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Architecture (Conceptual)

This document describes the intended architecture of Github Engine at a conceptual level. It does not represent implemented production modules yet.

Design Intent

Github Engine will evolve as a modular system where each layer has a clear responsibility and explicit handoff to the next layer.

Proposed Modules

1) Project Scanner

Responsibility: Inspect local repository files, directory structures, and foundational metadata.
Output: Raw repository signals (structure, scripts, configs, docs presence).

2) Repository Understanding Layer

Responsibility: Convert raw scan signals into an interpretable project model.
Output: Structured representation of project intent, architecture cues, and maturity indicators.

3) README Composer

Responsibility: Generate or improve README content based on observed repository state and project model.
Output: Suggested README updates with rationale.

4) Test & Quality Snapshotter

Responsibility: Aggregate latest test, build, and quality outputs into concise summaries.
Output: Normalized quality status snapshot.

5) Similar Project Research Layer

Responsibility: Analyze comparable projects and patterns in the same domain to inform recommendations.
Output: Comparative insights and pattern references.

6) MCP Recommendation Engine

Responsibility: Produce high-confidence, prioritized recommendations by combining repository understanding with MCP-sourced context.
Output: Improvement proposals spanning architecture, workflow, docs, and delivery quality.

7) Report Generator

Responsibility: Assemble outputs into consistent reports for builders and teams.
Output: Human-readable and machine-consumable repository intelligence reports.

8) Future CLI/API Surface

Responsibility: Expose engine capabilities to local workflows, automation systems, and platform integrations.
Output: Stable interfaces for execution, retrieval, and orchestration.

Module Interaction Flow

  1. Project Scanner collects repository signals.
  2. Repository Understanding Layer builds the project model.
  3. README Composer and Test & Quality Snapshotter generate domain outputs.
  4. Similar Project Research Layer enriches context.
  5. MCP Recommendation Engine prioritizes actionable improvements.
  6. Report Generator compiles results for delivery.
  7. CLI/API surfaces provide execution and integration paths.

Architectural Principles

  • Separation of responsibilities across layers
  • Traceable recommendation provenance
  • Extensible integration points for MCP and ecosystem connectors
  • Output formats that are easy to consume by both humans and automation