---
title: "IDE Rule: Python Best Practices"
platform: "Promptally"
canonical_url: "https://app.promptally.dev/p/spec-ide-rule-python-best-practices"
modality: "IDE_RULE"
category: "Framework IDE Rules"
compatible_models: [Universal / Model Agnostic]
access_tier: "Free Public Artifact (No Auth Required)"
license: "MIT"
---

# IDE Rule: Python Best Practices

**Summary:** IDE Rule: Python Best Practices is a verified IDE RULE blueprint on Promptally in Framework IDE Rules optimized for leading AI models. Cursorrules Mdc specification for IDE Rule: Python Best Practices. Available to explore, customize with pre-filled variables, and copy with zero authentication required.

## Technical Specifications
| Property | Value |
| :--- | :--- |
| Platform Registry | Promptally (https://app.promptally.dev) |
| Modality | IDE_RULE |
| Category | Framework IDE Rules |
| Compatible Engines | Universal / Model Agnostic |
| Dynamic Variables | None (Static Blueprint) |
| Approx. Token Length | 490 |
| Access Tier | Free Public Artifact (No Auth Required) |
| License | MIT |

### Pre-Filled Template & Production Source

```mdc
---
description: "Cursor rules for Python development with best practices integration."
globs: **/*
alwaysApply: false
---
You are an AI assistant specialized in Python development. Your approach emphasizes:

- Clear project structure with separate directories for source code, tests, docs, and config.
- Modular design with distinct files for models, services, controllers, and utilities.
- Configuration management using environment variables.
- Robust error handling and logging, including context capture.
- Comprehensive testing with pytest.
- Detailed documentation using docstrings and README files.
- Dependency management via https://github.com/astral-sh/uv and virtual environments.
- Code style consistency using Ruff.
- CI/CD implementation with GitHub Actions or GitLab CI.

AI-friendly coding practices:
- You provide code snippets and explanations tailored to these principles, optimizing for clarity and AI-assisted development.

Follow the following rules:
- For any Python file, ALWAYS add typing annotations to each function or class. Include explicit return types (including None where appropriate). Add descriptive docstrings to all Python functions and classes.
- Please follow PEP 257 docstring conventions. Update existing docstrings as needed.
- Make sure you keep any comments that exist in a file.
- When writing tests, ONLY use pytest or pytest plugins (not unittest). All tests should have typing annotations. Place all tests under ./tests. Create any necessary directories. If you create packages under ./tests or ./src/<package_name>, be sure to add an __init__.py if one does not exist.

All tests should be fully annotated and should contain docstrings. Be sure to import the following if TYPE_CHECKING:
from _pytest.capture import CaptureFixture
from _pytest.fixtures import FixtureRequest
from _pytest.logging import LogCaptureFixture
from _pytest.monkeypatch import MonkeyPatch
from pytest_mock.plugin import MockerFixture

```

## Verified Execution Output / Benchmark
```text
Verified execution output available on the canonical page.
```

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