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usmle-case-generatorUSMLE 案例生成器

Agent Skill

usmle-case-generator 用于查找、检索和筛选相关信息,适合在 OpenClaw 中需要根据关键词、任务场景或来源线索快速定位候选结果时使用。可结合来源仓库、安装命令和原始 README 继续核验具体用法。安装前建议确认权限范围、维护状态,以及是否会触发联网、命令执行或文件读写。

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安装说明

本站只整理中文说明和来源信息,不托管安装包,也不代用户安装。

GitHub

来源数

2

许可证

MIT-0

最后核验

2026-05-01

来源状态

来源可访问

安装方式

通过对话安装

复制提示词发给支持本地命令或 Skills 的 AI 助手,先确认命令和权限,再让它执行。

请帮我安装这个 Agent Skill:usmle-case-generator(USMLE 案例生成器)
来源仓库:https://github.com/aipoch-ai/usmle-case-generator
安装命令:
openclaw skills install usmle-case-generator
安装前请先检查当前环境是否支持对应 CLI,并向我确认将要执行的命令、安装目录、联网范围和文件读写权限;确认后再执行。

命令行安装

复制命令到本机终端执行。该命令会通过 OpenClaw 从第三方来源获取 Skill;本站只展示命令,不托管安装包,也不自动执行。

ClawHubOpenClaw
openclaw skills install usmle-case-generator

简介

生成符合 USMLE Step 1/2 标准的临床病例,包含病史与体格检查内容。

  • 适用于医学教育、考试准备或临床思维训练类研究检索场景。
  • 在 OpenClaw 中调用,支持按专科或难度定制病例输出。
  • 输出为教学用途,不能替代真实医疗诊断,请谨慎用于临床决策。
  • usmle-case-generator 属于研究检索类 Skill,可作为该场景下的辅助能力补充。

SKILL.md

name
usmle-case-generator
description
Generate USMLE Step 1/2 style clinical cases with patient history, physical.
license
MIT
skill-author
AIPOCH

USMLE Case Generator

Generate USMLE Step 1 and Step 2 CK style clinical cases for medical education and board exam preparation.

When to Use

  • Use this skill when the task is to Generate USMLE Step 1/2 style clinical cases with patient history, physical.
  • 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

See ## Features above for related details.

  • Scope-focused workflow aligned to: Generate USMLE Step 1/2 style clinical cases with patient history, physical.
  • 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 3.8+
  • No external API dependencies (template-based generation)
  • Optional: LLM integration for case variation

Example Usage

See ## Usage above for related details.

cd "20260318/scientific-skills/Academic Writing/usmle-case-generator"
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.

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

  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.

Features

  • Step 1 Cases: Basic science concepts, pathophysiology, pharmacology
  • Step 2 Cases: Clinical diagnosis, management, next best steps
  • Complete Vignettes: History, physical exam, labs, imaging
  • Multiple Choice Questions: Single best answer format
  • Answer Explanations: Detailed rationale for learning

Usage


# Generate a Step 1 case (pathophysiology focus)
python scripts/main.py --step 1 --topic cardiology --difficulty medium

# Generate a Step 2 case (clinical management focus)
python scripts/main.py --step 2 --topic nephrology --include-diagnosis

# Generate case with specific conditions
python scripts/main.py --step 2 --condition "diabetic ketoacidosis" --format json

Parameters

ParameterOptionsDescription
--step1, 2USMLE Step level
--topicSee references/topics.jsonMedical specialty
--conditionAny conditionSpecific disease/condition
--difficultyeasy, medium, hardCase complexity
--formattext, json, markdownOutput format
--include-diagnosisflagInclude answer key
--count1-10Number of cases to generate

Topics Covered

  • Cardiology
  • Pulmonology
  • Gastroenterology
  • Nephrology
  • Endocrinology
  • Hematology/Oncology
  • Infectious Disease
  • Neurology
  • Psychiatry
  • Musculoskeletal
  • Dermatology
  • Obstetrics/Gynecology
  • Pediatrics
  • Surgery

Case Structure

Each generated case includes:

  1. Patient Demographics: Age, gender, relevant background
  2. Chief Complaint: Presenting problem
  3. History of Present Illness: Detailed symptom timeline
  4. Past Medical History: Relevant comorbidities
  5. Medications: Current drug regimen
  6. Allergies: Drug/environmental allergies
  7. Family History: Genetic conditions
  8. Social History: Smoking, alcohol, occupation
  9. Physical Examination: Vital signs, relevant findings
  10. Laboratory Studies: CBC, CMP, specific markers
  11. Imaging/Diagnostics: X-ray, CT, ECG, etc.
  12. Question: USMLE-style multiple choice
  13. Answer Options: 5 choices (A-E)
  14. Correct Answer: With detailed explanation
  15. Educational Objectives: Key learning points

Output Formats

Text Format (Default)

Plain text suitable for printing or reading.

JSON Format

Structured data for integration with applications.

Markdown Format

Formatted for documentation or web display.

Technical Difficulty

High - Requires medical knowledge validation and clinical accuracy.

⚠️ Manual Review Required: Generated cases should be reviewed by medical professionals before use in high-stakes educational settings.

References

  • references/topics.json - Medical specialty taxonomy
  • references/case_templates.json - Case structure templates
  • references/usmle_patterns.md - USMLE question patterns
  • references/conditions/ - Condition-specific case data

Example Output

Case: A 58-year-old male with chest pain

A 58-year-old man presents to the emergency department with 
crushing substernal chest pain radiating to his left arm, 
beginning 2 hours ago at rest...

[History, physical, labs, ECG findings...]

Question: What is the most appropriate next step in management?

A. Administer aspirin and nitroglycerin
B. Order CT pulmonary angiography
C. Perform immediate synchronized cardioversion
D. Start heparin drip and call cardiology
E. Discharge with outpatient stress test

Correct Answer: D
Explanation: [Detailed rationale...]

Safety & Limitations

  • Cases are AI-generated and may contain inaccuracies
  • Not a substitute for professional medical education
  • Always verify clinical details with authoritative sources
  • Intended for educational purposes only

Risk Assessment

Risk IndicatorAssessmentLevel
Code ExecutionPython/R scripts executed locallyMedium
Network AccessNo external API callsLow
File System AccessRead input files, write output filesMedium
Instruction TamperingStandard prompt guidelinesLow
Data ExposureOutput files saved to workspaceLow

Security Checklist

  • [ ] No hardcoded credentials or API keys
  • [ ] No unauthorized file system access (../)
  • [ ] Output does not expose sensitive information
  • [ ] Prompt injection protections in place
  • [ ] Input file paths validated (no ../ traversal)
  • [ ] Output directory restricted to workspace
  • [ ] Script execution in sandboxed environment
  • [ ] Error messages sanitized (no stack traces exposed)
  • [ ] Dependencies audited

Prerequisites


# Python dependencies
pip install -r requirements.txt

Evaluation Criteria

Success Metrics

  • [ ] Successfully executes main functionality
  • [ ] Output meets quality standards
  • [ ] Handles edge cases gracefully
  • [ ] Performance is acceptable

Test Cases

  1. Basic Functionality: Standard input → Expected output
  2. Edge Case: Invalid input → Graceful error handling
  3. Performance: Large dataset → Acceptable processing time

Lifecycle Status

  • Current Stage: Draft
  • Next Review Date: 2026-03-06
  • Known Issues: None
  • Planned Improvements:

- Performance optimization - Additional feature support

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 usmle-case-generator 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:

usmle-case-generator 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.

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能力 2

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能力 3

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能力 4

补充不同宿主或平台的使用分布数据

能力 5

展示第三方安全扫描或审计结果

安装后应在对应宿主中按原始 README 的触发条件使用;具体调用方式请以来源页面和 README 为准。

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external-service

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安装前确认

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