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#27 — Top 97.8%

robjhyndman

Rob J Hyndman

B

Solid engineer

Overall

0.0

/ 100

01 · Roasts

HTML Professor

87% of your GitHub is HTML — for a Statistics professor who teaches R, your own repos suggest the web ate your codebase. forecast is carrying this portfolio harder than ARIMA carries a bad forecast.

License Dodger

forecast has 1,164 stars, 14 years of CRAN distribution, and... no open-source license. You've been shipping a legally ambiguous package to thousands of R users for over a decade. The OSS community thanks you for keeping them on their toes.

Test-Free Academic Zone

9 of your 12 scored repos have HAS_TESTS=no. You literally wrote a textbook on forecasting best practices, yet half your repos can't tell you if the code works after a push. Physician, heal thyself.

2,732 Commits, 0 Followers Gained

You committed 2,732 times this year — roughly 7.5 commits per day — yet you're following only 12 people. That's a 257:1 follower-to-following ratio. The most prolific hermit on GitHub.

Stars Live in One Zip Code

2,935 total stars but 1,164 of them (40%) belong to a single repo from 2012. Your portfolio is a one-hit wonder with a very long tail of personal LaTeX files and Quarto slides.

Built using

Zoral

Shadows one worker for a week, then takes over their job with zero extra setup. Behaves exactly like the original.

zoral.ai

02 · Category breakdown

  • Impact
    25% weight
    75B
  • Consistency
    20% weight
    90S
  • Quality
    20% weight
    72B
  • Depth
    15% weight
    75B
  • Breadth
    10% weight
    45D
  • Community
    10% weight
    55D

03 · Stats

365-day commit heatmap

343 active days

Less
More

Language distribution

6 langs
  • HTML87%
  • R8%
  • TeX4%
  • JavaScript1%
  • CSS0%
  • C++0%

04 · Numbers

Owned repos

non-fork

79

Commits

last 12 months

2,732

Followers

3,090

Joined GitHub

Sep 2009

05 · Top repos

robjhyndman /

forecast

72/100

Mature R forecasting library with 1.1k stars, comprehensive time series methods (ETS, ARIMA, TBATS, neural networks), shipped via CRAN, 14+ years active development, strong test coverage and CI/CD pipeline but no open license.

I70Q75D70
READMETestsCI
R1,1641mo ago

robjhyndman /

weird

67/100

Specialized R package for anomaly detection via kernel density estimation, supporting a published textbook; 113 MB codebase with thorough tests, CI/CD, and 2,097 observations in sample dataset; active indie project with concrete named product and author profile.

I55Q75D65
READMETestsCI
R211mo ago

robjhyndman /

CV

60/100

Sophisticated CV management system using R targets pipeline, Quarto, and RefManageR to auto-generate multiple publication-ready PDF variants from structured bibliographies and live Google Scholar data. Professional academic project with well-documented build architecture.

I55Q60D65
TeX1021mo ago

robjhyndman /

gh-dashboard

50/100

Personal Quarto dashboard monitoring GitHub repo metrics; automated daily R/GitHub API pipeline with CI deployment to gh-pages; thin scope, minimal stars but purposeful tooling.

I25Q60D50
READMECI
HTML11mo ago

robjhyndman /

ResearchHabits

50/100

Educational seminar material on research habits for honours students, maintained since 2018 with consistent updates. TeX-based slides with meaningful README and sustained commit history across 6+ years.

I35Q50D65
README
TeX172mo ago

robjhyndman /

lookout2

43/100

Research paper reproducibility repo comparing outlier detection algorithms (lookout, stray, HDoutliers, etc.) across synthetic and real datasets using R targets workflow, with 7 experiments and LaTeX compilation via Makefile.

I25Q55D50
README
TeX12mo ago

robjhyndman /

Surprisal_Theory

42/100

Academic research paper with reproducible workflow using R/targets and Quarto. Proposes surprisal theory framework for anomaly detection with supporting simulations and real-world applications (French mortality, cricket data). Well-documented paper manuscript but minimal package/library outputs.

I25Q60D45
README
TeX12mo ago

robjhyndman /

robjhyndman.com

40/100

Personal academic website source (Quarto-based) with minimal reuse potential. 355 MB of assets, regular updates since Aug 2022, but thin documentation and no structural metadata beyond README.

I25Q40D55
README
HTML201mo ago

robjhyndman /

quarto-password

40/100

Quarto template for password-protected static websites via GitHub Actions. Personal utility with clear README docs, structured workflow config, and modest adoption (30 stars). No license or tests; archived-feeling despite recent last push.

I25Q55D40
READMECI
R301mo ago

robjhyndman /

paperpile

28/100

TeX-based academic paper repository with 30 recent commits over ~3 years (2023–2026), minimal public documentation, no tests/CI/license, and 1 star. Personal scholarly project with sustained activity but limited adoption.

I15Q25D50
README
TeX11mo ago

robjhyndman /

positron_assistant

20/100

Educational demo repo showcasing Positron Assistant LLM features in R (code completion, chat agents, planning). Very recent one-off project with minimal commits, no tests/CI/docs/license, lacks README.

I15Q25D20
R01mo ago

robjhyndman /

robjhyndman.r-universe.dev

13/100

R universe metadata/configuration repo with no README, no source files sampled, minimal public scope. Recent pushes (29/30 commits recent) show ongoing maintenance but repo serves as package registry automation only.

I5Q10D25
CI
R01mo ago

06 · Timeline

  1. Sep 16, 2009
    Joined GitHub
  2. Apr 22, 2012
    Created forecast — Forecasting Functions for Time Series and Linear Models
  3. May 3, 2017
    Created CV — RJH Curriculum Vitae
  4. Aug 16, 2018
    Created ResearchHabits — Talk to honours students on developing good research habits
  5. Feb 14, 2021
    Created weird — All data and functions needed for the book "That's weird: Anomaly detection using R" by Rob J Hyndman <https://OTexts.com/weird>
  6. Jan 7, 2022
    Created robjhyndman.r-universe.dev — R universe packages
  7. Aug 27, 2022
    Created robjhyndman.com — Quarto source files for robjhyndman.com
  8. Mar 20, 2023
    Created paperpile
  9. May 29, 2024
    Created quarto-password
  10. Jun 13, 2024
    Created lookout2
  11. Sep 30, 2024
    Created gh-dashboard
  12. Aug 8, 2025
    Created Surprisal_Theory
  13. Apr 27, 2026
    Created positron_assistant — Thoughts on Positron Assistant for Numbats
  14. Apr 29, 2026
    Most recent push to positron_assistant

07 · Compare

github.com/
robjhyndman · 6dmedian coder

08 · Rubric

How this score was produced

Overall = Σ (category × weight) + gentle top-end curve

CategoryWeightScoreContrib.
Raw total72.4
Top-end curve+5.9
Final overall78.3

Tier thresholds

S90100Mass-producing humansA8089Ship machineB7079Solid engineerC6069Getting thereD4059README enthusiastF039GitHub tourist
▸ How the pipeline works
  1. 01Scrape.Pull every non-fork repo pushed in the last 90 days, plus your contribution calendar, followers, and language byte counts — straight from GitHub's REST & GraphQL APIs.
  2. 02Triage.A small model reads every repo's file tree + README and picks the 20 files per repo that actually reveal how you code.
  3. 03Grade each repo. All repos run in parallel through a fast scoring model that reads the picked files and rates each one independently on Impact, Quality, and Depth — with evidence citations.
  4. 04Aggregate. A larger reasoning model combines the per-repo scores with server-computed stats (heatmap, commit cadence, language entropy, follower count) to produce the 6-dimension profile score + roasts.
  5. 05Correct.Deterministic server-side checks enforce anchor-scale floors (e.g. a profile with 2,000+ public commits can't score 30 Consistency) and recompute the final verdict.

~90 seconds per profile, ~$0.25 in compute. Total of ~240 files read across your top-12 repos. One rating per GitHub account per day.

▸ Data sources & caveats
  • Heatmap & commit totals: GitHub GraphQL contributionsCollection — covers the last 365 days, includes private repos when the user has opted in (default).
  • Language %: byte totals across the top 30 owned non-fork repos.
  • Curve: a small upward nudge centered on raw score ≈ 70, capping at 100. Prevents specialists from being unfairly penalised for narrow breadth.
  • Anchor corrections: when server-measured signals (e.g. privateWorkLikely, multiRepoVolume, follower count) mandate a minimum category score, the aggregation step enforces it. These are signal-conditional, not identity-based floors.
robjhyndman · 78.3/100 — Rate My GitHub