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Evaluate trained YOLO model accuracy with mAP metrics and per-class error analysis. Identifies failure cases for targeted improvement. Use after training.
Codex 또는 Claude로 설치 이 Prompt를 복사해 Codex, Claude 또는 다른 어시스턴트에 붙여 넣으면 Skill 페이지를 검토하고 설치를 진행할 수 있습니다.
SOC 직업 분류 기준
| name | eval |
| description | Evaluate trained YOLO model accuracy with mAP metrics and per-class error analysis. Identifies failure cases for targeted improvement. Use after training. |
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.
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.
