python-development
Python 3.12+ development with FastAPI, Django, async patterns, and production tooling.
Works with
4
total installs
4
this week
990
GitHub stars
0
upvotes
Install Skill
Run in your terminal
4
installs
4
this week
990
stars
What it does
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
Installation Guide
How to use python-development on Cursor
AI-first code editor with Composer
Prerequisites
Before installing skills in Cursor, ensure your development environment meets these requirements:
- ›Cursor installed and configured on your machine
- ›Node.js 16+ with npm — verify with
node --version - ›Active project directory where you want to add
python-development
Run the install command
Execute the skills CLI command in your project's root directory to begin installation:
Fetches python-development from skillcreatorai/ai-agent-skills and configures it for Cursor.
Select Cursor when prompted
The CLI shows a list of agents. Use arrow keys and space to select Cursor:
Verify installation
Confirm successful installation by checking the skill directory location:
Restart Cursor to activate python-development. Access via /python-development in your agent's command palette.
Security 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 environment. Always review source, verify the publisher, and test in isolation before production.
Documentation
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
rufffor linting and formatting - Use
mypywith strict mode - Prefer
pathlib.Pathoveros.path - Use dataclasses or Pydantic for data structures
- Use
asynciofor I/O-bound operations - Use
contextlib.asynccontextmanagerfor async resources
List & Monetize Your Skill
Submit your Claude Code skill and start earning
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
Steps
- 1Install skill using provided installation command
- 2Test with simple use case relevant to your work
- 3Evaluate output quality and relevance
- 4Iterate on prompts to improve results
- 5Integrate 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
- 1Familiarize yourself with skill capabilities and limitations
- 2Start with low-risk, non-critical tasks
- 3Progress to more complex and valuable use cases
- 4Build expertise through regular use and experimentation
Related Skills
frontend-design
21skillcreatorai/ai-agent-skills
expo-app-design
14skillcreatorai/ai-agent-skills
python-expert-best-practices-code-review
34wispbit-ai/skills
fastapi-python
29mindrally/skills
backend-development
15mrgoonie/claudekit-skills
python-expert
13shubhamsaboo/awesome-llm-apps
Reviews
- OOlivia Dixit★★★★★Dec 28, 2024
We added python-development from the explainx registry; install was straightforward and the SKILL.md answered most questions upfront.
- MMichael Thompson★★★★★Dec 28, 2024
python-development reduced setup friction for our internal harness; good balance of opinion and flexibility.
- EEvelyn Kim★★★★★Dec 20, 2024
I recommend python-development for anyone iterating fast on agent tooling; clear intent and a small, reviewable surface area.
- IIra Dixit★★★★★Dec 20, 2024
python-development fits our agent workflows well — practical, well scoped, and easy to wire into existing repos.
- LLuis Iyer★★★★★Dec 16, 2024
I recommend python-development for anyone iterating fast on agent tooling; clear intent and a small, reviewable surface area.
- CChaitanya 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.
- LLuis Gupta★★★★★Dec 4, 2024
Registry listing for python-development matched our evaluation — installs cleanly and behaves as described in the markdown.
- DDiya 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.
- OOlivia Desai★★★★★Nov 19, 2024
python-development is among the better-maintained entries we tried; worth keeping pinned for repeat workflows.
- VValentina Brown★★★★★Nov 11, 2024
Solid pick for teams standardizing on skills: python-development is focused, and the summary matches what you get after install.
showing 1-10 of 57
Discussion
Comments — not star reviews- No comments yet — start the thread.