Churn rarely surprises anyone after the fact; the signals were sitting in usage data and the support queue for weeks. The problem is nobody was watching them together until the cancellation email arrived.
sonnet1 weekHubSpotMixpanelStripeSlack
Claude
90ROI
78Scale
$4k94Saved
ROI for
README.md
Why this subagent
Churn rarely surprises anyone after the fact; the signals were sitting in usage data and the support queue for weeks. The problem is nobody was watching them together until the cancellation email arrived.
This agent scores accounts daily on usage, billing, and ticket trends, then DMs the owner before the account goes cold. The alert names the actual warning sign and suggests a specific save play, so the rep reaches out with a reason instead of a vague check-in. You catch the slide while there is still time to act.
How it runs
Used at step 01 to kick off the pipeline.
Write
Used at step 01 to kick off the pipeline.
WebFetch
Used at step 01 to kick off the pipeline.
WebSearch
Used at step 01 to kick off the pipeline.
Score every account on trend direction and flag the ones sliding toward churn.
pending
Pull the likely cause, dropping logins, a billing failure, or a stack of open tickets.
pending
DM the account owner in Slack with the red flag and a suggested save play.
pending
Sample output
json
// Sample output
// (generated when the pipeline finishes)
For each account, return a risk_level, the top signal driving it, and one concrete save play tied to that signal.
Unlock the rest
The full agent definition, install snippet, and starter task are gated for community members.
Members get the full `.md` agent file, the npm / pnpm install one-liners, a starter prompt that we've tuned against real runs, and the open-source repo when this automation ships there. One email, magic link, done.