activation-onboarding

name	activation-onboarding
description	Designs "aha moment" flows and activation experiments to improve new user onboarding. Use when optimizing the path from sign-up to first value realization.

Activation & Onboarding

Reference

• ID: S-GRO-05
• Category: Growth
• Inputs: user journey data, sign-up funnel metrics, product analytics, ICP document
• Outputs: activation playbook → artifacts/growth/
• Used by: Growth Agent
• Tool scripts: ./tools/artifact/validate.sh

Purpose

Designs the onboarding experience that guides new users to their "aha moment" as quickly as possible, and defines activation experiments to systematically improve conversion from sign-up to active user.

Procedure

Step 0: Pre-Data Mode Check

Read company.stage from company.config.yaml. If idea/mvp with fewer than 100 users:

Pre-Data Mode — pivot to hypothesis-driven activation:

1. Define "aha moment" hypothesis from ICP document and customer conversations (not data)
2. Instrument the hypothesized aha moment immediately (use instrumentation skill)
3. Define "enough data" threshold before running experiments
4. Replace A/B test recommendations with manual interview protocol for 5 users
5. Use discovery-validation and customer-conversations skills to validate hypotheses

Proceed to full data-driven activation design once threshold is met.

1. Define the "aha moment": the specific action that correlates with long-term retention.
2. Map the current onboarding flow from sign-up to aha moment, step by step.
3. Identify drop-off points in the funnel using product analytics data.
4. For each drop-off point, hypothesize the friction cause.
5. Design interventions: simplify steps, add guidance, remove unnecessary steps, add progress indicators.
6. Define activation metrics: time-to-value, activation rate, Day 1/7/30 retention.
7. Design 2-3 activation experiments with clear hypotheses and success criteria.
8. Define the experiment execution plan: A/B test setup, sample size, duration.
9. Outline the post-experiment analysis framework.
10. Save the activation playbook to artifacts/growth/.
11. Validate the artifact using ./tools/artifact/validate.sh.

Progressive Discovery Integration

When the feature_flags.strategy in company.config.yaml includes progressive discovery (progressive-discovery or full), coordinate with the Feature Flags skill:

• Map aha moments to discovery levels: The aha moment from Step 1 should be the trigger that unlocks Level 1 (Foundations) features
• Align activation experiments with feature reveals: A/B tests can test different discovery level thresholds
• Time-to-value acceleration: Onboarding interventions should guide users toward the triggers that unlock the next discovery level
• Feature reveal coordination: When a user unlocks a new discovery level, trigger the corresponding guided tour (from the User Docs skill)

Discovery levels (from feature-flags skill):

• Level 0 (Core): Always visible — essential product value
• Level 1 (Foundations): Unlocked after aha moment / core flow completion
• Level 2 (Power): Unlocked after proficiency demonstrated
• Level 3 (Expert): Unlocked on request or sustained engagement

Quality Checklist

• "Aha moment" is data-backed or has a clear hypothesis
• Current onboarding flow is mapped end-to-end
• Drop-off points are identified with data
• Interventions are specific and actionable
• Activation metrics are defined with baseline values
• Experiments have clear hypotheses and success criteria
• Sample size and duration are calculated
• Artifact passes validation