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/stages/person-attribute-search.md

Person Attribute Search stage

The stage key is person_attribute_search; its image/build product is event-and-person-attribute-search-service. The service is an assembly and query-generation boundary. It never inspects media, captions images/video, runs Visual QA, detects people, copies crops, or creates tracking manifests.

Instructions

Before processing real individuals, confirm and document consent or another valid legal basis, the permitted purpose, the applicable retention period, and any required privacy or legal review.
Identify the source: one explicit --attribute-json, canonical single-person visual_qa/items.json, or video tracks plus namespaced per-track VQA windows.
Confirm every configured sidecar has an explicit upstream producer. For video, detection must produce detection_and_tracking/tracks.json; a separate Visual QA node must produce the configured item/window paths.
Configure the direct person_attribute_search: block. Use the shared LLM endpoint for per-person tiered queries or chunk-level caption/anomaly buckets. Do not add VLM fields or nested producer blocks.
For explicit attributes, pass --attribute-json as a node argument and keep exactly one service DataEntry. Bundle mode writes one matching entry per source image in the explicit JSON.
Suggest a workflow-runner dry-run and verify the PAS command contains its LLM settings and resolved source/config paths, but no VLM arguments.

Configuration examples

Single or multi-view canonical VQA input:

person_attribute_search:
  enabled: true
  dataset: upa
  visual_qa_item_sidecars: [visual_qa/items.json]
  visual_qa_window_sidecars: [visual_qa/windows.normalized.json]
  caption_sidecars: [captioning/image_caption.json]
  llm_query_generation: true
  use_template_for_medium: false
  bundle_query_generation: false
  bucket_query_generation: false

Video EPAS input and query buckets:

person_attribute_search:
  enabled: true
  dataset: upa
  tracks_sidecars: [detection_and_tracking/tracks.json]
  visual_qa_item_sidecars: [visual_qa_per_track/items.json]
  visual_qa_window_sidecars: [visual_qa_per_track/windows.normalized.json]
  video_captions_sidecars: [captioning/video_captions.json]
  anomaly_items_sidecars: [visual_qa_anomaly/items.json]
  llm_query_generation: true
  bucket_query_generation: true
  emit_contextual: true
  emit_daft_contextual: true

Explicit attribute bundle input belongs in node args:

workflow:
  nodes:
    person_attribute_search:
      stage: person_attribute_search
      args:
        - --attribute-json
        - data/input_media/attributes.json
        - --query-prompt-file
        - ../pas_synonymous_query_prompt.json
person_attribute_search:
  dataset: upa
  bundle_query_generation: true
  bundle_query_count: 3
  llm_query_generation: false
  bucket_query_generation: false

Outputs

Explicit bundle: bundle_attributes.json, bundle_queries.json, optional bundle_hitl.json.
Single identity: attributes.json, queries.json, optional hitl.json.
Video: pas.json, chunk_queries.json, optional pas_anomaly.json and contextual/person_attributes.json.

All PAS sidecars live under sidecars/person_attribute_search/.

Sensitive-data warning: These artifacts, including person_attributes.json and bundle_queries.json, may contain personal data, biometric-adjacent attributes, or other PII derived from images, crops, or video. Before using PAS on real individuals, identify and document consent or another valid legal basis and confirm compliance with all applicable laws, which may include the GDPR, CCPA/CPRA, and Illinois BIPA. Restrict input and output directories to authorized personnel and service accounts using least-privilege permissions; do not place artifacts in public or broadly shared locations. Encrypt artifacts at rest, apply a documented purpose-limited retention schedule, and securely delete the source media, crops, sidecars, query bundles, backups, and derived copies when that period expires. Obtain qualified privacy or legal review when requirements are uncertain.

Guardrails

Do not configure image_pas, crop discovery, synthetic tracks, captioning, or Visual QA inside this service. This architectural restriction does not reduce the privacy obligations for person data produced upstream or consumed by PAS.
Do not pass a VLM endpoint to PAS.
Do not claim PAS owns or reuses another service's artifacts.
Keep execution in the workflow-runner operator skill and require approval for real Docker runs.

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

From the author

AutoGineer summary

What it does

When to use it

Advantages

Usage examples

Install

Documentation by NVIDIA

CDF Auto-Labeling

Routing (Read First)

Instructions

Examples

Guardrails

Files

references/stages/person-attribute-search.md

Person Attribute Search stage

Instructions

Configuration examples

Outputs

Guardrails

Requirements and compatibility

Security

Attribution and licence

Related skills

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Emails

Conversion Tracking

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Download ZIP