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i4h Workflow End-to-End

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Execute the complete agentic pipeline seamlessly.

by nvidia2.8k stars on nvidia/skills
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Updated Aug 7, 2026
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What i4h Workflow End-to-End does

The i4h Workflow End-to-End skill is designed to facilitate the execution of a comprehensive agentic pipeline. This skill encompasses multiple stages including recording, mimicking, annotating, replaying, converting, visualizing, fine-tuning, and validating processes. It is particularly useful for users who need to run the entire workflow in one go, whether for testing, demonstration, or full operational runs. By automating these tasks, it allows developers and researchers to focus on higher-level objectives without getting bogged down in the intricacies of manual execution.

To utilize the skill, users must first configure their environment by setting the appropriate paths and ensuring that the necessary workflows are in place. The skill provides a straightforward command-line interface that guides users through a series of bash commands to set up and execute the pipeline. Each stage of the pipeline is executed in order, with real-time logging available to monitor progress and troubleshoot any issues that may arise during execution. The skill also allows for flexibility, enabling users to skip certain stages if desired or resume from a previous run.

This skill is particularly beneficial for developers and researchers working in fields that require complex workflows, such as robotics or machine learning. It simplifies the process of running extensive experiments and helps ensure that all necessary steps are completed systematically. The outputs generated at each stage, including logs and data artifacts, provide valuable insights and documentation of the workflow, which can be critical for further analysis and reporting.

However, users should be aware that this skill is not suitable for all scenarios. It is specifically tailored for running the full pipeline and may not be the best choice for isolated tasks that can be handled by more specialized skills. Additionally, certain limitations exist, such as the inference-only nature of the assemble_trocar stage, which may not meet the needs of users looking for comprehensive training capabilities.

When to use it

Use this skill when you need to run the entire end-to-end pipeline for agentic workflows, particularly for testing or demonstration purposes.

When not to use it

Avoid using this skill for isolated tasks or when only specific stages of the workflow are needed; it is designed for full pipeline execution only.

What you can build with it

Full Workflow Testing

Run the complete end-to-end pipeline to validate the entire workflow in a single command.

Demonstration Runs

Use this skill to showcase the capabilities of your agentic workflows during presentations or demos.

Automated Experimentation

Automate repetitive tasks in research by executing the full pipeline without manual intervention.

How to install i4h Workflow End-to-End

View source

1. Install with the skills CLI

npx skills add nvidia/skills/i4h-workflow-e2e --agent claude-code

2. Or install it manually

Download the skill folder and drop it into ~/.claude/skills/ for all projects, or .claude/skills/ to scope it to one repo. Restart Claude Code so it picks up the new skill.

Anthropic's agentic coding CLI, and the reference implementation of Agent Skills. Drop a skill folder into ~/.claude/skills and Claude Code loads it automatically whenever a task matches the skill's description. Claude Code docs

Inside SKILL.md

Written by nvidia

i4h Workflow — End-to-End

Purpose

Run the full end-to-end agentic pipeline (record, mimic, annotate/filter, replay, convert, visualize, finetune, validate). Use when the user asks to run the full pipeline, smoke the whole workflow, demo the workflow, or do an e2e run.

Base Code

These steps drive the i4h-workflows base code (the workflows/agentic/ tree). To reuse an existing checkout, set I4H_WORKFLOWS to its path (no clone happens). Otherwise this resolves the current repo, or clones to ~/i4h-workflows — pick that default without prompting. Run every command below from the resolved root:

# Resolve the i4h-workflows base code (provides workflows/agentic/).
ROOT="${I4H_WORKFLOWS:-$(git rev-parse --show-toplevel 2>/dev/null)}"
if [ ! -d "$ROOT/workflows/agentic" ]; then
  ROOT="${I4H_WORKFLOWS:-$HOME/i4h-workflows}"
  [ -d "$ROOT/workflows/agentic" ] || git clone https://github.com/isaac-for-healthcare/i4h-workflows "$ROOT"
fi
export I4H_WORKFLOWS="$ROOT"; cd "$ROOT"

Basics

  • Env config (source of truth): workflows/agentic/config/environments/<env>.yaml — drives every stage for <env> (robot, task, policy, cameras, arena.max_timesteps, dataset.* mappings).
  • Use the e2e script for full pipeline runs.
  • For per-stage work, use the corresponding dataset/finetune/validate skills.
  • assemble_trocar is inference-only; the e2e script skips finetune and checkpoint validation for it.

Dry Run

REPO_ROOT="${I4H_WORKFLOWS:-$(git rev-parse --show-toplevel 2>/dev/null)}"; [ -d "$REPO_ROOT/workflows/agentic" ] || REPO_ROOT="$HOME/i4h-workflows"
"${REPO_ROOT}/workflows/agentic/scripts/e2e/run.sh" --dry-run --env <env>

Run

Run the steps below in order. Each step is a separate bash call; variables persist in the local agent's tmux session.

For Claude Code --print or any other noninteractive runner, keep Step 2 in the foreground. This is a validation requirement: do not use Claude background tasks, async task mode, Bash background mode, &, nohup, tmux, disown, or any detached process/task id, and do not answer that the pipeline is still running. Do not return until run.sh exits and you have inspected logs/SUMMARY.txt on success, or the failing stage log on failure. Report the run dir, skipped stages, per-stage status, key artifacts, and cleanup/stop status before finishing.

Step 1 — setup

REPO_ROOT="${I4H_WORKFLOWS:-$(git rev-parse --show-toplevel 2>/dev/null)}"; [ -d "$REPO_ROOT/workflows/agentic" ] || REPO_ROOT="$HOME/i4h-workflows"

Step 2 — e2e pipeline

"${REPO_ROOT}/workflows/agentic/scripts/e2e/run.sh" --env <env>

Flags

  • --skip-mimic, --skip-annotate, --skip-replay, --skip-viz
  • --from-stage <stage> --run-dir <existing-run> resumes from a prior run.
  • Policy record/verify stages open the sim window by default. Set ARENA_HEADLESS=1 before run.sh only when the user explicitly asks for headless/no-window execution.

Stages: setup record mimic annotate replay convert viz finetune validate summary.

Outputs

The script prints RUN_DIR and symlinks it to runs/.latest. Subdirs:

  • logs/ — per-stage logs, workflow.log (full teed output), and logs/SUMMARY.txt (the final summary report)
  • data/
  • lerobot/
  • checkpoint/ (trainable envs only)

Monitor

run.sh runs every stage in the foreground and returns only when the whole pipeline ends, so track a long run from a separate shell (do not expect to query it from the shell that launched it):

tail -f "${REPO_ROOT}/workflows/agentic/runs/.latest/logs/workflow.log"   # live per-stage progress
cat    "${REPO_ROOT}/workflows/agentic/runs/.latest/logs/SUMMARY.txt"     # final report (once DONE)

Stop

Step 3 — stop (if needed)

"${REPO_ROOT}/workflows/agentic/stop.sh" all --env <env>

Prerequisites

  • Workflow set up via [[i4h-workflow-setup]] (the .venv must exist); setup is also the first pipeline stage.
  • A valid --env name to drive the run.
  • For per-stage work, use the corresponding dataset/finetune/validate skills instead.

Limitations

  • assemble_trocar is inference-only; the e2e script skips finetune and checkpoint validation for it.
  • checkpoint/ outputs are produced for trainable envs only.
  • Resuming requires both --from-stage <stage> and --run-dir <existing-run>.

Troubleshooting

  • Error: .venv not found / module import fails - Cause: workflow not set up. Fix: run [[i4h-workflow-setup]] first.
  • Error: env not recognized - Cause: wrong --env name. Fix: pass a valid env name; dry-run first with --dry-run --env <env>.
  • Error: resume fails to find prior outputs - Cause: --from-stage used without a matching --run-dir. Fix: pass --from-stage <stage> --run-dir <existing-run>.
  • Error: stale processes block a rerun - Cause: a previous pipeline session is still running. Fix: run stop.sh all --env <env> before retrying.

Final Response

Report env, run dir, skipped stages, per-stage success/failure, key artifact paths.

Frequently asked questions about i4h Workflow End-to-End

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