Skill UI

Max is a specialized agent designed for safe, non-behavioral code improvement. It systematically refactors existing, working code by optimizing time complexity (Big-O), abstracting duplicated logic, removing dead code, and enhancing readability. It operates under a strict constraint: no change may alter the original functionality, requiring all refactored code to pass existing test suites flawlessly.

Rex is a foundational agent designed to transform vague, high-level user intent into precise, unambiguous, and structured technical specifications. It acts as a requirements analyst, asking clarifying questions, identifying edge cases, and defining scope using frameworks like MoSCoW and User Stories. It ensures that all subsequent development stages (planning, architecture, implementation) work from a single, agreed-upon artifact, minimizing guesswork and scope creep.

An advanced ruleset for crafting clear, precise, and formal English technical prose. This guide integrates established literary principles (like those from Strunk & White and Orwell) with modern best practices for AI-generated content. It is ideal for writing research papers, technical documentation, proposals, and release notes, ensuring maximum clarity and professional tone.

This skill provides a deterministic, git-only method to verify claims made by AI agents (e.g., 'done', 'shipped', 'fixed'). Instead of trusting the agent's self-report, it audits the commit's diff and git history using the DOS kernel (`dos verify`, `dos commit-audit`) to confirm if the claimed change genuinely occurred and is backed by non-forgeable evidence. It acts as a critical quality gate.

This skill designs and audits the core engineering harness required for reliable AI agent collaboration within a codebase. It establishes critical infrastructure elements such as change tracking, linting rules, CI/CD gates, and standardized documentation, ensuring the repository remains the single source of truth for all intelligent agents. Ideal for setting up complex development workflows.

This skill enables the creation of a robust 'harness' system for coding agents within a repository. It addresses the need to prevent repeated AI-generated coding mistakes by implementing durable instructions, guardrails, regression tests, and failure memory. It treats the target repository as the source of truth, ensuring consistency and maintaining high reliability across all automated contributions.

This skill enforces a high standard for all generated code, prioritizing correctness, clarity, and idiomatic patterns across all languages. It helps minimize code bloat by removing unnecessary abstractions and verbose patterns, while also optimizing the agent's output by using targeted diffs and eliminating excessive conversational overhead. Use this for all code generation, refactoring, or review tasks.

A comprehensive style guide for implementing the Synthwave and Outrun aesthetic across web and mobile applications. This guide details the core principles, including dark mode backgrounds, glowing neon accents (magenta, cyan), and the signature perspective grid. It provides technical examples using CSS, SwiftUI, Flutter, and React Native to achieve authentic retro-futuristic visual fidelity.

A comprehensive guide for developers to implement the iconic Vaporwave aesthetic across multiple platforms, including Web (CSS), iOS (SwiftUI), and Flutter. It details core principles such as 90s OS motifs, cyan/magenta neon gradients, and surreal pastel color palettes, enabling the creation of nostalgic, retro-futuristic user interfaces.

This skill acts as a central router, helping users determine the correct specialized skill or workflow to tackle development challenges. It guides the entire software development lifecycle, from initial idea refinement (Idea -> Ship) and prototype testing, to managing complex multi-session builds and handling incoming bugs via triage. It is essential for navigating the vast ecosystem of AI coding agents.

This skill assists users in defining, adapting, and designing bounded, structured feedback loops for AI agents. Whether you need to find a suitable existing workflow, fine-tune a published loop, or design a new process from scratch, this tool guides you through a safe, iterative methodology. It emphasizes setting clear stopping rules, guardrails, and verifiable, measurable steps to ensure the automation remains controllable and focused.

This skill scaffolds the necessary configurations for advanced engineering workflows. It guides the user through setting up the issue tracking system (GitHub, GitLab, or local markdown), defining the canonical triage label vocabulary, and establishing the domain documentation context (single or multi-context). It must be run once to initialize the repository for subsequent agent-driven development skills.