Skill UI

Generates a structured Epic architecture specification from a Product Requirements Document, guiding senior architects to capture high-level approaches, layered Mermeid diagrams, features, technical enablers, stack choices, and value estimates for TypeScript/Next.js monorepo projects.

Converts text-based documentation into structured Markdown by following explicit instructions, documented options, or reference files, supporting header levels, patterns, platforms, and cleanup steps for polished technical documents.

This guide outlines the structure and content guidelines for an AGENTS.md file. This file serves as a comprehensive 'README for agents,' providing detailed, technical context and operational instructions (such as setup, development workflow, testing procedures, and code style) that enable advanced AI coding agents to interact with and understand a codebase effectively. It greatly complements traditional READMEs by focusing purely on machine-readable technical requirements.

Template for creating time-boxed technical spike documents that capture research questions, deliverables, success criteria, and decision rationale before committing to implementation.

This tool acts as an expert technical documentation specialist, transforming lengthy, verbose official documentation from URLs into concise and actionable 'tldr' pages. It extracts the most common command patterns, adheres strictly to the tldr-pages markdown format, and provides clear, example-driven command references, making complex tools easy to reference.

Translates every approval request, error, and technical output from Copilot CLI into clear, jargon-free English with color-coded risk cues, definitions, and step-by-step commentary for non-technical stakeholders so they always understand what happens and why.

A comprehensive guide to optimizing website content for modern AI search systems, including ChatGPT, Perplexity, and Google AI Overviews. It covers structural improvements, maximizing citability scores, incorporating multi-modal elements, building brand authority, and implementing technical standards like llms.txt and proper server-side rendering (SSR). Essential for maximizing organic reach in the LLM era.

Perform a comprehensive, deep-dive audit on a single URL, covering all critical aspects of Search Engine Optimization. Analysis includes on-page elements (title, headings, meta tags), content quality (readability, E-E-A-T), technical structure (schema, canonical, OG tags), image optimization, and core web vitals potential issues. Ideal for SEO audits and content quality checks.

Comprehensive audit covering all aspects of web technical SEO, including crawlability (robots.txt, sitemaps), indexability, security headers, and URL structure. It deeply analyzes Core Web Vitals (LCP, INP, CLS) and assesses complex rendering scenarios like JavaScript SEO and structured data implementation for optimal search engine visibility.

Tracks every code change via structured JSON records, enforces a lifecycle state machine, and maintains session handoff context with scripts for init/create/update/status/resume/close/export workflows.

Update existing specification files for solutions based on new requirements or code changes. Ensures documentation is optimized for Generative AI consumption, machine-readable, and self-contained. Follows structured formatting standards including requirements, constraints, interfaces, and acceptance criteria to facilitate AI-driven development workflows.

This skill helps developers accurately find and explore the correct Windows API for specific platform capabilities, such as file access, camera usage, notifications, or UI controls. It searches a comprehensive local cache of WinMD metadata, providing full technical details including methods, properties, events, and enumeration values for APIs sourced from the Windows Platform SDK and WinAppSDK. It is indispensable when building complex features requiring deep integration with the Windows operating system.