onboarding
π―SkillHelps new users see the value of your product quickly so they keep using it. It covers the first visit, setup checklists, empty screens and the moment users understand why the product matters.
Installation
npx skills add coreyhaines31/marketingskills --skill onboardingRun it in a terminal; it asks which AI coding tools to install it for (Claude Code, Codex, Cursor and more).
Uses skills.sh Β· Node.js 22.20+
After installing
Ask for related work and the agent uses it on its own, or call it yourself.
/onboardingInstall by hand
Copy the whole skill folder from GitHub to the place below. Copying only SKILL.md can leave out files the skill uses.
Open the skill folder β- This project
.claude/skills/onboarding/ - All projects
~/.claude/skills/onboarding/
These paths are for Claude Code. For other tools, the command above puts it in the right place.
SKILL.md
View on GitHub βWhen the user wants better onboarding and activation, or asks why new users don't stick or what the first session should do: first-run experience, aha moments, checklists, empty states, time to value. For the signup flow itself, see signup. For onboarding emails, see emails.
Onboarding CRO
You are an expert in user onboarding and activation. Your goal is to help users reach their "aha moment" as quickly as possible and establish habits that lead to long-term retention.
Initial Assessment
Check for product marketing context first:
If .agents/product-marketing.md exists (or .claude/product-marketing.md, or the legacy product-marketing-context.md filename, in older setups), read it before asking questions. Use that context and only ask for information not already covered or specific to this task.
Before providing recommendations, understand:
- Product Context - What type of product? B2B or B2C? Core value proposition?
- Activation Definition - What's the "aha moment"? What action indicates a user "gets it"?
- Current State - What happens after signup? Where do users drop off?
Core Principles
1. Time-to-Value Is Everything
Remove every step between signup and experiencing core value. Design the Minimum Path to Value (MPTV) β the least number of steps to experience enough value to make a confident decision (see references/minimum-path-to-value.md).
2. One Goal Per Session
Focus first session on one successful outcome. Save advanced features for later.
3. Do, Don't Show
Interactive > Tutorial. Doing the thing > Learning about the thing.
4. Progress Creates Motivation
Show advancement. Celebrate completions. Make the path visible. (See onboarding psychology below for the mechanisms.)
Onboarding Psychology
The principles that make progress mechanics, checklists, and prompts actually work:
- Endowed Progress Effect β people finish faster when progress is already started for them. A checklist that opens at "20% done" (a step pre-completed on their behalf) drives roughly +40% completion vs. starting at 0%. Give users a head start, don't make them start from nothing.
- Peak-End Rule β users remember an experience by its most intense moment (the peak) and its end, not the average. Engineer a clear high point (a win, a wow, a celebration) and end each session on a positive note.
- Goldilocks Rule β motivation peaks when a task is neither too easy nor too hard, but just right on the edge of ability. Tune early steps so they're achievable but not trivial.
- BJ Fogg Behavior Model β a behavior happens only when Motivation Γ Ability Γ Prompt converge at the same moment. If a step isn't happening, one of the three is missing: raise motivation, make it easier (Ability), or add a better-timed Prompt.
- Mario Kart boosters & blockers (Ramli John) β treat onboarding like a race track. Add boosters (accelerants: pre-filled data, templates, quick wins, celebrations) and remove blockers (friction: required fields, dead ends, confusing empty states). Speed users toward value and clear obstacles from the lane.
Onboarding Toolkit (10 Components)
The components you assemble an onboarding experience from. Use the fewest that reach value:
| Component | Purpose |
|---|---|
| Welcome forms | Capture role/goal to personalize the path (keep short β Hick's Law) |
| Initial screens | First-run screens that orient and point to one clear action |
| Drip emails | Multi-touch nurture β one concept per email, don't overload |
| Skippable tutorials | Optional guidance users can bypass β never trap them |
| Videos | Show complex workflows visually |
| Docs / help center | Self-serve reference for when users get stuck |
| Onboarding calls | Human touch for complex or high-value accounts |
| Data inputs | Getting the user's real data in so value feels "real" |
| Checklists | Ordered, value-first steps with visible progress (see below) |
| Empty states | Guided first-action opportunities, not dead ends (see below) |
Defining Activation
Judge activation by leadβcustomer conversion + 90-day retention, not lead volume. More signups mean nothing if they don't convert and stick.
Choose an activation model (freemium, free trial, paid trial, money-back, consultation) before designing the flow β the model shapes the whole onboarding path. See references/activation-models.md for the 5 models, the credit-card tradeoff, Model-Market Fit, and the Evernote-vs-Notion parable.
Find Your Aha Moment
Find early value actions associated with later retention, using a defined cohort and timeline:
- Define signup as time zero, an early activation window, and a separate retention outcome after the activation window. For example, first-seven-day actions and return activity in week four; choose windows that fit the product cadence.
- Include all eligible signups with full follow-up through the retention window. Show recent, immature cohorts separately; do not count them as churned or restrict the denominator to users who already activated.
- Compare actions recorded within the early window. Exclude behavior during the retention outcome window: a week-four return cannot also be the early activation predictor of week-four retention.
- Treat high-correlation events and frequency thresholds as candidate hypotheses. Report counts, uncertainty, and acquisition/product segments; confirm the definition on a later mature cohort before adopting it.
- Test whether an onboarding intervention improves later retention or customer conversion. Correlation alone does not prove that forcing the action causes retention.
Amplitude's Compass setup separates early actions from later retention and requires time for every cohort member to retain; its interpretation guide distinguishes correlation from causation.
Examples by product type:
- Project management: Create first project + add team member
- Analytics: Install tracking + see first report
- Design tool: Create first design + export/share
- Marketplace: Complete first transaction
Activation Metrics
- % of signups who reach activation
- Time to activation
- Steps to activation
- Activation by cohort/source
Onboarding Flow Design
Immediate Post-Signup (First 30 Seconds)
| Approach | Best For | Risk |
|---|---|---|
| Product-first | Simple products, B2C, mobile | Blank slate overwhelm |
| Guided setup | Products needing personalization | Adds friction before value |
| Value-first | Products with demo data | May not feel "real" |
Whatever you choose:
- Clear single next action
- No dead ends
- Progress indication if multi-step
Onboarding Checklist Pattern
When to use:
- Multiple setup steps required
- Product has several features to discover
- Self-serve B2B products
Best practices:
- 3-7 items (not overwhelming)
- Order by value (most impactful first)
- Start with quick wins
- Progress bar/completion %
- Celebration on completion
- Dismiss option (don't trap users)
Empty States
Empty states are onboarding opportunities, not dead ends.
Good empty state:
- Explains what this area is for
- Shows what it looks like with data
- Clear primary action to add first item
- Optional: Pre-populate with example data
Tooltips and Guided Tours
When to use: Complex UI, features that aren't self-evident, power features users might miss
Best practices:
- Max 3-5 steps per tour
- Dismissable at any time
- Don't repeat for returning users
Multi-Channel Onboarding
Email + In-App Coordination
Trigger-based emails:
- Welcome email (immediate)
- Incomplete onboarding (24h, 72h)
- Activation achieved (celebration + next step)
- Feature discovery (days 3, 7, 14)
Email should:
- Reinforce in-app actions, not duplicate them
- Drive back to product with specific CTA
- Be personalized based on actions taken
Handling Stalled Users
Detection
Define "stalled" criteria (X days inactive, incomplete setup)
Re-engagement Tactics
- Email sequence - Reminder of value, address blockers, offer help
- In-app recovery - Welcome back, pick up where left off
- Human touch - For high-value accounts, personal outreach
Measurement
Key Metrics
| Metric | Description |
|---|---|
| Activation rate | % reaching activation event |
| Time to activation | How long to first value |
| Onboarding completion | % completing setup |
| Day 1/7/30 retention | Return rate by timeframe |
Funnel Analysis
Track drop-off at each step:
Signup β Step 1 β Step 2 β Activation β Retention
100% 80% 60% 40% 25%
Identify biggest drops and focus there.
Output Format
Onboarding Audit
For each issue: Finding β Impact β Recommendation β Priority
Onboarding Flow Design
- Activation goal
- Step-by-step flow
- Checklist items (if applicable)
- Empty state copy
- Email sequence triggers
- Metrics plan
Common Patterns by Product Type
| Product Type | Key Steps |
|---|---|
| B2B SaaS | Setup wizard β First value action β Team invite β Deep setup |
| Marketplace | Complete profile β Browse β First transaction β Repeat loop |
| Mobile App | Permissions β Quick win β Push setup β Habit loop |
| Content Platform | Follow/customize β Consume β Create β Engage |
Experiment Ideas
When recommending experiments, consider tests for:
- Flow simplification (step count, ordering)
- Progress and motivation mechanics
- Personalization by role or goal
- Support and help availability
For comprehensive experiment ideas: See references/experiments.md
References
- references/minimum-path-to-value.md β MPTV, Hick's Law, the inventoryβremoveβreconstruct process, abandonment benchmarks (40β60% after one session; 75β80% within day one), and patterns (Stripe, Calendly, Notion).
- references/activation-models.md β the 5 activation models, credit-card tradeoff, Model-Market Fit, Evernote vs. Notion.
- references/experiments.md β comprehensive A/B test and experiment ideas.
Task-Specific Questions
- Which early action predicts later retention, using what activation window and fully observed signup cohort?
- What happens immediately after signup?
- Where do users currently drop off?
- What's your activation rate target?
- Do you have cohort analysis on successful vs. churned users?
Related Skills
- signup: For optimizing the signup before onboarding
- emails: For onboarding email series
- paywalls: For converting to paid during/after onboarding
- ab-testing: For testing onboarding changes
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