01 · Roasts
Portfolio, not pull requests
108 public repositories produced 1 total star and 0 forks: lots of shelves, little evidence anyone is borrowing the books.
Automation allergy
Most highlighted projects omit CI and licenses; even the well-structured Distributed-Task-Queue stops before automation.
Notebook monoculture
97% of language bytes are Jupyter Notebook, while the portfolio claims systems breadth through much smaller Java, Python, and shell work.
Scaffold collector
MusicBook and Build-a-Custom-Agent-Harness-from-scratch are essentially empty, and url-shortner-typescript is 0 KB.
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% weight48D
- Consistency20% weight55D
- Quality20% weight57D
- Depth15% weight50D
- Breadth10% weight55D
- Community10% weight40D
03 · Stats
365-day commit heatmap
151 active days
Language distribution
- Jupyter Notebook97%
- Java1%
- Shell1%
- Python1%
- JavaScript0%
- TypeScript0%
04 · Numbers
Owned repos
non-fork
89
Commits
last 12 months
265
Followers
15
Joined GitHub
Apr 2021
05 · Top repos
nikhil00shinde /
Distributed-Task-Queue
A documented Java 21 multi-module Spring Boot task-queue scaffold with solid domain validation and API tests, but Redis/worker modules remain planned and adoption is absent.
nikhil00shinde /
neetcode-submissions
A documented NeetCode submission archive with Java and Python solutions spanning algorithm topics, but no tests, CI, license, or evidence of external adoption.
nikhil00shinde /
dotfiles
A documented personal Fedora/Bluefin dotfiles repo with GNU Stow packaging and a substantial Neovim setup, but no adoption signals, tests, CI, license, or gitignore and only modest repository scope.
nikhil00shinde /
Incident-triage-agent
Small Python/OpenAI incident-triage prototype with Pydantic response models, CLI analysis, evaluation cases, and streaming demo, but minimal documentation and no tests, CI, license, or demonstrated adoption.
nikhil00shinde /
AI-Interview-Coach
A documented, small Python CLI that uses OpenAI streaming and Pydantic validation to generate five-question mock interviews, but it has no tests, CI, license, or demonstrated adoption.
nikhil00shinde /
nikhil00shinde
A documented GitHub profile configuration repository with 1 star and no sampled source files; it presents a professional skills and contact README but offers little evidence of a substantive software product.
nikhil00shinde /
lld
A small Java LLD practice repository with several design exercises, but no adoption signals, documentation, tests, CI, or license; sampled implementations contain multiple compile-time and logic errors.
nikhil00shinde /
MusicBook
MusicBook is an effectively empty 1 KB repository with a single commit and no fetched source files, documentation, tests, CI, or product evidence beyond its description.
nikhil00shinde /
Build-a-Custom-Agent-Harness-from-scratch
Empty repository scaffold with only a minimal README, no source files, tests, CI, license, or gitignore, and one sampled recent commit.
nikhil00shinde /
url-shortner-typescript
Empty repository with no source files, documentation, tests, CI, license, or recorded commits.
06 · Timeline
- Apr 22, 2021Joined GitHub
- May 4, 2021Created nikhil00shinde — Config files for my GitHub profile.
- May 23, 2025Created dotfiles — My simple dotfiles
- Dec 26, 2025Created lld — lld problems java
- Apr 7, 2026Created Distributed-Task-Queue
- Jun 10, 2026Created url-shortner-typescript
- Jul 4, 2026Created AI-Interview-Coach
- Jul 7, 2026Created Build-a-Custom-Agent-Harness-from-scratch
- Jul 26, 2026Created neetcode-submissions — My NeetCode.io problem submissions
- Aug 9, 2026Created Incident-triage-agent
- Sep 4, 2026Created MusicBook — Generate Music Using AI and Voice
- Sep 4, 2026Most recent push to MusicBook
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.