Skill UI

A comprehensive guide covering fundamental principles for creating 2D games. It details best practices for sprite organization (atlases, animation), tilemap design, physics implementation (collision shapes, fixed timestep), camera systems (follow, look-ahead), and genre-specific patterns like platformers and top-down games. Essential for game developers building robust 2D experiences.

Designs and implements immersive web experiences using Three.js, React Three Fiber, Spline, and WebGL, covering product configurators, portfolios, scroll-driven scenes, and performance-aware 3D delivery.

Guides teams through planning, designing, and running statistically sound A/B tests with hypothesis framing, sample sizing, metric selection, implementation strategies, and analysis checkpoints for systematic growth experimentation.

A comprehensive tool for designing, generating schemas, and evaluating complex multi-agent systems. It guides users through defining system requirements, selecting optimal orchestration patterns (Supervisor, Swarm, Pipeline), generating provider-ready tool schemas (Anthropic/OpenAI), and analyzing execution logs for performance bottlenecks, cost, and latency. This ensures robust, production-grade autonomous workflows.

This skill provides a comprehensive guide to designing and developing autonomous AI agents for code plugins. It covers the required file structure (YAML frontmatter), best practices for defining agent roles, designing robust system prompts, and setting up clear triggering conditions. Use this when you need to conceptualize, structure, or refine an agent's capabilities to handle complex, multi-step tasks autonomously.

A sophisticated hybrid memory system designed specifically for AI agents. It provides a persistent, searchable knowledge bank to store, retrieve, and manage long-term knowledge, including architectural designs, operational patterns, and critical decision points. By syncing with project documentation, it ensures that crucial institutional knowledge remains accessible throughout the development lifecycle, enhancing agent reliability and consistency.

A systematic workflow designed to significantly improve the reliability and performance of existing AI agents. It covers detailed performance analysis, including gathering metrics (e.g., task success rate, latency), classifying failure modes, and advanced prompt engineering techniques (like Chain-of-Thought and Constitutional AI). The process culminates in structured A/B testing and controlled deployment, ensuring measurable, robust improvements.

Covers designing agent tools from schema creation through validation and error handling, emphasizing descriptions that keep LLMs accurate and reusable via MCP standards.

A comprehensive, multi-phase workflow designed for building sophisticated AI systems. This guide covers everything from designing autonomous agents and implementing single-agent logic, to orchestrating complex multi-agent systems and integrating advanced tools and memory mechanisms. Ideal for developers building production-grade AI applications using frameworks like CrewAI and LangGraph.

A comprehensive, multi-phase workflow designed for building sophisticated AI systems. This guide covers everything from designing autonomous agents and implementing single-agent logic, to orchestrating complex multi-agent systems and integrating advanced tools and memory mechanisms. Ideal for developers building production-grade AI applications using frameworks like CrewAI and LangGraph.

This skill covers the comprehensive architecture design and construction of autonomous AI agents. Experts master advanced concepts including tool use, hierarchical memory systems (episodic, semantic, working), sophisticated planning strategies (ReAct, Plan-and-Execute), and multi-agent orchestration. It focuses on building systems that achieve high levels of autonomy while maintaining critical human oversight, designing for graceful degradation and rigorous error handling.

This skill covers the comprehensive architecture design and construction of autonomous AI agents. Experts master advanced concepts including tool use, hierarchical memory systems (episodic, semantic, working), sophisticated planning strategies (ReAct, Plan-and-Execute), and multi-agent orchestration. It focuses on building systems that achieve high levels of autonomy while maintaining critical human oversight, designing for graceful degradation and rigorous error handling.