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://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/workflow-runner-debugging/runner-debugging-patterns.md

Workflow Runner Debugging Patterns

Use the CDF workflow-runner service as the local execution layer. The runner owns Docker or Podman command construction, path normalization, identity bind mounts, stage ordering, image build dispatch, dry-run mode, and policy handling. It is not a scheduler; OSMO or Airflow should own production scheduling, distributed execution, retries, queues, and resource placement.

Raw docker run commands are useful as diagnostics, but they should not be the primary production path when the runner can express the workflow.

Runner Commands

Dry-run a cookbook:

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

Run the same cookbook and build only the missing images first (--container-ensure-images build-if-missing); use --container-build-images instead only when you intend to rebuild all images:

make run SCRIPT=workflow-runner:main \
  ARGS='--cookbook-file <cookbook-config> --container-ensure-images build-if-missing'

Run explicit stages (selected stages keep the canonical relative order):

make run SCRIPT=workflow-runner:main \
  ARGS='--input-file payloads/simple.jsonl --stages captioning visual_qa reasoning training_export'

Build Pattern

Prefer repo registrations:

make build IMAGE=super-resolution-service:build
make build IMAGE=detection-and-tracking-service:sam3
make build IMAGE=captioning-service:main
make build IMAGE=workflow-runner:build

Cookbooks should reference registered service images and any required build target through runner config and stage flags.

Runner Runtime Pattern

DataEntry local media parents are mounted read-only.
DataEntry local scene directories are mounted read-write.
Runner-generated input manifests are mounted read-only.
Cookbook prompt/question-bank/config paths are mounted read-only.
Model cache paths are mounted read-write when configured.
Remote URI-like paths are passed through and not mounted by the local runner.

SeedVR2 SR Pattern

The SR service image bakes the pinned SeedVR runtime into /opt/seedvr; do not mount or symlink SeedVR source for normal auto-labeling runs. The large checkpoints still come from the configured model cache unless the service is explicitly run with checkpoint download enabled.

Expected model-cache layout:

<model-cache>/seedvr2/ema_vae.pth
<model-cache>/seedvr2/seedvr2_ema_3b.pth

For mixed-resolution media, pass SR gate args through the cookbook or --stage-arg:

stage_args:
  super_resolution:
    - --resolution-policy
    - auto
    - --min-input-short-side
    - "720"
    - --min-input-long-side
    - "1280"

The gate probes each staged active media file. Inputs below either threshold run SeedVR2; inputs meeting both thresholds are recorded as skipped and keep the existing active media. Validate real SR on representative hardware before a large run; the 3B 720p path can exceed A40 memory depending on resolution, window size, and active GPU load.

Failure Handling

Stop after a failed stage unless the cookbook marks the stage as optional.
If a failed stage is marked optional, continue to the next stage, but still validate that any expected outputs from the optional stage exist before proceeding; a missing optional output can still break downstream inputs.
Keep the failed stage log and the exact command.
Do not repair downstream outputs manually; after fixing mounts, endpoints, or model availability, rerun from the failed stage. Rerun at most once per fixed root cause: if the same stage fails again with no new cause identified, STOP and escalate with the saved log and command instead of looping.

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

2026-11-09

AutoGineer is not affiliated with or endorsed by NVIDIA or NVIDIA.

Download ZIP

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