Skill UI

This skill integrates BuyWhere's product catalog surface, allowing AI agents to perform structured product searches, compare prices across various merchants, and discover deals. It is designed for developers building advanced e-commerce shopping agents, guiding them through the necessary API key setup and MCP configuration.

This skill pulls the latest GEO-SEO components, including skills, agents, scripts, and schema templates, from the official upstream repository. It performs a detailed comparison (diff) to show all changes—new, modified, or removed—before applying the update. It ensures local customizations are preserved while keeping the toolkit current. Use this whenever you suspect your local setup might be outdated.

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 skill implements advanced 'Gatekeeper' logic to optimize AI agent performance in long-running workflows. It prevents context window bloat by sharding metadata, allocating token budgets, and enforcing Atomic Precision to deliver purely functional, filler-free responses. Ideal for complex coding, multi-step reasoning, and large document analysis where conciseness is critical.

The Desktop Commander MCP grants agents deep reach across the user's entire operating environment, extending far beyond the typical IDE sandbox. Key capabilities include maintaining persistent shell sessions, managing long-running background processes, accessing arbitrary filesystems, and natively handling structured data formats (Excel, PDF, DOCX). It also enables remote operations via SSH and large-scale content search, making it ideal for complex, multi-turn development, DevOps, and data analysis workflows.

This tool searches a comprehensive database of over 2500 curated open-source repositories for tools related to Large Language Models (LLMs), ChatGPT, RAG, agents, and NLP. It interprets natural language queries, converts non-English inputs to core English keywords, and systematically filters results across multiple specialized categories (e.g., LangChain, Chatbots, CLIs). Use it whenever the user needs to find specific AI development libraries or open-source AI tooling.

This skill guides the design and optimization of production-grade multi-agent systems. It specializes in using LangGraph, LangChain, and DeepAgents to create structured, complex AI workflows. It covers state management, agent routing, memory integration, and tool calling, making it ideal for building robust, scalable agents like supervisors, planners, and coders.

This CLI alias executes a comprehensive teardown protocol for PUA agents, designed for development environments. It safely releases all active agent and loop states, ensuring a clean system reset. It automatically manages configuration files like ~/.pua/config.json, preserving unknown fields while providing reliable, evidence-based cleanup.

Provides a comprehensive toolkit for integrating X API functionality into applications, data pipelines, and AI agents. Features include robust tweet searching, user profile lookups, timeline fetching, media downloading, and webhook monitoring. Supports multiple languages via official SDKs, REST APIs, and MCP server integration for advanced automation workflows.

Design a persistent multi-agent operating system on Claude Code with kernel routing, specialist agents, slash commands, file-based memory, and scheduled automation for long-lived projects.

This guide helps users navigate and understand the complex components of the ECC (Everything Claude Code) ecosystem, including skills, commands, agents, hooks, and rules. It provides structured paths for new user onboarding, feature discovery, installation planning, and troubleshooting, ensuring answers are derived from the current repository state.

The Ejentum Reasoning Harness is a comprehensive library of cognitive operations designed to guide complex AI agents through critical thinking tasks. It exposes four specialized harnesses (reasoning, code, anti-deception, memory) that mitigate common failure modes like hallucination and reasoning decay. Instead of a fixed answer, the agent ingests a detailed, executable scaffold—including failure patterns, procedures, and falsification tests—to ensure robust, self-verified output.