Skill UI

Creates a Next.js page that renders App Store screenshots as marketing ads, exports Apple‑sized images via html-to-image, and supports branded styling, locale tabs, and reusable theme presets for iOS listings.

This skill enforces rigorous governance and discipline when AI modifies systems, debugs code, or makes root-cause claims. It mandates safety gates (e.g., backup, blast radius check), demands evidence for every diagnosis, and prevents common AI shortcuts like guessing or deflecting. Use this for critical code modifications, complex debugging, and high-stakes deployments to ensure thoroughness and safety.

A comprehensive guide to building robust database layers in Kotlin using the Exposed ORM framework. It covers advanced patterns including DSL queries, DAO entity management, transaction handling, connection pooling with HikariCP, schema migration via Flyway, and implementing the Repository pattern for clean, production-grade data access.

A comprehensive set of architectural patterns for building robust and maintainable HTTP servers using Kotlin Coroutines and the Ktor framework. It covers essential topics including REST routing DSL, plugin configuration (Authentication, CORS, StatusPages), Dependency Injection (Koin), serialization, WebSockets, and integration testing using testApplication.

A comprehensive guide detailing idiomatic Kotlin patterns, best practices, and conventions for building robust, efficient, and highly maintainable Kotlin applications. Topics include null safety, immutability, sealed classes, structured concurrency, and type-safe DSL builders.

A comprehensive guide to writing reliable and maintainable tests for Kotlin projects. This skill covers modern testing patterns using Kotest for various spec styles (FunSpec, BehaviorSpec), MockK for dependency mocking, and enforces TDD principles (RED/GREEN/REFACTOR). Learn how to implement unit tests, property-based testing, and ensure high code coverage with Kover.

A comprehensive guide to integrating privacy protections into software architecture from the ground up. This skill ensures compliance with major global regulations like GDPR, CCPA, and LGPD by implementing core principles such as data minimization, purpose limitation, and default privacy settings. Essential for developers building any application that handles personal user data.

This skill provides comprehensive integration with the X (Twitter) API, enabling users to programmatically post, read, search, and analyze tweets and timelines. It covers essential authentication patterns (OAuth 2.0 and OAuth 1.0a), rate limit handling, and best practices for building robust X integrations and bots.

Autoresearch Agent runs an autonomous optimization loop that edits a target file, invokes a provided evaluation command, keeps metric-improving commits, drops failures, and repeats indefinitely so engineers can optimize speed, bundle size, prompts, or other measurable goals overnight.

A forensic tool for performing post-mortem analysis on AI-assisted coding sessions. It goes beyond simply stating what was done, determining *why* work deviated (e.g., spec ambiguity, repo friction, agent error), identifying scope creep, and classifying rework patterns. It provides evidence-backed recommendations to improve prompts, codebase health, and future development workflows.

This skill automates the complex and tedious process of porting academic papers written in LaTeX from one publisher's journal template (e.g., Springer) to another (e.g., MDPI, IEEE). It executes a structured multi-stage workflow, including content extraction, structural mapping, systematic fixing of common LaTeX issues (like float placement and math environments), and final compilation to ensure the output adheres to the target journal's specifications.

A comprehensive strategy for regression testing in AI-assisted development. It focuses on identifying systematic blind spots, such as schema inconsistencies between sandbox and production paths, and integrating automated bug checks into the development workflow. This guide teaches how to build robust, database-free API tests to ensure code quality when AI models contribute to the codebase.