name: cv-notebook
description: Generate production-quality Computer Vision Jupyter notebooks. Supports detection, segmentation, classification, and VLM tasks. Follows roboflow/notebooks patterns with supervision visualization. Triggers on "CV notebook", "detection notebook", "segmentation notebook", "classification notebook", "VLM notebook", "train YOLO notebook", "fine-tune notebook", "inference notebook", "computer vision tutorial".
CV Notebook Generator
A skill for generating professional Computer Vision Jupyter notebooks following roboflow/notebooks patterns with Korean insights.
Design Principles
What to Apply (Roboflow Style)
- Banner image at top
- Colab/GitHub badges
- GPU check cell first
- supervision library for all visualizations
- Roboflow SDK for dataset management
- Clear section structure (Setup → Data → Model → Training → Evaluation)
What to Avoid
- Hardcoded API keys (use environment variables or secrets)
- Model-specific code outside templates
- Execution of cells (user runs in their environment)
- Direct .ipynb file manipulation (use NotebookEdit tool)
Supported Task Types
| Task |
Description |
Key Models |
detection |
Object detection |
YOLO, RT-DETR |
segmentation |
Instance/semantic segmentation |
SAM, YOLO-Seg |
classification |
Image classification |
ResNet, ViT, DINOv2 |
vlm |
Vision-Language Models |
Florence-2, PaliGemma, Qwen2.5-VL |
Parameters
| Parameter |
Type |
Default |
Description |
| task |
enum |
detection |
detection/segmentation/classification/vlm |
| model |
string |
auto |
Model name (YOLO, SAM, Florence, etc.) |
| level |
enum |
intermediate |
beginner/intermediate/expert |
| environment |
enum |
colab |
colab/kaggle/local |
| include_training |
bool |
true |
Include fine-tuning section |
| include_roboflow |
bool |
true |
Include Roboflow dataset integration |
| dataset_format |
enum |
yolov8 |
yolov8/coco/voc/pascal - Roboflow export format |
| language |
enum |
hybrid |
en/ko/hybrid (Korean insights) |
Notebook Structure
Standard section order for all CV notebooks:
| Section |
Cell Type |
Required |
Description |
| Header |
Markdown |
Yes |
Banner, badges, title, description |
| GPU Check |
Code |
Yes |
nvidia-smi and torch.cuda check |
| Setup |
Code |
Yes |
Package installation, imports |
| API Config |
Code |
Conditional |
Roboflow/HuggingFace API keys |
| Data |
Code+MD |
Yes |
Dataset download, exploration, visualization |
| Model |
Code+MD |
Yes |
Load pretrained, test inference |
| Training |
Code+MD |
Optional |
Fine-tuning workflow |
| Evaluation |
Code+MD |
Yes |
Metrics, confusion matrix, visualization |
| Deployment |
Code+MD |
Optional |
Export, Roboflow Deploy |
| Conclusion |
Markdown |
Yes |
Summary, next steps, resources |
User Level Configuration
Insight Density by Level
| Level |
Insight Blocks |
Inline Comments |
MD:Code Ratio |
| beginner |
15-20 per notebook |
80%+ of code lines |
1:1 |
| intermediate |
8-12 per notebook |
40% of code lines |
1:2 |
| expert |
3-5 per notebook |
10% of code lines |
1:4 |
Insight Injection Points
| Section |
Beginner |
Intermediate |
Expert |
| GPU Check |
Block after |
- |
- |
| Package Install |
All inline |
Key only |
- |
| Model Load |
Block after |
Block after |
- |
| Inference |
Both |
Inline |
Inline |
| Training Config |
Block after |
Block after |
- |
| Evaluation |
Block after |
Block after |
Block |
Usage Examples
Basic Detection Notebook
"Create a YOLOv8 detection notebook for beginners"
→ task=detection, model=yolov8, level=beginner, environment=colab
Custom Segmentation
"Generate SAM segmentation notebook for Kaggle, intermediate level"
→ task=segmentation, model=sam, level=intermediate, environment=kaggle
VLM Inference Only
"Create Florence-2 VLM notebook without training section"
→ task=vlm, model=florence-2, include_training=false
Expert Training Notebook
"Generate expert-level RT-DETR fine-tuning notebook with Roboflow dataset"
→ task=detection, model=rt-detr, level=expert, include_roboflow=true
Qwen2.5-VL Zero-Shot Detection
"Create Qwen2.5-VL notebook for zero-shot object detection"
→ task=vlm, model=qwen2.5-vl, include_training=false
Generation Workflow
- Identify parameters: Parse task, model, level, environment from request
- Select template: Load appropriate task template from references/templates/
- Apply environment: Insert Colab/Kaggle/Local specific setup
- Inject insights: Add Korean insights based on level density
- Generate notebook: Use NotebookEdit tool to create .ipynb file
- Validate structure: Ensure all required sections present
NotebookEdit Integration
This skill uses the NotebookEdit tool for .ipynb generation:
# Cell generation sequence
NotebookEdit(notebook_path="notebook.ipynb", edit_mode="insert", cell_type="markdown", new_source="# Header")
NotebookEdit(notebook_path="notebook.ipynb", edit_mode="insert", cell_id="<previous>", cell_type="code", new_source="!nvidia-smi")
Cell ID Strategy
- Generate cells sequentially (top to bottom)
- Track cell IDs returned from NotebookEdit responses
- Use
edit_mode="insert" with previous cell_id
- IMPORTANT: Cell IDs are returned in the NotebookEdit response and must be tracked for subsequent insertions
Additional Resources
By