GitHub Actions skill

The video demonstrates a workflow leveraging GitHub Actions and a custom CLI tool to diagnose and fix CI failures. The speaker emphasizes automating as much of the development process as possible, including linting, testing, and release management. He avoids publishing NPM binaries or tagging releases from his local machine, instead relying on GitHub Actions workflows.

Key points include:

• Automated Release Workflow: A workflow is triggered by specifying a tag name, which then checks if CI has passed, builds binaries, updates Homebrew, and performs smoke tests by installing the release via NPM, standalone download, and Homebrew, verifying the version after each installation.
• Diagnosis and Fix: A custom CLI tool ("GHA diagnose and fix") diagnoses Windows failures by downloading logs from GitHub Actions and identifying the root cause. It then automatically applies fixes and creates a pull request.
• Human-in-the-Loop: The speaker emphasizes the importance of understanding the fixes proposed by the tool, rather than blindly accepting them. He asks the tool to explain the changes and uses the new knowledge to identify potential gaps in existing tests.
• Learning and Improvement: The speaker demonstrates the value of learning from CI failures and using that knowledge to improve test coverage. In this specific instance, differences in how Windows and macOS handle the spawning of non-existent binaries uncovered a potential testing gap.
• Workflow Automation: The video highlights the automated workflow from code push to CI monitoring, using tools like gha monitor, commit, and hubcom (an alias to open the current project on GitHub in the browser) to streamline the development process.
• Cloud Code Integration: Demonstrates using Cloud Code (via a "bang" command) to execute underlying bash commands like git push for increased control over the push process.
• Value of Automation: The demonstrated workflow allows for quicker bug diagnosis, resolution, and release management, all while ensuring continuous learning and code quality.