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Coreweave ci integration

Skill jeremylongshore/claude-code-plugins-plus-skills/plugins/saas-packs/coreweave-pack/skills/coreweave-ci-integration

425 plugins, 2,810 skills, 200 agents for Claude Code. Open-source marketplace at tonsofskills.com with the ccpi CLI package manager.

Install
npx -y skills add jeremylongshore/claude-code-plugins-plus-skills --skill coreweave-ci-integration

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What its author says it does

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'Integrate CoreWeave deployments into CI/CD pipelines with GitHub Actions.

The file declares its own license as MIT. That is the author’s claim about this one file, and it is not the same thing as the license GitHub reports for the repository, which is listed with the other numbers below.

SKILL.md

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CoreWeave CI Integration

Community-contributed. Not affiliated with, endorsed by, or sponsored by CoreWeave, Inc. CoreWeave is a registered trademark of CoreWeave, Inc.

Overview

Set up CI/CD for CoreWeave GPU cloud workloads: run unit tests with mocked Kubernetes clients on every PR, deploy inference containers to CoreWeave namespaces on merge to main, and validate GPU resource requests against quota. CoreWeave uses standard Kubernetes APIs with GPU-specific scheduling, so CI pipelines authenticate via kubeconfig and manage deployments through kubectl.

GitHub Actions Workflow

# .github/workflows/coreweave-ci.yml
name: CoreWeave CI
on:
  pull_request:
    paths: ['src/**', 'k8s/**', 'Dockerfile']
  push:
    branches: [main]

jobs:
  unit-tests:
    runs-on: ubuntu-latest
    steps:
      - uses: actions/checkout@v4
      - uses: actions/setup-node@v4
        with: { node-version: '20' }
      - run: npm ci
      - run: npm test -- --reporter=verbose

  deploy:
    if: github.ref == 'refs/heads/main'
    needs: unit-tests
    runs-on: ubuntu-latest
    steps:
      - uses: actions/checkout@v4
      - name: Build and push container
        run: |
          echo "${{ secrets.GHCR_TOKEN }}" | docker login ghcr.io -u ${{ github.actor }} --password-stdin
          docker build -t ghcr.io/${{ github.repository }}/inference:${{ github.sha }} .
          docker push ghcr.io/${{ github.repository }}/inference:${{ github.sha }}
      - name: Deploy to CoreWeave
        env:
          KUBECONFIG_DATA: ${{ secrets.COREWEAVE_KUBECONFIG }}
        run: |
          echo "$KUBECONFIG_DATA" | base64 -d > /tmp/kubeconfig
          export KUBECONFIG=/tmp/kubeconfig
          kubectl set image deployment/inference \
            inference=ghcr.io/${{ github.repository }}/inference:${{ github.sha }}
          kubectl rollout status deployment/inference --timeout=300s

Mock-Based Unit Tests

// tests/coreweave-service.test.ts
import { describe, it, expect, vi } from 'vitest';
import { deployInferenceModel } from '../src/coreweave-service';

vi.mock('@kubernetes/client-node', () => ({
  KubeConfig: vi.fn().mockImplementation(() => ({
    loadFromDefault: vi.fn(),
    makeApiClient: vi.fn().mockReturnValue({
      patchNamespacedDeployment: vi.fn().mockResolvedValue({ body: { status: { readyReplicas: 1 } } }),
      listNamespacedPod: vi.fn().mockResolvedValue({
        body: { items: [{ metadata: { name: 'inference-abc' }, status: { phase: 'Running' } }] },
      }),
    }),
  })),
  AppsV1Api: vi.fn(),
}));

describe('CoreWeave Service', () => {
  it('deploys inference model with GPU requests', async () => {
    const result = await deployInferenceModel('llama-70b', { gpu: 'A100', count: 4 });
    expect(result.status).toBe('deployed');
    expect(result.gpuType).toBe('A100');
  });
});

Integration Tests

// tests/integration/coreweave.integration.test.ts
import { describe, it, expect } from 'vitest';
import { KubeConfig, CoreV1Api } from '@kubernetes/client-node';

const hasKubeconfig = !!process.env.COREWEAVE_KUBECONFIG;

describe.skipIf(!hasKubeconfig)('CoreWeave Live API', () => {
  it('lists GPU nodes in namespace', async () => {
    const kc = new KubeConfig();
    kc.loadFromString(Buffer.from(process.env.COREWEAVE_KUBECONFIG!, 'base64').toString());
    const k8sApi = kc.makeApiClient(CoreV1Api);
    const { body } = await k8sApi.listNamespacedPod('default');
    expect(Array.isArray(body.items)).toBe(true);
  });
});

Error Handling

CI IssueCauseFix
KUBECONFIG_DATA emptySecret not setRun gh secret set COREWEAVE_KUBECONFIG --body "$(base64 -w0 kubeconfig)"
Rollout timeoutGPU nodes unavailableIncrease --timeout or check CoreWeave GPU availability dashboard
Image pull backoffGHCR auth expiredVerify GHCR_TOKEN secret and image registry permissions
Quota exceededGPU request exceeds namespace limitCheck namespace quota with kubectl describe quota
Pod pendingNo matching GPU node typeVerify nodeSelector matches available GPU SKUs (A100, H100)

Resources

Next Steps

For deployment patterns, see coreweave-deploy-integration.

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