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 haoyu-haoyu · Claude Skill · ★ 125
Last updated: · Indexed by AgentSkillsHub · Auto-synced every 8h
🔒 Is Multi-AI-Workflow safe to install? View the security audit →
Multi-AI Workflow (MAW) Unleash the Power of AI Collaboration Claude + Codex + Gemini = Unstoppable Features Usage in Claude Code 中文文档 What is MAW? MAW (Multi-AI Workflow) is a revolutionary CLI framework that orchestrates multiple AI agents to work together seamlessly. Imagine having Claude's reasoning, Codex's code execution, and Gemini's multimodal capabilities all working in harmony on your projects. ┌──────────────────────────────────────────────────────────────┐ │ │ │ [CLAUDE]
| Stars | 125 |
| Forks | 5 |
| Language | TypeScript |
| Category | Claude Skill |
| License | MIT |
| Quality Score | 76.2397149432812/100 |
| Open Issues | 1 |
| Last Updated | 2026-04-16 |
| Created | 2026-01-19 |
| Platforms | claude-code, cli, codex, gemini, node |
| Est. Tokens | ~37k |
These tools work well together with Multi-AI-Workflow for enhanced workflows:
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Multi-AI-Workflow is Multi-AI orchestration framework for Claude Code — coordinate Claude, Codex, and Gemini with 7 workflow modes, parallel execution, and session unification. It is categorized as a Claude Skill with 125 GitHub stars.
Multi-AI-Workflow is primarily written in TypeScript. It covers topics such as ai-orchestration, claude-code, cli.
You can find installation instructions and usage details in the Multi-AI-Workflow GitHub repository at github.com/haoyu-haoyu/Multi-AI-Workflow. The project has 125 stars and 5 forks, indicating an active community.
Multi-AI-Workflow is released under the MIT license, making it free to use and modify according to the license terms.
The top alternatives to Multi-AI-Workflow on Agent Skills Hub include vnx-orchestration, sudocode, claude-code-open. 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: