Skill UI

This skill guides the full lifecycle of game development, from architecture design (ECS, component systems) to high-performance implementation. It covers integrating Unity/Unreal Engine features, implementing complex physics, multiplayer networking with lag compensation, and achieving consistent 60+ FPS performance. Key techniques include object pooling, state machines, and rigorous profiling to ensure production-ready, scalable game systems.

This orchestrator skill provides comprehensive core principles for game development, covering topics like the Game Loop, design patterns (State Machine, ECS), performance budgeting, and input abstraction. It guides users by routing them to specialized sub-skills based on required platforms (Web, Mobile, PC), dimensions (2D, 3D), and specialty areas (AI, Networking, Art). Ideal for structuring complex game projects from concept to implementation.

This skill guide helps developers master best practices and architectural patterns for Godot 4 game development using GDScript. It thoroughly covers implementing game systems, managing complex state machines, utilizing signals, and optimizing performance. Use this when building robust, scalable, or advanced Godot games.

This skill provides a comprehensive pattern for building robust, scalable, and fault-tolerant asynchronous video generation pipelines using Kling AI. It implements advanced software engineering patterns, including Redis queues (Producer/Consumer), State Machines, and event-driven callbacks. It is designed for managing complex, multi-step AI workflows—such as generate, extend, and download—ensuring reliable background task processing.

This skill orchestrates the entire coding lifecycle as a state machine, ensuring tasks proceed from issue intake through implementation, structured review, deployment, and final acceptance verification. It enforces a rigorous 'Definition of Done' (DoD) proof, moving beyond simple code changes. Ideal for complex feature development requiring structured handoffs and explicit verification evidence.

Transformers.js allows developers to run state-of-the-art machine learning models directly within JavaScript/TypeScript environments (browser, Node.js). It supports a comprehensive range of tasks including NLP (text generation, summarization, translation), Computer Vision (image classification, object detection), and Audio processing. This capability enables building fully client-side, multimodal AI applications without requiring a dedicated Python backend or server infrastructure.

This skill provides a robust system for tracking all code modifications using structured JSON records and enforcing a defined state machine (e.g., planned -> deployed). It is critical for AI coding workflows, ensuring that if a session expires or is abandoned, the next session receives the full context, progress summary, and blockers to resume seamlessly. Ideal for complex, multi-stage development projects and onboarding new codebases.

Tracks every code change via structured JSON records, enforces a lifecycle state machine, and maintains session handoff context with scripts for init/create/update/status/resume/close/export workflows.

A robust orchestration skill designed for managing large-scale, complex machine learning experiments on remote GPU clusters via SSH. It handles multi-seed sweeps, sequential wave transitions, and deep learning pipelines requiring state management. Features include OOM-aware automatic retries, cleanup of stale terminal sessions, and deterministic scheduling to prevent race conditions, making it ideal for grid computing and staged training.

A comprehensive TypeScript SDK providing production-ready patterns for interacting with the Fly.io Machines REST API. It includes a typed client for managing the entire machine lifecycle—creation, state polling, and deletion—across multiple global regions. Features like robust error handling, rate-limit retries, and deployment builders ensure reliable multi-region orchestration for edge computing applications.

This guide provides methods for robust monitoring and automation tailored for services deployed on Fly.io. It covers key areas including polling the Machines API to detect state changes, implementing comprehensive health check endpoints, processing structured logs using tools like `jq`, and automating deployment notifications within CI/CD pipelines. Essential for achieving high availability and reliable operations.

Manages the complete lifecycle of GitHub issues using a structured, label-based state machine. This skill assists maintainers in triaging incoming reports, determining if an issue is a bug or enhancement, and moving it through defined states (e.g., needs info, ready for agent, wontfix). It ensures systematic workflow management for development projects.