01 · Roasts
Serial Repo Abandoner
metaflow-nomad: 0 KB, 0 files, apparently created just to occupy a namespace. Meanwhile hsm-network went from zero to 2.5 MB in 5 days and then vanished. You don't abandon projects — you ghost them before they even meet your friends.
The 500-TPS Fantasist
upi claims '500 TPS production-grade capability' in the README but has 0 forks, 1 star (probably yours), no CI, no tests, and was fully abandoned after 6 days. The architecture doc is longer than the git log.
CI? Never Heard of Her
Zero repos with CI enabled across the entire account. You've written async ReAct loops, CUSUM drift monitors, and Ed25519 handshakes — but a GitHub Actions YAML file remains your final boss.
Heatmap Archaeologist
Your contribution heatmap looks like a lunar calendar — mostly dark voids with occasional isolated bursts. 184 commits in a year across 56 repos averages to 3.3 commits per repo. Quantity is not a strategy.
License? What License?
Not a single production repo has a license. sktime-agentic, hsm-network, upi — all license-free. Legally speaking, nobody can use any of your code. 'just a learner' indeed.
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% weight55D
- Consistency20% weight35F
- Quality20% weight69C
- Depth15% weight50D
- Breadth10% weight65C
- Community10% weight40D
03 · Stats
365-day commit heatmap
88 active days
Language distribution
- Python53%
- TypeScript31%
- Go7%
- JavaScript3%
- CSS1%
- C1%
- Other4%
04 · Numbers
Owned repos
non-fork
27
Commits
last 12 months
184
Followers
7
Joined GitHub
Jul 2021
05 · Top repos
nXtCyberNet /
upi
Sophisticated real-time UPI fraud detection engine in TypeScript/Python with Neo4j graph analytics, Redis streams, and explainable risk scoring. Production-grade architecture with comprehensive feature extractors and collusive fraud detection, but nascent project (6 days old) with no external visibility or adoption sig
nXtCyberNet /
sktime-agentic
Agent-driven time-series forecasting system using LLM + sktime + MCP tools. Python async orchestrator with drift monitoring, watchdog retraining, and FastAPI serving. Experimental stage with 0 stars; core cold-start demo (airline dataset) verified working.
nXtCyberNet /
hsm-network
ForgeSentinel: hardware-rooted tamper-proof private log mesh with Go backend, Ed25519+X25519 crypto, SQLite append-only chain, and Pico/mobile clients. 5 days old, ~2.5MB codebase with protocol layer, server, clients, firmware, and docs. No CI, no tests, no license—experimental research project exploring hardware-backe
nXtCyberNet /
upi-quant
Early-stage TypeScript fraud detection system for UPI payments with ambitious architecture (Neo4j, Redis Streams, GDS algorithms) documented in detailed README, but no implementation files sampled, no tests/CI, no license, created 2 days ago with only 8 commits.
nXtCyberNet /
nXtCyberNet
Personal portfolio README-only repo (21 KB) with no source code, tests, CI, or build artifacts. Serves as professional introduction rather than a shipping project. Last push 2026 suggests activity, but no executable content to evaluate.
nXtCyberNet /
Ntg
Empty scaffold created Feb 18, 2026 with single commit, no files sampled, no README, tests, CI, or documentation. Appears to be a placeholder/initial repo dump with no substantive content.
nXtCyberNet /
metaflow-nomad
Empty scaffold repo with 0 stars, 0 commits, created 2026-03-29. No files, no README, no documentation, no license, no tests. Appears to be an unused placeholder.
06 · Timeline
- Jul 4, 2021Joined GitHub
- Sep 22, 2025Created nXtCyberNet
- Feb 12, 2026Created upi-quant
- Feb 14, 2026Created upi
- Feb 18, 2026Created Ntg
- Mar 29, 2026Created metaflow-nomad
- Apr 4, 2026Created sktime-agentic
- Apr 19, 2026Created hsm-network
- Apr 29, 2026Most recent push to sktime-agentic
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.