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Export and integrate MULLER datasets with other formats - Arrow, Parquet, JSON, NumPy, MindRecord. Use when user wants to export data, convert formats, or integrate with other frameworks.
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 | muller-export |
| description | Export and integrate MULLER datasets with other formats - Arrow, Parquet, JSON, NumPy, MindRecord. Use when user wants to export data, convert formats, or integrate with other frameworks. |
| compatibility | Requires Python 3.11+, muller package installed |
DO NOT create new Python files. Always use the existing script:
scripts/export.py for all export and integration operationsExecute the script directly with python3 command. Never write new scripts to the project root.
When executing operations:
Use this skill when the user wants to:
Handles export and format conversion operations.
Operations:
to-arrow - Export to Apache Arrow formatto-parquet - Export to Parquet filesto-json - Export to JSON formatto-numpy - Convert tensors to NumPy arraysto-mindrecord - Export to MindRecord formatget-info - Get export informationUsage:
# Export to Arrow
python3 .claude/skills/muller-export/scripts/export.py to-arrow \
--path ./my_dataset --output ./output.arrow
# Export to Parquet
python3 .claude/skills/muller-export/scripts/export.py to-parquet \
--path ./my_dataset --output ./output_dir
# Export to JSON
python3 .claude/skills/muller-export/scripts/export.py to-json \
--path ./my_dataset --output ./output.json
# Convert tensor to NumPy
python3 .claude/skills/muller-export/scripts/export.py to-numpy \
--path ./my_dataset --tensor embeddings --output ./embeddings.npy
# Export to MindRecord
python3 .claude/skills/muller-export/scripts/export.py to-mindrecord \
--path ./my_dataset --output ./output.mindrecord
Export formats: Arrow (columnar exchange), Parquet (storage/analytics), JSON (web APIs), NumPy (ML frameworks), MindRecord (MindSpore).
For detailed workflows, check references/ directory.
Advanced query operations for MULLER datasets - indexing, vector search, aggregation, and complex filtering. Use when user wants to create indexes, perform vector similarity search, or run aggregation queries.
Create, query, and manage MULLER datasets (multimodal data lake with version control). Use when user wants to work with datasets, create tensors, append data, query samples, or inspect dataset information.
Git-like version control for MULLER datasets - commit, branch, merge, diff, and conflict resolution. Use when user wants to version datasets, create branches, merge changes, or view history.