Cdf Auto Labeling

NVIDIAOfficialCreated by NVIDIA

Skill
Workflow
Automation and integrations
Apache-2.0
Medium risk

From the author

Use when a user needs to get started with CDF Auto-Labeling, plan a scenario, run or debug a shipped cookbook, author prompts or cookbooks, migrate a pipeline, configure a stage, or pull the prebuilt stage containers from NGC (nvcr.io). Confirm critical inputs (data path, output path, endpoints) and ask when any are missing. This is a router: read the matching reference instead of inventing a workflow.

— the description by NVIDIA

AutoGineer summary

Written by AutoGineer from the skill’s own files, not by the author

CDF Auto-Labeling is a central routing skill designed to guide users through the lifecycle of automated data labeling tasks within the Cosmos Data Factory ecosystem. It provides a structured approach to onboarding, scenario planning, cookbook authoring, and pipeline execution. By acting as a router, it directs users to specialized documentation for specific tasks—such as migrating pipelines, debugging workflows, or managing containerized stages—while enforcing strict input validation and operational guardrails to ensure reproducible results.

What it does

Routes users to specific documentation based on their current task or stage in the pipeline.
Validates critical run inputs like data paths, output directories, and model endpoints.
Provides a standardized workflow for running, debugging, and customizing annotation cookbooks.
Facilitates the integration of new stages and the management of prebuilt container images.
Enforces operational guardrails to prevent common errors like hardcoding secrets or skipping validation steps.

When to use it

When you are a new user needing to get started with CDF Auto-Labeling.
When you need to plan a new annotation scenario or adapt an existing cookbook.
When you are troubleshooting a failed pipeline run or debugging a specific stage.
When you need to migrate an external annotation repository into the CDF framework.
When you need to pull or configure prebuilt stage containers from NGC.

Advantages

Ensures consistent and reproducible pipeline execution through standardized cookbooks.
Prevents configuration errors by mandating input validation before execution.
Simplifies complex workflows by breaking them down into manageable, modular tasks.
Promotes best practices by delegating specialized tasks to dedicated reference guides.

Usage examples

“I cloned the repo and have my own warehouse-safety video. How do I produce auto-labels for my domain?”: The agent will guide you to verify your environment, run a shipped example to confirm the stack, plan your scenario, adapt a cookbook, and execute a dry-run.
“How do I pull the prebuilt stage containers from NGC?”: The agent will direct you to the ngc-containers reference and provide instructions on using make pull to retrieve the necessary images.

Install

Download the skill as a ZIP:
https://mp-staging.autogineer.com/agent-skills/nvidia/cdf-auto-labeling/download

Unzip it into the cross-client skills folder of your project:

mkdir -p .agents/skills && unzip cdf-auto-labeling.zip -d .agents/skills

Documentation by NVIDIA

CDF Auto-Labeling

Use this skill when a user wants to kick off CDF Auto-Labeling on their own data, domain, or use case, or when the request matches a shipped cookbook, stage, authoring, or migration task. This is a router: sequence the specialized references instead of duplicating their detail.

Routing (Read First)

Request looks like	Read
New user, clean checkout, first validated run, "how do I get started"	This file, then the matching reference below
Choose annotation targets / stage subset for a domain	`references/scenario-planning.md`
Create, review, or adapt a cookbook	`references/cookbook-authoring.md`
Write or adapt VLM/LLM prompts or question banks	`references/prompt-authoring.md`
Migrate an existing annotation repo into this one	`references/pipeline-migration.md`
Run the video data augmentation cookbook	`references/video-data-augmentation.md`
Run or choose an EPAS / PAS cookbook	`references/event-and-person-attribute-search.md`
Run event-verification reasoning	`references/event-verification-reasoning.md`
Debug an already-integrated workflow	`references/workflow-runner-debugging.md`
Pull prebuilt stage images from NGC, log in to `nvcr.io`, or fix a missing `cdf-*` image	`references/ngc-containers.md`
Implement or review a new stage or Dockerized service	`references/workflow-stage-integration.md`
Configure or debug one production stage	The matching file under `references/stages/`

Stage references: super-resolution, detection-and-tracking, captioning, visual-qa, reasoning, person-attribute-search, grounding-2d, referring-expressions, training-export.

Instructions

Confirm the critical run inputs with the user before doing anything else, and ask a concise question whenever one is missing or ambiguous - never guess or silently invent a default. At minimum confirm: input data path, output path, VLM/LLM endpoint URLs and model names, model cache path, GPU ids, and (for reasoning-capable models) the max_tokens cap. Restate the confirmed values back to the user before the first execution.
Verify the environment: repository cloned, make targets available, the model cache path exists, the VLM/LLM endpoints are reachable, and a GPU is available. State any missing prerequisite as a blocker instead of assuming it. If the stage images are not present locally, offer to pull the prebuilt images from NGC with make pull instead of building them. Use ngc-containers.
Run a shipped example first to confirm the stack works end to end before customizing. Pick the closest operator pipeline - video data augmentation, event-and-person-attribute-search, or event-verification-reasoning - and run its committed cookbook. Use the matching operator reference.
Plan the target scenario: define modality, domain, intended consumer, and required annotations, and get a minimal stage subset. Use scenario-planning.
Adapt the closest shipped cookbook to the new domain rather than authoring from scratch. Use cookbook-authoring.
Author the domain prompts and question banks. Use prompt-authoring.
Configure the per-stage settings for the domain (detector classes or SAM3 prompts, endpoints, windowing, max_tokens). Use the relevant stage reference, starting with detection-and-tracking.
Dry-run the adapted cookbook, then execute and validate the outputs. Use workflow-runner-debugging.

Adopting an existing external annotation or dataset-generation repository into CDF instead of starting from a shipped cookbook is a migration task; use pipeline-migration for that path.

Examples

New user, new domain: "I cloned the repo and have my own warehouse-safety video. How do I produce auto-labels for my domain?"

Guided path:

Confirm env (model cache, VLM/LLM endpoints, GPU), then prove the stack on a shipped example before customizing:

make run SCRIPT=workflow-runner:main \
  ARGS='--cookbook-file cookbooks/video_data_augmentation/configs/pipeline_video.yaml --container-dry-run'

Plan the domain (scenario-planning) -> subset detection_and_tracking -> captioning -> visual_qa -> reasoning -> training_export (add grounding_2d for caption→boxes or referring_expressions for boxes→phrases; use grounding-2d / referring-expressions).
Copy the closest cookbook to cookbooks/warehouse_safety/configs/pipeline.yaml and adapt inputs, detector classes/SAM3 prompts, prompts, and question banks.
Dry-run the new cookbook, then run for real and validate outputs:

make run SCRIPT=workflow-runner:main \
  ARGS='--cookbook-file cookbooks/warehouse_safety/configs/pipeline.yaml --container-dry-run'

Guardrails

Do not guess or fabricate the critical inputs enumerated in step 1; if any is missing or ambiguous, ask the user and confirm before executing.
Do not customize a cookbook before a shipped example runs clean; a broken base makes domain debugging ambiguous.
Keep the first custom pipeline minimal - only the stages needed for the requested annotations - and expand later.
Verify that every selected stage's service package and image exist in the current branch before promising an end-to-end run.
Do not put secrets, tokens, or absolute home paths in committed cookbooks; use placeholders such as <model-cache> and env vars for endpoint keys.
For reasoning-capable models (for example Gemini 3 Flash), raise max_tokens on the visual_qa and reasoning LLM substages to avoid the thinking-token tax; keep the default cap for non-reasoning models.
Do not rely on non-CDF pipelines, commands, or file locations. A first run must be reproducible through workflow-runner:main inside this repo.

Files

ATTRIBUTION.mdAdded by AutoGineer909 B
BENCHMARK.md7.3 KiB
LICENSELicence file added by AutoGineer11.2 KiB
SKILL.md7.5 KiB
evals/evals.json5.2 KiB
references/README.md1.3 KiB
references/cookbook-authoring.md6.3 KiB
references/cookbook-authoring/cookbook-schema.md6.3 KiB
references/cookbook-authoring/scenario-authoring.md2.8 KiB
references/event-and-person-attribute-search.md5.1 KiB
references/event-and-person-attribute-search/run-reference.md2.4 KiB
references/event-verification-reasoning.md1.9 KiB
references/event-verification-reasoning/run-reference.md1.3 KiB
references/ngc-containers.md4.2 KiB
references/pipeline-migration.md7.2 KiB
references/pipeline-migration/migration-playbook.md3.8 KiB
references/pipeline-migration/nonlinear-video-pipeline-example.md2.7 KiB
references/pipeline-migration/service-reuse-matrix.md3.4 KiB
references/pipeline-migration/sidecar-contracts.md3.0 KiB
references/prompt-authoring.md3.3 KiB
references/prompt-authoring/domain-evidence-checklists.md2.2 KiB
references/prompt-authoring/prompt-patterns.md3.9 KiB
references/prompt-authoring/question-bank-authoring.md2.0 KiB
references/scenario-planning.md3.6 KiB
references/scenario-planning/annotation-recommendations.md5.0 KiB
references/scenario-planning/intake-and-decisions.md2.6 KiB
references/scenario-planning/pipeline-composition.md6.0 KiB
references/stages/captioning.md3.6 KiB
references/stages/detection-and-tracking.md6.2 KiB
references/stages/detection-tracking-patterns.md10.3 KiB
references/stages/grounding-2d-patterns.md1.6 KiB
references/stages/grounding-2d.md5.1 KiB
references/stages/guardrails.md532 B
references/stages/person-attribute-search.md4.5 KiB
references/stages/reasoning.md5.0 KiB
references/stages/referring-expressions-patterns.md1.2 KiB
references/stages/referring-expressions.md5.5 KiB
references/stages/super-resolution.md4.4 KiB
references/stages/training-export.md3.7 KiB
references/stages/visual-qa.md6.2 KiB
references/video-data-augmentation.md12.2 KiB
references/video-data-augmentation/run-reference.md3.7 KiB
references/workflow-runner-debugging.md6.0 KiB
references/workflow-runner-debugging/data-handoff.md3.5 KiB
references/workflow-runner-debugging/runner-debugging-patterns.md3.7 KiB
references/workflow-stage-integration.md7.1 KiB
references/workflow-stage-integration/stage-service-contract.md3.0 KiB
references/workflow-stage-integration/validation-checklist.md2.1 KiB
skill-card.md4.9 KiB
skill.oms.sig15.6 KiB

references/pipeline-migration/nonlinear-video-pipeline-example.md

Nonlinear Video Pipeline Example

Use this reference as an example pattern for source repos shaped like:

raw videos
  -> tracking/crops
  -> video chunking
  -> chunk VLM annotation
  -> anomaly votes
  -> retrieval query generation
  -> visualizer

Source Pipeline Concepts

Typical source steps map to capabilities like this:

Source concept	UPA target	Migration decision
SAM tracking and crop export	`detection_and_tracking`	extend existing service if crop/PAS handoff is missing
5-second video chunks	`media_chunking`	new generic service if not already available
Scene and dense captions over chunks	`captioning`	extend existing service with chunk input mode
PAS from track crops	`visual_qa` or generic extraction stage	choose based on whether it is question-answering or structured extraction
Multi-model anomaly vote	`anomaly_vote` or generic classification	new generic service if reusable
Retrieval query generation	`query_generation`	new generic service
HTML visualizer	optional report/export service	keep out of core annotation path unless productized

Cookbook Shape

Prefer dependency-shaped YAML over a new pipeline-branded service:

pipeline: video

workflow:
  nodes:
    tracking:
      stage: detection_and_tracking
    chunking:
      stage: media_chunking
    captions:
      stage: captioning
      needs: [chunking]
    anomaly:
      stage: anomaly_vote
      needs: [chunking]
    queries:
      stage: query_generation
      needs: [tracking, captions, anomaly]

If the current runner cannot execute these stage names yet, call that out as a runner registry gap instead of inventing source-repo-specific stage names.

Sidecar Sketch

One possible sidecar layout:

sidecars/detection_and_tracking/
  tracks.json
  crops/
  crop_manifest.json

sidecars/media_chunking/
  chunks.json
  chunks/
    chunk_000.mp4

sidecars/captioning/
  chunk_windows.json
  dense_captions.json

sidecars/anomaly_vote/
  votes.json

sidecars/query_generation/
  retrieval_queries.json

Downstream stages should discover these through pipeline_state.json or stable stage output conventions, not hard-coded source-repo folder names.

What Not To Do

Do not port the whole source repo as one opaque container if individual primitives can become reusable UPA services.
Do not split every source script into a pipeline-branded UPA service.
Do not make workflow-runner responsible for the script's internal thread pools. Stage-local concurrency can remain inside the service.
Do not require workflow-runner parallel scheduling for the first migration; use workflow.nodes for dependency intent and let platform schedulers own distributed execution when needed.

Requirements and compatibility

Works with: Claude Code, Claude apps, OpenAI Codex, GitHub Copilot, Cursor, Gemini CLI, Kiro, OpenCode, Goose, Amp, Roo Code, JetBrains Junie, Factory Droid, Devin (ex-Windsurf), Mistral Vibe, OpenHands, Letta

“Works with” means the tool reads the open Agent Skills format; a skill written for one tool may use features others lack.

Security

What our scanner noticed· Medium risk

Large base64-encoded blobs

A static scan is not a guarantee. Skills are third-party content: review a skill before you let an agent use it.

Attribution and licence

Created by

NVIDIA

Publisher

NVIDIA (Official)

Source

NVIDIA/skills · skills/cdf-auto-labeling · commit fce3cca

Licence

Apache License 2.0 (redistribution allowed)

LICENSE (added by AutoGineer from LICENSE-APACHE)
ATTRIBUTION.md (written by AutoGineer)

Synced

2026-11-08

AutoGineer is not affiliated with or endorsed by NVIDIA.

Download ZIP

The ZIP holds the skill’s files, its licence files and an ATTRIBUTION.md. Download cdf-auto-labeling.zip