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Large language models are powerful, but relying on prompts alone quickly becomes fragile and difficult to scale. As teams try to operationalize LLMs in real workflows, traditional documentation and ad-hoc prompting start to break down.

In this episode, we explore a new paradigm introduced with Claude Skills: packaging workflows, instructions, and resources into reusable capabilities that LLMs can execute.

You’ll learn:

* Why traditional documentation is poorly suited for LLMs and why workflow-first instructions are more effective.

* How Claude Skills structure tasks using a concise SKILL.md file that points to supporting files and scripts loaded on demand.

* How teams can design and deploy skills to turn LLMs into reliable task executors rather than prompt-driven tools.

By the end, you’ll understand how skills move us from prompt engineering to designing AI-native workflows.

If you’d rather read than listen, the full article (with diagrams, code examples, and implementation details) is available on Substack:

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