skill-pipeline

To enforce human confirmation checkpoints in an automated workflow, you can use a staged pipeline that pauses execution at designated points. This prevents skipping essential steps and requires human validation before proceeding to the next stage.

The best way to structure a multi-step release management process is by dividing it into ordered stages with clear inputs and outputs. Enforcing a strict stage order with checkpoint pauses reduces irreversible errors and aligns execution with governance needs.

Yes, you can create custom pipelines for data processing tasks. While predefined templates like data-pipeline.md are available for common workflows, you can design custom pipelines using a standardized stage definition format for any scenario.

When splitting a complex documentation generation task into a custom pipeline, you should define between 3 to 8 stages. This range provides guidance on marking checkpoints and defining clear stage inputs and outputs for effective task management.

Yes, the pipeline execution rules support rollback behavior across its fixed stage order. If a checkpoint validation fails, the structured execution model includes rollback provisions to manage the workflow state effectively.

You should not use a checkpoint-driven pipeline for simple, single-step tasks that do not require human validation. It is designed for complex, multi-step scenarios where pausing between stages is essential to prevent irreversible errors.