Trust Assessment
jarvis-skills received a trust score of 79/100, 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, 1 high, 1 medium, and 0 low severity. Key findings include Missing required field: name, Unpinned dependencies in skill manifest.
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 | |
|---|---|---|---|---|
| HIGH | Unpinned dependencies in skill manifest The skill's `skill.json` specifies dependencies (`openclaw`, `pyserial`, `numpy`) without pinning them to specific versions. This can lead to supply chain risks, including unexpected behavior, security vulnerabilities, or even malicious code injection if a dependency is compromised or a new version introduces breaking changes. It makes the build non-deterministic. Pin all dependencies to specific, known-good versions (e.g., `"openclaw": "1.2.3"`) to ensure deterministic builds and prevent unexpected changes or supply chain attacks. Regularly review and update pinned versions. | LLM | skill.json:16 | |
| MEDIUM | Missing required field: name The 'name' field is required for openclaw skills but is missing from frontmatter. Add a 'name' field to the SKILL.md frontmatter. | Static | skills/aly-joseph/jarvis-skills/SKILL.md:1 |
Scan History
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