Trust Assessment
cwicr-risk-calculator 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, Arbitrary File Write via export_analysis.
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/datadrivenconstruction/cwicr-risk-calculator/SKILL.md:1 | |
| MEDIUM | Arbitrary File Write via export_analysis The `export_analysis` method in the `CWICRRiskCalculator` class allows writing an Excel file to an arbitrary path specified by the `output_path` argument. If an AI agent exposes this parameter to untrusted user input without proper validation, an attacker could specify arbitrary file paths. This could lead to overwriting sensitive system files, creating malicious files in critical directories, or potentially exfiltrating data by writing to accessible network locations. This capability grants broad filesystem write access. Implement strict validation and sanitization of the `output_path` argument in the calling agent to ensure it points only to allowed, non-sensitive directories, or use a sandboxed environment for file operations. Consider restricting file writes to a designated temporary or output directory. | LLM | SKILL.md:360 |
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