Skill UI

This skill simulates an expert software engineer and systems analyst. It is designed for deep, evidence-based understanding of complex codebases. Users can ask "how does X work" or request architectural reviews, ensuring every claim is grounded in actual code paths, data flows, and dependency chains, following a rigorous multi-iteration process.

Guides execution of implementation plans by dispatching isolated subagents per task, enforcing a spec-first then quality review loop, and preserving overall coordination context for faster, high-quality iterations.

Allows users to resume an interrupted or paused autoresearch experiment. It automatically loads the full context, including configuration, strategy files, complete results history, and recent code changes. The system then summarizes the current best metrics and patterns, prompting the user to continue with a single iteration, set up an automated loop, or manually review the results.

A comprehensive multi-agent feedback loop inspired by Generative Adversarial Networks (GANs), designed to build complex, production-ready applications. It separates generation from evaluation, using a 'ruthlessly strict' evaluator (QA) to drive continuous iteration. This process ensures high design quality, robust functionality, and originality, overcoming the limitations of single-agent self-critique. Ideal for full-stack feature development and complex product specification implementation.

A comprehensive, generics-first utility library for Go, providing over 500 type-safe, functional helpers (like Map, Filter, Reduce, GroupBy) for slices, maps, and channels. Designed to replace manual, verbose loops with declarative code. It supports immutable operations by default and offers progressive complexity variants (lo, lop, lom, loi) to handle different performance needs, from standard transforms to CPU-bound parallelism and lazy iteration. Ideal for writing clean, maintainable, and performant Go code.

This guide details setting up a robust, local development loop for Adobe integrations (Firefly, PDF, Photoshop). It covers using the `aio` CLI for App Builder, implementing hot reload, and establishing unit/integration testing with mocking to ensure rapid, reliable iteration cycles for Adobe APIs.

This guide provides a comprehensive, reproducible workflow for setting up an Algolia local development environment. It covers creating separate development indices, seeding mock data, and implementing client mocking for unit tests. This process ensures fast, safe iteration cycles, allowing developers to build and test search features without ever touching production data.

This guide provides a comprehensive setup for a fast and reliable local development loop for applications powered by the Anthropic Claude API. It covers environment management, implementing hot reloading, tracking API usage costs, and utilizing mock responses to ensure a smooth, rapid iteration cycle for AI developers.

A comprehensive setup guide for establishing a fast, reproducible local development environment for AssemblyAI projects. It incorporates hot reloading, local caching of transcription results, and mocked testing utilities to ensure rapid iteration cycles without relying on live API keys or incurring costs. Ideal for setting up new services or enhancing existing AI workflows.

A comprehensive guide to setting up a fast, reproducible local development environment for BambooHR integrations. This workflow incorporates hot reloading, API mocking (using MSW), and robust unit/integration testing. It allows developers to establish a rapid, isolated, and stable iteration cycle for building or testing third-party HR platform connections.

A comprehensive setup guide for developing Canva Connect API integrations locally. It covers implementing the OAuth 2.0 flow with PKCE, setting up a robust mock API server (using MSW) for reliable unit and integration testing, and configuring hot reloading for fast iteration cycles. Ideal for developers building SaaS applications connected to Canva.

This guide details setting up a robust local development environment for Grammarly API integrations. It covers establishing the project structure, implementing mocked API responses, and utilizing vitest for comprehensive unit and integration testing. Ideal for developers who require a fast iteration cycle or need to configure reliable API test workflows.