fiftyone-dataset-curation
End-to-end dataset curation for FiftyOne: inspect schema and quality, audit annotations, analyze class distributions, explore embeddings, find duplicates, create curated subsets, and build train/val/test splits. Works with any computer vision dataset type.
Install
With your agent
Install OMGSkills skill "voxel51/fiftyone-skills:skills/fiftyone-dataset-curation" into this agent's normal skills directory: call the OMGSkills MCP get_install_plan tool for your agent and run the script it returns (it installs only the pinned commit and verifies SKILL.md), and stop if MCP or the pin is unavailable. Do not overwrite or change existing skills without asking me.
Manual command
git clone https://github.com/voxel51/fiftyone-skills /tmp/fiftyone-skills && ln -s /tmp/fiftyone-skills/skills/fiftyone-dataset-curation ~/.claude/skills/fiftyone-dataset-curationFrom README
FiftyOne Dataset Curation End-to-end curation pipeline for any FiftyOne dataset: images, video, point clouds, grouped multimodal. Run all phases sequentially or jump directly to any single phase. Key Directives ALWAYS follow these rules — no exceptions: Check for a loaded dataset first If none exists, offer to delegate to fiftyone-dataset-import skill. Set context before any operation Launch App before any brain operator Wait 5–10 seconds for initialization before executing delegated operators. Discover schema before referencing any field Never hardcode field names. Adapt all field references from schema discovery results. Discover operators dynamically Always call listoperators() before any executeoperator(). Confirm the operator exists. Confirm before mutating Before tagging, adding fields, or deleting — present findings to the user and ask for confirmation. Keep App open for interactive review Do NOT call closeapp() automatically.
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