[{"data":1,"prerenderedAt":-1},["ShallowReactive",2],{"skill:01M71CGMP28YXA78RAJF0DHFW8":3,"skill-same:01M71CGMP28YXA78RAJF0DHFW8":74},{"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":6,"ownerDescription":6,"updatedAt":53,"install":54,"resources":67},"01M71CGMP28YXA78RAJF0DHFW8","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.","strmt7\u002Fai_skills_collection_benchmarked","strmt7","https:\u002F\u002Favatars.githubusercontent.com\u002Fu\u002F28989754?v=4","01M71929P914SWKQ7QJV24NBBY","Stratos","01M71929Q9VGKQ7PWYR95VSQQQ",1,"MIT","https:\u002F\u002Fgithub.com\u002Fstrmt7\u002Fai_skills_collection_benchmarked\u002Fblob\u002Fmain\u002Fincluded\u002Fskills\u002Fby-category\u002Fscience-research-data-analysis\u002Flatest-release-community\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 available resources","get","resources","recommendations","running","at","information","GPU Acceleration","CPU","processing","scientific","as","memory","computational","strategies","parallel","disk","start","training","pytorch","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","# 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",{"name":5,"license":44,"metadata":45,"description":7},"MIT license",{"skill-author":46},"K-Dense Inc.","main","5c95bcac4646c5aed06bd9aed5235cfc7985aafc","included\u002Fskills\u002Fby-category\u002Fscience-research-data-analysis\u002Flatest-release-community\u002Fget-available-resources",0,"https:\u002F\u002Fgithub.com\u002Fstrmt7\u002Fai_skills_collection_benchmarked","https:\u002F\u002Fgithub.com\u002Fstrmt7","2026-11-15 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for interacting with and testing local web applications using Playwright. Supports verifying frontend functionality, debugging UI behavior, capturing browser screenshots, and viewing browser logs.","Apache-2.0","https:\u002F\u002Fgithub.com\u002Fstrmt7\u002Fai_skills_collection_benchmarked\u002Fblob\u002Fmain\u002Fincluded\u002Fskills\u002Fby-category\u002Ftesting-qa-benchmarking\u002Fofficial-reference\u002Fwebapp-testing\u002FLICENSE.txt","https:\u002F\u002Fgithub.com\u002Fstrmt7\u002Fai_skills_collection_benchmarked\u002Fblob\u002Fmain\u002Fincluded\u002Fskills\u002Fby-category\u002Ftesting-qa-benchmarking\u002Fofficial-reference\u002Fwebapp-testing\u002FSKILL.md",[83,88],{"categoryNo":84,"categoryName":85,"taxonomyType":86,"taxonomyTypeDesc":87},"01JBSEEDCAT000000000000008","测试与质量","SCENARIO","使用场景",{"categoryNo":89,"categoryName":90,"taxonomyType":86,"taxonomyTypeDesc":87},"01JBSEEDCAT000000000000009","前端开发",[92,93,94,95,96,97,98,99,100,101,102,103,104,105,106,107,108],"webapp","testing","webapp testing","for","applications","local","frontend","debugging","toolkit","logs","browser","with","playwright","web","behavior","ui","screenshots",{"skillNo":110,"name":111,"nameZh":6,"description":112,"descriptionZh":6,"repositoryFullName":8,"ownerLogin":9,"ownerAvatarUrl":10,"ownerAccountNo":11,"ownerDisplayName":12,"repositoryNo":13,"starsCount":14,"licenseSpdxId":79,"licenseFileUrl":113,"sourceHtmlUrl":114,"categories":115,"keywords":119,"publishedAt":39},"01M71D4CQR2A2TB7GASCR5ZBG3","skill-creator","Create new skills, modify and improve existing skills, and measure skill performance. Use when users want to create a skill from scratch, edit, or optimize an existing skill, run evals to test a skill, benchmark skill performance with variance analysis, or optimize a skill's description for better triggering accuracy.","https:\u002F\u002Fgithub.com\u002Fstrmt7\u002Fai_skills_collection_benchmarked\u002Fblob\u002Fmain\u002Fincluded\u002Fskills\u002Fby-category\u002Ftesting-qa-benchmarking\u002Fofficial-reference\u002Fskill-creator-anthropics\u002FLICENSE.txt","https:\u002F\u002Fgithub.com\u002Fstrmt7\u002Fai_skills_collection_benchmarked\u002Fblob\u002Fmain\u002Fincluded\u002Fskills\u002Fby-category\u002Ftesting-qa-benchmarking\u002Fofficial-reference\u002Fskill-creator-anthropics\u002FSKILL.md",[116],{"categoryNo":117,"categoryName":118,"taxonomyType":86,"taxonomyTypeDesc":87},"01JBSEEDCAT000000000000023","Skill与智能体开发",[120,121,122,123,124,125,126,127,128,129,130,131,132,133,134,135,136,137,95,138],"skill creator","creator","performance","improve","run evals","new","when","analysis","create","optimize","variance analysis","run","edit","benchmark","a skill","existing skill","evals","modify","accuracy",{"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":147,"publishedAt":39},"01M71D488XHEZVXXSXFYP8ENMH","playwright-skill","Complete browser automation with Playwright. Auto-detects dev servers, writes reusable test scripts, and supports screenshots, responsive checks, UX validation, login flows, link checks, and arbitrary browser automation. Use when the user wants to test a website, automate browser interactions, validate web functionality, or perform browser-based testing.","https:\u002F\u002Fgithub.com\u002Fstrmt7\u002Fai_skills_collection_benchmarked\u002Fblob\u002Fmain\u002Fincluded\u002Fskills\u002Fby-category\u002Ftesting-qa-benchmarking\u002Flatest-release-browser-automation\u002Fplaywright-skill\u002FSKILL.md",[145,146],{"categoryNo":84,"categoryName":85,"taxonomyType":86,"taxonomyTypeDesc":87},{"categoryNo":117,"categoryName":118,"taxonomyType":86,"taxonomyTypeDesc":87},[104,148,149,150,93,151,152,126,153,102,154,155,156,157,158,159,160,161,162,163],"playwright skill","browser automation","flows","auto","access","Bash","website","ux","login","scripts","read","write","node","install","network","responsive",{"skillNo":165,"name":166,"nameZh":6,"description":167,"descriptionZh":6,"repositoryFullName":8,"ownerLogin":9,"ownerAvatarUrl":10,"ownerAccountNo":11,"ownerDisplayName":12,"repositoryNo":13,"starsCount":14,"licenseSpdxId":79,"licenseFileUrl":168,"sourceHtmlUrl":169,"categories":170,"keywords":172,"publishedAt":39},"01M71CY25GM939M6RES91QSG3T","web-artifacts-builder","Suite of tools for creating elaborate, multi-component claude.ai HTML artifacts using modern frontend web technologies (React, Tailwind CSS, shadcn\u002Fui). Use for complex artifacts requiring state management, routing, or shadcn\u002Fui components - not for simple single-file HTML\u002FJSX artifacts.","https:\u002F\u002Fgithub.com\u002Fstrmt7\u002Fai_skills_collection_benchmarked\u002Fblob\u002Fmain\u002Fincluded\u002Fskills\u002Fby-category\u002Fsearch-retrieval-web-automation\u002Fofficial-reference\u002Fweb-artifacts-builder\u002FLICENSE.txt","https:\u002F\u002Fgithub.com\u002Fstrmt7\u002Fai_skills_collection_benchmarked\u002Fblob\u002Fmain\u002Fincluded\u002Fskills\u002Fby-category\u002Fsearch-retrieval-web-automation\u002Fofficial-reference\u002Fweb-artifacts-builder\u002FSKILL.md",[171],{"categoryNo":89,"categoryName":90,"taxonomyType":86,"taxonomyTypeDesc":87},[173,174,175,105,176,177,178,95,179,180,98,181,182,183,184,185,186,187,188,189],"builder","artifacts builder","artifacts","web artifacts builder","single-file","css","Tailwind","ui components","single","use","file","components","react","multi","state","routing","state management",{"skillNo":191,"name":192,"nameZh":6,"description":193,"descriptionZh":6,"repositoryFullName":8,"ownerLogin":9,"ownerAvatarUrl":10,"ownerAccountNo":11,"ownerDisplayName":12,"repositoryNo":13,"starsCount":14,"licenseSpdxId":79,"licenseFileUrl":194,"sourceHtmlUrl":195,"categories":196,"keywords":206,"publishedAt":39},"01M71CXZB60QJJ1H4S3NN94WMZ","frontend-design","Create distinctive, production-grade frontend interfaces with high design quality. Use this skill when the user asks to build web components, pages, artifacts, posters, or applications (examples include websites, landing pages, dashboards, React components, HTML\u002FCSS layouts, or when styling\u002Fbeautifying any web UI). Generates creative, polished code and UI design that avoids generic AI aesthetics.","https:\u002F\u002Fgithub.com\u002Fstrmt7\u002Fai_skills_collection_benchmarked\u002Fblob\u002Fmain\u002Fincluded\u002Fskills\u002Fby-category\u002Fsearch-retrieval-web-automation\u002Fofficial-reference\u002Ffrontend-design\u002FLICENSE.txt","https:\u002F\u002Fgithub.com\u002Fstrmt7\u002Fai_skills_collection_benchmarked\u002Fblob\u002Fmain\u002Fincluded\u002Fskills\u002Fby-category\u002Fsearch-retrieval-web-automation\u002Fofficial-reference\u002Ffrontend-design\u002FSKILL.md",[197,198,201],{"categoryNo":89,"categoryName":90,"taxonomyType":86,"taxonomyTypeDesc":87},{"categoryNo":199,"categoryName":200,"taxonomyType":86,"taxonomyTypeDesc":87},"01JBSEEDCAT000000000000015","视觉设计",{"categoryNo":202,"categoryName":203,"taxonomyType":204,"taxonomyTypeDesc":205},"01JBSEEDCAT000000000000032","设计师","PROFESSION","适用职业",[98,207,208,178,209,210,211,96,212,213,182,126,175,214,184,215,185,216,217,103],"design","frontend design","ui design","generic","aesthetics","landing","pages","code","web ui","react components","examples",{"skillNo":219,"name":220,"nameZh":6,"description":221,"descriptionZh":6,"repositoryFullName":8,"ownerLogin":9,"ownerAvatarUrl":10,"ownerAccountNo":11,"ownerDisplayName":12,"repositoryNo":13,"starsCount":14,"licenseSpdxId":15,"licenseFileUrl":6,"sourceHtmlUrl":222,"categories":223,"keywords":224,"publishedAt":39},"01M71CVZ54WFBTVA7R16PFY1ZE","zarr-python","Chunked N-D arrays for cloud storage. Compressed arrays, parallel I\u002FO, S3\u002FGCS integration, NumPy\u002FDask\u002FXarray compatible, for large-scale scientific computing pipelines.","https:\u002F\u002Fgithub.com\u002Fstrmt7\u002Fai_skills_collection_benchmarked\u002Fblob\u002Fmain\u002Fincluded\u002Fskills\u002Fby-category\u002Fscience-research-data-analysis\u002Flatest-release-community\u002Fzarr-python\u002FSKILL.md",[],[225,226,227,228,229,29,230,34,231,95,232,233,234,235,236,237,238,239],"zarr python","python","zarr","arrays","Cloud Storage","storage","dask","numpy","gcs","s3","integration","scientific computing","cloud","scale","pipelines"]