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#92 — Top 94.7%

shramanb113

S Banerjee

B

Solid engineer

Overall

0.0

/ 100

01 · Roasts

The CI split-brain

ZeroCache and courier-rto-fraud-audit automate the boring stuff; ZENITH's 35-star search engine still ships without CI.

Stars chose one child

ZENITH holds 35 of 42 total stars. The rest of the portfolio is shipping hard while adoption remains mostly theoretical.

Horizontal systems gremlin

168 recent commits across analyzed repos, 849 commits this year, and projects ranging from LSM storage to WASM board games: focus is apparently optional.

Bug report with teeth

mastra-issues contains four runnable high-severity reproductions, but its test command exits 1 and CI is absent. Chaos, documented.

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

  • Impact
    25% weight
    68C
  • Consistency
    20% weight
    65C
  • Quality
    20% weight
    75B
  • Depth
    15% weight
    60C
  • Breadth
    10% weight
    80A
  • Community
    10% weight
    50D

03 · Stats

365-day commit heatmap

140 active days

Less
More

Language distribution

7 langs
  • Rust33%
  • TypeScript30%
  • Go23%
  • Astro3%
  • CSS3%
  • HTML2%
  • Other6%

04 · Numbers

Owned repos

non-fork

22

Commits

last 12 months

849

Followers

6

Joined GitHub

Jan 2020

05 · Top repos

shramanb113 /

ZeroCache

61/100

ZeroCache is a substantial, documented Rust workspace for embedding and LLM-response caching, with provider adapters, Redis/sled backends, semantic search, streaming, a dashboard, demos, tests, CI, and a scratch Docker runtime; adoption remains minimal at 1 star.

I25Q78D60
READMETestsCITyped
Rust119d ago

shramanb113 /

courier-rto-fraud-audit

56/100

A documented, tested Python analytics application with Polars feature engineering, K-Means courier risk clustering, Streamlit visualization, SQL persistence, S3 ingestion, Docker packaging, and CI; adoption remains un demonstrated at 0 stars.

I20Q75D50
READMETestsCI
Python018d ago

shramanb113 /

Zerops-Swarmforge

54/100

SwarmForge is a substantial TypeScript Zerops hackathon system: a Fastify control plane coordinates Architect, Coder, and Deployer agents through NATS, Postgres, Valkey, generated code checks, and deployment manifests.

I35Q68D50
READMETestsTyped
TypeScript01mo ago

shramanb113 /

ZENITH

53/100

ZENITH is a substantial Go search engine combining hybrid ranking, fuzzy and semantic retrieval with WAL/LSM persistence, a CLI, gRPC service, and embedded ONNX inference; it is well documented and tested but lacks CI, licensing, and broad adoption evidence.

I30Q68D50
READMETestsTyped
Go353mo ago

shramanb113 /

9-men-morris

51/100

A shipped Nine Men's Morris product with a live playmorris.vercel.app browser UI, typed Rust engine/WASM adapter, React frontend, and unusually careful rule/search implementation, but only 1 star and no CI, license, or repository-level test automation.

I55Q62D35
READMETyped
Rust11mo ago

shramanb113 /

mastra-issues

39/100

A focused, well-documented JavaScript repository containing four high-severity, runnable Mastra bug reproductions with automated checks, but little demonstrated adoption and no formal test suite, CI, license, or typed implementation.

I22Q48D45
README
JavaScript125d ago

shramanb113 /

shramanb113

32/100

A polished GitHub profile repository centered on a detailed README, with named projects and OSS contributions documented, but no source code, tests, CI, or demonstrated adoption for this repo itself.

I22Q38D35
README
Unknown01mo ago

06 · Timeline

  1. Jan 19, 2020
    Joined GitHub
  2. Jan 24, 2026
    Created shramanb113 — My github front page
  3. Feb 1, 2026
    Created ZENITH — From scratch search engine in Go - no Elasticsearch, no Lucene, just LSM trees, and hybrid ranking
  4. Jul 8, 2026
    Created courier-rto-fraud-audit
  5. Jul 22, 2026
    Created mastra-issues — All of the mastra issues are to be bundled here
  6. Jul 23, 2026
    Created ZeroCache — Cache your embeddings, not your compute bill - Rust-native, provider-pluggable cache for RAG ingestion pipelines.
  7. Aug 8, 2026
    Created Zerops-Swarmforge — Multi-Agent engineering team using Zerops
  8. Aug 14, 2026
    Created 9-men-morris — Game engine for 9 men morris inspired by POPO
  9. Sep 2, 2026
    Most recent push to courier-rto-fraud-audit

07 · Compare

github.com/
shramanb113 · 6dmedian coder

08 · Rubric

How this score was produced

Overall = Σ (category × weight) + gentle top-end curve

CategoryWeightScoreContrib.
Raw total67.0
Top-end curve+5.9
Final overall72.9

Tier thresholds

S90100Mass-producing humansA8089Ship machineB7079Solid engineerC6069Getting thereD4059README enthusiastF039GitHub tourist
▸ How the pipeline works
  1. 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.
  2. 02Triage.A small model reads every repo's file tree + README and picks the 20 files per repo that actually reveal how you code.
  3. 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.
  4. 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.
  5. 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.
shramanb113 · 72.9/100 — Rate My GitHub