Skill · em Dados, IA e pesquisa

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…

Procedência

Antes de instalar

2 arquivos · 23,3 KB · inclui 1 script que executa: scripts/detect_resources.py

Instalar na sua CLI

O comando baixa a versão fixada (commit 57f899e) direto da origem, para a pasta que a CLI lê. Precisa de curl (macOS e Linux); no Windows não há comando, porque o Rook Labs é para macOS.

Claude Code

Neste projeto: instala em .claude/skills/get-available-resources/.

d=".claude/skills/get-available-resources"
u="https://raw.githubusercontent.com/davila7/claude-code-templates/57f899e5394bb8ca166f38eacae8f0853cbfe033/cli-tool/components/skills/scientific/get-available-resources"
curl -fsSL --create-dirs \
  -o "$d/SKILL.md" "$u/SKILL.md" \
  -o "$d/scripts/detect_resources.py" "$u/scripts/detect_resources.py" \
  -o "$d/LICENSE" "https://raw.githubusercontent.com/davila7/claude-code-templates/57f899e5394bb8ca166f38eacae8f0853cbfe033/LICENSE"

Global: instala em ~/.claude/skills/get-available-resources/.

d="$HOME/.claude/skills/get-available-resources"
u="https://raw.githubusercontent.com/davila7/claude-code-templates/57f899e5394bb8ca166f38eacae8f0853cbfe033/cli-tool/components/skills/scientific/get-available-resources"
curl -fsSL --create-dirs \
  -o "$d/SKILL.md" "$u/SKILL.md" \
  -o "$d/scripts/detect_resources.py" "$u/scripts/detect_resources.py" \
  -o "$d/LICENSE" "https://raw.githubusercontent.com/davila7/claude-code-templates/57f899e5394bb8ca166f38eacae8f0853cbfe033/LICENSE"

Codex

Neste projeto: instala em .agents/skills/get-available-resources/.

d=".agents/skills/get-available-resources"
u="https://raw.githubusercontent.com/davila7/claude-code-templates/57f899e5394bb8ca166f38eacae8f0853cbfe033/cli-tool/components/skills/scientific/get-available-resources"
curl -fsSL --create-dirs \
  -o "$d/SKILL.md" "$u/SKILL.md" \
  -o "$d/scripts/detect_resources.py" "$u/scripts/detect_resources.py" \
  -o "$d/LICENSE" "https://raw.githubusercontent.com/davila7/claude-code-templates/57f899e5394bb8ca166f38eacae8f0853cbfe033/LICENSE"

Global: instala em ~/.agents/skills/get-available-resources/.

d="$HOME/.agents/skills/get-available-resources"
u="https://raw.githubusercontent.com/davila7/claude-code-templates/57f899e5394bb8ca166f38eacae8f0853cbfe033/cli-tool/components/skills/scientific/get-available-resources"
curl -fsSL --create-dirs \
  -o "$d/SKILL.md" "$u/SKILL.md" \
  -o "$d/scripts/detect_resources.py" "$u/scripts/detect_resources.py" \
  -o "$d/LICENSE" "https://raw.githubusercontent.com/davila7/claude-code-templates/57f899e5394bb8ca166f38eacae8f0853cbfe033/LICENSE"

Antigravity

Neste projeto: instala em .agents/skills/get-available-resources/.

d=".agents/skills/get-available-resources"
u="https://raw.githubusercontent.com/davila7/claude-code-templates/57f899e5394bb8ca166f38eacae8f0853cbfe033/cli-tool/components/skills/scientific/get-available-resources"
curl -fsSL --create-dirs \
  -o "$d/SKILL.md" "$u/SKILL.md" \
  -o "$d/scripts/detect_resources.py" "$u/scripts/detect_resources.py" \
  -o "$d/LICENSE" "https://raw.githubusercontent.com/davila7/claude-code-templates/57f899e5394bb8ca166f38eacae8f0853cbfe033/LICENSE"

Global: instala em ~/.gemini/antigravity-cli/skills/get-available-resources/.

d="$HOME/.gemini/antigravity-cli/skills/get-available-resources"
u="https://raw.githubusercontent.com/davila7/claude-code-templates/57f899e5394bb8ca166f38eacae8f0853cbfe033/cli-tool/components/skills/scientific/get-available-resources"
curl -fsSL --create-dirs \
  -o "$d/SKILL.md" "$u/SKILL.md" \
  -o "$d/scripts/detect_resources.py" "$u/scripts/detect_resources.py" \
  -o "$d/LICENSE" "https://raw.githubusercontent.com/davila7/claude-code-templates/57f899e5394bb8ca166f38eacae8f0853cbfe033/LICENSE"

Peça ao Rook

Já usa o Rook Labs? Cole no chat do Rook: instale a skill https://rooklabs.sh/marketplace/cct.get-available-resources

Prévia do SKILL.md

---
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 …
---

# 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 appr…

## 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
- "Process 10,000 files in parallel" → Use this skill to determine optimal worker count
- "Run a computationally intensive simulation" → Use this skill to understand resource constraints

## How This Skill Works

### Resource Detection

The skill runs `scripts/detect_resources.py` to automatically detect:

1. **CPU Information**
   - Physical and logical core counts
   - Processor architecture and model
   - CPU frequency information

2. **GPU Information**
   - NVIDIA GPUs: Detects via nvidia-smi, reports VRAM, driver version, compute capability
…

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