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 abhi1693 · Codex Skill · ★ 4.1k
Last updated: · Indexed by AgentSkillsHub · Auto-synced every 8h
🔒 Is openclaw-mission-control safe to install? View the security audit →
OpenClaw Mission Control OpenClaw Mission Control is the centralized operations and governance platform for running OpenClaw across teams and organizations, with unified visibility, approval controls, and gateway-aware orchestration. It gives operators a single interface for work orchestration, agent and gateway management, approval-driven governance, and API-backed automation. Platform overview Mission Control is designed to be th
| Stars | 4,108 |
| Forks | 818 |
| Language | TypeScript |
| Category | Codex Skill |
| License | MIT |
| Quality Score | 66.1652778745866/100 |
| Open Issues | 85 |
| Last Updated | 2026-08-06 |
| Created | 2026-02-01 |
| Platforms | node |
| Est. Tokens | ~15k |
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openclaw-mission-control is AI Agent Orchestration Dashboard - Manage AI agents, assign tasks, and coordinate multi-agent collaboration via OpenClaw Gateway.. It is categorized as a Codex Skill with 4.1k GitHub stars.
openclaw-mission-control is primarily written in TypeScript. It covers topics such as ai-agents, automation, openclaw.
You can find installation instructions and usage details in the openclaw-mission-control GitHub repository at github.com/abhi1693/openclaw-mission-control. The project has 4.1k stars and 818 forks, indicating an active community.
openclaw-mission-control is released under the MIT license, making it free to use and modify according to the license terms.
The top alternatives to openclaw-mission-control on Agent Skills Hub include trigger.dev, commands, rowboat. 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: