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 weak across all dimensions. It reads like auto-generated boilerplate with a duplicated trigger term and no concrete actions or explicit usage guidance. It would be nearly impossible for Claude to reliably select this skill from a pool of ML-related skills.
Suggestions
Add specific concrete actions the skill performs, e.g., 'Constructs scikit-learn pipelines with preprocessing steps, feature transformers, and estimators. Handles ColumnTransformer, StandardScaler, OneHotEncoder, and model fitting.'
Add an explicit 'Use when...' clause with natural trigger terms, e.g., 'Use when the user asks about building sklearn/scikit-learn pipelines, chaining preprocessing and model steps, or creating ML training workflows in Python.'
Remove the duplicate trigger term and expand with natural variations users would say: 'scikit-learn', 'sklearn Pipeline', 'ML pipeline', 'preprocessing pipeline', 'model training pipeline', '.fit()', 'cross-validation'.
| 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', 'fits models', 'tunes hyperparameters', etc. | 1 / 3 |
Completeness | The 'what' is extremely vague (no concrete actions described) and the 'when' is only implied through the duplicate trigger term. There is no explicit 'Use when...' clause with meaningful trigger guidance. | 1 / 3 |
Trigger Term Quality | The trigger terms are just 'sklearn pipeline builder' repeated twice. Missing natural variations users would say like 'scikit-learn', 'machine learning pipeline', 'preprocessing', 'model training', 'Pipeline', 'ColumnTransformer', etc. | 1 / 3 |
Distinctiveness Conflict Risk | The mention of 'sklearn pipeline builder' is somewhat specific to scikit-learn pipelines, which narrows the domain. However, the vague 'ML Training' category could overlap with other ML-related skills, and the lack of specific actions makes it harder to distinguish from general ML 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 providing any concrete code, workflows, or actionable guidance. It fails on every dimension because it contains no substantive information that would help Claude build sklearn pipelines.
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 multi-step 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 patterns, gotchas, and copy-paste-ready code.
Add references to advanced topics in separate files (e.g., custom transformers, nested pipelines, experiment tracking integration) rather than listing vague capabilities.
| 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 delivering 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
3076d78
- Reviewed
Table of Contents
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