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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":45,"metadata":46,"description":7},"1.0.0",{"tags":47,"author":50,"github-url":51},[48,33,49,27,24],"holoscan","nvidia","Holoscan Team \u003Choloscan-team@nvidia.com>","https:\u002F\u002Fgithub.com\u002Fnvidia-holoscan\u002Fholoscan-sdk","main","7a940b8e5af16c95e5c190a524421d32fc7445a9",".agent\u002Fskills\u002Fholoscan-setup","https:\u002F\u002Fgithub.com\u002Fheadmktlucca-ui\u002Fneuroads-lp","https:\u002F\u002Fgithub.com\u002Fheadmktlucca-ui","https:\u002F\u002Fneuroads.com.br","2026-08-12 00:42:44",{"source":8,"skillName":5,"commands":60,"prompt":70,"zipAvailable":71},[61,64,67],{"tool":62,"command":63},"npx","npx skills add headmktlucca-ui\u002Fneuroads-lp --skill holoscan-setup",{"tool":65,"command":66},"bunx","bunx skills add headmktlucca-ui\u002Fneuroads-lp --skill holoscan-setup",{"tool":68,"command":69},"pnpm","pnpm dlx skills add headmktlucca-ui\u002Fneuroads-lp --skill holoscan-setup","安装技能，技能地址：https:\u002F\u002Fgithub.com\u002Fheadmktlucca-ui\u002Fneuroads-lp\u002Fblob\u002Fmain\u002F.agent\u002Fskills\u002Fholoscan-setup\u002FSKILL.md",true,[73,79,83,89,93,97],{"path":74,"resourceType":75,"resourceTypeDesc":76,"sizeBytes":77,"sourceHtmlUrl":78},"BENCHMARK.md","REFERENCE","参考文档",3743,"https:\u002F\u002Fgithub.com\u002Fheadmktlucca-ui\u002Fneuroads-lp\u002Fblob\u002Fmain\u002F.agent\u002Fskills\u002Fholoscan-setup\u002FBENCHMARK.md",{"path":80,"resourceType":75,"resourceTypeDesc":76,"sizeBytes":81,"sourceHtmlUrl":82},"evals\u002Fevals.json",4718,"https:\u002F\u002Fgithub.com\u002Fheadmktlucca-ui\u002Fneuroads-lp\u002Fblob\u002Fmain\u002F.agent\u002Fskills\u002Fholoscan-setup\u002Fevals\u002Fevals.json",{"path":84,"resourceType":85,"resourceTypeDesc":86,"sizeBytes":87,"sourceHtmlUrl":88},"scripts\u002Fcheck_conda.sh","SCRIPT","脚本",2798,"https:\u002F\u002Fgithub.com\u002Fheadmktlucca-ui\u002Fneuroads-lp\u002Fblob\u002Fmain\u002F.agent\u002Fskills\u002Fholoscan-setup\u002Fscripts\u002Fcheck_conda.sh",{"path":90,"resourceType":85,"resourceTypeDesc":86,"sizeBytes":91,"sourceHtmlUrl":92},"scripts\u002Fcheck_ngc_image.sh",360,"https:\u002F\u002Fgithub.com\u002Fheadmktlucca-ui\u002Fneuroads-lp\u002Fblob\u002Fmain\u002F.agent\u002Fskills\u002Fholoscan-setup\u002Fscripts\u002Fcheck_ngc_image.sh",{"path":94,"resourceType":75,"resourceTypeDesc":76,"sizeBytes":95,"sourceHtmlUrl":96},"skill-card.md",3785,"https:\u002F\u002Fgithub.com\u002Fheadmktlucca-ui\u002Fneuroads-lp\u002Fblob\u002Fmain\u002F.agent\u002Fskills\u002Fholoscan-setup\u002Fskill-card.md",{"path":98,"resourceType":99,"resourceTypeDesc":100,"sizeBytes":101,"sourceHtmlUrl":102},"skill.oms.sig","OTHER","其他",5057,"https:\u002F\u002Fgithub.com\u002Fheadmktlucca-ui\u002Fneuroads-lp\u002Fblob\u002Fmain\u002F.agent\u002Fskills\u002Fholoscan-setup\u002Fskill.oms.sig",[104,127,152,178,196,230],{"skillNo":105,"name":106,"nameZh":6,"description":107,"descriptionZh":6,"repositoryFullName":8,"ownerLogin":9,"ownerAvatarUrl":10,"ownerAccountNo":11,"ownerDisplayName":12,"repositoryNo":13,"starsCount":14,"licenseSpdxId":108,"licenseFileUrl":6,"sourceHtmlUrl":109,"categories":110,"keywords":115,"publishedAt":40},"01M6PTDCZTXTGSTX4RFK901YSZ","xunit-skill","Generates xUnit.net tests in C#. Covers Fact\u002FTheory, constructor injection, IClassFixture, and FluentAssertions. Use when user mentions \"xUnit\", \"[Fact]\", \"[Theory]\", \"Assert.Equal\", \"C# xUnit\". Triggers on: \"xUnit\", \"[Fact]\", \"[Theory]\", \"Assert.Equal C#\", \"xUnit.net\".","MIT","https:\u002F\u002Fgithub.com\u002Fheadmktlucca-ui\u002Fneuroads-lp\u002Fblob\u002Fmain\u002F.claude\u002Fskills\u002Fxunit-skill\u002FSKILL.md",[111,114],{"categoryNo":112,"categoryName":113,"taxonomyType":21,"taxonomyTypeDesc":22},"01JBSEEDCAT000000000000008","测试与质量",{"categoryNo":19,"categoryName":20,"taxonomyType":21,"taxonomyTypeDesc":22},[116,117,118,119,120,121,122,123,124,125,126],"xunit","xunit skill","testing","injection","user","use","tests","triggers","unit","unit-testing","C#",{"skillNo":128,"name":129,"nameZh":6,"description":130,"descriptionZh":6,"repositoryFullName":8,"ownerLogin":9,"ownerAvatarUrl":10,"ownerAccountNo":11,"ownerDisplayName":12,"repositoryNo":13,"starsCount":14,"licenseSpdxId":108,"licenseFileUrl":6,"sourceHtmlUrl":131,"categories":132,"keywords":135,"publishedAt":40},"01M6PTCB59ZTG5FTMWGPYEMMBP","xcuitest-skill","Generates XCUITest UI tests for iOS\u002FiPadOS apps in Swift. Apple's native testing framework for reliable, fast UI automation. Supports local simulators and TestMu AI cloud real devices. Use when user mentions \"XCUITest\", \"XCTest\", \"iOS UI test\", \"Swift test\", \"XCUIApplication\". Triggers on: \"XCUITest\", \"XCTest UI\", \"iOS UI test\", \"Swift UI test\", \"XCUIApplication\", \"TestMu\".","https:\u002F\u002Fgithub.com\u002Fheadmktlucca-ui\u002Fneuroads-lp\u002Fblob\u002Fmain\u002F.claude\u002Fskills\u002Fxcuitest-skill\u002FSKILL.md",[133,134],{"categoryNo":112,"categoryName":113,"taxonomyType":21,"taxonomyTypeDesc":22},{"categoryNo":19,"categoryName":20,"taxonomyType":21,"taxonomyTypeDesc":22},[136,137,118,138,139,140,141,142,121,143,144,145,146,147,122,123,148,149,150,151],"xcuitest","xcuitest skill","automation","test","testing framework","ios","devices","mobile","mobile-testing","ui test","local","apple","apps","native","for","objective",{"skillNo":153,"name":154,"nameZh":6,"description":155,"descriptionZh":6,"repositoryFullName":8,"ownerLogin":9,"ownerAvatarUrl":10,"ownerAccountNo":11,"ownerDisplayName":12,"repositoryNo":13,"starsCount":14,"licenseSpdxId":108,"licenseFileUrl":6,"sourceHtmlUrl":156,"categories":157,"keywords":162,"publishedAt":40},"01M6PTATMNTY9NF4J7KSR76YK6","world-builder","Create and manage generative game zones for Dorothy's Pokemon-style world. Use this skill when creating, updating, or designing game worlds via MCP tools.","https:\u002F\u002Fgithub.com\u002Fheadmktlucca-ui\u002Fneuroads-lp\u002Fblob\u002Fmain\u002F.claude\u002Fskills\u002Fworld-builder\u002FSKILL.md",[158,161],{"categoryNo":159,"categoryName":160,"taxonomyType":21,"taxonomyTypeDesc":22},"01JBSEEDCAT000000000000015","视觉设计",{"categoryNo":19,"categoryName":20,"taxonomyType":21,"taxonomyTypeDesc":22},[163,164,165,166,167,168,169,170,121,171,172,173,174,175,176,150,177],"builder","world","world builder","MCP","create","mcp tools","Tools","game","pokemon","manage","style","via","theme","zone","generative",{"skillNo":179,"name":180,"nameZh":6,"description":181,"descriptionZh":6,"repositoryFullName":8,"ownerLogin":9,"ownerAvatarUrl":10,"ownerAccountNo":11,"ownerDisplayName":12,"repositoryNo":13,"starsCount":14,"licenseSpdxId":108,"licenseFileUrl":6,"sourceHtmlUrl":182,"categories":183,"keywords":186,"publishedAt":40},"01M6PT9Z92JTSQEHE1QR0NNW5F","webdriverio-skill","Generates WebdriverIO (WDIO) automation tests in JavaScript or TypeScript. Supports local and TestMu AI cloud. Use when user mentions \"WebdriverIO\", \"WDIO\", \"wdio.conf\", \"browser.url\", \"$\", \"$$\". Triggers on: \"WebdriverIO\", \"WDIO\", \"wdio\", \"browser.$\".","https:\u002F\u002Fgithub.com\u002Fheadmktlucca-ui\u002Fneuroads-lp\u002Fblob\u002Fmain\u002F.claude\u002Fskills\u002Fwebdriverio-skill\u002FSKILL.md",[184,185],{"categoryNo":112,"categoryName":113,"taxonomyType":21,"taxonomyTypeDesc":22},{"categoryNo":19,"categoryName":20,"taxonomyType":21,"taxonomyTypeDesc":22},[187,118,188,138,189,190,191,192,120,121,193,146,122,123,194,195],"webdriverio skill","typescript","javascript","browser","cloud","e2e","e2e-testing","URL","conf",{"skillNo":197,"name":198,"nameZh":6,"description":199,"descriptionZh":6,"repositoryFullName":8,"ownerLogin":9,"ownerAvatarUrl":10,"ownerAccountNo":11,"ownerDisplayName":12,"repositoryNo":13,"starsCount":14,"licenseSpdxId":108,"licenseFileUrl":6,"sourceHtmlUrl":200,"categories":201,"keywords":211,"publishedAt":40},"01M6PT9NVK36ZWCQ69YXK3FR4K","web-design-guidelines","Web platform design and accessibility guidelines. Use when building web interfaces, auditing accessibility, implementing responsive layouts, or reviewing web UI code. Triggers on tasks involving HTML, CSS, web components, WCAG compliance, responsive design, or web performance.","https:\u002F\u002Fgithub.com\u002Fheadmktlucca-ui\u002Fneuroads-lp\u002Fblob\u002Fmain\u002F.claude\u002Fskills\u002Fweb\u002FSKILL.md",[202,205,206],{"categoryNo":203,"categoryName":204,"taxonomyType":21,"taxonomyTypeDesc":22},"01JBSEEDCAT000000000000009","前端开发",{"categoryNo":159,"categoryName":160,"taxonomyType":21,"taxonomyTypeDesc":22},{"categoryNo":207,"categoryName":208,"taxonomyType":209,"taxonomyTypeDesc":210},"01JBSEEDCAT000000000000032","设计师","PROFESSION","适用职业",[212,213,214,215,216,217,218,219,220,221,222,223,224,225,226,227,228,35,123,229],"design","web","guidelines","web design guidelines","web design","css","performance","components","web components","tasks","code","web performance","ui","platform design","html","responsive design","responsive","implementing",{"skillNo":231,"name":232,"nameZh":6,"description":233,"descriptionZh":6,"repositoryFullName":8,"ownerLogin":9,"ownerAvatarUrl":10,"ownerAccountNo":11,"ownerDisplayName":12,"repositoryNo":13,"starsCount":14,"licenseSpdxId":108,"licenseFileUrl":6,"sourceHtmlUrl":234,"categories":235,"keywords":238,"publishedAt":40},"01M6PT9A3JKRE3X34276F2FW5R","watchos-design-guidelines","Apple Human Interface Guidelines for Apple Watch. Use when building watchOS apps, complications, or workout features. Triggers on tasks involving Watch UI, Digital Crown, glanceable interfaces, or wrist-based interactions.","https:\u002F\u002Fgithub.com\u002Fheadmktlucca-ui\u002Fneuroads-lp\u002Fblob\u002Fmain\u002F.claude\u002Fskills\u002Fwatchos\u002FSKILL.md",[236,237],{"categoryNo":159,"categoryName":160,"taxonomyType":21,"taxonomyTypeDesc":22},{"categoryNo":207,"categoryName":208,"taxonomyType":209,"taxonomyTypeDesc":210},[212,214,239,221,224,121,240,241,147,35,242,123,148,243,150,244,245],"watchos design guidelines","workout","human","watch","interface","platform-design","features"]