Skill UI

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 guide covering the core principles of immersive game audio. Learn how to structure audio categories (SFX, Music, Ambient), implement adaptive music systems, master 3D spatialization, and apply professional mixing techniques (e.g., ducking, hierarchy) to elevate the emotional impact and technical quality of your game's soundscape. Essential for sound designers and game developers.

This expert skill reviews the architecture and design of the Bitcoin Lightning Network protocol. It analyzes complex Layer 2 scaling tradeoffs, compares various channel factory approaches, and evaluates critical technical aspects—including trust models, on-chain footprint, consensus requirements, HTLC/PTLC compatibility, liveness guarantees, and watchtower support. Use this when assessing the cryptographic and systemic soundness of L2 blockchain protocols.

This skill generates new UI components that strictly adhere to the StyleSeed design system conventions. It ensures the component is structurally sound, accessible, and uses specified design tokens for colors, spacing, and typography. Use this when creating isolated, reusable UI parts following the defined design system standards.

This skill acts as an elite Art Director for generating premium, highly actionable, and structurally sound frontend design references. It moves beyond generic AI aesthetics by enforcing principles like strong hierarchy, generous spacing, and image-led composition. Ideal for creating realistic, development-ready concepts for landing pages, hero sections, and multi-section websites.

This skill performs a rigorous, adversarial review of implemented code. It verifies that the task implementation is complete, structurally sound, respects defined boundaries, and aligns perfectly with approved requirements and design specifications. Use it after an implementer finishes work, or before final acceptance, to ensure code quality and architectural integrity.

A comprehensive audio generation skill designed for professional multimedia content creation. It handles three types of audio output: music (jingles, beds), voiceovers (TTS), and sound effects (SFX). The skill intelligently routes requests to advanced models like Suno V5, Udio, MiniMax TTS, and ElevenLabs, producing a single, high-quality MP3 or WAV file ready for use in marketing, video, or podcast production.

Design and audit automated email sequences for welcome, onboarding, nurture, and win-back flows. Covers sequence strategy, timing, subject lines, lifecycle email types, and copy guidelines without AI-sounding tells.

This skill acts as an expert guide for creating comprehensive, implementation-ready design system documentation. It structures guidelines by defining foundational tokens (color, typography, spacing), detailing component anatomy, interaction states, and adhering to strict accessibility standards (WCAG 2.2 AA). Use it to ensure your design standards are consistent, technically sound, and actionable for engineering and design teams.

A comprehensive guide for researchers and reviewers designing or auditing Automated Software Engineering (ASE) evaluations. It outlines the necessary rigor, including the use of real subject systems, establishing fair and runnable tool baselines, performing targeted ablations (especially for LLMs), defining explicit oracles, and ensuring full data and model provenance to guarantee reproducible and scientifically sound results.

This guide outlines best practices for designing and executing Computer Aided Verification (CAV) empirical evaluations. It emphasizes ensuring evidence matches claims by using standard, pinned benchmark sets, selecting fair baselines, respecting equal resource limits, and employing rigorous reporting methods like cactus and scatter plots to establish technical soundness and performance.

A comprehensive guide detailing best practices for designing and auditing empirical evaluations of Large Language Models (LLMs). It addresses critical pitfalls such as data contamination, baseline unfairness, model version drift, and decoding parameter sensitivity, ensuring that published results are robust, reproducible, and scientifically sound for academic publication.