Skill UI

Analyzes a project's StyleSeed engine files, compares them with a pinned upstream revision, and safely updates skills, .cursorrules, DESIGN-LANGUAGE.md, and Golden Rules with explicit user approval. Use for maintaining the design system engine without overwriting custom UI.

This skill inspects current product architecture, dependencies, and validation loops before planning or implementing changes. It reads documentation, tasks, and specs to trace dependencies and report risks. It performs read-only inspection and hands findings to planning or implementation tools for safe workflow integration.

This comprehensive skill transforms raw game concepts into fully actionable, structured Unity development projects. It guides users through a five-phase pipeline, generating detailed Game Design Documents (GDDs), technical scene blueprints, core system architectures, and ready-to-use AI prompts for assets (3D models, textures, music, SFX). Ideal for concept validation and starting a complex game project from scratch.

This skill provides comprehensive guidelines for upgrading Expo SDK versions and resolving dependency conflicts. It covers migration paths for React 19, new architecture, and deprecated packages like expo-av. Users will learn step-by-step processes including cache clearing, prebuild configuration, and updating app.json settings. It also addresses Hermes engine v1 adoption and Metro configuration changes. Essential for maintaining modern React Native projects.

This skill captures and persists critical user decisions, project constraints, and architectural choices across multiple sessions and agents. It saves this context into a structured memory database (`mdbase`), ensuring that future development steps or agents can retrieve the original intent without re-deriving it from scratch. Ideal for documenting project rules, tech stacks, and design rationale.

LWC is a durable memory system for coding agents that preserves project decisions, code structure, and verified context across sessions. It utilizes a physical Wiki document graph and a CodeGraph index to enable source-grounded recall. This tool helps agents search, update, and maintain knowledge without losing critical information during future coding-agent interactions.

uv is an extremely fast Python package installer and resolver written in Rust. This guide provides a comprehensive workflow for modern Python project management and dependency handling. Use this skill to set up new projects quickly, manage dependencies much faster than traditional tools like pip, resolve complex conflicts, and optimize CI/CD pipelines, especially when dealing with monorepos or needing reproducible builds.

Guides GitHub Copilot through V language development: installing the toolchain, structuring v.mod projects, building, formatting, vetting, testing, and writing idiomatic V with Option/Result error handling and immutability rules.

A comprehensive automation toolkit designed to manage the entire Vercel deployment lifecycle and associated infrastructure. Users can programmatically monitor, trigger, and manage deployments, configure custom domains and DNS records, manage environment variables across multiple targets, and oversee project settings and project listings. Essential for implementing robust CI/CD pipelines and managing cloud infrastructure.

This guide details how to deploy and manage web projects on Vercel using token-based authentication via the CLI. It provides comprehensive steps for locating and setting the VERCEL_TOKEN, identifying project IDs, and executing deployment commands (including linking and direct deployment) without relying on interactive login.

This skill performs an observability-first audit of deployed Vercel applications. It comprehensively analyzes production metrics, project configurations, and codebase scans to identify potential cost overruns and performance bottlenecks. It provides version-aware, actionable recommendations for improving application efficiency and reducing cloud expenditure.

Vibers provides a human-in-the-loop code review workflow for AI-generated GitHub projects. Users push code, and the service reviews it against a specification, checks for security issues, and submits a pull request with suggested fixes. It supports various languages and requires adding a collaborator and configuring a GitHub Action to trigger the review process.