Iván

Medicine

專業領域v1.0.0
作者:Iván更新於 2026-05-11medicine@clawhub_ivangdavila/medicine
醫療健康#Education

據 AlphaMoat 統計,截至 2026-09,Medicine 累計下載 2,900 次、獲 3 個星標,在全部 5,078 個 AI 技能中排名第 3,797 位,在「專業領域」分類(465 個)中排名第 318 位。

累計安裝
130
累計下載
2900
星標
3
全站排名
#3,797
全站前 75% · 「專業領域」前 69%
平台累計安裝累計下載星標評論
ClawHub ↗8928652-
SkillHub ↗13029003—

各平台的計數口徑不同,統計時間不一致。

這個技能是做什麼的

該技能按患者教育、醫學生、醫生或研究者的背景調整解釋方式,幫助理解醫學概念、準備就診問題、梳理鑑別診斷或分析研究。它不能檢查患者、替代醫生判斷或作出診斷。輸出可能被誤認為個體診療方案;具體治療和用藥資訊應由合格臨床人員結合當前權威指南核實,急症應及時尋求醫療評估。

資料釋出於 2026-10-29

可以用來做什麼

  • 需要醫學概念解釋、學習支援、研究討論或臨床資訊整理的患者、學習者和醫務人員。

什麼時候用

  • 將輸出作為一般資訊而非診斷或治療方案;患者特定決策和用藥須諮詢合格臨床人員並核對最新權威指南。急症應立即就醫。

示例提示詞

以下是根據技能文件生成的示例,不代表實際執行結果。

說明該技能可支援哪些醫學教育或研究任務以及安全邊界;不要診斷或提供個體治療方案。

月度資料趨勢

累計下載2900
1560228029992026-03 · 累計下載 16592026-05 · 累計下載 21922026-06 · 累計下載 23622026-07 · 累計下載 26202026-08 · 累計下載 27522026-09 · 累計下載 290026-0326-09
星標3
2332026-03 · 星標 22026-05 · 星標 22026-06 · 星標 22026-07 · 星標 32026-08 · 星標 32026-09 · 星標 326-0326-09

2026-09 起統計方法調整:同名技能按作者分別統計,並覆蓋全部技能。圖中虛線為調整前的月份,與之後的資料不直接比較,也不計算跨越調整的增長。

月份累計下載環比星標
2026-092900—3
2026-082752+5.0%3
2026-072620+10.9%3
2026-062362+7.8%2
2026-052192—2
2026-031659—2

SKILL.md 原文

以下是作者釋出的原始檔案,按純文字顯示,本站未做修改或稽核;其中的連結不可點選,命令請核實後再執行。抓取於 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
展開全部(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

「專業領域」分類下的熱門技能

Iván 的其他技能

資料月份:2026-09 · 下載/星標/安裝數聚合自公開技能註冊庫(ClawHub、SkillHub),按月更新。口徑說明見資料方法。