[{"data":1,"prerenderedAt":-1},["ShallowReactive",2],{"skill:01M7CYBEATS5M182BFPD8D2MJC":3,"skill-same:01M7CYBEATS5M182BFPD8D2MJC":53},{"skillNo":4,"name":5,"nameZh":6,"description":7,"descriptionZh":6,"repositoryFullName":8,"ownerLogin":9,"ownerAvatarUrl":10,"ownerAccountNo":11,"ownerDisplayName":12,"repositoryNo":13,"starsCount":14,"licenseSpdxId":15,"licenseFileUrl":6,"sourceHtmlUrl":16,"categories":17,"keywords":18,"publishedAt":19,"linkOnly":20,"markdownContent":21,"markdownBody":22,"frontMatter":23,"refName":27,"commitSha":28,"skillDirectory":29,"forksCount":14,"repositoryHtmlUrl":30,"repositoryHomepageUrl":6,"ownerHtmlUrl":31,"ownerBlogUrl":6,"ownerDescription":6,"updatedAt":32,"install":33,"resources":46},"01M7CYBEATS5M182BFPD8D2MJC","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.","Marvin-LiJunZan\u002Fskills-mcp","Marvin-LiJunZan","https:\u002F\u002Favatars.githubusercontent.com\u002Fu\u002F125227190?v=4","01M7CX941DQ6NV1MXY88ZDKVFM","lijunzan","01M7CX9421GJQ55X6W4259Y8V6",0,"MIT","https:\u002F\u002Fgithub.com\u002FMarvin-LiJunZan\u002Fskills-mcp\u002Fblob\u002Fmain\u002Fskills\u002Fclaude-scientific-skills\u002Fscientific-skills\u002Fget-available-resources\u002FSKILL.md",[],[],"2026-11-15 14:19:17",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":24,"metadata":25,"description":7},"MIT license",{"skill-author":26},"K-Dense Inc.","main","2b5bc01df8cc5820e97283a614d9873a8ac70ea1","skills\u002Fclaude-scientific-skills\u002Fscientific-skills\u002Fget-available-resources","https:\u002F\u002Fgithub.com\u002FMarvin-LiJunZan\u002Fskills-mcp","https:\u002F\u002Fgithub.com\u002FMarvin-LiJunZan","2026-10-17 16:33:48",{"source":8,"skillName":5,"commands":34,"prompt":44,"zipAvailable":45},[35,38,41],{"tool":36,"command":37},"npx","npx skills add Marvin-LiJunZan\u002Fskills-mcp --skill get-available-resources",{"tool":39,"command":40},"bunx","bunx skills add Marvin-LiJunZan\u002Fskills-mcp --skill get-available-resources",{"tool":42,"command":43},"pnpm","pnpm dlx skills add Marvin-LiJunZan\u002Fskills-mcp --skill get-available-resources","安装技能，技能地址：https:\u002F\u002Fgithub.com\u002FMarvin-LiJunZan\u002Fskills-mcp\u002Fblob\u002Fmain\u002Fskills\u002Fclaude-scientific-skills\u002Fscientific-skills\u002Fget-available-resources\u002FSKILL.md",true,[47],{"path":48,"resourceType":49,"resourceTypeDesc":50,"sizeBytes":51,"sourceHtmlUrl":52},"scripts\u002Fdetect_resources.py","SCRIPT","脚本",14083,"https:\u002F\u002Fgithub.com\u002FMarvin-LiJunZan\u002Fskills-mcp\u002Fblob\u002Fmain\u002Fskills\u002Fclaude-scientific-skills\u002Fscientific-skills\u002Fget-available-resources\u002Fscripts\u002Fdetect_resources.py",[54,62,69,76,83,90],{"skillNo":55,"name":56,"nameZh":6,"description":57,"descriptionZh":6,"repositoryFullName":8,"ownerLogin":9,"ownerAvatarUrl":10,"ownerAccountNo":11,"ownerDisplayName":12,"repositoryNo":13,"starsCount":14,"licenseSpdxId":58,"licenseFileUrl":6,"sourceHtmlUrl":59,"categories":60,"keywords":61,"publishedAt":19},"01M7CZ8DB1NGKBK8AC6BKFHYFK","vibe-research-workflow","Guides AI-assisted research across three sub-flows, Vibe Coding, Vibe Figure, and Vibe Writing, with behavioural rules that keep the user in charge of academic judgment while delegating mechanical work to AI. Recommends the right tool (Cursor, Claude Code, Codex, Figma, Gemini) for the current stage. Use when the user asks 'how to use AI for research', 'Vibe Coding tips', 'AI-assisted writing workflow', 'which AI tool for this', or starts an AI-assisted work session.","CC-BY-4.0","https:\u002F\u002Fgithub.com\u002FMarvin-LiJunZan\u002Fskills-mcp\u002Fblob\u002Fmain\u002Fskills\u002Fsupervisor-skills\u002Fvibe-research-workflow\u002FSKILL.md",[],[],{"skillNo":63,"name":64,"nameZh":6,"description":65,"descriptionZh":6,"repositoryFullName":8,"ownerLogin":9,"ownerAvatarUrl":10,"ownerAccountNo":11,"ownerDisplayName":12,"repositoryNo":13,"starsCount":14,"licenseSpdxId":58,"licenseFileUrl":6,"sourceHtmlUrl":66,"categories":67,"keywords":68,"publishedAt":19},"01M7CZ87M3Q1RGTAGMB61SJ18Q","tech-paper-template","Structures a technical paper's full logical skeleton using a thinking-template table (research background, limitations, key idea or goal, challenges, methodology modules, contributions), positions the paper as Technique or New Problem\u002FSetting, and runs a four-point self-consistency check. Use when the user is brainstorming a paper, discussing progress with an advisor, or planning the paper before drafting. Also use for 'paper skeleton', 'paper logic chain', 'thinking template', 'paper-structure planning'.","https:\u002F\u002Fgithub.com\u002FMarvin-LiJunZan\u002Fskills-mcp\u002Fblob\u002Fmain\u002Fskills\u002Fsupervisor-skills\u002Ftech-paper-template\u002FSKILL.md",[],[],{"skillNo":70,"name":71,"nameZh":6,"description":72,"descriptionZh":6,"repositoryFullName":8,"ownerLogin":9,"ownerAvatarUrl":10,"ownerAccountNo":11,"ownerDisplayName":12,"repositoryNo":13,"starsCount":14,"licenseSpdxId":58,"licenseFileUrl":6,"sourceHtmlUrl":73,"categories":74,"keywords":75,"publishedAt":19},"01M7CZ7ZZPBQ5C6G6YPBS2C87Q","pre-submission-reviewer","Runs a pre-submission review of a technical paper across five dimensions: macro logic, writing details, English grammar, LaTeX formatting, and figure quality. Uses a reviewer-style severity taxonomy (CRITICAL \u002F MAJOR \u002F MINOR) and flags banned AI-tone vocabulary and em-dash misuse. Use when the user asks to 'review this paper', 'audit before submission', 'check the draft', 'find issues', 'proofread', or within one week of a submission deadline.","https:\u002F\u002Fgithub.com\u002FMarvin-LiJunZan\u002Fskills-mcp\u002Fblob\u002Fmain\u002Fskills\u002Fsupervisor-skills\u002Fpre-submission-reviewer\u002FSKILL.md",[],[],{"skillNo":77,"name":78,"nameZh":6,"description":79,"descriptionZh":6,"repositoryFullName":8,"ownerLogin":9,"ownerAvatarUrl":10,"ownerAccountNo":11,"ownerDisplayName":12,"repositoryNo":13,"starsCount":14,"licenseSpdxId":58,"licenseFileUrl":6,"sourceHtmlUrl":80,"categories":81,"keywords":82,"publishedAt":19},"01M7CZ7RMCTTP08V2C7WVWZM0A","intro-drafter","Drafts a 6-paragraph Introduction outline for a technical paper from a structured Flowchart: background and running example, existing limitations, problem essence and goal, key challenges, solution overview, contributions. Positions the paper as Technique or New Problem\u002FSetting and aligns contributions with challenges. Use when the user asks to 'draft the Introduction', 'outline the Introduction', 'intro logic needs clarifying', 'help structure the paper story', or before writing any Introduction prose.","https:\u002F\u002Fgithub.com\u002FMarvin-LiJunZan\u002Fskills-mcp\u002Fblob\u002Fmain\u002Fskills\u002Fsupervisor-skills\u002Fintro-drafter\u002FSKILL.md",[],[],{"skillNo":84,"name":85,"nameZh":6,"description":86,"descriptionZh":6,"repositoryFullName":8,"ownerLogin":9,"ownerAvatarUrl":10,"ownerAccountNo":11,"ownerDisplayName":12,"repositoryNo":13,"starsCount":14,"licenseSpdxId":58,"licenseFileUrl":6,"sourceHtmlUrl":87,"categories":88,"keywords":89,"publishedAt":19},"01M7CZ7K5X92VSRM6WTHX91CDA","idea-evaluator","Evaluates a preliminary research idea against a five-dimension framework (Higher, Faster, Stronger, Cheaper, Broader) plus idea-lifecycle and student-capability matching, paradigm-shift probing, and a fatal-flaws audit. Returns a reviewer-style verdict. Use when the user has a draft research idea and asks whether it is worth pursuing, asks to 'evaluate this idea', 'score this idea', 'assess feasibility', 'novelty check', 'is this a good research direction', or before committing to a paper scope.","https:\u002F\u002Fgithub.com\u002FMarvin-LiJunZan\u002Fskills-mcp\u002Fblob\u002Fmain\u002Fskills\u002Fsupervisor-skills\u002Fidea-evaluator\u002FSKILL.md",[],[],{"skillNo":91,"name":92,"nameZh":6,"description":93,"descriptionZh":6,"repositoryFullName":8,"ownerLogin":9,"ownerAvatarUrl":10,"ownerAccountNo":11,"ownerDisplayName":12,"repositoryNo":13,"starsCount":14,"licenseSpdxId":58,"licenseFileUrl":6,"sourceHtmlUrl":94,"categories":95,"keywords":96,"publishedAt":19},"01M7CZ7D29XX445SD75ZT5E8FW","figure-designer","Advises on the design of the three core figures in a technical paper: the Motivated Example (Figure 1), the Solution Overview (Methodology), and the Experimental Results figures. Recommends the right design paradigm, layout, labelling, and tool for each figure type, then runs a quality-control audit. Use when the user asks to 'design a figure', 'draw Figure 1', 'plot experiment results', 'choose the right chart type', 'which figure tool to use', or 'figure looks unprofessional'.","https:\u002F\u002Fgithub.com\u002FMarvin-LiJunZan\u002Fskills-mcp\u002Fblob\u002Fmain\u002Fskills\u002Fsupervisor-skills\u002Ffigure-designer\u002FSKILL.md",[],[]]