Retention AnalysisCAUTION

1255 professional Agent Skills for Claude, ChatGPT, Gemini, Cursor & Codex — PRDs, postmortems, leases, medical bills, layoffs, go-bags, new countries. Plain markdown, MIT, in Anthropic's plugin directory. Plus a typed decision layer: which skill, is it safe, ship or slip. Free in-browser or npx pm-

The instruction file, verbatim from the audited commit — this is the text the model reads, and the surface the audit's instruction layer examines. Quoted here so you can judge it without cloning anything.

---
name: retention-analysis
description: "Structure a retention analysis, churn investigation, or engagement deep-dive for any product team. Use when asked to analyse user retention, investigate churn, measure DAU/MAU, or build a retention improvement plan. Produces a retention snapshot with root cause hypotheses, aha-moment correlation, and prioritised interventions."
---

# Retention Analysis Skill

Diagnose why users leave, identify what keeps them, and recommend specific, testable interventions — not vague "improve onboarding" suggestions.

## Retention Fundamentals

**The retention curve has two components:**
1. **Steepness of initial drop** (D1–D7) — onboarding problem
2. **Long-term floor level** — product-market fit indicator

A product with PMF has a retention curve that flattens. If it trends to zero, you have a PMF problem, not an onboarding problem. Name this distinction explicitly.

---

## Retention Metrics Definitions

| Metric | Formula | What It Tells You |
|---|---|---|
| D1 Retention | Users who return on day 2 ÷ new users day 1 | Quality of first experience |
| D7 Retention | Users active on day 8 ÷ users who joined 7 days ago | Early habit formation |
| D30 Retention | Users active on day 31 ÷ users who joined 30 days ago | Product-market fit signal |
| DAU/MAU Ratio | Daily active users ÷ monthly active users | Stickiness (>20% good, >50% excellent) |
| Churn Rate | Users lost in period ÷ users at start of period | Monthly or annual |
| Net Revenue Retention | MRR at end of period ÷ MRR at start (same cohort) | Revenue health including expansion |

---

## Retention Investigation Framework

### Step 1: Segment the problem
Don't analyse "retention" — analyse retention for specific cohorts:
- New vs returning users
- Paid vs free
- Acquisition channel (organic vs paid vs referral)
- Onboarding path completed vs not
- Feature usage (power users vs lurkers)

### Step 2: Find the inflection points
Where does the drop happen? D1? D7? Month 3?
- D1 drop → First session experience
- D7 drop → Habit loop not formed
- D30 drop → Value not delivered at depth
- Month 3+ drop → Boredom, competition, or lifecycle event

### Step 3: Identify the "aha moment" correlation
Which early behaviour predicts long-term retention?
- Run correlation: users who did [X] in first 7 days vs 30-day retention
- Common patterns: connected an integration, invited a teammate, completed a core action N times

### Step 4: Qualify the churn
Interview churned users — never skip this. Survey data alone is insufficient.
- "What was the trigger that led you to cancel/stop?"
- "What were you trying to accomplish that you couldn't?"
- "What would need to change for you to come back?"

---

## Output Format

### Retention Analysis — [Product/Segment] — [Date]

**Question:** [Specific retention question being answered]
**Period Analysed:** [Date range]
**Segment:** [Which users]

---

**Current Retention Snapshot:**

| Metric | Current | Industry Benchmark | Status |
|---|---|---|---|
| D1 Retention | [X%] | 25–40% | 🔴/🟡/🟢 |
| D7 Retention | [X%] | 10–25% | 🔴/🟡/🟢 |
| D30 Retention | [X%] | 5–15% | 🔴/🟡/🟢 |
| DAU/MAU | [X%] | 10–20% typical | 🔴/🟡/🟢 |

**Retention Curve Shape:** [Flattening / Still declining / Trending to zero]
**PMF Signal:** [Strong / Weak / Absent — based on curve shape]

---

**Root Cause Hypotheses:**

| Hypothesis | Evidence | Confidence | Test |
|---|---|---|---|
| [Cause] | [Data point] | H/M/L | [How to validate] |

**"Aha Moment" Correlation:**
Users who [specific action] in first [N] days retain at [X%] vs [Y%] for those who don't.

---

**Recommended Interventions:**

| Intervention | Target Drop | Expected Lift | Effort | Priority |
|---|---|---|---|---|
| [Specific change] | D1 / D7 / D30 | [X%] | S/M/L | 1/2/3 |

**Monitoring Plan:**
- Metric to track: [X]
- Review cadence: [Weekly / Monthly]
- Alert threshold: [If X drops below Y, investigate immediately]

---

## Required Inputs

Work from what is given. If any of these is missing, still produce the complete output: fill each gap with a reasonable assumption marked *(assumed, confirm)* and list those assumptions at the end. Ask a question first only when the core subject itself is missing (there is nothing to write about).
- **Product and business model** (SaaS / consumer app / marketplace / other)
- **Current retention metrics** (D1, D7, D30 if available)
- **Segment to analyse** (all users / paid / free / a specific cohort)
- **Key question to answer** (why is retention dropping? what drives retention?)
- **Available data** (analytics events, churn surveys, interview notes)

## Deeper Materials

This skill ships with support files — use them when they are available:

- **`references/curve-reading.md`** — Reading Retention Curves Without Fooling Yourself. Apply it while producing the output; it carries the calibration and judgment calls the method summary above compresses.
- **`templates/retention-readout.md`** — a fill-in version of the deliverable with the quality gates inline. Offer it when the user wants to work the document themselves rather than have it generated.

## Scoring Rubric (0–40)

Score any output of this skill before handing it over; 32+ is ship-quality.

| Dimension | 0 | 5 | 10 |
|---|---|---|---|
| Curve diagnosis | Reports a retention number without curve shape | Shape shown but not interpreted | Flattening vs trending-to-zero explicitly diagnosed and tied to what it means (PMF vs onboarding problem) |
| Cohort discipline | All users lumped into one blended rate | Cohorts split but read as a table dump | Cohorts segmented before analysis, with the divergent cohort called out and explained |
| Aha-moment linkage | Activation never connects to retention | Correlation claimed without data or caveat | The behavior separating retained from churned users identified with evidence, or honestly flagged unknown with a plan to find it |
| Intervention specificity | "Improve onboarding"-grade advice | Specific actions b

CAUTIONgrade B · trust 89/100 Install with care. The audit found things worth knowing before you trust its output.

What the source does

Filesystem

none-observed

Network

none-observed

Shell

none-observed

Dependencies

pinned

Secrets in source

none-found

Findings (5)

templates/pm-discovery-agent/skills/assumption-mapper/SKILL.md

templates/pm-discovery-agent/skills/discovery-interview-guide/SKILL.md

templates/pm-discovery-agent/skills/job-story-mapper/SKILL.md

templates/pm-discovery-agent/skills/user-interview-synthesis/SKILL.md

templates/pm-launch-agent/skills/content-calendar/SKILL.md

Gates applied: no_behavioural_pass.