Skill UI

This skill provides a deterministic, git-only method to verify claims made by AI agents (e.g., 'done', 'shipped', 'fixed'). Instead of trusting the agent's self-report, it audits the commit's diff and git history using the DOS kernel (`dos verify`, `dos commit-audit`) to confirm if the claimed change genuinely occurred and is backed by non-forgeable evidence. It acts as a critical quality gate.

Build reliable and maintainable end-to-end test suites to ensure code quality, catch regressions early, and validate critical user workflows across browsers and environments. Ideal for CI/CD pipelines and automation strategies.

This skill provides a comprehensive guide for implementing sophisticated, print-quality editorial design across web, iOS (SwiftUI), and Flutter platforms. It focuses on achieving an elegant, magazine-like aesthetic using classic techniques such as serif/sans-serif typography pairing, large drop caps, pull quotes, and columnar layouts. Ideal for content-heavy applications needing a sophisticated, literary feel.

This skill enables reviewing defined code diffs from correctness, authorization, and performance perspectives. It supports bounded multi-agent review when authorized, ensuring findings are reproduced centrally. Users must pin repository paths and intended behavior. It does not install orchestration engines but provides a structured procedure for code quality assurance and security validation without overstepping user authority.

Provides comprehensive guidance on building robust evaluation frameworks for complex agent systems. It addresses the inherent challenges of non-determinism and multiple valid paths, enabling systematic performance testing, validating context engineering choices, and measuring continuous improvements across multiple quality dimensions (e.g., factual accuracy, tool efficiency).

Review local branch changes to find bugs, security vulnerabilities, and code quality issues using a structured checklist-driven audit process for code review and security assessments.

This skill evaluates content for AI citability based on research from Princeton and Georgia Tech. It scores content on answer block quality, self-containment, and structural readability to optimize visibility in AI-generated responses. Ideal for SEO and GEO strategies to ensure AI models extract and cite your content effectively.

Evaluate content quality and E-E-A-T signals for AI citability. Assess experience, expertise, authoritativeness, trustworthiness, and content structure to optimize for AI search platforms.

This skill guides users on setting up automated code quality gates directly at the Git level. It covers best practices using frameworks like Husky, lint-staged, and pre-commit. Users can enforce linting, formatting, type-checking, and standardized commit message conventions (e.g., Conventional Commits) before code is committed or pushed, significantly improving code hygiene and development workflow consistency.

This skill provides comprehensive guidelines for writing idiomatic Go code. It covers error handling, slices, maps, concurrency with goroutines, structs, interfaces, and common anti-patterns. Use it to improve code quality and adhere to Go community standards.

This comprehensive guide outlines Apple's Human Interface Guidelines (HIG) for various technologies and user flows (e.g., Siri, Apple Pay, HealthKit, ARKit, Generative AI). It teaches developers how to ensure their applications feel natively integrated, respect user privacy, and adhere to established platform patterns across iOS, macOS, and other Apple devices. Essential reading for building high-quality, compliant, and seamless user experiences.

A comprehensive guide for continuous improvement, error proofing, and standardization. Use this skill to enhance code quality, refactor code, or discuss process improvements. It emphasizes incremental progress and prevention over massive effort.