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.

2.84x

Quality

63%

Does it follow best practices?

Impact

91%

2.84x

Average score across 3 eval scenarios

Securityby

Passed

No known issues

Optimize this skill with Tessl

npx tessl skill review --optimize ./scientific-skills/get-available-resources/SKILL.md

Quality

Discovery

85%

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 description that clearly articulates what the skill does (detects system resources and generates strategic recommendations) and when to use it (before computationally intensive tasks). The specificity is excellent with concrete tools and libraries mentioned. The main weakness is that trigger terms lean technical rather than matching natural user language, and the description is somewhat verbose.

Suggestions

Add more natural user-facing trigger terms such as 'check my hardware', 'system info', 'what resources are available', or 'resource profiling' to improve matching with how users naturally phrase requests.

Dimension	Reasoning	Score
Specificity	Lists multiple specific concrete actions: detect CPU cores, GPUs, memory, disk space; creates a JSON file with resource information; provides strategic recommendations for parallel processing (joblib, multiprocessing), out-of-core computing (Dask, Zarr), GPU acceleration (PyTorch, JAX), and memory-efficient strategies.	3 / 3
Completeness	Clearly answers both what (detect and report available system resources, create JSON file with recommendations) and when ('before running analyses, training models, processing large datasets, or any task where resource constraints matter'). Explicit trigger guidance is provided.	3 / 3
Trigger Term Quality	Includes some relevant keywords like 'CPU cores', 'GPUs', 'memory', 'disk space', 'system resources', 'parallel processing', 'GPU acceleration', but these are somewhat technical. Missing more natural user phrases like 'check my hardware', 'what resources do I have', 'system info', or 'resource check'. Users may not naturally say 'computationally intensive scientific task'.	2 / 3
Distinctiveness Conflict Risk	This skill occupies a clear niche: system resource detection and computational strategy recommendation. It is unlikely to conflict with other skills since it specifically targets pre-task resource assessment rather than the actual computation, analysis, or model training itself.	3 / 3
	Total	11 / 12 Passed

Implementation

42%

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

The skill is highly actionable with concrete, executable code examples and clear output format, but it is severely bloated with redundant explanations, exhaustive enumeration of detection categories, and inline content that should be split into referenced files. The workflow lacks validation checkpoints, and the monolithic structure makes it inefficient for context window usage.

Suggestions

Cut the content by at least 50%: remove the 'When to Use This Skill' examples (Claude can infer these), collapse the 'Resource Detection' enumeration into a single sentence pointing to the JSON schema, and remove 'Best Practices' and 'Troubleshooting' sections which explain things Claude already knows.

Move the full JSON output example and the detailed Strategic Recommendations tables into separate referenced files (e.g., SCHEMA.md and RECOMMENDATIONS.md) to improve progressive disclosure.

Add a validation step after running the detection script, e.g., 'Verify the file was created and contains valid JSON: `python -c "import json; json.load(open('.claude_resources.json'))"`'.

Include the actual `scripts/detect_resources.py` in the bundle since the skill depends on it entirely — without it, the skill is not self-contained or executable.

Dimension	Reasoning	Score
Conciseness	Extremely verbose. The 'When to Use This Skill' section repeats what the description already covers. The 'Resource Detection' section exhaustively lists what the JSON output already shows. The 'Strategic Recommendations' section restates logic that belongs in the script, not the skill. The 'Best Practices' and 'Troubleshooting' sections explain things Claude already knows. Much of this content could be cut by 60-70% without losing actionable information.	1 / 3
Actionability	Provides fully executable code examples for running the detection script, reading the JSON output, and applying recommendations for data loading, parallel processing, and GPU acceleration. The bash commands and Python snippets are copy-paste ready with concrete examples.	3 / 3
Workflow Clarity	The three-step workflow (run detection → read recommendations → make decisions) is clearly sequenced, but there are no validation checkpoints. There's no step to verify the JSON was generated correctly, no error handling if the script fails, and no feedback loop for re-running if resources change during a long task.	2 / 3
Progressive Disclosure	Monolithic wall of text with everything inline. The full JSON schema, all recommendation categories, all platform details, troubleshooting, and best practices are all in one file. The detailed recommendation logic tables and full JSON example could easily be in separate referenced files. No bundle files are provided despite references to 'scripts/detect_resources.py'.	1 / 3
	Total	7 / 12 Passed

Validation

90%

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 — 10 / 11 Passed

Validation for skill structure

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

	Total	10 / 11 Passed

Repository: K-Dense-AI/claude-scientific-skills
Commit: cbcae7b

Reviewed: 1 day ago

Table of Contents

Discovery Implementation Validation

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