codebase-consolidation

name	codebase-consolidation
description	Analyze, consolidate, and document codebases through multi-perspective analysis. Use when reviewing project structure, planning refactoring, creating documentation, or assessing technical debt.

Codebase Consolidation & Analysis

Systematically analyze codebases to identify consolidation opportunities, document architecture, and generate actionable insights.

Quick Reference

• Analysis Dimensions - 8 analysis dimensions with detailed criteria
• Consolidation Patterns - Common refactoring patterns with examples
• Report Templates - Output format templates

When to Use

• Starting on a new codebase - Understand structure quickly
• Planning refactoring - Identify consolidation opportunities
• Code review preparation - Comprehensive analysis before changes
• Documentation needs - Generate architecture docs
• Technical debt assessment - Quantify and prioritize improvements
• Onboarding new developers - Create codebase overview
• Pre-release audits - Quality and security review

Don't use for: Single file analysis, quick bug fixes, simple feature additions

Core Purpose

Comprehensive codebase analysis:

• Code Duplication - Find duplicate code for consolidation
• Architectural Analysis - Document system structure and patterns
• Refactoring Opportunities - Identify improvement areas
• Technical Debt Assessment - Quantify and prioritize debt
• Documentation Generation - Create architecture diagrams and docs
• Multi-Perspective Analysis - Review from architect, developer, product views
• Quality Metrics - Complexity, coverage, maintainability

Analysis Dimensions

Dimension	Focus
Code Duplication	Find duplicate/similar code blocks
Architectural Structure	System architecture and component relationships
Code Organization	Module structure and separation of concerns
Refactoring Opportunities	Large files, complex functions
Technical Debt	TODOs, missing tests, outdated deps
Quality Metrics	LOC, complexity, coverage
Design Patterns	Patterns and anti-patterns in use
Cross-Cutting Concerns	Error handling, logging, security

See analysis-dimensions.md for detailed criteria.

Analysis Workflow

1. Discovery - Project structure, file counts, configuration
2. Dependency Analysis - cargo tree, outdated, audit
3. Duplication Detection - Large files, tech debt markers
4. Complexity Analysis - LOC statistics, long functions
5. Architecture Mapping - Components, dependencies, data flow
6. Quality Assessment - Coverage, linting, formatting
7. Documentation Review - Doc generation, API documentation
8. Synthesis - Comprehensive report with recommendations

Output Formats

• Executive Summary - Health score, key metrics, priorities
• Architecture Documentation - System diagram, patterns, data flows
• Refactoring Roadmap - Phased plan with tasks and estimates
• Technical Debt Report - Quantified debt, payoff strategy
• Onboarding Document - Developer guide to codebase

See report-templates.md for complete templates.

Best Practices

✓ Start with high-level structure, use automated tools, prioritize findings, provide concrete examples with file paths, estimate effort, consider multiple perspectives

✗ Don't analyze without clear goals, only report problems, provide generic advice, ignore context, recommend big rewrites, overwhelm with detail

See consolidation-patterns.md for refactoring patterns and examples.