No red flags found in any of the 11 categories — no credential harvesting, no data exfiltration, no curl-pipe-shell installer. Scanned against the SlowMist agent-security taxonomy, refreshed every 8 hours. Full audit →
by mikeyobrien · Codex Skill · ★ 3.1k
Last updated: · Indexed by AgentSkillsHub · Auto-synced every 8h
🔒 Is ralph-orchestrator safe to install? View the security audit →
Ralph Orchestrator A hat-based orchestration framework that keeps AI agents in a loop until the task is done. "Me fail English? That's unpossible!" - Ralph Wiggum Documentation Presets Installation Via npm (Recommended) Via GitHub Releases installer bash curl --proto '=https' --tlsv1.2 -LsSf \ ht
| Stars | 3,107 |
| Forks | 291 |
| Language | Rust |
| Category | Codex Skill |
| License | MIT |
| Quality Score | 69.6464553784013/100 |
| Open Issues | 10 |
| Last Updated | 2026-08-25 |
| Created | 2025-09-07 |
| Platforms | claude-code, cli, codex, gemini, rust |
| Est. Tokens | ~24k |
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ralph-orchestrator is An improved implementation of the Ralph Wiggum technique for autonomous AI agent orchestration. It is categorized as a Codex Skill with 3.1k GitHub stars.
ralph-orchestrator is primarily written in Rust. It covers topics such as ai, ai-agents, ai-agents-framework.
You can find installation instructions and usage details in the ralph-orchestrator GitHub repository at github.com/mikeyobrien/ralph-orchestrator. The project has 3.1k stars and 291 forks, indicating an active community.
ralph-orchestrator is released under the MIT license, making it free to use and modify according to the license terms.
The top alternatives to ralph-orchestrator on Agent Skills Hub include ralph-claude-code, awesome-agent-orchestrators, ai-devkit. Each offers a different approach to the same problem space — compare them side-by-side by stars, quality score, and community activity.
Grades come from a rule-based scan built on the SlowMist agent-security taxonomy, covering 11 red-flag categories including credential harvesting, data exfiltration, and curl | sh installers. It is a first-layer scan, not a manual audit — we say so rather than overstate it.
The scale of the problem is documented independently: Liu et al. (2026), in a study of 31,132 agent skills, report that 26.1% contain security vulnerabilities. Our own full-catalog census is published as a citable open dataset.
Sources & who's responsible: