i4h-workflow-e2e
Run the maintained workflow data-to-policy pipeline from recording through checkpoint validation.
Skill by Isaac for Healthcare Team <isaac-for-healthcare-support@nvidia.com>
Install
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About
Use cases: testing, data, productivity, data-pipelines, llm-evaluation
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What this skill can do and what it asks for, from its SKILL.md. Checked Oct 25, 2026.
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codeload.github.com· found in install command
Files it touches
~/.claude/skills/i4h-workflow-e2e
What's inside
- Skill name
i4h-workflow-e2e- Description
- 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.
- License
- Apache-2.0
- Metadata
- author: Isaac for Healthcare Team <isaac-for-healthcare-support@nvidia.com> · version: 0.8.0 · verification-request: 2026-09-15
- Instructions
- 90 lines in SKILL.md · view on GitHub
Files (5)
- BENCHMARK.md
- SKILL.md
- evals.json · evals/
- skill-card.md
- skill.oms.sig
SKILL.md (first lines)
# 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
```bash
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
```bash
./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
```bash
./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.From NVIDIA/skills, Apache-2.0.
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Featured in
Signals
- Stars
- 3,718
- Forks
- 457
- Open issues
- 8
- Last commit (this folder)
- Sep 30, 2026 (27 days ago)
From GitHub (fetched Oct 24, 2026).
Details
- Version
- 0.8.0
- License
- Apache-2.0
- Repository
- github.com/NVIDIA/skills
- Website
- docs.nvidia.com/skills
- First seen
- Oct 24, 2026
- Last checked
- Oct 24, 2026
- Source
- GitHubgithub-graphql
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Cite this page
i4h-workflow-e2e skill. Agent Mart. https://agentmart.dev/skills/nvidia-i4h-workflow-e2e (last updated 2026-10-24)