tuning-hyperparameters

Optimize machine learning model hyperparameters using grid search, random search, or Bayesian optimization. Finds best parameter configurations to maximize performance. Use when asked to "tune hyperparameters" or "optimize model". Trigger with relevant phrases based on skill purpose.

Quality

48%

Does it follow best practices?

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tessl review fix ./plugins/ai-ml/hyperparameter-tuner/skills/tuning-hyperparameters/SKILL.md

Quality

Content

20%

Reviews the quality of instructions and guidance provided to agents. Good implementation is clear, handles edge cases, and produces reliable results.

The content is verbose and padded with generic filler, provides no executable code despite being a code-generation skill, and ignores the bundle files entirely. The workflow is sequenced but lacks the validation checkpoint a batch search operation requires.

Suggestions

Add concrete, executable code examples (e.g. a scikit-learn GridSearchCV snippet and an Optuna study snippet) instead of describing the generated code in prose.

Delete the generic filler sections (Prerequisites, Instructions, Output, Error Handling, Resources) that add no skill-specific value.

Insert an explicit validation/verification checkpoint in the search workflow (e.g. confirm cross-validation scores before reporting the best config), and link to the bundled asset templates and scripts rather than leaving them unreferenced.

Dimension	Reasoning	Score
Conciseness	The body explains concepts Claude already knows ('fine-tune machine learning models by automatically searching for the optimal hyperparameter configurations', 'leverages different search strategies') and is padded with low-value filler sections like Prerequisites ('Appropriate file access permissions'), Instructions ('Invoke this skill when the trigger conditions are met'), and Output ('The skill produces structured output relevant to the task').	1 / 3
Actionability	Despite being a skill whose purpose is generating Python code with scikit-learn/Optuna, the body contains zero executable code or commands; both 'How It Works' and the two 'Examples' describe in prose what the skill 'will' do rather than instructing.	1 / 3
Workflow Clarity	The 'How It Works' section lists a sequenced four-step process (Analyzing, Generating, Executing, Reporting), but there are no validation checkpoints; because hyperparameter search is a batch operation iterating over many combinations and training models, the rubric caps workflow clarity at 2.	2 / 3
Progressive Disclosure	The body is section-organized, but it never references the bundled assets that exist on disk (hyperparameter_space_template.json, visualization_template.html) or the scripts listed in scripts/README.md, so material that should be split out is left inline and references are entirely unsignaled.	2 / 3
	Total	6 / 12 Passed

Description

77%

Based on the skill's description, can an agent find and select it at the right time? Clear, specific descriptions lead to better discovery.

The description is strong on specificity and completeness, clearly stating concrete actions and an explicit 'Use when' trigger. It is held back by limited trigger-term coverage and a vague trailing meta-sentence that adds no value.

Suggestions

Remove the generic filler sentence 'Trigger with relevant phrases based on skill purpose.' which reads as an unfilled template instruction.

Broaden trigger terms to include natural variations like 'model tuning', 'parameter search', or 'find the best parameters'.

Tighten the broad 'optimize model' trigger to reduce overlap with general model-optimization skills, e.g. 'optimize model hyperparameters'.

Dimension	Reasoning	Score
Specificity	Lists multiple concrete actions — 'Optimize machine learning model hyperparameters using grid search, random search, or Bayesian optimization' and 'Finds best parameter configurations to maximize performance' — matching the multi-action anchor.	3 / 3
Completeness	Explicitly answers both what (optimize hyperparameters via grid/random/Bayesian search) and when ('Use when asked to "tune hyperparameters" or "optimize model"'), satisfying the explicit-trigger requirement.	3 / 3
Trigger Term Quality	Includes natural phrases users would say ('tune hyperparameters', 'optimize model') but covers only two variations, missing common ones like 'model tuning' or 'find best parameters'; the trailing 'Trigger with relevant phrases based on skill purpose' is generic filler, not a real trigger term.	2 / 3
Distinctiveness Conflict Risk	The hyperparameter-tuning niche is fairly distinct, but 'optimize model' is broad enough to overlap with general model-optimization skills, and the generic filler sentence weakens distinctiveness.	2 / 3
	Total	10 / 12 Passed

Validation

87%

Warnings & errors only

Checks the skill against the spec for correct structure and formatting. All validation checks must pass before discovery and implementation can be scored.

Validation — 14 / 16 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	14 / 16 Passed

Repository: jeremylongshore/claude-code-plugins-plus-skills
Path: plugins/ai-ml/hyperparameter-tuner/skills/tuning-hyperparameters/SKILL.md
Commit: 15940c5

Reviewed: 1 day ago

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