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Remote OpenClaw

Free plan available

A directory of AI agent skills, plugins, MCP servers, and workflows.

Workflow integration
Skill directory
Plugin resources
Mcp server list
Component comparison
Resource aggregation

About Remote OpenClaw

Launched Jul 03, 2026

Categories

Email :

[email protected]

Industry :

Horizontal

Website

Description

A directory of AI agent skills, plugins, MCP servers, and workflows.

Remote OpenClaw is a curated directory specifically designed for developers and teams working with AI agents. It aggregates and organizes resources like skills, plugins, MCP (Model Context Protocol) servers, and workflows for popular AI agent platforms including OpenClaw, Hermes, Claude Code, and Codex. The platform serves as a central hub to discover, compare, and integrate pre-built components, accelerating development and reducing the need to build everything from scratch. It is aimed at AI engineers, agent developers, and product teams who want to enhance their agent's capabilities with proven tools and community-contributed resources.

Remote OpenClaw Key Features

  • Directory of AI agent skills and plugins
  • Collection of MCP (Model Context Protocol) servers
  • Curated workflows for various agent platforms
  • Supports OpenClaw, Hermes, Claude Code, and Codex
  • Centralized resource hub for AI agent components

Remote OpenClaw Use Cases

  • Discovering pre-built skills to add functionality to an AI agent
  • Finding MCP servers to connect an agent to external tools and data
  • Browsing ready-made workflows to streamline agent development
  • Comparing available plugins and extensions for different agent platforms
  • Accelerating AI agent project setup by leveraging community resources

Pros

  • Centralized directory for AI agent resources, saving time in searching for tools.
  • Facilitates discovery and comparison of skills, plugins, and workflows.
  • Supports popular AI agent platforms like OpenClaw, Hermes, Claude Code, and Codex.
  • Accelerates development by integrating pre-built components.
  • Community-contributed resources enhance diversity and reliability.
  • Useful for teams and individual engineers, streamlining collaboration.

Cons

  • May become overwhelming due to the vast number of available resources.
  • The quality of community-contributed resources can vary.
  • Could require a learning curve to efficiently navigate and utilize all features.
  • May require frequent updates to stay current with evolving AI technologies.

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