story-splitting
Story Splitting (Vertical Slicing Patterns)
Overview
A pattern catalog for splitting epics and large stories into smaller, shippable, end-to-end slices that still deliver user value. Built on Richard Lawrence's canonical story-splitting flowchart (the 9 patterns most product teams converge on) with worked before/after examples and a quick-reference decision tree.
The single most common reason teams fail to deliver predictably is that stories are too large. Large stories balloon in cycle time (see cycle-time-analyzer/), create coordination overhead, and resist incremental release. The remedy is not to estimate more carefully; it is to split smaller — vertically, so each slice (1) delivers value the end user can perceive, (2) fits in a single sprint, (3) is independently shippable, and (4) passes INVEST quality gates (see wwas/). The skill is pattern-based — no Python tool is needed; the value is the recipes and the worked examples.
Core Capabilities
- 9 canonical Lawrence patterns — workflow steps, business-rule variations, happy/unhappy path, input/output variations, data variations, data-entry methods, deferred performance/quality, CRUD operations, and break-out-a-spike (plus optional "Major Effort First").
- Vertical-slicing decision tree — a top-down tree that names the first applicable pattern for any oversized story.
- Vertical-vs-horizontal discipline — keeps every slice end-to-end and demoable instead of layer-by-layer.
- Ordering & INVEST gating — sequences slices smallest-value-first and verifies each against I-N-V-E-S-T.