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

BENCHMARK.md

Skill Benchmark: cdf-auto-labeling

✅ Overall verdict: PASS — Recommended for publication

Publication Recommendation

Recommended for publication based on the completed evaluation evidence in this report.

Evaluation Metadata

Skill: cdf-auto-labeling
Evaluation date: 2026-10-20
Evaluator version: 1.5.6
Agents: Claude Code (aws/anthropic/bedrock-claude-opus-4-8), Codex (openai/openai/gpt-5.5)
Tasks: 4 evaluation tasks (4 positive)
Dataset digest: sha256:2c6d2a30a41329a86f39d94800eb60f640cf80f7382ff41245aa5d6301cf729c (skill-evaluator-dataset-snapshot/1)
Attempts per task: 1
Environment: k8s-sandbox
Tier 2 evidence: required for publication
Tier 3 evidence: required for publication

Each task attempt ran in its own isolated sandbox pod.

What This Report Answers

The three-tier evaluation checks whether the skill:

is safe to use;
produces correct answers;
is discovered and activated when needed;
helps the agent complete the user's goal and expected workflow; and
avoids wasted skill and tool usage.

Results at a Glance

Measure	Claude Code (Baseline → Skill Uplift)	Codex (Baseline → Skill Uplift)
Overall	96.1% — baseline ran, but no comparable score was available; uplift unavailable	97.1% — baseline ran, but no comparable score was available; uplift unavailable
Security	100.0% → 100.0% (±0.0 points)	62.5% → 100.0% (+37.5 points)
Correctness	25.0% → 100.0% (+75.0 points)	35.0% → 100.0% (+65.0 points)
Discoverability	96.3% — baseline ran, but no comparable score was available; uplift unavailable	91.3% — baseline ran, but no comparable score was available; uplift unavailable
Effectiveness	29.6% → 95.6% (+66.0 points)	25.0% → 96.3% (+71.3 points)
Efficiency	88.4% — baseline ran, but no comparable score was available; uplift unavailable	97.7% — baseline ran, but no comparable score was available; uplift unavailable

How to read this table: baseline is the same task attempted without the target skill. Scores are rounded to one decimal; threshold-adjacent values use additional precision so their displayed band matches the verdict. Uplift is derived from those displayed scores and shown in percentage points.

Example: 47.0% → 92.0% (+45.0 points) means the skill-assisted run scored 92.0%, 45.0 percentage points above its 47.0% no-skill baseline.

Token Usage

Actual Tier 3 execution usage is reported for every observed agent/case pair and both conditions.

Agent	Dataset case	With skill	Without skill	Delta	Change	Coverage
claude-code	All cases	585,270	674,144	-88,874	-13.18%	skill 4/4; base 4/4
claude-code	1	162,813	185,163	-22,350	-12.07%	skill 1/1; base 1/1
claude-code	2	163,551	214,272	-50,721	-23.67%	skill 1/1; base 1/1
claude-code	3	162,542	152,608	+9,934	+6.51%	skill 1/1; base 1/1
claude-code	4	96,364	122,101	-25,737	-21.08%	skill 1/1; base 1/1
codex	All cases	304,518	558,053	-253,535	-45.43%	skill 4/4; base 4/4
codex	1	86,351	42,081	+44,270	+105.20%	skill 1/1; base 1/1
codex	2	29,011	217,202	-188,191	-86.64%	skill 1/1; base 1/1
codex	3	141,216	128,791	+12,425	+9.65%	skill 1/1; base 1/1
codex	4	47,940	169,979	-122,039	-71.80%	skill 1/1; base 1/1
ALL AGENTS	Dataset aggregate	889,788	1,232,197	-342,409	-27.79%	skill 8/8; base 8/8

Prompt tokens include cached reads, so total tokens are prompt + completion (cached is not added twice). The Efficiency score uses (prompt - cached) + completion. N/A means the relevant trajectory counters were not available; coverage is never estimated.

Tier Status

Tier	Purpose	Status	Evidence
Tier 1	Static validation	PASSED WITH OBSERVATIONS	11 validator(s); 17 finding(s)
Tier 2	Semantic deduplication	PASSED	2 validator(s); 0 finding(s)
Tier 3	Live agent evaluation	PASS	2 agent(s); 4 task(s)

Findings and Observations

<details> <summary>Show detailed findings and successful checks</summary>

MEDIUM QUALITY/quality_efficiency: Deeply nested references in cookbook-authoring.md (skills/cdf-auto-labeling/SKILL.md)
MEDIUM SCHEMA/frontmatter_field_placement: Root field 'author' is ignored; use 'metadata.author' (skills/cdf-auto-labeling/SKILL.md)
MEDIUM SCHEMA/frontmatter_field_placement: Root field 'version' is ignored; use 'metadata.version' (skills/cdf-auto-labeling/SKILL.md)
MEDIUM SECURITY/Skill Enumeration (AS3): Agent Snooping: skills/cdf-auto-labeling/SKILL.md (BENCHMARK.md:81)
MEDIUM SECURITY/Skill Enumeration (AS3): Agent Snooping: skills/cdf-auto-labeling/SKILL.md (BENCHMARK.md:82)
12 additional finding(s) are available in the full evaluation artifacts.

</details>

Scoring Methodology

<details> <summary>Show dimension definitions, source signals, and thresholds</summary>

Dimension	Question	Scored signals
Security	Is it safe to use?	`security` (100%)
Correctness	Is the answer correct?	`accuracy` (100%)
Discoverability	Was the right skill loaded when needed?	`skill_execution` (100%)
Effectiveness	Did the skill help complete the task?	`goal_accuracy` (50%) + `behavior_check` (50%)
Efficiency	Did it avoid wasted tool calls and token usage?	`skill_efficiency` (50%) + `token_efficiency` (50%)

Dimension bands: PASS at 50% or above; NEUTRAL from 40% to below 50%; FAIL below 40%.
Overall Tier 3 lift: PASS at +5 points or more; FAIL at -10 points or less; values between those bands are NEUTRAL.
Overall verdict: PASS only when every configured dimension passes for at least one supported agent. Lift is reported as diagnostic evidence and does not override this gate.
The 50% attempt pass threshold is a separate per-task gate; it is not the dimension pass threshold.
Effectiveness is the equal-weight mean of goal completion (goal_accuracy) and expected workflow adherence (behavior_check).
Efficiency is 50% tool-call productivity (the backward-compatible skill_efficiency wire id) and 50% token_efficiency. Positive-case skill routing is scored under Discoverability, not Efficiency; a negative case without a routing target is N/A. N/A sources are omitted, remaining weights are renormalized, and the dimension is marked partial.

Signals present in this run:

security (Security): unsafe operations, secret leakage, and unauthorized access.
skill_execution (Skill Execution): whether the expected skill was selected, decoys were avoided, and the workflow executed.
skill_efficiency (Tool Productivity): tool-call productivity (legacy wire id; routing is scored under Discoverability).
accuracy (Accuracy): final-answer correctness against the reference answer.
goal_accuracy (Goal Accuracy): whether the user's goal was achieved.
behavior_check (Behavior Check): whether the expected workflow behavior was followed.
token_efficiency (Token Efficiency): actual uncached prompt plus completion usage (50% of Efficiency).

</details>

Freshness

Regenerate this benchmark when the skill, evaluation dataset, target agent/model, evaluator version, environment, or scoring policy changes.

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