Dev server
A collection of high-quality, opinionated agent skills for modern full-stack development. These skills are designed to be used with AI agents (like Cursor, Windsurf, or custom agents) to standardize project creation and maintenance.
npx -y skills add yugasun/skills --skill dev-serverAssembled from the repository path, not quoted from the project. Check it against their README if it does not work.
One thing to look at
- 1 stars1 stars. Stars are a popularity signal and not a quality one, but at this level it is likely that nobody has read this closely except its author, and you would be relying on your own review.
What its author says it does
Copied from the file, not written here
Create and manage Python backend services using uv, FastAPI, Pydantic, SQLAlchemy, and AI libraries. Use this skill when the user asks to build a backend, API, or server-side application.
SKILL.md
2.3 KB, as published. Nobody here has run it
Server Development Skill
Instructions
Use this skill to scaffold and maintain backend services in the server/ directory. Follow the stack preferences and configuration details below.
Quick Start
- Initialize:
uv init. - Manager: Use
uvfor all dependency operations. - Framework: Setup
FastAPIwithPydantic. - Database: configure
SQLAlchemy(Async) +Alembic.
Core Stack Preferences
Project Management (uv)
Use uv for all Python project management (scaffolding, dependency management, virtual environments).
| Command | Description |
|---|---|
uv init | Initialize a new project |
uv add <pkg> | Add dependency |
uv add --dev <pkg> | Add development dependency |
uv run <cmd> | Run command in virtual environment |
uv venv | Create virtual environment |
Project Location
The backend project should be initialized in the server/ directory.
Framework (FastAPI)
Use FastAPI for building APIs.
- Use
APIRouterfor modularizing routes. - Use
pydantic-settingsfor configuration management.
Database (SQLAlchemy + Alembic)
Use SQLAlchemy 2.0+ with AsyncIO support. Use Alembic for database migrations.
AI & LLM (LiteLLM + Docling)
- LiteLLM: For standardized access to various LLM providers.
- Docling: For parsing and processing documents.
References
Setup & Configuration
| Topic | Description | Reference |
|---|---|---|
| Project Setup | Using uv, strict python versioning, and environment variables | setup |
| API Development | FastAPI structure, error handling, and validation | api |
Data & Architecture
| Topic | Description | Reference |
|---|---|---|
| Database Access | Async SQLAlchemy strategies and Alembic migrations | database |
| AI Integration | using LiteLLM and Docling for AI features | ai |