Agent skill

Anatomy Quiz Master

by aipoch in aipoch/medical-research-skills

Generate interactive anatomy quizzes for medical education with multiple.

MITAuto-check passedEducation

Install Anatomy Quiz Master

skills CLI
$ npx skills add aipoch/medical-research-skills --skill anatomy-quiz-master -a claude-code

Project install by default; add -g for ~/.claude/skills/.

GitHub CLI
$ gh skill install aipoch/medical-research-skills anatomy-quiz-master --agent claude-code

Project scope by default; add --scope user for a personal install. Needs GitHub CLI 2.90.0 or later (public preview).

Manual copy
$ git clone --depth 1 https://github.com/aipoch/medical-research-skills.git skills-src && mkdir -p .claude/skills && cp -r skills-src/'scientific-skills/Academic Writing/anatomy-quiz-master' .claude/skills/anatomy-quiz-master && rm -rf skills-src

Use ~/.claude/skills/ instead of .claude/skills for a personal install. The folder must contain SKILL.md.

Claude Code skills documentation · loads skills from .claude/skills/

Facts

Skill name
anatomy-quiz-master
GitHub stars
1.9k
Token cost
~3.4k tokens
SKILL.md length
1,246 words
Files
5 (incl. scripts, references)
Skills in repo
578
Repo updated
First seen
Licence
MIT

At a glance

Generate interactive anatomy quizzes for medical education with multiple.

  • Works in 4 steps: Regional Anatomy Quizzes → Neuroanatomy Pathway Tracing → Clinical Correlation Questions → …
  • Tasks that involve Quizzes and assessments
  • SKILL.md covers When to Use, Key Features, Dependencies and Example Usage, plus 17 more sections
  • Runs Python scripts from its folder; calls python

What it does

Anatomy Quiz Master is an agent skill from aipoch/medical-research-skills. Generate interactive anatomy quizzes for medical education with multiple.

Its SKILL.md is about 3.4k tokens, which your agent loads only when the skill is triggered. The skill folder holds 6 other files, including scripts and reference files (for example `anatomy-quiz-master_audit_result_v2.json`, `references/guidelines.md` and `scripts/main.py`).

It sits in Education, covering Quizzes and assessments. The repository describes itself as: Hundreds of agent skills for medical research, including protocol design, data analysis, evidence insights, and academic writing. The licence is MIT.

When your agent uses it

  • Tasks that involve Quizzes and assessments

Example prompts

  • “/anatomy-quiz-master”

Requirements

  • Python 3

Workflow steps

4 steps, taken from the step headings in SKILL.md.

  1. Regional Anatomy Quizzes
  2. Neuroanatomy Pathway Tracing
  3. Clinical Correlation Questions
  4. Adaptive Learning System

What it can do on your machine

Read from SKILL.md and the folder at commit 686e09d. It shows what the files ask for, not the result of running them.

  • Tool permissions

    Pre-approves nothing: there is no allowed-tools line, so your agent's usual permission prompts apply.

    From allowed-tools in the SKILL.md frontmatter.

  • Runs code

    Ships 1 file in scripts/ (Python), which the agent can run.

    Shell commands in SKILL.md call:

    • python

    From the folder's file list and the shell code blocks in SKILL.md.

  • Network

    No URLs in SKILL.md.

    From URLs in SKILL.md, links to its own repository left out.

  • Credentials

    Names no API keys, tokens, secrets or passwords.

    From names ending in _API_KEY, _TOKEN, _SECRET, _KEY or _PASSWORD in SKILL.md.

Context cost

Anatomy Quiz Master loads about 3.4k tokens when it runs, and up to ~3.4k if it reads all its reference files. Until then it costs about 23 tokens; SKILL.md has 1,246 words of instructions outside code blocks.

Always · name and description, kept in context so the agent knows when to use it
~23
When it runs · the whole SKILL.md, loaded when a task matches
~3.4k
With references · SKILL.md plus every file in references/, read only if the agent opens them
~3.4k

Estimates: characters ÷ 4, the usual rule of thumb; real counts depend on the model's tokenizer. Scripts and assets cost tokens only if the agent reads them.

Safety

Auto-check passed

The automated check found no risky patterns in SKILL.md.

Automated static check — not a guarantee. Review scripts before installing. It scans the text of SKILL.md for risky patterns (piping downloads into a shell, reading credential files, hidden Unicode, destructive commands); the scripts in this folder are not scanned.

SKILL.md

The full file from aipoch/medical-research-skills at commit 686e09d, republished under its MIT licence (© aipoch). 1,246 words, ~3,370 tokens.

Download SKILL.mdSave it as .claude/skills/anatomy-quiz-master/SKILL.md (or your agent's skills folder). This skill also uses 4 other files; get the full folder from GitHub.
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.

bash
cd "20260318/scientific-skills/Academic Writing/anatomy-quiz-master"
python -m py_compile scripts/main.py
python scripts/main.py --help

Example run plan:

  1. Confirm the user input, output path, and any required config values.
  2. Edit the in-file CONFIG block or documented parameters if the script uses fixed settings.
  3. Run python scripts/main.py with the validated inputs.
  4. 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.

bash
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.

bash
python -m py_compile scripts/main.py
python scripts/main.py --help

Workflow

  1. Confirm the user objective, required inputs, and non-negotiable constraints before doing detailed work.
  2. Validate that the request matches the documented scope and stop early if the task would require unsupported assumptions.
  3. Use the packaged script path or the documented reasoning path with only the inputs that are actually available.
  4. Return a structured result that separates assumptions, deliverables, risks, and unresolved items.
  5. 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:

python
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:

RegionSubtopicsQuestion Types
Head & NeckSkull, cranial nerves, triangles, visceraIdentification, pathways, clinical
ThoraxHeart, lungs, mediastinum, pleuraRelations, auscultation, imaging
AbdomenGI tract, retroperitoneum, vesselsPeritoneal reflections, vascular supply
PelvisOrgans, perineum, wallsGender differences, clinical correlations
Upper LimbShoulder, arm, forearm, handMuscle actions, innervation, clinical
Lower LimbHip, thigh, leg, footGait, compartments, clinical exams
BackVertebral column, spinal cord, musclesLevels, landmarks, clinical
2. Neuroanatomy Pathway Tracing

Specialized quizzes for neural pathways:

python

# 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:

python
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:

python
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:

  • Anatomical accuracy verified against standard atlases (Netter, Gray's)
  • Clinical correlations reviewed by licensed physicians
  • Multiple difficulty levels appropriately calibrated
  • Distractors (wrong answers) are plausible and educational
  • Explications explain why correct answer is right
  • Image quality sufficient for identification (resolution, labeling)

Educational Value:

  • Questions test high-yield anatomy (clinically relevant)
  • Progressive difficulty builds knowledge systematically
  • Clinical scenarios reflect real patient presentations
  • Explanations include anatomical reasoning

Technical Quality:

  • Randomization prevents pattern recognition
  • No duplicate questions in quiz banks
  • Image files properly licensed or original
  • Accessibility compliance (alt text for images)

Before Use:

  • CRITICAL: Faculty review for anatomical accuracy
  • Pilot test with target student population
  • Time limits appropriate for difficulty
  • Answer key double-checked for errors
Show full SKILL.md (532 more words)Show less

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:

  • ❌ Predictable patterns → Students game the system

    • ✅ Randomize question order and distractor placement
  • ❌ No progress tracking → Can't identify weak areas

    • ✅ Implement analytics to guide focused study

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

ParameterTypeDefaultRequiredDescription
--region, -rstringupper_limbNoAnatomical region (upper_limb, lower_limb, thorax, abdomen, pelvis, head_neck, neuroanatomy)
--difficulty, -dstringintermediateNoDifficulty level (basic, intermediate, advanced)
--count, -cint1NoNumber of questions to generate
--output, -ostring-NoOutput file path (JSON format)
--formatstringjsonNoOutput format (json or text)
--list-regionsflag-NoList all available regions and exit

Usage

Basic Usage
text

# 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:

  1. Objective
  2. Inputs Received
  3. Assumptions
  4. Workflow
  5. Deliverable
  6. Risks and Limits
  7. Next Checks

If the request is simple, you may compress the structure, but still keep assumptions and limits explicit when they affect correctness.

© aipoch, MIT. Rendered from Markdown: HTML in the file is shown as text, images as links, and headings moved down two levels. Raw file

Files

SKILL.md and 4 other files (scripts, references) in scientific-skills/Academic Writing/anatomy-quiz-master of aipoch/medical-research-skills.

  • SKILL.md
  • anatomy-quiz-master_audit_result_v2.json
  • references/guidelines.md
  • requirements.txt
  • scripts/main.py

Open the folder on GitHubat commit 686e09d

Compare with similar skills

Anatomy Quiz Master next to the 5 skills that share the most tags, products or categories with it. Stars are the repository's; “used in” counts other GitHub owners with a copy.

Anatomy Quiz Master compared with similar skills
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Anatomy Quiz Master this skillaipoch/medical-research-skills1.9k—~3.4kAutomated safety check: PassMIT
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AI Engineering Placement Quizrohitg00/ai-engineering-from-scratch67k—~2kAutomated safety check: PassMIT
Codebase to Coursezarazhangrui/codebase-to-course5.7k—~4.4kAutomated safety check: PassNone
AI Engineering Phase Quizrohitg00/ai-engineering-from-scratch67k—~2.1kAutomated safety check: PassMIT
Scholar EvaluationK-Dense-AI/claude-scientific-writer2.4k2 repos~2.9kAutomated safety check: NotesMIT

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Categories

Questions about Anatomy Quiz Master

What does Anatomy Quiz Master do?

Generate interactive anatomy quizzes for medical education with multiple. Anatomy Quiz Master is an agent skill from aipoch/medical-research-skills. Generate interactive anatomy quizzes for medical education with multiple.

When should I use Anatomy Quiz Master?

Anatomy Quiz Master fits situations like: tasks that involve Quizzes and assessments.

How do I install Anatomy Quiz Master in Claude Code?

Run `npx skills add aipoch/medical-research-skills --skill anatomy-quiz-master -a claude-code`. Or copy the skill folder (scientific-skills/Academic Writing/anatomy-quiz-master in aipoch/medical-research-skills) into .claude/skills/anatomy-quiz-master in your project. Claude Code loads it when a task matches its description.

How do I install Anatomy Quiz Master in Codex?

Run `npx skills add aipoch/medical-research-skills --skill anatomy-quiz-master -a codex`. Or copy the skill folder (scientific-skills/Academic Writing/anatomy-quiz-master in aipoch/medical-research-skills) into .agents/skills/anatomy-quiz-master in your project. Codex loads it when a task matches its description.

Can I use Anatomy Quiz Master in Cursor, Gemini CLI or GitHub Copilot?

Cursor, Gemini CLI, GitHub Copilot and OpenCode also load SKILL.md folders. With the skills CLI, run `npx skills add aipoch/medical-research-skills --skill anatomy-quiz-master -a cursor` (or -a gemini-cli, github-copilot or opencode for the others). To copy it by hand, put the folder in .cursor/skills/anatomy-quiz-master, .gemini/skills/anatomy-quiz-master, .github/skills/anatomy-quiz-master and .opencode/skills/anatomy-quiz-master in your project.

What does Anatomy Quiz Master need to run?

Going by SKILL.md and its folder, Anatomy Quiz Master needs Python for the scripts in its folder and the command-line tools its instructions call (python). Our summary lists: Python 3.

Does Anatomy Quiz Master access the network?

SKILL.md contains no URLs. Any network use would come from the scripts or tools the agent runs. This is read from the text; nothing was executed.

Is Anatomy Quiz Master safe to install?

Our automated static check of SKILL.md found no risky patterns, such as piping downloads into a shell, reading credential files or hidden Unicode. It is not a guarantee. The check reads SKILL.md only: the scripts in the folder are not scanned, so read them before running anything.

What licence does Anatomy Quiz Master use?

Anatomy Quiz Master is published under the MIT licence (declared in SKILL.md). It allows redistribution, so the full SKILL.md is shown on this page.

How many tokens does Anatomy Quiz Master use?

About 3.4k tokens (SKILL.md is roughly 13k characters). Agents keep only the skill's name and description in context until a task matches; then they load SKILL.md in full. Its references folder adds about 67 tokens, read only when the agent opens those files.

What are the alternatives to Anatomy Quiz Master?

Skills that share tags, products or a category with Anatomy Quiz Master: DeepTutor CLI (HKUDS/DeepTutor, 41k stars), AI Engineering Placement Quiz (rohitg00/ai-engineering-from-scratch, 67k stars), Codebase to Course (zarazhangrui/codebase-to-course, 5.7k stars) and AI Engineering Phase Quiz (rohitg00/ai-engineering-from-scratch, 67k stars). The comparison table on this page puts their stars, adoption, token cost, safety result and licence side by side.

Who maintains Anatomy Quiz Master?

aipoch (a GitHub organization) maintains it in aipoch/medical-research-skills, which has 1,937 GitHub stars. The repository holds 578 skills in this directory. The repository was last updated on September 17, 2026.

Source: aipoch/medical-research-skills on GitHub. Facts on this page come from the repository at the commit we read; the author's words are quoted as theirs.