If you have a custom set of best practices or conventions that you want Gemini Code Assist to check for, you can add a styleguide.md file to the .gemini/ root folder of your repository. This file is treated as a regular text file, and expands the standard prompt that Gemini Code Assist uses.
Standard code review patterns
When custom style guides aren't specified, these are the main categories of areas where Gemini Code Assist focuses its code review on:
Correctness: Makes sure the code functions as intended and handles edge cases, checks for logic errors, race conditions, or incorrect API usage.
Efficiency: Identifies potential performance bottlenecks or areas for optimization, like excessive loops, memory leaks, inefficient data structures, redundant calculations, excessive logging, and inefficient string manipulation.
Maintainability: Assesses code readability, modularity, and adherence to language idioms and best practices. Targets poor naming for variables, functions, and classes, lack of comments or documentation, complex code, code duplication, inconsistent formatting, and magic numbers.
Security: Identifies potential vulnerabilities in data handling or input validation, like insecure storage of sensitive data, injection attacks, insufficient access controls, Cross-Site Request Forgery (CSRF), and Insecure Direct Object References (IDOR).
Miscellaneous: Other topics are considered when reviewing the pull request, like testing, performance, scalability, modularity and reusability, and error logging and monitoring.
Add configuration files
The .gemini/ folder hosts any Gemini Code Assist related configuration files, like config.yaml and styleguide.md.
The config.yaml file contains various configurable features that you can enable or disable. The styleguide.md text file is the style guide, which instructs Gemini Code Assist with some specific rules that you want it to follow when performing a code review.
To add these configuration files, create them in the .gemini/ folder of your repository and use the following table as a reference:
Default configuration
The following code snippet is an example of a config.yaml file with the default settings. If you don't include any particular settings, Gemini Code Assist resorts to the default settings. You can use this snippet as a template to create your own config.yaml file:
have_fun: true code_review: disable: false comment_severity_threshold: MEDIUM max_review_comments: -1 pull_request_opened: help: false summary: true code_review: true Custom configuration
The following code snippet is an example of a config.yaml file with customized settings:
$schema: "http://json-schema.org/draft-07/schema#" title: RepoConfig description: Configuration for Gemini Code Assist on a repository. All fields are optional and have default values. type: object properties: have_fun: type: boolean description: Enables fun features such as a poem in the initial pull request summary. Default: true. code_review: type: object description: Configuration for code reviews. All fields are optional and have default values. properties: disable: type: boolean description: Disables Gemini from acting on pull requests. Default: false. comment_severity_threshold: type: string enum: - LOW - MEDIUM - HIGH - CRITICAL description: The minimum severity of review comments to consider. Default: MEDIUM. max_review_comments: type: integer format: int64 description: The maximum number of review comments to consider. Use -1 for unlimited. Default: -1. pull_request_opened: type: object description: Configuration for pull request opened events. All fields are optional and have default values. properties: help: type: boolean description: Posts a help message on pull request open. Default: false. summary: type: boolean description: Posts a pull request summary on the pull request open. Default: true. code_review: type: boolean description: Posts a code review on pull request open. Default: true. Style guide
The following code snippet is an example of a styleguide.md file:
# Company X Python Style Guide # Introduction This style guide outlines the coding conventions for Python code developed at Company X. It's based on PEP 8, but with some modifications to address specific needs and preferences within our organization. # Key Principles * **Readability:** Code should be easy to understand for all team members. * **Maintainability:** Code should be easy to modify and extend. * **Consistency:** Adhering to a consistent style across all projects improves collaboration and reduces errors. * **Performance:** While readability is paramount, code should be efficient. # Deviations from PEP 8 ## Line Length * **Maximum line length:** 100 characters (instead of PEP 8's 79). * Modern screens allow for wider lines, improving code readability in many cases. * Many common patterns in our codebase, like long strings or URLs, often exceed 79 characters. ## Indentation * **Use 4 spaces per indentation level.** (PEP 8 recommendation) ## Imports * **Group imports:** * Standard library imports * Related third party imports * Local application/library specific imports * **Absolute imports:** Always use absolute imports for clarity. * **Import order within groups:** Sort alphabetically. ## Naming Conventions * **Variables:** Use lowercase with underscores (snake_case): `user_name`, `total_count` * **Constants:** Use uppercase with underscores: `MAX_VALUE`, `DATABASE_NAME` * **Functions:** Use lowercase with underscores (snake_case): `calculate_total()`, `process_data()` * **Classes:** Use CapWords (CamelCase): `UserManager`, `PaymentProcessor` * **Modules:** Use lowercase with underscores (snake_case): `user_utils`, `payment_gateway` ## Docstrings * **Use triple double quotes (`"""Docstring goes here."""`) for all docstrings.** * **First line:** Concise summary of the object's purpose. * **For complex functions/classes:** Include detailed descriptions of parameters, return values, attributes, and exceptions. * **Use Google style docstrings:** This helps with automated documentation generation. ```python def my_function(param1, param2): """Single-line summary. More detailed description, if necessary. Args: param1 (int): The first parameter. param2 (str): The second parameter. Returns: bool: The return value. True for success, False otherwise. Raises: ValueError: If `param2` is invalid. """ # function body here ``` ## Type Hints * **Use type hints:** Type hints improve code readability and help catch errors early. * **Follow PEP 484:** Use the standard type hinting syntax. ## Comments * **Write clear and concise comments:** Explain the "why" behind the code, not just the "what". * **Comment sparingly:** Well-written code should be self-documenting where possible. * **Use complete sentences:** Start comments with a capital letter and use proper punctuation. ## Logging * **Use a standard logging framework:** Company X uses the built-in `logging` module. * **Log at appropriate levels:** DEBUG, INFO, WARNING, ERROR, CRITICAL * **Provide context:** Include relevant information in log messages to aid debugging. ## Error Handling * **Use specific exceptions:** Avoid using broad exceptions like `Exception`. * **Handle exceptions gracefully:** Provide informative error messages and avoid crashing the program. * **Use `try...except` blocks:** Isolate code that might raise exceptions. # Tooling * **Code formatter:** [Specify formatter, e.g., Black] - Enforces consistent formatting automatically. * **Linter:** [Specify linter, e.g., Flake8, Pylint] - Identifies potential issues and style violations. # Example ```python """Module for user authentication.""" import hashlib import logging import os from companyx.db import user_database LOGGER = logging.getLogger(__name__) def hash_password(password: str) -> str: """Hashes a password using SHA-256. Args: password (str): The password to hash. Returns: str: The hashed password. """ salt = os.urandom(16) salted_password = salt + password.encode('utf-8') hashed_password = hashlib.sha256(salted_password).hexdigest() return f"{salt.hex()}:{hashed_password}" def authenticate_user(username: str, password: str) -> bool: """Authenticates a user against the database. Args: username (str): The user's username. password (str): The user's password. Returns: bool: True if the user is authenticated, False otherwise. """ try: user = user_database.get_user(username) if user is None: LOGGER.warning("Authentication failed: User not found - %s", username) return False stored_hash = user.password_hash salt, hashed_password = stored_hash.split(':') salted_password = bytes.fromhex(salt) + password.encode('utf-8') calculated_hash = hashlib.sha256(salted_password).hexdigest() if calculated_hash == hashed_password: LOGGER.info("User authenticated successfully - %s", username) return True else: LOGGER.warning("Authentication failed: Incorrect password - %s", username) return False except Exception as e: LOGGER.error("An error occurred during authentication: %s", e) return False