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.
Install with Tessl CLI
npx tessl i github:jeremylongshore/claude-code-plugins-plus-skills --skill sklearn-pipeline-builder35
Quality
3%
Does it follow best practices?
Impact
95%
0.98xAverage score across 3 eval scenarios
Optimize this skill with Tessl
npx tessl skill review --optimize ./planned-skills/generated/07-ml-training/sklearn-pipeline-builder/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, functioning more as a label than a useful skill description. It provides no information about what capabilities the skill offers, lists redundant trigger terms, and gives no guidance on when Claude should select it. The description would fail to help Claude distinguish this skill from others in a multi-skill environment.
Suggestions
Add specific concrete actions the skill performs, e.g., 'Builds scikit-learn pipelines with preprocessing steps, feature transformers, and estimators. Configures cross-validation, hyperparameter tuning, and model serialization.'
Expand trigger terms to include natural variations: 'sklearn', 'scikit-learn', 'ML pipeline', 'preprocessing pipeline', 'feature engineering', 'model training pipeline', 'ColumnTransformer', 'Pipeline object'.
Add an explicit 'Use when...' clause: 'Use when the user needs to create, configure, or debug scikit-learn Pipeline objects, or when building end-to-end ML workflows with preprocessing and model training.'
| Dimension | Reasoning | Score |
|---|---|---|
Specificity | The description only names the domain ('sklearn pipeline builder', 'ML Training') but provides no concrete actions. It doesn't explain what the skill actually does - no verbs describing capabilities like 'builds pipelines', 'configures transformers', or 'trains models'. | 1 / 3 |
Completeness | The description fails to answer 'what does this do' beyond naming itself, and the 'when' guidance is circular (use when user says the skill name). No explicit trigger guidance for actual use cases or user intents. | 1 / 3 |
Trigger Term Quality | The trigger terms are redundant ('sklearn pipeline builder' listed twice) and overly specific. Missing natural variations users would say like 'machine learning pipeline', 'scikit-learn', 'ML pipeline', 'preprocessing pipeline', 'model training', or 'feature engineering'. | 1 / 3 |
Distinctiveness Conflict Risk | The sklearn/pipeline focus provides some specificity that distinguishes it from generic ML skills, but 'ML Training' is broad enough to potentially conflict with other machine learning skills. The lack of specific capabilities makes boundaries unclear. | 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 actual content about sklearn pipeline building. It contains only generic meta-descriptions of what a skill should do, without any concrete guidance, code examples, or actionable instructions. The entire content could be replaced with a single sentence: 'This skill helps with sklearn pipelines' and convey the same (lack of) information.
Suggestions
Add executable Python code examples showing how to build sklearn pipelines (e.g., Pipeline, ColumnTransformer, make_pipeline)
Include concrete patterns for common pipeline tasks: preprocessing, feature engineering, model chaining
Provide specific guidance on pipeline best practices: naming conventions, caching, parameter access, serialization
Add validation/debugging steps for common pipeline errors (e.g., shape mismatches, transformer fitting issues)
| Dimension | Reasoning | Score |
|---|---|---|
Conciseness | The content is padded with generic boilerplate that explains nothing specific about sklearn pipelines. Phrases like 'provides automated assistance' and 'follows industry best practices' are filler that Claude doesn't need. | 1 / 3 |
Actionability | No concrete code, commands, or specific guidance is provided. The skill describes what it does abstractly ('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 building sklearn pipelines. | 1 / 3 |
Progressive Disclosure | No structure beyond generic headings. No references to detailed documentation, examples, or related files. The content is a shallow placeholder with no depth or navigation. | 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 | |
- Reviewed
Table of Contents
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