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Found 2,394 Skills
Detect the env manager and bootstrap the runtime, agent-tools, and composed development environments. Detect with `python -m skore_skills env detect`, persist the manager and `env.managed`, then `env init --manager`, `env sync --execute`, install Skore for a recorded hub or mlflow destination (plain Skore when unset or local), and `env verify --execute`. Does not add other stage ML libraries. TRIGGER when the user asks for the env manager, pixi, uv, or a Python environment, or when none is recorded yet. SKIP later packages (add-python-package). SKIP scaffolding src/ (setup-workspace). HOW TO USE: detect, ask managed vs user-managed, ask the manager when needed, then env init, env sync, add Skore, and env verify. When setup-ml-project dispatched this turn, skip each ask whose answer is already recorded. One recorded answer does not skip the other ask.
Write Python 3.13 for Factorial Code processes and modules — the main() entry point, fcode.context.parameters, fcode.import_module(), datastore/storage/env helpers, PEP 8 / snake_case, auto-installed dependencies, and return-value formats. Use when creating or editing .py process or module code for Factorial Code (fcode).
Subprocess and concurrency discipline for Python: how to run external programs safely, and how to pick the right concurrency model instead of the habitual one. Use when spawning processes with subprocess or asyncio subprocesses, writing threading or ThreadPoolExecutor code, using multiprocessing or ProcessPoolExecutor, sizing worker pools, bounding fan-out, or diagnosing deadlocks, hangs, orphaned children, and zombie processes. Covers argv vs shell, timeouts that kill the whole process tree, pipe deadlocks, start methods (spawn/forkserver/fork), pickling limits, GIL and free-threading, and cancellation. For event-loop and coroutine rules (TaskGroup, blocking calls, cancellation), use python-async-best-practices instead.
Temporal workflow orchestration in Python. Use when designing workflows, implementing activities, handling retries, managing workflow state, or building durable distributed systems.
This skill should be used when the user wants to "write agent code", "build an agent with ADK", "add a tool", "create a callback", "define an agent", "use state management", or needs ADK (Agent Development Kit) Python API patterns and code examples. Part of the Google ADK skills suite. It provides a quick reference for agent types, tool definitions, orchestration patterns, callbacks, and state management. Do NOT use for creating new projects (use google-agents-cli-scaffold) or deployment (use google-agents-cli-deploy).
Guide for creating high-quality MCP (Model Context Protocol) servers that enable LLMs to interact with external services through well-designed tools. Use when building MCP servers to integrate external APIs or services, whether in Python (FastMCP) or Node/TypeScript (MCP SDK).
Create a PR title and draft description after substantive code changes are finished. Trigger when wrapping up a moderate-or-larger change (runtime code, tests, build config, docs with behavior impact) and you need the PR-ready summary block with change summary plus PR draft text.
ALWAYS LOAD WHEN WORKING WITH PYSIDE6, QT, OR DESKTOP GUI CODE. PySide6 desktop apps: Manager→Service→Wrapper architecture, qasync integration, signals, system tray, testing.
Pydantic models and validation. Use when: (1) Defining schemas, (2) Validating input/output, (3) Generating JSON schema.
Guides the agent through scaffolding and building FastAPI applications, including project structure, API routes, request/response models, path and query parameters, dependency injection, middleware, error handling, and boilerplate generation. Triggered when the user asks to "scaffold a FastAPI project", "create a FastAPI app", "add an API endpoint", "create a router", "add middleware", "implement dependency injection", "handle errors", "set up CORS", "create background tasks", "implement WebSocket", "structure a FastAPI project", "generate boilerplate", or "add authentication".
FastAPI Secure Engineering
Test Temporal workflows with pytest, time-skipping, and mocking strategies. Covers unit testing, integration testing, replay testing, and local development setup. Use when implementing Temporal workflow tests or debugging test failures.