epic-breakdown-advisor — an installable skill for AI agents, published by deanpeters/product-manager-skills.
Break down large epics into vertical-slice user stories using Humanizing Work's 9 splitting patterns. Guides product managers through sequential pattern application (workflow steps, CRUD operations, business rules, data variations, UI methods, major effort, simple/complex, performance deferral, and spikes) to identify which pattern fits Validates epics against INVEST criteria before splitting to ensure stories are independent, negotiable, valuable, estimable, and testable Generates equal-sized, end-to-end user stories that preserve vertical value rather than creating horizontal technical slices Evaluates splits to reveal low-value work worth eliminating or deprioritizing, turning vague feature blobs into sprint-ready stories Purpose Guide product managers through breaking down epics into user stories using Richard Lawrence's complete Humanizing Work methodology—a systematic, flowchart-driven approach that applies 9 splitting patterns sequentially. Use this to identify which pattern applies, split while preserving user value, and evaluate splits based on what they reveal about low-value work you can eliminate. This ensures vertical slicing (end-to-end value) rather than horizontal slicing (technical layers). This is not arbitrary slicing—it's a proven, methodical process that starts with validation, walks through patterns in order, and evaluates results strategically. Key Concepts Core Principles: Vertical Slices Preserve Value A user story is "a description of a change in system behavior from the perspective of a user." Splitting must maintain vertical slices—work that touches multiple architectural layers and delivers observable user value—not horizontal slices addressing single components (e.g., "front-end story" + "back-end story"). The Three-Step Process Pre-Split Validation: Check if story satisfies INVEST criteria (except "Small") Apply Splitting Patterns: Work through 9 patterns sequentially until one fits Evaluate Splits: Choose the split that reveals low-value work or produces equal-sized stories
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