Trust Assessment
knhb-hockey 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 commands.
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 12, 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 commands The skill provides `bash` command examples that use `curl` to interact with an API. These commands include placeholders like `{clubId}` and `{teamId}` which are expected to be populated by the LLM, likely from user input. If user input is directly interpolated into these shell commands without proper sanitization (e.g., shell escaping or URL encoding), an attacker could inject arbitrary shell commands. For instance, if `{clubId}` were `123; rm -rf /`, it could lead to the execution of `rm -rf /`. The LLM orchestrating this skill must ensure all user-provided inputs used in shell commands are rigorously sanitized. For URL path segments, this means proper URL encoding. For arguments passed to `jq` or other tools, this means robust shell escaping. Alternatively, consider using a programmatic HTTP client (e.g., Python `requests`) instead of shell commands to reduce the attack surface. | LLM | SKILL.md:30 |
Scan History
Embed Code
[](https://skillshield.io/report/14fec2689ddfd9e6)
Powered by SkillShield