Trust Assessment
managing-apple-music received a trust score of 90/100, 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, 1 high, 0 medium, and 0 low severity. Key findings include Potential Command Injection via Unsanitized User Input to CLI 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 14, 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 | |
|---|---|---|---|---|
| HIGH | Potential Command Injection via Unsanitized User Input to CLI Arguments The skill's primary function is to execute `clawtunes` shell commands, often incorporating user-provided strings (e.g., song names, search queries, playlist names) as arguments. If the LLM agent directly interpolates untrusted user input into these commands without proper sanitization or shell-escaping, a malicious user could inject arbitrary shell commands. For instance, input like `Song Name"; rm -rf /; echo "` could lead to unintended command execution. While the `SKILL.md` examples demonstrate quoting, the LLM's implementation must ensure all user-supplied arguments are consistently and securely escaped. The LLM agent must be explicitly instructed and programmed to always sanitize and shell-escape all user-provided strings before incorporating them into `clawtunes` commands. For example, using a function like `shlex.quote()` in Python to properly quote arguments. | LLM | SKILL.md:18 |
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