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i4h Workflow Validate

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Validate and evaluate i4h environments efficiently.

by nvidia2.8k stars on nvidia/skills
Updated Aug 7, 2026
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Free · Opens the source repo

What i4h Workflow Validate does

The i4h Workflow Validate skill is designed to facilitate the validation, evaluation, and execution of policies or scripted state-machine controllers within i4h environments. This skill is particularly useful for developers and researchers working with AI models in healthcare settings, as it allows them to roll out policies and perform scripted evaluations while recording verification episodes to an HDF5 format. The skill streamlines the process of managing environment configurations and ensures that users can efficiently validate their models against specific scenarios.

To use the skill, users must configure their environment settings, which are defined in YAML files located in the workflows/agentic/config/environments directory. The skill supports both headless and interactive execution modes, providing flexibility depending on user requirements. Additionally, it includes specific commands for running evaluations, ensuring that the policy daemon and Arena simulation run in a controlled manner to avoid issues with background processes.

The skill is built for users who need to validate AI policies in a structured way, particularly those engaged in developing and testing AI systems for healthcare applications. It provides a clear workflow for setting up environments, running evaluations, and managing outputs, making it an essential tool for anyone involved in AI model validation in this domain. By following the provided scripts and commands, users can efficiently manage their workflows and ensure that their models are performing as expected.

When to use it

Use this skill when you need to validate or evaluate AI policies in i4h environments, especially during development or testing phases.

When not to use it

This skill is not suitable for users who require real-time interactive evaluations without a structured workflow, as it relies on specific command sequences.

What you can build with it

Validating AI Policies for Healthcare

Use this skill to validate AI policies in healthcare environments, ensuring they meet required standards.

Running Scripted Evaluations

Execute scripted evaluations of AI models to verify their performance against specific scenarios.

Managing Environment Configurations

Efficiently manage and configure your AI environment settings through YAML files for streamlined validation.

How to install i4h Workflow Validate

View source

1. Install with the skills CLI

npx skills add nvidia/skills/i4h-workflow-validate --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 — Validate

Purpose

Roll out a policy or scripted state-machine controller against an env and record verification episodes to an HDF5. Use when the user asks to validate, evaluate, run, or rollout a policy/checkpoint, or asks for surgical state-machine smoke runs.

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 — read it for the <env> defaults: policy.model_repo/model_revision, policy.task_description, policy.health_port, and arena.max_timesteps.
  • Validation runs the policy daemon and Arena together; both processes are required.
  • The policy daemon is headless. Arena opens the sim window by default; add --headless --enable_cameras --rendering_mode performance only when the user explicitly asks for headless/no-window execution.
  • In Claude Code --print, Codex exec, or any other non-interactive/fresh session, policy evaluation must use Step 2A as one foreground bash command. Do not start the policy and Arena in separate tool calls, do not use Claude background tasks for eval, and do not return to the user until Arena exits and the policy cleanup has run.
  • In Claude Code specifically, do not use the Bash tool's background mode for Evaluate ... prompts, do not launch a command ending in &, and do not say "the eval is running in the background." The answer is not complete until the HDF5/log summary has been inspected.
  • README quick-run prompts that say "with the state machine" use Arena --state-machine and do not start a policy daemon.
  • Do not run the VLM annotator unless the user asks for success labels.
  • assemble_trocar is inference-only — validate its YAML default model or a compatible N1.5 checkpoint.

Inputs

  • ENV_ID: env YAML id.
  • EPISODES: 1 for sanity, more for real eval.
  • MAX_TIMESTEPS: use the user-requested cap when the prompt gives one (for example, 300 timesteps -> MAX_TIMESTEPS=300); otherwise read arena.max_timesteps from the env YAML for normal evaluation. Use 200 only when the user explicitly asks for a smoke, sanity, or quick check.
  • MODEL_PATH (optional): path to a checkpoint-NNNN/ directory containing model-0000{N}-of-*.safetensors, experiment_cfg/, and processor/. Omit to use YAML policy.model_repo.
  • USE_LATEST_CHECKPOINT=1: set this when the prompt says "new checkpoint" or "latest checkpoint" and MODEL_PATH is not already known.
  • STATE_MACHINE: true only when the prompt explicitly says state machine.

Run

Run the steps below in order with the bash tool. Script paths like policy/run.sh, arena/run.sh, and stop.sh are commands inside bash, not tool names.

For policy/checkpoint evaluation in Claude Code --print, Codex, codex exec --ephemeral, or any other non-interactive fresh session, use Step 2A after setup. Background policy daemons launched by a finished shell can be cleaned up before Arena connects; the controlled shell keeps policy and Arena in one process lifetime and always stops the daemon afterward. In an interactive local-agent tmux session, the separate Step 2 / Step 3 / Step 4 flow is also acceptable.

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"
ENV_ID=scissor_pick_and_place
EPISODES=1
ENV_CONFIG="${REPO_ROOT}/workflows/agentic/config/environments/${ENV_ID}.yaml"
[ -f "${ENV_CONFIG}" ] || { echo "missing env config: ${ENV_CONFIG}" >&2; exit 1; }
PYTHON="${REPO_ROOT}/workflows/agentic/arena/.venv/bin/python"
[ -x "${PYTHON}" ] || PYTHON="${REPO_ROOT}/workflows/agentic/.venv/bin/python"
[ -x "${PYTHON}" ] || { echo "missing workflow python env; run i4h-workflow-setup first" >&2; exit 1; }
MAX_TIMESTEPS="${MAX_TIMESTEPS:-$("${PYTHON}" -c 'import sys, yaml; print(yaml.safe_load(open(sys.argv[1], encoding="utf-8"))["arena"]["max_timesteps"])' "${ENV_CONFIG}")}"
RUNS_ROOT="${REPO_ROOT}/workflows/agentic/runs"

# For prompts such as "Run eval using new checkpoint for 300 timesteps":
#   set MAX_TIMESTEPS=300 and USE_LATEST_CHECKPOINT=1 before this block.
if [ "${USE_LATEST_CHECKPOINT:-0}" = "1" ] && [ -z "${MODEL_PATH:-}" ]; then
  MODEL_PATH="$(find "${RUNS_ROOT}" -path '*/checkpoint/checkpoint-*' -type d -printf '%T@ %p\n' 2>/dev/null | sort -nr | head -1 | cut -d' ' -f2-)"
  [ -n "${MODEL_PATH}" ] || { echo "validate: no checkpoint found under ${RUNS_ROOT}; run finetune first or set MODEL_PATH" >&2; exit 1; }
fi

RUN_DIR="${RUNS_ROOT}/eval_${ENV_ID}_$(date +%Y%m%d_%H%M%S)"
mkdir -p "${RUN_DIR}/data" "${RUN_DIR}/logs"
ln -sfn "${RUN_DIR}" "${RUNS_ROOT}/.latest"

Step 2 — policy daemon

Skip this step when STATE_MACHINE=true. For Codex/non-interactive sessions, prefer Step 2A instead of this separate policy-daemon step.

POLICY_ARGS=(--env "${ENV_ID}" --ensure --log "${RUN_DIR}/logs/policy.log")
[ -n "${MODEL_PATH:-}" ] && POLICY_ARGS+=(--model-path "${MODEL_PATH}")
"${REPO_ROOT}/workflows/agentic/policy/run.sh" "${POLICY_ARGS[@]}"

Run this command exactly as a normal foreground bash command. Do not pipe it to head, cat, tee, or tail; do not add a separate stop, background launch, sleep, grep loop, curl check, or docker ps. policy/run.sh --ensure owns reuse, stop/restart, and start-and-ready behavior.

Step 2A — controlled policy rollout

Use this instead of separate Step 2 / Step 3 / Step 4 when running in Claude Code --print, Codex, codex exec --ephemeral, or another fresh non-interactive session. Run it as a normal foreground bash command; do not put it in the background and do not answer until it prints ARENA_STATUS.

POLICY_ARGS=(--env "${ENV_ID}" --ensure --log "${RUN_DIR}/logs/policy.log")
[ -n "${MODEL_PATH:-}" ] && POLICY_ARGS+=(--model-path "${MODEL_PATH}")

cleanup_policy() {
  "${REPO_ROOT}/workflows/agentic/stop.sh" policy --env "${ENV_ID}" >/dev/null 2>&1 || true
}
trap cleanup_policy EXIT

"${REPO_ROOT}/workflows/agentic/policy/run.sh" "${POLICY_ARGS[@]}"
ARENA_STATUS=0
"${REPO_ROOT}/workflows/agentic/arena/run.sh" --env "${ENV_ID}" \
  --episodes "${EPISODES}" \
  --max-timesteps "${MAX_TIMESTEPS}" \
  --max-attempts 1 \
  --record-to "${RUN_DIR}/data/verify.hdf5" \
  > "${RUN_DIR}/logs/arena.log" 2>&1 || ARENA_STATUS=$?
"${REPO_ROOT}/workflows/agentic/stop.sh" policy --env "${ENV_ID}"
trap - EXIT
echo "ARENA_STATUS=${ARENA_STATUS}"

After this block, skip directly to Step 5.

Step 3 — arena rollout

Skip this step if Step 2A was used.

For state-machine smoke runs, use this Arena command instead of the policy rollout command:

"${REPO_ROOT}/workflows/agentic/arena/run.sh" --env "${ENV_ID}" \
  --state-machine \
  --episodes "${EPISODES}" \
  --max-timesteps "${MAX_TIMESTEPS}" \
  --record-to "${RUN_DIR}/data/verify.hdf5" \
  > "${RUN_DIR}/logs/arena.log" 2>&1

For policy or checkpoint evaluation:

"${REPO_ROOT}/workflows/agentic/arena/run.sh" --env "${ENV_ID}" \
  --episodes "${EPISODES}" \
  --max-timesteps "${MAX_TIMESTEPS}" \
  --max-attempts 1 \
  --record-to "${RUN_DIR}/data/verify.hdf5" \
  > "${RUN_DIR}/logs/arena.log" 2>&1

Step 4 — stop policy

Skip this step when STATE_MACHINE=true or when Step 2A was used.

"${REPO_ROOT}/workflows/agentic/stop.sh" policy --env "${ENV_ID}"

Step 5 — summarize logs

grep -E "policy job complete|run complete|Traceback|Error|FAILED" "${RUN_DIR}/logs/arena.log" || tail -80 "${RUN_DIR}/logs/arena.log"
grep -E "policy ready|Traceback|Error|FAILED" "${RUN_DIR}/logs/policy.log" || tail -30 "${RUN_DIR}/logs/policy.log"

Notes

  • Launch the policy daemon with policy/run.sh --ensure, then launch Arena.
  • In non-interactive Codex runs, keep the policy daemon and Arena in one controlled shell with Step 2A so the daemon is not cleaned up between tool calls.
  • Once Arena exits — whether episodes succeeded or failed — shut down the policy daemon. It does not self-terminate, so leaving it running leaks GPU memory and holds its health port. Stop it with "${REPO_ROOT}/workflows/agentic/stop.sh" policy --env "${ENV_ID}".
  • --record-to must be absolute. The recorder resolves relative paths against workflows/agentic/arena (its CWD) and produces a nested orphan dir.
  • --max-attempts defaults to 1 for locomanip-family envs.

Optional Annotation

Run only on request:

"${REPO_ROOT}/workflows/agentic/annotator/run.sh" \
  --env "${ENV_ID}" \
  --output "${RUN_DIR}/annotations.jsonl" \
  offline \
  --hdf5-path "${RUN_DIR}/data/verify.hdf5"

Verify

  • verify.hdf5 exists under ${RUN_DIR}/data/.
  • Arena log shows run complete: N/M episodes succeeded.
  • Policy log contains no Traceback.

Prerequisites

  • Workflow set up via [[i4h-workflow-setup]] (.venv present); the policy/run.sh and arena/run.sh launches depend on it.
  • An ENV_ID matching an env YAML id.
  • A model source: either the env YAML policy.model_repo default, or a MODEL_PATH pointing at a checkpoint-NNNN/ dir (model-0000{N}-of-*.safetensors, experiment_cfg/, processor/).

Limitations

  • Both the policy daemon and Arena are required; the daemon is headless and Arena opens the sim window unless the user explicitly asks for headless/no-window execution.
  • assemble_trocar is inference-only — validate its YAML default model or a compatible N1.5 checkpoint.
  • --record-to must be absolute; relative paths resolve against workflows/agentic/arena and produce a nested orphan dir.
  • The VLM annotator is optional and run only on request; it is not part of the default rollout.

Troubleshooting

  • Error: .venv / import fails or run.sh missing - Cause: workflow not set up. Fix: run [[i4h-workflow-setup]] first.
  • Error: policy log shows Traceback / Error / FAILED before policy ready - Cause: the policy daemon failed to start (e.g. bad model source). Fix: inspect ${RUN_DIR}/logs/policy.log; verify ENV_ID / MODEL_PATH.
  • Error: Arena starts before the daemon is ready - Cause: launch order. Fix: launch the policy daemon first and wait for policy ready, then launch Arena.
  • Error: verify.hdf5 lands in a nested orphan dir - Cause: relative --record-to. Fix: pass an absolute path under ${RUN_DIR}/data/.
  • Error: PermissionError on /data/verify.hdf5 - Cause: RUN_DIR was unset when Arena ran (setup was skipped or run out of order). Fix: run the setup lines first so RUN_DIR exists before --record-to.

Final Response

Report env, model source, episodes saved vs requested, HDF5 path, log paths.

Frequently asked questions about i4h Workflow Validate

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