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.

Provides WCAG 2.1 based accessibility audit guidance for web interfaces, covering perceivable, operable, understandable, and robust principles along with code snippets for alt text, contrast, keyboard, focus, and media alternatives.

Automates adapting pretrained models via transfer learning by analyzing requirements, generating PyTorch or TensorFlow fine-tuning code, validating data, tracking KPIs, and saving artifacts so you can quickly reuse existing models for new datasets and tasks.

AFL++ is an advanced fork of AFL that runs multi-core campaigns, supports diverse mutations, and targets large C/C++ codebases with stable tooling via Docker or host builds.

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.

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.

Utilizes the Azure AI Projects SDK's ImageBasedHostedAgentDefinition to build robust, container-based AI agents deployed in Azure AI Foundry. This workflow is essential for creating production-grade agents that require custom logic, precise resource allocation (CPU/Memory), and integration of advanced tools like code interpreters.

Aggregates performance metrics from applications, systems, databases, caches, queues, and external services into a central platform, helping teams define a consistent taxonomy, choose aggregation tools, and create dashboards and alerts for faster monitoring and troubleshooting.

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.

A comprehensive, multi-phased workflow bundle designed for building production-grade AI and Machine Learning applications. It guides users through key stages, including AI application design, RAG implementation, autonomous agent development, robust ML pipelines, AI observability setup, and security hardening.

A comprehensive, multi-phased workflow bundle designed for building production-grade AI and Machine Learning applications. It guides users through key stages, including AI application design, RAG implementation, autonomous agent development, robust ML pipelines, AI observability setup, and security hardening.