Trust Assessment
nostr-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 shell command.
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 | |
|---|---|---|---|---|
| HIGH | Potential Command Injection via unsanitized user input in shell command The skill's usage example demonstrates a shell command: `script -q -c "nak req -k 4223 -a <pubkey> <relay> -l 1" /dev/null | cat`. This command includes placeholders (`<pubkey>`, `<relay>`) that are intended to be filled by user input. If these inputs are not rigorously validated and properly escaped or quoted before being interpolated into the shell command, an attacker could inject arbitrary shell commands. This could lead to remote code execution, data exfiltration, or system compromise. Ensure all user-provided inputs (`<pubkey>`, `<relay>`) are strictly validated and properly escaped or quoted when used in shell commands. It is highly recommended to use a dedicated API or library function for interacting with `nak` rather than direct shell execution. If shell execution is unavoidable, use a safe command execution mechanism (e.g., passing arguments as a list to `subprocess.run` in Python with `shell=False`) to prevent shell injection. | LLM | SKILL.md:17 |
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