Production-Ready Repository Analysis

Description

This prompt instructs AI coding assistants to conduct an in-depth code repository analysis and produce two complete, production-ready deliverables: a self-contained interactive HTML architecture visualizer and a structured JSON representation of nodes, edges, and flows.

Prompt

Analyze my entire code repository in depth.

Generate two complete, production-ready deliverables:

1. A single self-contained HTML file that includes:
- an interactive architecture diagram
- a flow panel
- tooltips
- a responsive design

2. A JSON file containing:
- nodes
- edges
- flows

The analysis must identify and represent the repository's architecture, modules, services, dependencies, integrations, data flows, and relationships between components. The output should be accurate, structured, and immediately usable for both human stakeholders and AI agents.

Output Example

{
  "nodes": [
    { "id": "api-gateway", "label": "API Gateway", "type": "service", "layer": "ingress" },
    { "id": "auth-service", "label": "Authentication Service", "type": "module", "layer": "core" },
    { "id": "db", "label": "PostgreSQL Database", "type": "database", "layer": "persistence" }
  ],
  "edges": [
    { "from": "api-gateway", "to": "auth-service", "relation": "routes request" },
    { "from": "auth-service", "to": "db", "relation": "reads/writes" }
  ],
  "flows": [
    {
      "name": "User Authentication Flow",
      "steps": ["api-gateway -> auth-service", "auth-service -> db"]
    }
  ]
}

Explanation

Key Features

1. Comprehensive Repository Analysis: Deeply inspects architecture, modules, services, dependencies, and integrations.
2. Dual Production-Ready Deliverables: Generates both an interactive, self-contained HTML visualizer with diagrams, tooltips, and flow panels, and a machine-readable JSON structure.
3. Multi-Stakeholder Utility: Designed to be instantly actionable for human developers, architects, and AI agent workflows alike.
4. Interactive Architecture Visualizer: The HTML output features responsive design, interactive nodes, and detailed tooltips for exploring complex system relationships.
5. Structured Data Representation: Clean node-edge-flow schema in JSON makes downstream automation and documentation seamless.

Best Use Cases

• Architectural auditing and documentation of large codebases
• Onboarding new engineers with interactive system maps
• Preparing structured knowledge graphs for AI agent consumption and code analysis
• Visualizing complex microservices or monolithic module interactions

Tips to Get the Best Results

• Provide repository context or key entry points in the prompt if the codebase is massive.
• Specify preferred styling frameworks or styling preferences (e.g., Tailwind CSS) for the HTML deliverable if desired.
• Use the generated JSON file as input for automated documentation generators or CI/CD architecture checks.