project-structure
About
This skill provides comprehensive project structure guidelines and best practices for organizing codebases across various project types. It offers standardized directory patterns for monorepos, web frameworks, backend services, and libraries to ensure scalable, maintainable architecture. Use it when designing new project structures, organizing monorepo workspaces, or establishing code organization conventions for teams.
Documentation
Project Structure Guide
Monorepo
project-root/
├── src/ # All services/apps
├── infra/ # Shared infrastructure
├── docs/ # Documentation
├── .devcontainer/ # Dev Container configuration
├── .github/ # Workflows, templates
├── .vscode/ # VSCode settings
├── .claude/ # Claude settings
├── .gemini/ # Gemini settings
├── package.json # Root package.json. For releases, version management
├── go.work # Go workspace (when using Go)
├── justfile # Just task runner
├── .gitignore
├── .prettierrc
├── .prettierignore
└── README.md
NestJS
project-root/
├── src/
│ ├── domains/
│ ├── common/
│ ├── config/
│ ├── database/
│ ├── app.module.ts
│ └── main.ts
├── tests/
├── package.json
└── tsconfig.json
React
project-root/
├── src/
│ ├── pages/ # Page modules
│ ├── domains/ # Domain-shared code
│ ├── components/ # Common UI components
│ ├── layouts/ # Layout-related
│ ├── libs/ # Feature libraries (auth, api, theme)
│ ├── shared/ # Pure utilities
│ ├── app.tsx
│ └── main.tsx
├── public/
├── package.json
├── vite.config.ts
└── tsconfig.json
Next.js
project-root/
├── app/
│ ├── (routes)/ # Pages (route groups)
│ ├── actions/ # Server Actions (internal mutations)
│ └── api/ # API Routes (external integrations only)
├── components/ # Shared components
├── lib/ # Utilities and clients
├── public/ # Static assets
├── middleware.ts # Edge/Node.js middleware
├── next.config.js
├── package.json
└── tsconfig.json
Go
project-root/
├── cmd/ # Execution entry points (main.go)
├── internal/ # Private packages
├── pkg/ # Public packages
├── configs/ # Configuration files
├── scripts/ # Utility scripts
├── tests/ # Integration tests
├── docs/ # Documentation
├── go.mod
└── go.sum
NPM
project-root/
├── cli/ # CLI execution entry point
├── internal/ # Private packages
├── pkg/ # Public packages
├── configs/ # Configuration files
├── scripts/ # Utility scripts
├── tests/ # Integration tests
├── docs/ # Documentation
├── dist/ # Build artifacts
├── package.json
├── tsconfig.json
└── README.md
IDE Extension
project-root/
├── extension/ # Extension entry point (activate/deactivate)
├── internal/ # Private packages
├── pkg/ # Public packages
├── view/ # WebView (if applicable)
├── configs/ # Configuration files
├── scripts/ # Utility scripts
├── tests/ # Integration tests
├── public/ # Static resources (icons, etc.)
├── dist/ # Build artifacts
├── package.json
├── tsconfig.json
└── .vscodeignore
Chrome Extension
project-root/
├── background/ # Service Worker (Background Script)
├── content/ # Content Scripts
├── popup/ # Popup (Extension UI)
├── internal/ # Private packages
├── pkg/ # Public packages
├── configs/ # Configuration files
├── scripts/ # Utility scripts
├── tests/ # Integration tests
├── public/ # Static resources
├── dist/ # Build artifacts
├── package.json
└── tsconfig.json
Quick Install
/plugin add https://github.com/KubrickCode/ai-config-toolkit/tree/main/project-structureCopy and paste this command in Claude Code to install this skill
GitHub 仓库
Related Skills
sglang
MetaSGLang is a high-performance LLM serving framework that specializes in fast, structured generation for JSON, regex, and agentic workflows using its RadixAttention prefix caching. It delivers significantly faster inference, especially for tasks with repeated prefixes, making it ideal for complex, structured outputs and multi-turn conversations. Choose SGLang over alternatives like vLLM when you need constrained decoding or are building applications with extensive prefix sharing.
llamaguard
OtherLlamaGuard is Meta's 7-8B parameter model for moderating LLM inputs and outputs across six safety categories like violence and hate speech. It offers 94-95% accuracy and can be deployed using vLLM, Hugging Face, or Amazon SageMaker. Use this skill to easily integrate content filtering and safety guardrails into your AI applications.
evaluating-llms-harness
TestingThis Claude Skill runs the lm-evaluation-harness to benchmark LLMs across 60+ standardized academic tasks like MMLU and GSM8K. It's designed for developers to compare model quality, track training progress, or report academic results. The tool supports various backends including HuggingFace and vLLM models.
langchain
MetaLangChain is a framework for building LLM applications using agents, chains, and RAG pipelines. It supports multiple LLM providers, offers 500+ integrations, and includes features like tool calling and memory management. Use it for rapid prototyping and deploying production systems like chatbots, autonomous agents, and question-answering services.