Skill UI

A skill for orchestrating parallel AI subagents that compete on the same task using isolated git worktrees. Handles session setup, agent spawning, progress monitoring, evaluation via metrics or LLM judge, and final merge of the winning branch. Ideal for code optimization, content variation, research exploration, and any task benefiting from parallel competition.

The /hub:spawn command launches N subagents simultaneously, each operating in an isolated git worktree to work on the same task. It supports custom templates (like optimizer or refactorer) and manages the entire parallel execution cycle, ensuring diverse strategies and structured results for maximum value exploration.

Guides creation of persona-targeted VS Code CodeTour .tour files, covering repo exploration, intent inference, step composition, and validation so each narrative walkthrough is anchored to real files and tailored depth.

This skill guides comprehensive website exploration using Playwright MCP for robust testing. It involves navigating a specified URL, identifying and simulating 3-5 core user flows, meticulously documenting user interactions, relevant UI locators, and expected outcomes. Upon completion, the skill summarizes findings and generates detailed, actionable test cases, ensuring thorough coverage of the application's functionality.

This micro-prompt guides AI agents to adopt the mindset of an interactive programmer. It emphasizes using the REPL (Read-Eval-Print Loop) for systematic system exploration and modification, treating the system state as the ultimate source of truth. It also reminds the agent to utilize structural editing tools and maintain todo lists, significantly improving the accuracy and efficiency of the development workflow in live coding environments.

A comprehensive skill designed to convert vague ideas, initial concepts, or high-level requirements into structured, detailed, and approved technical specifications. It enforces a rigorous, multi-step workflow, guiding users through context exploration, proposing multiple architectural trade-offs, and iteratively refining the design until a complete specification document is ready for the implementation phase.

This skill is designed for understanding complex database structures. It allows users to list all available tables, intelligently filter relevant tables based on natural language queries, retrieve detailed schemas (columns, types, primary/foreign keys), and map inter-table relationships. It culminates in generating the M-Schema format, which is essential for accurately preparing the context needed for advanced SQL query generation.

Generate comprehensive software requirements specifications following EARS format. Analyzes project context, creates acceptance criteria, and ensures scope boundaries are clearly defined before implementation. Supports both greenfield and brownfield projects with sub-agent delegation for codebase exploration and domain research.

This is a foundational primitive skill for managing complex browser interactions. It initiates a named, traceable browser session within an RVF cognitive container, recording every user action as a trajectory step. Use it when starting any multi-step web exploration, ensuring the session state, actions, and results are preserved for later analysis, testing, or replay by downstream skills.

This guide provides a comprehensive onboarding process for RuView (WiFi-DensePose). It details three deployment tiers: Docker simulation for quick testing and UI exploration, source code build verification (Rust/Python), and live sensing setup utilizing ESP32 hardware. Ideal for new users or rapid prototyping.

This skill guides users in evaluating whether their economics, management, or policy manuscript is a suitable fit for the journal 'Inquiry into Economic Issues.' It analyzes the journal's specific focus (regional development, Southwest opening, policy exploration), checks formatting requirements, and identifies common academic pitfalls and potential submission risks for high-quality Chinese academic publishing.

Systematically transforms high-level ideas or goals into a detailed, structured, and executable taskbook designed for AI agents. This process involves deep research, validation against existing codebases, adherence to strict formatting rules, and generating a single, copy-pastable block of instructions for immediate agent execution. It distinguishes between execution and exploration tasks, ensuring all steps are verifiable and anti-cheating measures are included.