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.
34
0%
Does it follow best practices?
Impact
99%
1.05xAverage score across 3 eval scenarios
Passed
No known issues
Optimize this skill with Tessl
npx tessl skill review --optimize ./planned-skills/generated/08-ml-deployment/gpu-resource-optimizer/SKILL.mdQuality
Discovery
0%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 an extremely weak description that appears to be auto-generated boilerplate. It provides no concrete actions, no natural trigger terms, no 'when to use' guidance, and nothing to distinguish it from other ML or GPU-related skills. It would be nearly useless for skill selection in a multi-skill environment.
Suggestions
Add specific concrete actions the skill performs, e.g., 'Allocates GPU resources across ML training jobs, monitors GPU memory utilization, configures CUDA settings, and optimizes batch sizes for available hardware.'
Add an explicit 'Use when...' clause with natural trigger terms, e.g., 'Use when the user mentions GPU allocation, CUDA memory errors, GPU utilization, training resource optimization, multi-GPU setup, or needs to optimize ML workloads for available GPU hardware.'
Replace the duplicated trigger term 'gpu resource optimizer' with diverse natural language variations users would actually say, such as 'GPU memory', 'out of memory', 'GPU utilization', 'optimize training speed', 'multi-GPU', 'VRAM'.
| Dimension | Reasoning | Score |
|---|---|---|
Specificity | The description only names the skill ('Gpu Resource Optimizer') and its category ('ML Deployment') but provides no concrete actions. There is no mention of what the skill actually does—no verbs describing specific capabilities like allocating GPUs, monitoring utilization, or optimizing memory. | 1 / 3 |
Completeness | The description fails to answer both 'what does this do' and 'when should Claude use it'. There is no explanation of capabilities and no explicit 'Use when...' clause or equivalent trigger guidance. | 1 / 3 |
Trigger Term Quality | The only trigger terms listed are 'gpu resource optimizer' repeated twice. These are not natural phrases a user would say; users are more likely to say things like 'GPU allocation', 'GPU memory', 'CUDA out of memory', 'optimize GPU usage', or 'ML training resources'. | 1 / 3 |
Distinctiveness Conflict Risk | The description is too vague to be distinctive. 'ML Deployment' is a broad category, and without specific actions or clear triggers, this could easily conflict with other ML-related skills covering deployment, infrastructure, or resource management. | 1 / 3 |
Total | 4 / 12 Passed |
Implementation
0%Reviews the quality of instructions and guidance provided to agents. Good implementation is clear, handles edge cases, and produces reliable results.
This skill is an empty template/placeholder with no actual content about GPU resource optimization. It contains no executable code, no specific techniques (e.g., GPU memory management, multi-GPU scheduling, CUDA optimization), no commands, and no workflows. It merely restates its own name repeatedly without teaching Claude anything it doesn't already know.
Suggestions
Add concrete, executable code examples for GPU resource optimization (e.g., PyTorch GPU memory management, NVIDIA MPS configuration, Kubernetes GPU scheduling with resource limits).
Define a clear multi-step workflow for GPU resource optimization tasks, such as: profile GPU usage → identify bottlenecks → apply optimization → validate improvements, with specific tools and commands at each step.
Remove all the meta-description sections ('When to Use', 'Example Triggers', 'Capabilities') that describe the skill rather than teaching the skill, and replace with actual technical content.
Add references to detailed guides for advanced topics like multi-GPU inference, GPU memory fragmentation, or CUDA stream optimization, following progressive disclosure patterns.
| Dimension | Reasoning | Score |
|---|---|---|
Conciseness | The content is entirely filler with no substantive information. It repeats 'gpu resource optimizer' numerous times without providing any actual technical content, commands, or code. Every section restates the same vague idea. | 1 / 3 |
Actionability | There is zero concrete, executable guidance. No code, no commands, no specific configurations, no examples of GPU resource optimization. The 'capabilities' section makes claims but delivers nothing actionable. | 1 / 3 |
Workflow Clarity | No workflow is defined at all. There are no steps, no sequence, no validation checkpoints. The skill claims to provide 'step-by-step guidance' but contains none. | 1 / 3 |
Progressive Disclosure | The content is a flat, repetitive structure with no references to detailed materials, no links to related files, and no meaningful organization of content across sections. | 1 / 3 |
Total | 4 / 12 Passed |
Validation
81%Checks the skill against the spec for correct structure and formatting. All validation checks must pass before discovery and implementation can be scored.
Validation — 9 / 11 Passed
Validation for skill structure
| Criteria | Description | Result |
|---|---|---|
allowed_tools_field | 'allowed-tools' contains unusual tool name(s) | Warning |
frontmatter_unknown_keys | Unknown frontmatter key(s) found; consider removing or moving to metadata | Warning |
Total | 9 / 11 Passed | |
- Commit
c8a915c
- Reviewed
Table of Contents
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