Agent skill · nvidia
physical-ai-event-video-generation
Run the PAIDF Orchestration Event Video Generation DAG on Kubernetes - image-to-video anomaly generation, auto-labeling, and anomaly dataset generation. Select for requests about event video generation, anomaly video generation, image-to-video synthesis, Cosmos3 image2video, anomaly dataset creation, safety/surveillance SDG, or generating person-falling, person-climbing, person-running, fighting, smoking/vaping, fire/smoke, or shoplifting video clips from a seed image. Runs environment setup first when controller readiness is unknown. Not for person-crop clothing/attribute augmentation (that is image-attribute-augmentation-workflow) and not for video style transfer.
What it needs
About 9k tokens when loaded.
What this skill does
PAIDF Orchestration — Event Video Generation Run the Event Video Generation DAG end to end: seed-image input preparation, Cosmos3 image-to-video anomaly augmentation, auto-labeling (detection and tracking, captioning, anomaly visual QA, person-attribute visual QA, person attribute search), anomaly dataset generation, and result retrieval. DAG selection The workflow builds one DAG per compute platform from airflow/dags/workflows/eventvideogenerationdag/: Platform DAG ID Manifest --- --- --- Kubernetes eventvideogenerationdagk8s eventvideogenerationk8smanifest.yaml Kubernetes is the only platform whose manifest is checked in, so eventvideogenerationdagk8s is the only DAG this repository registers. A DAG is registered only if its manifest exists; a missing manifest means the DAG is absent from Airflow rather than broken. List the DAGs Airflow actually loaded before triggering, and never name a DAG ID that is not in that list. There is a single end-to-end pipeline — there are no generation-only or labeling-only DAG variants. If a user asks for video generation without auto-labeling, tell them the checked-in DAG does not offer that flow rather than inventing a DAG ID. Manual payload entry in the Airflow UI If the user wants to enter their own payload directly in the Airflow UI rather than have you construct and trigger one, your job is limited to getting them to the UI: confirm controller readiness, ensure make port-forward is running (see airflow-direct-api.md), and report the reachable URL. Do not render a payload, run preflight, or trigger a run yourself in this case — the user is doing that from the UI. Resume monitoring (step 6 below) once they tell you a run has been triggered; you can find it via the Airflow API without needing the payload they used. Scope Before building any payload, collect all of the following from the user. Do not fall back to repository defaults, CI payloads, or any hardcoded endpoint URL or bucket path. …
How to use it
Reference it in AdaL, Claude Code, Cursor or any coding agent — nothing to install:
@skills nvidia/physical-ai-event-video-generation