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.
35
3%
Does it follow best practices?
Impact
94%
1.04xAverage 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/roc-curve-plotter/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 essentially a template placeholder with minimal useful content. It names the skill but fails to describe any concrete capabilities, lacks natural trigger terms users would use, and provides no 'Use when...' guidance. It would be very difficult for Claude to reliably select this skill from a pool of ML-related skills.
Suggestions
Add concrete actions the skill performs, e.g., 'Generates ROC (Receiver Operating Characteristic) curves, computes AUC scores, and compares classifier performance across multiple models.'
Add an explicit 'Use when...' clause with natural trigger terms, e.g., 'Use when the user asks to plot ROC curves, evaluate classifier performance, compute AUC, or visualize true positive vs false positive rates.'
Remove the duplicated trigger term and expand with natural variations users would say: 'ROC curve', 'AUC score', 'receiver operating characteristic', 'classification evaluation', 'TPR vs FPR plot'.
| Dimension | Reasoning | Score |
|---|---|---|
Specificity | The description names the tool ('Roc Curve Plotter') and mentions 'ML Training' as a category, but provides no concrete actions—it doesn't describe what the skill actually does (e.g., plot ROC curves, compute AUC, compare classifiers). | 1 / 3 |
Completeness | The description barely addresses 'what' (just the name) and has no explicit 'when' clause explaining when Claude should select this skill. The 'Triggers on' line is just a repeated label, not meaningful trigger guidance. | 1 / 3 |
Trigger Term Quality | The only trigger terms listed are 'roc curve plotter' repeated twice. It misses natural variations users would say such as 'ROC curve', 'AUC', 'receiver operating characteristic', 'plot ROC', 'classification performance', or 'true positive rate'. | 1 / 3 |
Distinctiveness Conflict Risk | The term 'ROC curve' is fairly specific to a niche ML visualization task, which provides some natural distinctiveness. However, the vague 'ML Training' category and lack of detail could cause overlap with other ML-related 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 an empty template with no actual content about ROC curve plotting. It contains only boilerplate descriptions of what the skill claims to do without providing any actionable guidance, code examples, or technical information. It would be entirely useless to Claude for performing ROC curve plotting tasks.
Suggestions
Add executable Python code examples showing how to plot ROC curves using sklearn (e.g., `sklearn.metrics.roc_curve`, `sklearn.metrics.auc`, and `matplotlib.pyplot` plotting code).
Include concrete guidance for common scenarios: binary classification, multi-class one-vs-rest, comparing multiple models on the same plot, and saving/exporting the figure.
Add a brief workflow: compute predictions → calculate FPR/TPR with `roc_curve()` → compute AUC → plot with labeled axes and legend → save output.
Remove all boilerplate sections ('When to Use', 'Example Triggers', 'Capabilities') that describe the skill meta-information rather than teaching how to plot ROC curves.
| Dimension | Reasoning | Score |
|---|---|---|
Conciseness | The content is entirely filler and boilerplate. It explains nothing Claude doesn't already know, repeats 'roc curve plotter' excessively, and provides zero actual technical content about plotting ROC curves. | 1 / 3 |
Actionability | There is no concrete code, no executable commands, no specific examples of how to plot an ROC curve. The content is entirely abstract descriptions like 'Provides step-by-step guidance' without any actual guidance. | 1 / 3 |
Workflow Clarity | No workflow is defined at all. There are no steps, no sequence, no validation checkpoints—just vague claims about providing 'step-by-step guidance' without delivering any. | 1 / 3 |
Progressive Disclosure | The content has section headers but they contain no meaningful information. There are no references to external files, no structured content to navigate, and no useful organization of actual knowledge. | 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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