Trust Assessment
frontend-design received a trust score of 72/100, placing it in the Caution category. This skill has some security considerations that users should review before deployment.
SkillShield's automated analysis identified 1 finding: 1 critical, 0 high, 0 medium, and 0 low severity. Key findings include Untrusted skill description attempts to manipulate LLM behavior.
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 12, 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 | |
|---|---|---|---|---|
| CRITICAL | Untrusted skill description attempts to manipulate LLM behavior The entire content of the `SKILL.md` file, which is explicitly marked as untrusted input, contains direct instructions and directives intended to guide the host LLM's creative process and output generation. This constitutes a prompt injection attempt, as the untrusted content is dictating the LLM's operational parameters and creative choices, such as 'Implement real working code' and 'NEVER use generic AI-generated aesthetics'. Remove all direct instructions and directives intended for the host LLM from the untrusted skill description. The skill description should only describe the skill's purpose and capabilities, not dictate the LLM's behavior or creative process. | LLM | SKILL.md:7 |
Scan History
Embed Code
[](https://skillshield.io/report/02b910ca58fe890c)
Powered by SkillShield