/i4h-catheter-navigation-setup
Verify host/GPU requirements and PYTHONPATH for the catheter navigation workflow. Use when asked to set up, install, or bootstrap catheter_navigation, or when hitting import/GPU/slangpy errors.
$ npx -y skills add NVIDIA/skills --skill i4h-catheter-navigation-setup --agent claude-codeHow it fires
How this skill gets triggered: by you, by Claude, or both.
- Fires itselfAuto-invocation. Claude auto-loads it when your prompt matches the work.Auto-invocation is when the right skill fires by itself at the right moment, driven by a FLOW.md router and a hook, instead of you invoking it by name. It is the difference between a skill being installed and a skill actually getting used.Read the full definition →
- You can call itInvoke it directly when you want it.
- Slash command
/i4h-catheter-navigation-setup
Context preview
The summary Claude sees to decide when to auto-load this skill.
Verify host/GPU requirements and PYTHONPATH for the catheter navigation workflow. Use when asked to set up, install, or bootstrap catheter_navigation, or when hitting import/GPU/slangpy errors.
SKILL.md
i4h-catheter-navigation-setup.SKILL.mdname: i4h-catheter-navigation-setup
version: "0.7.0"
description: Verify host/GPU requirements and PYTHONPATH for the catheter navigation workflow. Use when asked to set up, install, or bootstrap catheter_navigation, or when hitting import/GPU/slangpy errors.
license: Apache-2.0
metadata:
author: "Isaac for Healthcare Team <isaac-for-healthcare-support@nvidia.com>"
tags:
- isaac-for-healthcare
- i4h
- catheter-navigation
- setup
- installationi4h Catheter Navigation - Setup
Purpose
Verify host and GPU requirements, confirm the `./i4h` CLI sees the workflow, and run CPU smoke tests. Use when asked to set up catheter navigation or when hitting missing imports, GPU, or slangpy errors.
Base Code
These steps drive the i4h-workflows base code (the `workflows/catheter_navigation/` 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:
ROOT="${I4H_WORKFLOWS:-$(git rev-parse --show-toplevel 2>/dev/null)}"
if [ ! -d "$ROOT/workflows/catheter_navigation" ]; then
ROOT="${I4H_WORKFLOWS:-$HOME/i4h-workflows}"
[ -d "$ROOT/workflows/catheter_navigation" ] || git clone https://github.com/isaac-for-healthcare/i4h-workflows "$ROOT"
fi
export I4H_WORKFLOWS="$ROOT"; cd "$ROOT"Basics
- Catheter navigation registers via `workflows/catheter_navigation/metadata.json` and runs through `./i4h run catheter_navigation <mode>`.
- Runtime is package-first: `render_drr` uses installed `fluorosim` (`python -m fluorosim.examples.render_drr`), while `interactive_viewport` is launched from the local workflow script path in `metadata.json`.
- Docker image: `workflows/catheter_navigation/docker/Dockerfile` (drop `--local` on `./i4h run` to use it).
- GPU modes need slangpy, Warp, and CUDA; CPU smoke tests do not.
Preflight
command -v python3
command -v git
nvidia-smi
df -h .
Required: Linux x86_64 (Ubuntu 22.04/24.04 tested), NVIDIA GPU (CC >= 7.0), driver compatible with CUDA 12.8, >= 16 GB RAM, >= 20 GB disk.
Run the steps below in order. Each step is a separate bash call; variables persist in the local agent's tmux session.
Step 1 - resolve repo and export paths
REPO_ROOT="${I4H_WORKFLOWS:-$(git rev-parse --show-toplevel 2>/dev/null)}"; [ -d "$REPO_ROOT/workflows/catheter_navigation" ] || REPO_ROOT="$HOME/i4h-workflows"
WF_ROOT="${REPO_ROOT}/workflows/catheter_navigation"
SIM_ROOT="${WF_ROOT}/scripts/simulation"
export PYTHONPATH="${SIM_ROOT}:${PYTHONPATH:-}"
RUN_DIR="${WF_ROOT}/runs/setup_$(date +%Y%m%d_%H%M%S)"
mkdir -p "${RUN_DIR}/logs"
ln -sfn "${RUN_DIR}" "${WF_ROOT}/runs/.latest"Step 2 - verify CLI registration
"${REPO_ROOT}/i4h" modes catheter_navigation 2>&1 | tee "${RUN_DIR}/logs/modes.log"Step 3 - CPU smoke tests (no GPU)
python3 -m unittest workflows/catheter_navigation/tests/test_fluorosim_smoke.py \
2>&1 | tee "${RUN_DIR}/logs/smoke.log"Expected: `Ran 7 tests ... OK`. Parser error lines in stderr from negative test cases are expected.
Step 4 - optional GPU sanity (synthetic DRR)
Skip if no GPU or slangpy not installed.
"${REPO_ROOT}/i4h" run catheter_navigation render_drr --local \
--run-args="--output ${RUN_DIR}/drr.png" \
2>&1 | tee "${RUN_DIR}/logs/render_drr.log"Verify
test -f "${RUN_DIR}/logs/smoke.log"
grep -q "OK" "${RUN_DIR}/logs/smoke.log"
python3 -c "import fluorosim; print('fluorosim', fluorosim.__file__)"Prerequisites
- Repo checkout with `workflows/catheter_navigation/` present.
- Python 3 with numpy; full GPU stack (slangpy, torch CUDA, warp) for render/viewport modes.
Limitations
- No dedicated `setup.sh` yet - this skill verifies and documents host requirements; use Docker when host deps are incomplete.
- Step 4 requires a GPU; Step 3 alone is sufficient for CI-style verification.
Troubleshooting
- **Error:** `fluorosim` import fails - Cause: PYTHONPATH not set. Fix: re-run Step 1; confirm `SIM_ROOT` exists.
- **Error:** `./i4h` not found - Cause: not at repo root. Fix: `cd "$REPO_ROOT"` where `./i4h` lives.
- **Error:** render_drr fails with slang/GPU - Cause: missing CUDA or slangpy. Fix: use Docker (`./i4h run catheter_navigation render_drr` without `--local`) or install deps per README.
Final Response
Report setup status, smoke-test result, optional DRR output path, and recommend the next skill ([[i4h-catheter-navigation-digital-twin]] for patient data, [[i4h-catheter-navigation-viewport]] for interactive demo).
Read more
name: i4h-catheter-navigation-setup
version: "0.7.0"
description: Verify host/GPU requirements and PYTHONPATH for the catheter navigation workflow. Use when asked to set up, install, or bootstrap catheter_navigation, or when hitting import/GPU/slangpy errors.
license: Apache-2.0
metadata:
author: "Isaac for Healthcare Team <isaac-for-healthcare-support@nvidia.com>"
tags:
- isaac-for-healthcare
- i4h
- catheter-navigation
- setup
- installationi4h Catheter Navigation - Setup
Purpose
Verify host and GPU requirements, confirm the `./i4h` CLI sees the workflow, and run CPU smoke tests. Use when asked to set up catheter navigation or when hitting missing imports, GPU, or slangpy errors.
Base Code
These steps drive the i4h-workflows base code (the `workflows/catheter_navigation/` tree). To reuse an existing checkout, set `I4H_WORKFLOWS` to its path (no clone happens). Otherwise this resolves the current repo, or clones to `~/i4h-workflows` - pick that default without prompting. Run every command below from the resolved root:
ROOT="${I4H_WORKFLOWS:-$(git rev-parse --show-toplevel 2>/dev/null)}"
if [ ! -d "$ROOT/workflows/catheter_navigation" ]; then
ROOT="${I4H_WORKFLOWS:-$HOME/i4h-workflows}"
[ -d "$ROOT/workflows/catheter_navigation" ] || git clone https://github.com/isaac-for-healthcare/i4h-workflows "$ROOT"
fi
export I4H_WORKFLOWS="$ROOT"; cd "$ROOT"Basics
- Catheter navigation registers via `workflows/catheter_navigation/metadata.json` and runs through `./i4h run catheter_navigation <mode>`.
- Runtime is package-first: `render_drr` uses installed `fluorosim` (`python -m fluorosim.examples.render_drr`), while `interactive_viewport` is launched from the local workflow script path in `metadata.json`.
- Docker image: `workflows/catheter_navigation/docker/Dockerfile` (drop `--local` on `./i4h run` to use it).
- GPU modes need slangpy, Warp, and CUDA; CPU smoke tests do not.
Preflight
command -v python3 command -v git nvidia-smi df -h .
Required: Linux x86_64 (Ubuntu 22.04/24.04 tested), NVIDIA GPU (CC >= 7.0), driver compatible with CUDA 12.8, >= 16 GB RAM, >= 20 GB disk.
Run the steps below in order. Each step is a separate bash call; variables persist in the local agent's tmux session.
Step 1 - resolve repo and export paths
REPO_ROOT="${I4H_WORKFLOWS:-$(git rev-parse --show-toplevel 2>/dev/null)}"; [ -d "$REPO_ROOT/workflows/catheter_navigation" ] || REPO_ROOT="$HOME/i4h-workflows"
WF_ROOT="${REPO_ROOT}/workflows/catheter_navigation"
SIM_ROOT="${WF_ROOT}/scripts/simulation"
export PYTHONPATH="${SIM_ROOT}:${PYTHONPATH:-}"
RUN_DIR="${WF_ROOT}/runs/setup_$(date +%Y%m%d_%H%M%S)"
mkdir -p "${RUN_DIR}/logs"
ln -sfn "${RUN_DIR}" "${WF_ROOT}/runs/.latest"Step 2 - verify CLI registration
"${REPO_ROOT}/i4h" modes catheter_navigation 2>&1 | tee "${RUN_DIR}/logs/modes.log"Step 3 - CPU smoke tests (no GPU)
python3 -m unittest workflows/catheter_navigation/tests/test_fluorosim_smoke.py \
2>&1 | tee "${RUN_DIR}/logs/smoke.log"Expected: `Ran 7 tests ... OK`. Parser error lines in stderr from negative test cases are expected.
Step 4 - optional GPU sanity (synthetic DRR)
Skip if no GPU or slangpy not installed.
"${REPO_ROOT}/i4h" run catheter_navigation render_drr --local \
--run-args="--output ${RUN_DIR}/drr.png" \
2>&1 | tee "${RUN_DIR}/logs/render_drr.log"Verify
test -f "${RUN_DIR}/logs/smoke.log"
grep -q "OK" "${RUN_DIR}/logs/smoke.log"
python3 -c "import fluorosim; print('fluorosim', fluorosim.__file__)"Prerequisites
- Repo checkout with `workflows/catheter_navigation/` present.
- Python 3 with numpy; full GPU stack (slangpy, torch CUDA, warp) for render/viewport modes.
Limitations
- No dedicated `setup.sh` yet - this skill verifies and documents host requirements; use Docker when host deps are incomplete.
- Step 4 requires a GPU; Step 3 alone is sufficient for CI-style verification.
Troubleshooting
- **Error:** `fluorosim` import fails - Cause: PYTHONPATH not set. Fix: re-run Step 1; confirm `SIM_ROOT` exists.
- **Error:** `./i4h` not found - Cause: not at repo root. Fix: `cd "$REPO_ROOT"` where `./i4h` lives.
- **Error:** render_drr fails with slang/GPU - Cause: missing CUDA or slangpy. Fix: use Docker (`./i4h run catheter_navigation render_drr` without `--local`) or install deps per README.
Final Response
Report setup status, smoke-test result, optional DRR output path, and recommend the next skill ([[i4h-catheter-navigation-digital-twin]] for patient data, [[i4h-catheter-navigation-viewport]] for interactive demo).
Official, NVIDIA-verified Agent Skills for Claude Code, Codex, and other coding agents.
Other skills on nvidia-skills.
- /nvidia-skill-finder
Use for NVIDIA-related requests where an NVIDIA skill might help, even if the user did not ask for a skill. Trigger on NVIDIA products, hardware, software, SDKs, GPUs, Jetson/JetPack/L4T/BSP/SDK Manager/driver/flashing/setup, CUDA, NIM, NeMo, Omniverse/OpenUSD/SimReady,
Open skill - /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.
Open skill - /aiq-deploy
Use when asked to install, deploy, run, validate, troubleshoot, or stop NVIDIA AI-Q Blueprint infrastructure.
Open skill - /aiq-research
Use when asked to run deep research or AI-Q research through a reachable NVIDIA AI-Q Blueprint backend.
Open skill - /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'.
Open skill - /amc-run-video-calibration
Calibrate a new dataset from pre-recorded video files via the AutoMagicCalib REST API. Use when user has local MP4s and says 'calibrate my videos', 'run AMC on these videos', or similar. For RTSP/live streams, use amc-run-rtsp-calibration instead.
Open skill

