Encontre a capacidade ideal para o seu agente.
Set up a ragnarok development environment — checks Python 3.11+, installs via uv or pip with cloud extras, and verifies with doctor. Use when someone is onboarding, setting up a fresh clone, or troubleshooting their environment.
Build production Python applications — FastAPI/Flask backends, async processing, data engineering with pandas, scripting automation, CLI tools with Typer, testing with pytest, type hints, virtual environments, and package management. Use when building Python backends, data pipelines, scripts, or CLI tools.
Aids in writing Mojo code that interoperates with Python using current syntax and conventions. Use this skill in addition to mojo-syntax when writing Mojo code that interacts with Python, calls Python libraries from Mojo, or exposes Mojo types/functions to Python. Also use when the user wants to build Python extension modules in Mojo, wrap Mojo structs for Python consumption, or convert between Python and Mojo types.
HuggingFace Hub — download models/datasets, upload artifacts, search, and manage tokens via CLI and Python API.
Build, test, and develop Python bindings (runtimed-py, nteract MCP server). Use when working on Python code, maturin builds, or the MCP server.
Build, rebuild, test, and debug the Python bindings and MCP server in the nteract desktop repo. Use when working in `crates/runtimed-py/**`, `python/runtimed/**`, `python/nteract/**`, or `python/gremlin/**`, especially for choosing the correct venv, running `maturin develop`, wiring tests to the right daemon socket, or validating MCP behavior after Rust changes.
Publish CLIs/TUIs to Homebrew via a personal tap. Use when asked to create or manage a Homebrew tap repo, generate or update formulae, compute sha256, test installs, or ship new releases for Go, Rust, Node/TypeScript, Python, or prebuilt binaries.
In D&D, True Polymorph is the permanent transformation — the target becomes something fundamentally different, and if held long enough, the change is irreversible. Unlike Polymorph (temporary disguise or adaptation), True Polymorph means the old identity is gone. The real-world version is irreversible system transformation: rewriting a service from Python to Rust so thoroughly the Python version cannot be recovered, migrating from SQL to a graph database with no backward-compatible schema, or restructuring an organization so completely that the old org chart is not just outdated but nonsensical. True Polymorph is the point of no return.
In D&D, True Polymorph is the permanent transformation — the target becomes something fundamentally different, and if held long enough, the change is irreversible. Unlike Polymorph (temporary disguise or adaptation), True Polymorph means the old identity is gone. The real-world version is irreversible system transformation: rewriting a service from Python to Rust so thoroughly the Python version cannot be recovered, migrating from SQL to a graph database with no backward-compatible schema, or restructuring an organization so completely that the old org chart is not just outdated but nonsensical. True Polymorph is the point of no return.
数据分析提示词专家 - 代码执行模式、元数据注入、EDA优先、假设验证框架。Use when user mentions: 数据分析, data analysis, Python, Pandas, 代码执行, code execution, EDA, 探索性数据分析, exploratory data analysis, 数据可视化, data visualization, CSV, Excel, 数据清洗, data cleaning, 统计分析, statistical analysis, 趋势分析, trend analysis, 代码解释器, code interpreter, data interpreter
Use when Python data modeling with dataclasses, attrs, and Pydantic. Use when creating data structures and models.
Master Python asynchronous programming with asyncio, async/await, and concurrent.futures. Use for async code and concurrency patterns.
Use when Python's type system including type hints, mypy, Protocol, TypedDict, and Generics. Use when working with Python type annotations.
Guidelines for Flask Python development with best practices for blueprints, RESTful APIs, and application factories.
Best practices for Matplotlib data visualization, plotting, and creating publication-quality figures in Python
Guidelines for data analysis and Jupyter Notebook development with pandas, matplotlib, seaborn, and numpy.
Best practices for NumPy array programming, numerical computing, and performance optimization in Python
