A developer introduces a clever way to structure complex AI prompts into three clear layers—skills, agents, and commands—making them simpler to use, share, and more efficient in your daily work.
If you're someone who uses AI tools in your daily work, especially in programming, you might have found yourself with a growing pile of 'prompts' that are hard to manage and share with your team. The good news is there's a new, structured way to handle this, making your AI use much more effective for both you and your colleagues.
A developer recently came up with a clever system that breaks down these complex prompts into three clear layers: skills, agents, and commands. Imagine this system turning a messy collection of individual prompts into a well-organized, powerful toolkit.
* **Skills:** These are the core knowledge or 'how-to' guides the AI needs. They contain the specifics of how to perform a task, like best coding practices or design patterns. They are loaded only when they're actually needed.
* **Agents:** Think of these as the 'experts' or specialized roles, such as a 'code reviewer,' a 'project manager,' or a 'database administrator.' An agent doesn't carry all the knowledge itself; instead, it knows which skills it needs for its job and loads them on demand. This keeps agents lightweight and easy to manage.
* **Commands:** These are the simple starting points that kick off a specific workflow. You can think of them as 'buttons' you press to start a task, like 'review code' or 'write tests.'
The big advantage of splitting things this way is that when you update one 'skill,' all the 'agents' that rely on it automatically become smarter. This makes updating your AI tools much easier and ensures everyone is working with the latest information.
Thanks to this system, four new agents have been added to cover more aspects of the software development lifecycle, including a DBA, project manager, API engineer, and pentester, bringing the total number of agents to 20. This means comprehensive coverage for development tasks.
One final, important tip: if you plan to share your AI tools with others, it’s smart to generalize them as much as possible. This means removing any company-specific details or private file paths, so others can learn the method and approach rather than just copying your specific setup. This makes your tools much more useful to everyone.
A developer recently came up with a clever system that breaks down these complex prompts into three clear layers: skills, agents, and commands. Imagine this system turning a messy collection of individual prompts into a well-organized, powerful toolkit.
* **Skills:** These are the core knowledge or 'how-to' guides the AI needs. They contain the specifics of how to perform a task, like best coding practices or design patterns. They are loaded only when they're actually needed.
* **Agents:** Think of these as the 'experts' or specialized roles, such as a 'code reviewer,' a 'project manager,' or a 'database administrator.' An agent doesn't carry all the knowledge itself; instead, it knows which skills it needs for its job and loads them on demand. This keeps agents lightweight and easy to manage.
* **Commands:** These are the simple starting points that kick off a specific workflow. You can think of them as 'buttons' you press to start a task, like 'review code' or 'write tests.'
The big advantage of splitting things this way is that when you update one 'skill,' all the 'agents' that rely on it automatically become smarter. This makes updating your AI tools much easier and ensures everyone is working with the latest information.
Thanks to this system, four new agents have been added to cover more aspects of the software development lifecycle, including a DBA, project manager, API engineer, and pentester, bringing the total number of agents to 20. This means comprehensive coverage for development tasks.
One final, important tip: if you plan to share your AI tools with others, it’s smart to generalize them as much as possible. This means removing any company-specific details or private file paths, so others can learn the method and approach rather than just copying your specific setup. This makes your tools much more useful to everyone.
