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Publishing Kura-trained LoRA/model artifacts to Hugging Face or ModelScope. Use when preparing model cards, selecting safetensors outputs, checking metadata/license/sample images, or uploading releases without leaking secrets.
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 | publishing-huggingface-modelscope |
| description | Publishing Kura-trained LoRA/model artifacts to Hugging Face or ModelScope. Use when preparing model cards, selecting safetensors outputs, checking metadata/license/sample images, or uploading releases without leaking secrets. |
Use this skill for public/private model artifact publication.
uv run python scripts/check_secrets.py
uv run python scripts/check_model_artifacts.py
ComfyUI render workflow operations for Kura. Use when editing workflows/*.json, render run.yaml files, workflow_patches, promptsets, image comparison generation, ComfyUI endpoint behavior, or render result collection.
RunPod remote training lifecycle and billing safety for Kura. Use when working on kura run remote, runpod staging/upload/download/pull/stop/reconcile, Pod cleanup, max lease, notifications, GPU selection, Network Volumes, or RunPod docs/README updates.
Dataset preparation and validation for Kura training runs. Use when creating or editing datasets/, dataset.yaml, items.jsonl, captions, trigger words, paired/control datasets, dataset roles, or dataset validation behavior.
Local disk, WSL2, Docker, cache, cleanup, permission, and checkpoint safety workflow for Kura. Use before local Docker training, real model-download smoke tests, Docker image builds, RunPod downloads/pulls, cleanup, permission repair, or when the user mentions disk space, WSL, Docker storage, cache, checkpoints, root-owned files, cleanup, or a crash/reconnect incident.
Musubi Tuner backend guidance for Kura. Use when compiling or changing Musubi compile output, model bundle resolution, safetensors validation, LoRA output compatibility, dataset TOML generation, low-VRAM flags, batch/gradient accumulation, or Musubi Docker images.
Choosing training parameters and resource trade-offs for Kura LoRA runs. Use when proposing or reviewing run.yaml training parameters, judging VRAM fit from `kura run plan` Resources, reacting to CUDA OOM, choosing fp8/quantized artifacts, or deciding which memory-saving or quality trade-off to apply and what needs user approval.