cross-validation-setup
Cross Validation Setup - Auto-activating skill for ML Training. Triggers on: cross validation setup, cross validation setup Part of the ML Training skill category.
34
Quality
3%
Does it follow best practices?
Impact
89%
0.92xAverage 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/cross-validation-setup/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, essentially serving as a placeholder rather than a functional skill description. It lacks concrete actions, meaningful trigger terms, and explicit usage guidance. The redundant trigger term and boilerplate structure suggest this was auto-generated without customization.
Suggestions
Add specific concrete actions like 'Configure k-fold cross validation, implement stratified splits, set up leave-one-out validation, define train/validation/test ratios'
Include a 'Use when...' clause with natural trigger terms: 'Use when the user mentions k-fold, CV, validation splits, train-test split, holdout validation, or needs to evaluate model generalization'
Remove the redundant trigger term and expand with variations users actually say: 'cross-validation', 'CV setup', 'validation strategy', 'fold configuration'
| Dimension | Reasoning | Score |
|---|---|---|
Specificity | The description only names the domain 'cross validation setup' without describing any concrete actions. There are no specific capabilities listed like 'configure k-fold splits', 'stratify data', or 'implement leave-one-out validation'. | 1 / 3 |
Completeness | The description fails to answer 'what does this do' beyond naming the topic, and there is no explicit 'Use when...' clause or equivalent guidance for when Claude should select this skill. | 1 / 3 |
Trigger Term Quality | The trigger terms are redundant ('cross validation setup' listed twice) and miss natural variations users might say like 'k-fold', 'train-test split', 'validation strategy', 'CV', or 'holdout set'. | 1 / 3 |
Distinctiveness Conflict Risk | While 'cross validation' is a specific ML concept, the lack of detail means it could overlap with general ML training skills or data preprocessing skills. The category mention 'ML Training' provides some context but doesn't clearly carve out a distinct niche. | 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 template with no substantive content. It describes what a cross validation skill should do without providing any actual guidance, code, or instructions. The entire content is meta-description that tells Claude about the skill rather than teaching Claude how to perform cross validation setup.
Suggestions
Add executable code examples showing k-fold, stratified, and time-series cross validation implementations using sklearn or similar libraries
Include a concrete workflow: 1) Choose CV strategy based on data type, 2) Implement the split, 3) Validate fold distributions, 4) Integrate with training loop
Remove all meta-description sections (Purpose, When to Use, Capabilities, Example Triggers) and replace with actual technical content
Add specific guidance on common pitfalls like data leakage, choosing k value, and handling imbalanced datasets
| Dimension | Reasoning | Score |
|---|---|---|
Conciseness | The content is padded with generic boilerplate that explains nothing Claude doesn't already know. Phrases like 'provides automated assistance' and 'follows industry best practices' are meaningless filler with no actual information about cross validation. | 1 / 3 |
Actionability | No concrete code, commands, or specific guidance is provided. The skill describes what it claims to do ('provides step-by-step guidance') but contains zero actual steps, no code examples, and no executable instructions for implementing cross validation. | 1 / 3 |
Workflow Clarity | There is no workflow defined at all. The skill mentions 'step-by-step guidance' but provides none. No sequence of operations, no validation checkpoints, and no actual process for setting up cross validation. | 1 / 3 |
Progressive Disclosure | The content is a monolithic block of meta-description with no actual content to organize. There are no references to detailed materials, no links to examples or advanced topics, and no structure beyond generic section headers. | 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
994edc4
- Reviewed
Table of Contents
Is this your skill?
If you maintain this skill, you can claim it as your own. Once claimed, you can manage eval scenarios, bundle related skills, attach documentation or rules, and ensure cross-agent compatibility.