reversa-pricing-size

Deterministic feature sizing calculates structural metrics by extracting requirements, doubts, plan depth, and task counts from a selected feature directory. It applies a task-based T-shirt sizing formula with explicit risk adjustment based on doubts severity and principles touched to ensure consistent, traceable outputs.

Calculating T-shirt size with risk adjustment involves deriving a base complexity class from task counts, then modifying it using doubts severity, plan depth, and principles touched. This generates a traceable size.json file and a human-readable size.md summary for pricing decisions.

Yes, task-based sizing metrics are designed for legacy modernization projects. By selecting an active forward feature directory and extracting its operational specs, the process computes structural sizing outputs and persists them using strict JSON schema v1.1 for AI agent pricing decisions.

Dimensioning features for pricing generates a size.json file containing strict schema v1.1 persistence data and a human-readable size.md summary. These files are written to the _reversa_sdd/_pricing/<feature>/ directory to support AI agent pricing decisions.

The best way to generate deterministic size metrics for AI coding agents is to use a structured sizing formula that reads feature artifacts like requirements and tasks. This avoids guesswork by computing a risk-adjusted T-shirt size and persisting it in a standardized JSON schema.

Yes, you need specific operational specs to compute feature sizing outputs, including requirements, doubts, plan depth, and tasks. These forward-cycle artifacts are extracted from the selected feature folder to accurately calculate the risk-adjusted T-shirt size for pricing.