fastapi-router-py

Create FastAPI routers with CRUD operations, authentication dependencies, and proper response models. Use when building REST API endpoints, creating new routes, implementing CRUD operations, or add...

16 stars

Best use case

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

Create FastAPI routers with CRUD operations, authentication dependencies, and proper response models. Use when building REST API endpoints, creating new routes, implementing CRUD operations, or add...

Teams using fastapi-router-py 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/fastapi-router-py/SKILL.md --create-dirs "https://raw.githubusercontent.com/diegosouzapw/awesome-omni-skill/main/skills/backend/fastapi-router-py/SKILL.md"

Manual Installation

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

How fastapi-router-py Compares

Feature / Agentfastapi-router-pyStandard Approach
Platform SupportNot specifiedLimited / Varies
Context Awareness High Baseline
Installation ComplexityUnknownN/A

Frequently Asked Questions

What does this skill do?

Create FastAPI routers with CRUD operations, authentication dependencies, and proper response models. Use when building REST API endpoints, creating new routes, implementing CRUD operations, or add...

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.

SKILL.md Source

# FastAPI Router

Create FastAPI routers following established patterns with proper authentication, response models, and HTTP status codes.

## Quick Start

Copy the template from assets/template.py and replace placeholders:
- `{{ResourceName}}` → PascalCase name (e.g., `Project`)
- `{{resource_name}}` → snake_case name (e.g., `project`)
- `{{resource_plural}}` → plural form (e.g., `projects`)

## Authentication Patterns

```python
# Optional auth - returns None if not authenticated
current_user: Optional[User] = Depends(get_current_user)

# Required auth - raises 401 if not authenticated
current_user: User = Depends(get_current_user_required)
```

## Response Models

```python
@router.get("/items/{item_id}", response_model=Item)
async def get_item(item_id: str) -> Item:
    ...

@router.get("/items", response_model=list[Item])
async def list_items() -> list[Item]:
    ...
```

## HTTP Status Codes

```python
@router.post("/items", status_code=status.HTTP_201_CREATED)
@router.delete("/items/{id}", status_code=status.HTTP_204_NO_CONTENT)
```

## Integration Steps

1. Create router in `src/backend/app/routers/`
2. Mount in `src/backend/app/main.py`
3. Create corresponding Pydantic models
4. Create service layer if needed
5. Add frontend API functions

## When to Use
This skill is applicable to execute the workflow or actions described in the overview.

Related Skills

python-fastapi

16
from diegosouzapw/awesome-omni-skill

Python FastAPI development with uv package manager, modular project structure, SQLAlchemy ORM, and production-ready patterns.

python-fastapi-scalable-api-cursorrules-prompt-fil

16
from diegosouzapw/awesome-omni-skill

Apply for python-fastapi-scalable-api-cursorrules-prompt-fil. --- description: Defines conventions specific to FastAPI usage in the backend. globs: backend/src/**/*.py

python-fastapi-development

16
from diegosouzapw/awesome-omni-skill

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

fastapi

16
from diegosouzapw/awesome-omni-skill

FastAPI framework best practices including Pydantic schemas, dependency injection, and async patterns.

fastapi-templates

16
from diegosouzapw/awesome-omni-skill

Create production-ready FastAPI projects with async patterns, dependency injection, and comprehensive error handling. Use when building new FastAPI applications or setting up backend API projects.

fastapi-project-generator

16
from diegosouzapw/awesome-omni-skill

Generate complete FastAPI project skeleton with SQLAlchemy, MongoDB, JWT auth, Redis cache, Celery tasks, Docker, and CRUD generators. Use when user wants to create or scaffold a FastAPI project.

fastapi-expert

16
from diegosouzapw/awesome-omni-skill

Use when building high-performance async Python APIs with FastAPI and Pydantic V2. Invoke for async SQLAlchemy, JWT authentication, WebSockets, OpenAPI documentation. Keywords: FastAPI, Pydantic, async, SQLAlchemy, JWT, OpenAPI.

Build Your FastAPI Skill

16
from diegosouzapw/awesome-omni-skill

Create your FastAPI skill in one prompt, then learn to improve it throughout the chapter

agentbox-openrouter

16
from diegosouzapw/awesome-omni-skill

Set up OpenRouter as your LLM provider. Guides through account creation, API key setup, config, and making it the default model. Use when a user wants to use OpenRouter models like Claude Sonnet 4.5.

bgo

10
from diegosouzapw/awesome-omni-skill

Automates the complete Blender build-go workflow, from building and packaging your extension/add-on to removing old versions, installing, enabling, and launching Blender for quick testing and iteration.

Coding & Development

humanizer-ko

16
from diegosouzapw/awesome-omni-skill

Detects and corrects Korean AI writing patterns to transform text into natural human writing. Based on scientific linguistic research (KatFishNet paper with 94.88% AUC accuracy). Analyzes 19 patterns including comma overuse, spacing rigidity, POS diversity, AI vocabulary overuse, and structural monotony. Use when humanizing Korean text from ChatGPT/Claude/Gemini or removing AI traces from Korean LLM output.

huggingface-accelerate

16
from diegosouzapw/awesome-omni-skill

Simplest distributed training API. 4 lines to add distributed support to any PyTorch script. Unified API for DeepSpeed/FSDP/Megatron/DDP. Automatic device placement, mixed precision (FP16/BF16/FP8). Interactive config, single launch command. HuggingFace ecosystem standard.