Building Repeatables in Claude: Skills, CLI vs MCP and Token Discipline | Go With The Flow

Optimizing business operations through AI automation requires shifting from one-off prompts to building repeatable, efficient "skills" that leverage command-line interfaces (CLI) rather than resource-heavy Model Context Protocol (MCP) setups. By integrating tools like Google BigQuery or AWS CLI, businesses can create deterministic, scalable workflows that minimize token consumption and reduce reliance on manual intervention. Adopting a disciplined development approach—such as the "21-day rule" to avoid chasing transient trends and utilizing "plan mode" to force AI to refine and simplify its own logic—prevents over-engineering and decision fatigue. Separating deterministic tasks, which are best handled by scripts, from non-deterministic, creative tasks ensures higher reliability. This structured methodology transforms AI from a simple chatbot into a robust architectural foundation, allowing for consistent, high-quality output in complex operational environments.