LEAD GENERATION
Stargazer Enrichment Sync to BigQuery Warehouse
On a schedule, pulls the full current stargazer list, enriches new entries with company data, and upserts a clean.
How it runs
The automated pipeline, trigger to output.
- TriggerDaily schedule
- ActionFetch current stargazer listGitHub
- LogicDiff against warehouse table
- ActionEnrich new stargazersExa
- LogicNormalize + score fields
- OutputUpsert rows into BigQueryBigQuery
What it does
This workflow keeps a warehouse table of every enriched stargazer in sync. On each run it fetches the current stargazer list, enriches anyone new with firmographics, and upserts deduplicated rows into BigQuery so your data team can model lead quality and feed reverse-ETL pipelines.
When to use it
Use this when GitHub interest needs to live in the warehouse alongside product and revenue data, not just a CRM. Ideal for RevOps and data teams that drive routing and scoring from BigQuery rather than point tools.
How it works
- 1A schedule trigger runs daily.
- 2A GitHub action retrieves the complete current stargazer list for the repo.
- 3A logic step diffs against the existing BigQuery table to isolate new stargazers.
- 4An Exa action enriches only the new entries with company size, domain, and industry.
- 5A second logic step normalizes fields and computes a fit score column.
- 6An upsert action merges the enriched, deduped rows into the BigQuery lead table for downstream use.
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.
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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.
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