Trust Assessment
social-content received a trust score of 94/100, placing it in the Trusted category. This skill has passed all critical security checks and demonstrates strong security practices.
SkillShield's automated analysis identified 1 finding: 0 critical, 0 high, 1 medium, and 0 low severity. Key findings include Direct File Reading Instruction.
The analysis covered 4 layers: dependency_graph, manifest_analysis, llm_behavioral_safety, static_code_analysis. All layers scored 70 or above, reflecting consistent security practices.
Last analyzed on February 16, 2026 (commit a04cb61a). SkillShield performs automated 4-layer security analysis on AI skills and MCP servers.
Layer Breakdown
Behavioral Risk Signals
Security Findings1
| Severity | Finding | Layer | Location | |
|---|---|---|---|---|
| MEDIUM | Direct File Reading Instruction The skill explicitly instructs the AI to read a local file (`.claude/product-marketing-context.md`). While the file path is specific, this demonstrates the AI's capability to access the local filesystem. This could lead to information disclosure or data exfiltration if the file contains sensitive information, or if the instruction could be manipulated (e.g., via prompt injection) to read arbitrary files. Avoid instructing the LLM to directly read local files. If context is needed, it should be provided to the LLM through a secure, sandboxed mechanism, not by instructing it to read files from its environment. Ensure that the execution environment strictly limits file access to only necessary, explicitly allowed paths, and that sensitive files are never placed in such paths. | Unknown | SKILL.md:10 |
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