01 · Roasts
Scaffold Graveyard
data-ai and insyte were created, 'pushed', and immediately abandoned — both in the same second. That's not a project, that's a git init with commitment issues.
The 9-Hour Architect
pm-feedback has a D1 schema, Vectorize, Workers Workflows, and 20+ React components… built across 6 commits in 9 hours. Sir, that's a demo reel, not a product.
179 Commits, Zero PRs
You made 179 commits this year and opened exactly 0 pull requests to anyone else's code. The open-source world doesn't know you exist yet.
README-Driven Development
big-d has a 16 MB repo with Voronoi diagrams, gravity models, and network centrality planned in the README — and zero sampled implementation files. The ambition-to-execution ratio is astronomical.
Burst Builder
nectar: created April 18, abandoned April 19. via: 2 commits in 1 day. pm-feedback: 6 commits in 9 hours. Your git log looks like a series of energy drink-fueled sprints followed by weeks of silence.
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% weight35F
- Quality20% weight62C
- Depth15% weight50D
- Breadth10% weight65C
- Community10% weight25F
03 · Stats
365-day commit heatmap
76 active days
Language distribution
- Jupyter Notebook28%
- TypeScript26%
- Python19%
- JavaScript13%
- Swift8%
- CSS5%
- Other1%
04 · Numbers
Owned repos
non-fork
21
Commits
last 12 months
179
Followers
2
Joined GitHub
Feb 2025
05 · Top repos
sid-081205 /
pm-feedback
Cloudflare-native feedback analysis SPA (React + Vite frontend, TypeScript Workers backend, D1 + Workers AI + Vectorize). Feature-complete demo product with structured architecture, typed code, README, and tests—but zero stars, created April 2026, just 6 commits in 9 hours. Personal/portfolio project in experimental ph
sid-081205 /
macro
Personal iOS fitness/nutrition tracker with barcode scanning and workout logging. No stars, but has typed Swift code, SwiftData persistence, structured multi-view UI, and README. Recently active (April 2026) with ~114kb codebase.
sid-081205 /
ds-showcase
Jupyter Notebook-based data science portfolio project exploring music analysis and mood profiling via Spotify integration, with 21 commits over ~2.5 months and 59MB codebase, but lacks CI, tests infrastructure clarity, and typed code.
sid-081205 /
chatty-bot
A WhatsApp AI chatbot using Baileys and Gemini API with message batching, contact management, and chat history. Personal project created 2026-04-01 with minimal commit depth and no production indicators.
sid-081205 /
nectar
A lightweight interactive matching simulation tool (9 KB) exploring pair formation under tolerance rules, with live visualization and assortativity stats. Shipped with functional UI but minimal documentation and no tests.
sid-081205 /
big-d
Early-stage London transport planning analysis project with ambitious scope (Voronoi, gravity models, network analysis) but only a README outline; no implementation files sampled, no tests/CI/license, 3 commits in 4 days suggests exploratory phase.
sid-081205 /
via
Empty scaffold repo: 2 KB size, minimal README, no code files sampled, 2 commits in 1 day, no tests/CI/license/gitignore. Appears to be an abandoned initial commit dump.
sid-081205 /
data-ai
Empty scaffold repository with zero commits, no files, no documentation, and no discernible project intent. Created 2026-03-04 with only initial commit.
sid-081205 /
insyte
Empty scaffold with no README, no source files, and identical creation/push timestamps. No evidence of substantive work or implementation.
06 · Timeline
- Feb 6, 2025Joined GitHub
- Nov 21, 2025Created ds-showcase
- Jan 1, 2026Created macro
- Mar 4, 2026Created insyte — automate the entire data science pipeline
- Mar 4, 2026Created data-ai
- Mar 6, 2026Created big-d
- Apr 1, 2026Created pm-feedback — Customer Feedback Analyser
- Apr 1, 2026Created chatty-bot
- Apr 18, 2026Created nectar
- Apr 19, 2026Created via
- Apr 26, 2026Most recent push to macro
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.