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 win4r · Codex Skill · ★ 1.5k
Last updated: · Indexed by AgentSkillsHub · Auto-synced every 8h
🔒 Is ClawTeam-OpenClaw safe to install? View the security audit →
English | 简体中文 | 繁體中文 | 日本語 | 한국어 | Français | Español | Deutsch | Italiano | Русский | Português (Brasil) 🦞ClawTeam-OpenClaw Multi-agent swarm coordination for CLI coding agents — OpenClaw as default </p
| Stars | 1,458 |
| Forks | 320 |
| Language | Python |
| Category | Codex Skill |
| License | MIT |
| Quality Score | 71.9775218174472/100 |
| Open Issues | 4 |
| Last Updated | 2026-07-03 |
| Created | 2026-03-18 |
| Platforms | python |
| Est. Tokens | ~18k |
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ClawTeam-OpenClaw is ClawTeam fork fully adapted for OpenClaw — multi-agent swarm coordination with OpenClaw as the default agent. It is categorized as a Codex Skill with 1.5k GitHub stars.
ClawTeam-OpenClaw is primarily written in Python. It covers topics such as ai-agents, clawdbot, openclaw.
You can find installation instructions and usage details in the ClawTeam-OpenClaw GitHub repository at github.com/win4r/ClawTeam-OpenClaw. The project has 1.5k stars and 320 forks, indicating an active community.
ClawTeam-OpenClaw is released under the MIT license, making it free to use and modify according to the license terms.
The top alternatives to ClawTeam-OpenClaw on Agent Skills Hub include clawsec, secure-openclaw, powermem. 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: