radiology-image-quiz

Use when creating radiology educational quizzes, preparing board exam questions, or studying medical imaging cases. Generates interactive quizzes with X-ray, CT, MRI, and ultrasound images for medical education.

Quality

63%

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Securityby

Passed

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Fix and improve this skill with Tessl

tessl review fix ./scientific-skills/Academic Writing/radiology-image-quiz/SKILL.md

Quality

Content

42%Weight 40%Scale 1-5

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

The content is over-padded with generic process boilerplate and contains API/CLI examples that do not match the bundled script, undermining executability. Trimming the template sections and aligning code examples to the actual scripts/main.py surface would substantially improve quality.

Suggestions

Remove the duplicated description text in 'When to Use' and 'Key Features' and cut generic template sections (Output Requirements, Response Template, Input Validation, Error Handling) that restate process concepts Claude already knows.

Replace the fabricated Quick Start/Core Capabilities examples with the real API surface: scripts/main.py exposes RadiologyQuiz.generate_quiz(cases) with a --demo/--cases CLI, not create/create_case/set_difficulty or scripts/radiology_quiz.py.

Tie the Workflow steps to concrete validation checkpoints (e.g. run py_compile, then python scripts/main.py --demo) so the sequence has explicit feedback loops rather than generic fallback language.

Dimension	Reasoning	Score
Conciseness	The body is noticeably verbose with generic template boilerplate (Output Requirements, Response Template, Input Validation, Error Handling, Implementation Details) explaining process concepts Claude already knows, plus duplicated copies of the description text in 'When to Use' and 'Key Features'.	2 / 5
Actionability	The audit commands (py_compile, --help) are executable, but the Quick Start and Core Capabilities code examples reference non-existent methods (create, create_case, set_difficulty) and the CLI cites scripts/radiology_quiz.py instead of the actual scripts/main.py, leaving key details incomplete.	3 / 5
Workflow Clarity	A five-step Workflow and Example run plan are present, but validation checkpoints are implicit and untied to the actual quiz-generation steps, and the fallback path is described generically rather than as a concrete feedback loop.	3 / 5
Progressive Disclosure	There is one real, clearly signaled one-level reference (references/audit-reference.md) and a scripts/ entry, but the reference largely duplicates body content and large amounts of generic guidance are inlined rather than split out.	3 / 5
	Total	11 / 20 Passed

Description

83%Weight 40%Scale 1-5

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

The description is strong: it clearly states what the skill does and when to use it with concrete, natural trigger phrases and a well-scoped radiology niche. Minor improvements would add common terms like DICOM and clarify grading/explanation capabilities.

Dimension	Reasoning	Score
Specificity	Names several concrete actions (creating quizzes, preparing board exam questions, studying cases) across four imaging modalities (X-ray, CT, MRI, ultrasound), with only minor gaps such as scoring/grading or answer-key generation.	4 / 5
Completeness	Explicitly answers both 'what' ('Generates interactive quizzes with X-ray, CT, MRI, and ultrasound images') and 'when' ('Use when creating radiology educational quizzes...') with concrete trigger phrases, matching the top anchor.	5 / 5
Trigger Term Quality	Includes natural phrases a user would say ('radiology educational quizzes', 'board exam questions', 'medical imaging cases') plus modality synonyms, but misses common variations like 'DICOM' or 'radiology images'.	4 / 5
Distinctiveness Conflict Risk	Radiology image quizzes form a clear niche with distinct triggers and minimal conflict risk, though the broad 'medical education' framing allows minor overlap with general medical-education quiz skills.	4 / 5
	Total	17 / 20 Passed

Validation

87%

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 — 14 / 16 Passed

Validation for skill structure

Criteria	Description	Result
frontmatter_unknown_keys	Unknown frontmatter key(s) found; consider removing or moving to metadata	Warning
referenced_paths_exist	Referenced path issues: 1 missing	Warning

	Total	14 / 16 Passed

Repository: aipoch/medical-research-skills
Path: scientific-skills/Academic Writing/radiology-image-quiz/SKILL.md
Commit: 63c61d3

Reviewed: 8 hours ago

Table of Contents

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