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name: calibrate-sizing description: Use to improve story sizing accuracy by analyzing historical estimation data. Identifies patterns, updates size definitions, and refines PROBE proxy selections. version: 1.0.0

Calibrate Sizing Task

When to Use

• After completing 10+ stories to analyze estimation accuracy
• When estimation variance is consistently high (>30%)
• Before starting a new project phase or quarter
• When team composition changes significantly
• When introducing new technology stack

Quick Start

1. Load completed stories from last 10-20 completions
2. Run variance analysis (actual vs estimated)
3. Identify over/under estimation patterns
4. Update size category definitions based on actual data
5. Refresh PROBE proxy library with recent examples
6. Generate calibration report

Purpose

Continuously improve story sizing accuracy using PSP measurement data:

• Analyze actual vs estimated times
• Update size category definitions
• Refine PROBE proxy selections
• Identify estimation patterns
• Improve future sizing precision

Reference Materials

For detailed YAML examples of all structures below, see ./reference/probe-methodology.md.

SEQUENTIAL Task Execution

1. Gather Historical Data

Load completed stories from last 10-20 completions. Collect size distribution data including count, estimated average, actual average, and variance for each size category (VS, S, M, L, VL). Flag Very Large stories for potential breakdown.

2. Identify Patterns

Analyze Systematic Biases:

Identify overestimation patterns (e.g., Backend CRUD), underestimation patterns (e.g., third-party integrations), and accurate patterns. Document technology factors (new vs familiar tech multipliers) and complexity factors (high integration vs standalone).

3. Update Size Definitions

Based on actual data, compare old ranges to actual averages and propose new ranges. Update the story point mapping (1, 2, 3, 5, 8 points) accordingly. Flag 8-point stories as requiring split.

4. Refine PROBE Proxies

Update Proxy Story Library:

• Retire outdated proxies that no longer represent current patterns
• Add new proxies from recent well-estimated stories
• Organize proxies into categories: ui_simple, ui_with_api, backend_crud, integration
• Calculate average duration for each category

5. Create Regression Model

If sufficient data (>30 stories), build a regression model with:

• Base overhead (intercept)
• Factors per: acceptance criteria, API endpoints, UI components, database tables, integration points
• Formula combining all factors
• Model accuracy metrics (R-squared, mean error, confidence level)

6. Generate Recommendations

Specific Improvements:

Generate three categories of recommendations:

• Immediate actions: Size definition updates, buffer adjustments, estimate calibrations
• Process improvements: Flags for large stories, checklists for integrations
• Training needs: Team-specific guidance based on identified patterns

7. Update Estimation History

Record calibration details: date, stories analyzed, adjustments made (size ranges, proxy changes, patterns identified), accuracy improvement metrics, and next calibration trigger.

8. Generate Calibration Report

Generate a markdown report including:

• Executive Summary (stories analyzed, overall accuracy, key finding, primary action)
• Size Category Performance table
• Pattern Analysis (overestimation and underestimation patterns with actions)
• Updated Sizing Guide with new ranges
• Recommendations
• Next Steps

See ./reference/probe-methodology.md for the full report template.

Success Criteria

• Minimum 10 stories analyzed
• Patterns identified and documented
• Size definitions updated if needed
• Proxy library refreshed
• Recommendations actionable
• Team notified of changes
• History file updated

Output

• Calibration report (markdown)
• Updated estimation-history.yaml
• Modified PROBE proxy library
• New size range definitions
• Team communication of changes