Trust Assessment
as-built-tracker received a trust score of 79/100, 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, 1 high, 1 medium, and 0 low severity. Key findings include Missing required field: name, Arbitrary File Write via export_to_excel.
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 | |
|---|---|---|---|---|
| HIGH | Arbitrary File Write via export_to_excel The `export_to_excel` function accepts an `output_path` argument directly, which can be controlled by untrusted input. If the AI agent's execution environment grants broad filesystem write permissions, a malicious actor could specify an arbitrary path (e.g., `/etc/passwd` on Linux, `C:\Windows\System32\config\SAM` on Windows) to overwrite or create files in sensitive system locations. This could lead to denial of service, data corruption, or privilege escalation. Implement strict validation and sanitization of the `output_path` argument. Restrict output paths to a designated, sandboxed directory (e.g., a temporary directory or a user-specific output folder). Avoid allowing arbitrary user-provided paths for file write operations. Ensure the skill's execution environment has minimal necessary write permissions. | LLM | SKILL.md:247 | |
| 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/datadrivenconstruction/as-built-tracker/SKILL.md:1 |
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