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
Quality
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 severely underdeveloped, essentially serving as a placeholder rather than a functional skill description. It lacks any concrete actions, meaningful trigger terms, or guidance on when to use the skill. The only distinguishing element is the skill name itself, which is insufficient for Claude to make informed skill selection decisions.
Suggestions
Add specific concrete actions the skill performs, e.g., 'Analyzes GPU memory usage, optimizes batch sizes, configures CUDA settings, and manages multi-GPU allocation for ML workloads.'
Include a 'Use when...' clause with natural trigger terms like 'GPU memory', 'VRAM optimization', 'CUDA configuration', 'GPU utilization', 'out of memory errors', or 'multi-GPU training'.
Remove the duplicate trigger term and replace with varied natural language phrases users would actually say when needing GPU optimization help.
| Dimension | Reasoning | Score |
|---|---|---|
Specificity | The description only names the skill ('Gpu Resource Optimizer') without describing any concrete actions. There are no verbs or specific capabilities listed - it doesn't explain what optimizing GPU resources actually entails. | 1 / 3 |
Completeness | The description fails to answer 'what does this do' beyond the name, and provides no 'when should Claude use it' guidance. There is no 'Use when...' clause or equivalent explicit trigger guidance. | 1 / 3 |
Trigger Term Quality | The triggers listed are just the skill name repeated twice ('gpu resource optimizer, gpu resource optimizer'). No natural user keywords like 'GPU memory', 'CUDA', 'VRAM', 'GPU allocation', or 'optimize GPU' are included. | 1 / 3 |
Distinctiveness Conflict Risk | While 'GPU resource optimizer' is somewhat specific to a niche (GPU/ML optimization), the lack of detail means it could overlap with other ML deployment skills. The category mention provides some context but insufficient differentiation. | 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 essentially an empty placeholder that describes what a GPU resource optimizer skill might do without providing any actual content. It contains no executable code, no specific techniques, no concrete guidance, and no real information about GPU resource optimization. The entire content is meta-description that could apply to any skill topic.
Suggestions
Add concrete code examples for GPU memory management, multi-GPU allocation, and resource monitoring (e.g., using nvidia-smi, torch.cuda APIs, or TensorFlow GPU configuration)
Include specific techniques for GPU optimization such as memory pooling, batch size tuning, mixed precision training, and gradient checkpointing with executable examples
Define a clear workflow for diagnosing and optimizing GPU utilization with validation steps (e.g., profile -> identify bottleneck -> apply optimization -> verify improvement)
Remove all generic boilerplate text and replace with actionable content that teaches specific GPU resource optimization patterns
| 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 skill describes what it claims to do rather than actually instructing how to do anything. | 1 / 3 |
Workflow Clarity | No workflow is defined. There are no steps, no validation checkpoints, and no actual process for optimizing GPU resources. The content is entirely meta-description without substance. | 1 / 3 |
Progressive Disclosure | The content is a monolithic block of vague descriptions with no references to detailed materials, no links to examples, and no structured 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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