i4h-workflow-validate
Run the root-level workflow runtime policy or rule-based rollouts and verify simulator success. Use for evaluation, checkpoints, or local controllers; do not use for replay or dataset annotation.
Install / Use
npx skills add NVIDIA/skills --skill i4h-workflow-validateInstalls into whichever agent you are using.
SKILL.md
Installable skill definition
Quality Score
Category
AutomationSupported Platforms
Tags
Our assessment of i4h-workflow-validate
i4h-workflow-validate scores 94/100 on our quality scale, 353rd of 2,125 Automation skills we index (top 17%).
Its SKILL.md is 6.8 KB long, well organised into 11 sections with 4 code examples: a thorough specification that gives an agent plenty to work with.
With 3,421 GitHub stars, it is one of the more widely adopted skills in the catalogue.
Maintenance, license and trust
- The repository was last updated 5 days ago, so i4h-workflow-validate is actively maintained.
- It is released under the Apache-2.0 license, a permissive license that allows use, modification and commercial use with attribution.
- Its trust signals score 100/100, with no cautions. These come from repository metadata, not a code audit — read the skill file before letting an agent act on it.
Safety scan
No issues foundOur scan of the whole file found no instruction hijacking, hidden characters, credential access, data exfiltration or destructive commands.
Automated pattern scan on 2026-09-29. It catches known dangerous patterns, not every risk — read a skill before letting an agent act on it.
i4h-workflow-validate compared with similar skills
All 4 of these similar skills score higher than i4h-workflow-validate; compare them before choosing.
| Skill | Score | Stars | Updated | Format |
|---|---|---|---|---|
| i4h-workflow-validate (this skill)by NVIDIA | 94 | 3.4k | 5d ago | SKILL.md |
| Agent-Reachby Panniantong | 100 | 86.0k | 13d ago | CLAUDE.md |
| rufloby ruvnet | 100 | 73.4k | today | CLAUDE.md |
| Scraplingby D4Vinci | 100 | 84.4k | today | MCP Server |
| algorithmic-artby anthropics | 100 | 177.9k | 6d ago | SKILL.md |
Frequently asked questions
- How do I install i4h-workflow-validate?
- Run
npx skills add NVIDIA/skills --skill i4h-workflow-validate. The install tabs above show the steps for each supported agent. - Which AI agents does i4h-workflow-validate work with?
- It is written for Universal, as a SKILL.md file. Other agents that read the same format can often use it too.
- Is i4h-workflow-validate safe to use?
- Our scan of the whole file found no instruction hijacking, hidden characters, credential access, data exfiltration or destructive commands. It is Apache-2.0-licensed and scores 100/100 on trust signals. Skills are instructions an agent will follow, so read the file before installing it and do not approve commands you do not understand.
- Is i4h-workflow-validate still maintained?
- The repository was last updated 5 days ago, so i4h-workflow-validate is actively maintained.
Skill content
View source on GitHubname: i4h-workflow-validate description: Run the root-level workflow runtime policy or rule-based rollouts and verify simulator success. Use for evaluation, checkpoints, or local controllers; do not use for replay or dataset annotation. license: Apache-2.0 metadata: author: "Isaac for Healthcare Team isaac-for-healthcare-support@nvidia.com" version: "0.8.0" tags: - isaac-for-healthcare - i4h - simulation - evaluation
Validate a Workflow
Purpose
Run the selected workflow run mode through the unified launcher, inspect the completed recording, and report simulator success.
Instructions
- Resolve the base checkout and a live workflow run mode.
- Run the unified launcher in the foreground.
- Require the final episode success summary.
- Inspect visible behavior and every rollout artifact.
Resolve live support
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"
./run.sh list
Treat the resolver above 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.
Treat ./run.sh list output as the complete authoritative workflow-by-mode table; it is dependency-light and faster than scanning workflow modules. Do not duplicate that mutable table in this skill. Map the user's natural name to a listed workflow id, then choose only a mode shown on that same line:
| User intent | Required live mode | Launcher argument |
|---|---|---|
| Ordinary learned-policy evaluation | policy | --policy |
| Requested or only available local controller | rule-based | --rule-based |
| Explicit named alternative such as N1.7 | matching listed mode such as policy_n17 | --mode <name> |
Inspect ./run.sh show <workflow> --mode <mode>, the workflow module, Scene manifest, and selected Task manifest when model, prompt, checkpoint, goal, or step-cap behavior matters.
Use precise readiness language:
- Structurally valid:
show, per-mode lint, andlint --allpass. - Launchable: the selected simulator mode starts and every required backend/checkpoint preloads.
- Rollout-validated: the requested episodes complete and the recorded success evidence passes inspection.
Do not report “validated” without stating which level was actually reached.
Foreground execution rule
Keep run.sh as this agent's foreground tool call. Do not use a subagent, monitor task, shell backgrounding, nohup, tmux, or a detached process. Poll a yielded session until exit and inspect the final episode summary before responding. When the selected Task is remote, the policy backend subprocess internally owned by run.sh is expected; a simulator-compatible exported RSL-RL Task runs in-process.
Run visibly by default. If the user explicitly requests headless execution, or a documented environment constraint makes it necessary, say so before launch and include --headless; never switch to headless silently.
Policy:
./run.sh <workflow> --policy \
--episodes <N> --attempts 3 \
--record verify.hdf5
Rule-based:
./run.sh <workflow> --rule-based \
--episodes <N> --attempts 3 \
--record verify.hdf5
The launcher creates a unique canonical run directory and anchors the relative verify.hdf5 inside it. Resolve RUN_DIR from the ==> run dir ... line or machine-readable run.json; do not recreate the launcher's timestamp. Use --run-dir "$RUN_DIR" only when a caller-selected location must be shared with another stage; the launcher creates it. Absolute recording paths remain supported.
For another declared mode, use --mode <name>. For a supplied/new checkpoint, resolve its exact path, confirm it belongs to the selected policy Task, and pass --checkpoint /absolute/path; an exported RSL-RL Task expects its TorchScript policy.pt, while a remote Task expects the owning backend's loadable checkpoint format. For “300 timesteps,” pass --episode-steps 300.
Never raise the Scene manifest's cap. --episode-steps may only lower it. Remote inference waits do not consume simulation steps. Use a unique --namespace when another run of the same workflow is active.
Verify
Require exit status 0 and final N/N episodes succeeded. A failed attempt followed by a successful retry counts as a successful requested episode; report attempts and retries.
RUN_DIR="<absolute run_dir from run.json or launcher output>"
uv run --project tools/dataset i4h-dataset inspect "$RUN_DIR/verify.hdf5" --segments
Inspect episode metadata, action/state widths, camera frames, node segments, and success flags. For visible runs, observe Scene/camera behavior and final task outcome. On failure, use the first actionable backend, contract, graph, or simulator error; never switch modes or increase the cap silently. Run ./stop.sh all after crashes that leave processes.
For a success rule that excludes collision, inspect the owning contact-sensor configuration and require a forced-contact negative test at least once after authoring or changing that rule. A sensor that initializes but has never produced a non-zero filtered force does not establish collision rejection.
Troubleshooting
Use the first actionable backend, graph, contract, or simulator error. Retry within the same mode and cap only after correcting it.
Prerequisites
Require synced simulator assets and any backend/checkpoint declared by the selected run mode.
Limitations
Only modes from run.sh list are supported, and a runtime step override may lower but never raise the validated Scene cap.
Examples
Evaluate scissor pick and place for 2 episodes.→ run policy mode for two successful episodes, record, inspect, and report attempts plus visible outcome.Run surgical_reach_psm in rule-based mode for 1 episode.→ use only the declared local-controller mode.
Completion gate
Report workflow, mode, model/checkpoint source, requested successes, attempts/retries, completion steps, visible outcome, HDF5 path and inspection, final exit status, and first unresolved failure if any.
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Languages
Trust signals
From repository metadata: license, adoption, age and documentation. Not a code audit — see the Safety scan above for what the skill file itself contains.
