Skill UI

A comprehensive quick reference guide for functional programming types in TypeScript using the fp-ts library. It helps users decide which type (`Option`, `Either`, `Task`, etc.) to use based on whether operations are synchronous, asynchronous, and if they involve errors or missing values. It provides decision trees, common import patterns, and utility function patterns for robust TypeScript development.

This infrastructure provides a robust solution for running powerful AI agents in remote, sandboxed environments. It overcomes the limitations of local execution by ensuring unlimited concurrency, consistent environments, and collaborative features. Use this to build complex background coding agents, multi-client interfaces (e.g., Slack, Web), or scale agent workloads far beyond local machine constraints.

An expert assistant for the Makepad UI framework in Rust. This skill helps users generate correct, boilerplate-free Rust code, understand basic app structure, manage event handling, and troubleshoot issues related to cross-platform GUI development. Use it when you are starting a new Makepad project or need foundational guidance on widget implementation and application lifecycle.

This skill provides expert guidance on Makepad's cross-platform development lifecycle. It helps users understand platform-specific APIs, manage conditional compilation (e.g., macOS, Windows, WebGL2), and handle OS-specific behaviors across desktop, mobile, and web environments. Use it when targeting diverse deployment targets or integrating platform-level features.

This comprehensive reference guide for Makepad provides essential materials for developers. It covers common errors, debugging tips, API documentation, and advanced techniques for adaptive and high-quality layouts. Use this resource when troubleshooting, looking up specific API details, or ensuring code robustness across different platforms.

This skill generates a complete, opinionated, modern full-stack Rails application boilerplate. It sets up a robust tech stack including Rails 8, PostgreSQL, React, Inertia.js, Vite, Tailwind CSS, Sidekiq, and Redis. Designed for developers who need a starting point that adheres to best practices for database structure (UUIDs, JSONB), background job processing, comprehensive testing, and modern frontend development (TSX). Ideal for bootstrapping complex, production-grade web services.

This skill outlines a comprehensive methodology for designing, building, and deploying robust LLM-powered applications. It covers assessing task-model fit, designing modular pipelines (acquire -> prepare -> process -> parse -> render), and managing state using file systems to ensure scalability and reliability in AI systems.

This tool graduates proven patterns and recurring learnings stored in auto-memory into permanent, enforced rules within the project's rule system (CLAUDE.md or scoped rules). It formalizes best practices, ensuring the AI assistant consistently adheres to project conventions, architectural decisions, and development standards across all future sessions. Use it when a pattern has been corrected or observed multiple times and should become a mandatory guideline.

Varlock is a secure-by-default CLI tool designed to manage and validate sensitive environment variables and secrets during coding sessions, CI/CD, and development workflows. It ensures that credentials, API keys, and passwords are never exposed in logs, terminal output, error messages, or the assistant's context, providing a critical layer of security for modern development practices.

This skill monitors the performance of Claude Code and the local operating system. It diagnoses slowdowns, measures CPU/RAM/Disk usage, checks API latency, and generates detailed system health reports. It helps determine if the bottleneck is local hardware or the remote API connection, providing actionable corrective suggestions to improve the development workflow.

This skill guides the development of EarLLM One, a complex Android application. The app captures real-time audio from Bluetooth earbuds, transcribes the voice using STT, sends the text input to a Large Language Model (LLM) via a robust pipeline, and plays the response back using TTS. It requires deep knowledge of Kotlin, Android development, Bluetooth profiles (A2DP/HFP), and advanced audio routing.

This skill guides users on setting up automated code quality gates directly at the Git level. It covers best practices using frameworks like Husky, lint-staged, and pre-commit. Users can enforce linting, formatting, type-checking, and standardized commit message conventions (e.g., Conventional Commits) before code is committed or pushed, significantly improving code hygiene and development workflow consistency.