workflow

You can automate a multi-step research and writing pipeline by defining a reusable YAML workflow that chains multiple skill invocations together. This orchestrates knowledge-work processes by passing outputs via files from one step to the next, eliminating inconsistent manual handoffs.

Yes, you can reuse one workflow definition across different topics by using runtime parameterization. The system resolves {{topic}} and other YAML-defined variables at run start, allowing you to execute the same chained pipeline for various contexts without altering the core definition.

Background scheduling allows you to run workflows either in the foreground or as queued background jobs. You can schedule recurring daemon runs for repeated research-to-writing-to-review production, executing the same chain automatically across many topics over time.

If a workflow is interrupted midway, the system uses persisted per-step state to enable resumable execution. This means you can resume the pipeline from the exact point of interruption without losing completed steps or needing to restart the entire chain from the beginning.

Yes, the workflow engine enforces strict validation for unresolved tokens and detects circular dependencies before execution. It also validates skill references and limits workflow size, ensuring that the chained pipeline runs safely without structural errors or infinite loops.

Yes, parallel step grouping is supported in automated workflow pipelines. This allows multiple independent steps within the chain to run concurrently rather than sequentially, optimizing execution time for research and writing tasks that do not depend on each other's outputs.