Skill UI

This skill establishes an autonomous, iterative build loop for continuous website development. It functions as an agent that reads tasks from a 'baton' file, utilizes the Stitch MCP tools to generate structured web pages, integrates the generated HTML assets into the main site structure, and finally updates the baton file with the next required task. It supports end-to-end, programmatic site building and includes optional visual verification using Chrome DevTools.

This skill provides a comprehensive framework for security auditing LLM agents that possess external tools (file system access, command execution, network calls). It employs advanced dialogue probing techniques—such as command injection, path traversal, and SSRF attempts—to identify vulnerabilities where the agent might be induced to execute unexpected or malicious code, or access sensitive resources. Essential for red teaming and securing agent functionalities.

This internal agent skill defines the structured procedure for displaying bug triage results in terminal markdown format. It generates comprehensive summaries of bug clusters, provides detailed views of evidence and reports, and handles the parsing and execution flow for user review commands (e.g., `details`, `file`, `dismiss`, `escalate`). It is a core component for automated bug triage and defect management workflows.

This skill provides a structured framework for capturing, organizing, and maintaining persistent facts about the user. By segmenting facts into topic-specific files (e.g., coding preferences, tech stack, goals), it creates a reliable, objective knowledge base. This structure is crucial for AI agents and applications to achieve highly personalized interactions and provide accurate context.

This document outlines a comprehensive set of rules and guidelines for AI agents to ensure that relevant skills are called at every stage of interaction. It mandates the proactive use of skills—including brainstorming, systematic debugging, and specific process skills—before responding, clarifying, or accessing external resources, preventing assumptions, and enforcing structured, process-driven workflows.

Provides comprehensive migration patterns for converting existing v5 Webiny code to the v6 architecture. It details key architectural shifts, including replacing ContextPlugins with Dependency Injection (DI) Services, updating event subscriptions to specialized EventHandler Features, and adopting the feature-based container registration model. Essential for developers and AI agents performing major framework upgrades.

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 skill provides a comprehensive, structured process for creating and documenting new AI agent skills. It guides the user through gathering requirements, drafting structured files (like SKILL.md, REFERENCE.md), and optimizing descriptions to ensure the agent can correctly identify and utilize the new capability. Use when you need to build, write, or refine a complex, functional skill module for an AI agent.

Use this skill to prompt the agent to zoom out from a specific code section, providing a broader, higher-level perspective. It helps map out the overall system architecture, identifying relevant modules, calling relationships, and how the current code snippet fits into the larger picture. Ideal when you are unfamiliar with a specific area or need system-level understanding.

Use when executing an implementation plan with independent tasks in the current session. Assign fresh subagents per task, review each result, and finish with a broad branch-wide review to improve speed and quality.

A comprehensive guide on how to write, organize, and optimize skills for AI agents using a Test-Driven Development (TDD) approach, focusing on Claude Search Optimization (CSO) and token efficiency.

A comprehensive guide for simulating a full Software Development Life Cycle (SDLC) using specialized AI roles. It defines protocols for managing cross-functional teams (Dev, QA, DevOps, Product), covering sprint planning, codebase management, bug tracking, and deployment, ensuring context survival across multiple chat sessions.