Trust Assessment
bookameeting received a trust score of 87/100, placing it in the Mostly Trusted category. This skill has passed most security checks with only minor considerations noted.
SkillShield's automated analysis identified 2 findings: 0 critical, 0 high, 2 medium, and 0 low severity. Key findings include Missing required field: name, Potential Data Exfiltration via Public Summary Field.
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 Findings2
| Severity | Finding | Layer | Location | |
|---|---|---|---|---|
| MEDIUM | Missing required field: name The 'name' field is required for claude_code skills but is missing from frontmatter. Add a 'name' field to the SKILL.md frontmatter. | Static | skills/yzlee/bookameeting/skill.md:1 | |
| MEDIUM | Potential Data Exfiltration via Public Summary Field The skill allows `selfProfile.summary` and `targetProfile.summary` fields to be made public via the `summaryPublic` flag. Although the documentation explicitly warns against putting sensitive data (like contact details, emails, phone numbers, handles, or URLs) in these fields, an agent misconfiguration or oversight could lead to the public exposure of such data if `summaryPublic` is set to `true`. Ensure agents are strictly configured to never include sensitive or private information in `summary` fields, especially when `summaryPublic` is set to `true`. Implement robust input validation on the agent side to prevent accidental inclusion of sensitive data in public-facing fields. Consider server-side validation to detect and reject common sensitive patterns in public summaries. | LLM | skill.md:109 |
Scan History
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