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 gl0di · MCP Server · ★ 58
Last updated: · Indexed by AgentSkillsHub · Auto-synced every 8h
🔒 Is clawseccheck safe to install? View the security audit →
🔍 A free, local, read-only security self-audit for your own OpenClaw agent. Free · Local · Read-only · No API key · Your data never leaves your machine A one-command security self-audit for your own OpenClaw agent. It scores your setup A–F,
| Stars | 58 |
| Forks | 13 |
| Language | Python |
| Category | MCP Server |
| License | MIT |
| Quality Score | 57.8293241264795/100 |
| Last Updated | 2026-09-18 |
| Created | 2026-06-19 |
| Platforms | aws, cli, mcp, python |
| Est. Tokens | ~23k |
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clawseccheck is 🦞 Local, read-only security audit for your OpenClaw agent. Finds config, prompt-injection and supply-chain risks, watches for drift, and vets a skill before you install it. Offline, zero deps. A–F g. It is categorized as a MCP Server with 58 GitHub stars.
clawseccheck is primarily written in Python. It covers topics such as agent-security, ai-agents, ai-security.
You can find installation instructions and usage details in the clawseccheck GitHub repository at github.com/gl0di/clawseccheck. The project has 58 stars and 13 forks, indicating an active community.
clawseccheck is released under the MIT license, making it free to use and modify according to the license terms.
The top alternatives to clawseccheck on Agent Skills Hub include agent-security-scanner-mcp, agent-audit, agentseal. 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: