optuna-study-creator
Optuna Study Creator - Auto-activating skill for ML Training. Triggers on: optuna study creator, optuna study creator Part of the ML Training skill category.
36
Quality
3%
Does it follow best practices?
Impact
96%
1.02xAverage 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/optuna-study-creator/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 severely underdeveloped, functioning more as a label than a useful skill description. It lacks any concrete actions, meaningful trigger terms, or guidance on when to use the skill. The redundant trigger term and absence of capability details make it nearly useless for skill selection among multiple options.
Suggestions
Add specific actions the skill performs, e.g., 'Creates Optuna studies for hyperparameter optimization, defines search spaces, configures samplers and pruners, and analyzes optimization results.'
Include a 'Use when...' clause with natural trigger terms: 'Use when the user mentions hyperparameter tuning, HPO, parameter optimization, Optuna, or needs to optimize model training configurations.'
Remove the redundant duplicate trigger term and expand with variations users might naturally say like 'tune hyperparameters', 'optimize learning rate', 'parameter search', 'bayesian optimization'.
| Dimension | Reasoning | Score |
|---|---|---|
Specificity | The description only names the tool ('Optuna Study Creator') and category ('ML Training') without describing any concrete actions. No verbs indicating what the skill actually does (e.g., 'creates optimization studies', 'tunes hyperparameters'). | 1 / 3 |
Completeness | Missing both 'what does this do' (no actions described) and 'when should Claude use it' (no explicit use-case guidance beyond the redundant trigger phrase). No 'Use when...' clause present. | 1 / 3 |
Trigger Term Quality | The trigger terms are just 'optuna study creator' repeated twice - no natural variations users might say like 'hyperparameter tuning', 'optimize model', 'parameter search', or 'HPO'. | 1 / 3 |
Distinctiveness Conflict Risk | The mention of 'Optuna' provides some specificity to a particular library, but 'ML Training' is broad and could overlap with other ML-related skills. Without describing specific capabilities, it's unclear how this differs from other hyperparameter or training 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 only generic boilerplate text that could apply to any skill, with no actual information about Optuna, study creation, hyperparameter optimization, or any concrete implementation details. The skill fails to teach Claude anything it doesn't already know.
Suggestions
Add executable Python code showing how to create an Optuna study with create_study(), define an objective function, and run optimization with study.optimize()
Include concrete examples of common sampler and pruner configurations (e.g., TPESampler, MedianPruner) with their use cases
Define a clear workflow: 1) Create study 2) Define objective 3) Configure sampler/pruner 4) Run trials 5) Extract best parameters
Add references to separate files for advanced topics like distributed optimization, multi-objective studies, and integration with ML frameworks
| Dimension | Reasoning | Score |
|---|---|---|
Conciseness | The content is padded with generic boilerplate that provides no actual information about Optuna study creation. Phrases like 'provides automated assistance' and 'follows industry best practices' are meaningless filler that Claude doesn't need. | 1 / 3 |
Actionability | There is zero concrete guidance - no code examples, no specific commands, no actual instructions on how to create an Optuna study. The content describes rather than instructs, with vague statements like 'provides step-by-step guidance' without providing any actual steps. | 1 / 3 |
Workflow Clarity | No workflow is defined whatsoever. There are no steps, no sequence, and no validation checkpoints. The skill claims to provide 'step-by-step guidance' but contains no actual steps. | 1 / 3 |
Progressive Disclosure | The content is a monolithic block of generic text with no structure pointing to detailed materials. There are no references to additional documentation, examples, or API details that would be essential for Optuna study creation. | 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
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- Reviewed
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