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.
61
52%
Does it follow best practices?
Impact
Pending
No eval scenarios have been run
Passed
No known issues
Optimize this skill with Tessl
npx tessl skill review --optimize ./scientific-skills/Academic Writing/radiology-image-quiz/SKILL.mdQuality
Discovery
89%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 is a solid skill description with excellent trigger terms and completeness, clearly specifying both when to use it and what it does. The main weakness is that the specificity of capabilities could be stronger—it primarily describes one action (generating quizzes) rather than listing multiple concrete actions like creating case presentations, providing diagnostic explanations, or tracking learning progress. Overall, it would perform well in skill selection due to its distinct niche and natural trigger terms.
Suggestions
Add more specific concrete actions beyond 'generates interactive quizzes', such as 'presents diagnostic findings, provides answer explanations with anatomical annotations, supports case-based learning scenarios'.
| Dimension | Reasoning | Score |
|---|---|---|
Specificity | The description names the domain (radiology education) and mentions generating interactive quizzes with specific imaging modalities (X-ray, CT, MRI, ultrasound), but doesn't list multiple concrete actions beyond 'generates interactive quizzes'. It lacks detail on what the quizzes contain (e.g., answer explanations, difficulty levels, case presentations). | 2 / 3 |
Completeness | Clearly answers both 'what' (generates interactive quizzes with medical imaging modalities) and 'when' (creating radiology educational quizzes, preparing board exam questions, studying medical imaging cases) with an explicit 'Use when' clause. | 3 / 3 |
Trigger Term Quality | Includes strong natural keywords users would say: 'radiology', 'quizzes', 'board exam questions', 'medical imaging', 'X-ray', 'CT', 'MRI', 'ultrasound', 'medical education', 'studying'. Good coverage of terms a medical student or educator would naturally use. | 3 / 3 |
Distinctiveness Conflict Risk | Highly distinctive niche combining radiology, medical imaging modalities, and educational quiz generation. Very unlikely to conflict with other skills given the specific domain of radiology education and board exam preparation. | 3 / 3 |
Total | 11 / 12 Passed |
Implementation
14%Reviews the quality of instructions and guidance provided to agents. Good implementation is clear, handles edge cases, and produces reliable results.
This skill is heavily padded with generic boilerplate that applies to any skill, not specifically to radiology quiz generation. The domain-specific content (Quick Start, Core Capabilities, CLI Usage) is buried among repetitive process management sections. There are conflicting entry points (main.py vs radiology_quiz.py), and the workflow provides no radiology-specific guidance, validation of medical accuracy, or meaningful checkpoints.
Suggestions
Remove all generic boilerplate sections (Output Requirements, Response Template, Input Validation, Error Handling, Implementation Details) that don't contain radiology-quiz-specific information, and consolidate the remaining content to eliminate duplication.
Resolve the conflicting entry points (scripts/main.py vs scripts/radiology_quiz.py) and provide a single, verified executable example with expected output.
Replace the generic workflow with radiology-quiz-specific steps: e.g., 1) Select modality and cases, 2) Generate questions, 3) Validate medical accuracy of findings/diagnoses, 4) Format output, with explicit validation checkpoints for clinical correctness.
Add a concrete end-to-end example showing input parameters and expected quiz output format (e.g., a sample generated question with answer choices, correct answer, and explanation).
| Dimension | Reasoning | Score |
|---|---|---|
Conciseness | Extremely verbose and repetitive. The description is repeated verbatim in multiple sections ('When to Use', 'Key Features'). There are large boilerplate sections (Output Requirements, Response Template, Input Validation, Error Handling) that are generic process instructions Claude already knows. The 'Implementation Details' section says 'See Workflow above' then repeats vague platitudes. Multiple redundant validation commands appear in different sections. | 1 / 3 |
Actionability | There are some concrete code examples (Quick Start, Core Capabilities, CLI Usage) that show specific API calls and parameters, but they appear to be non-executable pseudocode referencing modules (scripts/radiology_quiz.py) that may not exist alongside the referenced scripts/main.py. The conflicting entry points (main.py vs radiology_quiz.py) create confusion about what actually runs. | 2 / 3 |
Workflow Clarity | The numbered workflow in the 'Workflow' section is entirely generic process management advice ('Confirm the user objective', 'Validate that the request matches documented scope') with no radiology-quiz-specific steps. There are no validation checkpoints for quiz content accuracy, no feedback loops for verifying generated questions are medically correct, and no clear sequence for the actual quiz generation process. | 1 / 3 |
Progressive Disclosure | The document is a monolithic wall of text with heavily duplicated content across sections. There's only one reference link (references/audit-reference.md) which is generic. Content is poorly organized with redundant sections (Example Usage, Quick Start, CLI Usage all partially overlap; Audit-Ready Commands and Quick Check are nearly identical). No clear hierarchy or navigation structure. | 1 / 3 |
Total | 5 / 12 Passed |
Validation
90%Checks the skill against the spec for correct structure and formatting. All validation checks must pass before discovery and implementation can be scored.
Validation — 10 / 11 Passed
Validation for skill structure
| Criteria | Description | Result |
|---|---|---|
frontmatter_unknown_keys | Unknown frontmatter key(s) found; consider removing or moving to metadata | Warning |
Total | 10 / 11 Passed | |
- Repository
- aipoch/medical-research-skills
- Commit
8277276
- Reviewed
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