train-test-splitter
Train Test Splitter - Auto-activating skill for ML Training. Triggers on: train test splitter, train test splitter Part of the ML Training skill category.
34
3%
Does it follow best practices?
Impact
89%
1.04xAverage 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/train-test-splitter/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 restates the skill name without providing any meaningful detail about what the skill does or when it should be used. It lacks concrete actions, natural trigger terms, and explicit usage guidance, making it nearly useless for skill selection among multiple options.
Suggestions
Add specific concrete actions the skill performs, e.g., 'Splits datasets into training and testing subsets with configurable ratios, supports stratified splitting, and handles CSV/DataFrame inputs.'
Add an explicit 'Use when...' clause with natural trigger terms, e.g., 'Use when the user asks to split data into train/test sets, create holdout sets, partition datasets, or mentions train-test split ratios.'
Include natural keyword variations users would actually say, such as 'train/test split', 'split dataset', 'holdout set', 'validation set', 'data partitioning', '.csv splitting'.
| Dimension | Reasoning | Score |
|---|---|---|
Specificity | The description only names the skill ('Train Test Splitter') and mentions 'ML Training' as a category, but does not describe any concrete actions like splitting datasets, specifying ratios, stratified sampling, or handling data formats. | 1 / 3 |
Completeness | The description fails to answer 'what does this do' beyond the name itself, and the 'when' clause is just a redundant restatement of the skill name rather than meaningful trigger guidance. There is no explicit 'Use when...' clause with real context. | 1 / 3 |
Trigger Term Quality | The trigger terms are just 'train test splitter' repeated twice. It misses natural variations users would say like 'split data', 'train/test split', 'holdout set', 'validation split', 'split dataset', or 'cross-validation'. | 1 / 3 |
Distinctiveness Conflict Risk | The term 'train test splitter' is somewhat specific to a particular ML task, which gives it some distinctiveness, but the vague 'ML Training' category and lack of concrete details 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 an empty template with no substantive content. It repeatedly references 'train test splitter' without ever providing actual instructions, code examples, or guidance on how to split data into training and test sets. It fails on every dimension as it contains zero actionable information.
Suggestions
Add executable code examples showing train-test splitting with sklearn's train_test_split, including common parameters like test_size, random_state, and stratify.
Include a concrete workflow: load data → check class balance → choose split strategy (random, stratified, time-based) → split → validate split proportions.
Replace the boilerplate 'Capabilities' and 'Example Triggers' sections with actual technical content covering common patterns like stratified splits, time-series splits, and k-fold cross-validation.
Add specific guidance on pitfalls such as data leakage, handling imbalanced datasets, and when to use different splitting strategies.
| Dimension | Reasoning | Score |
|---|---|---|
Conciseness | The content is entirely filler and boilerplate. It explains nothing Claude doesn't already know, repeats 'train test splitter' excessively, and provides zero actual technical content about splitting data into train/test sets. | 1 / 3 |
Actionability | There is no concrete code, no executable commands, no specific examples, and no actual guidance on how to perform train-test splitting. The content is entirely abstract and descriptive. | 1 / 3 |
Workflow Clarity | No workflow steps are provided whatsoever. There are no sequences, no validation checkpoints, and no actual process described for splitting data. | 1 / 3 |
Progressive Disclosure | The content has section headers but they contain only vague descriptions. There are no references to detailed materials, no examples, and no meaningful content organization beyond empty scaffolding. | 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
Table of Contents
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