01 · Roasts
The Godfather of Empty Scaffolds
You have 77 public repos, 0 total forks, and at least 6 repos that were created and abandoned on the same day within minutes. The Godfather didn't leave horse heads — you leave placeholder READMEs.
Speed Runner, Wrong Category
Code_Nakshatra-Metamorphosis: created 2026-04-23T18:42, last push 2026-04-23T18:49. A full-stack LLM simulation platform with 1,080 personas — speedrun in 6 minutes flat. No tests, no CI, no license. Any% glitchless.
159MB of Confidently Undocumented Code
pdf_chatbot_langchain weighs 159MB — heavier than most serious open-source projects — yet contains no README, no license, no gitignore, and exactly 2 commits. The repo is bigger than its ambitions.
Preferred Pronoun: 'Shipped'
Bio says 'Preferred pronoun — Godfather.' GitHub says 0 total forks, 2 PRs/year, and 0 issues opened. The streets don't know you yet.
Portfolio Quantity vs. Quality Gap
77 repos, 31 total stars, and a quality weighted mean that rounds to 18/100. That's 0.4 stars per repo on average. At this rate, you need 125 more repos to reach statistical relevance.
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
- Impact25% weight28F
- Consistency20% weight35F
- Quality20% weight18F
- Depth15% weight22F
- Breadth10% weight40D
- Community10% weight25F
03 · Stats
365-day commit heatmap
119 active days
Language distribution
- Python91%
- Cython2%
- C++2%
- Jupyter Notebook2%
- HTML1%
- C1%
- Other1%
04 · Numbers
Owned repos
non-fork
73
Commits
last 12 months
228
Followers
11
Joined GitHub
Dec 2021
05 · Top repos
Dhruv-Pahwa /
Code_Nakshatra-Metamorphosis
Recent full-stack CGE policy simulation platform (React + FastAPI) with 1,080 personas and LLM integration; incomplete and burst-built in 6 minutes with minimal commits; runs but lacks tests and CI.
Dhruv-Pahwa /
dhruvprep
Early-stage Python ML preprocessing toolkit with 6 core modules (missing, outliers, encoding, scaling, VIF, EDA), shipping via pyproject.toml but untyped, untested, and only 3 commits in 6 days.
Dhruv-Pahwa /
Dhruv-Pahwa
Personal profile repo with generic README; 1 star, minimal substance, no code artifacts—a one-off profile card rather than a working project.
Dhruv-Pahwa /
SLM_15MP
One-shot Jupyter notebook implementing a 15M-parameter GPT-2 style language model trained on TinyStories. Educational demonstration with comprehensive README but no production structure, tests, CI, or version control beyond initial commit.
Dhruv-Pahwa /
foss_proto
Single-day prototype of a CS learning platform with UI scaffolding (dashboard, lessons, battles) but no tests, CI, documentation, or type safety. 37 KB JavaScript codebase with hardcoded content data and no license.
Dhruv-Pahwa /
-Nemotron-Personas-India-_30
One-file data extraction script downloading Nemotron personas from Hugging Face with no documentation, tests, CI, or project structure. Created and pushed same day with minimal commits.
Dhruv-Pahwa /
repo-with-readme
Empty scaffold with minimal content: 3KB, 4 commits over 1 minute, README contains only two names. No code, tests, CI, license, or documentation.
Dhruv-Pahwa /
FOSS-Hack-Metamorphosis
Empty hackathon submission scaffold with minimal README, no code files, 2 commits in 21 seconds, untyped language, no tests/CI/license/gitignore.
Dhruv-Pahwa /
HoloVision
Empty scaffold repo created Feb 2026 with 1 star, 35 KB, no README, no tests, no CI, no documentation, and only 1 commit in the last 30 days.
Dhruv-Pahwa /
pdf_chatbot_langchain
Unpolished code dump: 159MB Python project with no README, tests, CI, license, or gitignore; created and pushed within 4 minutes on 2026-02-12; 0 stars/forks indicates no adoption or external validation.
Dhruv-Pahwa /
nlkjilj
Empty scaffold repository with 0 stars, no documentation, no code structure, and minimal same-day commits. Appears to be an abandoned or incomplete placeholder project.
Dhruv-Pahwa /
carma2.D
Empty scaffold with no files, no commits, and no documentation. Created 2026-03-28 with zero activity—placeholder only.
06 · Timeline
- Dec 21, 2021Joined GitHub
- Dec 23, 2024Created Dhruv-Pahwa
- Feb 11, 2026Created dhruvprep
- Feb 12, 2026Created pdf_chatbot_langchain
- Feb 25, 2026Created HoloVision
- Feb 28, 2026Created FOSS-Hack-Metamorphosis
- Mar 1, 2026Created nlkjilj
- Mar 8, 2026Created foss_proto
- Mar 11, 2026Created repo-with-readme
- Mar 17, 2026Created SLM_15MP
- Mar 28, 2026Created carma2.D
- Apr 17, 2026Created -Nemotron-Personas-India-_30
- Apr 23, 2026Created Code_Nakshatra-Metamorphosis
- Apr 23, 2026Most recent push to Code_Nakshatra-Metamorphosis
07 · Compare
08 · Rubric
How this score was produced
Overall = Σ (category × weight) + gentle top-end curve
Tier thresholds
▸ How the pipeline works
- 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.
- 02Triage.A small model reads every repo's file tree + README and picks the 20 files per repo that actually reveal how you code.
- 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.
- 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.
- 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.