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。
| 平台 | 累計安裝 | 累計下載 | 星標 | 評論 |
|---|---|---|---|---|
| ClawHub ↗ | 89 | 2865 | 2 | - |
| SkillHub ↗ | 130 | 2900 | 3 | — |
各平台的計數口徑不同,統計時間不一致。
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
以下是根據技能文件生成的示例,不代表實際執行結果。
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 |
|---|---|---|---|
| 2026-09 | 2900 | — | 3 |
| 2026-08 | 2752 | +5.0% | 3 |
| 2026-07 | 2620 | +10.9% | 3 |
| 2026-06 | 2362 | +7.8% | 2 |
| 2026-05 | 2192 | — | 2 |
| 2026-03 | 1659 | — | 2 |
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。
---
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## 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
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查詢 A 股行情、分析分時成交量並管理持倉的指令碼。
盯住自選股的行情與技術指標,觸發條件時按級別推送預警訊息。
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在保留公式、格式和活頁簿結構的前提下建立、檢查或編輯 Excel 檔案。
處理 Word 與 DOCX 文件的作業指南,著重維持樣式、編號與版面。
把資料庫、表格與匯出的原始資料變成可決策結論的資料分析技能。
協助編輯 PowerPoint 簡報,並在交付前檢查版面是否正常呈現。
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。