MARKET RESEARCH
New Dataset License Gatekeeper Alert
Watches for newly published datasets in your vertical and routes them by license: commercially-usable ones trigger a Linear task for evaluation.
How it runs
The automated pipeline, trigger to output.
- TriggerDaily schedule
- ActionQuery Hugging Face for new datasetsHugging Face
- ActionClassify each dataset's licenseOpenAI
- LogicBranch by license category
- ActionCreate Linear task for commercial-OK datasetsLinear
- OutputLog restricted datasets to Notion registerNotion
What it does
Adds a compliance lens to dataset discovery. As new datasets appear in your vertical, it inspects each one's license and splits the stream: anything you can legally use in a product flows into Linear as an evaluation task, while restricted or unclear-license datasets are recorded in a Notion register so nobody wastes time on data you can't ship.
When to use it
For teams shipping commercial models where dataset licensing is a real legal constraint. It keeps your backlog full of usable candidates and your engineers from accidentally training on data that can't go to production.
How it works
- 1A daily cron starts the scan.
- 2Hugging Face is queried for datasets newly published in the vertical.
- 3A license-classification step labels each as commercial-OK, restricted, or unknown.
- 4A branch routes the dataset by label.
- 5Commercial-OK datasets create a Linear evaluation task with metadata.
- 6Restricted and unknown datasets are appended to a Notion off-limits register with the blocking reason.
Set it up
What you configure once, before turning it on.
- 1Connect Hugging FaceModels, datasets, spaces — the open-source hub.
- 2Connect OpenAIModels, embeddings, files.
- 3Connect LinearIssues, projects, cycles, triage.
- 4Connect NotionPages, databases, comments.
- 5Set each agent's modelWe leave models unset so you pick the tier — fast + cheap, or top-quality.
- 6Tune it to your dataEdit the prompts, filters, and field mappings so it matches how your team works.
- 7Test, then turn it onRun once against a sample, confirm the output, then enable the trigger.
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Run this workflow in your colony.
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