Transforming Prompts into Reusable Skills: Ruben Hassid’s Guide for AI Users

In a recent LinkedIn post, Ruben Hassid outlines a streamlined method for converting effective AI prompts into reusable ‘skills,’ a feature available in platforms like Claude. Hassid emphasizes that this approach is significantly faster than manual creation and offers a more permanent integration of AI capabilities into daily workflows.

The Core Idea: Skills Over Single Prompts

Ruben Hassid’s central argument is that a well-crafted prompt, while useful once, is less impactful than a dedicated AI skill. He explains the fundamental difference in how AI models, specifically Claude, interpret these instructions. Hassid points out a critical detail often overlooked by users:

“Claude reads the 2-line description when it fires. Not the prompt inside. The description.”

This distinction, according to Hassid, is key to effective skill creation. He advises users to frame the skill’s description as a specific task rather than a general topic. For instance, instead of a vague description like “Helps with writing,” Hassid recommends a task-oriented description such as “Use when I ask for a LinkedIn hook.” This specificity ensures the AI triggers the skill only when genuinely needed for that particular task.

Optimizing Skills for Performance

Further elaborating on his strategy, Ruben Hassid advocates for a single-purpose design for each AI skill. He warns against creating multi-functional skills, stating:

“A skill that does 3 things fires for none of them. Hooks & emails & posts? It reaches for nothing. Boring and single-purpose wins.”

This principle of focused functionality, Hassid argues, leads to more reliable and predictable AI performance. He also provides a practical tip for testing skill implementation: users should not explicitly tell the AI to use the skill. Instead, they should simply ask for the task the skill is designed to perform. If the skill activates automatically, the user has successfully built it.

Leveraging AI Features for Better Results

Hassid also introduces a helpful tip for users who encounter difficulties or jargon when working with AI models. He suggests using the “explain like I’m 5” (ELI5) command.

“Claude drops the jargon instantly. I use this more”

This feature, as noted by Hassid, can demystify complex AI outputs and make the technology more accessible. He also touches upon optimizing the AI’s operational parameters, suggesting specific settings like “Cowork, Opus 4.8, effort on High” to potentially enhance output quality for the same skill.

The Long-Term Value of AI Skills

Ruben Hassid frames the creation of AI skills as an investment in long-term efficiency. He draws an analogy to illustrate this point:

“A great prompt helps you once. A skill turns it into a reflex Claude has forever. It’s the difference between driving a car & building a road you’ll drive every day for the rest of your life.”

To facilitate this process for others, Hassid has compiled 27 frequently used skills into a downloadable guide available at how-to-ai.guide. He encourages users to adopt these skills and share them within their teams, promoting wider adoption of efficient AI practices.