Developer Profile

Maximiliano Vargas

@mvargas33

Santiago, Chile

GitHub

Impact Score Overview

Developer Impact & Statistics

Final Score

30 / 100Raw: 135.00

Repository Score

17 / 100Raw: 21.00

PR Score

48 / 100Raw: 279.00

Contribution Score

5 / 100Raw: 5.00

Score Weight Distribution

finalScore = repoScore * 0.45 + prScore * 0.45 + contributionScore * 0.10

Repository Score (45%)7% of final signal

Pull Request Score (45%)93% of final signal

Contribution Score (10%)0% of final signal

Top Work

Top Work & Highlights

Top Repositories

Most impactful repositories and pull requests

#1Eolian-Auriga-backend

stars2

forks0

Java 9100%C++ 700%Python 200%

Score

#2Eolian-Telemetria-Auriga

stars5

forks1

C++ 4400%C 4300%Java 800%

Score

#3Eolian-Auriga-frontend

stars2

forks0

Vue 7100%JavaScript 2700%SCSS 100%

Score

Top Pull Requests

Most impactful repositories and pull requests

#1[MRG][DOC] Fixes almost all warnings in the docs

in scikit-learn-contrib/metric-learn

stars on the PR repository1439

+175/-117

Python 10000%

Score

#21. score_pairs refactor

in scikit-learn-contrib/metric-learn

stars on the PR repository1439

+360/-106

Python 10000%

Score

#3Rename variables, proposed by issue #257

in scikit-learn-contrib/metric-learn

stars on the PR repository1439

+305/-186

Python 10000%

Score

Community Contributions

comments

#1Discussion

The way to the OASIS

scikit-learn-contrib/metric-learn

stars1439

comments1

Score

Scoring Transparency

▼

Repositories analyzed

Pull requests analyzed

Merged external PRs counted

Own-repo PRs ignored

Unmerged PRs ignored

Unique external repositories

Issues analyzed

External issues counted

Discussions analyzed

External discussions counted

How this score was calculated

View the full scoring methodology with formulas, penalties, and step-by-step flow.

View full methodology