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.
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.