fiftyone-dataset-curation

Diagnose and fix common FiftyOne issues automatically. Use when a dataset disappeared, the App won't open, changes aren't saving, MongoDB errors occur, video codecs fail, notebook connectivity breaks, operators are missing, or any recurring FiftyOne pain point needs solving.

Generate a Data Lens connector from an external database schema. Use when users want to connect an external data source (PostgreSQL, BigQuery, Databricks, MySQL, etc.) to FiftyOne Data Lens, or when they have a database schema and want to browse/import that data through the FiftyOne App.

Triages FiftyOne GitHub issues by categorizing as fixed, won't fix, not reproducible, or still valid. Use when reviewing GitHub issues, triaging bugs, or closing stale issues in the voxel51/fiftyone repository.

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.

Answers FiftyOne Python SDK questions with accurate, runnable code. Use when the user asks how to do something in the FiftyOne Python SDK, asks a docs question, no skill or operator covers their goal, or the agent is unsure of the correct FiftyOne method, argument, or field path while writing code — e.g. "how do I do X in Python", "show me the SDK for filtering", "there's no operator for this, can I do it in code", "what's the API for embeddings", "write me a Python script to do Y".

Use when integrating a model into FiftyOne's remote model zoo — detection, classification, segmentation, embedding, keypoint, or vision-language (VLM) models loaded via `register_zoo_model_source` and `load_zoo_model`, then applied with `dataset.apply_model`. Also for debugging zoo registration, `manifest.json` issues, custom `fom.Model` / `TorchModelMixin` subclasses, DataLoader pickle errors, or worker `ModuleNotFoundError` from spawned DataLoader workers.

Connect a local AI agent (Claude Code, Cursor, or any MCP client) directly to a Voxel51 deployment over MCP, authenticating as yourself with your own account permissions. Use when a user wants to link their coding agent to a Voxel51 deployment, run FiftyOne operators from outside the App, or asks about remote/MCP access to a Voxel51 deployment.

Finds duplicate or near-duplicate images in FiftyOne datasets using brain similarity computation. Use when deduplicating datasets, finding similar images, or removing redundant samples.

Test-driven development. Use when the user wants to build features or fix bugs test-first, mentions "red-green-refactor", or wants integration tests.

Grill the user relentlessly about a plan, decision, or idea. Use when the user wants to stress-test their thinking, or uses any 'grill' trigger phrases.

Migrate test files from `as` type assertions to @total-typescript/shoehorn. Use when user mentions shoehorn, wants to replace `as` in tests, or needs partial test data.

Use when encountering any bug, test failure, or unexpected behavior, before proposing fixes

Use when implementing any feature or bugfix, before writing implementation code

Toolkit for interacting with and testing local web applications using Playwright. Supports verifying frontend functionality, debugging UI behavior, capturing browser screenshots, and viewing browser logs.

Run a binary industrial-inspection DEFT loop with TAO RT-DETR: measure on frozen KPI/test roles, mine gap-similar real and clean images with SigLIP, accumulate admitted COCO data, retrain from one base checkpoint, and select by KPI AP50. Use for iterative AOI defect detection, not generic multiclass OD.

Multi-step video annotation pipeline that turns raw videos into Chain-of-Thought training data — multi-level captions, structured descriptions, and QA pairs (MCQ, binary, open-ended) with reasoning traces, via VLM/LLM distillation. Use when the user wants to "create video training data", "generate video QA datasets", "build CoT reasoning traces from videos", "auto-label videos", or run the video_reasoning_annotation pipeline. Triggers include "video annotation", "video CoT", "video QA", "chain-of-thought", "video captioning pipeline", "video distillation".