01 · Roasts
Security Hazard Alert
social-media has hardcoded secrets in settings.py, plaintext password storage, AND SQL injection risks — all in one repo. That's a hat-trick of OWASP Top 10 violations.
README? Never Heard of Her
7 out of 10 scored repos have no README or a boilerplate Vite template copy-paste. The one 'profile README' you made is entirely commented out. Truly a mystery to future employers.
Test Coverage: 0%
Across 24 public repos — robotics pipelines, a RISC-V simulator, a full-stack AI app — not a single test file exists. datanexus has LangGraph agents and DuckDB and still no tests. Impressive commitment to flying blind.
Ship It and Ghost It
voyagerpro has 2 years of commits and 3,386 KB of Three.js and Gemini AI code, yet the README is still the default Vite template. Two years and you couldn't write one paragraph about what it does.
Solo Artist
93% solo commits, 0 PRs opened, 0 issues filed, 5 followers — you are coding in a soundproof room. The GitHub social graph doesn't know you exist.
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% weight52D
- Depth15% weight55D
- Breadth10% weight65C
- Community10% weight25F
03 · Stats
365-day commit heatmap
70 active days
Language distribution
- HTML33%
- Python24%
- JavaScript20%
- CSS11%
- TypeScript8%
- PHP1%
- Other3%
04 · Numbers
Owned repos
non-fork
23
Commits
last 12 months
105
Followers
5
Joined GitHub
Mar 2023
05 · Top repos
Sairoxs123 /
datanexus
Experimental full-stack data analytics app (Tauri+React+Python) with AI-powered SQL generation via LangGraph. Typed, structured, documented, but unproven adoption (0 stars, 0 forks). ~2mo old with 12 commits in last 30 days.
Sairoxs123 /
co-project
RISC-V assembler and simulator framework for coursework testing. Untyped Python (TYPED_LANG=no), no tests/CI/license, but well-structured modules (parser, decoder, execution, globals) with meaningful README, ~40 commits over 6 weeks of active development.
Sairoxs123 /
scrubby-grp5
Educational robotics project for autonomous whiteboard cleaning using YOLO object detection, ArUco markers, and a custom robot platform. Modular Python codebase with vision pipeline, serial communication, and multi-threaded scheduling, but lacks tests, CI, and production-ready documentation.
Sairoxs123 /
voyagerpro
Personal React+Vite travel planning web app with AI integration (Gemini), user auth, dashboard, and 3D visualization. No tests, CI, or production evidence. Experimental indie project.
Sairoxs123 /
attendance-app
React Native attendance management app with backend integration. Lacks README, tests, CI, and license. Untyped JavaScript with functional but loosely-structured components and hardcoded API endpoints.
Sairoxs123 /
custom-shell
Educational C shell implementation with working command execution, history navigation, and raw-mode terminal control. Lacks documentation, tests, and structured error handling.
Sairoxs123 /
voyage_backend
Django travel-planning backend with Gemini API integration. Zero adoption, no docs/tests/CI, minimal structure. Experimental prototype stage with hardcoded secrets and incomplete error handling.
Sairoxs123 /
social-media
Unpolished Django social media prototype with fundamental security flaws, missing documentation, untested code, and minimal architectural rigor. No README, no CI/tests, hardcoded secrets in settings.py, plaintext password storage, and SQL injection risks.
Sairoxs123 /
cv-simulator
Empty scaffold with minimal OpenGL triangle demo. Created and pushed within 7 minutes on 2026-04-26, no README, no tests, no CI, no commits beyond initial dump. Single practice exercise with boilerplate setup.
Sairoxs123 /
Sairoxs123
GitHub profile README scaffold with default template only; 1KB repo created Feb 2026 with single commit. No substantive code, documentation, or project content.
06 · Timeline
- Mar 10, 2023Joined GitHub
- Feb 27, 2024Created voyagerpro
- Feb 28, 2024Created voyage_backend
- Mar 16, 2024Created social-media
- Mar 16, 2024Created attendance-app
- Jan 19, 2026Created custom-shell
- Jan 30, 2026Created scrubby-grp5
- Feb 1, 2026Created datanexus
- Feb 15, 2026Created Sairoxs123
- Feb 28, 2026Created co-project
- Apr 26, 2026Created cv-simulator
- Apr 26, 2026Most recent push to cv-simulator
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.