In a recent LinkedIn post, Kieran Flanagan discusses the architecture and operational principles behind a sophisticated AI content system he developed using Claude Code. Flanagan emphasizes that while building the system required significant time investment (over 60 hours), the result functions as a “mini content agency,” capable of streamlining content production.
Flanagan highlights the critical role of a dedicated “builder” within a team for leveraging such AI tools. He explains that not every team member needs to be an expert in the underlying AI technology.
“Not everyone needs to know how Claude Code works. But someone does. That person builds the skills once and packages them into a custom MCP server. Everyone else connects from Claude Desktop and asks for what they need in plain English.”
The Power of Systems Thinking in AI Content Creation
A central theme in Flanagan’s analysis is the distinction between acquiring skills and developing systems. He argues that a systems-thinking approach yields greater long-term benefits than focusing solely on isolated skills. Flanagan illustrates this by explaining how his system centralizes information in a shared profiles folder, allowing for consistent updates that propagate across all content outputs.
“Systems thinkers will outperform those who just think about skills in isolation,” Flanagan states. “Every skill in my content system reads from a shared profiles folder. Change those files once, and every output adapts, hooks, posts, newsletters, all of it.” This approach mirrors the operational structure of a traditional content agency, focusing on “content architecture.”
Modular Design and Feedback Loops for AI Efficacy
Flanagan also details the importance of modularity within the AI system. He found that initially bundling research and creation tasks led to suboptimal outputs. By separating these functions into distinct “skills,” with each skill having a specific, singular purpose, the system’s performance improved significantly.
Separation of Concerns
For instance, Flanagan notes that his “talking point extractor” skill is not designed to understand the format of a LinkedIn post, and conversely, the “LinkedIn drafter” skill does not perform research. This division of labor, he suggests, enhances efficiency and output quality.
The Necessity of Feedback
Furthermore, Flanagan underscores the necessity of implementing feedback loops to prevent AI systems from degrading over time. He has integrated a performance logger that records key metrics for each published piece of content, such as platform, hook pattern, and engagement data. A subsequent “monthly review” skill analyzes these records to identify patterns and refine audience profiles, thereby improving content generation iteratively.
“Without a feedback loop, your AI system gets worse over time.”
The Orchestrator as the Core Product
Concluding his insights, Flanagan identifies the “orchestrator” as the key product of the system, rather than the individual skills which he likens to an “engine.” The orchestrator simplifies the user experience by managing the sequence and execution of various skills, eliminating the need for users to manually determine the correct workflow.
“Users don’t want to think about which skill to run in what order. I built the orchestrator skill last to make it really easy to use the system. That will use all skills on your behalf.”
Flanagan concludes by humorously noting that while the system is now largely automated, direct access is not publicly available, though some elements are shared with his Substack subscribers.
📝 About This Content
This article is based on insights shared by Kieran Flanagan on LinkedIn.
📅 Originally posted on March 26, 2026 | View original post on LinkedIn →