When many people come into contact with AI Agents for the first time, they still think of "letting AI help me do things": help me write articles, think about titles, make videos, and organize materials. But after truly putting AI into the content production process, you will find that the most troublesome problem is often not "AI can't write", but that there are a lot of trivial and error-prone links between the generation and final release of an idea: inspiration is not recorded, the material cannot be found, the topic selection is repeated, the ideas are not deepened, the article and the video cannot be reused, and there is no data left behind that can be used for the next creation. As a result, the question changed from "Can AI help me generate content" to "Can AI manage the entire content production process?"
on GitHub rnskill In the project, there is a person who is specially responsible for selecting topics ra-选题 Skill is worth observing from this perspective. It does not simply understand "topic selection" as asking AI to randomly generate several titles, but designs the topic selection as a content asset with a life cycle: an idea first enters the topic selection pool to form a topic selection card, and then goes through Deepening, judging and project approval, and then entering different workstations such as articles, videos, pictures and texts according to the final content form, and then returning to the entire content system after completing the production. The biggest change in this design is the separation of "topic selection" and "content". A topic selection is not an article or a video, but a long-standing content asset. The same topic selection can generate articles, videos and pictures at the same time. You only need to record in the topic selection card which production processes it eventually entered.
This design seems to be just file management, but in fact it reflects a very typical Agent thinking. Traditional Chatbot is more like a "question and answer": the user asks the question, the model generates the result, and the task is over. But a real Agent is more like a project manager. It needs to know what the current task is, what state it is in, what should be done next, and who should the task be handed over to. For example, if a user suddenly throws a link to the AI and says,"Make this an article." The system first needs to determine whether this is an existing topic topic selection; if not, create a topic selection card; then extract the original material, and then enter the article production process; Update the topic selection status after completion. The user may only see one sentence, but the task routing has actually been completed behind the scenes.
The most important thing here is actually the link of "deepening topic selection". Many AI tools understand topic selection as title generation, such as giving you 20 titles at a time, but a really good topic selection is not a beautiful sentence, but a question worth expressing. Why is a topic selection worth doing? Why is it worth doing now? What are the cases and evidence? What angle should we approach from? Is it ultimately suitable for articles or videos? These problems need to be resolved before formal production. Therefore, the topic selection stage and the writing stage should be separated: the topic selection stage is responsible for thinking clearly about "what to do", and the content stage is responsible for solving "how to say it." this is also ra-选题 One point in the design is very worth learning from.
Another valuable aspect is that it gives agents a large number of repetitive operations that should not be done by humans. Users don't need to create files, move files, update status, copy sources, establish associations themselves, nor do they need to remember whether they have done a topic before. The person is responsible for judging whether "this thing is worth doing", and the Agent is responsible for implementing this decision into the entire workflow. For content creators, this may be more important than simply improving AI's writing capabilities. Because what really consumes time is often not writing down the thousands of words, but the continuous information organization, material management and process switching around content production.
From this perspective,ra-选题 What is most worth learning is not a specific Prompt, but the system design behind it: a topic has a unique identity, a clear status, can be deepened, can be approved, can be routed to different content workstations, and at the same time Maintain clear data boundaries with the original material and the final product. In the end, what is formed is no longer a simple "inspiration → writing", but a complete content production chain:
Inspiration → topic selection → deepening → project establishment → production → review → next round of topic selection.
This also shows that the next stage of AI Agents may not be to make models "write more and more like people," but to make models more and more aware of how to participate in real work. A truly mature Agent is not just a smarter chatbot, but a system that can understand status, manage tasks, invoke tools, perform handovers, and continuously maintain work assets. Today we are discussing a topic selection Skill, which can be expanded into a complete set of content agents in the future: the Agent responsible for topic selection, the Agent responsible for research, the Agent responsible for writing, the Agent responsible for video, the Agent responsible for publishing and duplicating cooperate with each other, and the topic selection becomes the entrance of the whole system.
So, AI content production is really worth paying attention to changes, perhaps not "AI can help us write articles in the future", butContent production itself is evolving from a one-time authoring activity to a system that can be managed continuously by agents. When an idea can be recorded, understood, deepened, produced, published, and eventually fed back into the topic pool, it ceases to be an inspiration and becomes a content asset that can be operated for the long term.
And this may be a sign that the Agent has really begun to enter the productivity stage.