sklearn-pipeline-builder
Sklearn Pipeline Builder - Auto-activating skill for ML Training. Triggers on: sklearn pipeline builder, sklearn pipeline builder Part of the ML Training skill category.
Overall
score
28%
Does it follow best practices?
Validation for skill structure
Install with Tessl CLI
npx tessl i github:jeremylongshore/claude-code-plugins-plus-skills --skill sklearn-pipeline-builderActivation
22%This description is severely underdeveloped, functioning more as a label than a useful skill description. It lacks any concrete actions or capabilities, has redundant trigger terms, and provides no explicit guidance on when Claude should select this skill. The description would be nearly useless for skill selection among multiple ML-related skills.
Suggestions
Add specific concrete actions the skill performs, e.g., 'Builds sklearn preprocessing and model pipelines, configures transformers, handles feature engineering, and exports trained models'
Add an explicit 'Use when...' clause, e.g., 'Use when the user needs to create machine learning pipelines, combine preprocessing steps, or build end-to-end sklearn workflows'
Expand trigger terms to include natural variations: 'scikit-learn', 'ML pipeline', 'preprocessing pipeline', 'ColumnTransformer', 'feature pipeline', 'model training workflow'
| Dimension | Reasoning | Score |
|---|---|---|
Specificity | The description only names the domain ('ML Training', 'sklearn pipeline') but provides no concrete actions. It doesn't explain what the skill actually does - no verbs describing capabilities like 'builds', 'configures', 'trains', etc. | 1 / 3 |
Completeness | The 'what' is extremely weak (no actual capabilities described) and the 'when' is only implied through trigger terms rather than explicitly stated. Missing a proper 'Use when...' clause with clear guidance. | 1 / 3 |
Trigger Term Quality | Includes 'sklearn pipeline builder' as a trigger term which is relevant, but the trigger list is redundant (same term twice) and misses common variations users might say like 'machine learning pipeline', 'scikit-learn', 'ML model', 'preprocessing pipeline', or 'feature engineering'. | 2 / 3 |
Distinctiveness Conflict Risk | The 'sklearn' and 'pipeline builder' terms provide some specificity, but 'ML Training' is broad and could overlap with other machine learning skills. Without concrete actions, it's unclear how this differs from general ML or data science skills. | 2 / 3 |
Total | 6 / 12 Passed |
Implementation
7%This skill is essentially a placeholder with no substantive content. It describes what it claims to do but provides zero actual guidance on sklearn pipeline building - no code examples, no workflow steps, no concrete patterns. The entire content could be replaced with a single executable code example that would be far more useful.
Suggestions
Add executable Python code showing how to build a basic sklearn Pipeline with preprocessing and model steps (e.g., StandardScaler + LogisticRegression)
Include concrete examples of common pipeline patterns: ColumnTransformer for mixed data types, FeatureUnion for parallel feature extraction
Add a workflow section with steps for pipeline construction, fitting, cross-validation, and persistence (joblib.dump)
Remove the generic 'Capabilities' and 'Example Triggers' sections - they add no value and waste tokens
| Dimension | Reasoning | Score |
|---|---|---|
Conciseness | The content is padded with generic boilerplate that provides no actual value. Phrases like 'provides automated assistance' and 'follows industry best practices' are vague filler that Claude doesn't need. | 1 / 3 |
Actionability | No concrete code, commands, or specific guidance is provided. The skill describes what it does in abstract terms but never shows how to actually build an sklearn pipeline. | 1 / 3 |
Workflow Clarity | No workflow steps are defined. Claims to provide 'step-by-step guidance' but contains zero actual steps for building sklearn pipelines. | 1 / 3 |
Progressive Disclosure | The content is organized into clear sections with headers, but there are no references to detailed materials, examples, or external documentation that would help users learn more. | 2 / 3 |
Total | 5 / 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
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.