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AI-assisted annotation review — uses YOLO inference + Claude vision to auto-approve, correct, or flag images for human review in CVAT.
Install with Codex or Claude Copy this prompt, paste it into Codex, Claude, or another assistant, and let it review the skill page and install it for you.
Based on SOC occupation classification
Set up autonomous training monitoring — creates cron jobs to track long-running training, auto-continue pipeline when training completes.
Audit YOLO dataset quality — class distribution, annotation quality, image stats, and improvement suggestions.
Orchestrate the full active learning loop: train, analyze, push to CVAT, wait for review, pull, merge, retrain.
Analyze YOLO training runs — compares to baseline/best, checks per-class regression, analyzes training dynamics and tune convergence, writes actionable recommendations.
Run autonomous YOLO training experiments — reads training-plan.md, assesses bottlenecks, acts strategically, and delegates HP optimization to model.tune().
Profile YOLO model inference speed, FPS, and size across image sizes and export formats.
| name | review-annotations |
| description | AI-assisted annotation review — uses YOLO inference + Claude vision to auto-approve, correct, or flag images for human review in CVAT. |
Multi-pass annotation review: YOLO precision + Claude intelligence.
STATUS: EXPERIMENTAL
Read yolo-project.yaml for classes, model path. Get review folder from user.
Create: review_output/approved/, needs_review/, rejected/
For each image:
python scripts/multi_pass_annotate.py --image <path> --model <model> --conf 0.15python scripts/draw_annotations.pyWrite review_output/review_report.md with counts, corrections, common issues.