building-classification-models

tessl i github:jeremylongshore/claude-code-plugins-plus-skills --skill building-classification-models

Build and evaluate classification models for supervised learning tasks with labeled data. Use when requesting "build a classifier", "create classification model", or "train classifier". Trigger with relevant phrases based on skill purpose.

43%

Overall

Validation — 81%

Implementation — 7%

Activation — 67%

SKILL.md

Review

Evals

Validation

81%

Warnings & errors only

Criteria	Description	Result
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

	Total	13 / 16 Passed

Implementation

This skill content is almost entirely abstract description with no actionable guidance. It explains what a classification model builder would do conceptually but provides zero executable code, no specific library recommendations, no concrete workflows, and no actual implementation details Claude could use to build a classifier.

Suggestions

Replace abstract descriptions with executable Python code examples using specific libraries (e.g., scikit-learn) showing actual model training, evaluation, and prediction workflows

Add concrete validation steps with specific commands/code for checking data quality, model performance thresholds, and error handling

Remove sections that explain concepts Claude already knows (Overview, How It Works descriptions) and replace with copy-paste ready code snippets

Provide specific metric thresholds, algorithm selection criteria, and hyperparameter tuning examples rather than generic best practices

Dimension	Reasoning	Score
Conciseness	Extremely verbose with extensive padding explaining concepts Claude already knows (what classification is, how models work). Sections like 'How It Works', 'Overview', and generic 'Instructions' add no actionable value and waste tokens.	1 / 3
Actionability	No executable code, no concrete commands, no specific algorithms or libraries mentioned. Examples describe what 'the skill will do' abstractly rather than providing actual implementation guidance Claude can follow.	1 / 3
Workflow Clarity	Steps are vague abstractions ('analyze', 'generate code') with no concrete sequence. No validation checkpoints, no specific commands, and no feedback loops for error recovery in what should be a multi-step ML workflow.	1 / 3
Progressive Disclosure	Content has section headers providing some structure, but everything is inline in one monolithic file. References to 'classification-model-builder plugin' and 'project documentation' are vague with no actual links or file paths.	2 / 3
	Total	5 / 12 Passed

Activation

67%

The description adequately covers the what and when, earning good marks for completeness. However, it lacks specific concrete actions beyond 'build and evaluate', and the trigger terms are limited with a meaningless filler phrase at the end. The description would benefit from more specific capabilities and natural user language variations.

Suggestions

Replace the vague 'Trigger with relevant phrases based on skill purpose' with actual trigger terms like 'predict categories', 'binary classification', 'multi-class prediction', or specific algorithm names.

Add specific concrete actions such as 'train decision trees, evaluate with confusion matrices, perform hyperparameter tuning, compare model accuracy'.

Include common user phrases and file types like 'categorize data', 'predict labels', 'sklearn classifier', or 'classification accuracy'.

Dimension	Reasoning	Score
Specificity	Names the domain (classification models, supervised learning) and general action (build and evaluate), but lacks specific concrete actions like 'train decision trees, evaluate with confusion matrices, perform cross-validation'.	2 / 3
Completeness	Explicitly answers both what (build and evaluate classification models for supervised learning) and when (Use when requesting specific phrases). Has a clear 'Use when...' clause with trigger examples.	3 / 3
Trigger Term Quality	Includes some relevant keywords ('build a classifier', 'create classification model', 'train classifier') but the final sentence 'Trigger with relevant phrases based on skill purpose' is vague filler that adds no value. Missing common variations like 'predict categories', 'label data', 'random forest', 'logistic regression'.	2 / 3
Distinctiveness Conflict Risk	Somewhat specific to classification but could overlap with general ML skills, regression modeling, or data science skills. The 'supervised learning' qualifier helps but 'labeled data' is broad.	2 / 3
	Total	9 / 12 Passed

Reviewed

14 days ago

Table of Contents

Validation Implementation Activation

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