hyperparameter-tuner
Hyperparameter Tuner - Auto-activating skill for ML Training. Triggers on: hyperparameter tuner, hyperparameter tuner Part of the ML Training skill category.
Install with Tessl CLI
npx tessl i github:jeremylongshore/claude-code-plugins-plus-skills --skill hyperparameter-tunerOverall
score
19%
Does it follow best practices?
Validation for skill structure
Activation
7%This description is severely underdeveloped, essentially serving as a placeholder rather than a functional skill description. It lacks concrete actions, meaningful trigger terms, and explicit usage guidance. The redundant trigger term and absence of any capability details make it nearly useless for skill selection among multiple options.
Suggestions
Add specific concrete actions like 'Performs grid search, random search, and Bayesian optimization for hyperparameter tuning. Supports learning rate, batch size, regularization, and architecture parameters.'
Include a 'Use when...' clause with natural trigger terms: 'Use when the user mentions tuning hyperparameters, optimizing model parameters, grid search, learning rate tuning, or improving model performance.'
Add framework/context specificity to improve distinctiveness: 'Works with scikit-learn, PyTorch, and TensorFlow models' or specify the types of models supported.
| Dimension | Reasoning | Score |
|---|---|---|
Specificity | The description only names the domain ('Hyperparameter Tuner', 'ML Training') without describing any concrete actions. There are no specific capabilities listed like 'optimize learning rates', 'grid search', or 'cross-validation'. | 1 / 3 |
Completeness | The description fails to answer 'what does this do' beyond the name, and 'when should Claude use it' is only implied through the duplicate trigger term. There is no explicit 'Use when...' clause or meaningful guidance. | 1 / 3 |
Trigger Term Quality | The trigger terms are redundant ('hyperparameter tuner' listed twice) and miss natural variations users would say like 'tune hyperparameters', 'optimize parameters', 'grid search', 'learning rate', or 'model tuning'. | 1 / 3 |
Distinctiveness Conflict Risk | While 'hyperparameter tuner' is somewhat specific to ML, the lack of detail about what kind of tuning or which frameworks could cause overlap with other ML-related skills. The category mention helps slightly but is insufficient. | 2 / 3 |
Total | 5 / 12 Passed |
Implementation
0%This skill is essentially a placeholder template with no actual content. It contains only generic boilerplate text that could apply to any skill topic, with no specific information about hyperparameter tuning techniques, tools (like Optuna, Ray Tune, sklearn GridSearchCV), or concrete examples. The skill fails to teach Claude anything it doesn't already know.
Suggestions
Add concrete, executable code examples for hyperparameter tuning using specific libraries (e.g., Optuna, Ray Tune, sklearn's GridSearchCV/RandomizedSearchCV)
Include a clear workflow with steps: define search space → choose search strategy → run trials → analyze results → select best parameters
Provide specific guidance on search strategies (grid search, random search, Bayesian optimization) with when to use each
Add validation checkpoints such as checking for overfitting during tuning, cross-validation setup, and early stopping criteria
| Dimension | Reasoning | Score |
|---|---|---|
Conciseness | The content is padded with generic boilerplate that explains nothing Claude doesn't already know. Phrases like 'provides automated assistance' and 'follows industry best practices' are meaningless filler with no actual information. | 1 / 3 |
Actionability | There is zero concrete guidance - no code, no commands, no specific techniques for hyperparameter tuning. The skill describes what it claims to do rather than actually instructing how to do anything. | 1 / 3 |
Workflow Clarity | No workflow is provided whatsoever. There are no steps, no sequence, no validation checkpoints - just vague claims about 'step-by-step guidance' without any actual steps. | 1 / 3 |
Progressive Disclosure | The content is a flat, uninformative structure with no references to detailed materials, no links to examples, and no organization beyond generic section headers that contain no useful content. | 1 / 3 |
Total | 4 / 12 Passed |
Validation
69%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
Table of Contents
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