Problem Statement
LLM output quality varies by input register — casual phrasing costs Claude Opus 19% of correct answers and Gemini over half. The mapping is non-trivial: academic register halves reasoning accuracy on Opus, while technical outperforms direct on Gemini for analysis tasks. Transmogrifier normalizes register transparently as middleware.
MIT · Python