domains / families / science / get-available-resources

get-available-resources

combined

Detects host inventory and effective CPU, memory, disk, scheduler, container, and accelerator limits when a user asks for resource-aware planning or before a clearly resource-sensitive local workload. Produces a redacted JSON snapshot and conservative planning helpers without stress tests or assuming visible host hardware is usable. 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.

When to use

• **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
• "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
• "Process 10,000 files in parallel" → Use this skill to determine optimal worker count

When not to use

• stress tests
• benchmarks
• large allocations
• write probes
• device resets
• driver installation
• clock or power changes
• persisting fingerprints for every scientific task

Skill page

Conflict

The two copies describe the same detector but with materially different contracts. The K-Dense copy is a conservative, redacted, process-scoped inventory that forbids stress tests, hides host identifiers, treats unknowns as unknown, and separates host, cgroup, scheduler, and accelerator facts. The Vibe-Skills copy is a proactive, host-level inventory that writes a default JSON file, reports hostnames/paths/OS details, and emits strategic recommendations (worker counts, library choices, memory/disk strategies) that the K-Dense copy explicitly avoids. Because each contains steps and constraints the other lacks, crowning one would hide a real alternative.