data-normalization-tool
Data Normalization Tool - Auto-activating skill for ML Training. Triggers on: data normalization tool, data normalization tool Part of the ML Training skill category.
39
Quality
7%
Does it follow best practices?
Impact
99%
1.00xAverage 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/data-normalization-tool/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 just restating the skill name without providing any meaningful information about capabilities or usage triggers. It lacks specific actions, natural trigger terms, and explicit guidance on when Claude should select this skill. The redundant trigger terms suggest auto-generated content without human refinement.
Suggestions
Add specific concrete actions the tool performs, e.g., 'Applies min-max scaling, z-score standardization, and robust scaling to numerical features for ML model training'
Include a 'Use when...' clause with natural trigger terms like 'normalize data', 'scale features', 'standardize dataset', 'preprocessing for ML', 'feature scaling'
Remove the redundant trigger term and expand with variations users would naturally say, including file types (.csv, .parquet) or contexts (training data, feature engineering)
| Dimension | Reasoning | Score |
|---|---|---|
Specificity | The description only names the tool category ('Data Normalization Tool') without describing any concrete actions. There are no specific capabilities listed like 'scales features', 'handles missing values', or 'applies min-max normalization'. | 1 / 3 |
Completeness | The description fails to answer 'what does this do' beyond the name, and the 'when' clause is just a repetition of the skill name rather than meaningful trigger guidance. No explicit 'Use when...' clause with actionable triggers. | 1 / 3 |
Trigger Term Quality | The trigger terms are redundant (lists 'data normalization tool' twice) and overly generic. Missing natural variations users would say like 'normalize data', 'scale features', 'standardize', 'preprocessing', or 'feature scaling'. | 1 / 3 |
Distinctiveness Conflict Risk | While 'data normalization' is somewhat specific to ML preprocessing, the lack of detail means it could overlap with general data processing skills or other ML preprocessing tools. The 'ML Training' category helps slightly but isn't sufficient. | 2 / 3 |
Total | 5 / 12 Passed |
Implementation
7%Reviews the quality of instructions and guidance provided to agents. Good implementation is clear, handles edge cases, and produces reliable results.
This skill is a hollow template with no actual content. It describes capabilities in abstract terms but provides zero concrete guidance on data normalization—no code examples, no specific techniques (min-max scaling, z-score, etc.), no library usage, and no workflows. The skill would be completely useless for helping Claude perform data normalization tasks.
Suggestions
Add concrete, executable code examples for common normalization techniques (e.g., sklearn's StandardScaler, MinMaxScaler, or manual implementations)
Include specific guidance on when to use different normalization methods (e.g., min-max for bounded features, z-score for Gaussian distributions)
Provide a clear workflow: load data → identify columns → choose method → apply → validate results
Remove all generic boilerplate ('provides automated assistance', 'follows best practices') and replace with actual actionable content
| Dimension | Reasoning | Score |
|---|---|---|
Conciseness | The content is padded with generic boilerplate that explains nothing specific. Phrases like 'provides automated assistance' and 'follows industry best practices' are filler that Claude doesn't need and add no actionable value. | 1 / 3 |
Actionability | No concrete code, commands, or specific guidance is provided. The skill describes what it does in abstract terms ('provides step-by-step guidance') but never actually provides any guidance, examples, or executable content. | 1 / 3 |
Workflow Clarity | No workflow, steps, or process is defined. The skill claims to provide 'step-by-step guidance' but contains zero actual steps for data normalization tasks. | 1 / 3 |
Progressive Disclosure | The content is organized into sections with clear headers, but there are no references to detailed materials, no links to examples or advanced content, and the sections themselves contain no substantive information to disclose. | 2 / 3 |
Total | 5 / 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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