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virtual-patient-roleplay虚拟患者角色扮演

Agent Skill

virtual-patient-roleplay 用于补充效率相关能力,适合在 OpenClaw 中需要让 Agent 承接效率相关任务时使用。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。

总安装

3,208

周安装

135

GitHub Stars

1

下载量

1,123
OpenClaw

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:virtual-patient-roleplay(虚拟患者角色扮演)
来源仓库:https://github.com/aipoch-ai/virtual-patient-roleplay
安装命令:
openclaw skills install virtual-patient-roleplay
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 virtual-patient-roleplay

简介

模拟标准化患者进行医疗沟通培训练习。适用宿主包括 OpenClaw,接入前应确认版本、权限和运行环境要求。

  • 支持病史整理排练与医患沟通技巧演练。
  • 适合医学生与医护人员提升问诊能力使用。
  • 可配置不同病例类型与难度等级。virtual-patient-roleplay 属于效率类 Skill,可作为该场景下的辅助能力补充。
  • 输出为教学反馈,不构成临床诊断依据。

SKILL.md

name
virtual-patient-roleplay
description
Simulate standardized patient encounters for medical training, supporting OSCE-style history-taking practice, communication skills rehearsal, and educational debriefing.
license
MIT
skill-author
AIPOCH

Virtual Patient Roleplay

Structured standardized-patient simulation for medical training and clinical interview practice.

Educational Disclaimer: All output is for training simulation only. This skill does not provide real clinical diagnosis, treatment selection, or emergency instructions. Faculty supervision is required for formal assessment use.

Quick Check

python -m py_compile scripts/main.py
python -c "from scripts.main import PatientSimulator; sim=PatientSimulator('chest_pain'); print(sim.ask('Where does the pain go?')['patient_response'])"

When to Use

  • Use this skill for OSCE-style history-taking practice, communication skills rehearsal, or debrief planning.
  • Use this skill when a learner needs to practice clinical interviewing with a simulated patient response.
  • Do not use this skill for real patient triage, clinical diagnosis, treatment selection, or emergency guidance.

Workflow

  1. Confirm the training goal, scenario type, learner level, and output focus (questioning, bedside manner, or debriefing).
  2. Check whether the request is for live roleplay, case setup, feedback, or post-encounter summary.
  3. Use the packaged simulator for supported scenarios; otherwise provide a manual roleplay scaffold without inventing unsupported medical certainty.
  4. Return the patient response or teaching artifact with assumptions, missed-question prompts, and debrief notes.
  5. If the request exceeds educational scope, stop and restate the boundary explicitly.

Usage

python -c "from scripts.main import PatientSimulator; sim=PatientSimulator('chest_pain'); print(sim.ask('Where does the pain go?')['patient_response'])"
python -c "from scripts.main import PatientSimulator; sim=PatientSimulator('headache'); print(sim.ask('Did the pain start suddenly?')['patient_response'])"

Parameters

ParameterTypeRequiredDefaultDescription
scenariostringNochest_painScenario: chest_pain, headache, abdominal_pain
student_questionstringYes (for interaction)Learner question posed to the patient
difficultystringNointermediateScenario difficulty level

Output

  • Simulated patient response
  • Scenario-specific cues and debrief elements
  • Explicit reminder that output is educational, not clinical advice

Scope Boundaries

  • This skill supports training simulations, not real clinical triage.
  • This skill does not provide diagnosis, treatment selection, or emergency instructions.
  • This skill should not be used as a substitute for faculty supervision or patient care.

Stress-Case Rules

For complex multi-constraint requests, always include these explicit blocks:

  1. Training Objective
  2. Scenario Assumptions
  3. Roleplay Output
  4. Educational Limits
  5. Debrief and Next 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 clinical certainty, real patient data, or verified diagnostic outcomes.

Input Validation

This skill accepts: a scenario identifier and a learner question for standardized patient simulation in a medical training context.

If the request does not involve educational patient simulation — for example, asking for real clinical diagnosis, treatment recommendations, emergency triage, or non-medical roleplay — do not proceed with the workflow. Instead respond:

"virtual-patient-roleplay is designed for medical training simulations only. Your request appears to be outside this scope. Please provide a scenario and learner question for educational practice, or use a more appropriate tool."

References

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.

适合场景

01

OpenClaw 用户查找和安装 Skill 时

02

用户想查找某类 Agent Skill 时

03

需要根据任务场景推荐可安装能力包时

04

需要对比不同来源的安装命令和来源信息时

能力概览

能力 1

按任务关键词查找相关 Skills

能力 2

展示可复制的安装命令

能力 3

保留来源站点、仓库和原始说明,方便继续核验

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。

平台分布

OpenClaw

76.36%
按下载量换算858

安全审计

VirusTotal

通过

ClawScan

通过

Static analysis

通过

权限和风险

只读

该 Skill 主要提供规则、说明或参考内容,本身偏只读;真正读写文件、联网或执行命令仍取决于宿主 Agent 的任务。

安装前确认

本站仅展示第三方公开信息,不托管安装包,不提供自动安装或运行环境。安装前应自行审查源码、依赖和命令行为。当前只有一个来源,正式发布前建议补源仓库或其他目录站核验。

来源信息

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