Names the domain (classification models, supervised learning) and a general action (build and evaluate), but does not list multiple specific concrete actions like feature engineering, hyperparameter tuning, cross-validation, confusion matrix generation, etc.

Explicitly answers both 'what' (build and evaluate classification models for supervised learning tasks with labeled data) and 'when' (with a 'Use when' clause listing specific trigger phrases). Both components are present and explicit.

Includes some relevant trigger phrases like 'build a classifier', 'create classification model', 'train classifier', but misses many natural variations users might say such as 'predict categories', 'label prediction', 'logistic regression', 'random forest', 'classify data', 'categorize'. The final sentence 'Trigger with relevant phrases based on skill purpose' is vague filler that adds no value.

Somewhat specific to classification but could overlap with general ML/data science skills, regression modeling skills, or broader 'build a model' requests. The distinction between classification and other supervised learning tasks (e.g., regression) is mentioned but not strongly reinforced with unique triggers.

Extremely verbose with extensive padding. Explains concepts Claude already knows (what classification is, what data quality means, what hyperparameter tuning is). Sections like 'How It Works', 'When to Use This Skill', 'Overview', 'Best Practices', 'Integration', 'Prerequisites', 'Instructions', 'Output', 'Error Handling', and 'Resources' are all filler with no actionable content. References a nonexistent 'classification-model-builder plugin' repeatedly.

Contains zero executable code, no concrete commands, no specific library recommendations, no actual implementation guidance. The examples describe what 'the skill will' do in abstract terms rather than providing any concrete code or steps. Instructions like 'Invoke this skill when the trigger conditions are met' and 'Provide necessary context and parameters' are completely vague.

No clear, actionable workflow is defined. The 'How It Works' section describes an abstract 3-step process with no specifics. The 'Instructions' section is a generic 4-step placeholder that could apply to literally anything. No validation checkpoints, no error recovery loops, no concrete sequencing of actual ML pipeline steps.

Monolithic wall of text with no references to external files and no bundle files provided. Content is poorly organized with many redundant sections (Overview, How It Works, When to Use, Examples, Best Practices, Integration, Prerequisites, Instructions, Output, Error Handling, Resources) that mostly contain filler. The 'Resources' section lists 'Project documentation' and 'Related skills and commands' with no actual links.