A major new feature from Anthropic’s Claude, called “Skills,” represents a significant leap forward in interacting with AI by moving beyond the “tyranny of the prompt.” This innovation simplifies complex, multi-step tasks by storing detailed instructions and context in reusable modules, making it easier for anyone to accomplish difficult work with AI. The speaker argues this is one of the biggest AI news stories of the year because the underlying technique is portable to other models like ChatGPT and Gemini.
What Are AI ‘Skills’?
Skills are described as composable “Lego bricks” or capabilities that you can enable in your AI’s settings. Each skill contains specialized instructions and context for a specific, complex task. For example, a ‘Job Search Strategy’ skill could store your preferred job sites, roles, salary expectations, and outreach strategy. When you later ask for help with your job search, Claude automatically detects the context, accesses the relevant skill, and applies its stored knowledge without you needing to re-explain everything. This allows for more intuitive and less labor-intensive interactions for tasks that would otherwise require many detailed, sequential prompts.
A Portable Breakthrough for All Major AI Models
A key discovery highlighted in the video is that these skills are not locked into the Claude ecosystem. Because a skill is simply a collection of markdown files and other resources packaged in a zip file, it can be uploaded and used in other chat interfaces like ChatGPT or Gemini. You can instruct the model to use the uploaded file as a reference for its task. This transforms the feature from a single-platform tool into a new, universal methodology for working with AI, creating a standardized way to package and reuse complex instructions across different models.
How to Create and When to Use Skills
Users can create their own custom skills simply by describing the desired task and workflow to Claude, which is equipped with instructions on how to build them. The speaker recommends a “multi-LLM strategy”—for instance, using ChatGPT to critique and suggest improvements for a skill built by Claude—to enhance its quality. Skills are most valuable for high-value, repeatable tasks that would normally require training or onboarding a human employee, such as AI vendor risk assessment or creating detailed PowerPoint presentations. They are not intended for simple, one-off requests.
Conclusion: A ’10X Lever’ on Prompting, But Clarity is Still Key
The introduction of skills is framed as a “10X lever” on prompting, effectively creating “super prompts” that lift the heavy burden of providing exhaustive context for every complex task. However, this doesn’t eliminate the need for good prompting. While prompts no longer need to be as long or detailed, they must still be clear and unambiguous. The user’s role shifts from providing exhaustive instructions to giving clear direction for the powerful, pre-packaged skill the AI is using. This breakthrough significantly lowers the barrier to performing high-level work with AI, making it more accessible to a wider audience.
Mentoring question
What is a complex, repeatable process in your work that you currently spend a lot of time on, and how could you design an AI ‘skill’ to automate or streamline it?
Source: https://youtube.com/watch?v=UaJhYp7Tql4&si=sk6NwR1_5NciIFZ4
Leave a Reply