Add docker publish workflows

Here's a detailed code review for each file in your project, structured in Markdown format:

Overview

This GitHub Actions workflow is designed to automate the process of checking pull requests (PRs) by running code linting and tests, as well as providing AI-generated code reviews based on the changes made in the PR.

Review

Pull Request Trigger: The workflow is triggered on , which is useful for actions that require access to the pull request's context.
Parallel Jobs: There are two jobs defined ( and ), which are well-separated in terms of responsibilities.
Git Checkout: The use of is appropriate, and correctly utilizes the option to checkout the specific PR branch.
Environment Variables: The is calculated based on the number of commits, which is a good practice to avoid unnecessary fetches.
Linting and Testing: The linting step uses , making it easy to identify code quality issues early in the pipeline.
AI Code Review Call:
- Makes use of the input with a specific instruction for the AI to provide markdown-format code reviews, enhancing the clarity of the output.
- Ensure that is securely stored in GitHub secrets which is a recommended best practice.
Post Review Comment:
- Calls , which seamlessly integrates the review process with the actual PR by posting feedback.
- The output file is created before the comment, which is a neat idea.

Suggestions

Error Handling: There should be error handling after critical steps such as the AI review step and when posting comments back to the PR to ensure smooth operation and feedback on failure.
Fetching only Modified Files: If the list of changed files is large, consider paginating the review requests sent to the AI to manage output size and prevent issues with token limits.

Overview

This workflow focuses on building and publishing Docker images whenever commits are pushed to the main branch.

Review

Branch Trigger: It leverages a simple event on the branch, which aligns well with the goal of publishing stable releases.
Job Structure: The job is properly divided to use specific actions (e.g., ) for logging in and building Docker images.
Docker Login: The use of secrets for Docker credentials is a secure practice, ensuring sensitive information isn't exposed.
Build and Publish Workflow:
- The build and publish steps are clearly defined with distinct action calls, making it easy to understand the flow of operations.
- Using a timestamp tag for publishing Docker images is a good practice for traceability.

Suggestions

Error Handling in Steps: Add error handling for each step to capture failures (for example, if Docker login fails).
Cleaning Up Resources: Consider adding cleanup steps, if applicable, to remove unused images/repos to minimize resource utilization.

Overview

The README file provides essential information about the CLI tool, including installation guidance, usage examples, and workflows for CI.

Review

Clarity and Structure: The document is well-structured with a clear introduction, section headers, and usage examples.
Usage Examples: Provides command-line examples that illustrate commands and options, thus enhancing user understanding.
CI Integration: Clear instructions on how to integrate into CI workflows with example YAML configuration.

Suggestions

Command Output Efficiency: Consider adding more output examples for various complex commands to assist users in understanding what they can expect.
Troubleshooting Section: Include a section for common error messages and resolutions.

Overview

This file contains functions for Docker operations such as login, build, and publish.

Review

Function Naming: Function names are descriptive and follow a consistent naming pattern.
Error Handling: Each function gracefully returns errors with context, which is critical for debugging.
Command Execution: Commands are run using helper functions from , maintaining modularity.

Suggestions

Parameter Validation: While some parameters are validated (like checking for usernames), adding more validation (e.g., and ) might enhance robustness.
Output Improvement: Consider allowing the command outputs to be more detailed depending on user needs (e.g., verbosity option).

Overview

This file manages image building and publishing using the command.

Review

Clear Abstraction: Functions are well-organized, abstracting complexity away from the user.
Error Propagation: Errors are wrapped and propagated with detailed messages, which aids in troubleshooting.

Suggestions

Output Logging: Similar to previous suggestions, you may log output for build and publish steps to assist with debugging when builds fail.

Overview

This utility file provides essential command execution functions and helpers.

Review

Command Execution: The use of a regex to split commands is innovative and handles quoted arguments effectively.
Buffer for Capturing Output: The capturing of both stdout and stderr is well managed to help diagnose issues clearly.

Suggestions

Concurrency Handling: If commands take a long time, consider incorporating cancellation or context handling to manage execution more gracefully.
Function Documentation: Add documentation comments (with examples) to key functions to enhance maintainability.

Overview

This file contains unit tests for various utility functions in the package.

Review

Strongly Typed Tests: This makes tests easy to read and maintain as they are structured clearly.
Coverage: Tests cover various paths (both for valid commands and error cases).

Suggestions

More Diverse Test Cases: Add more tests that cover edge cases, such as unexpected command outputs or failures.
Performance Tests: Consider adding performance tests for long-running commands or commands expected to have large outputs.

Overview

This serves as the entry point for the Mage build process, orchestrating various Docker-related functions defined in the package.

Review

Simplicity: The magefile is succinct and easy to follow, which is beneficial for user comprehension.
Logger Initialization: Proper initialization of logging to stdout is good for tracking build processes.

Suggestions

Extending Functionality: If this file grows, consider organizing functions into logical sections or breaking them into separate files to maintain clarity and structure.

Overall, the code is structured and follows best practices, with clear functionality across separate files. With the suggestions mentioned, the maintainability and robustness of the system can be further improved.

go-zen-chu / aictl