Skill UI

This tool allows users to execute complex, multi-agent workflows defined in YAML format directly within the AI environment. It functions as an orchestration engine, sequentially invoking various specialized roles (agents) without requiring manual API key configuration. Ideal for structured tasks requiring collaboration between multiple personas.

This comprehensive guide serves as a decision flowchart for developers using Claude Code. It details when to use specific slash commands (/plan, /compact, /loop), select specialized agents (planner, code-reviewer, build-error-resolver), or invoke workflow skills (e2e-testing, tdd-workflow). It helps users select the optimal tool combination for complex tasks across the entire software development lifecycle, including feature planning, bug fixing, testing, and architectural design.

This skill analyzes your entire codebase to ensure it is optimized for AI contributions and developer tooling. It automatically generates essential configuration files such as `AGENTS.md`, Copilot instructions, CI workflows, and customizable issue templates. It learns from your existing PR review patterns to tailor the setup precisely to your project's stack. Use it when you need to prepare a repository for advanced AI-assisted development.

Squirrel is an advanced, full-cycle AI coding skill designed to guide software development from concept to production. It automatically detects the current project state (greenfield, in-progress, or mature) and adapts an 8-phase engineering pipeline. This comprehensive workflow covers planning, building, rigorous testing, bug hunting, code polishing, documentation, and deployment readiness, ensuring high-quality, maintainable results.

The Desktop Commander MCP grants agents deep reach across the user's entire operating environment, extending far beyond the typical IDE sandbox. Key capabilities include maintaining persistent shell sessions, managing long-running background processes, accessing arbitrary filesystems, and natively handling structured data formats (Excel, PDF, DOCX). It also enables remote operations via SSH and large-scale content search, making it ideal for complex, multi-turn development, DevOps, and data analysis workflows.

This skill guides the design and optimization of production-grade multi-agent systems. It specializes in using LangGraph, LangChain, and DeepAgents to create structured, complex AI workflows. It covers state management, agent routing, memory integration, and tool calling, making it ideal for building robust, scalable agents like supervisors, planners, and coders.

This guide provides a comprehensive framework for authoring new, structured AI agent skills. It details a three-phase workflow, including gathering requirements, drafting the main instructions (SKILL.md), and ensuring optimal structure. It emphasizes best practices for descriptions, scripting, and file splitting to improve agent reliability and usability.

A powerful tool for designing and writing deterministic, multi-agent workflow scripts in JavaScript. It allows users to orchestrate complex, repeatable, multi-step tasks using advanced patterns like fan-out, pipelines, loops, and judge-panels. Ideal for automating sophisticated business processes that require multiple specialized sub-agents and robust context management.

Polis Protocol transforms static AI agent workflows into a self-improving, dynamic team environment. It uses a learning router to assign tasks based on an agent's historical track record and effectiveness on specific domains, rather than fixed roles. It is vendor-agnostic, allowing diverse agents (e.g., Claude, Gemini) to collaborate under a unified, auditable governance structure where agents can propose and vote on amending the protocol's core rules.

This skill provides a comprehensive playbook for setting up and orchestrating multi-agent systems using the standalone Antigravity 2.0 Agent Manager alongside the Antigravity IDE. It guides developers on creating a dual-window workspace to coordinate specialized AI agents (e.g., front-end, back-end, QA) to work in parallel on a single codebase, significantly boosting development efficiency and managing concurrent code changes.

This skill provides a direct, fast shell invocation to capture the current git diff (staged, working-tree, or specific file list) as raw text. It is designed to replace slower LLM agent steps that merely execute 'git diff' commands, significantly speeding up CI/CD workflows and meta-steps that require repository state analysis.

A lightweight tool designed to fetch data from a single URL via HTTP/HTTPS using various methods (GET, POST, PUT, DELETE). It serves as a fast, direct shell wrapper, ideal for replacing complex agent steps that only require simple data retrieval. Supports configurable timeouts and response body capping, making it perfect for quick data fetching in meta-skill workflows.