Trust Assessment
agentpayy received a trust score of 97/100, placing it in the Trusted category. This skill has passed all critical security checks and demonstrates strong security practices.
SkillShield's automated analysis identified 1 finding: 0 critical, 0 high, 0 medium, and 1 low severity. Key findings include Unpinned Python Dependencies.
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 Findings1
| Severity | Finding | Layer | Location | |
|---|---|---|---|---|
| LOW | Unpinned Python Dependencies The skill's manifest specifies Python dependencies (`coinbase-cdp`, `requests`) without pinning them to exact versions. This can lead to non-deterministic builds, unexpected behavior changes, or introduce vulnerabilities if a future version of a dependency is compromised or contains breaking changes. It creates a supply chain risk where a malicious update to an unpinned dependency could be automatically pulled and executed. Pin all Python dependencies to exact versions (e.g., `coinbase-cdp==1.2.3`). Regularly audit and update these pinned versions to ensure security and stability. | LLM | SKILL.md |
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