name	senior-computer-vision
description	Computer vision engineering workflow for dataset design, model selection (detection/segmentation/classification), evaluation, inference optimization, and deployment. Use when planning or reviewing CV systems, auditing datasets, defining metrics/splits, or diagnosing model/inference issues.

Senior Computer Vision

Build CV systems that generalize, are measurable, and are deployable.

Specify the task precisely: classification vs detection vs segmentation; latency and target hardware.
Dataset first: define label taxonomy, edge cases, split strategy, and evaluation metrics.
Train with discipline: baselines, ablations, and error analysis (not just “more epochs”).
Deploy with realism: preprocessing parity, batching, quantization/trt where needed, monitoring.

For a directory like data/train/<class>/... or any image folder:

python ~/.codex/skills/senior-computer-vision/scripts/dataset_inventory.py data/ --out /tmp/dataset_report.json

name	senior-computer-vision
description	Computer vision engineering workflow for dataset design, model selection (detection/segmentation/classification), evaluation, inference optimization, and deployment. Use when planning or reviewing CV systems, auditing datasets, defining metrics/splits, or diagnosing model/inference issues.

Build CV systems that generalize, are measurable, and are deployable.

Specify the task precisely: classification vs detection vs segmentation; latency and target hardware.
Dataset first: define label taxonomy, edge cases, split strategy, and evaluation metrics.
Train with discipline: baselines, ablations, and error analysis (not just “more epochs”).
Deploy with realism: preprocessing parity, batching, quantization/trt where needed, monitoring.

For a directory like data/train/<class>/... or any image folder:

python ~/.codex/skills/senior-computer-vision/scripts/dataset_inventory.py data/ --out /tmp/dataset_report.json

senior-computer-vision