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Train a YOLO model on labeled dataset using ultralytics. Splits data into train/val, generates dataset.yaml, and produces trained weights. Use after labeling and augmenting.
Codex または Claude でインストール この Prompt をコピーして Codex、Claude、または他のアシスタントに貼り付けると、Skill ページを確認してインストールできます。
SOC 職業分類に基づく
| name | train |
| description | Train a YOLO model on labeled dataset using ultralytics. Splits data into train/val, generates dataset.yaml, and produces trained weights. Use after labeling and augmenting. |
Download YouTube videos and extract frames using yt-dlp and ffmpeg for YOLO training data. Use when you need to gather frames from a video URL.
Evaluate trained YOLO model accuracy with mAP metrics and per-class error analysis. Identifies failure cases for targeted improvement. Use after training.
Auto-label frames with bounding boxes. Supports four modes — CUA+SAM (best accuracy, OpenAI CUA clicks + SAM segmentation), Gemini (native bbox detection), GPT vision (API fallback), or Codex vision subagents (no API keys). Parallel dispatch via git worktrees. Use after collecting frames.
Train a custom YOLO object detection model from any YouTube gameplay video. Provide a video URL and target classes, and this skill handles the entire pipeline autonomously — frame extraction, AI-powered labeling, data augmentation, training, and evaluation with iterative improvement.
Generate synthetic training data variations using image transforms. Increases dataset diversity with flips, brightness jitter, and noise. Use after labeling.
