Run an autonomous Open Ralph Wiggum coding loop using OpenCode Zen with free models and automatic fallback.
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Select your agent
Option 1: Install via CLI (recommended)
Recommended (no pre-install needed)
npx clawhub@latest --dir ~/.claude/skills install open-ralphOr via clawhub CLI (if already installed)
clawhub --dir ~/.claude/skills install open-ralphβ οΈ Requires Node.js 18+. No Node? Use Option 2 below to download the ZIP instead. Install Node.js β
Option 2: Manual install (no Node required)
Download the ZIP, extract it, and place the folder at the path below. Restart your agent to activate.
Install path
~/.claude/skills/open-ralph/π‘Extract and place the folder at the path above, then restart your agent.
Category
π»Developer & DevOpsPlatforms
What open-ralph can do for your AI workflow
Autonomous open ralph wiggum directly from your Claude conversation
Works across Claude, Cursor, OpenClaw β install once, use everywhere
Trusted by 1,823+ developers worldwide
One-command installation β no complex setup required
Combine with other skills to build powerful multi-step AI workflows
Try these prompts with your AI agent after installing open-ralph
Help me get started with open-ralph
Explains what open-ralph does, walks through the setup, and runs a quick demo based on your current project
Use open-ralph to run an autonomous Open Ralph Wiggum coding loop using OpenCode Zen ...
Invokes open-ralph with the right parameters and returns the result directly in the conversation
What can I do with open-ralph in my developer & devops workflow?
Lists the top use cases for open-ralph, with example commands for each scenario
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open-ralph extends your AI assistant with the ability to run an autonomous Open Ralph Wiggum coding loop using OpenCode Zen with free models and automatic fallback. Rather than leaving your conversation to handle this manually, you can ask your Claude agent directly β and it will take care of the task end-to-end, using open-ralph as its underlying capability.
open-ralph works across Claude, Cursor, OpenClaw through the Model Context Protocol (MCP) β an open standard that lets AI clients share tools and skills without lock-in. Because MCP is platform-agnostic by design, you install open-ralph once and it becomes available across all your AI clients. Whether you're working in Claude for focused sessions or Cursor for integrated workflows, the skill behaves consistently.
Getting started with open-ralph takes about two minutes. Place the skill at `~/.claude/skills/open-ralph/` (personal, all projects) or `.claude/skills/open-ralph/` (project-specific), then restart your AI client. From that point, typing `/open-ralph` in any conversation activates it, or the AI will use it on its own when it detects a relevant request.
open-ralph has been installed 1,823 times, making it one of the more actively used skills in the Developer & DevOps category. The install rate suggests it solves a real, recurring need rather than a niche edge case. The source code is open on GitHub β you can inspect it, contribute fixes, or fork it to adapt the skill for your specific setup. Like all skills on DiscoverAISkills, it is free to install and use. The broader AI skills ecosystem continues to expand as developers contribute new capabilities across categories like developer tools, data analysis, writing, automation, and more.
Place the skill folder at ~/.claude/skills/open-ralph/ for personal use (all projects), or .claude/skills/open-ralph/ for project-specific use. Restart your AI client, then invoke with /open-ralph or let the AI discover it automatically.
open-ralph supports Claude, Cursor, OpenClaw. It integrates seamlessly with these AI platforms to extend their capabilities.
open-ralph is free to install. Check the repository for licensing information.
Run an autonomous Open Ralph Wiggum coding loop using OpenCode Zen with free models and automatic fallback.
Links
Automate my developer & devops tasks using open-ralph
Identifies repetitive steps in your workflow and sets up open-ralph to handle them automatically
open-ralph is categorized under Developer & DevOps. These skills help AI agents perform specialized tasks in this domain.
Local speech-to-text with the Whisper CLI (no API key).