fiftyone-dataset-import

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.

About 450 ready-made HyperFrames blocks and components installed as editable source - transitions, overlays, titles, kinetic type, captions, charts, map overlays, 3D, camera moves. Use it before hand-building any named visual (CRT scanlines, glitch, film grain, a chart); also for hyperframes add and catalog.

Read or write Azure SQL Database from an AIDP notebook through the AIDP `aidataplatform` Spark format handler. Use when the user mentions Azure SQL, Azure SQL Database, AZURE_SQLSERVER, or a database.windows.net endpoint. Auth is SQL username/password.

Field-level data lineage report per mapping via `infa2aidp cli lineage`. Use when the user asks "where does this field come from" or wants a lineage artifact for documentation/audit purposes. Operates on the Informatica export directly (source mappings), not on the generated PySpark notebooks.

Connect from an AIDP notebook to Oracle AI Lakehouse (ALH), Autonomous Data Warehouse (ADW), or Autonomous Transaction Processing (ATP). Prefer the AIDP `aidataplatform` Spark format handler for wallet/password and catalog.id paths (`ORACLE_ALH` / `ORACLE_ATP`), but use raw Spark JDBC for IAM DB-token because the AIDP connector does not support DB-token auth yet.

Provision the migration environment inside AIDP for the prod flow - the workspace (name auto-translated to the simplest safe charset), the migration-assets compute cluster, optional cluster libraries from requirements-aidp.txt, the backup-snowflake-migration/ workspace folder holding the data-migration scripts and the plan artifacts, and the migration jobs (discover, structure, reconcile, plus one copy job per schema of the approved plan, each passing its schema as a task parameter). Use when the user wants to set up AIDP for the migration, upload the migration scripts, create the migration workspace or cluster, or wire the migration jobs. Dry-run by default.

Create the medallion architecture on Oracle AI Data Platform - registering an EXTERNAL catalog of source type SNOWFLAKE by default, and generating Spark SQL for schemas, tables and views from an approved Snowflake plan only when the user has explicitly asked for a Standard catalog. This stage copies no data - rows are copied only by the snowmig_02_copy_schema job, when the operator runs it. Use when the user asks to create the medallion structure, soft clone, shallow clone, create the target catalog, or deploy the target schema.

Router skill. Read this first whenever the user mentions migrating Informatica ETL (IDMC/IICS cloud mappings, PowerCenter 10.x repository XML) onto Oracle AI Data Platform (AIDP). Lays out the toolkit, the CLI command sequence, which infa-* skill handles each phase, and the tool's honest limitations — no taskflow/Mapping Task/parameter-set support, no cell-by-cell execute-and-fix loop on a live cluster, nothing validated against an export from a production Informatica repository. Compose those skills; this one adds no API surface.

Connect to ANY database that has a JDBC driver from an AIDP notebook using Spark's native `format("jdbc")`. Use when the user mentions a DB without a dedicated AIDP connector — SQLite, ClickHouse, DuckDB, generic JDBC URL — or wants to use a custom JDBC driver they uploaded. Auth is driver-specific.