time-bounded-run

To enforce time limits on distinct phases in an AI workflow, you configure maximum execution durations for each operational phase like Discover or Implement. The process automatically saves intermediate state and notifies you upon timeout.

The best way to prevent runaway processes during long-running AI tasks is to set configurable phase timeouts. This ensures timely completion by stopping indefinite operations and preventing resource wastage.

To resume an AI operation after a phase timeout occurs, you follow the provided user notification instructions. The system automatically saves the current intermediate state during the timeout to allow for resumption.

Yes, you can set different maximum execution durations for each operational phase. Phase timeouts are fully configurable, allowing you to specify unique limits for distinct stages like Discover, Plan, and Implement.

State management for resumption does not require external dependencies. The Skill operates independently to automatically save intermediate operational state and notify the user when a phase time limit is reached.

Your AI code generation gets stuck because there is no phase execution limit. By enforcing time-bounded runs, you prevent the AI from spending excessive time on a single complex function and avoid runaway processes.