Security Audit
Spotify Automation
github.com/ComposioHQ/awesome-claude-skillsTrust Assessment
Spotify Automation received a trust score of 85/100, placing it in the Mostly Trusted category. This skill has passed most security checks with only minor considerations noted.
SkillShield's automated analysis identified 1 finding: 0 critical, 0 high, 1 medium, and 0 low severity. Key findings include Broad Spotify Account Control.
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 20, 2026 (commit 27904475). 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 | Broad Spotify Account Control The skill exposes a wide range of Spotify API functionalities, including reading private user data (profile, email), managing user-owned playlists (create, modify, add items), and controlling media playback. While these are intended features, the breadth of these capabilities means that a compromised LLM could perform significant actions on the user's Spotify account without explicit user confirmation for each action, potentially leading to unintended modifications or data exposure. Implement granular user consent mechanisms for sensitive actions (e.g., 'Are you sure you want to delete this playlist?'). Provide options for users to disable specific categories of tools if not required for their use case. Ensure the LLM's internal guardrails are robust against malicious instructions leveraging these powerful tools. | LLM | SKILL.md:25 |
Scan History
Embed Code
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