Trust Assessment
weather-api-1 received a trust score of 87/100, 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, Unpinned dependencies in Python code.
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 14, 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/phucanh08/weather-api-1/SKILL.md:1 | |
| MEDIUM | Unpinned dependencies in Python code The Python code imports `requests` and `pandas` without specifying exact versions. This can lead to supply chain vulnerabilities if a future version of these libraries introduces breaking changes, security flaws, or malicious code. It's a best practice to pin dependencies to specific versions to ensure reproducibility and security. Pin dependencies to specific versions (e.g., `requests==2.28.1`, `pandas==1.5.3`) in a `requirements.txt` file or similar dependency management system. This ensures that the skill always runs with tested and known-good versions of its dependencies. | LLM | SKILL.md:10 |
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