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.
39
38%
Does it follow best practices?
Impact
—
No eval scenarios have been run
Passed
No known issues
Optimize this skill with Tessl
npx tessl skill review --optimize ./plugins/ai-ml/hyperparameter-tuner/skills/tuning-hyperparameters/SKILL.mdQuality
Discovery
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 has strong specificity by naming concrete optimization methods (grid search, random search, Bayesian optimization) and includes explicit 'Use when' triggers. However, the final sentence 'Trigger with relevant phrases based on skill purpose' is meaningless filler that adds no information and slightly undermines the otherwise solid description. The trigger terms could be expanded to cover more natural user phrasings.
Suggestions
Remove the vague filler sentence 'Trigger with relevant phrases based on skill purpose' as it provides no actionable information for skill selection.
Expand trigger terms to include common variations like 'hyperparameter tuning', 'parameter sweep', 'model selection', 'cross-validation', or 'best parameters'.
| Dimension | Reasoning | Score |
|---|---|---|
Specificity | Lists multiple specific concrete actions: 'Optimize machine learning model hyperparameters using grid search, random search, or Bayesian optimization' and 'Finds best parameter configurations to maximize performance.' These are concrete methods and outcomes. | 3 / 3 |
Completeness | Clearly answers both 'what' (optimize hyperparameters using grid search, random search, or Bayesian optimization) and 'when' (Use when asked to 'tune hyperparameters' or 'optimize model'). Has an explicit 'Use when...' clause with trigger terms. | 3 / 3 |
Trigger Term Quality | Includes some natural keywords like 'tune hyperparameters', 'optimize model', 'grid search', 'random search', 'Bayesian optimization', but the final sentence 'Trigger with relevant phrases based on skill purpose' is vague filler that adds no value. Missing common variations like 'hyperparameter tuning', 'model selection', 'parameter sweep', 'cross-validation'. | 2 / 3 |
Distinctiveness Conflict Risk | The core domain of ML hyperparameter optimization is fairly specific, but 'optimize model' is broad enough to potentially conflict with other ML-related skills (e.g., model architecture optimization, feature engineering). The vague last sentence weakens distinctiveness. | 2 / 3 |
Total | 10 / 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 almost entirely boilerplate and abstract description with no actionable content. It explains what hyperparameter tuning is and describes in vague terms what the skill would do, but provides zero executable code, no concrete commands, no specific library usage patterns, and no validation steps. The majority of the content (Integration, Prerequisites, Instructions, Output, Error Handling, Resources sections) is generic filler that adds no value.
Suggestions
Replace the abstract descriptions with concrete, executable Python code examples showing grid search with scikit-learn's GridSearchCV, random search with RandomizedSearchCV, and Bayesian optimization with Optuna - each as a complete, runnable snippet.
Remove all boilerplate sections (Integration, Prerequisites, Instructions, Output, Error Handling, Resources) that contain only generic placeholder text and contribute nothing specific to hyperparameter tuning.
Add a clear workflow with validation checkpoints, e.g.: define search space → run search with cross-validation → verify results aren't overfit by checking train/test gap → report best parameters with confidence intervals.
Eliminate the 'Overview' and 'How It Works' sections that explain concepts Claude already knows, and instead focus on specific patterns, gotchas, and decision criteria (e.g., when to use grid vs random vs Bayesian based on parameter count).
| Dimension | Reasoning | Score |
|---|---|---|
Conciseness | Extremely verbose with extensive explanations of concepts Claude already knows. The 'Overview' section restates the title, 'How It Works' describes generic ML workflow steps Claude understands, 'When to Use' lists obvious triggers, and sections like 'Integration', 'Prerequisites', 'Instructions', 'Output', 'Error Handling', and 'Resources' are boilerplate filler with no substantive content. | 1 / 3 |
Actionability | No executable code anywhere in the skill. Examples describe what the skill 'will do' in abstract terms rather than providing concrete, copy-paste-ready code snippets. The 'Instructions' section is entirely generic ('Invoke this skill when the trigger conditions are met') with no specific commands or code. | 1 / 3 |
Workflow Clarity | The 'How It Works' section lists abstract phases (Analyzing, Generating, Executing, Reporting) without concrete steps, commands, or validation checkpoints. There is no guidance on how to actually perform hyperparameter tuning, no validation steps, and no error recovery loops. | 1 / 3 |
Progressive Disclosure | Monolithic wall of text with no references to external files and no bundle files to support it. Content is poorly organized with many low-value sections that could be removed entirely rather than split into separate files. The 'Resources' section references nothing specific. | 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 | |
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