abd-simple-validated-learning

To create a validated learning backlog, mine unverified assumptions from your business case and convert them into falsifiable hypotheses. The Skill prioritizes these items by uncertainty and impact, marking them as MUST, SHOULD, or NICE to test high-risk uncertainties early.

You prioritize a validated learning backlog by ranking extracted hypotheses based on their level of uncertainty and potential impact. The Skill automatically marks each backlog item as MUST, SHOULD, or NICE to ensure you test the most high-risk assumptions first.

The best way to structure experimentation workflows is using Plan, Validate, and Learn phases. The Skill enforces this by assigning an owner, timebox, method, evidence requirement, and decision to each item, utilizing validated-learning-backlog and experimentation-canvas templates.

Yes, you can extract assumptions from an opportunity canvas, impact map, journey map, or notes. The Skill processes these contexts to surface unverified assumptions and converts them into falsifiable hypotheses for your validated learning backlog.

You should not use a validated learning backlog when your project has zero uncertainty or when all assumptions are already verified. This approach is specifically designed to structure discovery and test high-risk uncertainties before building any new features.