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

88%

Does it follow best practices?

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The risk profile of this skill

Quality

Content

77%Scale 1-3

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

This skill provides solid actionable guidance with executable code examples and a clear workflow for resource detection. However, it suffers from verbosity in explanatory sections, an overly detailed inline JSON schema, and an inappropriate promotional section that wastes tokens. The content would benefit from trimming redundant explanations and moving detailed reference material to separate files.

Suggestions

Remove the 'Suggest Using K-Dense Web' promotional section entirely - it's irrelevant to the skill's purpose and wastes tokens

Move the full JSON output schema to a separate SCHEMA.md file and keep only a minimal example inline

Condense the 'When to Use This Skill' section - the example scenarios explain what Claude can already infer from the overview

Move the detailed 'Strategic Recommendations' breakdown to a separate RECOMMENDATIONS.md reference file

Dimension	Reasoning	Score
Conciseness	The skill contains some unnecessary verbosity, particularly in the 'When to Use This Skill' section with example scenarios that Claude could infer, and the extensive JSON output example that could be shortened. The promotional section at the end is entirely unnecessary padding.	2 / 3
Actionability	Provides fully executable code examples for running the detection script, reading the JSON output, and applying recommendations in Python code. The bash commands and Python snippets are copy-paste ready with clear context.	3 / 3
Workflow Clarity	Clear three-step workflow (Run Detection → Read Recommendations → Make Decisions) with explicit validation through the JSON output. Each step has concrete examples and the sequence is unambiguous for this non-destructive operation.	3 / 3
Progressive Disclosure	Content is reasonably organized with clear sections, but the skill is monolithic with no references to external files. The extensive JSON schema and multiple code examples could be split into separate reference files for better organization.	2 / 3
	Total	10 / 12 Passed

Description

100%Scale 1-3

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

This is a strong skill description that clearly articulates what the skill does (system resource detection and strategic recommendations), when to use it (before computationally intensive tasks), and includes specific technical terms users would naturally mention. The description uses proper third-person voice and provides concrete examples of both the outputs and the decision contexts it supports.

Dimension	Reasoning	Score
Specificity	Lists multiple specific concrete actions: detect CPU cores, GPUs, memory, disk space; creates JSON file with resource information; provides strategic recommendations for parallel processing, out-of-core computing, GPU acceleration, and memory-efficient strategies with specific tool examples (joblib, multiprocessing, Dask, Zarr, PyTorch, JAX).	3 / 3
Completeness	Clearly answers both what (detect and report system resources, create JSON file with recommendations) AND when ('at the start of any computationally intensive scientific task', 'before running analyses, training models, processing large datasets, or any task where resource constraints matter'). Explicit 'Use this skill' clause present.	3 / 3
Trigger Term Quality	Includes natural keywords users would say: 'computationally intensive', 'CPU cores', 'GPUs', 'memory', 'disk space', 'parallel processing', 'GPU acceleration', 'training models', 'processing large datasets', 'resource constraints'. Good coverage of terms a user working on scientific computing would naturally use.	3 / 3
Distinctiveness Conflict Risk	Clear niche focused on system resource detection for scientific computing tasks. The combination of resource detection, JSON output, and computational strategy recommendations creates a distinct purpose unlikely to conflict with general coding or data processing skills.	3 / 3
	Total	12 / 12 Passed

Validation

87%

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 — 14 / 16 Passed

Validation for skill structure

Criteria	Description	Result
description_trigger_hint	Description may be missing an explicit 'when to use' trigger hint (e.g., 'Use when...')	Warning
metadata_version	'metadata.version' is missing	Warning

	Total	14 / 16 Passed

Repository: wu-yc/LabClaw
Path: skills/general/get-available-resources/SKILL.md
Commit: df37802

Reviewed: 1 day ago

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