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.
35
3%
Does it follow best practices?
Impact
95%
0.98xAverage 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/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 extremely thin—it essentially just names the skill and its category without describing any concrete capabilities or providing meaningful trigger guidance. The trigger terms are duplicated and miss common natural language variations. It fails to answer both 'what does this do' and 'when should Claude use it' in any substantive way.
Suggestions
Add specific concrete actions the skill performs, e.g., 'Builds scikit-learn pipelines with preprocessing steps, feature transformers, and estimators. Configures ColumnTransformer, StandardScaler, OneHotEncoder, and model fitting.'
Add an explicit 'Use when...' clause with natural trigger terms, e.g., 'Use when the user asks about scikit-learn pipelines, sklearn Pipeline, ColumnTransformer, feature preprocessing, model training workflows, or building ML pipelines in Python.'
Remove the duplicated trigger term and expand coverage to include variations like 'scikit-learn', 'ml pipeline', 'preprocessing pipeline', 'model pipeline', '.fit()', 'train model'.
| Dimension | Reasoning | Score |
|---|---|---|
Specificity | The description names the domain ('sklearn pipeline builder', 'ML Training') but does not describe any concrete actions. There are no specific capabilities listed like 'builds preprocessing pipelines', 'configures estimators', or 'tunes hyperparameters'. | 1 / 3 |
Completeness | The 'what' is essentially absent—it only names itself without explaining what it does. The 'when' is limited to a duplicated trigger phrase with no explicit 'Use when...' clause or meaningful trigger guidance. | 1 / 3 |
Trigger Term Quality | The only trigger terms listed are 'sklearn pipeline builder' repeated twice. It misses natural variations users would say like 'scikit-learn', 'machine learning pipeline', 'feature engineering', 'model training', 'Pipeline', 'ColumnTransformer', etc. | 1 / 3 |
Distinctiveness Conflict Risk | The mention of 'sklearn' and 'pipeline builder' provides some specificity that distinguishes it from generic ML skills, but the lack of concrete actions and the broad 'ML Training' category label could cause overlap with other ML-related skills. | 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 actual instructional content. It repeatedly references 'sklearn pipeline builder' without ever providing concrete guidance, code examples, or workflows for building sklearn pipelines. It fails on every dimension because it contains no actionable information whatsoever.
Suggestions
Add executable Python code examples showing how to build sklearn pipelines (e.g., using Pipeline, ColumnTransformer, make_pipeline) with concrete preprocessing and model steps.
Define a clear workflow: data inspection → feature engineering → pipeline construction → cross-validation → hyperparameter tuning with GridSearchCV/RandomizedSearchCV, including validation checkpoints.
Remove all meta-description sections (Purpose, When to Use, Example Triggers, Capabilities) and replace with actual technical content—common pipeline patterns, best practices for column transformers, and serialization with joblib.
Add references to advanced topics in separate files (e.g., HYPERPARAMETER_TUNING.md, CUSTOM_TRANSFORMERS.md) rather than trying to cover everything inline.
| Dimension | Reasoning | Score |
|---|---|---|
Conciseness | The content is entirely filler and meta-description. It explains what the skill does in abstract terms without providing any actual technical content. Every section restates the same vague idea ('sklearn pipeline builder') without adding substance. | 1 / 3 |
Actionability | There is zero concrete guidance—no code examples, no commands, no specific steps for building sklearn pipelines. The content describes rather than instructs, offering only vague promises like 'provides step-by-step guidance' without actually providing any. | 1 / 3 |
Workflow Clarity | No workflow is defined at all. There are no steps, no sequence, no validation checkpoints. The skill claims to provide 'step-by-step guidance' but contains none. | 1 / 3 |
Progressive Disclosure | The content is a flat, repetitive document with no meaningful structure. There are no references to detailed files, no examples section, and no navigation to deeper content. The sections that exist are superficial and redundant. | 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
87f14eb
- Reviewed
Table of Contents
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