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

—

Does it follow best practices?

Impact

—

No eval scenarios have been run

Securityby

Passed

No known issues

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 body reads as a generic feature brochure: verbose, with no executable code and no links to the bundled scripts and templates that actually exist. The only structurally sound part is a basic step sequence that lacks validation checkpoints.

Suggestions

Replace the How It Works/Examples prose with one or two concrete, executable Python snippets (e.g., an Optuna or GridSearchCV call) and link to the bundled scripts/ implementations.

Delete the filler sections (Instructions, Output, Prerequisites, Integration) that restate what Claude already knows.

Reference the actual bundle files — assets/hyperparameter_space_template.json, assets/visualization_template.html, and the scripts/ modules — from the relevant sections so progressive disclosure is real.

Add an explicit validation/retry checkpoint to the search workflow (e.g., verify metric directionality and re-run on failed trials) to justify a higher workflow_clarity score.

Dimension	Reasoning	Score
Conciseness	The body is padded with boilerplate Claude already knows — "Instructions" (invoke/provide context/review output), "Output: The skill produces structured output relevant to the task", "Prerequisites", and marketing prose in Overview/Integration.	1 / 3
Actionability	Despite being a code-generation skill, the body contains zero executable code or commands; Examples and How It Works only describe what the skill "will" do rather than instructing.	1 / 3
Workflow Clarity	How It Works lists a 4-step sequence (analyze → generate → execute → report), but the batch hyperparameter search has no validation or verification checkpoints, capping the score.	2 / 3
Progressive Disclosure	Sections are present, but the body never references the real bundle files (scripts/ implementations and assets/ templates), so content that should be split out and signaled stays as inline prose.	2 / 3
	Total	6 / 12 Passed

Description

85%

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

A strong description: it names concrete actions, states the niche clearly, and includes an explicit Use-when trigger. Its main weakness is limited trigger-term coverage padded with a meaningless filler sentence.

Dimension	Reasoning	Score
Specificity	Lists multiple concrete actions — "grid search, random search, or Bayesian optimization" and "Finds best parameter configurations to maximize performance" — rather than vague language.	3 / 3
Completeness	Explicitly answers what it does (optimize hyperparameters via named strategies to maximize performance) and when to use it via an explicit "Use when asked to..." trigger clause.	3 / 3
Trigger Term Quality	Includes two natural phrases users would say ("tune hyperparameters", "optimize model"), but coverage is thin and "Trigger with relevant phrases based on skill purpose" is filler rather than a real keyword.	2 / 3
Distinctiveness Conflict Risk	Targets a clear niche (ML hyperparameter tuning) with distinct triggers, making it unlikely to fire for unrelated skills.	3 / 3
	Total	11 / 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
Commit: 2cafb23

Reviewed: 2 days ago

Table of Contents

Discovery Implementation Validation

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