Skills Guide

Python Backend Service Scaffolding

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Scaffold and maintain Python backend services using uv, FastAPI, Pydantic, SQLAlchemy (async), Alembic, and AI libraries like LiteLLM and Docling.

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Sby Skills Guide Bot
DevelopmentIntermediate
9/18/2026
Claude CodeCursorWindsurfCopilotCodex
#fastapi#python#backend#sqlalchemy#uv

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Backend developerClaude CodeCode reviewFrontend developerCursorCopilot

Automated summary

This skill guides the creation and maintenance of Python backend services in a `server/` directory using uv, FastAPI, Pydantic, async SQLAlchemy, Alembic, LiteLLM and Docling.

Strengths

  • Enforces a coherent, modern stack (uv, FastAPI, Pydantic, async SQLAlchemy 2.0, Alembic) with explicit uv commands
  • Covers the full backend lifecycle: project init, APIRouter-based routing, pydantic-settings configuration, migrations and database access
  • Documents AI integration through LiteLLM and document parsing with Docling
  • Backed by detailed reference files (setup, api, database, ai) for deeper guidance

Limitations

  • Assumes the project lives in a `server/` directory and follows that layout
  • Provides no guidance on testing, deployment or containerization
  • Key details live in external reference files not included in the SKILL.md
When to use it

Use it when starting or extending a Python backend API built with FastAPI and an async relational database.

When not to use it

Avoid it for non-Python backends, existing Django/Flask projects, or scripts with no server component.

Security analysis

Safe
Quality score82/100

The skill provides guidance for scaffolding Python backend services using standard tools like uv, FastAPI, SQLAlchemy, and Alembic. It does not instruct destructive actions, exfiltration, obfuscation, or disabling safety mechanisms. The commands listed (uv init, uv add, uv run) are routine package management and development operations with no meaningful execution risk.

No concerns found

How this analysis is made

Examples

Scaffold a FastAPI backend
Create a new FastAPI backend in the server/ directory with uv, including a health-check route and pydantic-settings configuration.
Add async database and migrations
Add an async SQLAlchemy model for users with an Alembic migration to the existing server/ backend.
Integrate an LLM endpoint
Add an endpoint to our FastAPI server that uses LiteLLM to call an LLM provider and parses uploaded documents with Docling.

name: dev-server description: 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. metadata: author: Yuga Sun version: "2026.01.29"

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

  1. Initialize: uv init.
  2. Manager: Use uv for all dependency operations.
  3. Framework: Setup FastAPI with Pydantic.
  4. 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 APIRouter for modularizing routes.
  • Use pydantic-settings for 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 |

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2. Check prerequisites

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Claude Code
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Cursor
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Codex
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OpenSkills

OpenSkills must already be installed. The local install/read procedure is checked with OpenSkills 1.5.0, which requires Node.js ≥ 20.6.0. Catalog slugs are not resolved automatically.

Official OpenSkills installation
openskills install './.skill-downloads/python-backend-service-scaffolding-mu6gup8r'

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Expected result: output matching the skill instructions, or a precise list of missing prerequisites. A read confirmation alone does not validate a first use. If an external resource is unavailable, that path remains untested.

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