agregadores-performance-potencial

Compute performance and potential axes from evidence events into AxesResult DTOs.

Updated Jan 23, 2026

Search Tags:#performance #aggregation #evidence #event-driven #deterministic #dto #potential

Ready to run

npx skills add https://github.com/VictorSmaniotto/degestao --skill agregadores-performance-potencial

💬 Prompt for ChatGPT / Claude Chat▼

Please help me install this Agent Skill.
Skill: agregadores-performance-potencial
Source: https://github.com/VictorSmaniotto/degestao/tree/main/.agent/skills/agregadores-calculo
Command: npx skills add https://github.com/VictorSmaniotto/degestao --skill agregadores-performance-potencial

SYSTEM DOCUMENTATION & REQUIREMENTS

What problem does it solve?

Este skill resolve a necessidade de criar agregadores puros e determinísticos que calculam os eixos de Performance e Potencial a partir de evidências, sem acoplamento a banco de dados ou UI.

Core Features & Use Cases

Implementação de agregadores separados: PerformanceAggregator e PotentialAggregator.
Regra de negócio: Potencial só cresce com progressão de complexidade.
Integração orientada a eventos: recebe evidências via eventos e retorna DTOs (AxesResult) em vez de models de domínio.
Testes abrangentes: cobrem cálculos em contextos múltiplos e cenários de progressão.

Quick Start

Implemente um pipeline de evidências que utilize PerformanceAggregator e PotentialAggregator para produzir um AxesResult a partir de inputs contextuais.

Frequently Asked Questions about agregadores-performance-potencial

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

FAQPage Schema

How do I calculate performance metrics from event-driven evidence deterministically?▼

A deterministic aggregator calculates performance metrics by processing event-driven evidence inputs and returning an AxesResult DTO, ensuring consistent, testable outputs without database or UI coupling.

What is the best way to structure an event-driven aggregation pipeline for performance axes?▼

Structure the pipeline by routing evidence events to separate PerformanceAggregator and PotentialAggregator components that return AxesResult DTOs instead of domain models, maintaining pure business logic separation.

Can I calculate potential metrics without a database connection?▼

Yes, you can calculate potential metrics without a database by using pure deterministic aggregators that process evidence events and return AxesResult DTOs, maintaining complete decoupling from infrastructure.

How does potential aggregation handle complexity progression rules?▼

Potential aggregation enforces a business rule where the potential axis only grows with the progression of complexity, ensuring metrics reflect meaningful contextual advancement rather than mere volume.

Why return DTOs instead of domain models from an evidence aggregator?▼

Returning AxesResult DTOs instead of domain models guarantees deterministic behavior and prevents side effects, allowing performance and potential axes to be calculated consistently across multiple test contexts.