skills-hub
Manage, sync, and apply AI agent skills, kits, and presets using the Skills Hub CLI. Streamline your project setup by browsing catalogs, inspecting configurations, and deploying curated instruction policies and skill packages.
Discover reusable agent skills, browse implementation details, and find the right skill for your workflow.
517 skills found
Manage, sync, and apply AI agent skills, kits, and presets using the Skills Hub CLI. Streamline your project setup by browsing catalogs, inspecting configurations, and deploying curated instruction policies and skill packages.
Generate and edit images, diagrams, and infographics using Google's Gemini 3 Pro model. Supports text-to-image, style transformation, and data-accurate visual creation.
Guidance for Model Context Protocol (MCP) server development, including tool design, resource handling, and AI/ML integration patterns.
Standardize, validate, and manage Netresearch AI agent skill repositories with automated structure enforcement, distribution workflows, and licensing compliance tools.
TanStack Router configuration, file-based routing, and type-safe navigation for React applications.
Fetch and parse Feishu (Lark) cloud documents into Markdown, with support for media handling and Wiki space navigation.
Retrieve real-time library documentation, code examples, and technical guidance using the Context7 API for frameworks like React, FastAPI, and Next.js.
Universal MCP client for connecting to any MCP server with progressive disclosure. Wraps MCP servers as skills to prevent context window bloat from tool definitions. Use for Zapier, GitHub, sequential thinking, and file operations.
A wise conductor of expert agents. It helps you achieve goals by summoning, orchestrating, and creating specialized AI experts. Features intellectual humility, multi-agent debate, and self-learning pattern capture.
Unified Python CLI for Tavily AI operations including web search, URL extraction, site crawling, link mapping, and automated deep research reports.
Enforces structured self-assessment checkpoints to validate approach, mitigate risks, and ensure quality before, during, and after task execution.
Resume a paused experimental loop by restoring branch context, loading configuration, reading history, and identifying optimization patterns for continued iteration.