Claude Code in DevOps

You've already seen how Claude Code works as a pair programmer, but it doesn't stop there. Beyond helping you write and refactor code, you can also integrate it into other phases of the DevOps life cycle. When you configure it, it helps with code reviews, task automation, and faster software delivery.

Let's see how you can use Claude Code in other phases of the DevOps life cycle.

Setting up your repository for Claude Code

Previously, you saw how to use Claude Code in your local development environment. Now, you will set up your repository so that Claude Code responds to commands within your version control system. We'll use GitHub here, but you can also use these capabilities in GitLab.

To set up Claude Code in GitHub, install the Claude GitHub app in the target repository. If you already have Claude Code set up in your local environment, you can use the /install-github-app slash command. Then, follow the prompts to set everything up:

If you'd like to do it manually, all you need to do is install the Claude GitHub app. Then, set your Anthropic API key as a repository secret in your GitHub repo with a name like ANTHROPIC_API_KEY. This method does not create default workflows for you, so you'll need to create them manually.

You don't need to give Claude Code access to all repositories or all permissions. Just choose the minimum permissions that Claude Code needs to perform its job in specific repositories.

Conversational agent in your repository

In a shared repository, teams use Issues to collaborate on different tasks. You can start a GitHub or GitLab Issue or open a PR and @mention Claude Code. It joins the conversation as a helpful partner for brainstorming ideas, performing code reviews, and even writing code. To enable this, you need to set up a GitHub Actions workflow.

GitHub Actions is an automation tool that allows you to run custom workflows directly in your repository. You can use it to run Claude Code when certain events occur in the repository. Let's start by creating the necessary configuration file that instructs GitHub Actions on what to do. If you used the automated setup, the workflow was already crafted for you.

Create a new file, .github/workflows/claude.yml, and add the following code. This file specifies when and how Claude Code should be activated.

# .github/workflows/claude.yml
name: 'Claude Code Planner'

on:
  issue_comment:
    types: [created]
  pull_request_review_comment:
    types: [created]

jobs:
  claude_code:
    permissions:
      contents: write
      pull-requests: write
      issues: write
      id-token: write # allows your workflow to authenticate with Anthropic
      actions: read # allows Claude to read CI results on PRs
    runs-on: ubuntu-latest

    steps:
      - name: Checkout repository
        uses: actions/checkout@v4

      - name: Run Claude Code
        uses: anthropics/claude-code-action@v1
        with:
          anthropic_api_key: ${{ secrets.ANTHROPIC_API_KEY }}
          trigger_phrase: "@claude" # you can modify the trigger phrase

The on key specifies that the workflow should run when new Issue or PR comments are created. The jobs section defines the tasks to be performed. You also need to grant the necessary permissions for Claude Code to retrieve context and write a response. Finally, it checks out your repository's code and runs the anthropics/claude-code-action@v1 action.

With this workflow in place, you have transformed your repository into an AI-powered collaborative space. This functionality is useful especially during the planning and review phases. Instead of just discussing ideas with your team, you can also bring Claude Code into the conversation:

This command triggers the workflow you just set up. Claude Code will then analyze the request and post a reply directly in the Issue. The response might include a high-level design, suggestions on specific libraries or APIs to use, and even boilerplate code for the main functions.

More automation with GitHub Actions

While using @claude mentions in comments is great for specific, on-demand tasks, automation is at the heart of CI/CD pipelines. You want Claude Code to act proactively without waiting for a manual trigger. This should happen automatically when specific events occur in your repository, such as when a developer opens a new pull request.

To enable this, you will modify the .github/workflows/claude.yml file to listen for different events. Instead of triggering on an issue_comment, you can set the workflow to run on a pull_request event (or any other event):

on:
  pull_request:
    types: [opened, synchronize, reopened]

This tells GitHub Actions to execute the job whenever a PR is opened, updated, or reopened. Then, add a prompt parameter to the with block to instruct Claude on what to do:

- name: Run Claude Code Review
  uses: anthropics/claude-code-action@v1
  with:
    anthropic_api_key: ${{ secrets.ANTHROPIC_API_KEY }}
    prompt: |
      Please review this pull request and provide feedback on:
      - Code quality and best practices
      - Potential bugs or issues
      - Performance considerations
      - Security concerns
      - Test coverage
      Use the repository's CLAUDE.md for guidance on style and conventions. Be constructive and helpful in your feedback. Use `gh pr comment` with your Bash tool to leave your review as a comment on the PR.

Other options like permissins and repository checkout remain the same. With this configuration, every time a developer opens a pull request or pushes new commits to it (synchronize), the workflow triggers automatically. The prompt parameter directs Claude Code to perform a comprehensive analysis of the changes. It will then post its findings directly as comments on the pull request.

To further tailor automated workflows, the with block offers several other parameters:

• prompt: This allows you to provide a custom, free-text instruction. This is useful for unique tasks, such as asking Claude Code to perform a code review as seen above.

• model: This parameter allows you to specify which Claude model the action should use.

• claude_args: For advanced control, this parameter lets you pass a string of command-line arguments. This is a flexible way to set multiple options at once, like the model, system prompt, and maximum interaction turns.

Here is an example:

  - name: Claude Code Review Action
    uses: anthropics/claude-code-action@v1
    env:
      GH_TOKEN: ${{ secrets.GITHUB_TOKEN }}
    with:
      anthropic_api_key: ${{ secrets.CLAUDE_AI_API_KEY }}
      prompt: |
        Act as an expert code reviewer.
        1. Provide a brief, high-level summary of the changes in this pull request.
        2. Review the code for potential security vulnerabilities ONLY.
        3. Post your findings as comments DIRECTLY on the relevant lines of code in the PR.
      claude_args: |
        --system-prompt "You are an expert Go developer. Always provide concise, actionable feedback with code examples."
        --model claude-sonnet-4-5-20250929
        --allowed-tools "Bash(gh search:*),Bash(gh issue list:*),Bash(gh pr comment:*),Bash(gh pr diff:*),Bash(gh pr view:*),Bash(gh pr list:*)"

Custom workflows

While GitHub Actions are helpful, some workflows require more flexibility or happen outside the repository. For these scenarios, you can use Claude Code in headless mode. This approach involves running Claude directly from the command line or within custom scripts, completely independent of a user interface or platform integration. It allows you to build custom tools and embed AI assistance into any part of your development and operations pipeline, giving you full control over how and when you use it.

This method is perfect for complex or unique tasks that don't fit into standard triggers. For instance, you might want to analyze logs from a failed build on a Jenkins server. Using scripts, you can pipe data from any source directly to Claude and integrate its response into your existing toolchains.

Here is an example of a shell script that debugs a failed build log:

#!/bin/bash

# A simple script to debug a failed build log.
# It assumes the error output is in a file named build_log.txt.

BUILD_LOG=$(cat build_log.txt)
PROMPT="The following build failed on our Jenkins server. Please analyze the error logs and suggest a solution. Log: ${BUILD_LOG}"

# Send the prompt to Claude via the command-line tool and print the response.
claude --print "$PROMPT"

When you run this script, Claude receives the full context of the failure. It can quickly pinpoint the exact error, whether it's a missing dependency or a syntax error in a build file. Given build logs from a Jenkins build server, you'd get something like:

If needed, you can also use the Claude Agent SDK to build highly customized workflows.

Observability and monitoring

Claude Code can also act as a helpful assistant for site reliability engineers (SREs) and on-call developers. Using scripts, Agent SDK, and MCP, operations teams can quickly diagnose issues and understand complex alerts. This speeds up incident response, reducing the mean time to resolution (MTTR) and minimizing the impact on users.

When an error occurs, Claude Code can directly summarize log data. MCP servers can connect Claude to logging platforms like the ELK stack (Elasticsearch, Logstash, and Kibana) to perform complex, context-aware queries across vast, structured log data. Instead of analyzing just a static file, Claude Code can identify patterns and correlate events across services.

Observability platforms like Prometheus and Datadog use complex query languages (e.g., PromQL). Engineers can use Claude Code to convert natural language questions into accurate queries:

Besides reactive analysis, Claude Code can help with proactive monitoring. It can analyze usage metrics and suggest updates to auto-scaling configurations. It can also find potential performance problems and suggest possible solutions. This helps teams optimize resource usage and infrastructure costs, and fix scaling issues before they affect users.

Conclusion

You have explored how Claude Code fits into the full DevOps lifecycle and helps automate important tasks in various phases. You started by setting up Claude Code in your GitHub repository. This allowed you to generate implementation plans and skeleton code directly from GitHub Issues.

Then, you automated code reviews to maintain quality. You saw that you can further customize your GitHub Actions workflows to perform arbitrary actions. Finally, you used scripts and MCP to analyze logs and enhance monitoring. By adding Claude Code to your CI/CD pipelines, you can increase reliability while delivering software faster.