Skill UI

A comprehensive reference architecture for building production-grade integrations with the Figma REST API. This guide covers the full design-to-code pipeline, including design token extraction, scalable asset export (icons/images), and robust webhook event handling. Ideal for architects planning new Figma services, establishing project structure, or implementing complex design system workflows.

This advanced skill generates high-quality, natural speech from text entirely offline using the VoxCPM2 model. Supporting 30 languages, it offers three modes: standard text-to-speech, descriptive voice design, and advanced voice cloning from reference audio. All processing runs on the local device (optimized for Apple Silicon), ensuring zero API calls, zero cost, and maximum privacy. Ideal for creating professional voiceovers, podcasts, and video narration without internet dependency.

Generates professional PNG icon image sets for the Xcode Asset Catalog, supporting both massive open-source collections via Iconify API and native Apple symbols via SF Symbols. Use this tool to streamline the creation, search, and replacement of app icons, ensuring consistency in style, size, and color across your iOS project.

A comprehensive guide to implementing the iconic Cyberpunk aesthetic across multiple platforms (Web, iOS, Android, Flutter). This guide details core principles like Neon-on-Black color schemes, sharply angled geometries (using clip-path or custom clippers), and glitch effects. It is ideal for developers needing to create high-tech, dystopian, or hacking interface experiences.

A comprehensive guide for developers to implement the iconic Vaporwave aesthetic across multiple platforms, including Web (CSS), iOS (SwiftUI), and Flutter. It details core principles such as 90s OS motifs, cyan/magenta neon gradients, and surreal pastel color palettes, enabling the creation of nostalgic, retro-futuristic user interfaces.

Unified design skill covering brand identity, design tokens, UI styling, AI logo generation, corporate identity programs, HTML presentations, banners, icons, and social media photos. Automates logo design, CIP mockups, slide decks, and multi-platform social images.

A comprehensive skill for preparing all metadata and visual assets required for App Store and Google Play listings. This guide covers strict character limits for copy (name, description, keywords) and exact pixel/format specifications for assets (icons, screenshots, feature graphics). It ensures the app listing is polished, compliant, and ready for submission across multiple platforms.

Recreate the modern, frosted glass aesthetic of macOS notification banners. This template is ideal for adding polished, system-like alerts to video overlays, product launch videos, or social media announcements. It features structured content areas for app icons, titles, and detailed text, mimicking the native OS look and feel.

Generates professional PNG icon image sets for the Xcode Asset Catalog, supporting both massive open-source collections via Iconify API and native Apple symbols via SF Symbols. Use this tool to streamline the creation, search, and replacement of app icons, ensuring consistency in style, size, and color across your iOS project.

This guide provides expert principles for designing and auditing system-level experiments in computer architecture (e.g., for ASPLOS-level conferences). It details the proper usage of various instruments—from real silicon and FPGAs to cycle-level simulators and analytical models—ensuring that every claim is matched to a valid, stated error model. It emphasizes workload selection, establishing strong baselines, performing rigorous ablation studies, and honestly reporting measurement noise and limitations to maintain scientific credibility.

A comprehensive guide for establishing research reproducibility in Electronic Design Automation (EDA). It details best practices for pinning benchmark suites, disclosing specific tool versions (open/commercial), documenting technology nodes (PDKs), and handling stochastic data flows (seeds, variance). Essential for ensuring that semiconductor design claims and ML-for-EDA results are credible and verifiable by the academic community.

This guide outlines the rigorous methodology for ensuring that computational research results, especially those involving hardware architecture and simulation, are fully reproducible. It emphasizes pinning the entire result chain—from simulator commits and configuration files to workloads, sampling recipes, and statistics scripts—and mandates creating a manifest for every figure. Key practices include recording machine state for silicon runs and containerizing the environment to prevent data drift.