> browse/registry

> read-repo-rules

Read specified repo rule files (AGENTS.md, docs/REPO_STYLE.md, docs/PYTHON_STYLE.md, docs/CHANGELOG.md) and return one-sentence summaries in a strict four-line format, including missing-file handling and no recursive listings. For docs/CHANGELOG.md, summarize only the latest dated entry instead of the full file. Use when a prompt asks for structured summaries of repo instruction files with exact output lines or similar constraints.

> computer-vision-expert

Design, implement, debug, and review computer vision systems in Python, including image processing, detection, segmentation, classification, tracking, OCR, camera pipelines, and dataset-driven evaluation. Use when working with OpenCV, PyTorch vision models, video/image analysis, model-selection tradeoffs, annotation strategy, failure analysis, or CV performance and robustness problems.

> doc

Use when the task involves reading, creating, or editing `.docx` documents, especially when formatting or layout fidelity matters; prefer `python-docx` plus the bundled `scripts/render_docx.py` for visual checks.

> 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.

> security-best-practices

Perform language and framework specific security best-practice reviews and suggest improvements. Trigger only when the user explicitly requests security best practices guidance, a security review/report, or secure-by-default coding help. Trigger only for supported languages (python, javascript/typescript, go). Do not trigger for general code review, debugging, or non-security tasks.

> coding-standards

Enforce language-specific coding standards (Python/TS/JS/Go/Rust/C/C++) + PR/commit conventions.

> mcp-server-builder

Build Model Context Protocol (MCP) servers that connect AI agents to external services and data sources. Use when a user asks to create an MCP server, build an MCP tool, connect an AI agent to an API, create a tool server for Claude, build MCP resources, or expose a database/service via MCP. Generates TypeScript or Python MCP servers with tools, resources, and prompts following the official MCP specification.

> 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.

> 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.

> 3dsmax-scripting

Automate 3ds Max with MAXScript and Python — scene manipulation, object creation, material assignment, camera setup, batch operations, UI tools, and file I/O. Use when tasks involve automating repetitive 3ds Max workflows, batch processing scenes, creating custom tools, or scripting scene setup for archviz, product visualization, or VFX.

> renovate

Assists with automating dependency updates across JavaScript, Python, Docker, Go, Rust, Terraform, and 50+ ecosystems using Renovate. Use when configuring update strategies, automerge policies, grouping rules, or scheduling to keep dependencies current. Trigger words: renovate, dependency updates, automerge, package rules, dependency management.

> mojo

Expert guidance for Mojo, the programming language by Modular that combines Python's usability with C-level performance. Helps developers write high-performance AI/ML code, optimize numerical computations with SIMD and parallelism, and gradually port Python code to Mojo for orders-of-magnitude speedups.

> fastapi

FastAPI is a modern, high-performance Python web framework for building APIs. It leverages Python type hints and Pydantic for automatic validation, serialization, and OpenAPI documentation generation with async/await support out of the box.

> onnx

Open Neural Network Exchange format for model interoperability across frameworks. Export models from PyTorch, TensorFlow, and other frameworks to ONNX, optimize with ONNX Runtime, and deploy for cross-platform inference on CPU, GPU, and edge devices.

> 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.

> 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.

> 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.

> 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.

> modal

Serverless GPU compute platform for running Python functions in the cloud. Deploy ML models, run training jobs, and serve inference endpoints without managing infrastructure. Supports A100/H100 GPUs, custom container images, and scales to zero automatically.

> structlog-python

Add structured logging to Python with structlog. Use when a user asks to implement structured logging, add context to Python logs, configure log processing pipelines, or replace standard logging with typed output.