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

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Goal

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

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MCP path

Ask your MCP client: 
Trigger a Managed Research data-factory flow for this project. Use directed effort, provider openrouter, and require an artifact manifest plus final report before publish.
The primary MCP tool is smr_trigger_data_factory.

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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

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Expected evidence

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

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Failure notes

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