[{"data":1,"prerenderedAt":-1},["ShallowReactive",2],{"skill-nvidia-i4h-catheter-navigation-digital-twin":3,"mdc-3prr2s-key":34,"related-org-nvidia-i4h-catheter-navigation-digital-twin":1882,"related-repo-nvidia-i4h-catheter-navigation-digital-twin":2043},{"slug":4,"name":4,"fn":5,"description":6,"org":7,"tags":11,"stars":23,"repoUrl":24,"updatedAt":25,"license":26,"forks":27,"topics":28,"repo":29,"sourceUrl":32,"mdContent":33},"i4h-catheter-navigation-digital-twin","build patient vasculature digital twins from CT","Build a patient vasculature digital twin from CT (preprocess + segment). Use when asked to preprocess CT, segment vessels, extract centerline, or prepare ct_cache for viewport\u002FDRR.",{"slug":8,"name":9,"logoUrl":10,"githubOrg":9},"nvidia","NVIDIA","https:\u002F\u002Fpexgzepcugksgbtrxkhf.supabase.co\u002Fstorage\u002Fv1\u002Fobject\u002Fpublic\u002Forg-logos\u002Fnvidia.png",[12,16,19,20],{"name":13,"slug":14,"type":15},"Robotics","robotics","tag",{"name":17,"slug":18,"type":15},"Healthcare","healthcare",{"name":9,"slug":8,"type":15},{"name":21,"slug":22,"type":15},"Imaging","imaging",2473,"https:\u002F\u002Fgithub.com\u002FNVIDIA\u002Fskills","2026-07-23T06:06:04.817145","Apache-2.0",281,[],{"repoUrl":24,"stars":23,"forks":27,"topics":30,"description":31},[],"AI agent skills published by NVIDIA","https:\u002F\u002Fgithub.com\u002FNVIDIA\u002Fskills\u002Ftree\u002FHEAD\u002Fskills\u002Fi4h-catheter-navigation-digital-twin","---\nname: i4h-catheter-navigation-digital-twin\nversion: \"0.7.0\"\ndescription: Build a patient vasculature digital twin from CT (preprocess + segment). Use when asked to preprocess CT, segment vessels, extract centerline, or prepare ct_cache for viewport\u002FDRR.\nlicense: Apache-2.0\nmetadata:\n  author: \"Isaac for Healthcare Team \u003Cisaac-for-healthcare-support@nvidia.com>\"\n  tags:\n    - isaac-for-healthcare\n    - i4h\n    - catheter-navigation\n    - digital-twin\n    - vasculature\n---\n\n# i4h Catheter Navigation - Digital Twin\n\n## Purpose\n\nDownload or locate a CT volume, preprocess it to an attenuation cache, and segment the arterial tree into vessel mask + centerline - the vasculature digital twin required for patient-specific viewport and DRR runs.\n\n## Base Code\n\n```bash\nROOT=\"${I4H_WORKFLOWS:-$(git rev-parse --show-toplevel 2>\u002Fdev\u002Fnull)}\"\nif [ ! -d \"$ROOT\u002Fworkflows\u002Fcatheter_navigation\" ]; then\n  ROOT=\"${I4H_WORKFLOWS:-$HOME\u002Fi4h-workflows}\"\n  [ -d \"$ROOT\u002Fworkflows\u002Fcatheter_navigation\" ] || git clone https:\u002F\u002Fgithub.com\u002Fisaac-for-healthcare\u002Fi4h-workflows \"$ROOT\"\nfi\nexport I4H_WORKFLOWS=\"$ROOT\"; cd \"$ROOT\"\n```\n\n## Basics\n\n- **Output cache layout:** `--output-dir` \u002F `--ct-dir` (e.g. `\u002Ftmp\u002Fct_cache`) holds `mu_volume.npy`, `metadata.json`, and after segmentation vessel mask + centerline artifacts.\n- Contrast-enhanced CTA subjects work best; TotalSegmentator small subset (~3.2 GB) is the documented public dataset.\n- Comply with the dataset license; no patient data is committed to the repo.\n\n## Run\n\nRun the steps below in order. Each step is a separate bash call; variables persist in the local agent's tmux session.\n\n### Step 1 - resolve paths\n\n```bash\nREPO_ROOT=\"${I4H_WORKFLOWS:-$(git rev-parse --show-toplevel 2>\u002Fdev\u002Fnull)}\"; [ -d \"$REPO_ROOT\u002Fworkflows\u002Fcatheter_navigation\" ] || REPO_ROOT=\"$HOME\u002Fi4h-workflows\"\nWF_ROOT=\"${REPO_ROOT}\u002Fworkflows\u002Fcatheter_navigation\"\nRUN_DIR=\"${WF_ROOT}\u002Fruns\u002Fdigital_twin_$(date +%Y%m%d_%H%M%S)\"\nmkdir -p \"${RUN_DIR}\u002Flogs\"\nln -sfn \"${RUN_DIR}\" \"${WF_ROOT}\u002Fruns\u002F.latest\"\n\n# User-supplied or downloaded subject directory (must contain ct.nii.gz + segmentations\u002F)\nSUBJ=\"${SUBJ:-}\"\nCACHE=\"${CACHE:-\u002Ftmp\u002Fct_cache}\"\n\nif [ -z \"${SUBJ}\" ] || [ ! -f \"${SUBJ}\u002Fct.nii.gz\" ]; then\n  echo \"digital-twin: set SUBJ to an extracted TotalSegmentator subject (got '${SUBJ:-\u003Cunset>}').\" >&2\n  echo \"Example: SUBJ=\u002Fpath\u002Fto\u002FTotalsegmentator_dataset_small_v201\u002Fs0011\" >&2\n  exit 1\nfi\n```\n\n### Step 2 - download dataset (skip if SUBJ already exists)\n\nOnly run when the user has no CT data yet.\n\n```bash\ncurl -L \"https:\u002F\u002Fwww.dropbox.com\u002Fscl\u002Ffi\u002Fpee5yxebfxrhz007cbuy5\u002FTotalsegmentator_dataset_small_v201.zip?rlkey=osvfk02jc4lw5gr6uhrldtb9e&dl=1\" \\\n  -o \"${RUN_DIR}\u002FTotalsegmentator_dataset_small_v201.zip\"\nunzip \"${RUN_DIR}\u002FTotalsegmentator_dataset_small_v201.zip\" -d \"${RUN_DIR}\u002FTotalsegmentator_dataset_small_v201\"\nls \"${RUN_DIR}\u002FTotalsegmentator_dataset_small_v201\"\n# Then set SUBJ to one extracted subject before continuing.\n```\n\n### Step 3 - preprocess CT\n\n```bash\n\"${REPO_ROOT}\u002Fi4h\" run catheter_navigation preprocess_ct --local \\\n  --run-args=\"--nifti ${SUBJ}\u002Fct.nii.gz --output-dir ${CACHE} --save-hu\" \\\n  2>&1 | tee \"${RUN_DIR}\u002Flogs\u002Fpreprocess_ct.log\"\n```\n\n### Step 4 - segment vessels\n\n```bash\n\"${REPO_ROOT}\u002Fi4h\" run catheter_navigation segment_vessels --local \\\n  --run-args=\"--ct-dir ${CACHE} --ts-gt-dir ${SUBJ}\u002Fsegmentations\" \\\n  2>&1 | tee \"${RUN_DIR}\u002Flogs\u002Fsegment_vessels.log\"\n```\n\n## Verify\n\n```bash\ntest -f \"${CACHE}\u002Fmu_volume.npy\"\ntest -f \"${CACHE}\u002Fmetadata.json\"\nls -la \"${CACHE}\"\n```\n\n## Notes\n\n- `SUBJ` must point at one extracted subject with `ct.nii.gz` and `segmentations\u002F` (TotalSegmentator layout).\n- `CACHE` is reused by [[i4h-catheter-navigation-viewport]] and cache-based [[i4h-catheter-navigation-render-drr]].\n- Segmentation is CPU\u002FGPU mixed and may take several minutes depending on volume size.\n\n## Prerequisites\n\n- [[i4h-catheter-navigation-setup]] completed (imports and CLI work).\n- A CT NIfTI and matching vessel segmentations (or TotalSegmentator subject).\n- >= 32 GB RAM recommended for large volumes.\n\n## Limitations\n\n- Does not ship data; user must download or provide their own CT.\n- Zenodo mirror is throttled; prefer the 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Use when users ask how to install, configure, operate, troubleshoot, secure, or learn NemoClaw with an AI coding assistant. Trigger keywords - nemoclaw docs, use nemoclaw with ai agent, nemoclaw mcp docs, nemoclaw install help, nemoclaw quickstart, nemoclaw markdown docs, llms.txt, agent skills.",{"slug":8,"name":9,"logoUrl":10,"githubOrg":9},[1890,1893,1896],{"name":1891,"slug":1892,"type":15},"Documentation","documentation",{"name":1894,"slug":1895,"type":15},"MCP","mcp",{"name":1897,"slug":1898,"type":15},"Search","search",21777,"https:\u002F\u002Fgithub.com\u002FNVIDIA\u002FNemoClaw","2026-07-20T06:00:01.461044",{"slug":1903,"name":1903,"fn":1904,"description":1905,"org":1906,"tags":1907,"stars":1917,"repoUrl":1918,"updatedAt":1919},"mcore-build-and-dependency","manage Megatron-LM development environments","Container-based dev environment setup and dependency management for Megatron-LM. Covers acquiring and launching the CI container, uv package management, and updating uv.lock.",{"slug":8,"name":9,"logoUrl":10,"githubOrg":9},[1908,1911,1914],{"name":1909,"slug":1910,"type":15},"Containers","containers",{"name":1912,"slug":1913,"type":15},"Deployment","deployment",{"name":1915,"slug":1916,"type":15},"Python","python",17049,"https:\u002F\u002Fgithub.com\u002FNVIDIA\u002FMegatron-LM","2026-07-27T06:06:11.249662",{"slug":1921,"name":1921,"fn":1922,"description":1923,"org":1924,"tags":1925,"stars":1917,"repoUrl":1918,"updatedAt":1930},"mcore-bump-base-image","update NVIDIA PyTorch base images","Bump the NVIDIA PyTorch base image (`nvcr.io\u002Fnvidia\u002Fpytorch:YY.MM-py3`) used by Megatron-LM CI. Covers the two pin sites (GitHub CI in `docker\u002F.ngc_version.dev` and GitLab CI in `.gitlab\u002Fstages\u002F01.build.yml`), the post-bump CI loop (re-run functional tests, refresh golden values, mark broken tests), and the gotchas that bit PRs",{"slug":8,"name":9,"logoUrl":10,"githubOrg":9},[1926,1929],{"name":1927,"slug":1928,"type":15},"CI\u002FCD","ci-cd",{"name":1912,"slug":1913,"type":15},"2026-07-14T05:25:59.97109",{"slug":1932,"name":1932,"fn":1933,"description":1934,"org":1935,"tags":1936,"stars":1917,"repoUrl":1918,"updatedAt":1942},"mcore-cicd","manage CI\u002FCD pipelines for Megatron-LM","CI\u002FCD reference for Megatron-LM. Covers CI pipeline structure, PR scope labels, triggering internal GitLab CI (which force-pushes the current branch to a pull-request\u002FBRANCH ref — always dry-run and verify the destination first; never run against shared or protected branches), and CI failure investigation.",{"slug":8,"name":9,"logoUrl":10,"githubOrg":9},[1937,1938,1939],{"name":1927,"slug":1928,"type":15},{"name":1912,"slug":1913,"type":15},{"name":1940,"slug":1941,"type":15},"GitHub","github","2026-07-27T06:06:12.278222",{"slug":1944,"name":1944,"fn":1945,"description":1946,"org":1947,"tags":1948,"stars":1917,"repoUrl":1918,"updatedAt":1956},"mcore-create-issue","investigate CI failures and create issues","Investigate a failing GitHub Actions run or job and create a GitHub issue for the failure.",{"slug":8,"name":9,"logoUrl":10,"githubOrg":9},[1949,1952,1953],{"name":1950,"slug":1951,"type":15},"Debugging","debugging",{"name":1940,"slug":1941,"type":15},{"name":1954,"slug":1955,"type":15},"Triage","triage","2026-07-14T05:25:57.442089",{"slug":1958,"name":1958,"fn":1959,"description":1960,"org":1961,"tags":1962,"stars":1917,"repoUrl":1918,"updatedAt":1969},"mcore-linting-and-formatting","lint and format Megatron-LM code","Linting and formatting for Megatron-LM. Covers running autoformat.sh, tools (ruff, black, isort, pylint, mypy), and code style rules.",{"slug":8,"name":9,"logoUrl":10,"githubOrg":9},[1963,1966],{"name":1964,"slug":1965,"type":15},"Best Practices","best-practices",{"name":1967,"slug":1968,"type":15},"Code Analysis","code-analysis","2026-07-14T05:25:56.18433",{"slug":1971,"name":1971,"fn":1972,"description":1973,"org":1974,"tags":1975,"stars":1917,"repoUrl":1918,"updatedAt":1983},"mcore-migrate-gpt-to-hybrid","migrate Megatron-LM models to HybridModel","Migration guide for moving Megatron Core GPTModel checkpoints, model providers, training commands, and layer mappings to HybridModel.",{"slug":8,"name":9,"logoUrl":10,"githubOrg":9},[1976,1979,1982],{"name":1977,"slug":1978,"type":15},"Machine Learning","machine-learning",{"name":1980,"slug":1981,"type":15},"Migration","migration",{"name":9,"slug":8,"type":15},"2026-07-17T06:07:11.777011",{"slug":1985,"name":1985,"fn":1986,"description":1987,"org":1988,"tags":1989,"stars":1917,"repoUrl":1918,"updatedAt":1996},"mcore-onboard-gb200-1node-tests","onboard functional tests for GB200","Onboard 1-node GitHub MR functional tests for GB200 from existing mr-scoped 2-node tests.",{"slug":8,"name":9,"logoUrl":10,"githubOrg":9},[1990,1993],{"name":1991,"slug":1992,"type":15},"QA","qa",{"name":1994,"slug":1995,"type":15},"Testing","testing","2026-07-14T05:25:53.673039",{"slug":1998,"name":1998,"fn":1999,"description":2000,"org":2001,"tags":2002,"stars":1917,"repoUrl":1918,"updatedAt":2007},"mcore-run-on-slurm","launch distributed training jobs on SLURM","How to launch distributed Megatron-LM training jobs on a SLURM cluster. Covers a minimal sbatch skeleton, environment-variable setup for torch.distributed.run, CUDA_DEVICE_MAX_CONNECTIONS rules across hardware and parallelism modes, container conventions, monitoring, and per-rank failure diagnosis.",{"slug":8,"name":9,"logoUrl":10,"githubOrg":9},[2003,2004],{"name":1912,"slug":1913,"type":15},{"name":2005,"slug":2006,"type":15},"Infrastructure","infrastructure","2026-07-14T05:25:49.362534",{"slug":2009,"name":2009,"fn":2010,"description":2011,"org":2012,"tags":2013,"stars":1917,"repoUrl":1918,"updatedAt":2021},"mcore-split-pr","split pull requests to reduce review load","Split a PR into multiple PRs to reduce the number of required CODEOWNERS reviewer groups.",{"slug":8,"name":9,"logoUrl":10,"githubOrg":9},[2014,2017,2018],{"name":2015,"slug":2016,"type":15},"Code Review","code-review",{"name":1940,"slug":1941,"type":15},{"name":2019,"slug":2020,"type":15},"Pull Requests","pull-requests","2026-07-14T05:26:01.226578",{"slug":2023,"name":2023,"fn":2024,"description":2025,"org":2026,"tags":2027,"stars":1917,"repoUrl":1918,"updatedAt":2030},"mcore-testing","run and manage Megatron-LM tests","Test system for Megatron-LM. Covers test layout, recipe YAML structure, adding and running unit and functional tests, golden values, marker filters, and CI parity.",{"slug":8,"name":9,"logoUrl":10,"githubOrg":9},[2028,2029],{"name":1991,"slug":1992,"type":15},{"name":1994,"slug":1995,"type":15},"2026-07-14T05:25:54.928983",{"slug":2032,"name":2032,"fn":2033,"description":2034,"org":2035,"tags":2036,"stars":1917,"repoUrl":1918,"updatedAt":2041},"nightly-sync","manage nightly main-to-dev sync workflows","Domain knowledge for the nightly main-to-dev sync workflow. Covers merge strategy, CI architecture, failure investigation, and known issues.",{"slug":8,"name":9,"logoUrl":10,"githubOrg":9},[2037,2040],{"name":2038,"slug":2039,"type":15},"Automation","automation",{"name":1927,"slug":1928,"type":15},"2026-07-30T05:29:03.275638",496,{"items":2044,"total":2138},[2045,2062,2072,2086,2096,2109,2124],{"slug":2046,"name":2046,"fn":2047,"description":2048,"org":2049,"tags":2050,"stars":23,"repoUrl":24,"updatedAt":2061},"accelerated-computing-cudf","accelerate data processing with cuDF","Official NVIDIA-authored guidance for NVIDIA cuDF GPU DataFrames, pandas acceleration, dask-cuDF, ETL, joins, groupby, CSV\u002FParquet I\u002FO, nullable semantics, and multi-GPU DataFrame workloads.",{"slug":8,"name":9,"logoUrl":10,"githubOrg":9},[2051,2054,2057,2058],{"name":2052,"slug":2053,"type":15},"Data Analysis","data-analysis",{"name":2055,"slug":2056,"type":15},"Data Engineering","data-engineering",{"name":9,"slug":8,"type":15},{"name":2059,"slug":2060,"type":15},"Performance","performance","2026-07-14T05:28:43.176466",{"slug":2063,"name":2063,"fn":2064,"description":2065,"org":2066,"tags":2067,"stars":23,"repoUrl":24,"updatedAt":2071},"aiq-deploy","deploy and manage NVIDIA AI-Q infrastructure","Use when asked to install, deploy, run, validate, troubleshoot, or stop NVIDIA AI-Q Blueprint infrastructure.\n",{"slug":8,"name":9,"logoUrl":10,"githubOrg":9},[2068,2069,2070],{"name":1912,"slug":1913,"type":15},{"name":2005,"slug":2006,"type":15},{"name":9,"slug":8,"type":15},"2026-07-14T05:29:06.667109",{"slug":2073,"name":2073,"fn":2074,"description":2075,"org":2076,"tags":2077,"stars":23,"repoUrl":24,"updatedAt":2085},"aiq-research","conduct deep research with AI-Q","Use when asked to run deep research or AI-Q research through a reachable NVIDIA AI-Q Blueprint backend.\n",{"slug":8,"name":9,"logoUrl":10,"githubOrg":9},[2078,2081,2082],{"name":2079,"slug":2080,"type":15},"Agents","agents",{"name":9,"slug":8,"type":15},{"name":2083,"slug":2084,"type":15},"Research","research","2026-07-14T05:28:06.816956",{"slug":2087,"name":2087,"fn":2088,"description":2089,"org":2090,"tags":2091,"stars":23,"repoUrl":24,"updatedAt":2095},"amc-run-sample-calibration","run AMC sample dataset 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'.",{"slug":8,"name":9,"logoUrl":10,"githubOrg":9},[2092,2093,2094],{"name":2052,"slug":2053,"type":15},{"name":9,"slug":8,"type":15},{"name":1994,"slug":1995,"type":15},"2026-07-17T05:29:03.913266",{"slug":2097,"name":2097,"fn":2098,"description":2099,"org":2100,"tags":2101,"stars":23,"repoUrl":24,"updatedAt":2108},"amc-run-video-calibration","calibrate video datasets with AutoMagicCalib","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\u002Flive streams, use amc-run-rtsp-calibration instead.",{"slug":8,"name":9,"logoUrl":10,"githubOrg":9},[2102,2103,2104,2105],{"name":2038,"slug":2039,"type":15},{"name":21,"slug":22,"type":15},{"name":9,"slug":8,"type":15},{"name":2106,"slug":2107,"type":15},"Video","video","2026-07-17T05:28:53.905004",{"slug":2110,"name":2110,"fn":2111,"description":2112,"org":2113,"tags":2114,"stars":23,"repoUrl":24,"updatedAt":2123},"amc-setup-calibration-stack","deploy AutoMagicCalib microservice with Docker","Launch AutoMagicCalib microservice and web UI from NGC release images via Docker Compose. Use when user says 'deploy auto calibration', 'launch auto calibration', 'launch AMC', 'start MS+UI', or 'set up auto-magic-calib'. Requires NGC API key.",{"slug":8,"name":9,"logoUrl":10,"githubOrg":9},[2115,2116,2119,2120],{"name":1912,"slug":1913,"type":15},{"name":2117,"slug":2118,"type":15},"Docker","docker",{"name":9,"slug":8,"type":15},{"name":2121,"slug":2122,"type":15},"Operations","operations","2026-07-17T05:28:56.913999",{"slug":2125,"name":2125,"fn":2126,"description":2127,"org":2128,"tags":2129,"stars":23,"repoUrl":24,"updatedAt":2137},"cudaq-guide","develop quantum applications with CUDA-Q","CUDA-Q onboarding guide for installation, test programs, GPU simulation, QPU hardware, and quantum applications.",{"slug":8,"name":9,"logoUrl":10,"githubOrg":9},[2130,2131,2134],{"name":9,"slug":8,"type":15},{"name":2132,"slug":2133,"type":15},"Quantum Computing","quantum-computing",{"name":2135,"slug":2136,"type":15},"Simulation","simulation","2026-07-14T05:26:58.898253",305]