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 hoangsonww · MCP Server · ★ 83
Last updated: · Indexed by AgentSkillsHub · Auto-synced every 8h
🔒 Is AI-Agents-Orchestrator safe to install? View the security audit →
AI Coding Tools Orchestrator and Agentic Team Runtime  to your Postman coll
Kindly Web Search MCP Server: Web search + robust content retrieval for AI coding tools (Claude Code, Codex, C
An MCP server that executes Python code in isolated rootless containers with optional MCP server proxying. Imp
K8s-mcp-server is a Model Context Protocol (MCP) server that enables AI assistants like Claude to securely exe
Explore other popular mcp server tools:
AI-Agents-Orchestrator is 🪈 Intelligent orchestration system that coordinates multiple AI coding assistants (Claude, Codex, Gemini CLI, Copilot CLI) to collaborate on complex software development tasks via REPL or a Vue/Nuxt . It is categorized as a MCP Server with 83 GitHub stars.
AI-Agents-Orchestrator is primarily written in Python. It covers topics such as agentic-ai, ai-agents, claude.
You can find installation instructions and usage details in the AI-Agents-Orchestrator GitHub repository at github.com/hoangsonww/AI-Agents-Orchestrator. The project has 83 stars and 28 forks, indicating an active community.
AI-Agents-Orchestrator is released under the MIT license, making it free to use and modify according to the license terms.
The top alternatives to AI-Agents-Orchestrator on Agent Skills Hub include kelos, Cerno-Agentic-Local-Deep-Research, postman-mcp-server. 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: