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81 changes: 81 additions & 0 deletions github-management/ai-code-review-tools.md
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# AI Code Review Tools

The Kubernetes project may evaluate AI-powered code review tools on a
per-repo opt-in basis. This document describes the process for requesting,
evaluating, and deciding on the use of such tools.

## Scope

This policy covers AI tools that automatically review pull requests, such as
CodeRabbit or GitHub Copilot code review. It does not cover other AI-powered
tooling such as CI/CD, security scanning, or code generation assistants.

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what about open source tools leveraging AI ? we could have something built in the open just for issue triage for example.

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@kannon92 kannon92 Apr 6, 2026

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The CNCF provides https://dosu.dev/ for this purpose already. ref: https://contribute.cncf.io/resources/services/hosted-tools/#tools

But imo I'm not sure this policy should be in scope for your question.

## Requesting a New Tool

A subproject lead listed in the repository's top-level [OWNERS] file files an
issue on [kubernetes/org]. The issue must:

- Identify the tool and link to its documentation
- Describe the use cases and what the subproject is trying to accomplish
- Explain why existing approved tools do not meet their needs
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Is there a list of already-approved tools somewhere?

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@janetkuo janetkuo Apr 16, 2026

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IIRC k8s repo admins can enable Copilot auto code reviews, but as @kannon92 mentioned in kubernetes/org#5930 (comment), it requires PR authors to have Copilot subscription first, or the maintainers need to manually request reviews from Copilot

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OK. I'm just saying that if we have existing tools, we should link to a list somewhere, so that it'll save a lot of duplicate requests.

- List the specific repositories for the pilot

## Privacy and Security Assessment

Upon receiving a request, the [GitHub Administration Team] conducts a privacy
and security assessment of the tool. The assessment documents:

- What GitHub permissions and OAuth scopes the tool requires
- What data the tool accesses and where it is sent
- What AI models are used to process the code
- Data retention and deletion policies
- Security certifications (SOC2, etc.)
- Whether access can be scoped to specific repositories

The assessment is documented in the [kubernetes/org] issue for transparency.

## Approval

The [GitHub Administration Team] reviews the request and the privacy and
security assessment, and approves or rejects the request. If approved, the
[GitHub Administration Team] enables the tool on the requested repositories
and applies an org-wide default configuration.

## Pilot Structure

- The pilot runs for a 90-day evaluation period starting from the date the
tool is enabled
- The tool is enabled only on the specific requested repositories, not org-wide
- An org-wide default configuration is applied; repositories may customize
within those bounds
- The sponsoring subproject is responsible for collecting feedback from
contributors and reviewers during the pilot

## Evaluation and Decision

At the end of the pilot period, the sponsoring subproject provides a summary
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how is this summary provided? is this going to be an artifact somewhere? is providing some structure to the summary a good idea so we can have consistency on the reports?

covering:

- Quality of reviews (signal vs noise)
- Contributor and reviewer feedback
- Any issues encountered

The [GitHub Administration Team], in consultation with the sponsoring
subproject, decides to:

- Continue and expand to additional repositories
- Continue with modifications
- Remove the tool

Expansion to additional repositories follows the same request process described
above.

## Removal

If a pilot is unsuccessful or a tool is no longer desired, the
[GitHub Administration Team] will disable the integration. Subproject leads may
request removal at any time by filing an issue on [kubernetes/org].

[GitHub Administration Team]: /github-management/README.md#github-administration-team
[OWNERS]: /contributors/guide/owners.md
[kubernetes/org]: https://github.com/kubernetes/org/issues