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.
36
3%
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
7%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 description is essentially a placeholder that provides almost no useful information beyond the skill's name and category. It lacks concrete actions, meaningful trigger terms, and any explicit guidance on when Claude should select this skill. The repeated trigger term adds no value.
Suggestions
Add specific concrete actions the skill performs, e.g., 'Allocates GPU memory, configures CUDA devices, monitors GPU utilization, and optimizes batch sizes for ML model deployment.'
Add an explicit 'Use when...' clause with natural trigger terms, e.g., 'Use when the user mentions GPU memory, CUDA allocation, GPU utilization, model deployment performance, or needs to optimize GPU resources for training or inference.'
Remove the duplicated trigger term and replace with diverse, natural keywords users would actually say, such as 'GPU memory', 'VRAM', 'CUDA', 'GPU optimization', 'model serving', 'inference performance'.
| Dimension | Reasoning | Score |
|---|---|---|
Specificity | The description names a domain ('GPU Resource Optimizer') but provides no concrete actions. There is no indication of what the skill actually does—no verbs like 'allocates', 'monitors', 'optimizes memory usage', etc. | 1 / 3 |
Completeness | The description fails to answer 'what does this do' beyond the name itself, and there is no explicit 'when to use' guidance. The 'Triggers on' line is just the skill name repeated, not meaningful trigger conditions. | 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 memory', 'CUDA allocation', 'GPU utilization', or 'optimize GPU usage'. | 1 / 3 |
Distinctiveness Conflict Risk | The mention of 'GPU' and 'ML Deployment' provides some domain specificity that distinguishes it from generic skills, but the lack of concrete actions or scope means it could overlap with other GPU or ML-related skills. | 2 / 3 |
Total | 5 / 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 substantive content. It contains no actionable guidance, no code, no workflows, and no real information about GPU resource optimization. Every section is generic boilerplate that could apply to any topic by swapping the skill name.
Suggestions
Add concrete, executable code examples for GPU resource optimization (e.g., CUDA memory management, GPU allocation configs for Kubernetes, nvidia-smi monitoring commands, PyTorch/TensorFlow GPU memory optimization patterns).
Define a clear workflow for GPU resource optimization tasks, such as: profile GPU usage → identify bottlenecks → apply optimization → validate improvements, with specific tools and validation checkpoints at each step.
Include specific configuration examples (e.g., Kubernetes GPU resource limits, NVIDIA MPS setup, multi-GPU serving configs) rather than abstract descriptions of capabilities.
Replace generic trigger descriptions with actual domain content — the 'When to Use' and 'Example Triggers' sections waste tokens on meta-information that provides no value.
| Dimension | Reasoning | Score |
|---|---|---|
Conciseness | The content is entirely filler and boilerplate. It explains nothing Claude doesn't already know, repeats 'gpu resource optimizer' excessively, and provides zero domain-specific information about GPU resource optimization. | 1 / 3 |
Actionability | There are no concrete code examples, commands, configurations, or specific instructions. Every section is vague and abstract — 'provides step-by-step guidance' without actually providing any steps. | 1 / 3 |
Workflow Clarity | No workflow is defined at all. There are no steps, no sequences, no validation checkpoints — just generic claims about capabilities without any actual process. | 1 / 3 |
Progressive Disclosure | No bundle files exist, no references to external documents, and the content itself is a flat, shallow placeholder with no meaningful structure or navigation to deeper content. | 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
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- Reviewed
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