MARKET RESEARCH
Weekly Dataset Radar for a Research Vertical
Every Monday, scans Hugging Face for datasets newly published in your research vertical, clusters them by theme.
How it runs
The automated pipeline, trigger to output.
- TriggerWeekly schedule (Monday AM)
- ActionQuery Hugging Face for new datasets in verticalHugging Face
- LogicFilter by size, license, recency
- ActionCluster into themes and rankOpenAI
- OutputPost ranked digest to SlackSlack
What it does
Runs a scheduled hunt across Hugging Face for datasets created or updated in the last seven days that match your vertical's keywords (e.g. "clinical NLP", "battery materials", "fraud detection"). It pulls metadata — task type, size, license, downloads — clusters the results into a handful of themes, ranks each by relevance and traction, and delivers a single skimmable digest to Slack.
When to use it
For research, ML, or competitive-intel teams who need to stay current on the open-data landscape but don't have time to browse the Hub manually. One standing report beats ten ad-hoc searches.
How it works
- 1A weekly cron fires Monday morning.
- 2Hugging Face is queried for datasets matching each vertical keyword, filtered to the last 7 days.
- 3A filter drops items below a minimum size or with non-permissive licenses.
- 4An LLM clusters the survivors into named themes and writes a one-line take on each.
- 5The ranked, clustered digest is posted to the team's Slack research channel.
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 SlackChannels, DMs, threads, mentions.
- 4Set each agent's modelWe leave models unset so you pick the tier — fast + cheap, or top-quality.
- 5Tune it to your dataEdit the prompts, filters, and field mappings so it matches how your team works.
- 6Test, 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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