Skill UI

This comprehensive guide details idiomatic best practices for writing high-performance, safe code in Rust. It covers advanced topics such as understanding Ownership and Borrowing rules, robust error handling using the '?' operator, optimizing functional programming with Iterators, implementing structured concurrency, and defining robust data models with Structs and Enums.

This skill analyzes your Gmail inbox over a specified time window, filtering out noise such as newsletters, receipts, and automated notifications. It surfaces only actionable items—emails requiring a direct reply, a decision, or follow-up—and even drafts suggested replies for efficiency.

Generates a structured, multi-section checklist and review framework for assessing tax efficiency for individuals and businesses. It covers key areas including income allowances, pension planning, capital gains tax, corporate reliefs (e.g., R&D, AIA), VAT, and estate planning. Use this tool when conducting year-end reviews or identifying tax-saving opportunities. Note: This is a framework for discussion, not qualified tax advice.

This comprehensive guide details how to configure, integrate, and troubleshoot live email sending using Mailtrap. It covers two primary methods: the Email API (REST) and SMTP. Learn the differences between core email streams—transactional (app-generated notifications), bulk (promotional marketing), and batch requests (high-volume efficiency)—along with recommended integration patterns and specific technical endpoints.

This skill helps developers achieve maximum AI readiness by automatically detecting the project stack and seeding context using a single, precise goal sentence. It systematically fills the essential project context (the 6 Ws), requiring the user only to answer the few remaining knowledge gaps (like the 'why' or 'when'). This creates a portable, structured context file, ensuring AI models have a complete understanding of the project before coding, minimizing guesswork and maximizing efficiency.

This skill provides comprehensive code review capabilities for analyzing git diffs or specific file scopes. It goes beyond simple syntax checks, assessing code for reuse opportunities, quality issues (e.g., redundant state, leaky abstractions), efficiency bottlenecks, and adherence to standards. It utilizes multiple specialized sub-agents (Reuse, Quality, Efficiency, Clarity) to perform parallel, read-only audits. Optionally, it can apply high-confidence, behavior-preserving fixes upon user request.

This meta-skill acts as a routing layer for complex UI development tasks. It guides the process of selecting the smallest, most specific, and useful skill context using the ui-skills CLI. Use it when starting UI work to analyze goals, identify the correct category, and pinpoint the precise skill set needed for implementation, ensuring development efficiency and accuracy.

Use this skill to define and set up self-running, recurring marketing workflows (loops). Instead of one-off tasks, these loops operate on defined cadences (weekly, daily, on-trigger), acting as always-on systems for continuous monitoring and optimization. It orchestrates multiple marketing skills—like SEO scanning, ad fatigue checks, and churn detection—to maintain marketing momentum and efficiency.

This skill enforces highly efficient coding practices, guiding the AI to write the simplest, most direct code possible. It strictly follows an 'Efficiency Ladder' (YAGNI, reusing existing code, standard library, etc.) to prevent over-engineering, unnecessary abstractions, and boilerplate. Use this when the goal is maximum efficiency with minimal code.

A comprehensive tool for establishing performance baselines across multiple dimensions. It measures Core Web Vitals (LCP, CLS, INP), API latency (p50, p95, p99), and development build efficiency. Essential for detecting performance regressions before merging code, ensuring stability in CI/CD pipelines, and comparing alternative stack implementations.

An advanced system that analyzes coding sessions via hooks to extract atomic, confidence-scored behaviors called "instincts." It tracks patterns, allowing users to evolve these instincts into comprehensive skills, commands, or agents. V2.1 introduces project-scoped learning, preventing knowledge contamination between different codebases and improving development workflow efficiency.

This skill runs comprehensive benchmarks against structural and adversarial corpora. It measures the performance and cost efficiency of a booster model compared to leading LLM baselines (Gemini, Anthropic). Results are persisted in verifiable JSON reports, crucial for auditing claims and verifying booster win rates before publishing.