BACKEND ARCHITECT NEURAL

An AI-native backend engineering knowledge base.

Organized as a neural knowledge graph — every file is a neuron with a defined purpose, activation conditions, and causal relationships. Built for AI agents (Claude, ChatGPT, Cursor, Codex, MiMoCode, and others) to dynamically activate only the relevant knowledge, apply quality gates, and produce production-grade decisions with reasoning traces.

Repository: https://github.com/mobinhasanghasemi/backend-skill

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Table of Contents

• What is this?
• How it Works — Neural Architecture
  • Cognitive Pipeline
  • Routing & Activation
  • The 4 Quality Engines
  • Evidence Protocol
  • Code Tiers
  • Architecture Linter
• What’s Inside
  • Knowledge Domains
  • Protocols & Engines
  • Brain
  • Playbooks
  • Checklists
  • Integrity Tools
• Installation
• How the Agent Should Use It
• Strengths & Current Limitations
• License

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What is this?

This is a pure Markdown knowledge base with zero dependencies. It’s called “Neural” because it’s modeled as a brain:

• Neurons = individual Markdown files, each containing a focused piece of backend knowledge.
• Root neurons (ROOT.md) = routers that tell the agent which child neurons to read.
• Graph = explicit connections between neurons (depends_on, influences, conflicts_with, etc.).
• Engines = always-on quality layers that filter every decision through security, simplicity, performance, and reliability lenses.
• Evidence = every claim carries a confidence label (VERIFIED / SUPPORTED / INFERRED / EXPERIMENTAL / UNCERTAIN), preventing hallucination.

The agent never reads the whole tree. It follows the graph, activating only the neurons relevant to the question.

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How it Works — Neural Architecture

Cognitive Pipeline

When the agent receives a question, it follows this pipeline (defined in BRAIN.md):

USER INPUT
    │
    ▼
PERCEPTION              ← parse problem, language, scope
    ▼
PROBLEM CLASSIFICATION  ← what kind of problem is this?
    ▼