Trust Assessment
padel received a trust score of 65/100, placing it in the Caution category. This skill has some security considerations that users should review before deployment.
SkillShield's automated analysis identified 2 findings: 1 critical, 1 high, 0 medium, and 0 low severity. Key findings include Unpinned Dependency in Nix Plugin, Potential Command Injection via Unsanitized Placeholders.
The analysis covered 4 layers: Manifest Analysis, Static Code Analysis, Dependency Graph, LLM Behavioral Safety. The LLM Behavioral Safety layer scored lowest at 55/100, indicating areas for improvement.
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 Findings2
| Severity | Finding | Layer | Location | |
|---|---|---|---|---|
| CRITICAL | Unpinned Dependency in Nix Plugin The skill's Nix plugin dependency `github:joshp123/padel-cli` is unpinned. This means the skill will always fetch the latest version from the main branch of the repository. If the upstream repository is compromised or updated with malicious code, it could lead to arbitrary code execution on the host system without warning. It is crucial to pin dependencies to a specific commit hash, tag, or version to ensure reproducibility and security. Pin the `nix.plugin` dependency to a specific commit hash or version tag (e.g., `github:joshp123/padel-cli/a1b2c3d4e5f6...` or `github:joshp123/padel-cli/v1.2.3`) to prevent unexpected or malicious changes from being introduced. | LLM | SKILL.md:1 | |
| HIGH | Potential Command Injection via Unsanitized Placeholders The skill defines shell command templates with placeholders (e.g., `VENUE1,VENUE2`, `YYYY-MM-DD`, `09:00-12:00`) that are expected to be filled by the LLM based on user input. If the LLM directly substitutes user-provided strings into these placeholders without proper sanitization or escaping, a malicious user could inject arbitrary shell commands. For example, providing `--venues 'foo,bar; rm -rf /'` could lead to command injection. Implement robust input validation and sanitization for all user-provided arguments before they are substituted into shell commands. Ensure that the LLM's command generation logic properly escapes or quotes arguments to prevent shell metacharacter interpretation. Consider using a safer command execution mechanism that separates commands from arguments. | LLM | SKILL.md:29 |
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