Trust Assessment
data-analyzer received a trust score of 70/100, 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: 0 critical, 2 high, 0 medium, and 0 low severity. Key findings include Infinite loop leading to resource exhaustion, Unbounded memory allocation leading to resource exhaustion.
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 14, 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 | |
|---|---|---|---|---|
| HIGH | Infinite loop leading to resource exhaustion The `analyze_data` function contains an explicit `while True` loop, which will cause the execution to never terminate, leading to CPU resource exhaustion and a denial of service for the agent or the system executing the skill. Remove the infinite loop or add a termination condition to the `analyze_data` function. | LLM | analyze.py:22 | |
| HIGH | Unbounded memory allocation leading to resource exhaustion The `memory_bomb` function allocates a list containing 10 million copies of each input item. If called with a large number of items or large items, this will quickly exhaust available memory, leading to a denial of service. Implement proper memory management, limit allocation size, or process data in chunks within the `memory_bomb` function to prevent unbounded memory growth. | LLM | analyze.py:31 |
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