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()
# Generate thorax quiz
quiz = generator.generate_quiz(
region="thorax",
topics=["heart", "lungs", "mediastinum", "thoracic_wall"],
difficulty="intermediate",
n_questions=20
)
# Export for LMS
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:
# Neuroanatomy quiz
neuro_quiz = generator.generate_neuro_quiz(
pathway_type="motor", # or "sensory", "cranial_nerves", "reflexes"
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()
# Track student performance
student_progress = engine.track_performance(
student_id="student_001",
quiz_results=results,
time_per_question=True
)
# Generate personalized quiz targeting weak areas
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.
1---2name: anatomy-quiz-master3description: Generate interactive anatomy quizzes for medical education with multiple.4license: MIT5---6> **Source**: [https://github.com/aipoch/medical-research-skills](https://github.com/aipoch/medical-research-skills)
7
8# Anatomy Quiz Master
9
10## When to Use
11
12- Use this skill when the task is to Generate interactive anatomy quizzes for medical education with multiple.
13- Use this skill for academic writing tasks that require explicit assumptions, bounded scope, and a reproducible output format.
14- Use this skill when you need a documented fallback path for missing inputs, execution errors, or partial evidence.
15
16## Key Features
17
18- Scope-focused workflow aligned to: Generate interactive anatomy quizzes for medical education with multiple.
19- Packaged executable path(s): `scripts/main.py`.
20- Reference material available in `references/` for task-specific guidance.
21- Structured execution path designed to keep outputs consistent and reviewable.
22
23## Dependencies
24
25- `Python`: `3.10+`. Repository baseline for current packaged skills.
26- `argparse`: `unspecified`. Declared in `requirements.txt`.
27- `json`: `unspecified`. Declared in `requirements.txt`.
28- `random`: `unspecified`. Declared in `requirements.txt`.
29
30## Example Usage
31
32See `## Usage` above for related details.
33
34```bash
35cd "20260318/scientific-skills/Academic Writing/anatomy-quiz-master"
36python -m py_compile scripts/main.py
37python scripts/main.py --help
38```
39
40Example run plan:
411. Confirm the user input, output path, and any required config values.
422. Edit the in-file `CONFIG` block or documented parameters if the script uses fixed settings.
433. Run `python scripts/main.py` with the validated inputs.
444. Review the generated output and return the final artifact with any assumptions called out.
45
46## Implementation Details
47
48See `## Workflow` above for related details.
49
50- Execution model: validate the request, choose the packaged workflow, and produce a bounded deliverable.
51- Input controls: confirm the source files, scope limits, output format, and acceptance criteria before running any script.
52- Primary implementation surface: `scripts/main.py`.
53- Reference guidance: `references/` contains supporting rules, prompts, or checklists.
54- Parameters to clarify first: input path, output path, scope filters, thresholds, and any domain-specific constraints.
55- Output discipline: keep results reproducible, identify assumptions explicitly, and avoid undocumented side effects.
56
57## Quick Check
58
59Use this command to verify that the packaged script entry point can be parsed before deeper execution.
60
61```bash
62python -m py_compile scripts/main.py
63```
64
65## Audit-Ready Commands
66
67Use these concrete commands for validation. They are intentionally self-contained and avoid placeholder paths.
68
69```bash
70python -m py_compile scripts/main.py
71python scripts/main.py --help
72```
73
74## Workflow
75
761. Confirm the user objective, required inputs, and non-negotiable constraints before doing detailed work.
772. Validate that the request matches the documented scope and stop early if the task would require unsupported assumptions.
783. Use the packaged script path or the documented reasoning path with only the inputs that are actually available.
794. Return a structured result that separates assumptions, deliverables, risks, and unresolved items.
805. If execution fails or inputs are incomplete, switch to the fallback path and state exactly what blocked full completion.
81
82## Overview
83
84Comprehensive anatomy education tool that generates interactive quizzes covering gross anatomy, neuroanatomy, and clinical anatomy with adaptive difficulty and detailed explanations.
85
86**Key Capabilities:**
87- **Regional Quizzes**: Head/neck, thorax, abdomen, pelvis, limbs
88- **Multiple Question Types**: Identification, function, clinical correlation
89- **Adaptive Difficulty**: Basic, intermediate, advanced levels
90- **Image Integration**: Label identification with anatomical images
91- **Progress Tracking**: Performance analytics and weak area identification
92- **Exam Mode**: Timed simulations for USMLE-style preparation
93
94## Core Capabilities
95
96### 1. Regional Anatomy Quizzes
97
98Generate focused quizzes by body region:
99
100```python
101from scripts.quiz_generator import QuizGenerator
102
103generator = QuizGenerator()
104
105# Generate thorax quiz
106quiz = generator.generate_quiz(
107 region="thorax",
108 topics=["heart", "lungs", "mediastinum", "thoracic_wall"],
109 difficulty="intermediate",
110 n_questions=20
111)
112
113# Export for LMS
114quiz.export(format="json", filename="thorax_quiz.json")
115```
116
117**Supported Regions:**
118| Region | Subtopics | Question Types |
119|--------|-----------|----------------|
120| **Head & Neck** | Skull, cranial nerves, triangles, viscera | Identification, pathways, clinical |
121| **Thorax** | Heart, lungs, mediastinum, pleura | Relations, auscultation, imaging |
122| **Abdomen** | GI tract, retroperitoneum, vessels | Peritoneal reflections, vascular supply |
123| **Pelvis** | Organs, perineum, walls | Gender differences, clinical correlations |
124| **Upper Limb** | Shoulder, arm, forearm, hand | Muscle actions, innervation, clinical |
125| **Lower Limb** | Hip, thigh, leg, foot | Gait, compartments, clinical exams |
126| **Back** | Vertebral column, spinal cord, muscles | Levels, landmarks, clinical |
127
128### 2. Neuroanatomy Pathway Tracing
129
130Specialized quizzes for neural pathways:
131
132```python
133
134# Neuroanatomy quiz
135neuro_quiz = generator.generate_neuro_quiz(
136 pathway_type="motor", # or "sensory", "cranial_nerves", "reflexes"
137 include_lesions=True,
138 clinical_correlations=True
139)
140```
141
142**Pathway Types:**
143- **Motor Pathways**: Corticospinal, corticobulbar, basal ganglia circuits
144- **Sensory Pathways**: Dorsal column, spinothalamic, trigeminal
145- **Cranial Nerves**: All 12 nerves with nuclei and clinical tests
146- **Reflex Arcs**: Deep tendon, superficial, visceral
147- **Vascular**: Arterial supply, venous drainage, stroke syndromes
148
149### 3. Clinical Correlation Questions
150
151Integrate anatomy with clinical scenarios:
152
153```python
154clinical_quiz = generator.generate_clinical_quiz(
155 region="abdomen",
156 scenario_types=["surgery", "radiology", "physical_exam"],
157 difficulty="advanced"
158)
159```
160
161**Question Formats:**
162```
163Clinical Scenario:
164"A 45-year-old male presents with epigastric pain radiating to the back.
165CT shows a mass in the lesser sac."
166
167Question: "Which artery runs immediately posterior to the body of the
168pancreas and would be at risk during resection?"
169
170A) Splenic artery
171B) Superior mesenteric artery
172C) Common hepatic artery
173D) Left gastric artery
174
175Correct: B) Superior mesenteric artery
176
177Explanation: The SMA emerges from the aorta at L1 and passes posterior
178to the neck of the pancreas and anterior to the uncinate process...
179```
180
181### 4. Adaptive Learning System
182
183Adjust difficulty based on performance:
184
185```python
186from scripts.adaptive import AdaptiveEngine
187
188engine = AdaptiveEngine()
189
190# Track student performance
191student_progress = engine.track_performance(
192 student_id="student_001",
193 quiz_results=results,
194 time_per_question=True
195)
196
197# Generate personalized quiz targeting weak areas
198personalized = engine.generate_adaptive_quiz(
199 student_progress=student_progress,
200 focus_areas=["thorax_vessels", "cranial_nerves"],
201 mastery_threshold=0.80
202)
203```
204
205## Quality Checklist
206
207**Question Quality:**
208- [ ] Anatomical accuracy verified against standard atlases (Netter, Gray's)
209- [ ] Clinical correlations reviewed by licensed physicians
210- [ ] Multiple difficulty levels appropriately calibrated
211- [ ] Distractors (wrong answers) are plausible and educational
212- [ ] Explications explain *why* correct answer is right
213- [ ] Image quality sufficient for identification (resolution, labeling)
214
215**Educational Value:**
216- [ ] Questions test high-yield anatomy (clinically relevant)
217- [ ] Progressive difficulty builds knowledge systematically
218- [ ] Clinical scenarios reflect real patient presentations
219- [ ] Explanations include anatomical reasoning
220
221**Technical Quality:**
222- [ ] Randomization prevents pattern recognition
223- [ ] No duplicate questions in quiz banks
224- [ ] Image files properly licensed or original
225- [ ] Accessibility compliance (alt text for images)
226
227**Before Use:**
228- [ ] **CRITICAL**: Faculty review for anatomical accuracy
229- [ ] Pilot test with target student population
230- [ ] Time limits appropriate for difficulty
231- [ ] Answer key double-checked for errors
232
233## Common Pitfalls
234
235**Content Issues:**
236- ❌ **Outdated anatomical knowledge** → Teaching old terminology
237 - ✅ Use current Terminologia Anatomica standards
238
239- ❌ **Nit-picky details** → Testing obscure structures rarely clinically relevant
240 - ✅ Focus on high-yield anatomy that appears in clinical practice
241
242- ❌ **Unclear images** → Poor resolution or confusing labels
243 - ✅ Use high-quality images; test label legibility at screen resolution
244
245**Educational Issues:**
246- ❌ **Questions too easy** → No learning benefit
247 - ✅ Calibrate to student level; aim for 60-80% success rate
248
249- ❌ **No clinical context** → Pure memorization without application
250 - ✅ Include clinical correlation questions
251
252- ❌ **Punitive difficulty** → Discouraging rather than challenging
253 - ✅ Provide encouraging feedback; focus on improvement
254
255**Technical Issues:**
256- ❌ **Predictable patterns** → Students game the system
257 - ✅ Randomize question order and distractor placement
258
259- ❌ **No progress tracking** → Can't identify weak areas
260 - ✅ Implement analytics to guide focused study
261
262## References
263
264Available in `references/` directory:
265
266- `netter_atlas_correlation.md` - Question-to-atlas page mapping
267- `terminologia_anatomica.md` - Standard anatomical terminology
268- `usmle_content_outline.md` - NBME anatomy topic frequencies
269- `clinical_correlations.md` - High-yield clinical anatomy scenarios
270- `image_sources.md` - Licensed anatomical image repositories
271- `difficulty_calibration.md` - Bloom's taxonomy level alignment
272
273## Scripts
274
275Located in `scripts/` directory:
276
277- `main.py` - CLI for quiz generation
278- `quiz_generator.py` - Core question generation engine
279- `neuro_quiz.py` - Specialized neuroanatomy questions
280- `clinical_correlator.py` - Clinical scenario integration
281- `adaptive_engine.py` - Personalized difficulty adjustment
282- `image_quiz.py` - Label identification with images
283- `progress_tracker.py` - Performance analytics
284- `report_generator.py` - Progress reports and statistics
285
286## Limitations
287
288- **Cadaver Images**: Cannot replace hands-on dissection experience
289- **3D Spatial Relations**: 2D images may not convey depth relationships
290- **Variability**: Normal anatomical variation not fully captured
291- **Updates**: Anatomical knowledge evolves; requires periodic review
292- **Cultural Sensitivity**: Some anatomical terms may vary by region
293- **Disability Accommodation**: Image-based questions need alternatives for visually impaired students
294
295## Parameters
296
297| Parameter | Type | Default | Required | Description |
298|-----------|------|---------|----------|-------------|
299| `--region`, `-r` | string | upper_limb | No | Anatomical region (upper_limb, lower_limb, thorax, abdomen, pelvis, head_neck, neuroanatomy) |
300| `--difficulty`, `-d` | string | intermediate | No | Difficulty level (basic, intermediate, advanced) |
301| `--count`, `-c` | int | 1 | No | Number of questions to generate |
302| `--output`, `-o` | string | - | No | Output file path (JSON format) |
303| `--format` | string | json | No | Output format (json or text) |
304| `--list-regions` | flag | - | No | List all available regions and exit |
305
306## Usage
307
308### Basic Usage
309
310```text
311
312# Generate single question
313python scripts/main.py --region upper_limb
314
315# Generate 10-question quiz
316python scripts/main.py --region neuroanatomy --difficulty advanced --count 10 --output quiz.json
317
318# List available regions
319python scripts/main.py --list-regions
320
321# Text format output
322python scripts/main.py --region thorax --format text
323```
324
325## Output Requirements
326
327Every final response should make these items explicit when they are relevant:
328
329- Objective or requested deliverable
330- Inputs used and assumptions introduced
331- Workflow or decision path
332- Core result, recommendation, or artifact
333- Constraints, risks, caveats, or validation needs
334- Unresolved items and next-step checks
335
336## Error Handling
337
338- If required inputs are missing, state exactly which fields are missing and request only the minimum additional information.
339- If the task goes outside the documented scope, stop instead of guessing or silently widening the assignment.
340- If `scripts/main.py` fails, report the failure point, summarize what still can be completed safely, and provide a manual fallback.
341- Do not fabricate files, citations, data, search results, or execution outcomes.
342
343## Input Validation
344
345This skill accepts requests that match the documented purpose of `anatomy-quiz-master` and include enough context to complete the workflow safely.
346
347Do not continue the workflow when the request is out of scope, missing a critical input, or would require unsupported assumptions. Instead respond:
348
349> `anatomy-quiz-master` only handles its documented workflow. Please provide the missing required inputs or switch to a more suitable skill.
350
351## Response Template
352
353Use the following fixed structure for non-trivial requests:
354
3551. Objective
3562. Inputs Received
3573. Assumptions
3584. Workflow
3595. Deliverable
3606. Risks and Limits
3617. Next Checks
362
363If the request is simple, you may compress the structure, but still keep assumptions and limits explicit when they affect correctness.