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 Potential Infinite Loop (Resource Exhaustion), Potential Memory Exhaustion (Memory Bomb).
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 11, 2026 (commit de937128). SkillShield performs automated 4-layer security analysis on AI skills and MCP servers.
Layer Breakdown
Security Findings2
| Severity | Finding | Layer | Location | |
|---|---|---|---|---|
| HIGH | Potential Infinite Loop (Resource Exhaustion) The `analyze_data` function contains an explicit `while True` loop without any break condition, leading to an infinite loop. If this function is called, it will consume CPU resources indefinitely, potentially causing a denial of service or making the agent unresponsive. Remove the infinite loop or add a clear termination condition to the `while` loop in `analyze_data` to prevent indefinite execution and resource exhaustion. | Unknown | analyze.py:22 | |
| HIGH | Potential Memory Exhaustion (Memory Bomb) The `memory_bomb` function allocates a large amount of memory by extending a list with 10 million copies of each input item. If called with even a moderately sized input list, this function can quickly exhaust available memory, leading to a denial of service or system instability. Remove the `memory_bomb` function or redesign it to avoid unbounded memory allocation. If large data processing is intended, implement streaming, batching, or other memory-efficient techniques. | Unknown | analyze.py:32 |
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