Skill UI

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.

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.

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.

Outlines WCAG 2.2 accessibility patterns for web apps, covering semantic HTML, ARIA roles, keyboard navigation, form handling, and contrast checks to keep interfaces usable for assistive technologies.

Generates and iterates high-performing ad creative—headlines, descriptions, primary text, and full variations—for any platform, using performance data to discover winning angles, stay within platform specs, and scale creative production.

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.

AddressSanitizer accelerates fuzz testing by instrumenting C/C++ and unsafe Rust code to detect buffer overflows, use-after-free, and related memory corruption bugs at runtime, with configurable ASAN_OPTIONS for verbosity, abort behavior, and leak control.

This skill provides comprehensive management for multiple local Command Line Interface (CLI) agents. It allows users to start, stop, monitor, and assign tasks to these agents, running them in separate tmux sessions for parallel execution. It is ideal for complex, scheduled workflows requiring multi-agent coordination, task monitoring, and log tailing, making it invaluable for advanced automation setups.

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.

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.

Automates the creation of Airflow DAGs for ETL and streaming data workflows, offering best-practice guidance, production-ready code samples, and validation against common pipeline standards.