Trust Assessment
research-company received a trust score of 86/100, placing it in the Mostly Trusted category. This skill has passed most security checks with only minor considerations noted.
SkillShield's automated analysis identified 1 finding: 0 critical, 1 high, 0 medium, and 0 low severity. Key findings include Unpinned Dependency Installation.
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 Findings1
| Severity | Finding | Layer | Location | |
|---|---|---|---|---|
| HIGH | Unpinned Dependency Installation The skill instructs to install the 'reportlab' Python package without specifying a version. This practice introduces a significant supply chain risk. An attacker could publish a malicious version of 'reportlab' (or a typosquat package) to public repositories, which would then be automatically installed when the skill is set up or run, leading to potential arbitrary code execution, data exfiltration, or system compromise. Pin the dependency to a specific, known-good version (e.g., `pip install reportlab==X.Y.Z`). Regularly review and update pinned versions to incorporate security patches while maintaining control over the installed package. | LLM | SKILL.md:51 |
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