LEAD GENERATION

New Stargazer to Lead: Enrich Recent Repo Stars and Push Buying-Org Signals to Attio

Watches new stars on your repo, enriches each stargazer's profile to find their employer, filters for target-company employees, and logs warm account-level interest in Attio.

CategoryLead Generation
Enginesim
Difficultyintermediate
Triggerevent
Steps5
Setup~15 min

How it runs

The automated pipeline, trigger to output.

  • TriggerGitHub star event fires on the repoGitHubGitHub
  • ActionPull the stargazer's profile, bio, and company fieldGitHubGitHub
  • ActionConfirm employer and domain when profile is sparseBraveBrave Search
  • LogicKeep only stargazers tied to target/ICP-fit companies
  • OutputUpsert company interest signal and person in AttioAttio

What it does

A fresh GitHub star is a quiet buying signal. This workflow catches each new stargazer, looks up their public profile and employer, checks whether their company matches your target list, and records the signal against that company in Attio so sales sees which accounts are warming up organically.

When to use it

Use it when stars trend upward and you want to convert anonymous interest into named accounts. Especially valuable for developer-tools companies where individual engineers evaluate before their org buys, and you want to spot a target logo accumulating multiple stars.

How it works

  1. 1A GitHub event fires whenever someone stars the repo.
  2. 2The stargazer's public profile, bio, and company field are pulled from GitHub.
  3. 3Brave Search confirms the employer and its domain when the profile is thin.
  4. 4A logic step keeps only stargazers tied to a target or ICP-fit company.
  5. 5The signal is upserted in Attio, incrementing an interest count on the company and attaching the person.

Set it up

What you configure once, before turning it on.

  1. 1
    Connect GitHubRepos, issues, pull requests, actions.
  2. 2
    Connect Brave SearchWeb, news, image, video search.
  3. 3
    Connect AttioReal-time CRM with structured data + powerful views.
  4. 4
    Set each agent's modelWe leave models unset so you pick the tier — fast + cheap, or top-quality.
  5. 5
    Tune it to your dataEdit the prompts, filters, and field mappings so it matches how your team works.
  6. 6
    Test, then turn it onRun once against a sample, confirm the output, then enable the trigger.

Run this workflow in your colony.

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