Automation of GitHub work flows with Claude 4

Claude Opus 4 is the most advanced and powerful AI Anthropic model, establishing a recent reference point for coding, reasoning and long -lasting task. It can autonomously support intricate, hours of tasks, maintain concentration and provide unique results in thousands of steps.

In this tutorial, we will learn how to employ Claude Opus 4 to automate GitHub work flows, which can be used to solve GitHub problems, perform code review and manage Pull (PRS) demands. We will learn how to configure the Claude application in your GitHub repository and call it directly through comments.

Configuring Claude code

1. Run the following command in the terminal to install the Claude code around the world:

   npm install -g @anthropic-ai/claude-code

2. Visit Anthropic console And create an account.
3. Add at least USD 10 loans to your account using a credit card or debit card.
4. Run the Claude code, navigating the project catalog and launching:
5. Follow the promises to connect to the anthropic console:

• The browser window will open, which prompted you to log in to the anthropic account.
• Copy the authentication code generated by the console and paste it into the Claude code terminal.

After authentication, the Claude code will be ready for employ.

Configuring the GitHub application using Claude code

1. Open your terminal and enter Claude to start the Claude code.
2. After entering the Claude code, enter /install-github -ppp to start configuring the Claude application for GitHub activities.
   
3. Go to the GitHub repository and create a Pull request. SCAL PULL request to make sure that the repository is ready to integrate Claude.
   
   Screenshot with Keybpro / BBC-News MLOPS
4. Open your GitHub work flow file (GitHub/Workflows/Claude.yml) and add the following configuration:

   model: 'claude-sonnet-4-20250514'

   
   Screenshot with .Github/workflows/claude.yml

   This ensures that the latest Claude 4 model is used in the Claude application.

5. Visit the Claude Github application page: https://github.com/apps/claude.
6. Install the application and give it access to the repository.

Creating a PULL request using GitHub problems

We chose in this tutorial Problem #9 With Keybpro / BBC-News MLOPS GitHub Repository.

1. Go to number 9 in the repository.
2. In the comments section on the case, enter the following command:

   @claude add the docker compose file based on the issue description

   . @claude The memory triggers the GitHub action, enabling clauda analysis of the description of the problem and using it as a context to generate the required code.

   
   Screenshot with Keybpro / BBC-News MLOPS
3. After completing the task, Claude will create all the necessary files and ensure the option of direct creation of the Pull request.
   
   Screenshot with Keybpro / BBC-News MLOPS
4. Click Create PR Link provided by Claude and Scal Changes in your repository.

Screenshot with Keybpro / BBC-News MLOPS

The changes will be implemented, and in most cases Claude solves this problem with 90% accuracy based on the context provided.

Other cases of using the GitHub Claude application

We used the description of the GitHub release to edit and create a Pull request. You can also try the following employ case using the same workflow:

1. Automated Code Review: Analyze Pull (PR) requests for code quality, potential errors and following standards.
2. PR: Create, update and manage the PUBLL demands automatically.
3. TRIAGE release: Analyze problems, categorize them and suggest or implement corrections.
4. Debugging and repairing errors: Find errors, implement corrections and create PR for review.
5. Documentation updates: Automatically update documentation based on code changes.
6. Re -invoice code: Improve the readability of the code, performance or possibility of maintaining.

Final thoughts

Anthropic quickly appears as a supplier of the AI ​​model in the scope of coding and software engineering tasks. The company offers a comprehensive ecosystem that automates the entire development process, including building, testing, debugging, implementation and monitoring of the application. Thanks to Claude Code and its extension, programmers can easily automatize all these steps, which makes it a powerful tool to improve work flows.

Abid Ali Awan (@1abidaliawan) is a certified scientist who loves to build machine learning models. Currently, it focuses on creating content and writing technical blogs on machine learning and data learning technologies. ABID has a master’s degree in technology management and a bachelor’s title in the field of telecommunications engineering. His vision is to build AI with a neural network for students struggling with mental illness.