cv-notebook

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

Hermes Agent Compatibility

When this skill is loaded through Hermes as ml-toolkit:cv-notebook, map Claude/Codex tool names to Hermes tools:

Claude/Codex term	Hermes tool
Read	read_file
Write	write_file
Edit	patch
NotebookEdit	Hermes "Jupyter Live Kernel" skill, or write_file / patch on the .ipynb JSON

Treat $ARGUMENTS as the natural-language arguments supplied when the user asks Hermes to load the skill. Plugin-provided skills are explicit opt-in loads in Hermes; use skill_view("ml-toolkit:cv-notebook") (or ask Hermes to load that qualified skill) rather than relying on bare text.

NotebookEdit across runtimes: Claude and Codex use the NotebookEdit tool to author .ipynb cells (the global rule forbids hand-editing notebook JSON under Claude). Under Hermes there is no NotebookEdit tool — use the Hermes "Jupyter Live Kernel" skill, or write/patch the .ipynb JSON directly with write_file / patch (Hermes' Claude/GPT brain can emit valid notebook JSON).

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

1. Identify parameters: Parse task, model, level, environment from request
2. Select template: Load appropriate task template from references/templates/
3. Apply environment: Insert Colab/Kaggle/Local specific setup
4. Inject insights: Add Korean insights based on level density
5. Generate notebook: Use NotebookEdit tool to create .ipynb file
6. 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

• Code patterns: references/patterns.md
• Environment setup: references/environment-setup.md
• Korean insights: references/insights-ko.md
• Architecture diagrams: references/diagrams.md
• Templates: references/templates/