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.

Quality

83%

Does it follow best practices?

Run evals on this skill

Adds up to 20 points to the overall score

View guide

Securityby

Passed

No findings from the security scan

Quality

Content

75%Weight 40%Scale 1-5

Reviews the quality of instructions and guidance provided to agents. Good implementation is clear, handles edge cases, and produces reliable results.

Highly actionable content with executable commands and code, organized into clear sections and backed by a real bundled script. The main weakness is verbosity—large inline JSON and duplicated recommendation tables could be trimmed or moved to a reference file.

Suggestions

Trim or move the full ~60-line JSON sample and Strategic Recommendations tables into a separate references/ file, keeping only a minimal example inline in SKILL.md.

Add an explicit validation checkpoint after Step 1 (e.g. confirm `.claude_resources.json` was created and is non-empty before proceeding to Step 2).

Condense the Best Practices and Troubleshooting sections to the few highest-value items to reduce token load.

Dimension	Reasoning	Score
Conciseness	Avoids explaining concepts Claude already knows, but the ~60-line full JSON sample plus duplicated Strategic Recommendations tables and lengthy Best Practices/Troubleshooting sections could be tightened; the recommendation tables largely restate the JSON.	3 / 5
Actionability	Provides a concrete runnable command (`python scripts/detect_resources.py`) with documented flags plus several complete, copy-paste-ready Python snippets for reading and applying the generated JSON across the common cases.	5 / 5
Workflow Clarity	A clear Step 1 (run detection) / Step 2 (read & apply recommendations) / Step 3 (make decisions) sequence with concrete commands; the operation is read-only so the destructive/batch validation cap does not apply, but no explicit output-validation checkpoint is given.	4 / 5
Progressive Disclosure	Well-organized into clearly headed sections and the `scripts/detect_resources.py` reference is real and one level deep; however the bulk of reference material (full JSON schema, recommendation tables) is inlined rather than split into a reference file.	4 / 5
	Total	16 / 20 Passed

Description

92%Weight 40%Scale 1-5

Based on the skill's description, can an agent find and select it at the right time? Clear, specific descriptions lead to better discovery.

A strong, specific description that clearly states both what the skill does and when to use it, with concrete library and resource mentions. Trigger-term coverage is good but could add a few more natural synonyms.

Dimension	Reasoning	Score
Specificity	Lists multiple concrete actions—"detect and report available system resources (CPU cores, GPUs, memory, disk space)" and "creates a JSON file with resource information and strategic recommendations"—and enumerates specific decision domains (parallel processing, out-of-core, GPU acceleration) with named libraries, giving comprehensive coverage.	5 / 5
Completeness	Explicitly answers both what (detect/report resources, produce a JSON file with recommendations) and when ("at the start of any computationally intensive scientific task", "Use this skill before running analyses, training models, processing large datasets").	5 / 5
Trigger Term Quality	Includes natural user-facing phrases like "computationally intensive scientific task", "training models", "processing large datasets", and "resource constraints matter", but misses some common synonyms a user might say (e.g. "simulation", "rendering", "slow run").	4 / 5
Distinctiveness Conflict Risk	Occupies a clear niche—pre-task system resource detection for compute-heavy scientific work—with distinct triggers that are unlikely to fire for unrelated skills.	5 / 5
	Total	19 / 20 Passed

Validation

93%

Warnings & errors only

Checks the skill against the spec for correct structure and formatting. All validation checks must pass before discovery and implementation can be scored.

Validation — 15 / 16 Passed

Validation for skill structure

Criteria	Description	Result
metadata_version	'metadata.version' is missing	Warning

	Total	15 / 16 Passed

Repository: foryourhealth111-pixel/Vibe-Skills
Path: bundled/skills/get-available-resources/SKILL.md
Commit: d5ae560

Reviewed: 1 day ago

Table of Contents

Discovery Implementation Validation

Is this your skill?

If you maintain this skill, you can claim it as your own. Once claimed, you can manage eval scenarios, bundle related skills, attach documentation or rules, and ensure cross-agent compatibility.