YOLO Skills for Codex

Codex skill — built for OpenAI Codex. Auto-loads by task description, or invoke with $yolo-training / $traffic-vehicle-count.

Two Codex skills I put together for my own image-processing and traffic work:

• yolo-training — turn a detection dataset into a trained, exported model (convert annotations → split → train → evaluate → export → infer).
• traffic-vehicle-count — count vehicles in a video, including directional flow across a virtual line you draw in the frame.

Both are plain folders (SKILL.md + a scripts/ dir) in .agents/skills/. Codex discovers them when you work inside this repo, and picks them up automatically when your task matches the skill’s description, or you can invoke them by name ($yolo-training, $traffic-vehicle-count).

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Layout

.
├── .agents/skills/
│   ├── yolo-training/
│   │   ├── SKILL.md
│   │   └── scripts/
│   │       ├── convert_to_yolo.py   VOC/COCO/txt -> YOLO labels
│   │       ├── prepare_dataset.py   train/val/test split + data.yaml
│   │       ├── evaluate_yolo.py     mAP + per-class report
│   │       ├── export_yolo.py       ONNX / TensorRT / TorchScript
│   │       └── infer_yolo.py        run the model on an image
│   └── traffic-vehicle-count/
│       ├── SKILL.md
│       └── scripts/
│           └── count_vehicles.py    crossing counts, directions, annotated video
├── requirements.txt
└── README.md

Skills installed here work repo-wide. To use them everywhere, copy (or symlink) the folders to ~/.agents/skills/.

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Setup

Tested on Windows 11 + Anaconda (CUDA, RTX 4080). Should work on Linux/macOS too.

# 1. install the Codex CLI
npm install -g @openai/codex

# 2. create a python environment and install the dependencies
conda create -n yolo python=3.12 -y
conda run -n yolo python -m pip install torch torchvision --index-url https://download.pytorch.org/whl/cu124
conda run -n yolo python -m pip install ultralytics

One gotcha: if conda create fails with UnavailableInvalidChannel, a dead mirror channel in your .condarc is usually the cause — add --override-channels -c conda-forge to get around it.

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Using the skills

You can hand the whole job to Codex in plain words — “train a detector on this dataset” — and the skill walks through it. Or run the steps yourself:

Train a detector

# 1. convert VOC annotations to YOLO
python .agents/skills/yolo-training/scripts/convert_to_yolo.py datasets/voc \
    --format voc --classes classes.txt --out datasets/yolo

# 2. split train/val and write data.yaml
python .agents/skills/yolo-training/scripts/prepare_dataset.py datasets/yolo --val 0.2

# 3. train
yolo detect train data=/abs/path/datasets/yolo/data.yaml \
    model=yolov8n.pt epochs=50 imgsz=640 device=0

# 4. evaluate
python .agents/skills/yolo-training/scripts/evaluate_yolo.py \