Trust Assessment
labor-allocation 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 output_path.
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 Findings2
| Severity | Finding | Layer | Location | |
|---|---|---|---|---|
| HIGH | Arbitrary File Write via output_path The `export_to_excel` method directly uses the `output_path` argument to create a file without any sanitization or restriction. An attacker could provide a malicious path (e.g., using directory traversal like `../../../../tmp/malicious.xlsx` or absolute paths like `/etc/passwd`) to write arbitrary Excel files to sensitive locations on the file system, potentially overwriting critical system files or exfiltrating data by writing to publicly accessible directories if the agent has the necessary permissions. Restrict the `output_path` to a designated, sandboxed directory. Implement path sanitization to prevent directory traversal (e.g., `os.path.basename` or `os.path.join` with a base directory). Alternatively, ensure the agent's execution environment has strict file system access controls. | LLM | SKILL.md:307 | |
| 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/labor-allocation/SKILL.md:1 |
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