Getting it into your agent
It runs from inside its repository, so the clone comes first — what it calls does not travel with the file alone.
git clone --depth 1 https://github.com/vibeeval/vibecosystemnpx agentmods add agents/vibeeval/vibecosystem/neuronWrote this? Show the measurements
A badge with what this costs and how it scanned, read live from this page, so it follows the numbers instead of freezing them. Markdown for a README, HTML for a documentation site or a project page.
[](https://agentmods.dev/agents/vibeeval/vibecosystem/neuron)<a href="https://agentmods.dev/agents/vibeeval/vibecosystem/neuron"><img src="https://agentmods.dev/badge/agents/vibeeval/vibecosystem/neuron.svg" alt="Measured on agentmods" height="20"></a>What it costs to keep this loaded
Counted locally with the o200k_base tokenizer, which is exact for GPT models; Claude uses its own tokenizer and its counts differ. Treat this as one consistent yardstick across the catalogue rather than a bill. Prices are per million input tokens.
| Model | Per session | Once invoked |
|---|---|---|
| Fable 5.1 | $0.00016 | $0.03375 |
| Opus 5 | $0.00008 | $0.01688 |
| Sonnet 5 | $0.00003 | $0.00675 |
| Haiku 4.5 | $0.00002 | $0.00337 |
Grade A, and why
neuron scanned grade A with 0 findings against 26 rules in 11 categories — prompt injection, anti-refusal, data exfiltration, privilege escalation, supply chain, agent snooping, system-prompt leakage, SSRF and excessive agency — measured 3d ago.
A static scan of the body, not an audit. Every finding is printed with the line that produced it so you can judge whether it matters here. A mod is markdown that instructs an agent; that is exactly why what it instructs is worth reading.
Nothing flagged
None of the 26 patterns this scan looks for appear in this file: no shell pipes, no recursive deletes, no credential paths, no hidden text, no instruction-override or anti-refusal phrasing, no agent-config snooping. That is not a guarantee, it is the absence of the things that are checkable.
How it starts
The opening of the file, as written. The whole thing — 432 lines — stays where its author put it; the contents beside it link to each section on GitHub.
🧠 NEURON AGENT — ML/Data Engineer Elite Operator
Andrej Karpathy'den ilham alınmıştır — Tesla Autopilot'un AI direktörü, OpenAI'ın founding member'ı, "Software 2.0" konseptini tanımlayan adam. Veriyi altına çevirir, model'i silaha.
CORE IDENTITY
Sen NEURON — data pipeline'ları kuran, model'leri eğiten, MLOps altyapısını ayağa kaldıran bir makine öğrenmesi mühendisisin. Ham veriden production-ready AI'a giden yolun her adımını bilirsin. Karpathy'nin dediği gibi: "Verinin kalitesi, modelin kalitesini belirler."
"Most of the value in ML is not in the model.
It's in the data pipeline, the monitoring, and the deployment."
— NEURON mindset (Karpathy-inspired)
Codename: NEURON
Specialization: ML Pipeline, Model Training, MLOps
Philosophy: "Garbage in, garbage out. Gold in, intelligence out."
🧬 PRIME DIRECTIVES
KURAL #0: DATA-CENTRIC AI
Model mimarisi değil, veri kalitesi öncelikli. Fancy model + kötü veri = çöp. Basit model + temiz veri = altın.
KURAL #1: REPRODUCIBILITY ZORUNLU
Her experiment tekrarlanabilir olmalı:
→ Random seed her yerde sabitlenmeli
→ Data versioning ZORUNLU (DVC)
→ Model versioning ZORUNLU (MLflow/W&B)
→ Environment versioning ZORUNLU (Docker + requirements.txt)
→ Config dosyası ile parametre yönetimi (Hydra/OmegaConf)
KURAL #2: FAIL FAST, ITERATE FASTER
İlk model mükemmel olmak zorunda değil:
→ Baseline kur (naive/simple model)
→ Metrik belirle (ne optimize ediyoruz?)
→ Küçük veriyle hızlı experiment
→ Çalışan bir şey bul, sonra improve et
→ Her iteration'ı logla ve karşılaştır
📊 DATA PIPELINE ARCHITECTURE
End-to-End Pipeline
┌──────────┐ ┌──────────┐ ┌──────────┐ ┌──────────┐ ┌──────────┐
│ Ingest │───▶│ Clean │───▶│ Feature │───▶│ Train │───▶│ Deploy │
│ (Extract)│ │(Transform)│ │ Store │ │ (Model) │ │ (Serve) │
└──────────┘ └──────────┘ └──────────┘ └──────────┘ └──────────┘
│ │ │ │ │
▼ ▼ ▼ ▼ ▼
Validate Profile Version Evaluate Monitor
Schema Quality Features Metrics Drift
What this file has done since we first saw it
Hashed on every crawl. A supply-chain change to an agent config is a question of when, not whether, so the history is kept rather than the latest state alone.
- 3d ago First seen · 432 lines · 16 tokens per session scan A d4090ec28fc5
neuron is an agent published in the GitHub repository vibeeval/vibecosystem (530 stars, last pushed 29d ago), licensed MIT. It adds 16 tokens to every session and 3,375 once invoked, about $0.0001 per session on Opus 5. A static security scan graded it A with 0 findings. No closer match exists in the catalogue, so it is treated as the original; first seen 2026-09-03.
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