System Resource Detector

What it does

When your agent uses it

• Before loading a large dataset, to see whether it fits in memory
• Before training a model, to find out whether a GPU backend is available
• Choosing a worker count for joblib, multiprocessing or Dask jobs
• Checking disk space before large file operations

Example prompts

• “Help me analyze this 50GB genomics dataset, but first check what resources this machine has.”
• “Check my GPUs and memory before I train a neural network on this data.”
• “Work out how many parallel workers I should use to process the files in the data folder.”

Requirements

• Python 3, to run `scripts/detect_resources.py`

Workflow steps

3 steps, taken from the step headings in SKILL.md.

1. 1Run Resource Detection
2. 2Read and Apply Recommendations
3. 3Make Informed Decisions

What it can do on your machine

Read from SKILL.md and the folder at commit c0ca7da. It shows what the files ask for, not the result of running them.

• Tool permissions

  Pre-approves nothing: there is no allowed-tools line, so your agent's usual permission prompts apply.

  From allowed-tools in the SKILL.md frontmatter.

• Runs code

  Ships 1 file in scripts/ (Python), which the agent can run.

  Shell commands in SKILL.md call:

  • uv
  • python

  From the folder's file list and the shell code blocks in SKILL.md.

• Network

  No URLs in SKILL.md. Its commands use uv, which can reach the network depending on how they are called.

  From URLs in SKILL.md, links to its own repository left out.

• Credentials

  Names no API keys, tokens, secrets or passwords.

  From names ending in _API_KEY, _TOKEN, _SECRET, _KEY or _PASSWORD in SKILL.md.

Context cost

System Resource Detector loads about 2.4k tokens when it runs. Until then it costs about 156 tokens; SKILL.md has 735 words of instructions outside code blocks.

Always · name and description, kept in context so the agent knows when to use it

~156

When it runs · the whole SKILL.md, loaded when a task matches

~2.4k

Estimates: characters ÷ 4, the usual rule of thumb; real counts depend on the model's tokenizer. Scripts and assets cost tokens only if the agent reads them.

Safety

Automated static check — not a guarantee. Review scripts before installing. It scans the text of SKILL.md for risky patterns (piping downloads into a shell, reading credential files, hidden Unicode, destructive commands); the scripts in this folder are not scanned.

SKILL.md

The full file from davila7/claude-code-templates at commit c0ca7da, republished under its MIT licence (© davila7). 735 words, ~2,442 tokens.

name

get-available-resources

description

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.

{
  "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"
    }
  }
}

python scripts/detect_resources.py

# 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

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')

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
)

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)

uv pip install psutil

© davila7, MIT. Rendered from Markdown: HTML in the file is shown as text, images as links, and headings moved down two levels. Raw file

Files

SKILL.md and 1 other file (scripts) in cli-tool/components/skills/scientific/get-available-resources of davila7/claude-code-templates.

• SKILL.md
• scripts/detect_resources.py

Open the folder on GitHubat commit c0ca7da

Used in 10 other repositories

We found 14 copies of this SKILL.md (exact, near-identical or edited) in other folders, from 10 other GitHub owners. This page covers the copy in davila7/claude-code-templates, which our catalogue first saw on October 7, 2026.

• jimmc414/Kosmoskosmos-claude-scientific-skills/scientific-skills/get-available-resources
• Microck/ordinary-claude-skillsskills_all/get-available-resourcesskills_categorized/finance-investment/get-available-resources+2 more
• aiskillstore/marketplaceskills/davila7/get-available-resourcesskills/k-dense-ai/get-available-resources
• lamm-mit/scienceclawskills/get-available-resources
• agent-skills-hub/agent-skills-hubskills/get-available-resources
• foryourhealth111-pixel/Vibe-Skillsbundled/skills/get-available-resources
• LeonChaoX/qinyan-academic-skillsskills/17-平台与基础设施/get-available-resources
• wu-yc/LabClawskills/general/get-available-resources
• BioTender-max/awesome-bio-agent-skillsskills/kdense/get-available-resources
• infometa/workbuddyskillsplugins/codebuddy-plugins-official/external_plugins/scientific-skills/get-available-resources