LEAD GENERATION

Active Discussion Commenters to Warm-Intro Brief

Triggers on new GitHub Discussions comments, identifies repeat engagers across a rolling window in Postgres, enriches them once they cross an engagement threshold.

CategoryLead Generation
Enginesim
Difficultyadvanced
Triggerevent
Steps6
Setup~25 min

How it runs

The automated pipeline, trigger to output.

  • TriggerNew Discussions commentGitHubGitHub
  • ActionRecord commenter in PostgresPostgreSQLPostgres
  • ActionCount interactions in rolling windowPostgreSQLPostgres
  • LogicBranch on threshold + not yet enriched
  • ActionEnrich person and employer via ExaExa
  • OutputFile warm-intro brief in AttioAttio

What it does

It tracks who comments in your GitHub Discussions, counts how often each person shows up over a rolling window, and only treats someone as a lead once they have engaged enough to be clearly invested — then builds a warm-intro brief.

When to use it

Use this when single interactions are too noisy but recurring community participation is a strong qualifier. It rewards genuine engagement over one-off comments, so reps reach out to people who already know and like your project.

How it works

  1. 1A new Discussions comment fires the trigger.
  2. 2Record the commenter and timestamp in a Postgres engagement table.
  3. 3Count the commenter's interactions in the rolling window.
  4. 4Branch: only continue when they cross the engagement threshold and have not been enriched yet.
  5. 5Enrich the person and employer with Exa.
  6. 6Create a warm-intro brief in Attio noting their top discussion topics and a suggested opener.

Set it up

What you configure once, before turning it on.

  1. 1
    Connect GitHubRepos, issues, pull requests, actions.
  2. 2
    Connect PostgresAny Postgres URL — query, write, migrate.
  3. 3
    Connect ExaNeural search across the web.
  4. 4
    Connect AttioReal-time CRM with structured data + powerful views.
  5. 5
    Set each agent's modelWe leave models unset so you pick the tier — fast + cheap, or top-quality.
  6. 6
    Tune it to your dataEdit the prompts, filters, and field mappings so it matches how your team works.
  7. 7
    Test, then turn it onRun once against a sample, confirm the output, then enable the trigger.

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