| name | anatomy-quiz-master |
| description | Generate interactive anatomy quizzes for medical education with multiple. |
| license | MIT |
| author | AIPOCH |
Source: https://github.com/aipoch/medical-research-skills
Anatomy Quiz Master
When to Use
- Use this skill when the task is to Generate interactive anatomy quizzes for medical education with multiple.
- Use this skill for academic writing tasks that require explicit assumptions, bounded scope, and a reproducible output format.
- Use this skill when you need a documented fallback path for missing inputs, execution errors, or partial evidence.
Key Features
- Scope-focused workflow aligned to: Generate interactive anatomy quizzes for medical education with multiple.
- Packaged executable path(s):
scripts/main.py.
- Reference material available in
references/ for task-specific guidance.
- Structured execution path designed to keep outputs consistent and reviewable.
Dependencies
Python: 3.10+. Repository baseline for current packaged skills.
argparse: unspecified. Declared in requirements.txt.
json: unspecified. Declared in requirements.txt.
random: unspecified. Declared in requirements.txt.
Example Usage
See ## Usage above for related details.
cd "20260318/scientific-skills/Academic Writing/anatomy-quiz-master"
python -m py_compile scripts/main.py
python scripts/main.py --help
Example run plan:
- Confirm the user input, output path, and any required config values.
- Edit the in-file
CONFIG block or documented parameters if the script uses fixed settings.
- Run
python scripts/main.py with the validated inputs.
- Review the generated output and return the final artifact with any assumptions called out.
Implementation Details
See ## Workflow above for related details.
- Execution model: validate the request, choose the packaged workflow, and produce a bounded deliverable.
- Input controls: confirm the source files, scope limits, output format, and acceptance criteria before running any script.
- Primary implementation surface:
scripts/main.py.
- Reference guidance:
references/ contains supporting rules, prompts, or checklists.
- Parameters to clarify first: input path, output path, scope filters, thresholds, and any domain-specific constraints.
- Output discipline: keep results reproducible, identify assumptions explicitly, and avoid undocumented side effects.
Quick Check
Use this command to verify that the packaged script entry point can be parsed before deeper execution.
python -m py_compile scripts/main.py
Audit-Ready Commands
Use these concrete commands for validation. They are intentionally self-contained and avoid placeholder paths.
python -m py_compile scripts/main.py
python scripts/main.py --help
Workflow
- Confirm the user objective, required inputs, and non-negotiable constraints before doing detailed work.
- Validate that the request matches the documented scope and stop early if the task would require unsupported assumptions.
- Use the packaged script path or the documented reasoning path with only the inputs that are actually available.
- Return a structured result that separates assumptions, deliverables, risks, and unresolved items.
- If execution fails or inputs are incomplete, switch to the fallback path and state exactly what blocked full completion.
Overview
Comprehensive anatomy education tool that generates interactive quizzes covering gross anatomy, neuroanatomy, and clinical anatomy with adaptive difficulty and detailed explanations.
Key Capabilities:
- Regional Quizzes: Head/neck, thorax, abdomen, pelvis, limbs
- Multiple Question Types: Identification, function, clinical correlation
- Adaptive Difficulty: Basic, intermediate, advanced levels
- Image Integration: Label identification with anatomical images
- Progress Tracking: Performance analytics and weak area identification
- Exam Mode: Timed simulations for USMLE-style preparation
Core Capabilities
1. Regional Anatomy Quizzes
Generate focused quizzes by body region:
from scripts.quiz_generator import QuizGenerator
generator = QuizGenerator()
quiz = generator.generate_quiz(
region="thorax",
topics=["heart", "lungs", "mediastinum", "thoracic_wall"],
difficulty="intermediate",
n_questions=20
)
quiz.export(format="json", filename="thorax_quiz.json")
Supported Regions:
| Region | Subtopics | Question Types |
|---|
| Head & Neck | Skull, cranial nerves, triangles, viscera | Identification, pathways, clinical |
| Thorax | Heart, lungs, mediastinum, pleura | Relations, auscultation, imaging |
| Abdomen | GI tract, retroperitoneum, vessels | Peritoneal reflections, vascular supply |
| Pelvis | Organs, perineum, walls | Gender differences, clinical correlations |
| Upper Limb | Shoulder, arm, forearm, hand | Muscle actions, innervation, clinical |
| Lower Limb | Hip, thigh, leg, foot | Gait, compartments, clinical exams |
| Back | Vertebral column, spinal cord, muscles | Levels, landmarks, clinical |
2. Neuroanatomy Pathway Tracing
Specialized quizzes for neural pathways:
neuro_quiz = generator.generate_neuro_quiz(
pathway_type="motor",
include_lesions=True,
clinical_correlations=True
)
Pathway Types:
- Motor Pathways: Corticospinal, corticobulbar, basal ganglia circuits
- Sensory Pathways: Dorsal column, spinothalamic, trigeminal
- Cranial Nerves: All 12 nerves with nuclei and clinical tests
- Reflex Arcs: Deep tendon, superficial, visceral
- Vascular: Arterial supply, venous drainage, stroke syndromes
3. Clinical Correlation Questions
Integrate anatomy with clinical scenarios:
clinical_quiz = generator.generate_clinical_quiz(
region="abdomen",
scenario_types=["surgery", "radiology", "physical_exam"],
difficulty="advanced"
)
Question Formats:
Clinical Scenario:
"A 45-year-old male presents with epigastric pain radiating to the back.
CT shows a mass in the lesser sac."
Question: "Which artery runs immediately posterior to the body of the
pancreas and would be at risk during resection?"
A) Splenic artery
B) Superior mesenteric artery
C) Common hepatic artery
D) Left gastric artery
Correct: B) Superior mesenteric artery
Explanation: The SMA emerges from the aorta at L1 and passes posterior
to the neck of the pancreas and anterior to the uncinate process...
4. Adaptive Learning System
Adjust difficulty based on performance:
from scripts.adaptive import AdaptiveEngine
engine = AdaptiveEngine()
student_progress = engine.track_performance(
student_id="student_001",
quiz_results=results,
time_per_question=True
)
personalized = engine.generate_adaptive_quiz(
student_progress=student_progress,
focus_areas=["thorax_vessels", "cranial_nerves"],
mastery_threshold=0.80
)
Quality Checklist
Question Quality:
Educational Value:
Technical Quality:
Before Use:
Common Pitfalls
Content Issues:
-
❌ Outdated anatomical knowledge → Teaching old terminology
- ✅ Use current Terminologia Anatomica standards
-
❌ Nit-picky details → Testing obscure structures rarely clinically relevant
- ✅ Focus on high-yield anatomy that appears in clinical practice
-
❌ Unclear images → Poor resolution or confusing labels
- ✅ Use high-quality images; test label legibility at screen resolution
Educational Issues:
-
❌ Questions too easy → No learning benefit
- ✅ Calibrate to student level; aim for 60-80% success rate
-
❌ No clinical context → Pure memorization without application
- ✅ Include clinical correlation questions
-
❌ Punitive difficulty → Discouraging rather than challenging
- ✅ Provide encouraging feedback; focus on improvement
Technical Issues:
References
Available in references/ directory:
netter_atlas_correlation.md - Question-to-atlas page mapping
terminologia_anatomica.md - Standard anatomical terminology
usmle_content_outline.md - NBME anatomy topic frequencies
clinical_correlations.md - High-yield clinical anatomy scenarios
image_sources.md - Licensed anatomical image repositories
difficulty_calibration.md - Bloom's taxonomy level alignment
Scripts
Located in scripts/ directory:
main.py - CLI for quiz generation
quiz_generator.py - Core question generation engine
neuro_quiz.py - Specialized neuroanatomy questions
clinical_correlator.py - Clinical scenario integration
adaptive_engine.py - Personalized difficulty adjustment
image_quiz.py - Label identification with images
progress_tracker.py - Performance analytics
report_generator.py - Progress reports and statistics
Limitations
- Cadaver Images: Cannot replace hands-on dissection experience
- 3D Spatial Relations: 2D images may not convey depth relationships
- Variability: Normal anatomical variation not fully captured
- Updates: Anatomical knowledge evolves; requires periodic review
- Cultural Sensitivity: Some anatomical terms may vary by region
- Disability Accommodation: Image-based questions need alternatives for visually impaired students
Parameters
| Parameter | Type | Default | Required | Description |
|---|
--region, -r | string | upper_limb | No | Anatomical region (upper_limb, lower_limb, thorax, abdomen, pelvis, head_neck, neuroanatomy) |
--difficulty, -d | string | intermediate | No | Difficulty level (basic, intermediate, advanced) |
--count, -c | int | 1 | No | Number of questions to generate |
--output, -o | string | - | No | Output file path (JSON format) |
--format | string | json | No | Output format (json or text) |
--list-regions | flag | - | No | List all available regions and exit |
Usage
Basic Usage
# Generate single question
python scripts/main.py --region upper_limb
# Generate 10-question quiz
python scripts/main.py --region neuroanatomy --difficulty advanced --count 10 --output quiz.json
# List available regions
python scripts/main.py --list-regions
# Text format output
python scripts/main.py --region thorax --format text
Output Requirements
Every final response should make these items explicit when they are relevant:
- Objective or requested deliverable
- Inputs used and assumptions introduced
- Workflow or decision path
- Core result, recommendation, or artifact
- Constraints, risks, caveats, or validation needs
- Unresolved items and next-step checks
Error Handling
- If required inputs are missing, state exactly which fields are missing and request only the minimum additional information.
- If the task goes outside the documented scope, stop instead of guessing or silently widening the assignment.
- If
scripts/main.py fails, report the failure point, summarize what still can be completed safely, and provide a manual fallback.
- Do not fabricate files, citations, data, search results, or execution outcomes.
Input Validation
This skill accepts requests that match the documented purpose of anatomy-quiz-master and include enough context to complete the workflow safely.
Do not continue the workflow when the request is out of scope, missing a critical input, or would require unsupported assumptions. Instead respond:
anatomy-quiz-master only handles its documented workflow. Please provide the missing required inputs or switch to a more suitable skill.
Response Template
Use the following fixed structure for non-trivial requests:
- Objective
- Inputs Received
- Assumptions
- Workflow
- Deliverable
- Risks and Limits
- Next Checks
If the request is simple, you may compress the structure, but still keep assumptions and limits explicit when they affect correctness.