roc-curve-plotter

Roc Curve Plotter - Auto-activating skill for ML Training. Triggers on: roc curve plotter, roc curve plotter Part of the ML Training skill category.

Install with Tessl CLI

npx tessl i github:jeremylongshore/claude-code-plugins-plus-skills --skill roc-curve-plotter

What are skills?

1.04x

Quality

11%

Does it follow best practices?

Impact

94%

1.04x

Average score across 3 eval scenarios

Optimize this skill with Tessl

npx tessl skill review --optimize ./planned-skills/generated/07-ml-training/roc-curve-plotter/SKILL.md

SKILL.md

Review

Evals

Quality

Discovery

22%

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, essentially just restating the skill name without explaining capabilities or providing meaningful trigger guidance. It lacks concrete actions (what it does), proper trigger term coverage, and explicit usage conditions. The description would fail to help Claude distinguish this skill from other ML-related tools.

Suggestions

Add specific actions the skill performs, e.g., 'Generates ROC curves from classifier predictions, calculates AUC scores, compares multiple models visually'

Include a 'Use when...' clause with natural trigger terms like 'ROC curve', 'AUC', 'receiver operating characteristic', 'classification performance', 'true positive rate', 'false positive rate'

Specify input requirements and output format, e.g., 'Takes prediction probabilities and true labels, outputs interactive ROC plots with AUC metrics'

Dimension	Reasoning	Score
Specificity	The description only names the tool ('Roc Curve Plotter') without describing any concrete actions. It doesn't explain what the skill actually does - no verbs like 'generates', 'plots', 'analyzes', or 'visualizes' are present.	1 / 3
Completeness	The description fails to answer 'what does this do' beyond the name, and while it mentions triggers, they are just the skill name repeated. There's no explicit 'Use when...' clause explaining when Claude should select this skill.	1 / 3
Trigger Term Quality	Includes 'roc curve plotter' as a trigger term (duplicated), which is relevant but misses common variations users might say like 'ROC', 'AUC', 'receiver operating characteristic', 'plot ROC', 'classification metrics', or 'model evaluation curve'.	2 / 3
Distinctiveness Conflict Risk	The 'ML Training' category and 'roc curve' terminology provide some specificity, but the vague description could overlap with other ML visualization or metrics skills. The lack of specific actions makes it harder to distinguish from general plotting tools.	2 / 3
	Total	6 / 12 Passed

Implementation

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 content about ROC curve plotting. It contains only generic boilerplate describing trigger conditions and vague capabilities, but provides zero actionable guidance, code examples, or specific instructions for the task it claims to support.

Suggestions

Add executable Python code showing how to plot ROC curves using sklearn (e.g., `from sklearn.metrics import roc_curve, auc` with a complete example)

Include specific guidance on handling multi-class ROC curves, threshold selection, and AUC interpretation

Provide concrete examples of input data format (y_true, y_scores) and expected output (matplotlib figure or saved file)

Remove all generic boilerplate text about 'automated assistance' and 'best practices' - replace with actual implementation details

Dimension	Reasoning	Score
Conciseness	The content is padded with generic boilerplate that explains nothing specific about ROC curves. Phrases like 'provides automated assistance' and 'follows industry best practices' are filler that Claude already understands.	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 for plotting ROC curves.	1 / 3
Workflow Clarity	No workflow, steps, or process is defined. The content only describes trigger conditions and vague capabilities without any actual instructions for creating ROC curve plots.	1 / 3
Progressive Disclosure	The content is a monolithic block of generic text with no references to detailed materials, examples, or related documentation. There's no structure that would help Claude navigate to actual implementation details.	1 / 3
	Total	4 / 12 Passed

Validation

81%

Warnings & errors only

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: about 16 hours ago

Table of Contents

Discovery Implementation Validation

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If you maintain this skill, you can claim it as your own. Once claimed, you can manage eval scenarios, bundle related skills, attach documentation or rules, and ensure cross-agent compatibility.