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d-o-hub

codebase-consolidation

by d-o-hub

A modular Rust-based self-learning episodic memory system for AI agents, featuring hybrid storage with Turso (SQL) and redb (KV), async execution tracking, reward scoring, reflection, and pattern-based skill evolution. Designed for real-world applicability, maintainability, and scalable agent workflows.

3🍴 0📅 Jan 23, 2026

SKILL.md


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

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

DimensionFocus
Code DuplicationFind duplicate/similar code blocks
Architectural StructureSystem architecture and component relationships
Code OrganizationModule structure and separation of concerns
Refactoring OpportunitiesLarge files, complex functions
Technical DebtTODOs, missing tests, outdated deps
Quality MetricsLOC, complexity, coverage
Design PatternsPatterns and anti-patterns in use
Cross-Cutting ConcernsError 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.

Score

Total Score

75/100

Based on repository quality metrics

SKILL.md

SKILL.mdファイルが含まれている

+20
LICENSE

ライセンスが設定されている

+10
説明文

100文字以上の説明がある

+10
人気

GitHub Stars 100以上

0/15
最近の活動

1ヶ月以内に更新

+10
フォーク

10回以上フォークされている

0/5
Issue管理

オープンIssueが50未満

+5
言語

プログラミング言語が設定されている

+5
タグ

1つ以上のタグが設定されている

+5

Reviews

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Reviews coming soon