dots-workstation-loop-runner

You can automate loop engineering by running `loop init <pattern>` to scaffold a new loop, which manages recursive AI coding iterations through the ai-workspace bin/loop CLI. This handles state management, safety gates, cost budgets, and rollout tiers for each iteration.

Loop engineering orchestrates recursive, autonomous processes in AI coding sessions by running sequential iterations from a starter pattern. It allows developers to execute tasks like automated code reviews while auditing past runs and calculating token costs for expensive loops.

Yes, you need both Python 3 and ai-workspace installed to manage loop operations. The skill wraps the ai-workspace bin/loop CLI to handle execution, state management, cost estimation, and safety gates for your autonomous coding iterations.

You can calculate token costs for expensive loops using the built-in loop cost estimation feature. This monitors the budget constraints of your autonomous AI coding sessions by projecting the token expenditure required for recursive loop iterations.

The best way to track autonomous coding loops is using the loop status feature, which displays all active loops with details on their tier, cadence, and last run. This provides visibility into the state and progress of your recursive AI coding iterations.

Yes, you can use the loop sync feature to push escalations directly to your knowledge base and todos. This ensures issues identified during autonomous AI coding iterations are tracked and managed outside the immediate loop execution context.