python-fastapi-development

Python FastAPI backend development with async patterns, SQLAlchemy, Pydantic, authentication, and production API patterns.

38 stars

Best use case

python-fastapi-development is best used when you need a repeatable AI agent workflow instead of a one-off prompt.

Python FastAPI backend development with async patterns, SQLAlchemy, Pydantic, authentication, and production API patterns.

Teams using python-fastapi-development should expect a more consistent output, faster repeated execution, less prompt rewriting.

When to use this skill

  • You want a reusable workflow that can be run more than once with consistent structure.

When not to use this skill

  • You only need a quick one-off answer and do not need a reusable workflow.
  • You cannot install or maintain the underlying files, dependencies, or repository context.

Installation

Claude Code / Cursor / Codex

$curl -o ~/.claude/skills/python-fastapi-development/SKILL.md --create-dirs "https://raw.githubusercontent.com/lingxling/awesome-skills-cn/main/antigravity-awesome-skills/plugins/antigravity-awesome-skills-claude/skills/python-fastapi-development/SKILL.md"

Manual Installation

  1. Download SKILL.md from GitHub
  2. Place it in .claude/skills/python-fastapi-development/SKILL.md inside your project
  3. Restart your AI agent — it will auto-discover the skill

How python-fastapi-development Compares

Feature / Agentpython-fastapi-developmentStandard Approach
Platform SupportNot specifiedLimited / Varies
Context Awareness High Baseline
Installation ComplexityUnknownN/A

Frequently Asked Questions

What does this skill do?

Python FastAPI backend development with async patterns, SQLAlchemy, Pydantic, authentication, and production API patterns.

Where can I find the source code?

You can find the source code on GitHub using the link provided at the top of the page.

Related Guides

SKILL.md Source

# Python/FastAPI Development Workflow

## Overview

Specialized workflow for building production-ready Python backends with FastAPI, featuring async patterns, SQLAlchemy ORM, Pydantic validation, and comprehensive API patterns.

## When to Use This Workflow

Use this workflow when:
- Building new REST APIs with FastAPI
- Creating async Python backends
- Implementing database integration with SQLAlchemy
- Setting up API authentication
- Developing microservices

## Workflow Phases

### Phase 1: Project Setup

#### Skills to Invoke
- `app-builder` - Application scaffolding
- `python-development-python-scaffold` - Python scaffolding
- `fastapi-templates` - FastAPI templates
- `uv-package-manager` - Package management

#### Actions
1. Set up Python environment (uv/poetry)
2. Create project structure
3. Configure FastAPI app
4. Set up logging
5. Configure environment variables

#### Copy-Paste Prompts
```
Use @fastapi-templates to scaffold a new FastAPI project
```

```
Use @python-development-python-scaffold to set up Python project structure
```

### Phase 2: Database Setup

#### Skills to Invoke
- `prisma-expert` - Prisma ORM (alternative)
- `database-design` - Schema design
- `postgresql` - PostgreSQL setup
- `pydantic-models-py` - Pydantic models

#### Actions
1. Design database schema
2. Set up SQLAlchemy models
3. Create database connection
4. Configure migrations (Alembic)
5. Set up session management

#### Copy-Paste Prompts
```
Use @database-design to design PostgreSQL schema
```

```
Use @pydantic-models-py to create Pydantic models for API
```

### Phase 3: API Routes

#### Skills to Invoke
- `fastapi-router-py` - FastAPI routers
- `api-design-principles` - API design
- `api-patterns` - API patterns

#### Actions
1. Design API endpoints
2. Create API routers
3. Implement CRUD operations
4. Add request validation
5. Configure response models

#### Copy-Paste Prompts
```
Use @fastapi-router-py to create API endpoints with CRUD operations
```

```
Use @api-design-principles to design RESTful API
```

### Phase 4: Authentication

#### Skills to Invoke
- `auth-implementation-patterns` - Authentication
- `api-security-best-practices` - API security

#### Actions
1. Choose auth strategy (JWT, OAuth2)
2. Implement user registration
3. Set up login endpoints
4. Create auth middleware
5. Add password hashing

#### Copy-Paste Prompts
```
Use @auth-implementation-patterns to implement JWT authentication
```

### Phase 5: Error Handling

#### Skills to Invoke
- `fastapi-pro` - FastAPI patterns
- `error-handling-patterns` - Error handling

#### Actions
1. Create custom exceptions
2. Set up exception handlers
3. Implement error responses
4. Add request logging
5. Configure error tracking

#### Copy-Paste Prompts
```
Use @fastapi-pro to implement comprehensive error handling
```

### Phase 6: Testing

#### Skills to Invoke
- `python-testing-patterns` - pytest testing
- `api-testing-observability-api-mock` - API testing

#### Actions
1. Set up pytest
2. Create test fixtures
3. Write unit tests
4. Implement integration tests
5. Configure test database

#### Copy-Paste Prompts
```
Use @python-testing-patterns to write pytest tests for FastAPI
```

### Phase 7: Documentation

#### Skills to Invoke
- `api-documenter` - API documentation
- `openapi-spec-generation` - OpenAPI specs

#### Actions
1. Configure OpenAPI schema
2. Add endpoint documentation
3. Create usage examples
4. Set up API versioning
5. Generate API docs

#### Copy-Paste Prompts
```
Use @api-documenter to generate comprehensive API documentation
```

### Phase 8: Deployment

#### Skills to Invoke
- `deployment-engineer` - Deployment
- `docker-expert` - Containerization

#### Actions
1. Create Dockerfile
2. Set up docker-compose
3. Configure production settings
4. Set up reverse proxy
5. Deploy to cloud

#### Copy-Paste Prompts
```
Use @docker-expert to containerize FastAPI application
```

## Technology Stack

| Category | Technology |
|----------|------------|
| Framework | FastAPI |
| Language | Python 3.11+ |
| ORM | SQLAlchemy 2.0 |
| Validation | Pydantic v2 |
| Database | PostgreSQL |
| Migrations | Alembic |
| Auth | JWT, OAuth2 |
| Testing | pytest |

## Quality Gates

- [ ] All tests passing (>80% coverage)
- [ ] Type checking passes (mypy)
- [ ] Linting clean (ruff, black)
- [ ] API documentation complete
- [ ] Security scan passed
- [ ] Performance benchmarks met

## Related Workflow Bundles

- `development` - General development
- `database` - Database operations
- `security-audit` - Security testing
- `api-development` - API patterns

## Limitations
- Use this skill only when the task clearly matches the scope described above.
- Do not treat the output as a substitute for environment-specific validation, testing, or expert review.
- Stop and ask for clarification if required inputs, permissions, safety boundaries, or success criteria are missing.

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