01 · Roasts
Docker, meet test suite
gym-program, Karamoozi, and django_dr_eskandari can orchestrate containers, but their test and CI coverage is essentially a ghost story.
Tutorial archive energy
telegram_bot weighs 22,560 KB, yet sampled core/main.py is largely commented experiments rather than a focused bot.
One-shot speedrun
The prime checker and keygen utility each show 2 sampled commits and same-day development windows: shipped fast, then vanished.
Security plot twist
Karamoozi hard-codes DEBUG=True and a django-insecure secret; keygen-clude-flare writes private keys with NoEncryption().
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% weight56D
- Consistency20% weight60C
- Quality20% weight37F
- Depth15% weight55D
- Breadth10% weight65C
- Community10% weight50D
03 · Stats
365-day commit heatmap
38 active days
Language distribution
- Jupyter Notebook37%
- HTML27%
- Python20%
- JavaScript10%
- CSS5%
- C++1%
04 · Numbers
Owned repos
non-fork
20
Commits
last 12 months
113
Followers
13
Joined GitHub
Sep 2024
05 · Top repos
Rezanikmanesh-79 /
django_dr_eskandari
A substantial but low-adoption Django repository combining social networking, blog, and shop implementations, with Docker documentation and domain models but limited verification and inconsistent polish.
Rezanikmanesh-79 /
gym-program
A small Django gym-management application with accounts, trainer/member workflows, workout plans, ticketing, and Docker deployment, but limited adoption and no documentation, tests, CI, or license.
Rezanikmanesh-79 /
auto-whatsapp-GROUP-ender
A small, early-stage Python/PyQt6 and Playwright WhatsApp group automation tool with GUI CRUD, scanning, categorization, and sending workflows, but no documentation, tests, CI, or typed-language classification.
Rezanikmanesh-79 /
Karamoozi
A small Django/OpenAI storefront chatbot with Playwright product scraping and Docker setup; it shows useful implementation scope but little public adoption, documentation, testing, or delivery automation.
Rezanikmanesh-79 /
telegram_bot
A 22,560 KB Python Telegram-bot learning repository with tests, Apache-2.0 licensing, and dependency pinning, but the sampled core/main.py is predominantly commented tutorial experiments rather than a focused production bot.
Rezanikmanesh-79 /
funn-class
Small educational Python repository demonstrating decorators, class methods, properties, and static methods; it has a minimal README and Apache-2.0 license but no tests, CI, typed code, or evidence of external adoption.
Rezanikmanesh-79 /
learn-some-html-and-css
A small Persian cafe landing page with responsive HTML/CSS, menu, contact and reservation forms, plus a separate homework exercise; it is functional-looking but undocumented and lacks tests, CI, and typed code.
Rezanikmanesh-79 /
Rezanikmanesh-79
A polished GitHub profile README documenting backend, Linux, networking, and security interests, but the repository contains no demonstrated software, tests, CI, license, or adoption beyond 4 stars.
Rezanikmanesh-79 /
keygen-clude-flare
A compact single-file Python utility that automates Cloudflare DNS-01 Let's Encrypt certificate issuance for one hardcoded domain, but has minimal repository hygiene and no demonstrated adoption or sustained development.
Rezanikmanesh-79 /
build-a-prime-number-checker-module
A small JavaScript CommonJS module exporting a basic primality check, with minimal packaging but no documentation, tests, CI, or evidence of adoption.
06 · Timeline
- Sep 24, 2024Joined GitHub
- Jun 27, 2025Created django_dr_eskandari
- Jun 30, 2025Created Karamoozi
- Jul 26, 2025Created Rezanikmanesh-79
- Sep 10, 2025Created funn-class
- Feb 9, 2026Created telegram_bot
- Apr 24, 2026Created learn-some-html-and-css
- Jun 23, 2026Created keygen-clude-flare
- Jul 30, 2026Created gym-program
- Aug 20, 2026Created auto-whatsapp-GROUP-ender
- Sep 6, 2026Created build-a-prime-number-checker-module
- Sep 6, 2026Most recent push to build-a-prime-number-checker-module
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.