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 linxidnju · MCP Server · ★ 567
Last updated: · Indexed by AgentSkillsHub · Auto-synced every 8h
🔒 Is OpenTag safe to install? View the security audit →
OpenTag Open-source, channel-native multiplayer AI gateway with shared, auditable team knowledge. Tag local AI agents into Slack threads, with local execution, visible progress, approvals, audit trails, and pluggable agent runtimes. English · 简体中文 · User Guide · Security · Contributing OpenTag brings AI agents into the place where team work already happens: Slack channels and threads. Instead of running an agent from one person's terminal and pasting results back into chat, a team can mention , discuss the task in the same thread, approve risky actions when needed, and keep the outcome visible to everyone who has access to that channel. ✨ What You Can Do Ask an agent to investigate a bug from a Slack thread. Let teammates add context before or
| Stars | 567 |
| Forks | 0 |
| Language | JavaScript |
| Category | MCP Server |
| Quality Score | 61.832162028052/100 |
| Last Updated | 2026-07-10 |
| Created | 2026-06-27 |
| Platforms | claude-code, cli, codex, docker, mcp, node |
| Est. Tokens | ~14k |
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OpenTag is Open-source, channel-native agent gateway for Slack. Route team threads to Claude Code, Codex, OpenCode, Docker, HTTP agents, and custom CLIs with policy, approvals, memory, audit logs, and artifacts.. It is categorized as a MCP Server with 567 GitHub stars.
OpenTag is primarily written in JavaScript. It covers topics such as agent-gateway, ai-agents, automation.
You can find installation instructions and usage details in the OpenTag GitHub repository at github.com/linxidnju/OpenTag. The project has 567 stars and 0 forks, indicating an active community.
The top alternatives to OpenTag on Agent Skills Hub include OpenContext, Overture, sandboxed.sh. 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: