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widget-inference

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Atualizado6 de julho de 2026 às 23:31

Guidelines for mathematically deducing advanced widget data on the backend.

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agussantinelli

agussantinelli/GitData

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Ocupações relacionadasSOC

Baseado na classificação ocupacional SOC

Desenvolvedores de softwareInformática e Matemática·SOC 15-1252

SKILL.md

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name	widget-inference
description	Guidelines for mathematically deducing advanced widget data on the backend.

Widget Inference Engine

Context

GitData's premium UI (like Tech Radar, Achievements, and Dev Clock) requires data that GitHub does not natively provide. Instead of adding complex databases, GitData infers these states algorithmically in the backend (OctokitGithubRepository.ts) using the raw metrics available.

Guidelines

Tech Radar Categorization: Languages must be statically mapped into three core pillars: Frontend, Backend, and DevOps/Data. The percentage is calculated by summing the total bytes of languages matching these buckets.
Achievements Deduction: Trophies are awarded based on absolute thresholds of existing data (e.g., prs >= 5 -> Pull Shark, followers >= 10 -> Influencer).
Time Analysis: Time-based widgets (Dev Clock, Hourly Frequency) rely exclusively on the created_at timestamp extracted from the last 50 REST API publicEvents.
Backend Processing: All inference MUST happen in the Backend. The Frontend should only receive the finalized, computed structures (e.g., arrays of objects) and should NOT perform heavy mapping or categorization logic.

Examples

// Inferring an achievement
const achievements = [];
if (prs >= 5) achievements.push({ id: 'pull-shark', title: 'Pull Shark', description: '...', icon: '🦈' });

// Inferring Tech Radar
let frontend = 0;
const feLangs = ['HTML', 'CSS', 'JavaScript', 'TypeScript', 'React'];
Object.keys(languageMap).forEach(lang => {
  if (feLangs.includes(lang)) frontend += languageMap[lang];
});

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name	widget-inference
description	Guidelines for mathematically deducing advanced widget data on the backend.

Widget Inference Engine

Context

Guidelines

Tech Radar Categorization: Languages must be statically mapped into three core pillars: Frontend, Backend, and DevOps/Data. The percentage is calculated by summing the total bytes of languages matching these buckets.
Achievements Deduction: Trophies are awarded based on absolute thresholds of existing data (e.g., prs >= 5 -> Pull Shark, followers >= 10 -> Influencer).
Time Analysis: Time-based widgets (Dev Clock, Hourly Frequency) rely exclusively on the created_at timestamp extracted from the last 50 REST API publicEvents.
Backend Processing: All inference MUST happen in the Backend. The Frontend should only receive the finalized, computed structures (e.g., arrays of objects) and should NOT perform heavy mapping or categorization logic.

Examples

// Inferring an achievement
const achievements = [];
if (prs >= 5) achievements.push({ id: 'pull-shark', title: 'Pull Shark', description: '...', icon: '🦈' });

// Inferring Tech Radar
let frontend = 0;
const feLangs = ['HTML', 'CSS', 'JavaScript', 'TypeScript', 'React'];
Object.keys(languageMap).forEach(lang => {
  if (feLangs.includes(lang)) frontend += languageMap[lang];
});