fiftyone-develop-plugin
Develop FiftyOne Plugins
Key Directives
ALWAYS follow these rules:
1. Understand before coding
Ask clarifying questions. Never assume what the plugin should do.
2. Plan before implementing
Present file structure and design. Get user approval before generating code.
3. Search existing plugins for patterns
# Clone official plugins for reference
git clone https://github.com/voxel51/fiftyone-plugins.git /tmp/fiftyone-plugins 2>/dev/null || true
# Search for similar patterns
grep -r "keyword" /tmp/fiftyone-plugins/plugins/ --include="*.py" -l
More from voxel51/fiftyone-skills
fiftyone-find-duplicates
Finds duplicate or near-duplicate images in FiftyOne datasets using brain similarity computation. Use when deduplicating datasets, finding similar images, or removing redundant samples.
20fiftyone-dataset-import
Imports datasets into FiftyOne with automatic format detection. Supports all media types (images, videos, point clouds), label formats (COCO, YOLO, VOC, KITTI), multimodal grouped datasets, and Hugging Face Hub datasets. Use when importing datasets from local files or Hugging Face, loading autonomous driving data, or creating grouped datasets.
14fiftyone-model-evaluation
Evaluate model predictions against ground truth using COCO, Open Images, or custom protocols. Use when computing mAP, precision, recall, confusion matrices, or analyzing TP/FP/FN examples for detection, classification, segmentation, or regression tasks.
12fiftyone-dataset-inference
Run ML model inference on FiftyOne datasets. Use when running models for detection, classification, segmentation, or embeddings. Discovers available models dynamically from the Zoo, plugin operators, or custom sources — never assumes a fixed model list.
12fiftyone-dataset-export
Exports FiftyOne datasets to standard formats (COCO, YOLO, VOC, CVAT, CSV, etc.) and Hugging Face Hub. Use when converting datasets, exporting for training, creating archives, sharing data in specific formats, or publishing datasets to Hugging Face.
11fiftyone-embeddings-visualization
Visualizes datasets in 2D using embeddings with UMAP or t-SNE dimensionality reduction. Use when exploring dataset structure, finding clusters, identifying outliers, or understanding data distribution.
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