Can I use this for teams using different technology stacks?

Yes, the skill specifically analyzes technology factors and documents multipliers for new versus familiar tech to ensure estimations remain accurate regardless of the stack.

What kind of output does the Calibrate Sizing skill provide?

It generates a comprehensive Markdown calibration report, an updated estimation history file, modified PROBE proxy libraries, and actionable recommendations for team process improvements.

How does this skill handle large or complex stories?

The skill identifies patterns in 'Very Large' stories and flags them for breakdown, while also proposing new size ranges to ensure future 8-point stories are split for better manageability.

What is the PROBE methodology used in this skill?

PROBE (Proxy Based Estimation) is a technique that uses historical data and representative 'proxies' from previous tasks to accurately estimate the size and time required for new software development work.

When is the best time to run the calibration task?

It is recommended to run this skill after completing at least 10-20 stories, or whenever you notice estimation variance consistently exceeding 30%.

Calibrate Sizing

Name: Calibrate Sizing
Author: resolve-io

byresolve-io

•

生産性とワークフロー

Improves software project estimation accuracy by analyzing historical variance and refining story-sizing definitions using the PROBE methodology.

概要

Part of the PRISM framework, the Calibrate Sizing skill automates the retrospective analysis of development task estimations to bridge the gap between predicted and actual delivery times. By applying Personal Software Process (PSP) principles and PROBE (Proxy Based Estimation) methodology to your historical data, it identifies systematic biases, updates size category definitions from VS to VL, and refreshes proxy libraries. This ensures that software engineering teams achieve higher predictability, reduced variance, and more reliable sprint planning through data-driven recalibration.

主な機能

Historical variance analysis comparing estimated vs. actual story duration
Automated updates for story point mappings and size category definitions
PROBE proxy library refinement based on recent high-quality completions
Statistical regression modeling to improve future sizing precision
Systematic bias identification for specific technology stacks and complexities
1 GitHub stars

ユースケース

Refining project roadmaps by reducing estimation error margins below 30%
Recalibrating team velocity when introducing new technologies or changing team composition
Conducting data-driven retrospectives after completing a project phase or 10+ stories

概要

主な機能

Historical variance analysis comparing estimated vs. actual story duration
Automated updates for story point mappings and size category definitions
PROBE proxy library refinement based on recent high-quality completions
Statistical regression modeling to improve future sizing precision
Systematic bias identification for specific technology stacks and complexities
1 GitHub stars

ユースケース

Refining project roadmaps by reducing estimation error margins below 30%
Recalibrating team velocity when introducing new technologies or changing team composition
Conducting data-driven retrospectives after completing a project phase or 10+ stories