reversa-pricing-size

Parse forward-cycle artifacts into deterministic S/M/L/XL/XXL sizing with risk adjustment.

1|Updated Dec 20, 2025

Search Tags:#pricing #engineering planning #risk scoring #task analysis #feature sizing #deterministic metrics

Ready to run

npx skills add https://github.com/Rdinda/FMA_Pontos --skill reversa-pricing-size-rdinda

💬 Prompt for ChatGPT / Claude Chat▼

Please help me install this Agent Skill.
Skill: reversa-pricing-size
Source: https://github.com/Rdinda/FMA_Pontos/tree/main/.agents/skills/reversa-pricing-size
Command: npx skills add https://github.com/Rdinda/FMA_Pontos --skill reversa-pricing-size-rdinda

SYSTEM DOCUMENTATION & REQUIREMENTS

💡 This Skill includes references (resource) components.

What problem does it solve?

It eliminates guesswork when estimating a feature by transforming existing forward-cycle artifacts into a deterministic structural size class and score, including an explicit risk adjustment derived from uncertainties and depth.

Core Features & Use Cases

Counts structural metrics from forward artifacts: Reads requirements, doubts, plan depth, tasks, and principles touched to build a sizing input set.
Computes T shirt sizing with risk adjustment: Produces base complexity (S/M/L/XL/XXL) from task volume, then adjusts it upward using high and medium doubts plus plan depth and principles touched.
Writes versioned, schema validated outputs: Generates size.json and a human readable size.md into the pricing output folder for downstream estimation.

Quick Start

Use the reversa-pricing-size skill to size the currently active feature by running /reversa-pricing-size after /reversa-to-do.

Frequently Asked Questions about reversa-pricing-size

High-intent search queries and answers about installing and using this skill.

FAQPage Schema

How do I deterministically size features using task analysis and risk scoring?▼

Feature sizing determines structural size classes by parsing task volume, plan depth, and principles touched from forward-cycle artifacts, then applying a fixed T-shirt sizing formula with risk adjustment based on captured doubts.

Can I use existing engineering planning artifacts to calculate T-shirt sizes for pricing?▼

Yes, feature sizing reads existing requirements, doubts, plans, tasks, and principles captured under the forward directory to build a consistent sizing input set, eliminating guesswork for pricing workflows.

How does risk adjustment work when estimating feature complexity?▼

Risk adjustment computes base complexity from task volume, then adjusts the size upward using high and medium doubts alongside plan depth and principles touched to produce the final deterministic score.

What is the best way to persist feature sizing metrics for downstream estimation?▼

The sizing process writes versioned, schema-validated size.json and human-readable size.md files into the pricing output folder, ensuring consistent structural metrics for downstream estimation workflows.

Do I need prior forward-cycle artifacts before running feature sizing?▼

Yes, feature sizing requires active forward-cycle artifacts where requirements, doubts, plans, tasks, and principles are already captured under the forward directory to parse sizing inputs into metrics.

Why does my feature sizing output lack a risk adjustment component?▼

Risk adjustment requires captured doubts within forward artifacts; if high and medium doubts are missing from the engineering planning inputs, the sizing formula cannot apply upward complexity adjustments.