Trust Assessment
context-compression received a trust score of 95/100, 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 Potential Data Exfiltration to External LLM Provider.
The analysis covered 4 layers: manifest_analysis, llm_behavioral_safety, static_code_analysis, dependency_graph. All layers scored 70 or above, reflecting consistent security practices.
Last analyzed on February 15, 2026 (commit 3e75fabd). 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 | Potential Data Exfiltration to External LLM Provider The `scripts/compression_evaluator.py` file outlines a probe-based evaluation system where an external LLM acts as a judge. The `_evaluate_response_with_llm_judge` method is designed to construct a prompt containing `probe.question`, `probe.ground_truth`, and the agent's `response`. The `ground_truth` is derived from the `conversation_history`, which can include sensitive details like file paths, error messages, and internal decisions. While the `_llm_judge_api_call` is currently stubbed with a `NotImplementedError`, the 'PRODUCTION NOTES' explicitly state: 'Production systems should implement actual API calls to GPT-5.2 or equivalent.' If implemented as intended, this would transmit potentially sensitive user data, internal file paths, and conversation details to a third-party LLM provider, leading to data exfiltration. 1. **Anonymize Data**: Before sending any data to external LLMs, ensure all personally identifiable information (PII), sensitive business data, or proprietary code snippets are removed or anonymized. 2. **On-Premise/Private LLM**: Consider using an LLM deployed on-premise or within a private cloud environment where data residency and security controls can be fully managed. 3. **Explicit User Consent**: Obtain explicit user consent before transmitting any data to third-party LLM providers for evaluation purposes. 4. **Data Minimization**: Only send the absolute minimum amount of information required for the evaluation. 5. **Security Review**: Conduct a thorough security review of the LLM integration, including data handling, access controls, and compliance with relevant regulations. | Unknown | scripts/compression_evaluator.py:190 |
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