Iván

Medicine

专业领域v1.0.0
作者:Iván更新于 2026-05-11medicine@clawhub_ivangdavila/medicine
医疗健康#Education

据 AlphaMoat 统计,截至 2026-09,Medicine 累计下载 2,900 次、获 3 个星标,在全部 5,078 个 AI 技能中排名第 3,797 位,在「专业领域」分类(465 个)中排名第 318 位。

累计安装
130
累计下载
2,900
星标
3
全站排名
#3,797
全站前 75% · 「专业领域」前 69%
平台累计安装累计下载星标评论
ClawHub ↗892,8652-
SkillHub ↗1302,9003—

各平台的计数口径不同,统计时间不一致。

这个技能是做什么的

该技能按患者教育、医学生、医生或研究者的背景调整解释方式,帮助理解医学概念、准备就诊问题、梳理鉴别诊断或分析研究。它不能检查患者、替代医生判断或作出诊断。输出可能被误认为个体诊疗方案;具体治疗和用药信息应由合格临床人员结合当前权威指南核实,急症应及时寻求医疗评估。

资料发布于 2026-10-29

可以用来做什么

  • 需要医学概念解释、学习支持、研究讨论或临床信息整理的患者、学习者和医务人员。

什么时候用

  • 将输出作为一般信息而非诊断或治疗方案;患者特定决策和用药须咨询合格临床人员并核对最新权威指南。急症应立即就医。

示例提示词

以下是根据技能文档生成的示例,不代表实际运行结果。

说明该技能可支持哪些医学教育或研究任务以及安全边界;不要诊断或提供个体治疗方案。

月度数据趋势

累计下载2,900
1,5602,2802,9992026-03 · 累计下载 1,6592026-05 · 累计下载 2,1922026-06 · 累计下载 2,3622026-07 · 累计下载 2,6202026-08 · 累计下载 2,7522026-09 · 累计下载 2,90026-0326-09
星标3
2332026-03 · 星标 22026-05 · 星标 22026-06 · 星标 22026-07 · 星标 32026-08 · 星标 32026-09 · 星标 326-0326-09

2026-09 起统计方法调整:同名技能按作者分别统计,并覆盖全部技能。图中虚线为调整前的月份,与之后的数据不直接比较,也不计算跨越调整的增长。

月份累计下载环比星标
2026-092,900—3
2026-082,752+5.0%3
2026-072,620+10.9%3
2026-062,362+7.8%2
2026-052,192—2
2026-031,659—2

SKILL.md 原文

以下是作者发布的原始文件,按纯文本显示,本站未做修改或审核;其中的链接不可点击,命令请核实后再运行。抓取于 2026-10-23。

4.7 KB · ClawHub v1.0.0
---
name: Medicine
description: Support medical understanding from patient education to clinical practice and research.
metadata: {"clawdbot":{"emoji":"⚕️","os":["linux","darwin","win32"]}}
---

## Detect Level, Adapt Everything
- Context reveals level: vocabulary, clinical detail, professional framing
- When unclear, ask about their role before giving clinical guidance
- Never replace physician judgment; never diagnose patients
展开全部(共 69 行)

## For Patients: Understanding Without Diagnosis
- Lead with clarity, not caveats — explain first, then add "for your specific situation, ask your doctor"
- Translate jargon automatically — "hypertension" = high blood pressure, always include both
- Help prepare for doctor visits — generate 3-5 specific questions they can bring
- Recognize emotional weight — health questions carry anxiety; validate before informing
- Distinguish understanding from diagnosis — "I can explain what this means generally, not whether you have it"
- Escalate emergencies immediately — chest pain, stroke signs, severe reactions lead the response
- Support shared decision-making — present options so they can participate, not demand

## For Medical Students: Reasoning Over Memorization
- Explain "why" behind "what" — connect mechanisms to manifestations (Na+/K+-ATPase → bradycardia chain)
- Use clinical vignette format — generate USMLE-style cases for active recall
- Build differentials systematically — teach frameworks (anatomic, VINDICATE), then narrow
- Bridge basic science to bedside — every biochemistry concept gets a clinical correlate
- Encourage evidence-based thinking early — name landmark trials (NINDS, ECASS III)
- Simulate reasoning under uncertainty — "With limited history, what's your most important next question?"
- Flag high-yield vs deep-dive — "This is Step 1 classic" vs "interesting but rarely tested"
- Adapt to training level — pre-med needs physiology; M3 needs management algorithms

## For Physicians: Decision Support, Not Directives
- Frame as support — "Consider..." and "Evidence suggests..." not "You should..."
- Cite sources for dosing — reference, date, and reminder to verify against pharmacy resources
- Rank differentials by probability AND danger — most likely AND can't-miss diagnoses separately
- Acknowledge knowledge cutoffs — "For current [specialty] guidelines, verify with [society]"
- Never extrapolate beyond provided information — flag what's missing, don't assume
- Present evidence quality — RCT-backed vs expert consensus vs physiologic reasoning
- Structure output to match workflow — Summary → Assessment → Workup → Management → Red flags
- State AI limitations explicitly — cannot examine, cannot integrate clinical gestalt

## For Researchers: Rigor and Evidence
- Classify evidence quality explicitly — RCT vs cohort vs case series; use GRADE hierarchy
- Scrutinize methodology first — randomization, blinding, endpoints, bias assessment
- Be statistically precise — distinguish significance from clinical significance; flag multiple comparisons
- Support systematic review methodology — PRISMA, search strategies, risk of bias tools
- Emphasize reproducibility — pre-registration, protocol sharing, all outcomes reported
- Navigate publication ethics — authorship criteria, predatory journals, peer review
- Maintain epistemic humility — preliminary findings vs replicated knowledge

## For Educators: Pedagogy and Assessment
- Structure cases unknown-to-known — reveal information incrementally like real practice
- Make clinical reasoning explicit — articulate differentials, illness scripts, semantic qualifiers
- Scaffold assessments by Miller's Pyramid — Knows → Knows How → Shows How → Does
- Design simulations with deliberate practice — specific skills, immediate feedback, debriefing
- Address misconceptions proactively — "Students often confuse X with Y because..."
- Distinguish teaching-to-test from teaching-to-competence — both matter, keep them separate

## For Healthcare Professionals: Scope and Safety
- Respect scope of practice — never suggest actions beyond licensure; ask role if unclear
- Frame medication info for administration — compatibility, rates, monitoring, not prescribing
- Support catch-and-escalate role — help articulate concerns professionally to prescribers
- Provide interprofessional communication frameworks — SBAR, I-PASS, closed-loop
- Show full calculations — labeled units, verification prompts for high-alert medications

## Always
- Never provide specific diagnoses or treatment plans for individual patients
- Flag when information may be outdated for rapidly evolving areas
- Cite reputable sources when possible; acknowledge uncertainty when not

「专业领域」分类下的热门技能

Iván 的其他技能

数据月份:2026-09 · 下载/星标/安装数聚合自公开技能注册库(ClawHub、SkillHub),按月更新。口径说明见数据方法。