Skills for “data”

Prepare payloads, run and monitor the Autonomous Vehicle Dataset Generation Airflow workflow, and inspect results for driving-clip retrieval and Cosmos3-Nano variants. Existing clips with no retrieval use…

Run the CDF Video Data Augmentation Airflow workflow on Kubernetes or OSMO to create Cosmos video-to-video variants and auto-label both source and generated videos. Use for VDA payload authoring, preflight, execution,…

Use this skill for Hugging Face Dataset Viewer API workflows that fetch subset/split metadata, paginate rows, search text, apply filters, download parquet URLs, and read size or statistics.

Imports datasets into FiftyOne with automatic format detection. Supports all media types (images, videos, point clouds, MCAP multimodal recordings), label formats (COCO, YOLO, VOC, KITTI), multimodal grouped datasets,…

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…

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 —…

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…

Collect and package NVSHMEM put/get bandwidth, latency, and other perftest results with system and topology evidence for performance sanity checks.

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…

Use when running video data augmentation and auto-labeling workflows on OSMO: flow selection, preflight, submit-time interpolation, monitoring, and output retrieval. Trigger keywords: video data augmentation, data…

The data-mover for TAO jobs — decides the storage tier (A pre-positioned mount with zero fetch / B volume-from-S3 / C ephemeral in-compute fetch), stages inputs (bulk + annotation-selective + archive extract + HF/NGC…

Run `tao-daft convert` to convert NVIDIA TAO DAFT datasets between supported formats. Do not use for non-DAFT data. Use when the user asks to convert a DAFT dataset, change DAFT format, change a TAO dataset format, or…

…requirement, row caps, unique-measure rule), the call-metadata argument convention (`rationale`, `skill`), and domain resolution. Also covers parsing of pasted `console.noibu.com` URLs. Loads alongside topical Noibu…

Pull and combine business data from any of the 350+ sources connected to Windsor.ai inside a coding workflow, for example writing campaign or CRM data to a local JSON or CSV file, generating typed data for a dashboard,…

Pre-migration data-availability scan. Reads a migration manifest, probes every spark.read.* / saveAsTable target on the AIDP cluster, reports OK / MISSING / EMPTY with remediation tips per category. Use before any…

Read a single Databricks notebook (.ipynb / .py / .scala source format) and produce a migration-readiness report — what it does, what it depends on, what risks the migrator will hit, which gotchas (from…

Run `tao-daft validate` to check NVIDIA TAO DAFT datasets for structure, schema, and cross-reference errors. Do not use for non-DAFT formats. Use when the user asks to validate a DAFT dataset, check DAFT schema,…

Share AIDP data via Delta Sharing — create/manage shares, add data assets, manage recipients, and grant access/permissions. Use when the user wants to share a table/schema with another team or org, set up Delta Sharing,…

Run data-quality rule checks on AIDP tables — not-null, uniqueness, allowed ranges/sets, referential integrity, and freshness. Use when the user wants to validate data, check for nulls/duplicates/orphans, assert a…

…AIDP lakehouse with Spark SQL. Use when the user asks a data question ("how many…", "top N…", "show me…", "trend of…", "revenue by…") or wants to run ad-hoc Spark SQL. Grounds in .aidp/catalog.md + .aidp/semantic.md…