get-available-resources by davila7/claude-code-templates
npx skills add https://github.com/davila7/claude-code-templates --skill get-available-resources检测可用的计算资源,并为科学计算任务生成策略性建议。此技能自动识别 CPU 能力、GPU 可用性(NVIDIA CUDA、AMD ROCm、Apple Silicon Metal)、内存限制和磁盘空间,以帮助就计算方法做出明智决策。
在任何计算密集型任务之前主动使用此技能:
示例场景:
该技能运行 scripts/detect_resources.py 以自动检测:
CPU 信息
GPU 信息
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内存信息
磁盘空间信息
操作系统信息
该技能在当前工作目录中生成一个 .claude_resources.json 文件,包含:
{
"timestamp": "2025-10-23T10:30:00",
"os": {
"system": "Darwin",
"release": "25.0.0",
"machine": "arm64"
},
"cpu": {
"physical_cores": 8,
"logical_cores": 8,
"architecture": "arm64"
},
"memory": {
"total_gb": 16.0,
"available_gb": 8.5,
"percent_used": 46.9
},
"disk": {
"total_gb": 500.0,
"available_gb": 200.0,
"percent_used": 60.0
},
"gpu": {
"nvidia_gpus": [],
"amd_gpus": [],
"apple_silicon": {
"name": "Apple M2",
"type": "Apple Silicon",
"backend": "Metal",
"unified_memory": true
},
"total_gpus": 1,
"available_backends": ["Metal"]
},
"recommendations": {
"parallel_processing": {
"strategy": "high_parallelism",
"suggested_workers": 6,
"libraries": ["joblib", "multiprocessing", "dask"]
},
"memory_strategy": {
"strategy": "moderate_memory",
"libraries": ["dask", "zarr"],
"note": "Consider chunking for datasets > 2GB"
},
"gpu_acceleration": {
"available": true,
"backends": ["Metal"],
"suggested_libraries": ["pytorch-mps", "tensorflow-metal", "jax-metal"]
},
"large_data_handling": {
"strategy": "disk_abundant",
"note": "Sufficient space for large intermediate files"
}
}
}
该技能生成上下文感知的建议:
并行处理建议:
内存策略建议:
GPU 加速建议:
大数据处理建议:
在任何计算密集型任务开始时执行检测脚本:
python scripts/detect_resources.py
可选参数:
-o, --output <path>:指定自定义输出路径(默认:.claude_resources.json)-v, --verbose:将完整的资源信息打印到标准输出运行检测后,读取生成的 .claude_resources.json 文件以指导计算决策:
# 示例:在代码中使用建议
import json
with open('.claude_resources.json', 'r') as f:
resources = json.load(f)
# 检查并行处理策略
if resources['recommendations']['parallel_processing']['strategy'] == 'high_parallelism':
n_jobs = resources['recommendations']['parallel_processing']['suggested_workers']
# 使用 joblib、Dask 或 multiprocessing,工作进程数为 n_jobs
# 检查内存策略
if resources['recommendations']['memory_strategy']['strategy'] == 'memory_constrained':
# 使用 Dask、Zarr 或 H5py 进行核外处理
import dask.array as da
# 分块加载数据
# 检查 GPU 可用性
if resources['recommendations']['gpu_acceleration']['available']:
backends = resources['recommendations']['gpu_acceleration']['backends']
# 根据可用后端使用适当的 GPU 库
使用资源信息和建议做出策略性选择:
对于数据加载:
memory_available_gb = resources['memory']['available_gb']
dataset_size_gb = 10
if dataset_size_gb > memory_available_gb * 0.5:
# 数据集相对于内存较大,使用 Dask
import dask.dataframe as dd
df = dd.read_csv('large_file.csv')
else:
# 数据集适合内存,使用 pandas
import pandas as pd
df = pd.read_csv('large_file.csv')
对于并行处理:
from joblib import Parallel, delayed
n_jobs = resources['recommendations']['parallel_processing'].get('suggested_workers', 1)
results = Parallel(n_jobs=n_jobs)(
delayed(process_function)(item) for item in data
)
对于 GPU 加速:
import torch
if 'CUDA' in resources['gpu']['available_backends']:
device = torch.device('cuda')
elif 'Metal' in resources['gpu']['available_backends']:
device = torch.device('mps')
else:
device = torch.device('cpu')
model = model.to(device)
检测脚本需要以下 Python 包:
uv pip install psutil
所有其他功能使用 Python 标准库模块(json、os、platform、subprocess、sys、pathlib)。
.claude_resources.json 文件保留在项目目录中,以记录基于资源的决策未检测到 GPU:
脚本执行失败:
uv pip install psutilchmod +x scripts/detect_resources.py内存读数不准确:
每周安装数
125
仓库
GitHub 星标数
22.6K
首次出现
Jan 21, 2026
安全审计
安装于
claude-code105
opencode98
cursor95
gemini-cli93
antigravity85
codex84
Detect 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.
Use this skill proactively before any computationally intensive task:
Example scenarios:
The skill runs scripts/detect_resources.py to automatically detect:
CPU Information
GPU Information
Memory Information
Disk Space Information
Operating System Information
The skill generates a .claude_resources.json file in the current working directory containing:
{
"timestamp": "2025-10-23T10:30:00",
"os": {
"system": "Darwin",
"release": "25.0.0",
"machine": "arm64"
},
"cpu": {
"physical_cores": 8,
"logical_cores": 8,
"architecture": "arm64"
},
"memory": {
"total_gb": 16.0,
"available_gb": 8.5,
"percent_used": 46.9
},
"disk": {
"total_gb": 500.0,
"available_gb": 200.0,
"percent_used": 60.0
},
"gpu": {
"nvidia_gpus": [],
"amd_gpus": [],
"apple_silicon": {
"name": "Apple M2",
"type": "Apple Silicon",
"backend": "Metal",
"unified_memory": true
},
"total_gpus": 1,
"available_backends": ["Metal"]
},
"recommendations": {
"parallel_processing": {
"strategy": "high_parallelism",
"suggested_workers": 6,
"libraries": ["joblib", "multiprocessing", "dask"]
},
"memory_strategy": {
"strategy": "moderate_memory",
"libraries": ["dask", "zarr"],
"note": "Consider chunking for datasets > 2GB"
},
"gpu_acceleration": {
"available": true,
"backends": ["Metal"],
"suggested_libraries": ["pytorch-mps", "tensorflow-metal", "jax-metal"]
},
"large_data_handling": {
"strategy": "disk_abundant",
"note": "Sufficient space for large intermediate files"
}
}
}
The skill generates context-aware recommendations:
Parallel Processing Recommendations:
Memory Strategy Recommendations:
GPU Acceleration Recommendations:
Large Data Handling Recommendations:
Execute the detection script at the start of any computationally intensive task:
python scripts/detect_resources.py
Optional arguments:
-o, --output <path>: Specify custom output path (default: .claude_resources.json)-v, --verbose: Print full resource information to stdoutAfter running detection, read the generated .claude_resources.json file to inform computational decisions:
# Example: Use recommendations in code
import json
with open('.claude_resources.json', 'r') as f:
resources = json.load(f)
# Check parallel processing strategy
if resources['recommendations']['parallel_processing']['strategy'] == 'high_parallelism':
n_jobs = resources['recommendations']['parallel_processing']['suggested_workers']
# Use joblib, Dask, or multiprocessing with n_jobs workers
# Check memory strategy
if resources['recommendations']['memory_strategy']['strategy'] == 'memory_constrained':
# Use Dask, Zarr, or H5py for out-of-core processing
import dask.array as da
# Load data in chunks
# Check GPU availability
if resources['recommendations']['gpu_acceleration']['available']:
backends = resources['recommendations']['gpu_acceleration']['backends']
# Use appropriate GPU library based on available backend
Use the resource information and recommendations to make strategic choices:
For data loading:
memory_available_gb = resources['memory']['available_gb']
dataset_size_gb = 10
if dataset_size_gb > memory_available_gb * 0.5:
# Dataset is large relative to memory, use Dask
import dask.dataframe as dd
df = dd.read_csv('large_file.csv')
else:
# Dataset fits in memory, use pandas
import pandas as pd
df = pd.read_csv('large_file.csv')
For parallel processing:
from joblib import Parallel, delayed
n_jobs = resources['recommendations']['parallel_processing'].get('suggested_workers', 1)
results = Parallel(n_jobs=n_jobs)(
delayed(process_function)(item) for item in data
)
For GPU acceleration:
import torch
if 'CUDA' in resources['gpu']['available_backends']:
device = torch.device('cuda')
elif 'Metal' in resources['gpu']['available_backends']:
device = torch.device('mps')
else:
device = torch.device('cpu')
model = model.to(device)
The detection script requires the following Python packages:
uv pip install psutil
All other functionality uses Python standard library modules (json, os, platform, subprocess, sys, pathlib).
.claude_resources.json file in project directories to document resource-aware decisionsGPU not detected:
Script execution fails:
uv pip install psutilchmod +x scripts/detect_resources.pyInaccurate memory readings:
Weekly Installs
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Repository
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First Seen
Jan 21, 2026
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Installed on
claude-code105
opencode98
cursor95
gemini-cli93
antigravity85
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