gpu-resource-optimizer

Gpu Resource Optimizer - Auto-activating skill for ML Deployment. Triggers on: gpu resource optimizer, gpu resource optimizer Part of the ML Deployment skill category.

Install with Tessl CLI

npx tessl i github:jeremylongshore/claude-code-plugins-plus-skills --skill gpu-resource-optimizer

What are skills?

Overall
score

19%

Review — 4%

Does it follow best practices?

Validation — 11 / 16 Passed

Validation for skill structure

SKILL.md

Review

Evals

Activation

This description is severely underdeveloped, consisting only of the skill name, duplicate trigger terms, and a category label. It provides no information about what the skill actually does (optimize memory? allocate resources? monitor usage?) or when Claude should select it. Users searching for GPU optimization help would not find clear guidance here.

Suggestions

Add specific concrete actions the skill performs, e.g., 'Optimizes GPU memory allocation, monitors CUDA utilization, configures multi-GPU training setups, and manages GPU resource scheduling for ML workloads.'

Include a 'Use when...' clause with natural trigger terms: 'Use when the user mentions GPU memory issues, CUDA out-of-memory errors, GPU utilization optimization, or needs to configure GPU resources for model training/inference.'

Remove the duplicate trigger term and expand with varied natural phrases users would actually say, such as 'GPU allocation', 'VRAM optimization', 'multi-GPU setup', 'GPU memory management'.

Dimension	Reasoning	Score
Specificity	The description only names the skill ('Gpu Resource Optimizer') without describing any concrete actions. There are no specific capabilities listed - no verbs describing what the skill actually does with GPU resources.	1 / 3
Completeness	The description fails to answer both 'what does this do' and 'when should Claude use it'. There is no 'Use when...' clause and no explanation of the skill's functionality beyond its name and category.	1 / 3
Trigger Term Quality	The trigger terms are just the skill name repeated twice ('gpu resource optimizer, gpu resource optimizer'). Missing natural user terms like 'GPU allocation', 'CUDA memory', 'GPU utilization', 'optimize GPU', or 'ML training resources'.	1 / 3
Distinctiveness Conflict Risk	The 'ML Deployment' category and 'GPU' focus provide some specificity, but without concrete actions described, it could overlap with other GPU or ML-related skills. The niche is somewhat defined but not clearly bounded.	2 / 3
	Total	5 / 12 Passed

Implementation

This skill is essentially a placeholder template with no actual content about GPU resource optimization. It contains only generic boilerplate text that could apply to any skill topic, providing zero actionable guidance, no code examples, and no specific techniques for the stated purpose.

Suggestions

Add concrete code examples for GPU resource optimization (e.g., CUDA memory management, batch size tuning, multi-GPU allocation strategies)

Include specific commands or configurations for common GPU optimization tools (nvidia-smi, torch.cuda utilities, TensorRT)

Define a clear workflow for GPU resource optimization with validation steps (e.g., profile -> identify bottlenecks -> optimize -> validate improvements)

Replace generic capability descriptions with actual techniques and patterns specific to GPU optimization in ML deployment contexts

Dimension	Reasoning	Score
Conciseness	The content is padded with generic boilerplate that provides no actual information about GPU resource optimization. Phrases like 'provides automated assistance' and 'follows industry best practices' are meaningless filler that waste tokens.	1 / 3
Actionability	There is zero concrete guidance - no code, no commands, no specific techniques for GPU resource optimization. The content only describes what the skill claims to do without actually providing any executable instructions.	1 / 3
Workflow Clarity	No workflow is defined. There are no steps, no validation checkpoints, and no actual process for optimizing GPU resources. The skill mentions 'step-by-step guidance' but provides none.	1 / 3
Progressive Disclosure	The content is a flat, uninformative structure with no references to detailed materials, no links to examples or advanced topics, and no organization beyond generic section headers.	1 / 3
	Total	4 / 12 Passed

Validation

69%

Warnings & errors only

Validation — 11 / 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
allowed_tools_field	'allowed-tools' contains unusual tool name(s)	Warning
metadata_version	'metadata' field is not a dictionary	Warning
frontmatter_unknown_keys	Unknown frontmatter key(s) found; consider removing or moving to metadata	Warning
body_steps	No step-by-step structure detected (no ordered list); consider adding a simple workflow	Warning

	Total	11 / 16 Passed

Reviewed

21 days ago

Table of Contents

Activation Implementation Validation

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