
Galaxy code linting, formatting, and type checking. Run checks, auto-fix formatting, Python lint, client lint, mypy type checks. Use for: ruff, flake8, black, isort, darker, autoflake, pyupgrade, eslint, prettier, mypy, tox, make format, make diff-format, code style, lint failures, CI lint checks, formatting errors, type errors, codespell, redocly, api schema, xsd, config lint.
Captures quality metrics baseline (tests, coverage, type errors, linting, dead code) by running quality gates and storing results in memory for regression detection. Use at feature start, before refactor work, or after major changes to establish baseline. Triggers on "capture baseline", "establish baseline", or PROACTIVELY at start of any feature/refactor work. Works with pytest output, pyright errors, ruff warnings, vulture results, and memory MCP server for baseline storage.
Guide for migrating Apache Airflow 2.x projects to Airflow 3.x. Use when the user mentions Airflow 3 migration, upgrade, compatibility issues, breaking changes, or wants to modernize their Airflow codebase. If you detect Airflow 2.x code that needs migration, prompt the user and ask if they want you to help upgrade. Always load this skill as the first step for any migration-related request.
Run code quality checks (ruff, mypy, pytest) and optionally simplify code. This skill should be used when the user wants to check code quality, run linters, run tests, or simplify recently modified code. Triggered by /lint, /check, or /code-quality commands.
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.
Iterative code refinement through plan → code → evaluate → refine cycles. Runs lint checks (ruff), tests (pytest), and structured self-evaluation each cycle, then diagnoses failures and refines. Decomposes complex tasks into sequential phases, iterates up to 3 times per phase (10 total). Use when: the main agent delegates a code task with 'MODE: MORE_EFFORT', the user selects 'More Effort' code generation mode, or the task explicitly requests iterative refinement for higher code quality. Do NOT use for single-pass code generation (Lite mode), experiment pipeline orchestration (use experiment-pipeline), or diagnosing a specific experiment failure (use experiment-craft).
Work with the Inpoxia repository's local tools and workflows for CLI usage, GraphMail library changes, and quality checks. Use when tasks involve running or updating `inpoxia` commands, modifying files under `src/inpoxia/**`, validating behavior with `pytest`, or enforcing style/type checks with `ruff` and `pyright`.