[{"data":1,"prerenderedAt":-1},["ShallowReactive",2],{"skill:01M79FAMHAXX78S9FAQRCV83Z7":3,"skill-same:01M79FAMHAXX78S9FAQRCV83Z7":84},{"skillNo":4,"name":5,"nameZh":6,"description":7,"descriptionZh":6,"repositoryFullName":8,"ownerLogin":9,"ownerAvatarUrl":10,"ownerAccountNo":11,"ownerDisplayName":12,"repositoryNo":13,"starsCount":14,"licenseSpdxId":15,"licenseFileUrl":6,"sourceHtmlUrl":16,"categories":17,"keywords":18,"publishedAt":19,"linkOnly":20,"markdownContent":21,"markdownBody":22,"frontMatter":23,"refName":34,"commitSha":35,"skillDirectory":36,"forksCount":14,"repositoryHtmlUrl":37,"repositoryHomepageUrl":6,"ownerHtmlUrl":38,"ownerBlogUrl":6,"ownerDescription":6,"updatedAt":39,"install":40,"resources":53},"01M79FAMHAXX78S9FAQRCV83Z7","holoscan-setup",null,"Guides Holoscan SDK installation: inspects the host, assesses platform compatibility, recommends an install method, and delegates to the matching install skill.","Elogic360\u002Fsoftware-factory","Elogic360","https:\u002F\u002Favatars.githubusercontent.com\u002Fu\u002F156535519?v=4","01M74NH3F0RB13PWB7JZDE2V80","elogic360","01M74NH3FPC65082MDZVMFHDM3",0,"Apache-2.0","https:\u002F\u002Fgithub.com\u002FElogic360\u002Fsoftware-factory\u002Fblob\u002Fmain\u002Fskills\u002Fnvidia\u002Fholoscan-setup\u002FSKILL.md",[],[],"2026-11-15 14:19:17",false,"---\nname: holoscan-setup\nversion: \"1.0.0\"\ndescription: \"Guides Holoscan SDK installation: inspects the host, assesses platform compatibility, recommends an install method, and delegates to the matching install skill.\"\nlicense: Apache-2.0\nmetadata:\n  author: \"Holoscan Team \u003Choloscan-team@nvidia.com>\"\n  github-url: \"https:\u002F\u002Fgithub.com\u002Fnvidia-holoscan\u002Fholoscan-sdk\"\n  tags:\n    - holoscan\n    - installation\n    - nvidia\n    - sdk\n    - setup\n---\n\n# Holoscan SDK Setup\n\n## Purpose\n\nDetermines the correct Holoscan SDK installation method for the current host by inspecting hardware, OS, CUDA driver, and existing tooling, then delegates to a method-specific install skill. Covers NGC container, Debian\u002Fapt, pip wheel, Conda, and source builds across Ubuntu, RHEL, IGX Orin, Jetson, and DGX Spark \u002F Grace-Hopper platforms.\n\n## Prerequisites\n\n- Linux host (Ubuntu 22.04\u002F24.04, RHEL 9.x, IGX Orin, Jetson, or DGX Spark \u002F Grace-Hopper)\n- NVIDIA GPU with a working driver (`nvidia-smi` returns a CUDA Version)\n- Network access to `docs.nvidia.com` and NGC\n- One of: Docker + NVIDIA Container Toolkit, `apt`, Python 3.10–3.13 with `pip`, Conda, or a build toolchain — depending on chosen method\n\n## Available Scripts\n\n| Script | Purpose | Arguments |\n|--------|---------|-----------|\n| `scripts\u002Fcheck_conda.sh` | Detects Conda installs even when not on PATH (searches `~\u002Fminiconda3`, `~\u002Fminiforge3`, `~\u002Fanaconda3`, `~\u002Fmambaforge`, `\u002Fopt\u002Fconda`, and shell rc files); reports envs and which have `holoscan` importable. | none |\n| `scripts\u002Fcheck_ngc_image.sh` | Checks whether the NGC Holoscan container image for a given CUDA tag suffix is pulled or available. | `\u003Ccuda-tag-suffix>` — one of `cuda13`, `cuda12-dgpu`, `cuda12-igpu` |\n\nInvoke scripts with `run_script(\"scripts\u002Fcheck_conda.sh\")` and `run_script(\"scripts\u002Fcheck_ngc_image.sh\", \"cuda13\")`. Trust the script output over bare commands such as `which conda` or `docker images`.\n\n## Instructions\n\nBe conversational and step-by-step — do not front-load all the information. Complete each step and report back before moving on.\n\n### Workflow rules (must follow)\n\n1. End Step 5 with a **bolded one-line recommendation** that names the method (e.g. `**Recommendation:** NGC Container — bundles all deps, fastest path to a working install.`).\n2. For a first-time user on a supported x86_64 host with Docker available, that recommendation **must** be **NGC Container**.\n3. After the recommendation, **stop and ask** which method to use. Do not paste `docker pull`, `docker run`, `apt install`, `pip install`, or other install commands in that turn — those belong to the delegated install skill in Step 6.\n4. If the container path is in play, verify Docker + GPU passthrough **yourself** in Step 4 (run the command shown there). Do not ask the user to run `nvidia-smi` or `docker --version` for you.\n\n### Step 1: Read the Docs First\n\nFetch `https:\u002F\u002Fdocs.nvidia.com\u002Fholoscan\u002Fsdk-user-guide\u002F` then `sdk_installation.html` to get the current release's supported platforms, package names, and install requirements. Do not rely on hardcoded assumptions.\n\n### Step 2: Inspect the Machine\n\nRun in parallel:\n\n```bash\nuname -a && (lsb_release -a 2>\u002Fdev\u002Fnull || cat \u002Fetc\u002Fos-release)\nuname -m\nnvidia-smi 2>&1 | head -10\nnproc && free -h | head -2\n```\n\n**Key:** Read the \"CUDA Version\" field from `nvidia-smi` (top-right of the table header) — this is the *maximum* CUDA version the driver supports, and drives `cuda12` vs `cuda13` package selection.\n\n### Step 3: Assess Compatibility\n\n| Platform | Methods Available |\n|----------|-------------------|\n| Ubuntu 22.04\u002F24.04, x86_64 | Container, Debian\u002Fapt, pip wheel, Conda, Source |\n| RHEL 9.x, x86_64 | Container only |\n| IGX Orin (ARM64) | Container, Debian\u002Fapt, Source |\n| Jetson AGX Orin \u002F Orin Nano | Container, Debian\u002Fapt (iGPU) |\n| Jetson AGX Thor | Container, Debian\u002Fapt |\n| DGX Spark \u002F Grace-Hopper | Container (check docs for OS requirements) |\n| Other Linux, x86_64 | Container may work; pip wheel if glibc ≥ 2.35 |\n\n### Step 4: Check Tools and Present Options\n\nRun in parallel:\n\n```bash\ndocker --version 2>&1 | head -1; python3 --version 2>&1; pip3 --version 2>&1\ndpkg -l | grep holoscan || true\npip3 show holoscan 2>\u002Fdev\u002Fnull | grep -E \"^(Name|Version)\" || true\n~\u002Fholoscan\u002Fvenv\u002Fbin\u002Fpip show holoscan 2>\u002Fdev\u002Fnull | grep -E \"^(Name|Version)\" | sed 's\u002F^\u002Fvenv: \u002F' || true\n```\n\nThen verify GPU passthrough yourself — do **not** ask the user to run this:\n\n```bash\ndocker run --rm --gpus all ubuntu:22.04 nvidia-smi 2>&1 | tail -5 || true\n```\n\nInterpret the result for the Status column in Step 5:\n- `docker` missing → container row Status `✗ — Docker not installed`.\n- Docker present but `could not select device driver \"nvidia\"` → `✗ — NVIDIA Container Toolkit missing`.\n- `nvidia-smi` output appears → `✓`.\n\nThen invoke the detection scripts via `run_script`:\n\n- `run_script(\"scripts\u002Fcheck_conda.sh\")` — see Available Scripts above for why this is preferred over `conda --version`.\n- `run_script(\"scripts\u002Fcheck_ngc_image.sh\", \"\u003Ccuda-tag-suffix>\")` — replace `\u003Ccuda-tag-suffix>` with the tag determined from Step 2 (e.g. `cuda13`, `cuda12-dgpu`, `cuda12-igpu`).\n\nIf Holoscan is already installed, note the version and ask whether to upgrade or verify the existing install.\n\n**CUDA variant rule** (canonical reference — apply this in all steps below):\n\n| nvidia-smi CUDA Version | Native packages | Container tag |\n|------------------------|-----------------|---------------|\n| 13.x+ | `holoscan-cu13` \u002F `holoscan-cuda-13` | `cuda13` |\n| 12.x, Blackwell GPU | `holoscan-cu12` \u002F `holoscan-cuda-12` | `cuda13` (Forward Compat) or `cuda12-dgpu` |\n| 12.x, Ampere\u002FAda dGPU | `holoscan-cu12` \u002F `holoscan-cuda-12` | `cuda12-dgpu` |\n| ARM64 iGPU (Jetson, IGX) | `holoscan` | `cuda12-igpu` |\n\nNative installs treat the driver CUDA version as a hard ceiling. Containers support Forward Compatibility (banner saying \"CUDA Forward Compatibility mode ENABLED\" is expected, not an error).\n\n### Step 5: Present Options and Recommend\n\nAlways present **all methods** in the table — never omit a row. Use the Status column to indicate availability on the host (unavailable methods show ✗ with a short reason). Use this table format:\n\n| Method | Best for | Status |\n|--------|----------|--------|\n| **NGC Container** | All deps bundled (CUDA, TensorRT, LibTorch, ONNX Runtime, Vulkan); C++ + Python. Needs Docker + NVIDIA Container Toolkit. | ✓\u002F✗ based on docker presence |\n| **Debian\u002Fapt** | Native Ubuntu; C++ only | ✓\u002F✗ if package is installed |\n| **pip wheel** | Python-only projects; needs CUDA Toolkit on PATH; Python 3.10–3.13. | ✓\u002F✗ if wheel is installed in virtual env at ~\u002Fholoscan\u002Fvenv |\n| **Conda** | CUDA 13 only; good if already in a conda environment. | ✓\u002F✗ based on `check_conda.sh` output (not just `which conda`) |\n| **Source** | Modifying SDK internals, custom CMake flags, debug symbols, unsupported platform, or unreleased branch. | ✓\u002F✗ if already cloned at ~\u002Fholoscan\u002Fholoscan-sdk |\n\nAfter the table, end the turn with this exact two-line shape:\n\n> **Recommendation:** `\u003Cmethod>` — `\u003Cone-line why>`\n>\n> **Which method would you like to use?** (container \u002F apt \u002F wheel \u002F conda \u002F source)\n\nIf the user is new to Holoscan and the host is a supported x86_64 platform with Docker available, recommend **NGC Container**. For RHEL 9 or other container-only hosts, recommend container. For Python-only projects on a Docker-less host, recommend pip wheel.\n\nDo **not** include `docker pull`, `docker run`, `apt install`, or `pip install` commands in this turn — those live in the install skill invoked in Step 6. Keep this response short to avoid being truncated mid-table.\n\n### Step 6: Delegate to the Install Skill\n\nOnce a method is picked, invoke the corresponding skill — do not repeat the install steps inline:\n\n| Method | Skill to invoke |\n|--------|-----------------|\n| NGC Container | `\u002Fholoscan-install-container` |\n| Debian\u002Fapt | `\u002Fholoscan-install-debian` |\n| pip wheel | `\u002Fholoscan-install-wheel` |\n| Conda | `\u002Fholoscan-install-conda` |\n| Source | `\u002Fholoscan-install-source` |\n\nPass the CUDA variant (cu12\u002Fcu13\u002Figpu) and any other relevant facts from Steps 2–4 as context when invoking the skill.\n\nThe install skill owns the full command set — including the recommended container flags (`--gpus all`, `--ipc=host`, `--ulimit memlock=-1`, `--ulimit stack=67108864`, inner `ulimit -s 32768`) and verification examples. Do not restate them from `holoscan-setup`; delegate and let the install skill produce them.\n\n### Step 7: Summary\n\nIf installation was successful and tests were run, print a table summary of test results.\n\n## Limitations\n\n- RHEL 9.x supports the NGC container method only — native packages are not published.\n- Conda packages are CUDA 13 only; CUDA 12 hosts must use container, apt, pip wheel, or source.\n- Debian\u002Fapt installs C++ only since Holoscan v3.0.0; Python support requires an additional pip wheel install.\n- pip wheel requires glibc ≥ 2.35 and Python 3.10–3.13.\n- Native installs cannot exceed the driver's reported CUDA Version; only containers can use CUDA Forward Compatibility.\n- DGX Spark \u002F Grace-Hopper OS requirements change between releases — always re-check `sdk_installation.html`.\n\n## Troubleshooting\n\n- **`conda --version` says \"command not found\" but Conda is installed** — common in zsh setups with lazy-loaded conda or when only `.bashrc` ran `conda init`. Use `run_script(\"scripts\u002Fcheck_conda.sh\")`; it searches install dirs and rc files.\n- **`nvidia-smi` shows a lower CUDA Version than expected** — that field is the driver's max supported CUDA, not the installed toolkit. Upgrade the driver before installing a newer-CUDA package.\n- **Debian install succeeds but `import holoscan` fails in Python** — apt installs C++ only since v3.0.0. Follow up with `\u002Fholoscan-install-wheel`.\n- **`pip install holoscan` fails with glibc errors** — host glibc is \u003C 2.35. Use container or apt instead.\n- **`check_ngc_image.sh` reports image missing** — confirm NGC login (`docker login nvcr.io`) and that the tag suffix matches the CUDA variant rule in Step 4.\n","# Holoscan SDK Setup\n\n## Purpose\n\nDetermines the correct Holoscan SDK installation method for the current host by inspecting hardware, OS, CUDA driver, and existing tooling, then delegates to a method-specific install skill. Covers NGC container, Debian\u002Fapt, pip wheel, Conda, and source builds across Ubuntu, RHEL, IGX Orin, Jetson, and DGX Spark \u002F Grace-Hopper platforms.\n\n## Prerequisites\n\n- Linux host (Ubuntu 22.04\u002F24.04, RHEL 9.x, IGX Orin, Jetson, or DGX Spark \u002F Grace-Hopper)\n- NVIDIA GPU with a working driver (`nvidia-smi` returns a CUDA Version)\n- Network access to `docs.nvidia.com` and NGC\n- One of: Docker + NVIDIA Container Toolkit, `apt`, Python 3.10–3.13 with `pip`, Conda, or a build toolchain — depending on chosen method\n\n## Available Scripts\n\n| Script | Purpose | Arguments |\n|--------|---------|-----------|\n| `scripts\u002Fcheck_conda.sh` | Detects Conda installs even when not on PATH (searches `~\u002Fminiconda3`, `~\u002Fminiforge3`, `~\u002Fanaconda3`, `~\u002Fmambaforge`, `\u002Fopt\u002Fconda`, and shell rc files); reports envs and which have `holoscan` importable. | none |\n| `scripts\u002Fcheck_ngc_image.sh` | Checks whether the NGC Holoscan container image for a given CUDA tag suffix is pulled or available. | `\u003Ccuda-tag-suffix>` — one of `cuda13`, `cuda12-dgpu`, `cuda12-igpu` |\n\nInvoke scripts with `run_script(\"scripts\u002Fcheck_conda.sh\")` and `run_script(\"scripts\u002Fcheck_ngc_image.sh\", \"cuda13\")`. Trust the script output over bare commands such as `which conda` or `docker images`.\n\n## Instructions\n\nBe conversational and step-by-step — do not front-load all the information. Complete each step and report back before moving on.\n\n### Workflow rules (must follow)\n\n1. End Step 5 with a **bolded one-line recommendation** that names the method (e.g. `**Recommendation:** NGC Container — bundles all deps, fastest path to a working install.`).\n2. For a first-time user on a supported x86_64 host with Docker available, that recommendation **must** be **NGC Container**.\n3. After the recommendation, **stop and ask** which method to use. Do not paste `docker pull`, `docker run`, `apt install`, `pip install`, or other install commands in that turn — those belong to the delegated install skill in Step 6.\n4. If the container path is in play, verify Docker + GPU passthrough **yourself** in Step 4 (run the command shown there). Do not ask the user to run `nvidia-smi` or `docker --version` for you.\n\n### Step 1: Read the Docs First\n\nFetch `https:\u002F\u002Fdocs.nvidia.com\u002Fholoscan\u002Fsdk-user-guide\u002F` then `sdk_installation.html` to get the current release's supported platforms, package names, and install requirements. Do not rely on hardcoded assumptions.\n\n### Step 2: Inspect the Machine\n\nRun in parallel:\n\n```bash\nuname -a && (lsb_release -a 2>\u002Fdev\u002Fnull || cat \u002Fetc\u002Fos-release)\nuname -m\nnvidia-smi 2>&1 | head -10\nnproc && free -h | head -2\n```\n\n**Key:** Read the \"CUDA Version\" field from `nvidia-smi` (top-right of the table header) — this is the *maximum* CUDA version the driver supports, and drives `cuda12` vs `cuda13` package selection.\n\n### Step 3: Assess Compatibility\n\n| Platform | Methods Available |\n|----------|-------------------|\n| Ubuntu 22.04\u002F24.04, x86_64 | Container, Debian\u002Fapt, pip wheel, Conda, Source |\n| RHEL 9.x, x86_64 | Container only |\n| IGX Orin (ARM64) | Container, Debian\u002Fapt, Source |\n| Jetson AGX Orin \u002F Orin Nano | Container, Debian\u002Fapt (iGPU) |\n| Jetson AGX Thor | Container, Debian\u002Fapt |\n| DGX Spark \u002F Grace-Hopper | Container (check docs for OS requirements) |\n| Other Linux, x86_64 | Container may work; pip wheel if glibc ≥ 2.35 |\n\n### Step 4: Check Tools and Present Options\n\nRun in parallel:\n\n```bash\ndocker --version 2>&1 | head -1; python3 --version 2>&1; pip3 --version 2>&1\ndpkg -l | grep holoscan || true\npip3 show holoscan 2>\u002Fdev\u002Fnull | grep -E \"^(Name|Version)\" || true\n~\u002Fholoscan\u002Fvenv\u002Fbin\u002Fpip show holoscan 2>\u002Fdev\u002Fnull | grep -E \"^(Name|Version)\" | sed 's\u002F^\u002Fvenv: \u002F' || true\n```\n\nThen verify GPU passthrough yourself — do **not** ask the user to run this:\n\n```bash\ndocker run --rm --gpus all ubuntu:22.04 nvidia-smi 2>&1 | tail -5 || true\n```\n\nInterpret the result for the Status column in Step 5:\n- `docker` missing → container row Status `✗ — Docker not installed`.\n- Docker present but `could not select device driver \"nvidia\"` → `✗ — NVIDIA Container Toolkit missing`.\n- `nvidia-smi` output appears → `✓`.\n\nThen invoke the detection scripts via `run_script`:\n\n- `run_script(\"scripts\u002Fcheck_conda.sh\")` — see Available Scripts above for why this is preferred over `conda --version`.\n- `run_script(\"scripts\u002Fcheck_ngc_image.sh\", \"\u003Ccuda-tag-suffix>\")` — replace `\u003Ccuda-tag-suffix>` with the tag determined from Step 2 (e.g. `cuda13`, `cuda12-dgpu`, `cuda12-igpu`).\n\nIf Holoscan is already installed, note the version and ask whether to upgrade or verify the existing install.\n\n**CUDA variant rule** (canonical reference — apply this in all steps below):\n\n| nvidia-smi CUDA Version | Native packages | Container tag |\n|------------------------|-----------------|---------------|\n| 13.x+ | `holoscan-cu13` \u002F `holoscan-cuda-13` | `cuda13` |\n| 12.x, Blackwell GPU | `holoscan-cu12` \u002F `holoscan-cuda-12` | `cuda13` (Forward Compat) or `cuda12-dgpu` |\n| 12.x, Ampere\u002FAda dGPU | `holoscan-cu12` \u002F `holoscan-cuda-12` | `cuda12-dgpu` |\n| ARM64 iGPU (Jetson, IGX) | `holoscan` | `cuda12-igpu` |\n\nNative installs treat the driver CUDA version as a hard ceiling. Containers support Forward Compatibility (banner saying \"CUDA Forward Compatibility mode ENABLED\" is expected, not an error).\n\n### Step 5: Present Options and Recommend\n\nAlways present **all methods** in the table — never omit a row. Use the Status column to indicate availability on the host (unavailable methods show ✗ with a short reason). Use this table format:\n\n| Method | Best for | Status |\n|--------|----------|--------|\n| **NGC Container** | All deps bundled (CUDA, TensorRT, LibTorch, ONNX Runtime, Vulkan); C++ + Python. Needs Docker + NVIDIA Container Toolkit. | ✓\u002F✗ based on docker presence |\n| **Debian\u002Fapt** | Native Ubuntu; C++ only | ✓\u002F✗ if package is installed |\n| **pip wheel** | Python-only projects; needs CUDA Toolkit on PATH; Python 3.10–3.13. | ✓\u002F✗ if wheel is installed in virtual env at ~\u002Fholoscan\u002Fvenv |\n| **Conda** | CUDA 13 only; good if already in a conda environment. | ✓\u002F✗ based on `check_conda.sh` output (not just `which conda`) |\n| **Source** | Modifying SDK internals, custom CMake flags, debug symbols, unsupported platform, or unreleased branch. | ✓\u002F✗ if already cloned at ~\u002Fholoscan\u002Fholoscan-sdk |\n\nAfter the table, end the turn with this exact two-line shape:\n\n> **Recommendation:** `\u003Cmethod>` — `\u003Cone-line why>`\n>\n> **Which method would you like to use?** (container \u002F apt \u002F wheel \u002F conda \u002F source)\n\nIf the user is new to Holoscan and the host is a supported x86_64 platform with Docker available, recommend **NGC Container**. For RHEL 9 or other container-only hosts, recommend container. For Python-only projects on a Docker-less host, recommend pip wheel.\n\nDo **not** include `docker pull`, `docker run`, `apt install`, or `pip install` commands in this turn — those live in the install skill invoked in Step 6. Keep this response short to avoid being truncated mid-table.\n\n### Step 6: Delegate to the Install Skill\n\nOnce a method is picked, invoke the corresponding skill — do not repeat the install steps inline:\n\n| Method | Skill to invoke |\n|--------|-----------------|\n| NGC Container | `\u002Fholoscan-install-container` |\n| Debian\u002Fapt | `\u002Fholoscan-install-debian` |\n| pip wheel | `\u002Fholoscan-install-wheel` |\n| Conda | `\u002Fholoscan-install-conda` |\n| Source | `\u002Fholoscan-install-source` |\n\nPass the CUDA variant (cu12\u002Fcu13\u002Figpu) and any other relevant facts from Steps 2–4 as context when invoking the skill.\n\nThe install skill owns the full command set — including the recommended container flags (`--gpus all`, `--ipc=host`, `--ulimit memlock=-1`, `--ulimit stack=67108864`, inner `ulimit -s 32768`) and verification examples. Do not restate them from `holoscan-setup`; delegate and let the install skill produce them.\n\n### Step 7: Summary\n\nIf installation was successful and tests were run, print a table summary of test results.\n\n## Limitations\n\n- RHEL 9.x supports the NGC container method only — native packages are not published.\n- Conda packages are CUDA 13 only; CUDA 12 hosts must use container, apt, pip wheel, or source.\n- Debian\u002Fapt installs C++ only since Holoscan v3.0.0; Python support requires an additional pip wheel install.\n- pip wheel requires glibc ≥ 2.35 and Python 3.10–3.13.\n- Native installs cannot exceed the driver's reported CUDA Version; only containers can use CUDA Forward Compatibility.\n- DGX Spark \u002F Grace-Hopper OS requirements change between releases — always re-check `sdk_installation.html`.\n\n## Troubleshooting\n\n- **`conda --version` says \"command not found\" but Conda is installed** — common in zsh setups with lazy-loaded conda or when only `.bashrc` ran `conda init`. Use `run_script(\"scripts\u002Fcheck_conda.sh\")`; it searches install dirs and rc files.\n- **`nvidia-smi` shows a lower CUDA Version than expected** — that field is the driver's max supported CUDA, not the installed toolkit. Upgrade the driver before installing a newer-CUDA package.\n- **Debian install succeeds but `import holoscan` fails in Python** — apt installs C++ only since v3.0.0. Follow up with `\u002Fholoscan-install-wheel`.\n- **`pip install holoscan` fails with glibc errors** — host glibc is \u003C 2.35. Use container or apt instead.\n- **`check_ngc_image.sh` reports image missing** — confirm NGC login (`docker login nvcr.io`) and that the tag suffix matches the CUDA variant rule in Step 4.\n",{"name":5,"license":15,"version":24,"metadata":25,"description":7},"1.0.0",{"tags":26,"author":32,"github-url":33},[27,28,29,30,31],"holoscan","installation","nvidia","sdk","setup","Holoscan Team \u003Choloscan-team@nvidia.com>","https:\u002F\u002Fgithub.com\u002Fnvidia-holoscan\u002Fholoscan-sdk","main","c475fc64e94e803b50d14a791dfee8853fbb2c5c","skills\u002Fnvidia\u002Fholoscan-setup","https:\u002F\u002Fgithub.com\u002FElogic360\u002Fsoftware-factory","https:\u002F\u002Fgithub.com\u002FElogic360","2026-11-08 22:21:22",{"source":8,"skillName":5,"commands":41,"prompt":51,"zipAvailable":52},[42,45,48],{"tool":43,"command":44},"npx","npx skills add Elogic360\u002Fsoftware-factory --skill holoscan-setup",{"tool":46,"command":47},"bunx","bunx skills add Elogic360\u002Fsoftware-factory --skill holoscan-setup",{"tool":49,"command":50},"pnpm","pnpm dlx skills add Elogic360\u002Fsoftware-factory --skill holoscan-setup","安装技能，技能地址：https:\u002F\u002Fgithub.com\u002FElogic360\u002Fsoftware-factory\u002Fblob\u002Fmain\u002Fskills\u002Fnvidia\u002Fholoscan-setup\u002FSKILL.md",true,[54,60,64,70,74,78],{"path":55,"resourceType":56,"resourceTypeDesc":57,"sizeBytes":58,"sourceHtmlUrl":59},"BENCHMARK.md","REFERENCE","参考文档",3743,"https:\u002F\u002Fgithub.com\u002FElogic360\u002Fsoftware-factory\u002Fblob\u002Fmain\u002Fskills\u002Fnvidia\u002Fholoscan-setup\u002FBENCHMARK.md",{"path":61,"resourceType":56,"resourceTypeDesc":57,"sizeBytes":62,"sourceHtmlUrl":63},"evals\u002Fevals.json",4718,"https:\u002F\u002Fgithub.com\u002FElogic360\u002Fsoftware-factory\u002Fblob\u002Fmain\u002Fskills\u002Fnvidia\u002Fholoscan-setup\u002Fevals\u002Fevals.json",{"path":65,"resourceType":66,"resourceTypeDesc":67,"sizeBytes":68,"sourceHtmlUrl":69},"scripts\u002Fcheck_conda.sh","SCRIPT","脚本",2798,"https:\u002F\u002Fgithub.com\u002FElogic360\u002Fsoftware-factory\u002Fblob\u002Fmain\u002Fskills\u002Fnvidia\u002Fholoscan-setup\u002Fscripts\u002Fcheck_conda.sh",{"path":71,"resourceType":66,"resourceTypeDesc":67,"sizeBytes":72,"sourceHtmlUrl":73},"scripts\u002Fcheck_ngc_image.sh",360,"https:\u002F\u002Fgithub.com\u002FElogic360\u002Fsoftware-factory\u002Fblob\u002Fmain\u002Fskills\u002Fnvidia\u002Fholoscan-setup\u002Fscripts\u002Fcheck_ngc_image.sh",{"path":75,"resourceType":56,"resourceTypeDesc":57,"sizeBytes":76,"sourceHtmlUrl":77},"skill-card.md",3785,"https:\u002F\u002Fgithub.com\u002FElogic360\u002Fsoftware-factory\u002Fblob\u002Fmain\u002Fskills\u002Fnvidia\u002Fholoscan-setup\u002Fskill-card.md",{"path":79,"resourceType":80,"resourceTypeDesc":81,"sizeBytes":82,"sourceHtmlUrl":83},"skill.oms.sig","OTHER","其他",5057,"https:\u002F\u002Fgithub.com\u002FElogic360\u002Fsoftware-factory\u002Fblob\u002Fmain\u002Fskills\u002Fnvidia\u002Fholoscan-setup\u002Fskill.oms.sig",[85,92,99,106,113,120],{"skillNo":86,"name":87,"nameZh":6,"description":88,"descriptionZh":6,"repositoryFullName":8,"ownerLogin":9,"ownerAvatarUrl":10,"ownerAccountNo":11,"ownerDisplayName":12,"repositoryNo":13,"starsCount":14,"licenseSpdxId":15,"licenseFileUrl":6,"sourceHtmlUrl":89,"categories":90,"keywords":91,"publishedAt":19},"01M79G206MT3J6S0GY3C45P2KV","vss-summarize-video","Use to summarize a recorded video via the LVS summarization microservice (HITL-gated) with a VLM fallback. Not for report generation or live RTSP captioning.","https:\u002F\u002Fgithub.com\u002FElogic360\u002Fsoftware-factory\u002Fblob\u002Fmain\u002Fskills\u002Fnvidia\u002Fvss-summarize-video\u002FSKILL.md",[],[],{"skillNo":93,"name":94,"nameZh":6,"description":95,"descriptionZh":6,"repositoryFullName":8,"ownerLogin":9,"ownerAvatarUrl":10,"ownerAccountNo":11,"ownerDisplayName":12,"repositoryNo":13,"starsCount":14,"licenseSpdxId":15,"licenseFileUrl":6,"sourceHtmlUrl":96,"categories":97,"keywords":98,"publishedAt":19},"01M79G1WN63PGRE8CKD47WEBQ5","vss-setup-video-analytics-api","Use to deploy the vss-video-analytics-api REST service standalone (config-source, data-log bind, Elasticsearch, optional Kafka). Not for full warehouse deploy.","https:\u002F\u002Fgithub.com\u002FElogic360\u002Fsoftware-factory\u002Fblob\u002Fmain\u002Fskills\u002Fnvidia\u002Fvss-setup-video-analytics-api\u002FSKILL.md",[],[],{"skillNo":100,"name":101,"nameZh":6,"description":102,"descriptionZh":6,"repositoryFullName":8,"ownerLogin":9,"ownerAvatarUrl":10,"ownerAccountNo":11,"ownerDisplayName":12,"repositoryNo":13,"starsCount":14,"licenseSpdxId":15,"licenseFileUrl":6,"sourceHtmlUrl":103,"categories":104,"keywords":105,"publishedAt":19},"01M79G1S9F7VY3EF6H53PRP5AN","vss-setup-behavior-analytics","Use to deploy the vss-behavior-analytics service standalone (entrypoint, config-source, optional calibration). Not for the full warehouse deploy.","https:\u002F\u002Fgithub.com\u002FElogic360\u002Fsoftware-factory\u002Fblob\u002Fmain\u002Fskills\u002Fnvidia\u002Fvss-setup-behavior-analytics\u002FSKILL.md",[],[],{"skillNo":107,"name":108,"nameZh":6,"description":109,"descriptionZh":6,"repositoryFullName":8,"ownerLogin":9,"ownerAvatarUrl":10,"ownerAccountNo":11,"ownerDisplayName":12,"repositoryNo":13,"starsCount":14,"licenseSpdxId":15,"licenseFileUrl":6,"sourceHtmlUrl":110,"categories":111,"keywords":112,"publishedAt":19},"01M79G1N1S1Y27PFZPYMNK1MTQ","vss-search-archive","Use this skill to run top-level VSS fusion search on archived video, or to ingest video files \u002F RTSP streams for search. Do NOT use for ad-hoc visual Q&A (use vss-ask-video), live captioning (use vss-deploy-dense-captioning), or video summarization and reports (use vss-summarize-video).","https:\u002F\u002Fgithub.com\u002FElogic360\u002Fsoftware-factory\u002Fblob\u002Fmain\u002Fskills\u002Fnvidia\u002Fvss-search-archive\u002FSKILL.md",[],[],{"skillNo":114,"name":115,"nameZh":6,"description":116,"descriptionZh":6,"repositoryFullName":8,"ownerLogin":9,"ownerAvatarUrl":10,"ownerAccountNo":11,"ownerDisplayName":12,"repositoryNo":13,"starsCount":14,"licenseSpdxId":15,"licenseFileUrl":6,"sourceHtmlUrl":117,"categories":118,"keywords":119,"publishedAt":19},"01M79G1H6WN0Q6C0VCAXCZX6B7","vss-query-analytics","Use this skill when reading video-analytics metrics, incidents, alerts, and sensor data via the VA-MCP server (port 9901). Not for live VLM or incident-range narrative reports.","https:\u002F\u002Fgithub.com\u002FElogic360\u002Fsoftware-factory\u002Fblob\u002Fmain\u002Fskills\u002Fnvidia\u002Fvss-query-analytics\u002FSKILL.md",[],[],{"skillNo":121,"name":122,"nameZh":6,"description":123,"descriptionZh":6,"repositoryFullName":8,"ownerLogin":9,"ownerAvatarUrl":10,"ownerAccountNo":11,"ownerDisplayName":12,"repositoryNo":13,"starsCount":14,"licenseSpdxId":15,"licenseFileUrl":6,"sourceHtmlUrl":124,"categories":125,"keywords":126,"publishedAt":19},"01M79G1DYPNXQS7QHZ0AAF5FX5","vss-manage-video-io-storage","Use to call the VIOS REST API (sensor list, timelines, clip extraction, snapshots, add\u002Fdelete sensors and streams). Not for VLM inference or search.","https:\u002F\u002Fgithub.com\u002FElogic360\u002Fsoftware-factory\u002Fblob\u002Fmain\u002Fskills\u002Fnvidia\u002Fvss-manage-video-io-storage\u002FSKILL.md",[],[]]