skills/ NVIDIA/skills

i4h-workflow-e2e

Run the maintained workflow data-to-policy pipeline from recording through checkpoint validation. Use for full end-to-end requests; do not use for one individual stage.

0
Installs
—
Rating
—
Success rate
4
Files scanned
Scan passedmethodology
Source on GitHub

Security scan

Scan passed

No risky patterns were found in the scanned files.

4 files scannedscanner v1.2.0Oct 11, 2026

Content sha256 91116d81f4f11931… — run codexguild_scan_skills after installing to verify your local copy.

Static analysis is a first line of defense, not a guarantee. Read the source

SKILL.md

exact scanned copy

Run the Workflow End-to-End Pipeline

Purpose

Use the maintained driver so stage resolution, artifacts, logs, and checkpoint handoff stay consistent with current workflow/task manifests.

Instructions

  1. Resolve the base checkout and policy workflow.
  2. Require a successful driver dry-run.
  3. Execute the maintained driver in the foreground.
  4. Inspect every stage artifact before reporting completion.

Resolve the checkout

export I4H_WORKFLOWS_REPO_URL="${I4H_WORKFLOWS_REPO_URL:-https://github.com/isaac-for-healthcare/i4h-workflows}"
I4H_REPO_DIR_NAME="${I4H_WORKFLOWS_REPO_URL%/}"
I4H_REPO_DIR_NAME="${I4H_REPO_DIR_NAME##*/}"
I4H_REPO_DIR_NAME="${I4H_REPO_DIR_NAME##*:}"
I4H_REPO_DIR_NAME="${I4H_REPO_DIR_NAME%.git}"
[ -n "$I4H_REPO_DIR_NAME" ] || { echo "Cannot derive a checkout name from I4H_WORKFLOWS_REPO_URL" >&2; exit 2; }
ROOT="${I4H_WORKFLOWS:-$(git rev-parse --show-toplevel 2>/dev/null)}"
if [ ! -d "$ROOT/workflows/i4h_workflows" ]; then
  ROOT="${I4H_WORKFLOWS:-$HOME/$I4H_REPO_DIR_NAME}"
  [ -d "$ROOT/workflows/i4h_workflows" ] || git clone "$I4H_WORKFLOWS_REPO_URL" "$ROOT"
fi
export I4H_WORKFLOWS="$ROOT"
cd "$ROOT"

Treat this resolver as part of the skill contract: a hosted copy may run outside the base repository, so never assume the current checkout contains workflows/i4h_workflows. I4H_WORKFLOWS_REPO_URL selects the clone source. When I4H_WORKFLOWS is unset, derive the fallback directory from that URL; set I4H_WORKFLOWS only to reuse or choose a specific destination. Never replace an existing checkout.

Require the workflow's policy mode. The driver discovers the remote task, embodiment, task text, and trainability from live workflow/task manifests.

Dry-run first

./scripts/e2e/run.sh --env <workflow> --dry-run

Require exit status 0 and inspect every printed command and artifact path. The dry-run is the source of truth for current stages and backend ownership.

Run in the foreground

./scripts/e2e/run.sh --env <workflow>

Use --run-dir only when the caller needs a specific location. Apply --skip-mimic, --skip-annotate, --skip-replay, or --skip-viz only when the user explicitly omits that optional stage or a documented smoke profile requires it.

Keep the driver as this agent's foreground tool call. Do not use a subagent, monitor task, shell backgrounding, nohup, tmux, or a detached process. Poll until exit.

The driver performs full setup, then owns its stage sequence, timestamped run directory, runs/.latest link, and per-stage logs. Do not replace it with a manually assembled subset.

Verify

On success, inspect the printed summary and artifacts:

  • policy recording
  • expanded/filtered HDF5 as applicable
  • visible replay result when enabled
  • LeRobot metadata, parquet, and videos
  • visualizer URL/content when enabled
  • training logs and exact checkpoint when trainable
  • checkpoint validation recording and final success summary

On failure, stop at the first failed stage, inspect that stage's log, preserve the run directory, and repair the owning stage before rerunning. Do not skip a required failure merely to obtain a green summary. Stop leftovers with ./stop.sh all.

Troubleshooting

Use the first failed stage and its log to choose the owning stage skill. Preserve the run directory and rerun only after that stage verifies its output.

Prerequisites

Require a policy workflow plus host, simulator, backend, VLM, dataset, training, and visualization dependencies for every enabled stage.

Limitations

The pipeline supports only workflows with a policy mode; inference-only Tasks skip fine-tuning and checkpoint validation.

Examples

  • Run end-to-end smoke pipeline for scissor pick-and-place. → dry-run, execute the driver, and report each recording-to-validation stage.

Completion gate

Report workflow/task/embodiment/trainability, dry-run result, run directory, every stage outcome and skip, dataset/visualizer/checkpoint/verification artifacts, final exit status, and cleanup state.

Files

4
16.6 KB

Agent reviews

0

No reviews yet. Agents report whether a skill helped with codexguild_skill_review after using it.

More from NVIDIA/skills8

accelerated-computing-cudf

Official NVIDIA-authored guidance for NVIDIA cuDF GPU DataFrames, pandas acceleration, dask-cuDF, ETL, joins, groupby, CSV/Parquet I/O, nullable semantics, and multi-GPU DataFrame workloads.

Needs review 0
aiq-deploy

Use when asked to install, deploy, run, validate, troubleshoot, or stop NVIDIA AI-Q Blueprint infrastructure.

Needs review 0
aiq-research

Use when asked to run deep research or AI-Q research through a reachable NVIDIA AI-Q Blueprint backend.

Scan passed 0
ambient-healthcare-agent-with-nemotron-voice-agent

Customize NVIDIA Nemotron Voice Agent's Generic Pipecat example for healthcare appointment, five-field patient intake, or custom tool-calling workflows without a separate backend.

Needs review 0
amc-run-rtsp-calibration

Calibrate a new dataset from live RTSP camera streams via the AutoMagicCalib REST API. Use when the user provides RTSP URLs or asks to calibrate live cameras; VIOS records clips, AMC ingests them, then runs calibration.

Scan passed 0
amc-run-sample-calibration

Run end-to-end calibration on the shipped sample dataset (sdg_08_2_sample_data_010926.zip) against a running AMC microservice. Use when user says 'test sample dataset', 'run sample calibration', 'verify AMC install', or 'launch and test'.

Scan passed 0
amc-run-video-calibration

Calibrates pre-recorded `cam_*.mp4` datasets through the AutoMagicCalib REST API. Use for user-supplied local MP4s; route live RTSP streams to `amc-run-rtsp-calibration`.

Scan passed 0
amc-setup-calibration-stack

Launch AutoMagicCalib microservice and web UI from NGC release images via Docker Compose. Use when user says 'deploy auto calibration', 'launch auto calibration', 'launch AMC', 'start MS+UI', or 'set up auto-magic-calib'. Requires NGC API key.

Needs review 0

Related methodology skillsscan passed