# Cvat Deploy

> Deploy a trained YOLO model as a Nuclio serverless function for CVAT auto-annotation.

- **Type:** Skill
- **Install:** `agentstack add skill-macroman5-autotrain-yolo-cvat-deploy`
- **Verified:** Yes — security-reviewed for prompt injection and unsafe behavior
- **Seller:** [MacroMan5](https://agentstack.voostack.com/s/macroman5)
- **Installs:** 0
- **Category:** [Cloud & Infrastructure](https://agentstack.voostack.com/c/cloud-infrastructure)
- **Latest version:** 0.1.0
- **License:** MIT
- **Upstream author:** [MacroMan5](https://github.com/MacroMan5)
- **Source:** https://github.com/MacroMan5/autotrain-yolo/tree/master/.claude/skills/cvat-deploy

## Install

```sh
agentstack add skill-macroman5-autotrain-yolo-cvat-deploy
```

Requires the [AgentStack CLI](https://agentstack.voostack.com/docs/cli). Works with Claude Code, Cursor, and any MCP-compatible agent.

## 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.yaml` has 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.md` for the best-performing model path

### 2. Generate Nuclio Function
```bash
yolo-cvat deploy --model  --name 
```
This generates:
- `serverless//function.yaml` — Nuclio config with class spec
- `serverless//main.py` — Inference handler
- `serverless//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:
```bash
nuctl deploy --path ./serverless/ --platform local
```

### 5. Verify Deployment
```bash
nuctl get functions
```
Check 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 normally
```

## Guardrails
- 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](https://github.com/MacroMan5)
- **Source:** [MacroMan5/autotrain-yolo](https://github.com/MacroMan5/autotrain-yolo)
- **License:** MIT

Install and usage instructions live in the source repository linked above.

## Pricing

- **Free** — Free

## Security capabilities

Automated source analysis of v0.1.0 — what this tool can access:

- **Network access:** no
- **Filesystem access:** no
- **Shell / process execution:** no
- **Environment & secrets:** no
- **Dynamic code execution:** no

*"Yes" means the capability is present in the source — more access means more to trust, not that it is unsafe.*


## Versions

- **0.1.0** — security scan: passed — Imported from the upstream source.

## Links

- Listing page: https://agentstack.voostack.com/l/skill-macroman5-autotrain-yolo-cvat-deploy
- Seller: https://agentstack.voostack.com/s/macroman5
- Browse the marketplace: https://agentstack.voostack.com/browse

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