Humor generation remains one of the most challenging tasks in natural language processing, requiring creativity, incongruity resolution, cultural sensitivity, and strict structural control. We present a fully prompt-based system for headline-conditioned joke generation in SemEval-2026 Task 1 (MWAHAHA) for both English and Spanish. Deliberately avoiding fine-tuning, our approach relies on structured prompt engineering combined with a multi-stage heuristic pipeline. For Spanish, we extract a “stylistic-humor DNA” from a public joke corpus to guide generation. The pipeline integrates multi-candidate generation, diversity enhancement, iterative refinement, LLM-based rewriting, and constraint-aware selection. Human evaluation performed by the team (n=180) shows substantial gains over single-pass generation, particularly in funniness and punchline clarity. Official shared-task results were modest (12th/16 Spanish, 24th/31 English), underscoring that limited originality remains a key bottleneck. In an era dominated by large language models (LLMs) such as GPT-4o and Grok, our work demonstrates the value of linguistically grounded heuristics as an efficient, interpretable, and low-cost complement to black-box generation systems.