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.
Overall
score
24%
Does it follow best practices?
Validation for skill structure
Install with Tessl CLI
npx tessl i github:jeremylongshore/claude-code-plugins-plus-skills --skill roc-curve-plotterActivation
22%This description is severely underdeveloped, essentially just restating the skill name without explaining capabilities or usage triggers. It relies entirely on the name 'Roc Curve Plotter' to convey meaning, providing no actionable information about what the skill does or when Claude should select it over other ML-related skills.
Suggestions
Add specific actions the skill performs, e.g., 'Generates ROC curves from classification model predictions, calculates AUC scores, compares multiple models' performance'
Include a 'Use when...' clause with natural trigger scenarios, e.g., 'Use when the user asks to visualize classifier performance, plot ROC curves, calculate AUC, or compare model discrimination ability'
Expand trigger terms to include variations users might say: 'ROC', 'AUC', 'receiver operating characteristic', 'true positive rate', 'false positive rate', 'classification threshold'
| Dimension | Reasoning | Score |
|---|---|---|
Specificity | The description only names the tool ('Roc Curve Plotter') and category ('ML Training') but provides no concrete actions. It doesn't explain what the skill actually does - no mention of plotting, analyzing, comparing curves, or any specific capabilities. | 1 / 3 |
Completeness | The description fails to answer 'what does this do' beyond the name, and has no explicit 'when to use' guidance. The 'Triggers on' line is just the skill name repeated, not meaningful trigger scenarios. | 1 / 3 |
Trigger Term Quality | Includes 'roc curve plotter' as a trigger term (duplicated), which is a natural phrase users might say. However, it misses common variations like 'ROC', 'AUC', 'receiver operating characteristic', 'classification performance', or 'model evaluation'. | 2 / 3 |
Distinctiveness Conflict Risk | The 'roc curve' terminology is fairly specific to a particular ML visualization task, reducing conflict risk. However, the vague 'ML Training' category and lack of specific actions could cause overlap with other ML-related skills. | 2 / 3 |
Total | 6 / 12 Passed |
Implementation
0%This skill is essentially an empty template with no actual content about ROC curve plotting. It contains only generic placeholder text that could apply to any skill, with zero domain-specific information, code examples, or actionable guidance. The skill completely fails to teach Claude anything about ROC curves, AUC metrics, or visualization techniques.
Suggestions
Add executable Python code showing how to plot ROC curves using sklearn and matplotlib (e.g., `from sklearn.metrics import roc_curve, auc; fpr, tpr, _ = roc_curve(y_true, y_scores)`)
Include a concrete example with sample input data and expected output visualization
Remove all generic boilerplate text ('provides automated assistance', 'follows industry best practices') and replace with actual ROC-specific guidance
Add specific guidance on handling multi-class ROC curves, choosing thresholds, and interpreting AUC values
| Dimension | Reasoning | Score |
|---|---|---|
Conciseness | The content is padded with generic boilerplate that explains nothing Claude doesn't already know. Phrases like 'provides automated assistance' and 'follows industry best practices' are meaningless filler with no actual information about ROC curves. | 1 / 3 |
Actionability | No concrete code, commands, or executable guidance whatsoever. The skill describes what it supposedly does but provides zero actual instructions on how to plot ROC curves - no matplotlib code, no sklearn examples, nothing actionable. | 1 / 3 |
Workflow Clarity | No workflow is defined. The content mentions 'step-by-step guidance' but provides none. There are no steps, no sequence, and no validation checkpoints for what should be a straightforward plotting task. | 1 / 3 |
Progressive Disclosure | The content is a monolithic block of vague descriptions with no structure pointing to actual resources. No references to example code, no links to detailed documentation, and no organized sections with real content. | 1 / 3 |
Total | 4 / 12 Passed |
Validation
69%Validation — 11 / 16 Passed
Validation for skill structure
| Criteria | Description | Result |
|---|---|---|
description_trigger_hint | Description may be missing an explicit 'when to use' trigger hint (e.g., 'Use when...') | Warning |
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 |
body_steps | No step-by-step structure detected (no ordered list); consider adding a simple workflow | Warning |
Total | 11 / 16 Passed | |
Reviewed
Table of Contents
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