flask

Flask Python microframework with blueprints and extensions. Use for lightweight APIs.

7 stars

Best use case

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

Flask Python microframework with blueprints and extensions. Use for lightweight APIs.

Teams using flask 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/flask/SKILL.md --create-dirs "https://raw.githubusercontent.com/G1Joshi/Agent-Skills/main/skills/frameworks/flask/SKILL.md"

Manual Installation

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

How flask Compares

Feature / AgentflaskStandard Approach
Platform SupportNot specifiedLimited / Varies
Context Awareness High Baseline
Installation ComplexityUnknownN/A

Frequently Asked Questions

What does this skill do?

Flask Python microframework with blueprints and extensions. Use for lightweight APIs.

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

# Flask

Flask is a lightweight WSGI web application framework. It is designed to make getting started quick and easy, with the ability to scale up to complex applications. Flask 3.0 (2025) fully supports async routes.

## When to Use

- **Microservices**: Minimal boilerplate makes it great for small services.
- **Flexibility**: You validly choose your ORM (SQLAlchemy, Peewee) and Auth provider.
- **Data Science APIs**: The standard for wrapping ML models (PyTorch/TensorFlow) in an API.

## Quick Start (Async)

```python
from flask import Flask
import asyncio

app = Flask(__name__)

@app.route("/")
async def hello():
    await asyncio.sleep(1)
    return "Hello form Async Flask!"
```

## Core Concepts

### The Application Context

Flask uses thread-locals (or context-vars in async) to make `request` and `g` globally accessible during a request.

### Blueprints

Organize a group of related views and other code. `auth_bp = Blueprint('auth', __name__)`.

## Best Practices (2025)

**Do**:

- **Use `Quart` or Flask 3.0+**: Ensure you are using modern async features if your app is I/O bound.
- **Use `pydantic`**: Use Pydantic for request validation (via libraries like `flask-pydantic` or just raw).
- **Application Factory Pattern**: Always use `create_app()` to ensure your app is testable.

**Don't**:

- **Don't use global state**: Use `current_app` or `g` to store request-scoped data.

## References

- [Flask Documentation](https://flask.palletsprojects.com/)