SKILL.md
Retention Loop Design Skill
Acquisition without retention is a leaky bucket — you pay to fill it and it drains. This skill diagnoses where and why users drop, then designs the loop that makes the product habitual: the trigger that brings them back, the value they get, and the investment that makes the next visit more likely. Retention is the truest measure of product-market fit.
Required Inputs
Ask for these only if they aren't already provided:
- The retention curve — how usage decays over time (D1/D7/D30, or weekly cohorts); does it flatten or go to zero?
- The core value & natural frequency — what users come for, and how often they'd genuinely need it.
- Activation definition — the early action that correlates with sticking (or note it's unknown).
- Current loops — any notifications, streaks, or re-engagement already in place.
Output Format
Retention Design: [product]
1. Curve diagnosis — read the retention curve: does it flatten (a retained core exists — good) or decay to zero (no PMF for this segment)? Identify the drop-off point and the cohort that retains best (your beachhead).
2. Activation → habit — the early "setup moment" and the habit milestone (e.g. "3 sessions in week 1"); the shortest path to it, since activation is the strongest lever on long-term retention.
3. The core loop — design the engagement loop explicitly:
- Trigger — external (notification, email) and the internal trigger you want to own (the felt need).
- Action — the simplest behaviour that delivers value.
- Reward — the value/variable reward received.
- Investment — what the user puts in (data, content, social, configuration) that makes the next loop better and raises switching cost.
4. Natural frequency match — align the loop's cadence to how often the job actually recurs; don't manufacture engagement the product doesn't warrant.
5. Re-engagement — triggered winback for users sliding toward churn (behavioural signal → message → return path); pair with [lifecycle-crm-plan](../lifecycle-crm-plan/SKILL.md).
6. Metrics — the retention metric and cohort view to watch, plus the leading indicator (habit-milestone rate) that predicts it.
Quality Checks
Anti-Patterns
Based On
The Hook Model (Nir Eyal) and cohort-retention analysis practice (flattening curve = PMF signal).