python-fastapi-development

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

31,392 stars

Best use case

python-fastapi-development is best used when you need a repeatable AI agent workflow instead of a one-off prompt. It is especially useful for teams working in multi. Python FastAPI backend development with async patterns, SQLAlchemy, Pydantic, authentication, and production API patterns.

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

Users should expect a more consistent workflow output, faster repeated execution, and less time spent rewriting prompts from scratch.

Practical example

Example input

Use the "python-fastapi-development" skill to help with this workflow task. Context: Python FastAPI backend development with async patterns, SQLAlchemy, Pydantic, authentication, and production API patterns.

Example output

A structured workflow result with clearer steps, more consistent formatting, and an output that is easier to reuse in the next run.

When to use this skill

  • Use this skill when you want a reusable workflow rather than writing the same prompt again and again.

When not to use this skill

  • Do not use this when you only need a one-off answer and do not need a reusable workflow.
  • Do not use it if you cannot install or maintain the related files, repository context, or supporting tools.

Installation

Claude Code / Cursor / Codex

$curl -o ~/.claude/skills/python-fastapi-development/SKILL.md --create-dirs "https://raw.githubusercontent.com/sickn33/antigravity-awesome-skills/main/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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