yolo 플러그인
Agent skills for Ultralytics YOLO and Platform: model selection, dataset annotation, training, tuning, inference, export, and deployment through the Platform web UI, the Python package, and the yolo and ul CLIs.
yolo 플러그인 설치 방법
두 명령을 모두 Claude Code 안에서 실행하십시오. 첫 번째는 마켓플레이스를 한 번 추가하고, 두 번째는 플러그인을 설치합니다.
/plugin marketplace add ultralytics/skills
/plugin install yolo@ultralyticsyolo 구성 요소
플러그인 파일에서 읽어온 8개의 항목입니다.
Skills 8
작업이 일치하면 Claude가 로드합니다- platform-cli
Use when operating Ultralytics Platform from a terminal or script with the ul cloud CLI (ul cloud RESOURCE OPERATION key=value, PyPI package ultralytics-platform) — listing, creating, updating, cloning, or moving projects, datasets, and models; inspecting dataset images, statistics, metadata, duplicates, and similar images; starting and monitoring cloud training runs; per-image model validation analysis; downloading weights; exports, deployments, uploads, trash and restore, storage integrations, and account usage, storage, and billing; and building Agents (YOLO, LLM, gate, and alert workflows). For the Platform web UI or local yolo commands, see yolo; for choosing what to train and how to improve it, see yolo-training and yolo-tuning.
- yolo
Use for ANY task involving Ultralytics Platform, the ultralytics Python package, yolo CLI, YOLO model weights (.pt), dataset annotation, training, validation, prediction, tracking, export, deployment, or the detect / segment / semantic / depth / classify / pose / OBB vision tasks.
- yolo-datasets
Use when uploading, annotating, building, converting, analyzing, or debugging datasets in Ultralytics Platform or local YOLO — Platform dataset management and Smart Annotation, data.yaml, YOLO label .txt formats, COCO/DOTA/mask conversion, auto-labeling, splits, validation, and errors like "no labels found" or mAP near 0. Covers detect, segment, semantic, depth, classify, pose, and OBB data.
- yolo-export
Use when exporting or deploying Ultralytics YOLO models in Platform or code — the Platform Export tab and yolo export/model.export() for ONNX, TensorRT, CoreML, Core AI, OpenVINO, LiteRT, NCNN, ExecuTorch, and NPUs (RKNN, QNN, Hailo, Ascend, IMX, Axelera, DeepX, Xilinx, NXP), FP16/INT8 quantization, benchmarking, and non-Python runtimes. For inference with .pt weights or Platform endpoints, see yolo-inference.
- yolo-inference
Use when testing, running, or deploying Ultralytics YOLO inference in Platform or code on images, video, webcams, or streams — Platform Predict and dedicated endpoints, model.predict()/yolo predict, Results API boxes/masks/keypoints, persistent tracking, annotated video, and Solutions such as counting, heatmaps, speed, queues, and parking. For optimized runtime exports, see yolo-export.
- yolo-models
Use when choosing or comparing Ultralytics models in Platform or code — picking a model family (YOLO26/YOLO11/YOLOv8, YOLO-World, YOLOE, SAM/SAM2/FastSAM, RT-DETR, YOLO-NAS), size (n/s/m/l/x), task variant (-seg, -sem, -cls, -pose, -obb, -depth), pretrained checkpoint, open-vocabulary or promptable detection/segmentation, custom architecture, Platform Explore, or exact weight names and availability.
- yolo-training
Use when training, fine-tuning, or validating Ultralytics YOLO models in Platform, cloud GPUs, or local code — model.train(), yolo train/val, remote metric streaming, epochs, batch, imgsz, devices, augmentation, multi-GPU, resumes, results, and fixing OOM, NaN loss, low mAP, or overfitting. For hyperparameter search and systematic improvement loops, see yolo-tuning.
- yolo-tuning
Use when improving or comparing Ultralytics YOLO models in Platform or code, or running hyperparameter search/autotraining — Platform experiment comparison, the systematic improvement playbook, model.tune() genetic evolution, Ray Tune, search spaces, and deciding whether tuning is worthwhile. For one training run and its arguments, see yolo-training.
Claude Code에서 yolo 사용하기
Skills는 명령어가 필요하지 않습니다. 요청이 설명과 일치하면 Claude가 이를 읽습니다.
업데이트, 비활성화 또는 삭제
- 플러그인 업데이트 (shell)
- claude plugin update yolo@ultralytics
- marketplace 새로고침
- /plugin marketplace update ultralytics
- 일시적으로 비활성화
- /plugin disable yolo@ultralytics
- 삭제
- /plugin uninstall yolo@ultralytics
yolo의 스킬
- platform-cliultralytics/skills
Use when operating Ultralytics Platform from a terminal or script with the ul cloud CLI (ul cloud RESOURCE OPERATION key=value, PyPI package ultralytics-platform) — listing, creating, updating, cloning, or moving projects, datasets, and models; inspecting dataset images, statistics, metadata, duplicates, and similar images; starting and monitoring cloud training runs; per-image model validation analysis; downloading weights; exports, deployments, uploads, trash and restore, storage integrations, and account usage, storage, and billing; and building Agents (YOLO, LLM, gate, and alert workflows). For the Platform web UI or local yolo commands, see yolo; for choosing what to train and how to improve it, see yolo-training and yolo-tuning.
2개 파일38스타 - yoloultralytics/skills
Use for ANY task involving Ultralytics Platform, the ultralytics Python package, yolo CLI, YOLO model weights (.pt), dataset annotation, training, validation, prediction, tracking, export, deployment, or the detect / segment / semantic / depth / classify / pose / OBB vision tasks.
2개 파일38스타 - yolo-datasetsultralytics/skills
Use when uploading, annotating, building, converting, analyzing, or debugging datasets in Ultralytics Platform or local YOLO — Platform dataset management and Smart Annotation, data.yaml, YOLO label .txt formats, COCO/DOTA/mask conversion, auto-labeling, splits, validation, and errors like "no labels found" or mAP near 0. Covers detect, segment, semantic, depth, classify, pose, and OBB data.
3개 파일38스타 - yolo-exportultralytics/skills
Use when exporting or deploying Ultralytics YOLO models in Platform or code — the Platform Export tab and yolo export/model.export() for ONNX, TensorRT, CoreML, Core AI, OpenVINO, LiteRT, NCNN, ExecuTorch, and NPUs (RKNN, QNN, Hailo, Ascend, IMX, Axelera, DeepX, Xilinx, NXP), FP16/INT8 quantization, benchmarking, and non-Python runtimes. For inference with .pt weights or Platform endpoints, see yolo-inference.
3개 파일38스타 - yolo-inferenceultralytics/skills
Use when testing, running, or deploying Ultralytics YOLO inference in Platform or code on images, video, webcams, or streams — Platform Predict and dedicated endpoints, model.predict()/yolo predict, Results API boxes/masks/keypoints, persistent tracking, annotated video, and Solutions such as counting, heatmaps, speed, queues, and parking. For optimized runtime exports, see yolo-export.
3개 파일38스타 - yolo-modelsultralytics/skills
Use when choosing or comparing Ultralytics models in Platform or code — picking a model family (YOLO26/YOLO11/YOLOv8, YOLO-World, YOLOE, SAM/SAM2/FastSAM, RT-DETR, YOLO-NAS), size (n/s/m/l/x), task variant (-seg, -sem, -cls, -pose, -obb, -depth), pretrained checkpoint, open-vocabulary or promptable detection/segmentation, custom architecture, Platform Explore, or exact weight names and availability.
3개 파일38스타 - yolo-trainingultralytics/skills
Use when training, fine-tuning, or validating Ultralytics YOLO models in Platform, cloud GPUs, or local code — model.train(), yolo train/val, remote metric streaming, epochs, batch, imgsz, devices, augmentation, multi-GPU, resumes, results, and fixing OOM, NaN loss, low mAP, or overfitting. For hyperparameter search and systematic improvement loops, see yolo-tuning.
3개 파일38스타 - yolo-tuningultralytics/skills
Use when improving or comparing Ultralytics YOLO models in Platform or code, or running hyperparameter search/autotraining — Platform experiment comparison, the systematic improvement playbook, model.tune() genetic evolution, Ray Tune, search spaces, and deciding whether tuning is worthwhile. For one training run and its arguments, see yolo-training.
2개 파일38스타
yolo 플러그인 질문
yolo 플러그인은 어떻게 설치하나요?
Claude Code에서 /plugin marketplace add ultralytics/skills를 실행한 다음 /plugin install yolo@ultralytics을 실행하십시오.
yolo 플러그인에는 무엇이 포함되나요?
yolo은(는) 8개 스킬을(를) 묶습니다.
yolo 플러그인을 어떻게 업데이트하거나 삭제하나요?
shell에서 claude plugin update yolo@ultralytics를 사용하여 업데이트하거나 /plugin의 Installed 탭에서 수행하세요. 삭제하려면 /plugin uninstall yolo@ultralytics을 사용하세요.
yolo 플러그인의 라이선스는 무엇인가요?
플러그인 manifest에 명시된 대로 AGPL-3.0입니다.