python-development

skillcreatorai/ai-agent-skills · updated May 27, 2026

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$npx skills add https://github.com/skillcreatorai/ai-agent-skills --skill python-development
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summary

Python 3.12+ development with FastAPI, Django, async patterns, and production tooling.

  • Covers modern project structure, type hints with generics, and async/await patterns for I/O-bound operations
  • Includes FastAPI patterns for building APIs with dependency injection, Pydantic models, and async request handlers
  • Demonstrates testing strategies using pytest, async test fixtures, and mocking for async functions
  • Recommends ruff for linting, mypy in strict mode, and pathlib for file oper
skill.md

Python Development

Project Setup

Modern Python Project Structure

my-project/
├── src/
│   └── my_project/
│       ├── __init__.py
│       ├── main.py
│       └── utils.py
├── tests/
│   ├── __init__.py
│   └── test_main.py
├── pyproject.toml
├── README.md
└── .gitignore

pyproject.toml

[project]
name = "my-project"
version = "0.1.0"
requires-python = ">=3.12"
dependencies = [
    "fastapi>=0.100.0",
    "pydantic>=2.0",
]

[project.optional-dependencies]
dev = [
    "pytest>=7.0",
    "ruff>=0.1.0",
    "mypy>=1.0",
]

[tool.ruff]
line-length = 88
select = ["E", "F", "I", "N", "W"]

[tool.mypy]
strict = true

Type Hints

from typing import TypeVar, Generic
from collections.abc import Sequence

T = TypeVar('T')

def process_items(items: Sequence[str]) -> list[str]:
    return [item.upper() for item in items]

class Repository(Generic[T]):
    def get(self, id: int) -> T | None: ...
    def save(self, item: T) -> T: ...

Async Patterns

import asyncio
from collections.abc import AsyncIterator

async def fetch_all(urls: list[str]) -> list[dict]:
    async with aiohttp.ClientSession() as session:
        tasks = [fetch_one(session, url) for url in urls]
        return await asyncio.gather(*tasks)

async def stream_data() -> AsyncIterator[bytes]:
    async with aiofiles.open('large_file.txt', 'rb') as f:
        async for chunk in f:
            yield chunk

FastAPI Patterns

from fastapi import FastAPI, Depends, HTTPException
from pydantic import BaseModel

app = FastAPI()

class UserCreate(BaseModel):
    email: str
    name: str

class UserResponse(BaseModel):
    id: int
    email: str
    name: str

@app.post("/users", response_model=UserResponse)
async def create_user(
    user: UserCreate,
    db: Database = Depends(get_db)
) -> UserResponse:
    result = await db.users.create(user.model_dump())
    return UserResponse(**result)

Testing

import pytest
from unittest.mock import AsyncMock, patch

@pytest.fixture
def mock_db():
    db = AsyncMock()
    db.users.get.return_value = {"id": 1, "name": "Test"}
    return db

@pytest.mark.asyncio
async def test_get_user(mock_db):
    result = await get_user(1, db=mock_db)
    assert result["name"] == "Test"
    mock_db.users.get.assert_called_once_with(1)

Best Practices

  • Use ruff for linting and formatting
  • Use mypy with strict mode
  • Prefer pathlib.Path over os.path
  • Use dataclasses or Pydantic for data structures
  • Use asyncio for I/O-bound operations
  • Use contextlib.asynccontextmanager for async resources
how to use python-development

How to use python-development on Cursor

AI-first code editor with Composer

1

Prerequisites

Before installing skills in Cursor, ensure your development environment meets these requirements:

  • Cursor installed and configured on your development machine
  • Node.js version 16.0+ with npm package manager (verify with node --version)
  • Active project directory or workspace where you want to add python-development
2

Execute installation command

Execute the skills CLI command in your project's root directory to begin installation:

$npx skills add https://github.com/skillcreatorai/ai-agent-skills --skill python-development

The skills CLI fetches python-development from GitHub repository skillcreatorai/ai-agent-skills and configures it for Cursor.

3

Select Cursor when prompted

The CLI will show a list of available agents. Use arrow keys to navigate and space to select Cursor:

◆ Which agents do you want to install to?
│ ── Universal (.agents/skills) ── always included ────
│ • Amp
│ • Antigravity
│ • Cline
│ • Codex
│ ●Cursor(selected)
│ • Cursor
│ • Windsurf
4

Verify installation

Confirm successful installation by checking the skill directory location:

.cursor/skills/python-development

Reload or restart Cursor to activate python-development. Access the skill through slash commands (e.g., /python-development) or your agent's skill management interface.

Security & Verification Notice

We perform automated surface-level scans (Gen AI Scanner, Socket, Snyk) during installation. These checks detect common vulnerabilities but do not guarantee complete security. Always review skill source code and verify the publisher's reputation before production use.

Skills execute code in your development environment. Always verify the publisher's identity, review recent commits, and test in isolated environments before production deployment.

List & Monetize Your Skill

Submit your Claude Code skill and start earning

GET_STARTED →

Use Cases

Task Automation & Efficiency

Automate repetitive workflows and reduce manual effort

Example

Generate reports, summarize documents, draft communications

Save 3-5 hours per week on routine tasks

Knowledge Enhancement

Learn new skills, understand complex topics, get expert guidance

Example

Explain concepts, provide examples, suggest learning resources

Accelerate learning and skill development by 2x

Quality Improvement

Enhance output quality through reviews, suggestions, and refinements

Example

Review drafts, suggest improvements, catch errors

Improve work quality by 30-40% with less effort

Implementation Guide

Prerequisites

  • Claude Desktop or compatible AI client with skill support
  • Clear understanding of task or problem to solve
  • Willingness to iterate and refine outputs

Time Estimate

15-45 minutes depending on use case complexity

Installation Steps

  1. 1.Install skill using provided installation command
  2. 2.Test with simple use case relevant to your work
  3. 3.Evaluate output quality and relevance
  4. 4.Iterate on prompts to improve results
  5. 5.Integrate into regular workflow if valuable

Common Pitfalls

  • Expecting perfect results without iteration
  • Not providing enough context in prompts
  • Using skill for tasks outside its intended scope
  • Accepting outputs without review and validation

Best Practices

✓ Do

  • +Start with clear, specific prompts
  • +Provide relevant context and constraints
  • +Review and refine all outputs before using
  • +Iterate to improve output quality
  • +Document successful prompt patterns

✗ Don't

  • Don't use without understanding skill limitations
  • Don't skip validation of outputs
  • Don't share sensitive information in prompts
  • Don't expect skill to replace human judgment

💡 Pro Tips

  • Be specific about desired format and style
  • Ask for multiple options to choose from
  • Request explanations to understand reasoning
  • Combine AI efficiency with human expertise

When to Use This

✓ Use When

Use when skill capabilities match your task, clear ROI on time saved, and you can validate outputs. Best for repetitive tasks, learning, and quality improvement.

✗ Avoid When

Avoid when task requires deep expertise you can't validate, involves sensitive decisions, or when learning process is more valuable than speed of completion.

Learning Path

  1. 1Familiarize yourself with skill capabilities and limitations
  2. 2Start with low-risk, non-critical tasks
  3. 3Progress to more complex and valuable use cases
  4. 4Build expertise through regular use and experimentation

Discussion

Product Hunt–style comments (not star reviews)
  • No comments yet — start the thread.
general reviews

Ratings

4.657 reviews
  • Olivia Dixit· Dec 28, 2024

    We added python-development from the explainx registry; install was straightforward and the SKILL.md answered most questions upfront.

  • Michael Thompson· Dec 28, 2024

    python-development reduced setup friction for our internal harness; good balance of opinion and flexibility.

  • Evelyn Kim· Dec 20, 2024

    I recommend python-development for anyone iterating fast on agent tooling; clear intent and a small, reviewable surface area.

  • Ira Dixit· Dec 20, 2024

    python-development fits our agent workflows well — practical, well scoped, and easy to wire into existing repos.

  • Luis Iyer· Dec 16, 2024

    I recommend python-development for anyone iterating fast on agent tooling; clear intent and a small, reviewable surface area.

  • Chaitanya Patil· Dec 12, 2024

    Keeps context tight: python-development is the kind of skill you can hand to a new teammate without a long onboarding doc.

  • Luis Gupta· Dec 4, 2024

    Registry listing for python-development matched our evaluation — installs cleanly and behaves as described in the markdown.

  • Diya Srinivasan· Nov 23, 2024

    Keeps context tight: python-development is the kind of skill you can hand to a new teammate without a long onboarding doc.

  • Olivia Desai· Nov 19, 2024

    python-development is among the better-maintained entries we tried; worth keeping pinned for repeat workflows.

  • Valentina Brown· Nov 11, 2024

    Solid pick for teams standardizing on skills: python-development is focused, and the summary matches what you get after install.

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