ci: Script for checking-out experimental types

[lkawka]

Description

Added a script for creating a new feature branch with types generated from unmerged A2A spec changes.

We need to begin development on several upcoming spec changes that are currently unmerged but are highly likely to be included in the next release, such as the task/list method. To do that, we need updated types, which this script provides.

Prerequisites

• Follow the CONTRIBUTING Guide.
• Make your Pull Request title in the https://www.conventionalcommits.org/ specification.
  • Important Prefixes for release-please:
    • fix: which represents bug fixes, and correlates to a SemVer patch.
    • feat: represents a new feature, and correlates to a SemVer minor.
    • feat!:, or fix!:, refactor!:, etc., which represent a breaking change (indicated by the !) and will result in a SemVer major.
• Ensure the tests and linter pass (Run bash scripts/format.sh from the repository root to format)
• Appropriate docs were updated (if necessary)

[gemini-code-assist]

Summary of Changes

Hello @lkawka, I'm Gemini Code Assist¹! I'm currently reviewing this pull request and will post my feedback shortly. In the meantime, here's a summary to help you and other reviewers quickly get up to speed!

This pull request introduces a new shell script designed to streamline the development process for features based on unmerged A2A specification changes. By automating the generation of Python types from a specified A2A spec branch, it enables developers to create dedicated feature branches with the necessary updated types, facilitating early integration and testing of experimental functionalities. This enhancement significantly reduces manual setup time and ensures consistency when working with evolving specifications.

Highlights

• New Script for Experimental Types: A new shell script, checkout_experimental_types.sh, has been added to automate the generation of Python types from unmerged A2A specification changes. This allows developers to work with upcoming spec features before they are officially integrated into the main branch.
• Automated Type Generation: The script clones the A2A spec repository, checks out a specified branch, and uses datamodel-codegen to generate pydantic_v2.BaseModel types from the A2A JSON schema. It then formats the generated file using ruff.
• Feature Branch Creation: After generating and formatting the types, the script automatically creates a new Git feature branch, commits the newly generated types, and cleans up temporary files, streamlining the setup for experimental development.
• Configurable Options: The script is highly configurable, allowing users to specify the A2A spec repository URL, the experimental spec branch name, the desired feature branch name, and the temporary working directory via command-line arguments.

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[gemini-code-assist]

Code Review

This pull request introduces a helpful script for generating Python types from experimental A2A spec changes. The script is well-structured with argument parsing and a usage function. My review focuses on improving the script's robustness and safety, particularly concerning file system operations like temporary directory handling and path calculations. I've also suggested changes to make the script less dependent on specific repository naming conventions, which will enhance its reliability and maintainability.

[holtskinner]

Note: I changed the conventionalcommits.org type to be ci: instead of feat: this isn't actually a feature for the SDK, it's for internal development.

[holtskinner]

/gemini review

[gemini-code-assist]

Code Review

This pull request introduces a helpful script for generating experimental types from the specification repository, which will streamline development against unmerged changes. The modifications to generate_types.sh enhance its flexibility by allowing local file inputs. The new checkout_experimental_types.sh script is well-structured, but I've identified a few areas for improvement regarding robustness, clarity, and the removal of non-functional code. My suggestions aim to make the scripts more reliable and easier to maintain.