Trust Assessment
cal-com received a trust score of 88/100, 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 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 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 Input in Shell Commands The skill documentation provides `curl` command examples that directly embed variables (e.g., `EVENT_TYPE_ID`, `BOOKING_ID`, `subscriberUrl`, etc.) into shell command strings. If an AI agent implements these commands by directly substituting untrusted user input into these variables without proper shell escaping or validation, it could lead to command injection. An attacker could craft malicious input (e.g., `123; rm -rf /`) that would be executed by the underlying shell when the command is constructed and executed. When constructing shell commands from user input, ensure all variables are properly quoted and escaped. For example, use `shlex.quote()` in Python or similar functions in other languages. Alternatively, use API client libraries that handle parameter serialization safely instead of raw shell commands. | LLM | SKILL.md:30 |
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