Skill UI

This framework provides a comprehensive methodology for defining, measuring, and implementing Service Level Indicators (SLIs), Service Level Objectives (SLOs), and managing error budgets. It helps engineering teams quantify service reliability (e.g., availability, latency) and establish clear, measurable targets (SLOs). By tracking error budgets, teams can strategically balance rapid feature development (innovation velocity) against maintaining high system stability and reliability.

A comprehensive guide and setup for building a fast, reliable local development workflow for language learning applications. It includes setting up mock tutor responses for offline testing, generating audio test fixtures using TTS, and implementing a debug mode for detailed pronunciation scoring analysis. Ideal for engineers setting up a new Speak-based integration or refining existing test pipelines.

This skill provides expertise in building robust, scalable, and intelligent test automation ecosystems. It covers AI-powered test generation, self-healing tests, Test-Driven Development (TDD), cross-platform automation (Web/Mobile/API), advanced CI/CD integration, and comprehensive quality engineering practices, ensuring high quality across the entire software development lifecycle.

This expert guide provides comprehensive best practices for developing robust, high-performance C++ code within Unreal Engine 5.x. It covers critical topics such as UObject lifecycle management, adhering to Epic Games' strict coding conventions, optimizing performance-critical sections (e.g., avoiding unnecessary Ticking), and correctly using soft references for asset loading. Use this skill for professional game development tasks requiring deep knowledge of the Unreal reflection system and advanced C++ patterns.

Guides building async worker pipelines for real-time conversational voice agents with streaming transcription, LLM reasoning, and interrupt-aware TTS across providers.

This comprehensive guide outlines the core principles for developing immersive Virtual and Augmented Reality (VR/AR) applications. It covers critical aspects such as platform selection (Quest, ARKit, ARCore), mandatory performance metrics (FPS, frame budget), user comfort techniques (motion sickness prevention), and best practices for spatial interaction and world design. Essential reading for engineers building next-generation spatial computing experiences.

Guides product managers from discovery notes to an aligned PRD by orchestrating problem framing, user context, solution definition, and success criteria so engineering and stakeholders share a clear source of truth.

This comprehensive guide outlines a rigorous, human-in-the-loop engineering workflow for leveraging advanced AI agents to complete complex implementation tasks. It mandates defining clear completion criteria, decomposing work into manageable units, and strategically routing models (Haiku, Sonnet, Opus) based on task complexity. The workflow emphasizes continuous evaluation, regression testing, security review of invariants and edge cases, and strict cost discipline to ensure robust and reliable software development using AI assistance.

This model provides an engineering operational framework for teams that leverage AI agents to generate large volumes of code. It shifts the focus from simple syntax to process quality, robust architecture design, rigorous testing, and systematic risk control, ensuring the development of highly stable and reliable systems in AI-assisted environments.

This skill simulates a highly advanced 'Board of Experts,' consulting multiple specialized personas (e.g., Musk, Buffett, Jobs) simultaneously. It gathers distinct, multi-perspective analyses on any complex topic—from engineering feasibility to market strategy—and synthesizes them into a single, comprehensive, and actionable decision. Ideal for complex strategic planning, product development, and high-stakes investment decisions.

This utility converts a single, high-level objective into a comprehensive, step-by-step build plan suitable for complex, multi-agent, or multi-session engineering projects. It automatically handles dependency mapping, detects parallel steps, and structures each step with self-contained context briefs, ensuring any new agent can execute it without prior knowledge. Ideal for large-scale refactoring or feature development spanning multiple PRs.

This skill enforces rigorous governance and discipline when AI modifies systems, debugs code, or makes root-cause claims. It mandates safety gates (e.g., backup, blast radius check), demands evidence for every diagnosis, and prevents common AI shortcuts like guessing or deflecting. Use this for critical code modifications, complex debugging, and high-stakes deployments to ensure thoroughness and safety.