Trust Assessment
Agent Vibes OpenClaw Skill received a trust score of 95/100, placing it in the Trusted category. This skill has passed all critical security checks and demonstrates strong security practices.
SkillShield's automated analysis identified 1 finding: 0 critical, 0 high, 1 medium, and 0 low severity. Key findings include Potential Command Injection via User-Supplied Arguments.
The analysis covered 4 layers: Manifest Analysis, Static Code Analysis, Dependency Graph, LLM Behavioral Safety. All layers scored 70 or above, reflecting consistent security practices.
Last analyzed on February 13, 2026 (commit 13146e6a). SkillShield performs automated 4-layer security analysis on AI skills and MCP servers.
Layer Breakdown
Behavioral Risk Signals
Security Findings1
| Severity | Finding | Layer | Location | |
|---|---|---|---|---|
| MEDIUM | Potential Command Injection via User-Supplied Arguments The skill defines several commands that accept arbitrary string arguments from the user (e.g., voice names, custom text, provider names). If the underlying implementation of the 'agent-vibes' skill processes these arguments by directly embedding them into shell commands without proper sanitization or escaping, it could lead to command injection. An attacker could craft malicious input containing shell metacharacters (e.g., ';', '`', '$()') to execute arbitrary commands on the host system. This risk applies to commands like `/agent-vibes:switch <voice_name>`, `/agent-vibes:add <name> <voice_id>`, and `/agent-vibes:set-pretext <word>`. Ensure all user-supplied arguments to skill commands are properly sanitized and escaped before being used in any shell execution context. Prefer using argument lists with `subprocess.run(shell=False)` over `shell=True` or `os.system`. Implement strict input validation, such as whitelisting allowed characters or patterns, especially for arguments like voice names and custom text. | LLM | SKILL.md:36 |
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