[{"data":1,"prerenderedAt":-1},["ShallowReactive",2],{"skill:01M71J6DSYFCPZS7Z0N0FAYXCE":3,"skill-same:01M71J6DSYFCPZS7Z0N0FAYXCE":82},{"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":39,"linkOnly":40,"markdownContent":41,"markdownBody":42,"frontMatter":43,"refName":47,"commitSha":48,"skillDirectory":49,"forksCount":50,"repositoryHtmlUrl":51,"repositoryHomepageUrl":6,"ownerHtmlUrl":52,"ownerBlogUrl":53,"ownerDescription":54,"updatedAt":55,"install":56,"resources":69},"01M71J6DSYFCPZS7Z0N0FAYXCE","get-available-resources",null,"This skill should be used at the start of any computationally intensive scientific task to detect and report available system resources (CPU cores, GPUs, memory, disk space). It creates a JSON file with resource information and strategic recommendations that inform computational approach decisions such as whether to use parallel processing (joblib, multiprocessing), out-of-core computing (Dask, Zarr), GPU acceleration (PyTorch, JAX), or memory-efficient strategies. Use this skill before running analyses, training models, processing large datasets, or any task where resource constraints matter.","iDorgham\u002FWorkspace-Factory","iDorgham","https:\u002F\u002Favatars.githubusercontent.com\u002Fu\u002F136521493?v=4","01M6CNGBDAHE4ARW3YJEF4X2FD","Dorgham","01M71E8H3CXZ7DJX59MTM9JEB2",1,"MIT","https:\u002F\u002Fgithub.com\u002FiDorgham\u002FWorkspace-Factory\u002Fblob\u002Fmaster\u002Ffactory\u002Flibrary\u002Fskills\u002Fgithub_imports\u002Fget-available-resources\u002FSKILL.md",[],[19,20,21,22,23,24,25,26,27,28,29,30,31,32,33,34,35,36,37,38],"get","get available resources","resources","as","parallel","running","start","at","recommendations","CPU","scientific","strategies","disk","computational","information","GPU Acceleration","pytorch","training","memory","processing","2026-11-10 10:12:30",false,"---\nname: get-available-resources\ndescription: This skill should be used at the start of any computationally intensive scientific task to detect and report available system resources (CPU cores, GPUs, memory, disk space). It creates a JSON file with resource information and strategic recommendations that inform computational approach decisions such as whether to use parallel processing (joblib, multiprocessing), out-of-core computing (Dask, Zarr), GPU acceleration (PyTorch, JAX), or memory-efficient strategies. Use this skill before running analyses, training models, processing large datasets, or any task where resource constraints matter.\nlicense: MIT license\nmetadata:\n    skill-author: K-Dense Inc.\n---\n\n# Get Available Resources\n\n## Overview\n\nDetect available computational resources and generate strategic recommendations for scientific computing tasks. This skill automatically identifies CPU capabilities, GPU availability (NVIDIA CUDA, AMD ROCm, Apple Silicon Metal), memory constraints, and disk space to help make informed decisions about computational approaches.\n\n## When to Use This Skill\n\nUse this skill proactively before any computationally intensive task:\n\n- **Before data analysis**: Determine if datasets can be loaded into memory or require out-of-core processing\n- **Before model training**: Check if GPU acceleration is available and which backend to use\n- **Before parallel processing**: Identify optimal number of workers for joblib, multiprocessing, or Dask\n- **Before large file operations**: Verify sufficient disk space and appropriate storage strategies\n- **At project initialization**: Understand baseline capabilities for making architectural decisions\n\n**Example scenarios:**\n- \"Help me analyze this 50GB genomics dataset\" → Use this skill first to determine if Dask\u002FZarr are needed\n- \"Train a neural network on this data\" → Use this skill to detect available GPUs and backends\n- \"Process 10,000 files in parallel\" → Use this skill to determine optimal worker count\n- \"Run a computationally intensive simulation\" → Use this skill to understand resource constraints\n\n## How This Skill Works\n\n### Resource Detection\n\nThe skill runs `scripts\u002Fdetect_resources.py` to automatically detect:\n\n1. **CPU Information**\n   - Physical and logical core counts\n   - Processor architecture and model\n   - CPU frequency information\n\n2. **GPU Information**\n   - NVIDIA GPUs: Detects via nvidia-smi, reports VRAM, driver version, compute capability\n   - AMD GPUs: Detects via rocm-smi\n   - Apple Silicon: Detects M1\u002FM2\u002FM3\u002FM4 chips with Metal support and unified memory\n\n3. **Memory Information**\n   - Total and available RAM\n   - Current memory usage percentage\n   - Swap space availability\n\n4. **Disk Space Information**\n   - Total and available disk space for working directory\n   - Current usage percentage\n\n5. **Operating System Information**\n   - OS type (macOS, Linux, Windows)\n   - OS version and release\n   - Python version\n\n### Output Format\n\nThe skill generates a `.claude_resources.json` file in the current working directory containing:\n\n```json\n{\n  \"timestamp\": \"2025-10-23T10:30:00\",\n  \"os\": {\n    \"system\": \"Darwin\",\n    \"release\": \"25.0.0\",\n    \"machine\": \"arm64\"\n  },\n  \"cpu\": {\n    \"physical_cores\": 8,\n    \"logical_cores\": 8,\n    \"architecture\": \"arm64\"\n  },\n  \"memory\": {\n    \"total_gb\": 16.0,\n    \"available_gb\": 8.5,\n    \"percent_used\": 46.9\n  },\n  \"disk\": {\n    \"total_gb\": 500.0,\n    \"available_gb\": 200.0,\n    \"percent_used\": 60.0\n  },\n  \"gpu\": {\n    \"nvidia_gpus\": [],\n    \"amd_gpus\": [],\n    \"apple_silicon\": {\n      \"name\": \"Apple M2\",\n      \"type\": \"Apple Silicon\",\n      \"backend\": \"Metal\",\n      \"unified_memory\": true\n    },\n    \"total_gpus\": 1,\n    \"available_backends\": [\"Metal\"]\n  },\n  \"recommendations\": {\n    \"parallel_processing\": {\n      \"strategy\": \"high_parallelism\",\n      \"suggested_workers\": 6,\n      \"libraries\": [\"joblib\", \"multiprocessing\", \"dask\"]\n    },\n    \"memory_strategy\": {\n      \"strategy\": \"moderate_memory\",\n      \"libraries\": [\"dask\", \"zarr\"],\n      \"note\": \"Consider chunking for datasets > 2GB\"\n    },\n    \"gpu_acceleration\": {\n      \"available\": true,\n      \"backends\": [\"Metal\"],\n      \"suggested_libraries\": [\"pytorch-mps\", \"tensorflow-metal\", \"jax-metal\"]\n    },\n    \"large_data_handling\": {\n      \"strategy\": \"disk_abundant\",\n      \"note\": \"Sufficient space for large intermediate files\"\n    }\n  }\n}\n```\n\n### Strategic Recommendations\n\nThe skill generates context-aware recommendations:\n\n**Parallel Processing Recommendations:**\n- **High parallelism (8+ cores)**: Use Dask, joblib, or multiprocessing with workers = cores - 2\n- **Moderate parallelism (4-7 cores)**: Use joblib or multiprocessing with workers = cores - 1\n- **Sequential (\u003C 4 cores)**: Prefer sequential processing to avoid overhead\n\n**Memory Strategy Recommendations:**\n- **Memory constrained (\u003C 4GB available)**: Use Zarr, Dask, or H5py for out-of-core processing\n- **Moderate memory (4-16GB available)**: Use Dask\u002FZarr for datasets > 2GB\n- **Memory abundant (> 16GB available)**: Can load most datasets into memory directly\n\n**GPU Acceleration Recommendations:**\n- **NVIDIA GPUs detected**: Use PyTorch, TensorFlow, JAX, CuPy, or RAPIDS\n- **AMD GPUs detected**: Use PyTorch-ROCm or TensorFlow-ROCm\n- **Apple Silicon detected**: Use PyTorch with MPS backend, TensorFlow-Metal, or JAX-Metal\n- **No GPU detected**: Use CPU-optimized libraries\n\n**Large Data Handling Recommendations:**\n- **Disk constrained (\u003C 10GB)**: Use streaming or compression strategies\n- **Moderate disk (10-100GB)**: Use Zarr, H5py, or Parquet formats\n- **Disk abundant (> 100GB)**: Can create large intermediate files freely\n\n## Usage Instructions\n\n### Step 1: Run Resource Detection\n\nExecute the detection script at the start of any computationally intensive task:\n\n```bash\npython scripts\u002Fdetect_resources.py\n```\n\nOptional arguments:\n- `-o, --output \u003Cpath>`: Specify custom output path (default: `.claude_resources.json`)\n- `-v, --verbose`: Print full resource information to stdout\n\n### Step 2: Read and Apply Recommendations\n\nAfter running detection, read the generated `.claude_resources.json` file to inform computational decisions:\n\n```python\n# Example: Use recommendations in code\nimport json\n\nwith open('.claude_resources.json', 'r') as f:\n    resources = json.load(f)\n\n# Check parallel processing strategy\nif resources['recommendations']['parallel_processing']['strategy'] == 'high_parallelism':\n    n_jobs = resources['recommendations']['parallel_processing']['suggested_workers']\n    # Use joblib, Dask, or multiprocessing with n_jobs workers\n\n# Check memory strategy\nif resources['recommendations']['memory_strategy']['strategy'] == 'memory_constrained':\n    # Use Dask, Zarr, or H5py for out-of-core processing\n    import dask.array as da\n    # Load data in chunks\n\n# Check GPU availability\nif resources['recommendations']['gpu_acceleration']['available']:\n    backends = resources['recommendations']['gpu_acceleration']['backends']\n    # Use appropriate GPU library based on available backend\n```\n\n### Step 3: Make Informed Decisions\n\nUse the resource information and recommendations to make strategic choices:\n\n**For data loading:**\n```python\nmemory_available_gb = resources['memory']['available_gb']\ndataset_size_gb = 10\n\nif dataset_size_gb > memory_available_gb * 0.5:\n    # Dataset is large relative to memory, use Dask\n    import dask.dataframe as dd\n    df = dd.read_csv('large_file.csv')\nelse:\n    # Dataset fits in memory, use pandas\n    import pandas as pd\n    df = pd.read_csv('large_file.csv')\n```\n\n**For parallel processing:**\n```python\nfrom joblib import Parallel, delayed\n\nn_jobs = resources['recommendations']['parallel_processing'].get('suggested_workers', 1)\n\nresults = Parallel(n_jobs=n_jobs)(\n    delayed(process_function)(item) for item in data\n)\n```\n\n**For GPU acceleration:**\n```python\nimport torch\n\nif 'CUDA' in resources['gpu']['available_backends']:\n    device = torch.device('cuda')\nelif 'Metal' in resources['gpu']['available_backends']:\n    device = torch.device('mps')\nelse:\n    device = torch.device('cpu')\n\nmodel = model.to(device)\n```\n\n## Dependencies\n\nThe detection script requires the following Python packages:\n\n```bash\nuv pip install psutil\n```\n\nAll other functionality uses Python standard library modules (json, os, platform, subprocess, sys, pathlib).\n\n## Platform Support\n\n- **macOS**: Full support including Apple Silicon (M1\u002FM2\u002FM3\u002FM4) GPU detection\n- **Linux**: Full support including NVIDIA (nvidia-smi) and AMD (rocm-smi) GPU detection\n- **Windows**: Full support including NVIDIA GPU detection\n\n## Best Practices\n\n1. **Run early**: Execute resource detection at the start of projects or before major computational tasks\n2. **Re-run periodically**: System resources change over time (memory usage, disk space)\n3. **Check before scaling**: Verify resources before scaling up parallel workers or data sizes\n4. **Document decisions**: Keep the `.claude_resources.json` file in project directories to document resource-aware decisions\n5. **Use with versioning**: Different machines have different capabilities; resource files help maintain portability\n\n## Troubleshooting\n\n**GPU not detected:**\n- Ensure GPU drivers are installed (nvidia-smi, rocm-smi, or system_profiler for Apple Silicon)\n- Check that GPU utilities are in system PATH\n- Verify GPU is not in use by other processes\n\n**Script execution fails:**\n- Ensure psutil is installed: `uv pip install psutil`\n- Check Python version compatibility (Python 3.6+)\n- Verify script has execute permissions: `chmod +x scripts\u002Fdetect_resources.py`\n\n**Inaccurate memory readings:**\n- Memory readings are snapshots; actual available memory changes constantly\n- Close other applications before detection for accurate \"available\" memory\n- Consider running detection multiple times and averaging results\n\n","# Get Available Resources\n\n## Overview\n\nDetect available computational resources and generate strategic recommendations for scientific computing tasks. This skill automatically identifies CPU capabilities, GPU availability (NVIDIA CUDA, AMD ROCm, Apple Silicon Metal), memory constraints, and disk space to help make informed decisions about computational approaches.\n\n## When to Use This Skill\n\nUse this skill proactively before any computationally intensive task:\n\n- **Before data analysis**: Determine if datasets can be loaded into memory or require out-of-core processing\n- **Before model training**: Check if GPU acceleration is available and which backend to use\n- **Before parallel processing**: Identify optimal number of workers for joblib, multiprocessing, or Dask\n- **Before large file operations**: Verify sufficient disk space and appropriate storage strategies\n- **At project initialization**: Understand baseline capabilities for making architectural decisions\n\n**Example scenarios:**\n- \"Help me analyze this 50GB genomics dataset\" → Use this skill first to determine if Dask\u002FZarr are needed\n- \"Train a neural network on this data\" → Use this skill to detect available GPUs and backends\n- \"Process 10,000 files in parallel\" → Use this skill to determine optimal worker count\n- \"Run a computationally intensive simulation\" → Use this skill to understand resource constraints\n\n## How This Skill Works\n\n### Resource Detection\n\nThe skill runs `scripts\u002Fdetect_resources.py` to automatically detect:\n\n1. **CPU Information**\n   - Physical and logical core counts\n   - Processor architecture and model\n   - CPU frequency information\n\n2. **GPU Information**\n   - NVIDIA GPUs: Detects via nvidia-smi, reports VRAM, driver version, compute capability\n   - AMD GPUs: Detects via rocm-smi\n   - Apple Silicon: Detects M1\u002FM2\u002FM3\u002FM4 chips with Metal support and unified memory\n\n3. **Memory Information**\n   - Total and available RAM\n   - Current memory usage percentage\n   - Swap space availability\n\n4. **Disk Space Information**\n   - Total and available disk space for working directory\n   - Current usage percentage\n\n5. **Operating System Information**\n   - OS type (macOS, Linux, Windows)\n   - OS version and release\n   - Python version\n\n### Output Format\n\nThe skill generates a `.claude_resources.json` file in the current working directory containing:\n\n```json\n{\n  \"timestamp\": \"2025-10-23T10:30:00\",\n  \"os\": {\n    \"system\": \"Darwin\",\n    \"release\": \"25.0.0\",\n    \"machine\": \"arm64\"\n  },\n  \"cpu\": {\n    \"physical_cores\": 8,\n    \"logical_cores\": 8,\n    \"architecture\": \"arm64\"\n  },\n  \"memory\": {\n    \"total_gb\": 16.0,\n    \"available_gb\": 8.5,\n    \"percent_used\": 46.9\n  },\n  \"disk\": {\n    \"total_gb\": 500.0,\n    \"available_gb\": 200.0,\n    \"percent_used\": 60.0\n  },\n  \"gpu\": {\n    \"nvidia_gpus\": [],\n    \"amd_gpus\": [],\n    \"apple_silicon\": {\n      \"name\": \"Apple M2\",\n      \"type\": \"Apple Silicon\",\n      \"backend\": \"Metal\",\n      \"unified_memory\": true\n    },\n    \"total_gpus\": 1,\n    \"available_backends\": [\"Metal\"]\n  },\n  \"recommendations\": {\n    \"parallel_processing\": {\n      \"strategy\": \"high_parallelism\",\n      \"suggested_workers\": 6,\n      \"libraries\": [\"joblib\", \"multiprocessing\", \"dask\"]\n    },\n    \"memory_strategy\": {\n      \"strategy\": \"moderate_memory\",\n      \"libraries\": [\"dask\", \"zarr\"],\n      \"note\": \"Consider chunking for datasets > 2GB\"\n    },\n    \"gpu_acceleration\": {\n      \"available\": true,\n      \"backends\": [\"Metal\"],\n      \"suggested_libraries\": [\"pytorch-mps\", \"tensorflow-metal\", \"jax-metal\"]\n    },\n    \"large_data_handling\": {\n      \"strategy\": \"disk_abundant\",\n      \"note\": \"Sufficient space for large intermediate files\"\n    }\n  }\n}\n```\n\n### Strategic Recommendations\n\nThe skill generates context-aware recommendations:\n\n**Parallel Processing Recommendations:**\n- **High parallelism (8+ cores)**: Use Dask, joblib, or multiprocessing with workers = cores - 2\n- **Moderate parallelism (4-7 cores)**: Use joblib or multiprocessing with workers = cores - 1\n- **Sequential (\u003C 4 cores)**: Prefer sequential processing to avoid overhead\n\n**Memory Strategy Recommendations:**\n- **Memory constrained (\u003C 4GB available)**: Use Zarr, Dask, or H5py for out-of-core processing\n- **Moderate memory (4-16GB available)**: Use Dask\u002FZarr for datasets > 2GB\n- **Memory abundant (> 16GB available)**: Can load most datasets into memory directly\n\n**GPU Acceleration Recommendations:**\n- **NVIDIA GPUs detected**: Use PyTorch, TensorFlow, JAX, CuPy, or RAPIDS\n- **AMD GPUs detected**: Use PyTorch-ROCm or TensorFlow-ROCm\n- **Apple Silicon detected**: Use PyTorch with MPS backend, TensorFlow-Metal, or JAX-Metal\n- **No GPU detected**: Use CPU-optimized libraries\n\n**Large Data Handling Recommendations:**\n- **Disk constrained (\u003C 10GB)**: Use streaming or compression strategies\n- **Moderate disk (10-100GB)**: Use Zarr, H5py, or Parquet formats\n- **Disk abundant (> 100GB)**: Can create large intermediate files freely\n\n## Usage Instructions\n\n### Step 1: Run Resource Detection\n\nExecute the detection script at the start of any computationally intensive task:\n\n```bash\npython scripts\u002Fdetect_resources.py\n```\n\nOptional arguments:\n- `-o, --output \u003Cpath>`: Specify custom output path (default: `.claude_resources.json`)\n- `-v, --verbose`: Print full resource information to stdout\n\n### Step 2: Read and Apply Recommendations\n\nAfter running detection, read the generated `.claude_resources.json` file to inform computational decisions:\n\n```python\n# Example: Use recommendations in code\nimport json\n\nwith open('.claude_resources.json', 'r') as f:\n    resources = json.load(f)\n\n# Check parallel processing strategy\nif resources['recommendations']['parallel_processing']['strategy'] == 'high_parallelism':\n    n_jobs = resources['recommendations']['parallel_processing']['suggested_workers']\n    # Use joblib, Dask, or multiprocessing with n_jobs workers\n\n# Check memory strategy\nif resources['recommendations']['memory_strategy']['strategy'] == 'memory_constrained':\n    # Use Dask, Zarr, or H5py for out-of-core processing\n    import dask.array as da\n    # Load data in chunks\n\n# Check GPU availability\nif resources['recommendations']['gpu_acceleration']['available']:\n    backends = resources['recommendations']['gpu_acceleration']['backends']\n    # Use appropriate GPU library based on available backend\n```\n\n### Step 3: Make Informed Decisions\n\nUse the resource information and recommendations to make strategic choices:\n\n**For data loading:**\n```python\nmemory_available_gb = resources['memory']['available_gb']\ndataset_size_gb = 10\n\nif dataset_size_gb > memory_available_gb * 0.5:\n    # Dataset is large relative to memory, use Dask\n    import dask.dataframe as dd\n    df = dd.read_csv('large_file.csv')\nelse:\n    # Dataset fits in memory, use pandas\n    import pandas as pd\n    df = pd.read_csv('large_file.csv')\n```\n\n**For parallel processing:**\n```python\nfrom joblib import Parallel, delayed\n\nn_jobs = resources['recommendations']['parallel_processing'].get('suggested_workers', 1)\n\nresults = Parallel(n_jobs=n_jobs)(\n    delayed(process_function)(item) for item in data\n)\n```\n\n**For GPU acceleration:**\n```python\nimport torch\n\nif 'CUDA' in resources['gpu']['available_backends']:\n    device = torch.device('cuda')\nelif 'Metal' in resources['gpu']['available_backends']:\n    device = torch.device('mps')\nelse:\n    device = torch.device('cpu')\n\nmodel = model.to(device)\n```\n\n## Dependencies\n\nThe detection script requires the following Python packages:\n\n```bash\nuv pip install psutil\n```\n\nAll other functionality uses Python standard library modules (json, os, platform, subprocess, sys, pathlib).\n\n## Platform Support\n\n- **macOS**: Full support including Apple Silicon (M1\u002FM2\u002FM3\u002FM4) GPU detection\n- **Linux**: Full support including NVIDIA (nvidia-smi) and AMD (rocm-smi) GPU detection\n- **Windows**: Full support including NVIDIA GPU detection\n\n## Best Practices\n\n1. **Run early**: Execute resource detection at the start of projects or before major computational tasks\n2. **Re-run periodically**: System resources change over time (memory usage, disk space)\n3. **Check before scaling**: Verify resources before scaling up parallel workers or data sizes\n4. **Document decisions**: Keep the `.claude_resources.json` file in project directories to document resource-aware decisions\n5. **Use with versioning**: Different machines have different capabilities; resource files help maintain portability\n\n## Troubleshooting\n\n**GPU not detected:**\n- Ensure GPU drivers are installed (nvidia-smi, rocm-smi, or system_profiler for Apple Silicon)\n- Check that GPU utilities are in system PATH\n- Verify GPU is not in use by other processes\n\n**Script execution fails:**\n- Ensure psutil is installed: `uv pip install psutil`\n- Check Python version compatibility (Python 3.6+)\n- Verify script has execute permissions: `chmod +x scripts\u002Fdetect_resources.py`\n\n**Inaccurate memory readings:**\n- Memory readings are snapshots; actual available memory changes constantly\n- Close other applications before detection for accurate \"available\" memory\n- Consider running detection multiple times and averaging results\n\n",{"name":5,"license":44,"metadata":45,"description":7},"MIT license",{"skill-author":46},"K-Dense Inc.","master","c2e410e11df4d80d885e0ecf0583ef3c926df736","factory\u002Flibrary\u002Fskills\u002Fgithub_imports\u002Fget-available-resources",0,"https:\u002F\u002Fgithub.com\u002FiDorgham\u002FWorkspace-Factory","https:\u002F\u002Fgithub.com\u002FiDorgham","Dorgham.me","hey I'm Dorgham \r\nI code, I create, and AI codes back. \r\nMaybe we can give AI the chance!! now we begin. 🚀🤖\r\n... . . . git commit -m \"Hello, Future\" ","2026-05-04 18:39:09",{"source":8,"skillName":5,"commands":57,"prompt":67,"zipAvailable":68},[58,61,64],{"tool":59,"command":60},"npx","npx skills add iDorgham\u002FWorkspace-Factory --skill get-available-resources",{"tool":62,"command":63},"bunx","bunx skills add iDorgham\u002FWorkspace-Factory --skill get-available-resources",{"tool":65,"command":66},"pnpm","pnpm dlx skills add iDorgham\u002FWorkspace-Factory --skill 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for implementing smooth, native-feeling animations using React's View Transition API (`\u003CViewTransition>` component, `addTransitionType`, and CSS view transition pseudo-elements). Use this skill whenever the user wants to add page transitions, animate route changes, create shared element animations, animate enter\u002Fexit of components, animate list reorder, implement directional (forward\u002Fback) navigation animations, or integrate view transitions in Next.js. Also use when the user mentions view transitions, `startViewTransition`, `ViewTransition`, transition types, or asks about animating between UI states in React without third-party animation libraries.","https:\u002F\u002Fgithub.com\u002FiDorgham\u002FWorkspace-Factory\u002Fblob\u002Fmaster\u002Ffactory\u002Flibrary\u002Fskills\u002Fofficial_vercel_labs_react_view_transitions\u002Fskill.md",[89],{"categoryNo":90,"categoryName":91,"taxonomyType":92,"taxonomyTypeDesc":93},"01JBSEEDCAT000000000000009","前端开发","SCENARIO","使用场景",[95,96,97,98,99,100,101,102,103,104,105,106,107,108,109,110,111,112,113,114],"react","vercel react view transitions","view","react view transitions","vercel","transitions","shared","back","Animation","css","types","states","element","implementing","when","components","api","route","create","libraries","2026-11-10 13:02:02",{"skillNo":117,"name":118,"nameZh":6,"description":119,"descriptionZh":6,"repositoryFullName":8,"ownerLogin":9,"ownerAvatarUrl":10,"ownerAccountNo":11,"ownerDisplayName":12,"repositoryNo":13,"starsCount":14,"licenseSpdxId":15,"licenseFileUrl":6,"sourceHtmlUrl":120,"categories":121,"keywords":123,"publishedAt":115},"01M71P8TDGHZ289TSAC5VTG2EW","vercel-react-native-skills","React Native and Expo best practices for building performant mobile apps. Use when building React Native components, optimizing list performance, implementing animations, or working with native modules. Triggers on tasks involving React Native, Expo, mobile performance, or native platform APIs.","https:\u002F\u002Fgithub.com\u002FiDorgham\u002FWorkspace-Factory\u002Fblob\u002Fmaster\u002Ffactory\u002Flibrary\u002Fskills\u002Fofficial_vercel_labs_react_native_skills\u002Fskill.md",[122],{"categoryNo":90,"categoryName":91,"taxonomyType":92,"taxonomyTypeDesc":93},[95,124,125,99,126,127,128,129,108,130,109,131,110,132,133,134,135,136,137,138],"react native","native","vercel react native skills","react native skills","modules","performance","apps","tasks","mobile","expo","list","mobile performance","platform","animations","for",{"skillNo":140,"name":141,"nameZh":6,"description":142,"descriptionZh":6,"repositoryFullName":8,"ownerLogin":9,"ownerAvatarUrl":10,"ownerAccountNo":11,"ownerDisplayName":12,"repositoryNo":13,"starsCount":14,"licenseSpdxId":15,"licenseFileUrl":6,"sourceHtmlUrl":143,"categories":144,"keywords":146,"publishedAt":115},"01M71P8QK4JF5BYRGYDDGPZQG9","vercel-react-best-practices","React and Next.js performance optimization guidelines from Vercel Engineering. This skill should be used when writing, reviewing, or refactoring React\u002FNext.js code to ensure optimal performance patterns. Triggers on tasks involving React components, Next.js pages, data fetching, bundle optimization, or performance improvements.","https:\u002F\u002Fgithub.com\u002FiDorgham\u002FWorkspace-Factory\u002Fblob\u002Fmaster\u002Ffactory\u002Flibrary\u002Fskills\u002Fofficial_vercel_labs_react_best_practices\u002Fskill.md",[145],{"categoryNo":90,"categoryName":91,"taxonomyType":92,"taxonomyTypeDesc":93},[95,147,148,99,149,129,150,109,151,131,110,152,153,154,155,156,157,158,159,160],"react best practices","vercel react best practices","best practices","writing","code","data fetching","react components","next","refactoring","data","patterns","Optimization","guidelines","triggers",{"skillNo":162,"name":163,"nameZh":6,"description":164,"descriptionZh":6,"repositoryFullName":8,"ownerLogin":9,"ownerAvatarUrl":10,"ownerAccountNo":11,"ownerDisplayName":12,"repositoryNo":13,"starsCount":14,"licenseSpdxId":15,"licenseFileUrl":6,"sourceHtmlUrl":165,"categories":166,"keywords":168,"publishedAt":115},"01M71P8HVEA70K5N6ZVJCG2ED3","vercel-composition-patterns","React composition patterns that scale. Use when refactoring components with boolean prop proliferation, building flexible component libraries, or designing reusable APIs. Triggers on tasks involving compound components, render props, context providers, or component architecture. Includes React 19 API changes.","https:\u002F\u002Fgithub.com\u002FiDorgham\u002FWorkspace-Factory\u002Fblob\u002Fmaster\u002Ffactory\u002Flibrary\u002Fskills\u002Fofficial_vercel_labs_composition_patterns\u002Fskill.md",[167],{"categoryNo":90,"categoryName":91,"taxonomyType":92,"taxonomyTypeDesc":93},[169,99,170,157,171,172,173,174,160,175,176,177,109,178,131,110,179,180,111,95],"Composition","composition patterns","vercel composition patterns","react composition patterns","react 19","render","boolean","component architecture","use","reusable","architecture","react composition",{"skillNo":182,"name":183,"nameZh":6,"description":184,"descriptionZh":6,"repositoryFullName":8,"ownerLogin":9,"ownerAvatarUrl":10,"ownerAccountNo":11,"ownerDisplayName":12,"repositoryNo":13,"starsCount":14,"licenseSpdxId":15,"licenseFileUrl":6,"sourceHtmlUrl":185,"categories":186,"keywords":190,"publishedAt":115},"01M71P60XXKKP222ZFJRG7R373","supabase-postgres-best-practices","Postgres performance optimization and best practices from Supabase. Use this skill when writing, reviewing, or optimizing Postgres queries, schema designs, or database configurations.","https:\u002F\u002Fgithub.com\u002FiDorgham\u002FWorkspace-Factory\u002Fblob\u002Fmaster\u002Ffactory\u002Flibrary\u002Fskills\u002Fofficial_supabase_supabase_postgres_best_practices\u002Fskill.md",[187],{"categoryNo":188,"categoryName":189,"taxonomyType":92,"taxonomyTypeDesc":93},"01JBSEEDCAT000000000000010","后端与接口",[191,192,193,194,149,195,196,197,138,158,129,198,199,150,200,177,201,202,203,109],"postgres best practices","supabase","postgres","supabase postgres best practices","categories","postgres performance","advanced","correct","schema","impact","Rules","critical","database",{"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":15,"licenseFileUrl":6,"sourceHtmlUrl":208,"categories":209,"keywords":211,"publishedAt":227},"01M71P2ZCEJD0PKC458YTP1FJW","portable-text-serialization","Render and serialize Portable Text to React, Svelte, Vue, Astro, HTML, Markdown, and plain text. Use when implementing Portable Text rendering in any frontend framework, building custom serializers for non-standard block types, converting Portable Text to HTML strings server-side, converting Portable Text to Markdown, extracting plain text from Portable Text, or troubleshooting rendering issues with marks, blocks, lists, or custom types.","https:\u002F\u002Fgithub.com\u002FiDorgham\u002FWorkspace-Factory\u002Fblob\u002Fmaster\u002Ffactory\u002Flibrary\u002Fskills\u002Fofficial_sanity_io_portable_text_serialization\u002Fskill.md",[210],{"categoryNo":90,"categoryName":91,"taxonomyType":92,"taxonomyTypeDesc":93},[212,213,214,215,216,105,217,174,218,108,219,220,109,95,221,222,223,224,225,226],"text","portable text serialization","portable","serialization","extracting","strings","issues","frontend","svelte","sanity","astro","server","markdown","rendering","vue","2026-11-10 10:56:23"]