get-available-resources
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.
npx skills add LeonChaoX/qinyan-academic-skills --skill get-available-resources --agent claude-code
Same command for any agent — swap --agent for codex, cursor, copilot.
Weekly change comes from our own snapshots, not the repository page — it measures attention, not adoption.
# Get Available Resources ## Overview 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. ## When to Use This Skill Use this skill proactively before any computationally intensive task: - **Before data analysis**: Determine if datasets can be loaded into memory or require out-of-core processing - **Before model training**: Check if GPU acceleration is available and which backend to use - **Before parallel processing**: Identify optimal number of workers for joblib, multiprocessing, or Dask - **Before large file operations**: Verify sufficient disk space and appropriate storage strategies - **At project initialization**: Understand baseline capabilities for making architectural decisions **Example scenarios:** - "Help me analyze this 50GB genomics dataset" → Use this skill first to determine if Dask/Zarr are needed - "Train a neural network on this data" → Use this skill to detect available GPUs and backends - "
- Overview
- When to Use This Skill
- How This Skill Works
- Resource Detection
- Output Format
- Strategic Recommendations
- Usage Instructions
- Step 1: Run Resource Detection
- Step 2: Read and Apply Recommendations
- Step 3: Make Informed Decisions
- Dependencies
- Platform Support
- Best Practices
- Troubleshooting
python scripts/detect_resources.py uv pip install psutil
What does the get-available-resources skill do?
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.
How do I install it?
Run `npx skills add LeonChaoX/qinyan-academic-skills --skill get-available-resources --agent claude-code` — it drops the skill into your project so the agent can pick it up. Swap the --agent value for codex, cursor or copilot if you use one of those.
Where does this skill come from?
From LeonChaoX/qinyan-academic-skills, a repository with 759 stars. We read it straight from the repository tree rather than a submitted listing, so what you see here is what is actually published.
Is a popular skill a good skill?
Not necessarily. Stars measure attention, not adoption — a repository can trend for a week and be abandoned. That is why we show the weekly change from our own snapshots next to the total, instead of a single flattering number.