Cvat Deploy
Deploy a trained YOLO model as a Nuclio serverless function for CVAT auto-annotation.
Install
$ agentstack add skill-macroman5-autotrain-yolo-cvat-deploy ✓ scanned · ✓ verified, works with Claude Code, Cursor, and more.
Security review
✓ PassedNo issues found. Passed automated security review. · v0.1.0 How review works →
- ✓ Prompt-injection patterns
- ✓ Secret / credential exfiltration
- ✓ Dangerous shell & filesystem operations
- ✓ Untrusted network calls
- ✓ Known-malicious package signatures
What it can access
- ✓ Network access No
- ✓ Filesystem access No
- ✓ Shell / process execution No
- ✓ Environment & secrets No
- ✓ Dynamic code execution No
From automated source analysis of v0.1.0. “Used” means the capability is present in the source — more access means more to trust, not that it’s unsafe.
Verified badge
Passed review? Show it. Paste this badge into your README, it links to the public security report.
Links to your public security report.[](https://agentstack.voostack.com/security/report/skill-macroman5-autotrain-yolo-cvat-deploy) Reliability & compatibility
Declared compatibility
Compatibility is declared by the source manifest. End-to-end runtime verification is coming, see below.
We're building live execution health for every listing: tool-call success rate, median latency, uptime, and last-checked timestamps, measured, not self-reported. It isn't live yet, so we don't show numbers we can't stand behind.
How agent discovery & health will work →About
Deploy Model to CVAT
Deploy a trained YOLO model as a Nuclio serverless function so CVAT users can auto-annotate images directly from the CVAT UI.
Pre-Flight Checklist
- [ ] Trained model exists (best.pt or user-specified)
- [ ] Nuclio is running (check port 8070)
- [ ]
yolo-project.yamlhas class definitions - [ ] Docker is running (required for Nuclio builds)
Workflow
1. Identify the Model
- Use the best model from the latest experiment, or ask the user
- Check
experiments/summary.mdfor the best-performing model path
2. Generate Nuclio Function
yolo-cvat deploy --model --name This generates:
serverless//function.yaml— Nuclio config with class specserverless//main.py— Inference handlerserverless//best.onnx— Exported model
3. Verify Generated Files
Read the generated function.yaml and confirm:
- Class names match
yolo-project.yaml - Image name is unique
- ONNX model was exported correctly
4. Deploy to Nuclio
Provide the command for the user to run:
nuctl deploy --path ./serverless/ --platform local5. Verify Deployment
nuctl get functionsCheck that the function is running.
Decision Tree
Model is .pt format?
├── Yes → Export to ONNX first (yolo-export)
└── Already .onnx → Copy directly
Nuclio reachable on port 8070?
├── Yes → Ready to deploy
└── No → Warn user, suggest checking Docker and CVAT stack
Function with same name exists?
├── Yes → Ask user: overwrite or use different name?
└── No → Deploy normallyGuardrails
- NEVER deploy without confirming class mapping matches CVAT project labels
- ALWAYS verify the ONNX export succeeded before deploying
- Document the deployed model version and metrics in experiments/
- If deployment fails, show the Nuclio dashboard URL for debugging (http://localhost:8070)
Source & license
This open-source skill is cataloged on AgentStack and links to its original source — we do not rehost the code.
- Author: MacroMan5
- Source: MacroMan5/autotrain-yolo
- License: MIT
Install and usage instructions live in the source repository linked above.
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Versions
- v0.1.0 Imported from the upstream source.