medical-doctor

当用户需要根据主诉、病史、地区和就诊偏好，推荐最合适的医院与医生，并在可行时生成模拟挂号与陪诊安排时使用。

Security Scan

The skill appears internally consistent: it matches its stated purpose (recommend hospitals/doctors and produce simulated bookings) and only operates on bundled local mock data and user-provided inputs without requesting external credentials or network access.

Purpose & Capability

Name/description (doctor/hospital recommendation + simulated registration/escort) match the included files: SKILL.md, reference docs, schemas, mock data, and scripts/recommend.py. The code and docs implement filtering, scoring, and simulated booking/escort consistent with the stated purpose. (Minor metadata mismatch: SKILL.md lists version 0.1.0 while registry metadata lists 1.0.0 — cosmetic, not a functional concern.)

Instruction Scope

Runtime instructions limit actions to collecting user clinical/profile info, consulting local reference docs, and running scripts/recommend.py against bundled mock data. The skill processes sensitive user health data (chief complaint, history, age, region) but does not instruct reading unrelated system files or environment variables. Note: because it handles personal health information, treat outputs as sensitive and do not feed real patient identifiers into the sample/mock-only workflow unless you audit data handling and privacy.

Install Mechanism

No install specification; skill is delivered as files (SKILL.md + script + data). No downloads, package installs, or archive extraction are present. scripts/recommend.py is a local Python script that reads bundled JSON files — no network calls or subprocess execution are used.

Credentials

The skill declares no required environment variables, no credentials, and no config paths. The included code likewise does not read environment variables or secret files. This is proportionate to the described functionality (local recommendation + simulated booking).

Persistence & Privilege

always is false and the skill is user-invocable; it does not request persistent agent-wide privileges or modify other skills. It does not persist external credentials or install background services.

Assessment

This skill is coherent and implements what it claims: local recommendation logic and simulated booking using bundled mock data and a Python script. Before installing or using it with real users: (1) remember it processes sensitive health information — avoid supplying real patient identifiers unless you review and adapt the code for proper privacy/security and compliance; (2) the current implementation uses only local mock data and does not call external APIs, but if you (or someone else) modify it to integrate real booking or escort services you will need to provide external credentials and re-evaluate those changes; (3) the skill explicitly marks simulated bookings/escorts and is not a replacement for clinical diagnosis — continue to surface emergency instructions when appropriate. If you want higher assurance, review scripts/recommend.py and any future changes for network calls, credential use, or attempts to read system files.

医疗就诊匹配助手

Overview

把用户的主诉、病史、年龄、地区、就诊偏好转成结构化画像，完成科室判断、医院筛选、医生匹配、Top 3 推荐，并在有需要时生成模拟挂号和陪诊安排。

When to Use

在下面场景触发本技能：

1. 用户说不清该去哪个医院、哪个科、哪个医生。
2. 用户给出症状、病史、检查结果，希望获得就诊建议与医生推荐。
3. 用户希望按地区、医院等级、可挂号时间、是否需要陪诊做联合筛选。
4. 用户需要自动挂号或陪诊安排的模拟流程。

不要在下面场景单独依赖本技能：

1. 明显急危重症：胸痛持续加重、呼吸困难、意识障碍、大出血、抽搐、卒中征象。此时先建议急诊/120。
2. 用户要求明确诊断、处方、治疗方案。本技能只做就医分流与匹配，不替代医生诊疗。
3. 缺少最基本的主诉或地区信息且无法合理补全时。

Quick Workflow

1. 先收集信息：主诉、持续时间、病史、过敏史、年龄、性别、地区、预算、是否要挂号、是否要陪诊。
2. 参考 references/symptom_to_specialty.md 完成科室初筛与风险识别。
3. 需要可执行演示时，使用 scripts/recommend.py 读取输入 JSON 与 data/ 下模拟数据，输出 Top 3 推荐。
4. 输出必须包含：推荐理由、医院信息、医生信息、匹配分、风险提示、是否已生成模拟挂号/陪诊安排。
5. 若用户症状涉及急危重症，先给风险提示，再提供急诊方向，不做普通门诊优先推荐。

Core Rules

1. 始终先做风险分层，再做医院与医生匹配。
2. 不把“推荐医院/医生”表达成“已经完成诊断”。
3. 推荐结果至少给 3 个选项，且说明为什么排在前面。
4. 当地区信息不足时，优先询问或使用用户提供的城市/区域；没有时只能给出“示例推荐”。
5. 自动挂号与陪诊在本技能中默认是模拟流程，除非你已经接入真实挂号/陪诊 API。
6. 输出时优先使用结构化格式，参考 schemas/response_schema.json。
7. 需要更详细的匹配规则时，按需读取 references/ 下文档，不要把全部规则一次性塞进回答。

Bundled Files

文件	用途
references/workflow.md	完整工作流与交互步骤
references/symptom_to_specialty.md	症状到科室映射与风险规则
references/output_template.md	建议输出模板
schemas/request_schema.json	输入结构
schemas/response_schema.json	输出结构
data/*.json	模拟医院、医生、号源、陪诊数据
scripts/recommend.py	基于模拟数据生成推荐、挂号、陪诊结果

Recommended Output Sections

1. 用户病情摘要
2. 风险等级与建议
3. 推荐科室
4. Top 3 医院医生推荐
5. 模拟挂号结果（如需要）
6. 模拟陪诊安排（如需要）
7. 注意事项

Example Trigger

• “我发烧咳嗽三天，在朝阳区，应该挂哪个医院哪个医生？”
• “妈妈膝盖痛半年了，想在海淀区找骨科专家，并且最好这两天能挂上号。”
• “小孩反复发热，帮我找儿科医院和医生，顺便安排陪诊。”