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In the evolving landscape of software development and system operations, the demand for automating traditionally manual tasks has surged. Continuous operation and minimal downtimes highlight the need for automated detection and remediation of runtime anomalies. Ansible, known for its scalable features, including high-level abstraction and modularity, stands out as a reliable solution for managing complex systems securely. The challenge lies in creating an on-the-spot Ansible solution for dynamic auto-remediation, requiring a substantial dataset for in-context tuning of large language models (LLMs). Our research introduces KubePlaybook, a curated dataset with 130 natural language prompts for generating automation-focused remediation code scripts. After rigorous manual testing, the generated code achieved an impressive 98.86% accuracy rate, affirming the solution's reliability and performance in …