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Automating Pull Request Workflows with Claude Task Master

#automation #claude #cli #softwaredevelopment

Introduction

Developers spend a lot of time managing pull requests, fixing CI failures, and responding to review comments. Claude Task Master provides a command line interface that automates this entire loop. By giving the tool a high level goal, it plans the work, writes code, pushes commits, opens a pull request, handles CI, addresses feedback, and merges when all checks pass.

How It Works

The CLI reads the repository, creates a task list, and defines success criteria before any code is changed. Each task is executed in isolation: the tool makes the required modifications, runs the test suite, and creates a commit. After the commit is pushed, a pull request is opened automatically. The pull request enters the CI stage; if any checks fail, Claude Task Master updates the code, runs the tests again, and pushes a new commit. Review comments are fetched via the GitHub API, and the tool can apply suggested changes without human intervention. When the pull request passes all checks and receives approval, the tool performs an auto merge.

Benefits

  • Hands off - set a goal and let the tool run until the pull request is merged.
  • State persistence - the CLI stores its progress on disk, so a stopped session can resume exactly where it left off.
  • Profile isolation - multiple Claude subscriptions can be used in parallel by creating separate profiles, each with its own configuration directory.
  • Extensible integration - a REST API, an MCP server, and signed webhooks expose the same lifecycle to external systems, enabling custom dashboards or CI pipelines.

Example Usage 

# Install the CLI (uv, pip, or Docker are supported)
uv tool install claude-task-master

# Authenticate with Claude Code
claude login

# Run a task in your project directory
cd my-project
claudetm start "Add user authentication with tests"
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The command above creates a plan, writes the authentication code, adds tests, pushes the changes, opens a pull request, and watches the CI pipeline. If the CI fails, the tool fixes the issue and pushes a new commit. Once the pull request is approved, it merges automatically.

Integration Points

Claude Task Master can be invoked from other tools via its REST API. For example, a CI job can POST a JSON payload with a goal description, and the server will start a new task. Webhooks can be configured to notify a Slack channel when a pull request is merged or when a CI failure occurs. The MCP server provides a lightweight message queue for coordinating multiple instances of the CLI.

Conclusion

Automating the pull request workflow reduces manual overhead and speeds up delivery. Claude Task Master combines planning, execution, and verification in a single tool that works with the Claude Code session or a direct Anthropic compatible API. It is open source under the MIT license and can be installed via PyPI or Docker. Try it today and see how a fully autonomous pull request loop can improve your development process.

Repository: https://github.com/developerz-ai/claude-task-master

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