hyperparameter-tuner
Hyperparameter Tuner - Auto-activating skill for ML Training. Triggers on: hyperparameter tuner, hyperparameter tuner Part of the ML Training skill category.
32
3%
Does it follow best practices?
Impact
77%
0.88xAverage score across 3 eval scenarios
Passed
No known issues
Optimize this skill with Tessl
npx tessl skill review --optimize ./planned-skills/generated/07-ml-training/hyperparameter-tuner/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 essentially a placeholder that names the skill and its category but provides no substantive information about what it does or when to use it. It lacks concrete actions, meaningful trigger terms, and explicit usage guidance. The repeated trigger term ('hyperparameter tuner' listed twice) suggests auto-generated content with no human refinement.
Suggestions
Add specific concrete actions the skill performs, e.g., 'Performs grid search, random search, and Bayesian optimization to find optimal hyperparameters for ML models including learning rate, batch size, regularization, and architecture choices.'
Add an explicit 'Use when...' clause with natural trigger terms, e.g., 'Use when the user asks about tuning hyperparameters, optimizing model performance, grid search, random search, learning rate scheduling, or finding the best model configuration.'
Remove the duplicate trigger term and expand with natural variations users would actually say, such as 'HP tuning', 'parameter search', 'model optimization', 'tune learning rate', 'hyperparameter optimization'.
| Dimension | Reasoning | Score |
|---|---|---|
Specificity | The description names the domain ('ML Training') and the concept ('Hyperparameter Tuner') but does not describe any concrete actions. There are no specific capabilities listed such as 'tunes learning rate', 'performs grid search', 'optimizes batch size', etc. | 1 / 3 |
Completeness | The description barely addresses 'what' (just names the concept) and has no meaningful 'when' clause. There is no 'Use when...' guidance, and the trigger terms are just the skill name repeated. Both what and when are very weak. | 1 / 3 |
Trigger Term Quality | The only trigger term listed is 'hyperparameter tuner' repeated twice. It misses natural variations users would say like 'tune hyperparameters', 'learning rate', 'grid search', 'bayesian optimization', 'model tuning', 'HP search', etc. | 1 / 3 |
Distinctiveness Conflict Risk | The term 'hyperparameter tuner' is fairly specific to a niche within ML, which provides some distinctiveness. However, the lack of concrete actions and the vague 'ML Training' category could cause overlap with other ML-related skills. | 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 a placeholder with no substantive content. It contains no actionable guidance, no code examples, no specific hyperparameter tuning techniques (e.g., grid search, Bayesian optimization, Optuna/Ray Tune usage), and no workflows. It reads like an auto-generated template that was never filled in with actual skill content.
Suggestions
Add concrete, executable code examples for hyperparameter tuning using specific frameworks (e.g., Optuna, Ray Tune, sklearn GridSearchCV) with copy-paste ready snippets.
Define a clear workflow: 1) Define search space, 2) Choose tuning strategy, 3) Run trials, 4) Validate best params on held-out set, with explicit validation checkpoints.
Remove all meta-description sections ('When to Use', 'Example Triggers', 'Capabilities') that describe the skill rather than teaching the task—replace with actual technical content.
Add references to detailed guides for specific tuning approaches (e.g., Bayesian optimization guide, distributed tuning setup) to enable progressive disclosure.
| Dimension | Reasoning | Score |
|---|---|---|
Conciseness | The content is almost entirely filler and meta-description. It explains what the skill does ('provides automated assistance') without providing any actual technical content. Every section restates the same vague idea in different words. | 1 / 3 |
Actionability | There is zero concrete guidance—no code, no commands, no specific hyperparameter tuning techniques, no library usage examples, no configurations. It only describes what it could do rather than instructing how to do anything. | 1 / 3 |
Workflow Clarity | No workflow, steps, or process is defined. The skill claims to provide 'step-by-step guidance' but contains none. There are no validation checkpoints or sequenced operations. | 1 / 3 |
Progressive Disclosure | The content is a flat, 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
3076d78
- Reviewed
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