Skill UI

Expert assistance for building, modifying, and debugging Minecraft server plugins for the Paper, Spigot, and Bukkit ecosystems. This includes implementing complex gameplay mechanics such as minigames, advanced combat systems, persistent player progression (perks, economy, quests), custom command handling, and robust state management. We handle configuration, listener registration, and adherence to best practices for Java-based server API usage.

This tool reads your local GitHub Copilot CLI conversation history using the specialized `vardoger` CLI. It analyzes your behavioral patterns, coding conventions, and usage style to generate a personalized instructions block. This block is written directly to your local Copilot configuration file, allowing the assistant to adapt its guidance and suggestions specifically to your unique habits and preferences. The process runs entirely offline, ensuring maximum privacy.

Cavecrew provides a set of specialized subagents (investigator, builder, reviewer) designed for efficient code development delegation. Unlike vanilla agents, Cavecrew compresses output results, drastically reducing the token usage injected back into the main context. This allows users to maintain context over much longer, complex coding sessions, effectively preventing context exhaustion during large-scale refactoring, debugging, or multi-step feature development.

This utility displays the precise, real-time token usage and estimated cost savings for the current session. It reads directly from the internal session log, ensuring objective and accurate data without requiring AI computation. Triggered by /caveman-stats, it provides immediate performance metrics to monitor API costs and usage patterns.

This skill generates comprehensive, tailored instruction files for AI coding assistants like GitHub Copilot and Claude. It supports multiple strategies (flat or nested) and is specifically designed for managing complex monorepos by creating area-scoped instructions using `applyTo` globs. Use it to standardize AI tooling usage across large codebases.

A comprehensive pre-production audit tool that scans a codebase for critical issues across eight dimensions: security, database, deployment, code quality, AI/LLM usage, dependencies, frontend, and observability. It intercepts deployment commands, forcing users to resolve critical failures before shipping code. The audit is stack-agnostic and provides detailed pass/fail/manual reports.

This file serves as a placeholder indicating that the Japanese translation for the 'motion-advanced' skill is currently under development. Users should consult the original English skill file for the definitive functionality and usage guide, as this translation is not yet functional.

This file serves as a placeholder indicating that Japanese localization for the 'motion-foundations' skill is currently in progress. Users should refer to the original English skill file for comprehensive functionality details and usage instructions.

This guide provides expert CRO strategies for optimizing in-app monetization moments, such as paywalls, upgrade modals, and upsell prompts. It covers the entire user journey from freemium conversion and usage limit management to trial expiration. Learn core principles like 'Value Before Ask' and how to design friction-free, high-converting components to maximize paid adoption.

This skill analyzes and reports on the token usage, expenditure, and budget for AI models (like Claude Code) by querying a local SQLite database. It is essential for users who need detailed financial accountability, tracking costs by project, tool, or date, and managing operational budgets.

This skill is designed to enhance the overall feel and usability of user interfaces. Currently, this file is undergoing Japanese translation. Please refer to the original English skill file for detailed functionality and usage scenarios.

A specialized assistant designed to navigate and accurately retrieve commands and parameters from the offline Alibaba Cloud CLI Chinese documentation mirror. It enforces a strict, hierarchical workflow: first consulting quick usage guides, then checking the main index, and finally drilling down to specific chapter pages. This structured approach ensures all provided information is accurate, evidence-based, and highly relevant to the user's intent.