community-automations/churn-modeler

Research & Intel

PublicClaude subagent

Churn Modeler

Teams know churn is a problem but stall at building a first model from scratch. This automation ships a working baseline you can act on.

sonnet1 weekPythonscikit-learnPostgresSHAP
ClaudeClaude
ROI for
README.md

Why this subagent

Teams know churn is a problem but stall at building a first model from scratch. This automation ships a working baseline you can act on.

It pulls account history, builds standard features, and trains a gradient-boosted model, then scores live accounts by churn risk. Each at-risk account comes with its top drivers from SHAP, so the success team sees not just who might leave but the reason behind the score.

How it runs

    • Read

      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.

Sample output

json
// Sample output
// (generated when the pipeline finishes)

Train a baseline churn model, score active accounts, and return a ranked at-risk list with the top driver per account.

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.