Trust Assessment
daily-progress-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, Unrestricted 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 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/daily-progress-report/SKILL.md:1 | |
| MEDIUM | Unrestricted file write via output_path The `export_to_excel` function allows writing to an arbitrary file path specified by the `output_path` parameter. The skill does not perform any validation or sanitization on this path. If the calling agent passes untrusted user input directly to `output_path`, an attacker could potentially write files to sensitive system locations, leading to denial of service, data corruption, or, in conjunction with other vulnerabilities, data exfiltration or remote code execution. Implement path validation within the `export_to_excel` function to restrict `output_path` to a safe, designated directory (e.g., a sandbox or temporary directory). Prevent absolute paths or paths containing directory traversal sequences (`..`). Alternatively, ensure the calling agent strictly sanitizes or restricts `output_path` values. | LLM | SKILL.md:173 |
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