spark-engineer
Pass
Audited by Gen Agent Trust Hub on Apr 29, 2026
Risk Level: SAFE
Full Analysis
- [SAFE]: The skill provides detailed guidance and code examples for Apache Spark engineering without introducing any malicious patterns. It correctly uses placeholders for sensitive parameters such as JDBC credentials and storage paths.\n- [CREDENTIALS_UNSAFE]: Examples involving database and cloud storage access utilize clearly identified placeholders for authentication details, following security best practices for documentation.\n- [DYNAMIC_EXECUTION]: Mention of standard library features like Spark's pickle-based persistence for RDDs is presented within the context of legitimate API documentation and does not represent a malicious attempt at unsafe deserialization.\n- [INDIRECT_PROMPT_INJECTION]: The skill acknowledges data ingestion from distributed sources like Kafka and S3. While these represent potential attack surfaces for processing untrusted data, the skill focuses on engineering efficiency and follows standard patterns for Spark-based data processing.\n
- Ingestion points: Data is read via standard Spark APIs from sources such as S3, HDFS, and Kafka topics.\n
- Boundary markers: None explicitly required as this is a technical implementation guide, not a data-parsing utility.\n
- Capability inventory: Includes distributed file operations and partitioned processing across cluster nodes.\n
- Sanitization: Not applicable to the provided architectural guidelines.
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