Skill UI

A comprehensive guide to optimizing operating costs when utilizing Together AI's OpenAI-compatible API. This resource details strategies for cost reduction across various AI tasks, including inference, fine-tuning, and model deployment. Learn how to select appropriate model sizes, implement caching, leverage batch inference, and utilize Turbo variants to maximize efficiency and minimize spending.

This skill provides comprehensive, expert-level guidelines for authoring robust design systems. It covers foundational style tokens, strict accessibility requirements (WCAG 2.2 AA), and detailed component anatomy (states, variants, interaction behavior). Use this to create implementation-ready documentation that ensures visual consistency and optimal user experience across all digital platforms.

This expert skill guides the creation of comprehensive, highly structured, and implementation-ready design system guidelines based on Material Design principles. It ensures consistency across visual style, accessibility (WCAG 2.2 AA), and component behavior. The output is actionable documentation covering tokens, states, variants, and anti-patterns, suitable for guiding both engineers and designers.

A comprehensive guide for creating a robust design system based on perspective techniques. It details foundational tokens (color, typography, spacing), component rules (states, variants), and accessibility criteria (WCAG 2.2 AA). Use this skill to ensure scalable, realistic, and highly usable UI components with structured implementation.

This guide teaches you how to function as an expert design-system guideline author for the 'Vibrant' brand. It provides comprehensive, implementation-ready documentation covering foundational tokens (color, typography, spacing), detailed component rules (states, variants, interactions), and adherence to WCAG 2.2 AA accessibility standards. The output is structured, actionable, and includes quality assurance checklists for both designers and engineers.

This tool helps you draft, rewrite, or condense professional emails with precise control over tone, length, and audience. Whether you are performing cold outreach, providing status updates, or handling escalations, it ensures your communication is clear, concise, and impactful. It guides the process from outlining key points to providing structured variants and QA checks.

Unship is a dedicated local workflow for front-end developers to compare multiple AI-generated UI alternatives (such as layouts, copy, or flows) directly within a running application environment. Instead of committing to one generated version, it adds temporary source-level variants, allowing for visual comparison. After the user selects the final version, the skill automatically cleans up all unused temporary code and attributes, ensuring a clean, production-ready codebase. Note: This tool is for local iteration only, not for production A/B testing or analytics.

This skill systematically defines all necessary variants, states, and properties for a Figma component before development. It ensures comprehensive coverage of all states (e.g., Hover, Disabled, Loading) and properties (e.g., Size, Type), generating a robust variant matrix that prevents critical design gaps and streamlines the entire component building process in Figma.

This guide explains how to register custom model slugs (variants) within the Pi Agent's configuration files. It is essential for ensuring that specialized OpenRouter variants or brand-new provider models, which are not part of the static bundled registry, resolve correctly and are available for accurate agent model invocation.

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 skill provides a rigorous, measured framework for optimizing system performance. It guides users to convert vague goals (e.g., 'make it faster') into a bounded, repeatable process. The loop requires establishing a clear baseline, systematically testing hypotheses (variants), identifying bottlenecks, and promoting the safest, most measurably improved solution, ensuring reproducibility and stability throughout the process.

Compares two recorded browser sessions step-by-step by matching steps via shared step IDs. It performs both pixel-level diffing on screenshots (for visual regression) and node-by-node diffing on accessible DOM snapshots (for structural changes). Ideal for visual regression testing, comparing A/B flow variants, and verifying replay consistency in UI development.