Robots require ethical sensitivity, not just functional competence, to make decisions in human settings. While AI planning generates action sequences for goals, few approaches incorporate ethical rules. Manually defining such rules is context-specific and time-consuming, and to our knowledge, no work automates this process. We propose a pipeline that uses Large Language Models (LLMs) to generate context-specific ethical rules grounded in high-level principles like privacy and beneficence. Rules are generated by an LLM and compiled into action costs, enabling classical planners to produce ethically informed plans. We evaluate our pipeline on nine ethical planning scenarios across three domains. Rule generation achieves an average Sentence-BERT similarity of 0.82, while code generation succeeds in 82.2% of cases with minimal manual edits. This work presents a novel approach to automating ethical rule generation, enabling context-based ethical decision-making.