Trust Assessment
tavily-web received a trust score of 85/100, 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, 0 high, 1 medium, and 0 low severity. Key findings include Unpinned skill dependency.
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 20, 2026 (commit e36d6fd3). SkillShield performs automated 4-layer security analysis on AI skills and MCP servers.
Layer Breakdown
Behavioral Risk Signals
Security Findings1
| Severity | Finding | Layer | Location | |
|---|---|---|---|---|
| MEDIUM | Unpinned skill dependency The skill installation command references a GitHub repository without specifying a version, commit hash, or tag. This means that future installations could fetch different or potentially malicious code if the upstream repository is compromised or updated with breaking/malicious changes. This introduces a supply chain risk as the integrity of the installed skill is not guaranteed over time. Pin the skill dependency to a specific version, commit hash, or tag (e.g., `npx skills add -g BenedictKing/tavily-web@v1.0.0` or `npx skills add -g BenedictKing/tavily-web#<commit_hash>`) to ensure consistent and verifiable installations. | LLM | SKILL.md:15 |
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