Skill UI

This skill provides a comprehensive framework for deploying Apple Notes automation as a persistent local service on macOS. It supports three distinct deployment models: using `launchd` agents for scheduled background tasks, creating Shortcuts for user-triggered actions, and includes a robust installer script. It is essential for building cross-application, system-level automation workflows within the Apple ecosystem.

This guide details how to use sophisticated coding agents (such as Codex, Claude Code, or Pi) through a structured bash workflow. It provides methods for complex development tasks, including building new features, refactoring large codebases, and reviewing pull requests (PRs). Key best practices covered include managing pseudo-terminals (PTY), defining focused working directories (`workdir`), and executing tasks in the background for robust, multi-step automation.

This skill helps users design robust and optimal software interfaces (APIs) for a module by applying the 'Design It Twice' philosophy. It generates multiple, radically different architectural designs using parallel agents, allowing for comprehensive comparison of trade-offs (e.g., simplicity vs. flexibility, specialized vs. general-purpose). Use this when you need to explore various structural options or compare module shapes before committing to a final design.

This powerful orchestrator automates the entire GitHub issue lifecycle. It fetches issues based on criteria (labels, milestones, assignees), spawns specialized sub-agents to implement necessary code fixes, and automatically opens Pull Requests (PRs). Furthermore, it monitors open PRs, addresses review comments, and manages the remediation process, streamlining the development workflow from issue tracking to merge.

This skill orchestrates the entire development lifecycle for approved software tasks. It operates in autonomous or manual modes, rigorously applying Test-Driven Development (TDD). It automatically gathers all necessary context (specs, requirements, design, and historical learnings), validates the repository state, and dispatches specialized sub-agents to implement tasks one by one, ensuring high code quality, adherence to design, and process traceability.

This tool systematically converts a project roadmap into actionable specifications. It first analyzes features by grouping them into dependency waves. It then automatically dispatches sub-agents to generate complete feature specs (requirements, design, tasks, and JSON) for each wave. The core value lies in its comprehensive cross-spec review, which verifies data model consistency, interface alignment, and architectural integrity across the entire project, ensuring a cohesive final product.

This guide teaches how to build robust, production-grade ReAct agents using LangGraph 1.0's `create_react_agent`. It specifically addresses common agent failure modes, such as preventing infinite loops (recursion caps) and ensuring proper error propagation. Learn to define typed tools using Pydantic, manage state with checkpointers, and set precise recursion limits. Perfect for users migrating from legacy AgentExecutor or building reliable, multi-step tool-calling workflows.

A comprehensive guide to ensuring robust state management for LangGraph agents. This skill addresses common checkpointing pitfalls (like missing thread_id or non-JSON serializable state) and details best practices for implementing reliable state persistence using MemorySaver or PostgresSaver. It covers thread discipline, schema migration, and time-travel debugging for production-ready applications.

This skill defines canonical, ordered middleware patterns for LangChain and LangGraph agents. It addresses critical production-level LLM deployment issues, including PII leakage across tenants, token budget overconsumption, prompt injection defense (Guardrails), and cache key corruption. Use this to build secure, scalable, and cost-aware AI applications.

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 document outlines strict, mandatory rules for AI agents regarding the invocation of specialized skills (Superpowers). It establishes that agents must check for and proactively use relevant skills in every response or operation, regardless of perceived simplicity. It details platform-specific tool usage and priority rules, ensuring user instructions always take precedence.

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.