> ## Documentation Index
> Fetch the complete documentation index at: https://docs.usesynth.ai/llms.txt
> Use this file to discover all available pages before exploring further.

# Data Factory Flow

> Use Managed Research data-factory tools for intake, generation, review, and publish flows.

Use the data-factory flow when the run should create or refine datasets, examples, labels, or research inputs before downstream work.

## Goal

Start with clear intake criteria, generation constraints, review policy, and publish expectations.

## MCP path

Ask your MCP client:

```text theme={null}
Start a Managed Research project run for a data-factory workflow. Use directed effort, provider openrouter, and require an artifact manifest plus final report before publish.
```

The primary MCP path is the normal project-run launch path: create or select a project, attach the relevant repo or files, preflight, then call `smr_trigger_run`. For isolated work, use `smr_start_one_off_run`.

## Good instructions

* describe the target dataset or artifact
* specify accepted source material
* specify validation or review criteria
* require rejected-example notes
* require artifact manifest and publish summary

## Expected evidence

* generated or revised data files
* review notes
* rejected or deferred examples
* final report
* usage summary

## Failure notes

Use project-scoped context for data-factory work. One-off runs are better for isolated repo tasks.