Skill UI

Creates a comprehensive onboarding guide for new teammates by extracting project context from the knowledge graph, layering architecture descriptions, guiding tours, and highlighting complex files so newcomers can ramp up quickly.

An advanced debugging skill designed for AI agents. It utilizes gdb-cli to provide intelligent assistance for C/C++ programs, enabling the analysis of core dumps, debugging live processes, and investigating complex issues like crashes, deadlocks, and memory corruption. It correlates runtime state inspection with source code context for deeper insights.

This skill systematically audits a given project by analyzing its historical memory, rollout summaries, and local skill definitions. It identifies repeated complex workflows, recurring operational patterns, or areas where existing skills are stale. The audit process guides the user to recommend the highest-value skills to either add (if a new pattern emerges) or update (if current skills are outdated or incomplete), ensuring the AI agent operates with the most accurate and context-specific knowledge base for optimal project execution.

Blueprint turns a single objective into an executable, multi-step construction plan tailored for coding agents and multi-session teams, complete with context briefs, review gates, dependency awareness, and installable via `/blueprint <project> <objective>`.

Guides frontend designer-engineers to deliver memorable, production-ready interfaces by defining an aesthetic stance, scoring feasibility with DFII, and translating every intentional decision into code, typography, motion, and texture.

This tool serves as a persistent memory and state manager for complex coding projects within the Claude environment. It allows users to initialize new projects with structured metadata, track detailed session progress (including summaries, decisions, and next steps), and save the entire context reliably. By ensuring project continuity across disconnected sessions, it drastically improves the flow and consistency of the development workflow.

A comprehensive workflow for preparing and maintaining grounded, venue-compliant rebuttals to external academic reviews. It handles strict text-only character limits, manages responses for multiple reviewers (e.g., OpenReview style), supports follow-up rounds, and enforces safety gates against fabrication or overpromising. Ideal for the post-submission phase of research publishing.

Transformers.js allows developers to run state-of-the-art machine learning models directly within JavaScript/TypeScript environments (browser, Node.js). It supports a comprehensive range of tasks including NLP (text generation, summarization, translation), Computer Vision (image classification, object detection), and Audio processing. This capability enables building fully client-side, multimodal AI applications without requiring a dedicated Python backend or server infrastructure.

This harness transforms standard code environments into fully persistent, autonomous agent systems. It leverages native features like scheduled tasks (Crons), remote dispatching, and structured memory storage (MCP) to enable continuous, self-directing automation. Ideal for building AI assistants that require long-term context, recurring workflows, and complex, multi-step operations, eliminating the need for external agent frameworks like AutoGPT.

Crafts polished internal communications by matching request types (3P updates, newsletters, FAQs, etc.) to reference templates, gathering context, confirming scope, and producing concise, audience-aware drafts with metrics and links.

This skill is designed to detect and rewrite text that exhibits common characteristics of AI-generated Chinese writing, such as rigid structures, mechanical connectors, and over-polished phrasing. It helps users 'de-AI' their content by restoring natural human rhythm. Key use cases include reducing AIGC signals in academic papers (for CNKI/VIP checks), and converting the writing tone to specific styles like Zhihu, Xiaohongshu, or literary prose.

A comprehensive set of specialized tools for deep interaction with source codebases. This context-mode is mandatory for advanced tasks such as running code snippets, analyzing files, batch processing repository commands, indexing knowledge, searching content, and fetching web data. It ensures that interactions are context-aware and highly efficient, moving beyond basic file listing or general chat prompts.