Skill UI

Implements a robust, four-stage coding discipline (Plan, Isolate, Test First, Double Review) to prevent premature code delivery. This skill forces the AI to structure complex tasks by planning, implementing in isolation, writing tests first, and performing deep self-review, significantly improving code correctness and quality.

This skill generates a complete and detailed specification for an ETL/ELT data pipeline. It covers everything from source systems, transformation logic, and data quality rules to destination data warehousing and required SLAs. Use this when planning data ingestion, designing complex data flows, or needing comprehensive architecture documentation for engineering handoff.

This skill provides a comprehensive, structured audit of any design system. It assesses component coverage gaps against production UI, monitors design token consistency (color, spacing, typography), evaluates documentation quality, and checks for accessibility compliance. The output includes an overall health score and a prioritized remediation roadmap, making it essential for design leadership and engineering teams maintaining shared component libraries.

This skill automates the creation of high-quality, structured pull request descriptions. By analyzing git diffs, commit lists, or raw changes, it generates comprehensive sections covering the summary, motivation, specific changes, detailed testing procedures, and crucial reviewer guidance. Use it to ensure every code change is fully documented for effective peer review.

Conducts a comprehensive, scored audit of a brand's social media presence across multiple platforms. It assesses profile completeness, content quality, audience engagement, posting consistency, and competitive positioning. The output includes a detailed health score, platform-by-platform analysis, competitor gap analysis, and a prioritized, actionable strategy plan ready for immediate implementation by marketing teams.

This skill generates formal, audit-ready Standard Operating Procedures (SOPs) suitable for regulated industries, ISO certification, or internal process standardization. It structures the document with defined sections including Purpose, Scope, Definitions, Responsibilities, detailed Step-by-step Procedures, Quality Checks, and Non-Conformance handling, ensuring compliance and clarity for operational scaling.

This skill generates a comprehensive and structured test strategy document. Given a feature specification, PRD, or system description, it systematically outlines the test scope, performs a detailed risk assessment, defines necessary test types (Unit, E2E, Performance, Security), and provides a prioritized, actionable test case outline to ensure robust quality assurance before release.

This skill automates end-to-end (E2E) functional testing for Android applications. It parses an XML specification defining a multi-step user 'journey.' The executor sequentially performs interactive UI actions (taps, swipes, text input) using ADB commands and verifies application states through assertions. It outputs a detailed, standardized JSON report, making it ideal for robust CI/CD integration and quality assurance.

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.

Use Mailtrap Email Sandbox to capture and inspect outbound emails without sending them to real recipients. This is crucial for development, staging, and CI/CD environments. It allows developers to test email content, headers, attachments, and spam scores using dedicated APIs, SMTP settings, or SDKs, ensuring mail integrity before deployment. Ideal for automation and rigorous quality checks.

This document provides a comprehensive guide to the principles and vocabulary needed to write high-quality, predictable, and effective skills. It covers advanced concepts like model-invoked vs. user-invoked skills, managing context load, structuring information hierarchies (steps vs. reference), and implementing progressive disclosure to improve agent predictability.

A comprehensive, adaptive framework for developing and implementing features, adhering to a strict, multi-phase workflow: Specify, Design, Tasks, and Execute. This skill ensures feature quality through granular task breakdown, atomic Git commits, requirement traceability, and mandatory, independent verification. It automatically adjusts its depth based on feature complexity (Small to Complex), providing a robust mechanism for managing technical debt and capturing project lessons.