Skill UI

A powerful agentic workflow for complex software development. Cascade Write mode acts as an autonomous coding agent, allowing users to build features, refactor code across multiple files, run terminal commands, and debug errors—all from a single natural language prompt. Ideal for full-stack development, complex system building, and large-scale code refactoring.

Windsurf is a powerful, AI-powered code editor built on VS Code. This guide covers the complete setup process, including initial installation for macOS/Linux/Windows, crucial Codeium authentication using Google/GitHub, and advanced configuration for enterprise or team deployments. Learn to use Supercomplete and interact with the Cascade AI agent to boost your development workflow.

A comprehensive directory designed for discovering, researching, and comparing autonomous AI agents, frameworks, and tools across diverse ecosystems. This skill helps users map the current AI agent landscape, identify existing solutions, gather best practices, and pinpoint market gaps before initiating custom development or workflow design.

Denario orchestrates a multi-agent AI to automate research workflows, covering idea generation, methodology development, computational experiments, and LaTeX publication output.

This powerful skill manages Git worktrees to enable safe, isolated parallel feature development. It standardizes branch isolation, automatically allocates unique network ports, and synchronizes environments, allowing multiple agents or terminal sessions to work on different branches simultaneously without conflicts. It also provides mechanisms for detecting and safely cleaning up stale worktrees.

A comprehensive guide covering patterns and architectures for automating code execution using LLMs. It spans simple sequential pipelines (e.g., daily dev scripts) to advanced, RFC-driven multi-agent Directed Acyclic Graph (DAG) orchestration. Ideal for building CI/CD style continuous development pipelines, managing complex state, and automating quality gates without human intervention.

Guides an agent to critically review a written implementation plan, execute every outlined step with required verifications, and hand off completion via finishing-a-development-branch procedures, ensuring blockers prompt clarification.

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.

Guides authors through adapting test-driven development to agent skills documentation, covering when to create skills, required background, directory structure, and how to run pressure scenarios to ensure compliance before publishing.

This comprehensive guide outlines a rigorous, human-in-the-loop engineering workflow for leveraging advanced AI agents to complete complex implementation tasks. It mandates defining clear completion criteria, decomposing work into manageable units, and strategically routing models (Haiku, Sonnet, Opus) based on task complexity. The workflow emphasizes continuous evaluation, regression testing, security review of invariants and edge cases, and strict cost discipline to ensure robust and reliable software development using AI assistance.

This model provides an engineering operational framework for teams that leverage AI agents to generate large volumes of code. It shifts the focus from simple syntax to process quality, robust architecture design, rigorous testing, and systematic risk control, ensuring the development of highly stable and reliable systems in AI-assisted environments.

This infrastructure provides a robust solution for running powerful AI agents in remote, sandboxed environments. It overcomes the limitations of local execution by ensuring unlimited concurrency, consistent environments, and collaborative features. Use this to build complex background coding agents, multi-client interfaces (e.g., Slack, Web), or scale agent workloads far beyond local machine constraints.