What is the target size for a decomposed story?

This skill aims to create stories that take between 4 and 24 hours (roughly 0.5 to 3 days) to complete, which is the optimal range for predictable delivery.

What estimation methodology does this skill use?

It applies the PROBE (Proxy-Based Estimation) method combined with Personal Software Process (PSP) discipline to compare new tasks against historical actuals.

Does it support vertical or horizontal slicing?

The skill supports both vertical slices (end-to-end functionality) and horizontal layers (technical boundaries), though it prioritizes vertical slices for independent value.

How does this help with project predictability?

By forcing decomposition of any task exceeding 3 days and using data-driven estimation, it reduces the risk of scope creep and helps maintain a continuous development flow.

PRISM Story Decomposition

Name: PRISM Story Decomposition
Author: resolve-io

byresolve-io

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Productivity & Workflow

Breaks down complex software requirements into manageable, 1-3 day work items using PSP measurement discipline.

About

The PRISM Story Decomposition skill refracts development complexity by transforming large user stories into granular, high-value sub-stories through coordinated AI analysis. By applying Personal Software Process (PSP) measurement discipline and PROBE estimation, it ensures tasks are optimally sized between 4 and 24 hours while maintaining architectural alignment and independent value. This skill is essential for engineering teams using the PRISM framework to ensure a predictable, resilient, and sustainable flow of ready-to-develop stories during sprint planning or requirement analysis.

Key Features

Vertical slice identification to ensure independent story value
PROBE estimation calibration using historical sizing data
Quality validation checks for story coverage and testability
1 GitHub stars
Automated story splitting based on architectural and functional boundaries
Comprehensive decomposition reports with dependency mapping

Use Cases

Breaking down monolithic legacy tickets into manageable technical tasks
Right-sizing large feature requests during sprint planning
Aligning frontend, backend, and database work across vertical slices

About

Key Features

Vertical slice identification to ensure independent story value
PROBE estimation calibration using historical sizing data
Quality validation checks for story coverage and testability
1 GitHub stars
Automated story splitting based on architectural and functional boundaries
Comprehensive decomposition reports with dependency mapping

Use Cases

Breaking down monolithic legacy tickets into manageable technical tasks
Right-sizing large feature requests during sprint planning
Aligning frontend, backend, and database work across vertical slices