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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.

74

2.84x
Quality

63%

Does it follow best practices?

Impact

91%

2.84x

Average score across 3 eval scenarios

SecuritybySnyk

Passed

No known issues

Optimize this skill with Tessl

npx tessl skill review --optimize ./scientific-skills/get-available-resources/SKILL.md
SKILL.md
Quality
Evals
Security

Evaluation results

94%

59%

Spectroscopy Batch Processor

Resource-aware parallel processing

Criteria
Without context
With context

psutil via uv

0%

100%

Runs detect_resources.py

0%

100%

.claude_resources.json created

100%

100%

Reads suggested_workers

0%

90%

Parallel library used

100%

70%

Detection before processing

66%

100%

n_jobs from resources

50%

90%

Handles sequential fallback

0%

100%

83%

62%

Climate Station Data Aggregator

Memory-adaptive data loading strategy

Criteria
Without context
With context

Runs detect_resources.py

0%

33%

.claude_resources.json used

0%

100%

Memory threshold comparison

0%

100%

Dask for memory-constrained

0%

100%

Pandas for memory-abundant

80%

100%

Disk strategy applied

20%

53%

Dynamic branching

66%

100%

96%

54%

Portable Neural Network Training Harness

GPU-aware training setup

Criteria
Without context
With context

Runs detect_resources.py

0%

100%

Reads available_backends

0%

100%

CUDA device selection

46%

100%

Metal device selection

46%

100%

CPU fallback

100%

100%

CUDA library suggestion

90%

80%

Metal library suggestion

90%

80%

Verbose flag used

0%

100%

Repository
K-Dense-AI/claude-scientific-skills
Evaluated
Agent
Claude Code
Model
Claude Sonnet 4.6

Table of Contents

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