Skip to main content
codebase-analysis-patterns Comprehensive codebase analysis framework covering 14 technical areas including system overview, architecture, modules, integrations, workflows, and deployment patterns. Use when analyzing a codebase structure for documentation or understanding.
インストールへ移動 Skills Marketplace コミュニティが作成したAIスキルを発見・探索
Codex または Claude でインストール この Prompt をコピーして Codex、Claude、または他のアシスタントに貼り付けると、Skill ページを確認してインストールできます。
直接コマンドでは確認用 Prompt が省略されます。実行前にソースを確認してください。
npx skills add https://github.com/gravity9-tech/claude_code_marketplace_demo --skill codebase-analysis-patternsコマンドは1行のまま表示されます。コピー前に横へスクロールして全体を確認してください。
ローカルで確認しますか？SkillsMP が現在取得できるファイルをダウンロードできます。
Zipをダウンロード ダウンロード中... name codebase-analysis-patterns description Comprehensive codebase analysis framework covering 14 technical areas including system overview, architecture, modules, integrations, workflows, and deployment patterns. Use when analyzing a codebase structure for documentation or understanding. user-invocable false
Codebase Analysis Patterns
A systematic framework for analyzing codebases across 14 technical areas. This skill provides the analysis structure and signals to look for when examining a repository.
Technical Areas
1. System Overview
Purpose: Understand the project at a high level.
Signals to extract:
Project name (from package.json, pyproject.toml, go.mod, etc.)
Project type: monolith, microservices, fullstack, library, CLI tool
Purpose/domain: e-commerce, SaaS, developer tool, etc.
Maturity level: prototype, MVP, production, legacy
Detection patterns:
package.json, pyproject.toml, Cargo.toml, go.mod, pom.xml
README.md (first paragraph often describes purpose)
2. Technology Stack
Purpose: Identify languages, frameworks, and tools.
Signals to extract:
Primary languages (by file extension distribution)
Frameworks (React, Vue, Django, FastAPI, Spring, etc.)
Databases (PostgreSQL, MongoDB, Redis, etc.)
Cloud providers (AWS, GCP, Azure indicators)
API styles (REST, GraphQL, gRPC, WebSocket)
*.ts, *.tsx → TypeScript
*.py → Python
*.go → Go
*.rs → Rust
*.java → Java
next.config.* → Next.js
angular.json → Angular
settings.py + urls.py → Django
main.py + FastAPI import → FastAPI
3. Architecture Pattern Purpose: Identify the structural design.
Type: layered, hexagonal, microservices, serverless, event-driven
Layers: presentation, business logic, data access, infrastructure
Communication: sync HTTP, async messaging, event bus
Design patterns: repository, factory, observer, CQRS, etc.
# Layered architecture signals
/controllers or /handlers → presentation layer
/services or /usecases → business logic layer
/repositories or /dal → data access layer
# Microservices signals
Multiple Dockerfiles or docker-compose services
/services/* with independent package files
API gateway configuration
4. Frontend Structure Purpose: Understand client-side organization.
Framework: React, Vue, Angular, Svelte, etc.
State management: Redux, Zustand, Vuex, NgRx, etc.
Build tool: Vite, Webpack, esbuild, Turbopack
Entry point: index.html, main.tsx, App.vue
Component organization: atomic, feature-based, page-based
# React patterns
/components, /hooks, /contexts
useState, useEffect, useContext imports
# State management
/store, /redux, /state directories
createStore, configureStore, create (zustand)
5. Backend Structure Purpose: Understand server-side organization.
Framework: Express, FastAPI, Django, Spring Boot, etc.
API style: REST, GraphQL, gRPC
Module decomposition: by feature, by layer, by domain
Entry points: main.py, index.ts, Application.java
# API route patterns
/routes, /api, /endpoints directories
@app.route, @router.get, @GetMapping decorators
# Module patterns
/modules/*/ → feature-based
/domain/*/ → domain-driven
6. Module/Service Inventory Purpose: Catalog major components.
Module/service names
Types: API, worker, scheduler, gateway
Paths and entry points
Dependencies (internal and external)
Public interfaces (exported functions, API endpoints)
Module Type Path Dependencies Interface auth API /services/auth db, redis POST /login, /register worker Background /workers/email queue, smtp processEmailQueue()
7. Data Stores Purpose: Identify persistence mechanisms.
Databases: PostgreSQL, MySQL, MongoDB, DynamoDB
Caches: Redis, Memcached
Search: Elasticsearch, Algolia
File storage: S3, GCS, local filesystem
Purpose of each store
# Database configuration
DATABASE_URL, MONGO_URI environment variables
/migrations, /prisma, /drizzle directories
*.sql files, schema definitions
# Cache patterns
REDIS_URL, redis.createClient()
@Cacheable annotations
8. External Integrations Purpose: Map third-party connections.
Service names (Stripe, Twilio, SendGrid, etc.)
Integration methods (SDK, REST API, webhook)
Authentication (API keys, OAuth, certificates)
Failure handling (retries, circuit breakers, fallbacks)
# SDK imports
import stripe, from '@stripe/stripe-js'
import twilio, from 'twilio'
# API client patterns
/integrations, /clients, /external directories
fetch('https://api.stripe.com/...')
9. Key Workflows Purpose: Trace critical user journeys.
Workflow names (checkout, onboarding, report generation)
Actors (user, admin, system, scheduler)
Steps and sequence
Services involved
Error scenarios and handling
# Workflow indicators
/workflows, /flows, /sagas directories
State machines, step functions
Transaction boundaries
10. Deployment Architecture Purpose: Understand infrastructure and deployment.
Containerization: Docker, Podman
Orchestration: Kubernetes, ECS, Docker Compose
IaC: Terraform, CloudFormation, Pulumi
Environments: dev, staging, production
CI/CD: GitHub Actions, GitLab CI, Jenkins
# Container configuration
Dockerfile, docker-compose.yml
.dockerignore
# IaC patterns
/terraform, /infrastructure, /infra directories
*.tf, *.yaml (CloudFormation), Pulumi.*
# CI/CD
.github/workflows/, .gitlab-ci.yml, Jenkinsfile
11. Testing & CI/CD Purpose: Understand quality assurance approach.
Test structure: unit, integration, e2e
Test frameworks: Jest, Pytest, JUnit, Cypress
Coverage targets and tools
Linting and formatting: ESLint, Prettier, Black, Ruff
Deployment process and stages
# Test directories
/tests, /__tests__, /spec, /e2e
*.test.ts, *.spec.py, *_test.go
# CI configuration
.github/workflows/*.yml
test, lint, build, deploy stages
12. Security Architecture Purpose: Identify security mechanisms.
Authentication: JWT, OAuth2, SAML, API keys
Authorization: RBAC, ABAC, policies
Secrets management: environment variables, Vault, AWS Secrets
Encryption: at-rest, in-transit, field-level
Security headers and middleware
# Auth patterns
/auth, /security directories
passport, next-auth, @nestjs/passport imports
JWT verification middleware
# Secrets
.env files (check .gitignore)
secrets/, vault configuration
13. File Structure & Entry Points Purpose: Map the repository organization.
Top-level directory purposes
Critical entry points (main files, CLI entry)
Configuration files and their roles
Generated vs source directories
/
├── src/ → Source code
├── tests/ → Test files
├── docs/ → Documentation
├── scripts/ → Build/deploy scripts
├── config/ → Configuration files
└── dist/ → Build output (generated)
14. Other Technical Areas Purpose: Capture anything not covered above.
Observability: logging, metrics, tracing
Feature flags and A/B testing
Internationalization (i18n)
Accessibility (a11y)
Performance optimization patterns
Background job processing
Real-time features (WebSocket, SSE)
Analysis Process
Scan project structure — Glob for directories and key filesDetect technology — Check manifest files (package.json, etc.)Identify entry points — Find main files for each componentMap dependencies — Internal and external dependenciesExtract architecture — Recognize patterns from code structureDocument modules/services — Create inventory of major componentsDiscover integrations — Find external API calls and webhooksMap workflows — Trace major user journeys through codeValidate completeness — Ensure all 14 areas are examined
Completeness Scoring Coverage Score Action 12-14 areas populated 85-100% Proceed with documentation 9-11 areas populated 70-84% Review gaps, may proceed < 9 areas populated < 70% Request additional context
Version: 1.0
Last Updated: 2026-01-22
