WildClawBench

InternLM/WildClawBench

An in-the-wild benchmark for AI agents in the OpenClaw Environment.

418 stars

39 forks

Python

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Installation

Option 1: Use slash command in Claude Code

/install-skill https://github.com/InternLM/WildClawBench

Option 2: Clone to skills directory

# Global (all projects)

git clone https://github.com/InternLM/WildClawBench ~/.claude/skills/WildClawBench

# Project-specific

git clone https://github.com/InternLM/WildClawBench .claude/skills/WildClawBench

Add MCP server to .cursor/mcp.json:

{
  "mcpServers": {
    "skillz": {
      "command": "npx",
      "args": ["-y", "skillz-mcp", "https://github.com/InternLM/WildClawBench"]
    }
  }
}

Restart Cursor after adding the configuration.

Option 1: Use Gemini CLI command

gemini extensions install https://github.com/InternLM/WildClawBench

Option 2: Clone to extensions directory

git clone https://github.com/InternLM/WildClawBench ~/.gemini/extensions/WildClawBench

Topics

agentic-ai agentic-evaluation agents benchmarks openclaw

Related Skills

Claude Code is an agentic coding tool that lives in your terminal, understands your codebase, and helps you code faster by executing routine tasks, explaining complex code, and handling git workflows - all through natural language commands.

skill-writer

Tensors and Dynamic neural networks in Python with strong GPU acceleration