Spark Engineer

Use when building Apache Spark applications, distributed data processing pipelines, or optimizing big data workloads. Invoke for DataFrame API, Spark SQL, RDD operations, performance tuning, streaming analytics.

This skill is an offline reference and looks internally consistent with its Spark-focused purpose. Before running any provided code in your environment: 1) review and supply only the credentials your cluster/storage requires (the skill does not request any itself), 2) avoid running example collect() or large broadcasts on production data without safeguards, and 3) inspect any mapPartitions/foreachPartition code that opens external DB/HTTP connections to ensure it uses approved endpoints and secure credentials. If you plan to let an agent execute code from this skill automatically, ensure the agent does not have unrestricted access to production cluster credentials or sensitive storage buckets.

Type: OpenClaw Skill Name: spark-engineer Version: 0.1.0 The OpenClaw AgentSkills skill bundle for 'spark-engineer' is benign. The `SKILL.md` provides instructions for an AI agent to act as a Spark engineer, focusing on best practices and technical guidance. All referenced markdown files (`references/*.md`) contain documentation and code examples (PySpark/Scala) for Apache Spark operations, performance tuning, and structured streaming. There is no evidence of data exfiltration, malicious execution, persistence mechanisms, prompt injection attempts to subvert the agent's role, or obfuscation. Generic placeholders for S3 buckets, Kafka brokers, and database connection strings are used for illustrative purposes and do not indicate malicious intent.