Trust Assessment
daily-report received a trust score of 88/100, 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, Unsanitized path variable in file write operation.
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 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/visualdeptcreative/daily-report/SKILL.md:1 | |
| MEDIUM | Unsanitized path variable in file write operation The skill instructs the agent to save data to a file path `memory/{{date}}.md`. If the `{{date}}` variable is populated by the LLM based on untrusted input without proper sanitization, it could be vulnerable to path traversal attacks. An attacker could inject characters like `../` to write files to arbitrary locations on the filesystem, potentially leading to data corruption, information disclosure, or even remote code execution if critical system files are overwritten. Ensure that any variables used in file paths (e.g., `{{date}}`) are strictly validated and sanitized to prevent path traversal. Only allow expected characters and enforce a specific directory for file writes. Consider using a dedicated file system abstraction layer that prevents writing outside a designated sandbox. | LLM | SKILL.md:43 |
Scan History
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