> browse/registry

> moviepy

Edit and compose video with Python using MoviePy. Use when a user asks to programmatically edit videos, create video montages, add text overlays, build automated video pipelines, composite multiple clips, apply video effects, generate social media videos from templates, concatenate clips, extract audio, create GIFs, build slideshows, add transitions, resize and crop videos, or integrate video editing into Python applications. Covers MoviePy 2.x for compositing, effects, text, and rendering.

> apache-arrow

Expert guidance for Apache Arrow, the cross-language columnar memory format for analytics workloads. Helps developers use Arrow for high-performance data interchange between systems, zero-copy reads, and efficient columnar processing in Python (PyArrow) and JavaScript (Arrow JS).

> microsoft-word

Create, edit, and manipulate Word documents (.docx) programmatically. Use when someone asks to "generate a Word document", "create a report in docx", "mail merge", "fill Word template", "extract text from Word", "convert markdown to Word", "add tables to Word", or "automate document generation". Covers python-docx for file manipulation, docxtpl for templates, and Microsoft Graph API for cloud documents.

> gradio

Python library for building ML demo UIs with minimal code. Create interactive web interfaces for models with text, image, audio, and video inputs/outputs. Share demos via public links or deploy to Hugging Face Spaces.

> django

Django is a batteries-included Python web framework that follows the model-template-view pattern. It provides an ORM, admin interface, authentication, and everything needed to build full-featured web applications rapidly and securely.

> instructor

You are an expert in Instructor, the library for getting structured, validated output from LLMs. You help developers extract typed data from unstructured text using Pydantic models (Python) or Zod schemas (TypeScript), with automatic retries on validation failures, streaming partial objects, and support for OpenAI, Anthropic, Google, and local models — turning LLMs into reliable data extraction engines.

> jupyter

Assists with interactive data analysis, visualization, and reproducible research using Jupyter notebooks. Use when building notebooks that combine code with rich output, managing kernels, converting to reports, or parameterizing notebooks for batch execution. Trigger words: jupyter, notebook, jupyterlab, ipynb, nbconvert, papermill.

> outlines

You are an expert in Outlines, the Python library for reliable structured text generation with LLMs. You help developers generate guaranteed-valid JSON, regex-matching text, and grammar-constrained output from open-source models — using finite state machine guided generation that constrains the token sampling process to produce only valid output on the first try.

> dspy

You are an expert in DSPy, the Stanford framework that replaces prompt engineering with programming. You help developers define LLM tasks as typed signatures, compose them into modules, and automatically optimize prompts/few-shot examples using teleprompters — so instead of manually crafting prompts, you write Python code and DSPy finds the best prompts for your task.

> pandera

Expert guidance for Pandera, the Python library for validating pandas and Polars DataFrames with expressive schemas. Helps developers define data contracts, validate data pipelines, and catch data quality issues before they corrupt downstream systems.

> blender-vse-pipeline

Automate video editing in Blender's Video Sequence Editor with Python. Use when the user wants to add video, image, or audio strips, create transitions, apply effects, build edit timelines, batch assemble footage, estimate render times, or script any VSE workflow from the command line.

> polars

Polars is a blazingly fast DataFrame library written in Rust with a Python API. Learn eager and lazy evaluation, expressions, groupby, joins, and how Polars outperforms pandas for large datasets through parallel execution.

> poetry

Manage Python projects with Poetry. Use when a user asks to manage Python dependencies, create virtual environments, publish packages to PyPI, handle dependency resolution, or set up a Python project structure.

> prefect

Prefect is a modern workflow orchestration framework for Python data pipelines. Learn to define flows and tasks with decorators, handle retries and scheduling, create deployments, and monitor via the Prefect UI.

> jupyter-notebook

Use when the user asks to create, scaffold, or edit Jupyter notebooks (`.ipynb`) for experiments, explorations, or tutorials; prefer the bundled templates and run the helper script `new_notebook.py` to generate a clean starting notebook.

> celery

Run background tasks in Python with Celery. Use when a user asks to process tasks asynchronously, schedule periodic jobs, run background workers, build task queues in Python, or offload heavy processing from web requests.

> great-expectations

Great Expectations is a Python framework for data quality testing and validation. Learn to define expectations, create validation suites, build data docs, and integrate with data pipelines for automated quality checks.

> pytorch

Assists with building, training, and deploying neural networks using PyTorch. Use when designing architectures for computer vision, NLP, or tabular data, optimizing training with mixed precision and distributed strategies, or exporting models for production inference. Trigger words: pytorch, torch, neural network, deep learning, training loop, cuda.

> godot

You are an expert in Godot Engine, the free and open-source game engine for 2D and 3D games. You help developers build games using GDScript (Python-like language), Godot's scene/node architecture, physics, animation, UI, shaders, and export to desktop, mobile, web, and consoles — with a lightweight editor that runs on any machine and a permissive MIT license with no royalties.

> pipedream

You are an expert in Pipedream, the workflow automation platform built for developers. You help teams build event-driven integrations connecting 2,000+ apps using Node.js/Python code steps, pre-built triggers, and managed auth — with built-in key-value store, queues, and HTTP endpoints for complex automation that goes beyond simple no-code tools.