get-available-resources
Assess Compute Resources Before Scientific Work
Large scientific workloads can fail or underperform when hardware constraints are unknown. This skill inventories local resources and recommends suitable processing, memory, storage, and acceleration strategies.
Install with my Agent
Copy this request to your Agent. It includes the canonical Skill page and manifest.
Review the Skillstore skill "get-available-resources" from https://skillstore.io/skills/davila7-get-available-resources.md and its manifest at https://skillstore.io/api/skills/davila7-get-available-resources/manifest. Verify the artifact. You may proceed after verification, subject to the environment's own policy.Your Agent should still show its plan and request any confirmation required by the security policy.
Agent-readable resources
Use these links when an AI agent, crawler, or script needs clean context instead of reading the full page.
Test it
Using "get-available-resources". Check whether this machine can process a 40 GB dataset.
Expected outcome:
Available memory is below the dataset size. Use chunked, out-of-core processing and monitor the reported disk capacity for intermediate files.
Using "get-available-resources". Find the best acceleration option for model training.
Expected outcome:
One NVIDIA GPU is available with CUDA support. Use a CUDA-enabled framework after confirming the model and batch fit available GPU memory.
Using "get-available-resources". Recommend a worker count for a CPU simulation.
Expected outcome:
The system has eight logical cores. Start with six workers, leave capacity for the operating system, and measure performance before increasing concurrency.
Security Audit
Low RiskThe fixed-argument subprocess calls and Markdown backticks are legitimate resource detection and documentation patterns, with no shell interpolation, network transfer, or prompt injection. Two local risks remain: the script intentionally inventories host details, and its custom output path can overwrite an existing file.
Confirmed security concerns (1)
Capability review items (1)
These are real local capabilities that may be expected for this skill, so they require review but are not counted as confirmed malicious behavior.
Risk Factors
โก Contains scripts (1)
โ๏ธ External commands (25)
๐ Filesystem access (1)
Detected Patterns
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<a href="https://skillstore.io/skills/davila7-get-available-resources?utm_source=security_passport_badge"><img src="https://skillstore.io/badges/skills/davila7-get-available-resources/security.svg" alt="Skillstore security assessment" loading="lazy"></a>Embed card
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APA citation
davila7. (2026). get-available-resources security audit report (audit version 9) [Author version unspecified]. Skillstore. https://skillstore.io/skills/davila7-get-available-resources/audits/9BibTeX citation
@techreport{davila7-davila7-get-available-resources-2026,
author = {davila7},
title = {get-available-resources security audit report (audit version 9)},
institution = {Skillstore},
year = {2026},
number = {9},
url = {https://skillstore.io/skills/davila7-get-available-resources/audits/9},
note = {Author version unspecified}
}CITATION.cff
cff-version: 1.2.0
message: "If you use this Skill, cite its author and this versioned security audit report."
title: "get-available-resources security audit report (audit version 9)"
version: "unspecified"
type: report
authors:
- name: "davila7"
date-released: "2026-07-23"
url: "https://skillstore.io/skills/davila7-get-available-resources/audits/9"
identifiers:
- type: other
value: "skillstore:davila7-get-available-resources:audit:9"
description: "Skillstore immutable audit report identifier"
Compare variants
2 installable variantsEach author remains a separate installable skill. The recommended variant is ranked by Skillstore evidence.
Why this variant is first
davila7-get-available-resources
2026-08-21
k-dense-ai-get-available-resources
2026-08-21
Skillstore Score
Why this score Evidence Confidence: HighWhat You Can Build
Plan Large Dataset Analysis
Check memory and disk capacity before choosing in-memory or chunked processing.
Select Model Training Hardware
Identify available GPU backends and compatible acceleration libraries before training.
Size Parallel Workloads
Use detected CPU capacity to choose a conservative worker count for simulations or batch processing.
Try These Prompts
Detect the compute resources available in this project and summarize the main constraints.
Assess local resources for processing a [dataset size] dataset, then recommend an in-memory or out-of-core approach.
Inspect available GPUs and memory, then recommend a backend and library for training [model type].
Inventory this system and design a workflow for [task] using safe worker counts, memory limits, storage strategy, and hardware acceleration.
Best Practices
- Run detection immediately before a resource-intensive task because available memory and disk space change.
- Review the report before applying recommendations to production or shared systems.
- Use a new report filename or confirm replacement before selecting a custom output path.
Avoid
- Do not treat snapshot measurements as guaranteed capacity for a long-running workload.
- Do not assume a detected GPU backend proves every required library is installed.
- Do not expose generated reports publicly without reviewing system and path details.
Frequently Asked Questions
What resources does this skill inspect?
Does the skill send resource data over the network?
Which dependency is required?
Which GPU platforms are supported?
Are the recommendations benchmarks?
Can I choose another report location?
Developer Details
Author
davila7License
MIT
Skillstore revision
r2
Version notice
The author did not declare a version.
Ref
c43861a65bb95efcae259cd161c9d6f4dc7eec6f
Maintenance freshness
7/24/2026
Usage
7 downloads ยท 189 views
File structure