General-OAT-Skills

Prepares and triggers a new Python package release: runs cleanup, validates semantic versioning, updates pyproject.toml and docs, builds with uv, creates a GitHub release, and monitors the publishing workflow. Supports patch/minor/major auto-increment. Use when the user wants to cut a release, bump the version, or publish a new Python package version.

Verify and release a Rust crate or workspace. Bumps version, commits, tags, pushes. CI-driven by default; --local publishes directly.

Interactive lesson-based teaching skill. Researches a topic via the web, builds a structured curriculum of up to 10 lessons, and delivers them one at a time with comprehension questions. Supports difficulty flags (--beginner, --advanced, --expert) and lesson count (--lessons N). Use when the user wants to learn about any topic in a guided, incremental way.

Run a comprehensive multi-agent QA audit of any project. Auto-detects language and framework, then launches parallel agents to audit code quality, architecture, error handling, test coverage, CI/CD, documentation, and security. Use for pre-release validation, periodic quality reviews, or when the user asks for a full project audit.

Runs a quick project health check covering git status, recent commits, GitHub Actions workflow runs, and package build verification. Use when starting a session, before a release, or to get a fast overview of project state.

Runs a complete code quality pipeline: ruff linting and formatting, mypy type checking, and pytest test suite. Use for end-of-session quality checks, pre-release validation, or when the user asks to lint, format, or clean up code.

Loads all markdown files from the current directory or a specified directory into context for analysis and reference. Non-recursive, loads only top-level .md files. Use when the user wants to read all docs in a folder, review documentation, or bring markdown content into the conversation.

Runs Google Agent Development Kit (ADK) evaluations in sorted order with detailed results, failure summaries, and rate limiting between tests. Use when the user wants to run ADK evals, test agent performance, or validate agent behavior against evaluation datasets.

Creates a new project from templates with full setup including directory structure, dependency management, git initialization, CI/CD, testing, and documentation. Supports Python, Node.js, Rust, Go, and 14 AI agent framework templates. Use when the user wants to start a new project, bootstrap a codebase, or create a project from a template.

Runs the comprehensive test suite with pytest, reports coverage statistics, analyzes failures, and attempts automatic fixes. Use when the user wants to run tests, check coverage, or validate that code changes pass the test suite.