Skill UI

This skill acts as a comprehensive Go modernization engineer, scanning entire codebases to update outdated patterns, deprecated APIs, and standard library usage. It utilizes multi-agent orchestration to check language features, testing patterns, and tooling/infra, ensuring the codebase adheres to the latest Go idioms for maximum safety and readability. Ideal for CI/CD pipelines or large-scale code reviews.

This mode simulates a P9 Tech Lead role, specializing in strategic project management and complex task orchestration. It does not write code itself, but focuses on generating detailed, structured Task Prompts (six elements) and managing the entire workflow delivery from multiple specialized agents (P8 teams). Ideal for coordinating large, complex projects requiring strategic decomposition and coordination of three or more parallel agents.

Planificación persistente basada en archivos para tareas multipaso de agentes de IA. Mantiene task_plan.md, findings.md y progress.md en disco; los hooks del ciclo de vida inyectan contexto seleccionado de planificación del proyecto. Diseñado para investigación o trabajo que requiera 5 o más llamadas a herramientas, con recuperación automática y opciones de gate.

An advanced, AI-powered agent designed to generate comprehensive and workable unit tests for virtually any programming language (e.g., Python, C#, Java, Go, TypeScript). It operates via a sophisticated multi-agent pipeline (Research → Plan → Implement → Build → Test → Fix → Lint). This ensures generated tests are not only written following project conventions but also compile, pass all tests, and effectively improve code coverage.

Provides comprehensive patterns and techniques for securing AI agent systems. Learn how to enforce governance policies, including tool allow/block lists, content filtering, rate limiting, and human approval workflows. Essential for building robust, compliant, and trustworthy agents in multi-agent or production environments that interact with external APIs and sensitive data.

This skill guides the integration of Arize AX tracing into existing applications, especially complex LLM applications. It follows a structured, two-phase, agent-assisted flow: first analyzing the codebase (read-only) to understand dependencies, and then implementing instrumentation. It supports multi-language environments (Python, TypeScript, Java, Go) and integrates deeply with OpenTelemetry and various LLM providers, ensuring comprehensive observability without altering core business logic.

This skill provides comprehensive tools to download, export, and deeply inspect existing Arize traces and spans. It is essential for understanding the full execution flow of complex LLM applications, debugging runtime errors, and performing root-cause analysis. Users can retrieve detailed data filtered by Trace ID, Span ID, or Session ID, enabling thorough analysis of conversational or multi-step agent behaviors.

Continuous file-based planning system for multi-step AI agents. Maintains task_plan.md, findings.md, and progress.md files on disk to track project progress. Injects planning context into lifecycle hooks and supports session recovery. Designed for complex research or development tasks requiring 5+ tool calls.

Provides persistent Markdown-based planning for multi-step AI agent work, keeping task_plan.md, findings.md, and progress.md on disk with lifecycle hooks and recovery.

Provides advanced control over browser debugging protocols (CDP). Use this skill when the user needs to connect to an external, running Chrome instance, scan local CDP ports, or explicitly launch the browser with a fixed debug port. It is specifically designed for complex automation scenarios where multiple tools or agents need to attach to the browser instance.

This skill is designed to manage complex, multi-step tasks by coordinating the efforts of multiple specialized AI agents. It provides structured methods for listing available agents, initiating real-time conversations, submitting complex background tasks (such as data analysis or report generation), tracking task status, and maintaining conversational context using session IDs. It is essential when a core task requires diverse expert knowledge or sequential collaboration.

This advanced agent acts as a highly constrained implementation expert, designed to execute complex, multi-step plans with absolute fidelity. It strictly adheres to the provided workflow, making only specified changes, updating the plan status in real-time, and running tests until completion. Ideal for disciplined software development, detailed project execution, or formal process adherence.