[{"data":1,"prerenderedAt":-1},["ShallowReactive",2],{"skill:01M6PHFDCQPDQ3EH1Z6P072AM7":3,"skill-same:01M6PHFDCQPDQ3EH1Z6P072AM7":126},{"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":34,"linkOnly":35,"markdownContent":36,"markdownBody":37,"frontMatter":38,"refName":43,"commitSha":44,"skillDirectory":45,"forksCount":14,"repositoryHtmlUrl":46,"repositoryHomepageUrl":6,"ownerHtmlUrl":47,"ownerBlogUrl":48,"ownerDescription":6,"updatedAt":49,"install":50,"resources":63},"01M6PHFDCQPDQ3EH1Z6P072AM7","nv-generate-mr",null,"Used for generating synthetic body MRI volumes with NV-Generate-CTMR rflow-mr. Not for paired masks or production training data.","headmktlucca-ui\u002Fneuroads-lp","headmktlucca-ui","https:\u002F\u002Favatars.githubusercontent.com\u002Fu\u002F247379838?v=4","01M6PDVAV6M1W96D1SR1PDF8CS","NeuroAds","01M6PDVAZ4P281GNDKD45CYTF5",0,"Apache-2.0","https:\u002F\u002Fgithub.com\u002Fheadmktlucca-ui\u002Fneuroads-lp\u002Fblob\u002Fmain\u002F.agent\u002Fskills\u002Fnv-generate-mr\u002FSKILL.md",[],[19,20,21,22,23,24,25,26,27,28,29,30,31,32,33],"generate","mr","nv generate mr","training","NVIDIA","data","Production","team","Bash","generation","synthetic","MRI","with","for","body","2026-11-10 10:12:30",false,"---\nname: nv-generate-mr\ndescription: Used for generating synthetic body MRI volumes with NV-Generate-CTMR rflow-mr. Not for paired masks or production training data.\nlicense: Apache-2.0\nallowed-tools: Bash\nmetadata:\n  author: NVIDIA MedTech Team\n  tags:\n    - MedTech\n    - MRI\n    - generation\n---\n\n# NV-Generate-MR\n\n## Purpose\n- Used for generating synthetic body MRI volumes with NV-Generate-CTMR rflow-mr. Not for paired masks or production training data.\n- Use the wrapper exactly as documented; do not replace the upstream entrypoint with a handwritten implementation.\n- Do not write custom inference code for normal runs. The wrapper owns config staging, output paths, and validation.\n- Manifest I\u002FO: inputs are `model_config_override`; outputs are `synthetic_mr_volumes` and `result_json`.\n\n## Instructions\n- Read `skill_manifest.yaml` before changing arguments, side effects, or validation gates.\n- Run `scripts\u002Frun_mr.py` through the documented command below; keep outputs under a caller-provided run directory.\n- If a host agent exposes `run_script`, use `run_script(\"scripts\u002Frun_mr.py\", args=[...])`; otherwise run the Bash\u002FPython command shown below.\n- Emit a single bash code block, and keep the `python -m pip install -r \"$NV_GENERATE_ROOT\u002Frequirements.txt\"` step in that same command — the runtime may be a fresh environment without `nibabel`\u002FMONAI, so dropping the install fails with `ModuleNotFoundError`.\n- Do not add `rm`, `mkdir`, or any cleanup of `--output-dir`; the wrapper creates it. Use a fresh `--output-dir` instead of deleting one.\n- Check the emitted JSON and paired verifier guidance before treating the run as evidence.\n\n## Available Scripts\n| Script | Purpose | Arguments |\n|---|---|---|\n| `scripts\u002Frun_mr.py` | Primary entrypoint declared by skill_manifest.yaml. | `MODEL_CONFIG.json --output-dir OUT_DIR --modality mri_t1 [--random-seed N] [--yes]` |\n\n## Prerequisites\n- Runtime requirements: GPU\u002FCUDA when declared by the manifest; Python packages listed in `runtime.side_effects.pip_packages`.\n- Side effects: writes generated outputs under the caller's `--output-dir`, may cache model assets under `~\u002F.cache\u002Fhuggingface\u002F`, and may contact `https:\u002F\u002Fhuggingface.co` or `https:\u002F\u002Fgithub.com` during setup.\n- Run commands from the repository root unless an existing section below says otherwise.\n\n## Limitations\n- This is a thin wrapper. Inference, sampling, and decoding are delegated entirely to NVIDIA-Medtech\u002FNV-Generate-CTMR's `scripts.diff_model_infer`. Do not modify code under $NV_GENERATE_ROOT or the repo-local fallback at .workbench_data\u002Fupstreams\u002FNV-Generate-CTMR.\n- rflow-mr generates image-only synthetic MRI volumes. It does not emit paired segmentation masks.\n- The upstream README recommends `rflow-mr-brain` instead for brain MRI synthesis; use `skills\u002Fnv-generate-mr-brain` for that path.\n- NV-Generate-MR weights are listed by upstream as NVIDIA Non-Commercial. Do not use outputs as production training data without legal and quality review.\n- Not for clinical deployment, clinical interpretation, autonomous diagnosis, regulatory submission.\n\n## Troubleshooting\n| Error | Cause | Fix |\n|---|---|---|\n| Missing dependency or import error | Runtime package drift from `skill_manifest.yaml`. | Install the packages declared in the manifest or use the documented setup command. |\n| Empty or schema-invalid output | Wrong input path, unsupported modality, or upstream failure. | Re-run with a known fixture and inspect the wrapper JSON plus stderr. |\n| Validation gate failure | Output violated a declared engineering invariant. | Keep the failed evidence pack and use the gate message to repair inputs or wrapper code. |\n\nWraps the upstream\n[`NVIDIA-Medtech\u002FNV-Generate-CTMR`](https:\u002F\u002Fgithub.com\u002FNVIDIA-Medtech\u002FNV-Generate-CTMR#25-mr-image-generation)\nMR image-only generation workflow. The wrapper does not reimplement diffusion\nsampling or autoencoder decoding. It stages config overrides, runs the\ndocumented `python -m scripts.diff_model_infer` command for `rflow-mr`, then\nsummarizes the generated NIfTI volume.\n\n\n## Exact Runnable Surface\n\nFor user run commands in a fresh benchmark environment, use this setup plus\nrepo-root wrapper command exactly:\n\n```bash\nexport NV_GENERATE_ROOT=\"${NV_GENERATE_ROOT:-.workbench_data\u002Fupstreams\u002FNV-Generate-CTMR}\" && \\\npython -m pip install -r \"$NV_GENERATE_ROOT\u002Frequirements.txt\" && \\\npython skills\u002Fnv-generate-mr\u002Fscripts\u002Frun_mr.py PATH_TO_MR_CONFIG.json --output-dir OUT_DIR --modality mri_t1 --random-seed 0\n```\n\nDo not invent `generate.sh`, `infer.py`, `Medical AI Skills run`, or `python -m nv_generate_mr` commands. `PATH_TO_MR_CONFIG.json` must be the user's supplied request path.\n\n## Preconditions\n\nClone and install the upstream repo once. In this Medical AI Skills checkout, prefer\nthe repo-local cache path when it exists:\n\n```bash\nmkdir -p .workbench_data\u002Fupstreams\ntest -d .workbench_data\u002Fupstreams\u002FNV-Generate-CTMR\u002F.git || \\\n  git clone https:\u002F\u002Fgithub.com\u002FNVIDIA-Medtech\u002FNV-Generate-CTMR.git \\\n    .workbench_data\u002Fupstreams\u002FNV-Generate-CTMR\nexport NV_GENERATE_ROOT=.workbench_data\u002Fupstreams\u002FNV-Generate-CTMR\npip install -r \"$NV_GENERATE_ROOT\u002Frequirements.txt\"\n```\n\nDownload the MR weights:\n\n```bash\ncd \"$NV_GENERATE_ROOT\"\npython -m scripts.download_model_data --version rflow-mr --root_dir .\u002F --model_only\n```\n\nRuntime needs an NVIDIA GPU with at least 16 GB VRAM. There is no CPU\nfallback in the upstream path.\n\nThe wrapper also searches `.workbench_data\u002Fupstreams\u002FNV-Generate-CTMR` if\n`NV_GENERATE_ROOT` is unset or points at a stale clone.\n\nFor agent-generated user run commands, use the command in Usage. Do not prepend\nclone or model-download setup steps when the repo-local upstream cache already\nexists. In a fresh Python environment, still include\n`pip install -r \"$NV_GENERATE_ROOT\u002Frequirements.txt\"` before the wrapper unless\nthe active environment has already proven those imports are available; cached\nweights do not imply cached Python packages. If setup requires `cd \"$NV_GENERATE_ROOT\"`, return to the Medical AI Skills repo before invoking\n`skills\u002Fnv-generate-mr\u002Fscripts\u002Frun_mr.py`.\n\n## Usage\n\n```bash\nexport NV_GENERATE_ROOT=\"${NV_GENERATE_ROOT:-.workbench_data\u002Fupstreams\u002FNV-Generate-CTMR}\" && \\\npython -m pip install -r \"$NV_GENERATE_ROOT\u002Frequirements.txt\" && \\\npython skills\u002Fnv-generate-mr\u002Fscripts\u002Frun_mr.py \\\n  PATH_TO_MR_CONFIG.json \\\n  --output-dir runs\u002Fnv_generate_mr_demo \\\n  --modality mri_t1 \\\n  --random-seed 0\n```\n\nReplace `PATH_TO_MR_CONFIG.json` with the user's actual request\u002Fconfig path.\nDo not copy the fixture path from this document unless the user explicitly\nasked to run that fixture. If the user says \"the request is at\n`runs\u002F...\u002Fdefault_mri_t1.json`\", that exact path is the first positional\nargument to `scripts\u002Frun_mr.py`.\n\nSupported rflow-mr modality names are `mri`, `mri_t1`, `mri_t2`, and\n`mri_flair`, matching the upstream MR image-generation guide. The upstream\nREADME recommends `rflow-mr-brain` instead when synthesizing brain images;\nuse `skills\u002Fnv-generate-mr-brain` for that path.\nFor FOV and setup details, see `references\u002Ffov-and-downloads.md`.\n\nThe fixture argument is a small JSON override for\n`configs\u002Fconfig_maisi_diff_model_rflow-mr.json`. Pass `default` to use the\nupstream defaults plus the CLI modality and random seed. Common override keys\nare `dim`, `spacing`, `num_inference_steps`, `cfg_guidance_scale`, and\n`modality`.\n\nEach run records the staged config, model inventory, upstream command, output\ngeometry, spacing, affine, intensity range, and non-constant \u002F finite-data\nchecks. Output volumes are synthetic and are not safe as production training\ndata without independent review.\n\nNot for clinical interpretation, production deployment, autonomous diagnosis,\nor regulatory submission.\n","# NV-Generate-MR\n\n## Purpose\n- Used for generating synthetic body MRI volumes with NV-Generate-CTMR rflow-mr. Not for paired masks or production training data.\n- Use the wrapper exactly as documented; do not replace the upstream entrypoint with a handwritten implementation.\n- Do not write custom inference code for normal runs. The wrapper owns config staging, output paths, and validation.\n- Manifest I\u002FO: inputs are `model_config_override`; outputs are `synthetic_mr_volumes` and `result_json`.\n\n## Instructions\n- Read `skill_manifest.yaml` before changing arguments, side effects, or validation gates.\n- Run `scripts\u002Frun_mr.py` through the documented command below; keep outputs under a caller-provided run directory.\n- If a host agent exposes `run_script`, use `run_script(\"scripts\u002Frun_mr.py\", args=[...])`; otherwise run the Bash\u002FPython command shown below.\n- Emit a single bash code block, and keep the `python -m pip install -r \"$NV_GENERATE_ROOT\u002Frequirements.txt\"` step in that same command — the runtime may be a fresh environment without `nibabel`\u002FMONAI, so dropping the install fails with `ModuleNotFoundError`.\n- Do not add `rm`, `mkdir`, or any cleanup of `--output-dir`; the wrapper creates it. Use a fresh `--output-dir` instead of deleting one.\n- Check the emitted JSON and paired verifier guidance before treating the run as evidence.\n\n## Available Scripts\n| Script | Purpose | Arguments |\n|---|---|---|\n| `scripts\u002Frun_mr.py` | Primary entrypoint declared by skill_manifest.yaml. | `MODEL_CONFIG.json --output-dir OUT_DIR --modality mri_t1 [--random-seed N] [--yes]` |\n\n## Prerequisites\n- Runtime requirements: GPU\u002FCUDA when declared by the manifest; Python packages listed in `runtime.side_effects.pip_packages`.\n- Side effects: writes generated outputs under the caller's `--output-dir`, may cache model assets under `~\u002F.cache\u002Fhuggingface\u002F`, and may contact `https:\u002F\u002Fhuggingface.co` or `https:\u002F\u002Fgithub.com` during setup.\n- Run commands from the repository root unless an existing section below says otherwise.\n\n## Limitations\n- This is a thin wrapper. Inference, sampling, and decoding are delegated entirely to NVIDIA-Medtech\u002FNV-Generate-CTMR's `scripts.diff_model_infer`. Do not modify code under $NV_GENERATE_ROOT or the repo-local fallback at .workbench_data\u002Fupstreams\u002FNV-Generate-CTMR.\n- rflow-mr generates image-only synthetic MRI volumes. It does not emit paired segmentation masks.\n- The upstream README recommends `rflow-mr-brain` instead for brain MRI synthesis; use `skills\u002Fnv-generate-mr-brain` for that path.\n- NV-Generate-MR weights are listed by upstream as NVIDIA Non-Commercial. Do not use outputs as production training data without legal and quality review.\n- Not for clinical deployment, clinical interpretation, autonomous diagnosis, regulatory submission.\n\n## Troubleshooting\n| Error | Cause | Fix |\n|---|---|---|\n| Missing dependency or import error | Runtime package drift from `skill_manifest.yaml`. | Install the packages declared in the manifest or use the documented setup command. |\n| Empty or schema-invalid output | Wrong input path, unsupported modality, or upstream failure. | Re-run with a known fixture and inspect the wrapper JSON plus stderr. |\n| Validation gate failure | Output violated a declared engineering invariant. | Keep the failed evidence pack and use the gate message to repair inputs or wrapper code. |\n\nWraps the upstream\n[`NVIDIA-Medtech\u002FNV-Generate-CTMR`](https:\u002F\u002Fgithub.com\u002FNVIDIA-Medtech\u002FNV-Generate-CTMR#25-mr-image-generation)\nMR image-only generation workflow. The wrapper does not reimplement diffusion\nsampling or autoencoder decoding. It stages config overrides, runs the\ndocumented `python -m scripts.diff_model_infer` command for `rflow-mr`, then\nsummarizes the generated NIfTI volume.\n\n\n## Exact Runnable Surface\n\nFor user run commands in a fresh benchmark environment, use this setup plus\nrepo-root wrapper command exactly:\n\n```bash\nexport NV_GENERATE_ROOT=\"${NV_GENERATE_ROOT:-.workbench_data\u002Fupstreams\u002FNV-Generate-CTMR}\" && \\\npython -m pip install -r \"$NV_GENERATE_ROOT\u002Frequirements.txt\" && \\\npython skills\u002Fnv-generate-mr\u002Fscripts\u002Frun_mr.py PATH_TO_MR_CONFIG.json --output-dir OUT_DIR --modality mri_t1 --random-seed 0\n```\n\nDo not invent `generate.sh`, `infer.py`, `Medical AI Skills run`, or `python -m nv_generate_mr` commands. `PATH_TO_MR_CONFIG.json` must be the user's supplied request path.\n\n## Preconditions\n\nClone and install the upstream repo once. In this Medical AI Skills checkout, prefer\nthe repo-local cache path when it exists:\n\n```bash\nmkdir -p .workbench_data\u002Fupstreams\ntest -d .workbench_data\u002Fupstreams\u002FNV-Generate-CTMR\u002F.git || \\\n  git clone https:\u002F\u002Fgithub.com\u002FNVIDIA-Medtech\u002FNV-Generate-CTMR.git \\\n    .workbench_data\u002Fupstreams\u002FNV-Generate-CTMR\nexport NV_GENERATE_ROOT=.workbench_data\u002Fupstreams\u002FNV-Generate-CTMR\npip install -r \"$NV_GENERATE_ROOT\u002Frequirements.txt\"\n```\n\nDownload the MR weights:\n\n```bash\ncd \"$NV_GENERATE_ROOT\"\npython -m scripts.download_model_data --version rflow-mr --root_dir .\u002F --model_only\n```\n\nRuntime needs an NVIDIA GPU with at least 16 GB VRAM. There is no CPU\nfallback in the upstream path.\n\nThe wrapper also searches `.workbench_data\u002Fupstreams\u002FNV-Generate-CTMR` if\n`NV_GENERATE_ROOT` is unset or points at a stale clone.\n\nFor agent-generated user run commands, use the command in Usage. Do not prepend\nclone or model-download setup steps when the repo-local upstream cache already\nexists. In a fresh Python environment, still include\n`pip install -r \"$NV_GENERATE_ROOT\u002Frequirements.txt\"` before the wrapper unless\nthe active environment has already proven those imports are available; cached\nweights do not imply cached Python packages. If setup requires `cd \"$NV_GENERATE_ROOT\"`, return to the Medical AI Skills repo before invoking\n`skills\u002Fnv-generate-mr\u002Fscripts\u002Frun_mr.py`.\n\n## Usage\n\n```bash\nexport NV_GENERATE_ROOT=\"${NV_GENERATE_ROOT:-.workbench_data\u002Fupstreams\u002FNV-Generate-CTMR}\" && \\\npython -m pip install -r \"$NV_GENERATE_ROOT\u002Frequirements.txt\" && \\\npython skills\u002Fnv-generate-mr\u002Fscripts\u002Frun_mr.py \\\n  PATH_TO_MR_CONFIG.json \\\n  --output-dir runs\u002Fnv_generate_mr_demo \\\n  --modality mri_t1 \\\n  --random-seed 0\n```\n\nReplace `PATH_TO_MR_CONFIG.json` with the user's actual request\u002Fconfig path.\nDo not copy the fixture path from this document unless the user explicitly\nasked to run that fixture. If the user says \"the request is at\n`runs\u002F...\u002Fdefault_mri_t1.json`\", that exact path is the first positional\nargument to `scripts\u002Frun_mr.py`.\n\nSupported rflow-mr modality names are `mri`, `mri_t1`, `mri_t2`, and\n`mri_flair`, matching the upstream MR image-generation guide. The upstream\nREADME recommends `rflow-mr-brain` instead when synthesizing brain images;\nuse `skills\u002Fnv-generate-mr-brain` for that path.\nFor FOV and setup details, see `references\u002Ffov-and-downloads.md`.\n\nThe fixture argument is a small JSON override for\n`configs\u002Fconfig_maisi_diff_model_rflow-mr.json`. Pass `default` to use the\nupstream defaults plus the CLI modality and random seed. Common override keys\nare `dim`, `spacing`, `num_inference_steps`, `cfg_guidance_scale`, and\n`modality`.\n\nEach run records the staged config, model inventory, upstream command, output\ngeometry, spacing, affine, intensity range, and non-constant \u002F finite-data\nchecks. Output volumes are synthetic and are not safe as production training\ndata without independent review.\n\nNot for clinical interpretation, production deployment, autonomous diagnosis,\nor regulatory submission.\n",{"name":5,"license":15,"metadata":39,"description":7,"allowed-tools":27},{"tags":40,"author":42},[41,30,28],"MedTech","NVIDIA MedTech Team","main","7a940b8e5af16c95e5c190a524421d32fc7445a9",".agent\u002Fskills\u002Fnv-generate-mr","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":51,"prompt":61,"zipAvailable":62},[52,55,58],{"tool":53,"command":54},"npx","npx skills add headmktlucca-ui\u002Fneuroads-lp --skill nv-generate-mr",{"tool":56,"command":57},"bunx","bunx skills add headmktlucca-ui\u002Fneuroads-lp --skill nv-generate-mr",{"tool":59,"command":60},"pnpm","pnpm dlx skills add 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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",[134,139],{"categoryNo":135,"categoryName":136,"taxonomyType":137,"taxonomyTypeDesc":138},"01JBSEEDCAT000000000000008","测试与质量","SCENARIO","使用场景",{"categoryNo":140,"categoryName":141,"taxonomyType":137,"taxonomyTypeDesc":138},"01JBSEEDCAT000000000000023","Skill与智能体开发",[143,144,145,146,147,148,149,150,151,152,153],"xunit","xunit skill","testing","injection","user","use","tests","triggers","unit","unit-testing","C#",{"skillNo":155,"name":156,"nameZh":6,"description":157,"descriptionZh":6,"repositoryFullName":8,"ownerLogin":9,"ownerAvatarUrl":10,"ownerAccountNo":11,"ownerDisplayName":12,"repositoryNo":13,"starsCount":14,"licenseSpdxId":131,"licenseFileUrl":6,"sourceHtmlUrl":158,"categories":159,"keywords":162,"publishedAt":34},"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",[160,161],{"categoryNo":135,"categoryName":136,"taxonomyType":137,"taxonomyTypeDesc":138},{"categoryNo":140,"categoryName":141,"taxonomyType":137,"taxonomyTypeDesc":138},[163,164,145,165,166,167,168,169,148,170,171,172,173,174,149,150,175,176,32,177],"xcuitest","xcuitest skill","automation","test","testing framework","ios","devices","mobile","mobile-testing","ui test","local","apple","apps","native","objective",{"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":131,"licenseFileUrl":6,"sourceHtmlUrl":182,"categories":183,"keywords":188,"publishedAt":34},"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",[184,187],{"categoryNo":185,"categoryName":186,"taxonomyType":137,"taxonomyTypeDesc":138},"01JBSEEDCAT000000000000015","视觉设计",{"categoryNo":140,"categoryName":141,"taxonomyType":137,"taxonomyTypeDesc":138},[189,190,191,192,193,194,195,196,148,197,198,199,200,201,202,32,203],"builder","world","world builder","MCP","create","mcp tools","Tools","game","pokemon","manage","style","via","theme","zone","generative",{"skillNo":205,"name":206,"nameZh":6,"description":207,"descriptionZh":6,"repositoryFullName":8,"ownerLogin":9,"ownerAvatarUrl":10,"ownerAccountNo":11,"ownerDisplayName":12,"repositoryNo":13,"starsCount":14,"licenseSpdxId":131,"licenseFileUrl":6,"sourceHtmlUrl":208,"categories":209,"keywords":212,"publishedAt":34},"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",[210,211],{"categoryNo":135,"categoryName":136,"taxonomyType":137,"taxonomyTypeDesc":138},{"categoryNo":140,"categoryName":141,"taxonomyType":137,"taxonomyTypeDesc":138},[213,145,214,165,215,216,217,218,147,148,219,173,149,150,220,221],"webdriverio skill","typescript","javascript","browser","cloud","e2e","e2e-testing","URL","conf",{"skillNo":223,"name":224,"nameZh":6,"description":225,"descriptionZh":6,"repositoryFullName":8,"ownerLogin":9,"ownerAvatarUrl":10,"ownerAccountNo":11,"ownerDisplayName":12,"repositoryNo":13,"starsCount":14,"licenseSpdxId":131,"licenseFileUrl":6,"sourceHtmlUrl":226,"categories":227,"keywords":237,"publishedAt":34},"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",[228,231,232],{"categoryNo":229,"categoryName":230,"taxonomyType":137,"taxonomyTypeDesc":138},"01JBSEEDCAT000000000000009","前端开发",{"categoryNo":185,"categoryName":186,"taxonomyType":137,"taxonomyTypeDesc":138},{"categoryNo":233,"categoryName":234,"taxonomyType":235,"taxonomyTypeDesc":236},"01JBSEEDCAT000000000000032","设计师","PROFESSION","适用职业",[238,239,240,241,242,243,244,245,246,247,248,249,250,251,252,253,254,255,150,256],"design","web","guidelines","web design guidelines","web design","css","performance","components","web components","tasks","code","web performance","ui","platform design","html","responsive design","responsive","platform","implementing",{"skillNo":258,"name":259,"nameZh":6,"description":260,"descriptionZh":6,"repositoryFullName":8,"ownerLogin":9,"ownerAvatarUrl":10,"ownerAccountNo":11,"ownerDisplayName":12,"repositoryNo":13,"starsCount":14,"licenseSpdxId":131,"licenseFileUrl":6,"sourceHtmlUrl":261,"categories":262,"keywords":265,"publishedAt":34},"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",[263,264],{"categoryNo":185,"categoryName":186,"taxonomyType":137,"taxonomyTypeDesc":138},{"categoryNo":233,"categoryName":234,"taxonomyType":235,"taxonomyTypeDesc":236},[238,240,266,247,250,148,267,268,174,255,269,150,175,270,32,271,272],"watchos design guidelines","workout","human","watch","interface","platform-design","features"]