Agent Skills Hub

react-typescript

Production-grade React 19 and TypeScript patterns featuring hooks, state management, TanStack Query, form validation with Zod, and performance optimization workflows.

karpathy-guidelines

Behavioral guidelines for LLMs to reduce coding mistakes, follow best practices, and improve output quality by enforcing simplicity, surgical changes, and goal-driven verification.

components-build

Build modern, composable, and accessible React UI components following the components.build specification. Use for design systems, component libraries, and reusable UI architectures.

generate-sdk-types

Automates TypeScript type generation from Rust tool schemas to ensure end-to-end type safety between server and client in MCP-based projects.

matlab-live-script

Generate professional MATLAB plain text Live Scripts (.m files) with rich text documentation, mathematical equations, and visualization code following specific formatting standards.

tool-integration

Create, register, and manage custom agent tools and MCP servers to extend AI agent capabilities with external APIs and custom logic.

image-rotator

Reliably rotate images by 90-degree increments using a deterministic Python script. Supports PNG, JPG, GIF, BMP, and TIFF, preserving quality with automated file handling.

maestro

Intelligent RAG-based gateway that routes coding tasks to specialized Swift/iOS expertise without context window bloat. Uses MCP to retrieve precise patterns from 100+ indexed skills.

voice-apply

Transforms content to match specific voice profiles, tones, or styles using configurable YAML templates for consistent brand and narrative output.

litestream

Expert technical support for the Litestream disaster recovery tool, covering WAL monitoring, LTX replication, cloud storage backends, and SQLite page management.

executing-plans

Execute implementation plans in separate sessions with review checkpoints, ensuring task-by-task verification and robust code quality.

project-development

A framework for managing the end-to-end LLM project lifecycle, from evaluating task-model fit and pipeline architecture design to implementing structured output parsing and agent-assisted development.