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tooluniverse-diagnostic-test-evaluation

Diagnostic test / biomarker accuracy — sensitivity, specificity, PPV, NPV, likelihood ratios, accuracy from a 2x2 table; ROC curve, AUC, and the optimal cutoff (Youden) for a continuous biomarker; and post-test probability via Bayes. Use when you have test results vs a gold standard (binary 2x2, or a continuous score + true labels) and need to judge how good the test is, pick a threshold, or compute the probability of disease given a result. Emphasizes the prevalence-dependence of PPV/NPV.

76

Quality

95%

Does it follow best practices?

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SecuritybySnyk

Low

Low-risk findings worth noting

SKILL.md
Quality
Evals
Security

Quality

Content

100%

Reviews the quality of instructions and guidance provided to agents. Good implementation is clear, handles edge cases, and produces reliable results.

An exemplary skill body: concise, fully actionable, with a clear routing workflow and appropriately split-out detail in a real bundle script.

DimensionReasoningScore

Conciseness

Lean and action-dense with no prose padding: commands, compact reference tables, and targeted gotchas; assumes Claude's competence without explaining what a ROC curve or AUC is in narrative form.

3 / 3

Actionability

Fully executable, copy-paste-ready invocations throughout — `tu run Epidemiology_diagnostic '{...}'`, `ROC_analysis(scores=[...], labels=[...])`, `python skills/.../scripts/roc_analysis.py --input scores.csv`, and `tu run Epidemiology_bayesian '{...}'`.

3 / 3

Workflow Clarity

A "Which case are you in?" routing table funnels into sequenced Steps 1–3 with explicit cross-links ("Once you choose a cutoff, build its 2×2 and run Step 1") and a held-out-validation gotcha; not a batch/destructive task so no validate-fix-retry loop is required.

3 / 3

Progressive Disclosure

Well-organized overview with a one-level-deep, clearly-signaled bundle reference (scripts/roc_analysis.py, verified present on disk) and a Related-skills section; no nested reference chains.

3 / 3

Total

12

/

12

Passed

Description

90%

Based on the skill's description, can an agent find and select it at the right time? Clear, specific descriptions lead to better discovery.

A specific, well-triggered description that names concrete actions and explicit use-conditions; the only weakness is second-person voice ("you have"), which the rubric penalizes on specificity.

Suggestions

Rewrite the trigger clause in third person to avoid the voice penalty, e.g. "Use when test results vs a gold standard are available and one needs to judge test quality, pick a threshold, or compute the probability of disease given a result."

DimensionReasoningScore

Specificity

Lists multiple concrete actions ("sensitivity, specificity, PPV, NPV, likelihood ratios, accuracy from a 2x2 table; ROC curve, AUC, and the optimal cutoff (Youden)... post-test probability via Bayes"), which is anchor-3 content, but the second-person voice ("Use when you have test results") triggers the rubric's -1 specificity penalty.

2 / 3

Completeness

Clearly answers both what (the metric families computed) and when ("Use when you have test results vs a gold standard ... and need to judge how good the test is, pick a threshold, or compute the probability of disease given a result"), with explicit triggers.

3 / 3

Trigger Term Quality

Good coverage of natural terms a user would say — "diagnostic test", "biomarker", "sensitivity/specificity/PPV/NPV", "ROC", "AUC", "gold standard", "probability of disease" — matching the anchor-3 example.

3 / 3

Distinctiveness Conflict Risk

A clear niche (diagnostic-test / biomarker accuracy evaluation) with distinct triggers and prevalence-dependence framing, unlikely to fire for unrelated skills.

3 / 3

Total

11

/

12

Passed

Validation

100%

Checks the skill against the spec for correct structure and formatting. All validation checks must pass before discovery and implementation can be scored.

Validation16 / 16 Passed

Validation for skill structure

No warnings or errors.

Repository
mims-harvard/ToolUniverse
Reviewed

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