How to teach a neural network a custom style without fine-tuning

The author proposes an alternative to traditional fine-tuning for mimicking an author's style with neural networks. Instead of using heavy computing power and creating datasets, the approach uses two skills for Claude Code or Codex. The first skill analyzes the author's existing texts, measuring dozens of parameters—from sentence length to emoji frequency—to form a set of stylistic rules. The second skill applies these rules to any draft, rewriting it in the desired style. The resulting style profile is saved as a text file, allowing it to be reused with any model without modifying its weights. This method significantly reduces the time required to adapt content to a brand's voice while ensuring high accuracy in meeting specified characteristics. This solution makes the process of personalizing texts an accessible and flexible tool for working with LLMs.