LEAD GENERATION
Enrich and warehouse every GitHub stargazer to BigQuery for ICP analytics
Continuously capture each new stargazer, enrich their profile and company, compute an ICP fit score, and append the full record to BigQuery for funnel reporting and segmentation.
How it runs
The automated pipeline, trigger to output.
- TriggerGitHub star event on repoGitHub
- ActionFetch stargazer profile + activityGitHub
- ActionEnrich company firmographics via ExaExa
- LogicCompute ICP score + normalize to schema
- OutputAppend enriched row to BigQueryBigQuery
What it does
Builds a durable analytics layer on top of your stargazer signal. Every new star is enriched and scored, then written as a structured row to BigQuery — giving your team a queryable history of who's interested, how they score, and which repos and segments drive the best fits over time.
When to use it
You want to analyze stargazer trends, measure conversion by ICP segment, or feed a BI dashboard — not just react to individual stars. Best when an analyst or RevOps team owns the data and reps consume it downstream.
How it works
- 1A GitHub webhook fires on each new star event.
- 2The flow fetches the stargazer's GitHub profile, company, and activity stats.
- 3Exa enriches the company with firmographic and funding data.
- 4A scoring step computes an ICP fit score and segment label.
- 5A logic step normalizes the record into the warehouse schema.
- 6The enriched, scored row is appended to a BigQuery table for reporting and segmentation.
Set it up
What you configure once, before turning it on.
- 1Connect GitHubRepos, issues, pull requests, actions.
- 2Connect ExaNeural search across the web.
- 3Connect BigQueryDatasets, queries, schemas.
- 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 Lead Generation workflows
Webhook-triggered Brave rising-keyword check into a Notion trend queue
When an external trend or alert tool fires a webhook with a keyword, checks Brave for current intent volume and freshness, has an LLM judge whether it's a real warm signal.
Fuzzy-match badge companies to Salesforce accounts and enrich
Resolves messy hand-typed company names from badge scans to canonical Salesforce accounts using domain and fuzzy-name matching, enriches missing firmographics.
Manual Brave keyword sweep into an Airtable research board
On demand, sweeps a topic across Brave Search, clusters the results by buying stage with an LLM, and writes a deduplicated research board to Airtable with company, source URL.
Daily rollup of scored webinar leads from Airtable into HubSpot lists
On a schedule, read newly scored webinar leads from Airtable, sync each into the matching HubSpot tiered list (Hot/Warm/Cold).
Fast-track hot webinar leads into HubSpot and ping the rep on Slack
After a webinar, identify attendees whose poll answers signal high purchase intent, create or update their HubSpot contact with a lead score.
Classify open-text webinar poll answers with AI and enrich the lead record
For webinars that use free-text poll questions, an AI step reads each attendee's written answers, classifies intent and pain points into structured fields.
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.
