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By voxel51
Build, curate, and evaluate computer vision datasets and models within FiftyOne — import data from multiple formats, run inference, detect duplicates, visualize embeddings, evaluate predictions, and export to standard formats.
npx claudepluginhub anthropics/claude-plugins-official --plugin fiftyoneWrites Python code following FiftyOne's official conventions. Use when contributing to FiftyOne, developing plugins, or writing code that integrates with FiftyOne's codebase.
Creates Jupyter notebooks for FiftyOne workflows including getting-started guides, tutorials, recipes, and full ML pipelines. Use when creating notebooks, writing tutorials, building demos, or generating FiftyOne walkthroughs covering data loading, exploration, inference, evaluation, and export.
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.
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.
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.
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Computer vision image processing and analysis
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Comprehensive skill pack with 66 specialized skills for full-stack developers: 12 language experts (Python, TypeScript, Go, Rust, C++, Swift, Kotlin, C#, PHP, Java, SQL, JavaScript), 10 backend frameworks, 6 frontend/mobile, plus infrastructure, DevOps, security, and testing. Features progressive disclosure architecture for 50% faster loading.
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Comprehensive .NET development skills for modern C#, ASP.NET, MAUI, Blazor, Aspire, EF Core, Native AOT, testing, security, performance optimization, CI/CD, and cloud-native applications

Expert workflows for computer vision powered by AI assistants
Skills are packaged workflows that teach AI assistants to perform complex computer vision tasks autonomously. Combined with the FiftyOne MCP Server, you can find duplicates, run inference, and explore datasets using natural language.
"Find and remove duplicate images from my dataset"
"Import this COCO dataset and run object detection"
"Visualize my embeddings and identify outliers"
Skills bridge the gap between natural language and FiftyOne's 80+ operators, providing step-by-step guidance that AI assistants follow to complete complex workflows.
| Skill | Description | MCP |
|---|---|---|
| 📥 Dataset Import | Universal import for all media types, label formats, multimodal groups, and Hugging Face Hub | Yes |
| 📤 Dataset Export | Export datasets to COCO, YOLO, VOC, CVAT, CSV, Hugging Face Hub, and more | Yes |
| 🔍 Find Duplicates | Find and remove duplicate images using brain similarity | Yes |
| 🤖 Dataset Inference | Run Zoo models for detection, classification, segmentation, embeddings | Yes |
| 📈 Model Evaluation | Compute mAP, precision, recall, confusion matrices, analyze TP/FP/FN | Yes |
| 📊 Embeddings Visualization | Visualize datasets in 2D, find clusters, identify outliers | Yes |
| 🔌 Develop Plugin | Create custom FiftyOne plugins (operators and panels) | — |
| 🎨 VOODO Design | Build UIs with VOODO React components and design tokens | — |
| 📝 Code Style | Write Python code following FiftyOne's official conventions | — |
| 📓 Create Notebook | Create Jupyter notebooks: getting-started guides, tutorials, recipes, ML pipelines | — |
| 🏷️ Issue Triage | Triage GitHub issues: validate status, categorize, generate responses | — |
| 🧹 Dataset Curation | End-to-end curation: quality checks, annotation audit, duplicates, class distribution, splits | Yes |
| 🔧 Troubleshoot | Fix common issues: dataset persistence, App connection, MongoDB errors, codecs, performance | — |
| 🛡️ Eval Plugin | Evaluate plugins for quality, security, and agent-readiness. Produces a structured report | — |