MARKET RESEARCH
Open a Linear eval ticket when a trending HuggingFace model scores relevant to your roadmap
Scans trending HuggingFace models daily, uses an LLM to score each against your team's current roadmap.
How it runs
The automated pipeline, trigger to output.
- TriggerDaily schedule
- ActionFetch trending HuggingFace modelsHugging Face
- ActionScore relevance to roadmap and write rationaleOpenAI
- LogicKeep only models over relevance and download bars
- OutputCreate a Linear eval ticket with rationale and checklistLinear
What it does
Turns model discovery into action. It pulls the day's trending models, has an LLM judge each one against a description of your roadmap and use cases, and for the ones that are both relevant and adopted enough, it creates a Linear ticket pre-filled with why the model matters and what to evaluate. Models that don't clear the bar are silently ignored.
When to use it
Use it when your team commits to evaluating promising open models but loses candidates in chat noise. This routes only roadmap-relevant, traction-backed models straight into your tracked work queue with context attached.
How it works
- 1A daily schedule fires the run.
- 2HuggingFace returns the current trending models with their download and like counts.
- 3OpenAI scores each model's relevance to your roadmap prompt and writes a short rationale.
- 4A logic gate keeps only models above both the relevance score and the download threshold.
- 5For each survivor, a Linear issue is created in the eval project with the rationale and a checklist of what to test.
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.
- 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.
More Market Research workflows
Hiring Surge Detector with Slack Alert
Detects when a target account's open-role count jumps above its recent baseline and posts a ranked Slack alert to the GTM channel so reps can act on a company that is clearly…
Tech-Stack Shift Inference from Job Descriptions
Reads new job descriptions for target accounts, uses an LLM to extract named technologies and infer stack changes.
Enrich Inbound Accounts with BigQuery Firmographics and Score Fit
When a new account row lands in Airtable, joins it against BigQuery public business datasets to attach firmographic attributes.
Blend BigQuery TAM with Live Competitor Signals into a Notion Brief
On demand, sizes a chosen segment from BigQuery public data, gathers current competitor signals via Brave Search, and synthesizes a one-page market brief into Notion.
Allocate Sales Territory TAM from BigQuery Geo Data to HubSpot
When triggered by a webhook, queries BigQuery public ZIP-level business data to compute TAM per sales territory.
Discover Adjacent Segments from BigQuery and Rank Expansion Bets
On a schedule, scans BigQuery public industry data for fast-growing NAICS sectors adjacent to your core segments, scores them as expansion bets.
Run it inside a business
This workflow drops into a full company template. Import the org, and this is one of the playbooks its agents run.
Run this workflow in your colony.
14-day trial. No DevOps. No Sales call. Provisioned in under a minute.