Churn Analysis Skill

Produce a structured churn analysis that goes beyond the headline rate — identifying why customers leave, which segments are most at risk, and what interventions will have the highest impact on retention.

Reads from / Writes to the Brain

If a professional-brain (brain/) exists, ground in it instead of re-asking for what you already know:

• Read first: context.md (metric definitions — what "churn" means here), knowledge/, and related segment entities/. Run python3 ../professional-brain/scripts/brain_query.py ./brain "churn" and carry each fact's provenance tag through.
• 📥 Propose to the Brain: after producing, propose recording the headline retention finding to knowledge/ ([data]), any retention decision to decisions/, and at-risk drivers as hypotheses/. Show them, get a yes, then write with ../professional-brain/scripts/brain_write.py … --commit (append-only, dry-run by default).

Required Inputs