1. Introduction workflow working once. It is much harder to make it repeatable. Prompting ChatGPT or Claude for each run is fast, but the results are inconsistent and hard to reproduce. Building everything in Python or locking down the workflow improves reliability, but often removes the flexibility that makes LLMs useful for exploration. A Claude Code skill can bridge this gap. It preserves the flexibility of natural language, while SKILL.md and bundled scripts provide enough structure to keep the workflow consistent. This approach works best for tasks that repeat with small changes, where natural-language instructions are important, and where hardcoding everything would add unnecessary complexity. In my previous article , I walked through how to design, build, and distribute a Claude Code skill from scratch. In this article, I will focus on a concrete case study to show where a skill adds real value. 2.…