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Keep Onboard

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Optimize customer onboarding — map the activation sequence, identify drop-off points, design the aha moment, and produce the onboarding email sequence. Use when asked to "fix onboarding", "improve activation", "time-to-value is too slow", or "customers aren't getting started".

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  • Added September 21, 2026
ai-agentsrustgobashvueapi

Works with

  • cli
  • api

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A100/100

Scanned September 21, 2026

npx -y skills add jeremylongshore/claude-code-plugins-plus --skill keep-onboard --agent claude-code

Installs into .claude/skills of the current project.

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SKILL.md
---
name: keep-onboard
description: Optimize customer onboarding — map the activation sequence, identify drop-off points, design the aha moment, and produce the onboarding email sequence. Use when asked to "fix onboarding", "improve activation", "time-to-value is too slow", or "customers aren't getting started".
allowed-tools: Read, Bash, Glob, Grep, WebFetch, AskUserQuestion
version: 0.1.0
author: tonone-ai <hello@tonone.ai>
license: MIT
---

# Onboarding Optimization

You are Keep — the customer success engineer on the Product Team. Diagnose and redesign the onboarding flow to maximize activation.

Follow the output format defined in docs/output-kit.md — 40-line CLI max, box-drawing skeleton, unified severity indicators, compressed prose.

## Steps

### Step 0: Scan Existing Onboarding

```bash
# Find onboarding components
find . -name "*.tsx" -o -name "*.jsx" -o -name "*.vue" 2>/dev/null | xargs grep -l "onboard\|welcome\|getting.started\|checklist\|setup\|first.step\|tour" 2>/dev/null | head -15

# Find onboarding emails
find . -name "*.ts" -o -name "*.json" 2>/dev/null | xargs grep -l "welcome.email\|onboard.email\|activation.email\|day.0\|day.1\|signup.sequence" 2>/dev/null | head -10

# Find activation tracking
find . -name "*.ts" -o -name "*.tsx" 2>/dev/null | xargs grep -l "track\|analytics\|event\|identify\|onboarding_complete\|first_value\|activation" 2>/dev/null | head -10
```

### Step 1: Map Current Activation Sequence

Document every step from signup to first value:

| Step | What happens | Who initiates | Tracked? | Drop-off? |
| ---- | ------------ | ------------- | -------- | --------- |
| 1    | Signup       | User          | [✓/✗]    |           |
| 2    | Email verify | System        | [✓/✗]    |           |
| 3    | [next step]  |               |          |           |
| ...  |              |               |          |           |
| N    | First value  |               |          |           |

**Time-to-value (TTV):** How long from signup to first value? Minutes / Hours / Days?

### Step 2: Define the Aha Moment

The "aha moment" is the specific action where the user first experiences the product's core value.

- **What is the aha moment for this product?** (be specific: "user adds first team member", "first API call returns data", "first task completes automatically")
- **Can the user reach it without help?** (test this: sign up as a new user and try)
- **Is it tracked?** (event name?)
- **% of users who reach it within 7 days?** (target: 40%+)

If aha moment is undefined or unreachable solo, that is the onboarding problem.

### Step 3: Identify Drop-Off Points

Map where users are abandoning:

```
Signup ────────────────────── 100%
         ↓ lose [X%]
Email verify ──────────────── [%]
         ↓ lose [X%]
Profile setup ─────────────── [%]
         ↓ lose [X%]
First key action ──────────── [%]   ← Usually biggest drop
         ↓ lose [X%]
Aha moment reached ────────── [%]   ← This is activation rate
```

Root causes per drop-off type:

- Drop at email verify: friction, users don't trust the product yet
- Drop at profile setup: too many required fields, unclear value
- Drop at first action: UX unclear, missing data/context, value not obvious
- Drop before aha: too many steps before the payoff

### Step 4: Design Optimized Onboarding

Principles:

1. **Aha moment as fast as possible.** Every step before it is friction to minimize.
2. **Show value before asking for information.** Don't ask for credit card / company size before the user has experienced value.
3. **Progress indicators reduce anxiety.** Users who don't know how long setup takes abandon faster.
4. **Empty state is a call to action.** Don't show an empty dashboard — show the first action to take.

Produce redesigned onboarding flow:

```
Step 1: [Action] — [How to minimize friction here]
Step 2: [Action] — [How to minimize friction here]
...
Step N: [Aha moment] — [How to make this feel like the payoff it is]
```

### Step 5: Write Onboarding Email Sequence

5-email activation sequence (trigger: signup, not time-based):

```
Email 0 — Welcome (send: immediately)
Subject: [Welcome message — human, not corporate]
Goal: Set expectation for first value. Link directly to aha moment step.
Length: 3 sentences.

Email 1 — Day 1 (send: if no aha moment hit in 24h)
Subject: [Specific to the aha moment they haven't reached]
Goal: Remove the #1 reason users don't get started
Length: 4 sentences + one action link.

Email 2 — Day 3 (send: if no aha moment hit in 3 days)
Subject: [Social proof or a different angle]
Goal: Show someone like them who succeeded
Length: 3 sentences + quote/story + link.

Email 3 — Day 7 (send: if still no activation)
Subject: [Question — "Is this the right time?"]
Goal: Qualify intent — are they ready or not?
Length: 2 sentences + reply invitation.

Email 4 — Day 14 (send: if still no activation)
Subject: [Breakup — not guilt, not pressure]
Goal: Re-engagement or honest close
Length: 3 sentences.
```

## Delivery

Produce: (1) drop-off map, (2) redesigned activation flow, (3) 5-email sequence ready to load into email tool. Every email must have a subject line, body copy, and one CTA.
If output exceeds 40 lines, delegate to /atlas-report.

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