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.

A comprehensive guide covering core principles for building high-performance 3D game systems. Topics include the rendering pipeline, shader types, physics collision detection, camera control, and advanced optimization techniques like Frustum Culling and LOD. Ideal for developers needing to build visually rich and technically robust 3D environments.

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.

This skill outlines a systematic workflow for addressing review comments and issue feedback on GitHub Pull Requests (PRs) using the GitHub CLI (`gh`). It guides users through inspecting comments, planning fixes, applying code changes, and finally responding to the threads as resolved, ensuring a streamlined code review process.

Specializes in the expert deployment and orchestration of production-grade multi-agent systems using Google's Agent Development Kit (ADK). This skill manages the complex lifecycle, including AgentCard discovery, A2A protocol communication, state management via Memory Bank, and secure execution within the Code Execution Sandbox. Ideal for setting up robust, enterprise-level AI workflows on the Vertex AI Agent Engine, especially during CI/CD pipelines.

A specialized skill for architecting, building, and productionizing complex Agent Development Kit (ADK) agents and multi-agent systems. This process emphasizes best practices, including clean module structure, robust tool integration, comprehensive regression testing, and deployment automation for reliable, shippable AI solutions. Use this when requiring the creation of structured, enterprise-grade agent code.

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.

This comprehensive guide details the architecture of memory for intelligent agents. It covers the distinction between short-term context, long-term vector storage, and advanced cognitive models (CoALA framework: semantic, episodic, procedural). Learn how to implement robust memory retrieval mechanisms, select appropriate vector databases (Pinecone, Qdrant, etc.), and utilize embedding models to build truly context-aware, persistent AI applications.

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.

An advanced AI-driven framework for holistic performance engineering of multi-agent systems. It provides coordinated profiling, workload distribution, and cost-aware orchestration across various domains (database, application, frontend). Use this tool to identify bottlenecks, improve throughput, enhance reliability, and manage resource constraints in complex agent workflows.