Documentation
Kiro provides powerful AI-assisted development capabilities for Python projects, helping you write, debug, and maintain code more efficiently.
Before diving into Python development with Kiro, ensure you have:
venv, virtualenv, or conda for dependency managementKiro supports extensions from Open VSX that can enhance your Python development experience. Here are some helpful extensions you can install:
Python - Python language support with extension access points for IntelliSense (Pylance), Debugging (Python Debugger), linting, formatting, refactoring, unit tests, and more.
PyLint - Linting support for Python files.
Jupyter - Jupyter notebook support, interactive programming and computing that supports Intellisense, debugging and more.
Python Debugger - Python debugger (debugpy) extension providing debugging capabilities for Python applications.
Rainbow CSV - Highlight CSV and TSV files, Run SQL-like queries
You can install these extensions in Kiro, use the Extensions panel and search for the extension names listed above.
With Kiro, you can leverage the chat capabilities to setup a new project or work on an existing one.
Kiro can help you set up and maintain configuration files for your Python projects, and organize your project following Python best practices.
Example prompts:
"Set up a requirements.txt with development dependencies" "Configure a .env file for my Django application" "Set up a Python package structure with proper __init__.py files" "Create a Flask project structure with blueprints" "Organize my data science project with notebooks and modules" "Create a pyproject.toml for a FastAPI project with pytest and black"
Kiro can analyze your Python code to identify issues and suggest improvements:
Example prompts:
"Analyze this function for potential bugs and performance issues" "Refactor this code to follow PEP 8 style guidelines" "Add type hints to this Python module" "Convert this synchronous code to use async/await"
When you encounter errors in your Python code:
Examples:
"Explain this Python error: AttributeError: 'NoneType' object has no attribute 'split'" "Help me debug this Django view that's returning a 500 error" "Why is my pandas DataFrame operation so slow?"
Steering allows you to provide Kiro with project specific context and guidelines. Kiro can generate steering files which you can refine:
product.md) - Contains information about the product, its purpose, and key featurestech.md) - Details the technologies, frameworks, and development guidelinesstructure.md) - Provides information about how the project is organizedFor Python projects, you can create additional custom steering files to provide more specific guidance:
Use the following instructions to add new steering documents to your project.
To add new steering documents
+ button to create a new steering file.Custom steering files are stored in the .kiro/steering/ directory and are automatically recognized by Kiro during interactions.
For example, you can define custom naming conventions, file structure, or practices for your project.
Create a python-conventions.md steering file to define your team's coding standards:
Python Conventions Naming Conventions - Use snake_case for variables and functions - Use PascalCase for classes - Use UPPER_SNAKE_CASE for constants - Use descriptive names that explain purpose Code Style - Follow PEP 8 guidelines - Use Black for code formatting - Maximum line length of 88 characters - Use type hints for all public functions File Structure - One class per file for large classes - Group related functions in modules - Use __init__.py files for package organization - Separate tests in tests/ directory Documentation - Use docstrings for all public functions and classes - Follow Google or NumPy docstring style - Include type information in docstrings
For Django projects, create a django-patterns.md steering file:
Django Development Guidelines Model Design - Use descriptive model names - Add __str__ methods to all models - Use model managers for complex queries - Follow Django naming conventions for fields View Structure - Prefer class-based views for complex logic - Use function-based views for simple operations - Keep business logic in models or services - Use proper HTTP status codes Template Organization - Use template inheritance effectively - Keep templates DRY with includes and tags - Use meaningful template names - Organize templates by app Performance Best Practices - Use select_related and prefetch_related for queries - Implement database indexing for frequently queried fields - Use caching for expensive operations - Profile database queries in development
For data science projects, create a data-science-patterns.md steering file:
Data Science Development Guidelines Notebook Organization - Use clear section headers and markdown cells - Keep notebooks focused on single analyses - Export reusable code to Python modules - Include data source documentation Data Handling - Validate data quality early in pipelines - Use consistent column naming conventions - Document data transformations clearly - Handle missing values explicitly Model Development - Use cross-validation for model evaluation - Track experiments with clear versioning - Document model assumptions and limitations - Implement proper train/validation/test splits Code Organization - Separate data processing, modeling, and visualization - Use configuration files for parameters - Implement logging for long-running processes - Create reproducible environments with requirements files
These steering files help Kiro generate code that follows your team's specific conventions and best practices.
Kiro's Agent Hooks can automate common Python development tasks:
Here are some hook examples:
Automatically generate tests when you save a Python file:
"Create a hook that generates pytest tests when I save a new Python module"
Keep your dependencies up to date:
"Create a hook that checks for outdated pip packages and suggests updates"
When a Python file is saved: 1. Run flake8 or pylint on the file 2. Report any style or quality issues 3. Suggest fixes for common problems 4. Update docstrings if missing
When requirements.txt or pyproject.toml is modified: 1. Check if virtual environment is activated 2. Install or update dependencies automatically 3. Report any dependency conflicts 4. Update requirements-dev.txt if needed
#docsKiro provides built-in access to documentation for Python and popular frameworks through the #docs reference system. This allows you to quickly bring relevant documentation into your conversations with Kiro.
Simply type #docs in the chat and select from the available documentation sources, such as:
Example usage:
"#Python How do I use context managers effectively?" "#Pytorch how can I add a custom operator?" "#PySide6 What is the best way to add a button?"
You can also reference specific documentation URLs using #URL:
"#URL https://docs.python.org/3/library/asyncio.html"
When you encounter issues, Kiro can help diagnose and fix them:
Cmd/Ctrl + I to open the inline chat.Cmd/Ctrl + L to add the current file to the chat.Quick fix and Ask Kiro.
Python