/cdf-curation-and-retrieval

name: cdf-curation-and-retrieval
description: >-
  NVIDIA CDF curation and retrieval. Cosmos Curator video and image
  pipelines (split, dedup, shard, annotate) with filtering, captioning, and
  embedding. Cosmos Dataset Search ingest, search, and capability
  qualification. TAO data mining embeddings and neighbor matching. Ships
  domain event catalogs, including workplace employee conduct monitoring,
  which requires legal review before use. Use when a request names Cosmos
  Curator, Cosmos Dataset Search, CDF, or their Make and CLI surfaces.
  Not for model training, orchestration, or unrelated dataset questions.
license: CC-BY-4.0 AND Apache-2.0
owner: NVIDIA
service: cosmos-data-factory
reviewed: 2026-10-20
metadata:
  author: "NVIDIA <opensource@nvidia.com>"
  version: 1.2.0
  tags:
    - data-curation
    - dataset-retrieval
    - cosmos-curator
    - tao
    - physical-ai

CDF Curator Operator Skill

GPU-accelerated video and image curation via NVIDIA Cosmos Curator inside **Cosmos Data Factory — Curation and Retrieval** (`cdf-curation-and-retrieval`). This skill is a short Curator index. Embedding handoff boundaries live in [data-mining.md](references/data-mining.md) and [curation-retrieval-workflow.md](references/curation-retrieval-workflow.md); mining execution is `make help` and the repository cookbooks.

• **Video**: `split`, `dedup`, `shard`.
• **Image**: `annotate` (load → filter → embed → caption → write).
• **Handoff**: Curator IV2 or CE1 parquet that downstream mining can consume.

See [data-mining.md](references/data-mining.md) and [curation-retrieval-workflow.md](references/curation-retrieval-workflow.md).

• **CDS stitch**: Curator → CDS ingest/search. Operator Path D (public GA

`1.2.0`; not supported on Blackwell GPUs): see [cosmos-dataset-search.md](references/cosmos-dataset-search.md).

Purpose

Turn raw video and image collections into curated, training-ready datasets. This skill configures and runs cosmos-curator pipelines (clip splitting, filtering, captioning, embeddings, SAM3 event verification, dedup, WebDataset sharding, image annotate) and supports KPI-driven, distribution-aware, and restrictive curation.

Instructions

1. Classify the request as advisory, config, run, TAO handoff, or CDS ingest/search. Do not mix those routes. A config request with no KPI output or samples goes through the calibration interview, which can stop with unresolved requirements before any YAML is written. 2. For config work, complete the mandatory pre-flight below before writing YAML. 3. For an explicit run, load [running-pipelines.md](references/running-pipelines.md), validate the config, obtain credentials only through approved injection, then execute after authorization. 4. For a TAO handoff, validate Curator output and the declared embedding family using [data-mining.md](references/data-mining.md) before mining. 5. For CDS ingest or search, confirm CDS is healthy first, then follow [cosmos-dataset-search.md](references/cosmos-dataset-search.md). 6. Return the Output Format below. Never print secret values.

Examples

• Advisory: "How much SHM should I set?" → read

[running-pipelines.md](references/running-pipelines.md) and report guidance. Do not run Docker.

• Config with no KPI: complete the calibration interview in

[calibration-config.md](references/calibration-config.md), then emit YAML.

• Run: after sample clips are staged, `make run-pipeline` with the traffic

`split-minimal` cookbook recipe only after preflight and user authorization.

• FFmpeg missing in the container: install the host sidecar

(`make ffmpeg-install`) per [ffmpeg-sidecar.md](references/ffmpeg-sidecar.md).

Inputs

Required inputs depend on the route:

• **Advisory request:** the question plus relevant repository and config context.
• **Config request:** input and output locations, domain and goal, available KPI

output or representative samples, and hardware constraints. If neither KPI output nor samples exist, complete the calibration interview before writing YAML.

• **Run request:** reviewed config path, data and model paths, runtime, GPU,

and SHM constraints, and explicit authorization to execute.

• **TAO handoff:** validated artifact paths and declared embedding family.

Optional inputs include target distributions, event taxonomy, prompt choices, existing output metadata, and user-approved operational constraints.

Resolve inputs in this order: repository configuration and validated run artifacts; explicit prompt arguments and corrections; available agent context; then the broad user prompt. Explicit user instructions remain authoritative unless unsafe or incompatible, in which case stop and explain the conflict. Never infer secret values: credentials come only from approved runtime injection.

Prerequisites

• **Repository access**: the agent must be able to read the Makefile, the

`configs/` and `cookbook/` trees, and every file under `references/`.

• **GPU host** with NVIDIA drivers + `nvidia-container-toolkit`; Docker. SHM

sized from host RAM (`SHM_SIZE`, default 24gb).

• **`cosmos-curator` image**: `make pull` uses the pin configured by the

example env file and Make. No separate product engine image. Source builds are developer-only; see [cosmos-curator.md](references/cosmos-curator.md).

• **FFmpeg host sidecar** for distributable images (`make ffmpeg-install`) —

they do not bundle FFmpeg. See [ffmpeg-sidecar.md](references/ffmpeg-sidecar.md).

• **Credentials** as needed: inject S3 and captioning API keys at runtime

through an approved secret manager or operator deployment mechanism. Never put secret values in repository files, commands, logs, or examples. The env file is for non-secret image and CDS profile overrides copied from the example env file.

Mandatory pre-flight: do NOT emit a pipeline config without context

Before writing any `*.yaml` pipeline config, the agent MUST verify that one of