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Initialize a local git repository for a dataset, with the conventional Hugging Face layout, LFS configuration, license, and a stub dataset card. Use when the user wants to start a new dataset repo, or as a step inside `hf-setup`.
기본적으로 소스를 먼저 확인하는 Prompt가 선택됩니다. 직접 명령으로 전환하거나 로컬 사본을 다운로드할 수도 있습니다.
설치 여부를 결정하기 전에 SKILL.md와 SkillsMP에 표시된 보조 파일을 읽어 보세요.
Codex 또는 Claude로 설치 이 Prompt를 복사해 Codex, Claude 또는 다른 어시스턴트에 붙여 넣으면 Skill 페이지를 검토하고 설치를 진행할 수 있습니다.
직접 명령은 검토 Prompt를 거치지 않습니다. 실행하기 전에 소스를 확인하세요.
npx skills add https://github.com/danielrosehill/Claude-Data-Annotation-Plugin --skill init-dataset-repo명령은 한 줄로 유지됩니다. 복사하기 전에 가로로 스크롤해 전체 내용을 확인하세요.
로컬 사본을 원하시나요? SkillsMP에서 현재 제공할 수 있는 파일을 다운로드하세요.
Annotate a small dataset directly inside the Claude Code session — Claude reads each record, applies the locked schema, and writes structured annotations to disk. For datasets where standing up Label Studio or a Gemini batch job would be overkill (typically tens to low hundreds of records). Use when the user says "annotate this dataset", "label these records with Claude", "do the annotations yourself", or "small-scale annotation".
Set up a Hugging Face dataset repository — create the remote repo (asking public/private), copy prepared data over, generate a dataset card, and push. Uses the huggingface-cli, not an MCP. Use when the user says "set up a HF dataset", "publish to Hugging Face", "create the HF dataset repo", or after annotation/prep is complete.
Stage raw data from a GitHub repo, local path, or remote URL into a working directory so downstream prep skills can operate on it. Use when the user provides a data source and wants it pulled in, or when `shape-dataset` needs to ingest before profiling.
SOC 직업 분류 기준
SKILL.md 표시 중
| name | init-dataset-repo |
| description | Initialize a local git repository for a dataset, with the conventional Hugging Face layout, LFS configuration, license, and a stub dataset card. Use when the user wants to start a new dataset repo, or as a step inside `hf-setup`. |
Creates a clean local git repository structured for Hugging Face hosting. Does not push anywhere — hf-setup handles remote creation and push.
<workspace>/dataset-repo/ if a shape-dataset workspace exists, otherwise ask.mit if the user hasn't decided; flag this in the card as a TODO).<dataset-name>/
├── .gitattributes # LFS rules
├── .gitignore # excludes scratch/, .env, __pycache__/
├── LICENSE # full license text
├── README.md # dataset card stub
└── data/ # empty; prepared splits go here later
*.parquet filter=lfs diff=lfs merge=lfs -text
*.arrow filter=lfs diff=lfs merge=lfs -text
*.bin filter=lfs diff=lfs merge=lfs -text
*.h5 filter=lfs diff=lfs merge=lfs -text
*.zip filter=lfs diff=lfs merge=lfs -text
*.tar.gz filter=lfs diff=lfs merge=lfs -text
*.jsonl filter=lfs diff=lfs merge=lfs -text
*.csv filter=lfs diff=lfs merge=lfs -text
*.png filter=lfs diff=lfs merge=lfs -text
*.jpg filter=lfs diff=lfs merge=lfs -text
*.wav filter=lfs diff=lfs merge=lfs -text
*.mp3 filter=lfs diff=lfs merge=lfs -text
If certain formats are known to be small in this dataset, the user can prune later — better to over-include LFS rules at init time.
Minimal HF dataset card with YAML frontmatter and section headers. Every value is <!-- TODO --> until prep produces real metadata. The full population happens in hf-setup.
mkdir -p <path>/data.gitattributes, .gitignore, LICENSE, README.md.git init and git lfs install inside the repo.git add -A && git commit -m "Initialize dataset repo skeleton".Do not add any HF remote here. hf-setup does that after asking public/private.