coreweave-ci-integration
Integrate CoreWeave deployments into CI/CD pipelines with GitHub Actions. Use when automating container builds, deploying inference services from CI, or validating GPU manifests in pull requests. Trigger with phrases like "coreweave CI", "coreweave github actions", "coreweave pipeline", "automate coreweave deploy".
Best use case
coreweave-ci-integration is best used when you need a repeatable AI agent workflow instead of a one-off prompt.
Integrate CoreWeave deployments into CI/CD pipelines with GitHub Actions. Use when automating container builds, deploying inference services from CI, or validating GPU manifests in pull requests. Trigger with phrases like "coreweave CI", "coreweave github actions", "coreweave pipeline", "automate coreweave deploy".
Teams using coreweave-ci-integration should expect a more consistent output, faster repeated execution, less prompt rewriting.
When to use this skill
- You want a reusable workflow that can be run more than once with consistent structure.
When not to use this skill
- You only need a quick one-off answer and do not need a reusable workflow.
- You cannot install or maintain the underlying files, dependencies, or repository context.
Installation
Claude Code / Cursor / Codex
Manual Installation
- Download SKILL.md from GitHub
- Place it in
.claude/skills/coreweave-ci-integration/SKILL.mdinside your project - Restart your AI agent — it will auto-discover the skill
How coreweave-ci-integration Compares
| Feature / Agent | coreweave-ci-integration | Standard Approach |
|---|---|---|
| Platform Support | Not specified | Limited / Varies |
| Context Awareness | High | Baseline |
| Installation Complexity | Unknown | N/A |
Frequently Asked Questions
What does this skill do?
Integrate CoreWeave deployments into CI/CD pipelines with GitHub Actions. Use when automating container builds, deploying inference services from CI, or validating GPU manifests in pull requests. Trigger with phrases like "coreweave CI", "coreweave github actions", "coreweave pipeline", "automate coreweave deploy".
Where can I find the source code?
You can find the source code on GitHub using the link provided at the top of the page.
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SKILL.md Source
# CoreWeave CI Integration
## 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
```yaml
# .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
```typescript
// 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
```typescript
// 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 Issue | Cause | Fix |
|----------|-------|-----|
| `KUBECONFIG_DATA` empty | Secret not set | Run `gh secret set COREWEAVE_KUBECONFIG --body "$(base64 -w0 kubeconfig)"` |
| Rollout timeout | GPU nodes unavailable | Increase `--timeout` or check CoreWeave GPU availability dashboard |
| Image pull backoff | GHCR auth expired | Verify `GHCR_TOKEN` secret and image registry permissions |
| Quota exceeded | GPU request exceeds namespace limit | Check namespace quota with `kubectl describe quota` |
| Pod pending | No matching GPU node type | Verify `nodeSelector` matches available GPU SKUs (A100, H100) |
## Resources
- [CoreWeave Kubernetes Docs](https://docs.coreweave.com/coreweave-kubernetes/)
- [GitHub Actions Secrets](https://docs.github.com/en/actions/security-guides/encrypted-secrets)
## Next Steps
For deployment patterns, see `coreweave-deploy-integration`.Related Skills
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