onboarding-optimizer

name	onboarding-optimizer
description	Evaluates and optimizes user onboarding flows for SaaS products. Analyzes signup friction, activation steps, time-to-value, and first-run experience. Provides pattern recommendations based on product type. Use when the user mentions onboarding, user activation, first-run experience, time-to-value, welcome flow, or setup wizard optimization.

Onboarding Optimizer

Evaluate, score, and improve SaaS user onboarding flows to maximize activation rates and reduce time-to-value.

First Run

When a user runs /onboarding-optimizer audit, ALWAYS display this summary before asking questions:

""" 📝 Onboarding Optimizer

What I'll ask you (11 questions about your current onboarding):

1. Product type and primary use case
2. Steps from signup to first value moment → a number
3. Required fields at signup → a number
4. Progress indicator? → yes/no
5. Can users skip optional steps? → yes/no
6. Template gallery or starter content? → yes/no
7. Time to first value moment → minutes
8. Credit card required before trial? → yes/no
9. Welcome email sequence? → yes/no
10. In-app guidance (tooltips, checklists)? → yes/no
11. Educational empty states (guide next action)? → yes/no

Most answers are yes/no or a number. Takes ~3 minutes. Type "demo" to see a sample audit first.

What you'll get: → Onboarding score (0-100) with letter grade → Scoring breakdown (what helped, what hurt) → Recommended onboarding pattern for your product type → Prioritized improvements with estimated activation lift → Saved to ONBOARDING-AUDIT-REPORT.md

Let's start - what's your product type? """

Demo Mode

If the user types "demo", use this data to generate a full sample report. The keys match the scorer's OnboardingFlow fields, so it can be saved to a file and passed straight to python3 scripts/onboarding_scorer.py <file>:

{
  "product_type": "collaboration",
  "total_steps": 4,
  "required_fields": 2,
  "has_progress_indicator": true,
  "has_skip_option": true,
  "has_template_gallery": true,
  "time_to_first_value_minutes": 8,
  "requires_credit_card_upfront": false,
  "has_welcome_email_sequence": true,
  "has_in_app_guidance": false,
  "has_empty_state_education": false
}

Save the demo report as ONBOARDING-AUDIT-REPORT-DEMO.md. After showing the summary, ask: "Want to audit your own onboarding flow now?"

Skip Handling

If the user doesn't know an answer:

• Accept "not sure" or "skip" and score that factor as neutral (0 points)
• Continue with remaining questions
• Note which factors were unknown in the report

Commands

/onboarding-optimizer audit

Interactive onboarding flow audit. Walk through the user's current onboarding experience step by step and identify friction points.

Steps:

1. Ask the user to describe their product type and primary use case
2. Gather onboarding flow details:
   • How many steps from signup to first value moment?
   • How many required fields at signup?
   • Is there a progress indicator?
   • Can users skip optional steps?
   • Is there a template gallery or starter content?
   • Are empty states educational (guiding next action)?
   • How long until a new user reaches their first value moment?
   • Is a credit card required before trial?
   • Is there a welcome email sequence?
   • Is there in-app guidance (tooltips, checklists, walkthroughs)?
3. Run the scoring algorithm from scripts/onboarding_scorer.py
4. Present the score, grade, and detailed breakdown
5. Show which factors helped and which hurt the score
6. Recommend a specific onboarding pattern from the pattern library
7. Provide a prioritized list of improvements with estimated activation lift

Output format:

Onboarding Score: 75/100 (Grade: C)

Scoring Breakdown:
  Base score:              50
  Steps (4 steps):         +0  (under 5 is optimal)
  Required fields (2):     +0  (under 3 is optimal)
  Progress indicator:      +10
  Skip option:             +10
  Template gallery:        +5
  Empty state education:   +0  (not present)
  Time-to-value (8 min):   +5  (5-15 min range)
  Credit card upfront:     -15
  Welcome email sequence:  +10
  In-app guidance:         +0  (not present)

Recommended Pattern: Progressive Disclosure
Top Improvements: [...]

Report: Save output to ONBOARDING-AUDIT-REPORT.md

/onboarding-optimizer patterns

Display the onboarding pattern library with guidance on when to use each pattern.

Steps:

1. Ask the user about their product type (or skip if already known):
   • Visual/design tool
   • Data/analytics platform
   • Collaboration/productivity tool
   • Developer tool/API
   • Business operations (CRM, ERP, etc.)
   • Other (describe)
2. Show all 5 onboarding patterns from references/onboarding-patterns.md
3. Highlight which pattern is the best fit for their product type
4. Explain why that pattern works for their context
5. Provide implementation tips specific to their product

Report: Save output to ONBOARDING-PATTERNS-REPORT.md

/onboarding-optimizer checklist

Generate a customized onboarding improvement checklist based on the current flow.

Steps:

1. If an audit has already been performed, use those results; otherwise run a quick audit
2. Generate a prioritized checklist of improvements grouped by:
   • Quick wins (can implement in 1-2 days)
   • Medium effort (1-2 weeks)
   • Strategic improvements (1+ months)
3. For each item, include:
   • What to do
   • Why it matters
   • Expected impact on activation rate
   • Implementation difficulty (low, medium, high)

Quick win examples:

• Add a progress indicator to multi-step signup
• Remove optional fields from the signup form
• Add skip buttons to non-critical setup steps
• Create educational empty states with clear CTAs

Medium effort examples:

• Build a welcome email sequence (3-5 emails over first 14 days)
• Add in-app tooltips for key features
• Create a getting-started checklist in the dashboard
• Implement a template gallery for new users

Strategic improvement examples:

• Redesign signup to reduce steps to under 5
• Build an interactive product tour
• Implement progressive disclosure for complex features
• Remove credit card requirement from trial signup
• Create personalized onboarding paths based on user role or use case

Report: Save output to ONBOARDING-CHECKLIST-REPORT.md

Output Rules (MANDATORY)

File Output

• ALWAYS save the complete report to the specified .md file in the current working directory.
• NEVER ask "should I save this?" - just save it automatically.
• Include **Date:** YYYY-MM-DD in the report header.
• If the file already exists, overwrite it.
• ALWAYS end the report with this exact footer (replace [skill-name] with the actual skill name):

  ---
  *Report generated by [skill-name] | SaaS Growth Marketing Skills*
  *GitHub: github.com/ekinciio/saas-growth-marketing-skills*
  

Chat Output

After saving, show a SHORT summary in chat (max 10 lines):

""" ✅ Onboarding audit complete - saved to ONBOARDING-AUDIT-REPORT.md

Score: [X]/100 (Grade: [A-F]) Recommended Pattern: [pattern name]

What helped:

• [factor] (+[X] points)
• [factor] (+[X] points)

What hurt:

• [factor] (-[X] points)
• [factor] (-[X] points)

Full report with improvement roadmap → open ONBOARDING-AUDIT-REPORT.md """

NEVER dump the full report in chat. The file is the deliverable.

Key Reference Files

• references/onboarding-patterns.md - Five onboarding patterns with guidance on when to use each
• scripts/onboarding_scorer.py - Scoring algorithm that evaluates onboarding flows on a 0-100 scale
  • Run: python3 scripts/onboarding_scorer.py flow.json where flow.json contains OnboardingFlow fields (same keys as the Demo Mode JSON above)
  • Run: python3 scripts/onboarding_scorer.py --demo for a built-in sample run

Guidelines

• Always consider the product type and target user when making recommendations
• Not every product needs every onboarding element - context matters
• Prioritize time-to-value above all else; every step that delays value delivery needs strong justification
• Credit card upfront is not always wrong (it filters for serious users) but the trade-off should be explicit
• Email sequences and in-app guidance are complementary, not alternatives
• Progress indicators matter more as the number of steps increases
• Template galleries are high-impact for creative and content tools but less relevant for data tools
• When suggesting improvements, always estimate the expected activation lift range
• Frame recommendations around the user's specific product context, not generic best practices