Skill UI

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 meta-skill establishes a rigorous and mandatory workflow for all AI agent interactions. It dictates that before generating any response, the agent must check for and invoke all relevant specialized skills, regardless of the perceived complexity or simplicity of the user request. It defines strict rules for skill prioritization, establishes user instructions as the highest authority, and outlines structured processes for various development scenarios, ensuring systematic and complete task execution.

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.

A powerful tool for executing complex, multi-step, and multi-agent workflows defined in YAML format directly within the AI session. It acts as an orchestration engine, simulating various roles (agents) to complete tasks sequentially or in parallel, without requiring external API keys or configuration. Ideal for complex business process automation and multi-role content generation.

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.

This skill is designed for tackling complex, multi-step software development tasks. It generates highly detailed, structured implementation plans that strictly adhere to best practices like TDD, YAGNI, and DRY. The plan breaks down features into small, atomic, executable tasks, specifying required file changes, writing initial failing tests, implementing minimum necessary code, and committing. It ensures the resulting plan is ready for efficient, autonomous execution by AI agents or human teams.

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.

This guide outlines a sophisticated development workflow using dedicated subagents for each independent task within a plan. It mandates a two-stage review process—first for specification compliance, then for code quality—ensuring highly reliable, high-quality, and rapidly iterative development cycles while maintaining strict context isolation.

This guide provides comprehensive best practices for creating, structuring, and optimizing reusable 'Skills' for advanced AI agents. It maps the concept of Test-Driven Development (TDD) onto documentation writing, covering YAML frontmatter rules, optimal description writing (focusing on trigger conditions, not workflows), and maximizing token efficiency for superior AI compliance.

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.

This powerful compiler ingests heterogeneous research inputs, including PDF papers, code repositories, experiment logs, and raw notes. It transforms this diverse data into a comprehensive, structured Agent-Native Research Artifact (ARA). The system extracts core knowledge atoms, builds cognitive maps (claims, concepts, heuristics), and generates a verifiable, executable knowledge package for advanced AI consumption and research iteration.

This tool performs a rigorous Level 2 semantic epistemic review of Agent-Native Research Artifacts (ARAs). It assesses six critical dimensions—including evidence relevance, falsifiability, and methodological rigor—to determine the content's intellectual soundness. It is used after initial structural validation to provide objective, constructive criticism, scoring the work from Strong Accept to Reject.