fastapi-development
Build async APIs with FastAPI, including endpoints, dependency injection, validation, and testing. Use when creating REST APIs, web backends, or microservices.
Best use case
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. Build async APIs with FastAPI, including endpoints, dependency injection, validation, and testing. Use when creating REST APIs, web backends, or microservices.
Build async APIs with FastAPI, including endpoints, dependency injection, validation, and testing. Use when creating REST APIs, web backends, or microservices.
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 "fastapi-development" skill to help with this workflow task. Context: Build async APIs with FastAPI, including endpoints, dependency injection, validation, and testing. Use when creating REST APIs, web backends, or microservices.
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
Manual Installation
- Download SKILL.md from GitHub
- Place it in
.claude/skills/fastapi-development/SKILL.mdinside your project - Restart your AI agent — it will auto-discover the skill
How fastapi-development Compares
| Feature / Agent | fastapi-development | Standard Approach |
|---|---|---|
| Platform Support | Not specified | Limited / Varies |
| Context Awareness | High | Baseline |
| Installation Complexity | Unknown | N/A |
Frequently Asked Questions
What does this skill do?
Build async APIs with FastAPI, including endpoints, dependency injection, validation, and testing. Use when creating REST APIs, web backends, or microservices.
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
# FastAPI Development
## Quick start
Create a basic FastAPI application:
```python
from fastapi import FastAPI
app = FastAPI()
@app.get("/")
async def read_root():
return {"Hello": "World"}
@app.get("/items/{item_id}")
async def read_item(item_id: int, q: str | None = None):
return {"item_id": item_id, "q": q}
```
Run with:
```bash
uv run uvicorn main:app --reload
```
## Common patterns
### Pydantic models for validation
```python
from pydantic import BaseModel
from typing import Optional
class Item(BaseModel):
name: str
description: Optional[str] = None
price: float
tax: Optional[float] = None
@app.post("/items/")
async def create_item(item: Item):
return item
```
### Dependency injection
```python
from typing import Annotated
from fastapi import Depends
async def common_parameters(
q: str | None = None,
skip: int = 0,
limit: int = 100
):
return {"q": q, "skip": skip, "limit": limit}
CommonsDep = Annotated[dict, Depends(common_parameters)]
@app.get("/items/")
async def read_items(commons: CommonsDep):
return commons
```
### Database dependencies with cleanup
```python
async def get_db():
db = connect_to_database()
try:
yield db
finally:
db.close()
@app.get("/query/")
async def query_data(db: Annotated[dict, Depends(get_db)]):
return {"data": "query results"}
```
### Error handling
```python
from fastapi import HTTPException
@app.get("/items/{item_id}")
async def read_item(item_id: int):
if item_id < 1:
raise HTTPException(status_code=404, detail="Item not found")
return {"item_id": item_id}
```
### Path and query validation
```python
from typing import Annotated
from fastapi import Path, Query
@app.get("/items/{item_id}")
async def read_item(
item_id: Annotated[int, Path(gt=0, le=1000)],
q: Annotated[str, Query(max_length=50)] = None
):
return {"item_id": item_id, "q": q}
```
### Response models
```python
from pydantic import BaseModel
class ItemPublic(BaseModel):
id: int
name: str
price: float
@app.get("/items/{item_id}", response_model=ItemPublic)
async def read_item(item_id: int):
return ItemPublic(id=item_id, name="Laptop", price=999.99)
```
## Testing with TestClient
```python
from fastapi.testclient import TestClient
client = TestClient(app)
def test_read_root():
response = client.get("/")
assert response.status_code == 200
assert response.json() == {"Hello": "World"}
def test_read_item():
response = client.get("/items/42?q=test")
assert response.status_code == 200
assert response.json() == {"item_id": 42, "q": "test"}
```
## Requirements
```bash
uv add fastapi uvicorn
uv add "fastapi[all]" # Includes all optional dependencies
```
## Key concepts
- **Async/await**: Use `async def` for I/O operations
- **Automatic validation**: Request/response validation with Pydantic
- **Dependency injection**: Share logic across endpoints with `Depends`
- **Type hints**: Full editor support and validation
- **Interactive docs**: Auto-generated Swagger/OpenAPI at `/docs`
- **Background tasks**: Run tasks after response using `BackgroundTasks`Related Skills
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