Skill UI

This skill provides a comprehensive, 4-phase workflow for managing and reviewing a pull request backlog. It automatically audits open PRs, classifying them by size, staleness, overlap, and CI status. Users can then initiate deep code reviews, generate comment drafts, and set up local worktrees for systematic maintenance and quality assurance.

A comprehensive, multi-step framework for conducting systematic QA testing on web applications. This guide covers scope detection (diff-aware), tiered testing depth (Quick, Standard, Exhaustive), detailed issue taxonomy (Functional, Visual, UX, etc.), structured bug documentation, and the critical fix-and-verify loop, ensuring high code quality throughout the development cycle.

This tool analyzes a given codebase to automatically identify high-value, actionable automation routines. It surfaces use cases across three primary trigger types: scheduled cron jobs, external API calls, and GitHub repository events. By mapping manual pain points to autonomous workflows, it helps teams implement robust CI/CD and DevOps practices.

This command inspects the native code execution sandbox environment. It reports on platform compatibility (e.g., bubblewrap, Seatbelt), active security configurations (auto-allow status, filesystem policies, network allowlists), and details recent security violations. Use it to audit the execution environment before running autonomous workflows.

This orchestrator manages the entire end-to-end pipeline for preparing technical talks, transforming raw materials—whether code commits (REX mode) or conceptual articles—into polished revision sheets. It sequentially runs through multiple stages (Research, Concepts, Scripting, Revision) with human-in-the-loop checkpoints, ensuring a structured and comprehensive content creation workflow.

This skill processes verbose, stream-of-consciousness voice dictations (from tools like macOS Dictation or Wispr Flow) and transforms them into highly structured, token-efficient prompts. It removes filler words, repetitions, and tangents, organizing the core intent into standard sections (Context, Objective, Constraints) suitable for advanced LLMs like Claude Code.