KubeVirt development involves a lot of repetitive workflow — reviewing PRs against project conventions, triaging CI failures across dozens of job lanes, tracking enhancement proposals through the release process. These tasks are well-defined but time-consuming, and they’re exactly the kind of thing an AI coding assistant can help with if you give it the right context.
kubevirt-ai-helpers is a collection of Claude Code plugins that encode KubeVirt-specific knowledge into reusable slash commands. Instead of pasting reviewer checklists or manually crawling Prow logs, you run a command and get structured output that follows the project’s conventions.
What’s in the box
The main plugin ships 18 commands organized around four areas of KubeVirt development:
Code review — /kubevirt:review runs a multi-pass review of your local branch changes (general design, detailed code, standards compliance, commit history). /kubevirt:review-pr does the same against a remote PR via the gh CLI, posting pending review comments with verified line numbers so you can edit before submitting.
CI analysis — /kubevirt:ci-health gives a quick overview of failure trends across merge-time jobs using pre-aggregated data from kubevirt/ci-health. /kubevirt:ci-lane digs deeper into a specific job with live Prow data. /kubevirt:ci-search finds test failures by regex pattern across all jobs, and /kubevirt:ci-triage layers on root cause analysis with cross-job correlation. /kubevirt:review-ci ties it back to a specific PR, categorizing its failures and suggesting fixes.
VEP management — KubeVirt Enhancement Proposals have their own lifecycle tracked across GitHub issues, PRs, and project boards. /kubevirt:vep-list shows open proposals with release tracking status. /kubevirt:vep-summary gives a TL;DR of any VEP. /kubevirt:vep-groom reviews a proposal PR against template requirements. /kubevirt:vep-find-reviewers identifies potential reviewers based on SIG ownership and recent git activity.
Development workflow — /kubevirt:dev implements what I’ve been calling the “harness pattern”: it loads project conventions and code context upfront (feedforward), then iterates through build, test, lint, and self-review sensors (feedback) to converge on correct code. It accepts GitHub issues, Jira tickets, or plain text as input and produces a development session with changes ready to commit.
How it works
These are Claude Code plugins — markdown files with structured frontmatter that Claude Code loads as slash commands. Each command is essentially a detailed prompt that encodes project-specific knowledge: reviewer checklists, coding conventions, CI infrastructure details, and the VEP process. When you invoke a command, Claude Code follows the steps in the prompt, using its tools (file reads, shell commands, gh CLI) to gather context and produce output.
There’s no custom runtime or API. The plugins run inside Claude Code’s existing agent loop, which means they get tool use, context management, and conversation history for free.
Getting started
Install the plugin from the Claude Code plugin marketplace:
claude plugins:install kubevirt/kubevirt-ai-helpers
Or clone the repo and point Claude Code at it locally. Commands are available immediately as /kubevirt:* slash commands.
Contributing
The project has 36 contributors so far. The plugin architecture makes it straightforward to add new commands — each one is a self-contained markdown file with frontmatter defining its name, arguments, and description, followed by the implementation steps. The hello-world plugin serves as a reference template.
Source at github.com/kubevirt/kubevirt-ai-helpers. Issues and PRs welcome.