In a recent LinkedIn post, Kieran Flanagan discusses a powerful, yet under-utilized, skill for users of AI conversational tools like Claude. Flanagan emphasizes the potential to transform everyday interactions into reusable AI capabilities.
According to Flanagan, the key lies in how users label and manage their conversations. He suggests that by consistently naming chats according to the specific task being addressed, users can effectively train the AI for future use. Flanagan highlights this approach as a method to harness the full potential of AI assistants.
“This is such a powerful skill. And mostly under-used by people using Claude.”
Flanagan’s core argument is that the conversational history with an AI is not just a record of past interactions, but a foundation for creating specialized tools. He proposes a practical method for users to leverage these past successes.
Transforming Conversations into Reusable AI Skills
The process, as outlined by Kieran Flanagan, involves a straightforward yet impactful workflow. The first step is diligent labeling of chat sessions. Flanagan advises users to name their conversations based on the objective they aim to achieve. This initial step is crucial for organizing and identifying productive exchanges.
Once a user has achieved a satisfactory outcome from a particular chat session, Flanagan introduces a specific command to solidify that learning. He explains:
“If you start labeling your chats after the task you’re trying to complete. Once you’ve got the outcome you’re happy with from that chat. Simply run /skill-creator and ask Claude to turn that chat into a skill.”
In Flanagan’s view, this command acts as a catalyst, enabling the AI to internalize the successful interaction pattern. This allows the AI to replicate that specific task or problem-solving approach in the future, effectively creating a custom skill or function for the user.
The Underexplored Potential of AI Skill Creation
Flanagan points out that this capability is frequently overlooked by many users. He suggests that a significant portion of AI users are not leveraging their interactions to build a personalized toolkit of AI functions. This represents a missed opportunity for enhanced efficiency and productivity.
The implication of Flanagan’s insight is that AI tools are not just for one-off queries, but can be systematically developed into more sophisticated assistants tailored to individual or business needs. By treating each successful chat as a potential building block, users can gradually enhance the AI’s utility.
As Flanagan notes, this method encourages a more proactive engagement with AI technology. Instead of passively receiving answers, users are empowered to actively shape the AI’s capabilities, turning it into a more versatile and personalized tool.
📝 About This Content
This article is based on insights shared by Kieran Flanagan on LinkedIn.
📅 Originally posted on April 15, 2026 | View original post on LinkedIn →