Dirty13itch/deploy-model
.claude/skills/deploy-model/SKILL.md
Deploy or redeploy an inference model on the cluster. Use when asked to deploy, update, or restart a model.
devops
Updated Apr 16, 2026
$ install --global
skillsauthnpx skillsauth add Dirty13itch/kaizen deploy-modelInstall this skill globally with one command. Works with Claude Code, Cursor, and Windsurf.
Security Scan Results
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Some scanners were skipped, did not run, or reported a non-clean status. Review each row below.
3
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9
Scanners in report
Clean
TrivyContainer and dependency vulnerability scanner
95%
Clean
SemgrepStatic code analysis for vulnerabilities
95%
Clean
mcp-scan (Snyk)Model Context Protocol security validation
95%
Skipped
Snyk (dep)Open source security scanning
50%
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Socket.devSupply chain security analysis
50%
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VirusTotalMulti-engine malware detection
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CrowdStrikeAdvanced threat intelligence
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OSV-ScannerOpen Source Vulnerability database check
50%
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OWASP Dep-Check
50%
Last scanned: Apr 16, 2026, 2:38 PM6.3s1 file scanned
SKILL.md
- name:
- deploy-model
- description:
- Deploy or redeploy an inference model on the cluster. Use when asked to deploy, update, or restart a model.
- disable-model-invocation:
- true
- allowed-tools:
- Bash, Read, Edit
Deploy Model
Deploy or manage inference models on Kaizen. Argument should be one of: reasoning, embedding, heavy, or a manifest path.
Pre-flight Checks
- Verify target node is Ready:
kubectl get nodes - Check GPU availability:
kubectl describe node <node> | grep -A3 nvidia.com/gpu - Check existing deployments:
kubectl get pods -n inference
Available Manifests
k8s/apps/inference/— All inference deploymentsk8s/apps/inference/sglang-core.yaml— Heavy model (Qwen2.5-72B, TP=4, CORE)
GPU Constraints
- INTERFACE: RTX 5090 (32GB) + RTX 4090 (24GB) — NO tensor parallelism
- CORE: 4x RTX 5070 Ti (16GB each) — TP=4 supported
- Deployment order determines GPU assignment on INTERFACE
Deploy Steps
- Read the manifest file
- Validate:
kubectl apply --dry-run=client -f <manifest> - Show the user what will be deployed (model, GPU, node, port)
- Ask for confirmation before applying
- Apply:
kubectl apply -f <manifest> - Watch:
kubectl rollout status -n inference deploy/<name> --timeout=300s - Test:
curl http://10.10.10.10:<port>/v1/models
Restart
kubectl rollout restart -n inference deploy/<name>
kubectl rollout status -n inference deploy/<name>
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