Optimizes post-signup user onboarding, activation rates, and time-to-value by designing flows, checklists, and empty states. It analyzes onboarding funnels to identify drop-off points and recommends strategies to reach the 'aha moment' faster.
Say it in your own words; the assistant finds this recipe by itself. Examples:
Use this skill when the user wants to improve the experience after a user has signed up, specifically focusing on activation and first-run value. It is appropriate for designing onboarding checklists, defining activation metrics, optimizing empty states, or analyzing drop-off in the post-signup funnel. It applies when the goal is to reduce time-to-value or increase the percentage of users who reach a key 'aha moment'.
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.
Check for product marketing context first:
If .claude/product-marketing-context.md exists, 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:
Remove every step between signup and experiencing core value.
Focus first session on one successful outcome. Save advanced features for later.
Interactive > Tutorial. Doing the thing > Learning about the thing.
Show advancement. Celebrate completions. Make the path visible.
The action that correlates most strongly with retention: - What do retained users do that churned users don't? - What's the earliest indicator of future engagement?
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
| 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
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 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
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
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
Define "stalled" criteria (X days inactive, incomplete setup)
| 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 |
Track drop-off at each step:
Signup → Step 1 → Step 2 → Activation → Retention
100% 80% 60% 40% 25%
Run the bundled analyzer on your step counts instead of eyeballing:
python3 scripts/activation_funnel_analyzer.py funnel.json --json # no arg = embedded demo
It computes per-step drop-off, an activation score 0-100, and names the biggest-loss step. That step is where the audit focuses first.
Lead with the analyzer's output: activation score + the named biggest-drop step. Then, for each issue: Finding → Impact → Recommendation → Priority
| Product Type | Key Steps |
|---|---|
| B2B SaaS | Setup wizard → First value action → Team invite → Deep setup |
| Marketplace | Complete profile → Browse → First transaction → Repeat loop |
| Mobile App | Permissions → Quick win → Push setup → Habit loop |
| Content Platform | Follow/customize → Consume → Create → Engage |
When recommending experiments, consider tests for: - Flow simplification (step count, ordering) - Progress and motivation mechanics - Personalization by role or goal - Support and help availability
Deliver recommendations following the output quality standard: lead with the highest-leverage finding, provide a clear activation definition, then prioritize experiments by expected impact. Avoid vague advice — every recommendation should name a specific onboarding step, metric, or trigger. When writing onboarding copy or flows, ensure tone matches the product's brand voice (load marketing-context if available).
| Artifact | Description |
|---|---|
| Activation Definition Doc | Clearly defined aha moment, correlated action, and success metric |
| Onboarding Flow Diagram | Step-by-step post-signup flow with drop-off points and decision branches |
| Checklist Copy | 3–7 onboarding checklist items ordered by value, with completion messaging |
| Email Trigger Map | Trigger conditions, timing, and goals for each onboarding email in the sequence |
| Experiment Backlog | Prioritized A/B test ideas for onboarding steps, sorted by expected impact |
LICENSESKILL.mdscripts/activation_funnel_analyzer.pyVendored from alirezarezvani/claude-skills, commit 27cfcd0fb0a4, path marketing-skill/skills/onboarding-cro, licence MIT. The upstream licence file ships inside the skill.