Trust Assessment
context7 received a trust score of 73/100, placing it in the Caution category. This skill has some security considerations that users should review before deployment.
SkillShield's automated analysis identified 1 finding: 1 critical, 0 high, 0 medium, and 0 low severity. Key findings include Potential Command Injection via unsanitized `curl` parameters.
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 11, 2026 (commit 326f2466). SkillShield performs automated 4-layer security analysis on AI skills and MCP servers.
Layer Breakdown
Behavioral Risk Signals
Security Findings1
| Severity | Finding | Layer | Location | |
|---|---|---|---|---|
| CRITICAL | Potential Command Injection via unsanitized `curl` parameters The skill provides `curl` commands that construct URLs using user-provided parameters such as `libraryName`, `query`, and `libraryId`. If an agent directly interpolates untrusted user input into these parameters without proper shell escaping and URL encoding before executing the `curl` command, an attacker could inject arbitrary shell commands. For example, a malicious `libraryName` like `'; rm -rf /'` could lead to arbitrary code execution on the host system if the agent executes the command directly in a shell. The agent implementing this skill must ensure that all user-provided inputs (`libraryName`, `query`, `libraryId`) are properly shell-escaped and URL-encoded before being incorporated into the `curl` command string for execution. A more robust solution is to use a dedicated HTTP client library in the agent's programming language (e.g., Python's `requests` library) which handles parameter encoding safely, rather than constructing and executing shell commands directly. | LLM | SKILL.md:14 |
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