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.
Install with Tessl CLI
npx tessl i github:jeremylongshore/claude-code-plugins-plus-skills --skill data-normalization-toolOverall
score
19%
Does it follow best practices?
Validation for skill structure
Activation
7%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 guidance on when to use it. The redundant trigger term and absence of capability details would make it nearly impossible for Claude to reliably select this skill from a larger skill library.
Suggestions
Add specific concrete actions the skill performs, e.g., 'Applies min-max scaling, z-score standardization, and log transformations to numerical features for ML model preparation'
Include a 'Use when...' clause with natural trigger terms like 'normalize data', 'scale features', 'standardize columns', 'preprocessing for ML', 'feature scaling'
Remove the redundant trigger term and expand with variations users would naturally say when needing data normalization
| 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 while it mentions 'Triggers on', the triggers are just the skill name repeated. There is no explicit 'Use when...' clause explaining when Claude should select this skill. | 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 | Being part of 'ML Training skill category' provides some context, and 'data normalization' is a specific enough domain. However, without concrete actions, it could overlap with general data preprocessing or feature engineering skills. | 2 / 3 |
Total | 5 / 12 Passed |
Implementation
0%This skill is entirely meta-content that describes what a skill should do without providing any actual substance. It contains zero actionable information about data normalization - no techniques (min-max, z-score, etc.), no code examples, no library recommendations, and no workflows. The content is pure boilerplate that wastes tokens without teaching Claude anything.
Suggestions
Replace the generic 'Capabilities' section with actual normalization techniques: min-max scaling, z-score standardization, robust scaling, with executable Python code using sklearn or numpy
Add a concrete workflow showing: 1) Analyze data distribution, 2) Choose normalization method, 3) Apply transformation, 4) Validate results with specific code for each step
Include specific examples showing input data, the normalization code, and expected output for at least 2-3 common scenarios
Remove all meta-descriptions ('This skill provides...', 'Follows best practices...') and replace with actual technical content
| Dimension | Reasoning | Score |
|---|---|---|
Conciseness | The content is padded with generic boilerplate that provides no actual information. Phrases like 'provides automated assistance' and 'follows industry best practices' are meaningless filler that Claude doesn't need. | 1 / 3 |
Actionability | No concrete code, commands, or specific guidance is provided. The skill describes what it claims to do but never shows how to actually normalize data - no algorithms, no code examples, no specific techniques. | 1 / 3 |
Workflow Clarity | No workflow is defined. The skill mentions 'step-by-step guidance' but provides none. There are no actual steps, no validation checkpoints, and no process to follow. | 1 / 3 |
Progressive Disclosure | The content is a monolithic block of vague descriptions with no references to detailed materials, no links to examples, and no structured navigation to actual implementation details. | 1 / 3 |
Total | 4 / 12 Passed |
Validation
69%Validation — 11 / 16 Passed
Validation for skill structure
| Criteria | Description | Result |
|---|---|---|
description_trigger_hint | Description may be missing an explicit 'when to use' trigger hint (e.g., 'Use when...') | Warning |
allowed_tools_field | 'allowed-tools' contains unusual tool name(s) | Warning |
metadata_version | 'metadata' field is not a dictionary | Warning |
frontmatter_unknown_keys | Unknown frontmatter key(s) found; consider removing or moving to metadata | Warning |
body_steps | No step-by-step structure detected (no ordered list); consider adding a simple workflow | Warning |
Total | 11 / 16 Passed | |
Reviewed
Table of Contents
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