decision-tree-design

Decision-tree design transforms goals into hierarchical epic → sub-epic → story structures using a Socratic discovery process. This approach systematically maps product decisions, ensuring each level defines clear data contracts and dependencies, making your backlog formally specified and traceable from high-level goals to actionable stories.

Map user journeys by decomposing them into epic-driven hierarchies where each epic captures a decision point, sub-epics break down workflows, and stories define executable steps with explicit data contracts. This ensures feature sets align with user workflows and decision dependencies are visible across your backlog.

Data contracts are generated through the Socratic workflow during story definition within the epic hierarchy. Each story explicitly documents its inputs, outputs, and decision dependencies, creating a formal specification that ensures consistency and clarity across interconnected epics and workflows.

Yes. The Socratic approach requires three formal steps: design (decompose goals into hierarchies), review (validate decision dependencies and data contracts), and validation (ensure edge cases are handled). This structured workflow prevents gaps and ensures your decision-tree backlog is complete and specification-ready.

Yes. The skill explicitly includes edge-case handling and validation as part of the hierarchical decomposition. By mapping decisions and dependencies systematically, you identify edge cases early and document them within the story-level data contracts, reducing implementation surprises.

Decision-tree design imposes hierarchical structure and explicit data contracts, making dependencies and decision logic visible. Flat backlogs lack this formality, often resulting in ambiguous stories and missed dependencies. The structured approach ensures specification completeness and cross-epic consistency.