Skill UI

This pattern is designed to solve context limitations in complex multi-agent workflows. Instead of relying on a single, massive context dump, it implements a structured, multi-cycle loop (Dispatch -> Evaluate -> Refine -> Loop). This process iteratively refines the search query by scoring content relevance and identifying missing context gaps, ensuring sub-agents gather the most targeted and highly relevant information needed for tasks like debugging or feature implementation.

Uses the Monitor tool to stream real-time events from a swarm system instead of inefficient polling. It provides live observability for key actions like agent spawns, task completions, memory writes, and health status. This is ideal for continuous monitoring and debugging of complex, distributed processes.

Collects distributed trace spans for a specific task ID, building a comprehensive, visual trace tree. Users can analyze the timing of individual spans, identify performance bottlenecks, understand agent coordination, and synthesize a narrative summary of the task's execution flow. Ideal for debugging complex multi-agent systems and improving system observability.

This optimizer guides the acceleration of complex, multi-stage processes, such as CI/CD pipelines, large-scale refactoring, or data validation. It instructs users to decompose large tasks into independent execution lanes, build a dependency graph, batch read/check operations, and manage writes with strict isolation to maximize speed without sacrificing correctness. Ideal for optimizing development workflows and testing cycles.

This skill is designed for orchestrating complex, multi-step processes. It helps manage workflows, coordinate different components, and ensure systematic execution of tasks from planning to completion. Ideal for complex automation scenarios and structured project management.

The Safety Guard skill is designed to prevent destructive actions when working in production systems or when autonomous agents are executing tasks. It offers three protection modes (Careful, Freeze, and Guard) to monitor dangerous commands, lock specific directories, and ensure code changes are confined to intended areas, enhancing system stability and security.

This tool initializes a structured multi-agent swarm with built-in anti-drift configuration. It is designed for orchestrating complex, multi-file tasks that require coordinated efforts from three or more specialized AI agents, such as feature implementation, cross-module refactoring, or comprehensive security audits. It supports various topologies (hierarchical, mesh, etc.) to ensure robust and manageable parallel work execution across large teams.

A guide to the Swift 6.2 concurrency model, which mandates default single-threaded execution to eliminate accidental data races. Learn how to use MainActor for isolated consistency and utilize the @concurrent attribute for explicitly offloading CPU-intensive tasks to background threads, ensuring safe and predictable application architecture.

This skill provides a structured framework for managing complex, multi-agent development tasks. It treats agents as a collaborative team, utilizing structured work items, defined ownership, Agent Kanban boards, and explicit merge gates. This ensures that large-scale, multi-tool, or cross-branch development efforts are predictable, highly visible, and result in reliably mergeable, production-ready artifacts.

A powerful skill leveraging OpenAI Codex/GPT for autonomous, hands-off code generation, implementation, and complex task execution. It operates in sandboxed modes (Read-Only, Workspace-Write) to allow developers to refactor, fix bugs, and build features with minimal user intervention. Ideal for complex programming tasks, code reviews, and structured workflow automation.