Build a dedicated AI skill that reviews your usage patterns. It can identify weak prompting habits, suggest new skills to build for repetitive tasks, and flag when your core context files need updating, creating a self-improving system.

Instead of pre-designing AI skills, Brown first uses AI for various tasks. When he identifies a useful, repeatable workflow, he instructs the agent to "turn this into a skill." This creates a personalized, practical, and organically-grown toolkit perfectly tailored to his needs.

Current AI tools require users to define and set up workflows. The next generation of agents will observe user patterns—like handling email intros or forwarding receipts—and proactively suggest automating them. This removes the setup friction and makes AI accessible to a broader, non-technical audience.

Establish a powerful feedback loop where the AI agent analyzes your notes to find inefficiencies, proposes a solution as a new custom command, and then immediately writes the code for that command upon your approval. The system becomes self-improving, building its own upgrades.

Avoid brittle, high-maintenance productivity systems by letting your AI agent learn from your actual behavior over time. Instead of extensive setup, the AI observes what you do and don't accomplish, organically building a system that reflects reality, not your idealized intentions.

Elevate your AI from a reactive tool to a proactive employee by setting up scheduled routines. Instead of just coding, task it with recurring operator work like creating a 'morning brief' from customer notes or running a 'weekly ops review' of open issues. This maintains business momentum and surfaces key insights.

Instead of asking an AI for a one-off task, identify recurring workflows and have the AI turn them into a "skill." This creates a reusable asset that dramatically improves efficiency and output quality over time, turning the user into a system builder.

To scale your use of AI agents, move beyond single-use builds. Identify recurring capabilities and package them as reusable 'skills.' This modular approach makes your work transportable, allowing you to easily apply successful processes across different projects and agents, which compounds your efficiency over time.

Treat AI 'skills' as Standard Operating Procedures (SOPs) for your agent. By packaging a multi-step process, like creating a custom proposal, into a '.skill' file, you can simply invoke its name in the future. This lets the agent execute the entire workflow without needing repeated instructions.