Skill UI

This skill guides developers through the complex process of integrating an iOS App Clip target into an existing Expo project. It covers setting up the necessary bundle identifiers, configuring associated domains (AASA file), and deploying the required metadata to allow the lightweight clip to be invoked directly from a URL alongside the parent application.

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 guides users through an in-depth interview process to achieve maximum project context completeness (100% "Gold Code") for their .faf file. By systematically asking questions about project goals, architecture, and technology stack, it ensures all critical context slots are filled, making the project fully structured and highly readable by AI systems.

This guide details best practices for implementing robust, type-safe GraphQL queries and mutations. It covers using separate .gql files, running code generation tools, and implementing mandatory error handling (onError) and loading states. Use this when building complex, reliable data fetching layers with Apollo Client.

This skill is designed to accurately convert complex, Hugo-managed documentation sites and content into standard Markdown format. It goes beyond generic knowledge by inspecting the entire local Hugo repository structure, including `hugo.toml`, custom shortcodes, layouts, and content files. It preserves the original content's semantic meaning while materializing Hugo-specific constructs into clean Markdown.

This skill is designed to identify subtle and complex logic bugs within a single code file or function. It employs a semi-formal execution tracing methodology, systematically breaking down the suspected bug into five stages: Premises (expected state), Trace (execution path), Divergence (actual deviation), Trigger (reproduction case), and Remedy (fix). It is ideal for scenarios where users suspect code is wrong but cannot specify a concrete failure point.

Neon Object Storage provides S3-compatible, branch-aware object storage directly integrated with your Neon Postgres project. It ensures that files (images, documents, user uploads) remain perfectly synchronized with the database schema across all development branches (dev, preview, staging, production). Use this skill when you need a single backend for both database rows and file assets, eliminating the need for separate cloud storage providers.

This skill automatically generates fully structured, import-ready Postman Collection v2.1 JSON files. It can interpret both natural language descriptions of REST APIs (endpoints, methods, parameters) and raw cURL commands. Use it whenever you need to quickly set up a comprehensive API testing suite in Postman without manual JSON construction, ensuring all variables and environments are properly extracted.

This skill converts standard API specifications, including OpenAPI 3.x and Swagger 2.0 (in YAML or JSON format), into fully functional, import-ready Postman Collection v2.1 JSON files. It automatically maps paths, parameters, request bodies, authentication schemes, and generates corresponding environment variables, allowing users to immediately test defined APIs.

A powerful skill for generating scripts using Puppeteer, enabling robust browser automation, data scraping, and PDF creation. Use this when you need to simulate user interactions, perform complex web testing, or extract content from dynamic websites, including handling wait strategies, network interception, and generating media files.

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 skill performs a comprehensive, read-only, multi-agent review of a git diff or file scope. It launches four specialized sub-agents—covering Regression, Security, Performance, and Contracts—in parallel. The main agent then aggregates, filters, and summarizes critical findings, ensuring the code change is robust, secure, and maintainable. Ideal for pre-commit or Pull Request review workflows.