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Expert patterns for AI-powered image editing including inpainting, outpainting, ControlNet, image-to-image, and API integration with Replicate, Stability AI, and FalUse when "ai image editing, inpainting, outpainting, controlnet, image to image, remove object from image, extend image, flux inpaint, sdxl editing, image-editing, inpainting, outpainting, controlnet, stable-diffusion, flux, replicate, stability-ai, comfyui" mentioned.

日本語の概要は準備中です。原文の説明を表示しています。

临床诊断思维 (临床诊断思维 / 临床推理 (Clinical Diagnostic Reasoning) — 医生『怎么想病』的元学科:从症状/体征/检查到诊断结论的认知操作系统,从业者(临床医生/住院医/规培生,尤其全科/急诊/大内科等以未分化主诉为生的科室)、医学生与医学教育者、诊断安全与质量改进研究者、以及做医疗 AI 辅助诊断产品的人的视角。覆盖: (a) 第一性张力 — **直觉模式识别 (System 1: illness scripts 疾病脚本 / pattern recognition / gestalt, 『资深人一眼认出 aunt Minnie』) ⇄ 分析性推理 (System 2: hypothetico-deductive 假设演绎 / Bayesian 概率更新)** 的 dual-process 双过程理论 (Kahneman→Croskerry/Norman 谱系), 资深≠更会分析而是『脚本库更大+校准更好』; 更深层论战 — **『认知去偏可教 (Croskerry: bias awareness / cognitive forcing strategies / diagnostic timeout) ⇄ 偏倚标签是马后炮、知识结构才是主因 (Norman/Sherbino/Monteiro: debiasing 干预 transfer 证据弱, bias 是 hindsight 标签)』** — 本行最核心学术对垒; 『概率思维 (验前概率 × 似然比 → 验后概率, Pauker-Kassirer test/treatment threshold 阈值模型) ⇄ 穷尽式排查 (rule-out everything / 防御性医疗 / VOMIT)』; 『临床 gestalt ⇄ 结构化临床决策规则 (Wells/PERC/HEART)』; 『诊断简约 Occam's razor ⇄ Hickam's dictum (病人可以同时得 N 个病)』; 『床旁体格检查复兴 (Verghese Stanford 25 / McGee 循证体检) ⇄ 影像检验替代床旁』; (b) 方法论正典 — illness script theory (Schmidt/Boshuizen: enabling conditions/fault/consequences 三段结构), problem representation + semantic qualifiers 问题表征与语义限定词 (Bowen NEJM 2006, 把病人翻成 one-liner), hypothetico-deductive model (Elstein 1978《Medical Problem Solving》: 早期假设生成+定向检验), Bayesian 工具箱 (sensitivity/specificity/LR/Fagan nomogram/SnNout-SpPin), threshold model (Pauker-Kassirer NEJM 1975/1980), 认知偏倚分类学 (anchoring/premature closure/availability/confirmation/base-rate neglect/search satisficing/diagnostic momentum — Croskerry 偏倚清单), 去偏与元认知策略 (diagnostic timeout/cognitive forcing/Ely checklist/calibration), schema-based reasoning (Clinical Problem Solvers schemas: 按 pivot 症状走分支), reflective practice 结构化反思 (Mamede/Schmidt), 诊断错误科学 (NAM 2015 报告定义 / Newman-Toker Big Three / Hardeep Singh e-triggers / SAFER Dx 框架); (c) 行业结构与角色 — 医学生→实习/住院医 (晨会 morning report/查房被 pimping/汇报训练)→主治 attending→master clinician (NEJM CPC discussant/晨会大师如 Dhaliwal); 配套生态: 医学教育者 (clinical reasoning curriculum + 评估: script concordance test/key features exam/OSCE), 诊断安全研究者 (SIDM/AHRQ), 诊断辅助与 CDS 工具开发者 (DDx generator/AI); (d) 核心工作流 — 数据采集 (病史为王 + 循证体检 + 针对性检验影像) → 问题表征 (one-liner + semantic qualifiers) → 鉴别诊断生成 (schema / 解剖定位法 / VINDICATE-M 病因筛, 按『常见可能 × 致命不能漏 can't-miss』双轴排序) → 假设定向检验 (按 LR 选检查 / 阈值决策) → working diagnosis + 安全网 (red flags 交代 / test of time / test of treatment) → 反馈校准 (follow-up / M&M / diagnostic timeout); 教学工作流: 晨会渐进披露汇报 / CPC / SNAPPS / one-minute preceptor / virtual morning report; AI 增强工作流 (2023-2026: LLM 鉴别诊断头脑风暴 / OpenEvidence 检索 / ambient scribe 释放认知带宽 + 自动化偏倚 guardrails); (e) 产出物 — one-liner, problem list, prioritized DDx, assessment & plan (按问题分层), 晨会/CPC 汇报, M&M 复盘, 诊断不确定性沟通 (『最可能是 X, 但出现 Y 红旗立刻回来』); (f) 教育与评估 — script concordance test / key feature exam / EPA / 里程碑; 中国语境: 人卫《诊断学》教材 / 执业医师考试 / 规培结业临床思维考核; (g) 争议/批判 — debiasing 之争 (Croskerry vs Norman-Sherbino『knowledge is the cure』, 去偏 RCT 效果弱 / bias 标签不可证伪), 诊断错误率数字之争 (『10-15%』经典估计 / Newman-Toker 79.5 万美国年严重伤害外推方法被质疑 / 尸检符合率), dual-process 二分被批过度简化 (连续谱 / 难以实证分离), 决策规则 vs gestalt (资深 gestalt 常不输 Wells 类规则 / 算法厌恶), vignette 研究外推性 + context/case specificity (推理高度内容绑定不可通用迁移 [Norman/Eva] — 对『教推理通用课』产业的根本批判), AI 辅助诊断 2023-2026 (GPT-4 在 NEJM CPC/vignette 追平或超医生 [Kanjee 2023 JAMA / Goh 2024 JAMA Netw Open『AI alone > physician+AI』悖论] / Google AMIE / OpenEvidence 爆发 vs 自动化偏倚 / 去技能化 / 真实环境验证缺失), 防御性医疗与过度检查 (incidentaloma 瀑布 / VOMIT), 体检衰亡之争, pimping 教学法争议; (h) 流派/思想谱系 — 决策科学/Bayesian 派 (Ledley-Lusted 1959 → Elstein/Kassirer/Sox/Pauker/Eddy → Brush) vs 认知心理/双过程派 (Kahneman-Tversky → Croskerry/Graber) vs 教育认知派 (Schmidt/Boshuizen illness scripts → Norman/Eva/Mamede/Durning 情境认知) vs 诊断安全/系统派 (NAM 2015 / Newman-Toker / Hardeep Singh / Graber-SIDM: 错误=系统×认知共因) vs 床旁临床派 (Osler 传统 → Tierney aphorisms / Verghese / McGee / Dhaliwal / Saint: 病史体检为王 + 刻意练习) vs AI/计算派 (INTERNIST-1/DXplain 专家系统 → Isabel → LLM 世代 Rodman/Topol)。不含: 具体专科治疗方案与手术决策深度 (足踝外科/种植牙等另有 skill), 中医辨证 (另有 skill), EBM 文献批判性评价方法学 (相关但本 skill 聚焦诊断推理本身), 护理诊断体系 (NANDA), 影像/病理知觉型读片训练 (相邻但独立), 精神科 DSM 结构化访谈细节, 医患沟通技巧全集 (只覆盖诊断不确定性沟通), 患者自查/自我诊断指

日本語の概要は準備中です。原文の説明を表示しています。

中国网站备案 与 互联网内容合规 (含 ICP 备案 主体+网站 / 公安备案 / ICP 经营许可证 增值电信 / 域名实名认证 / APP 备案 2024 工信部新规 / 等级保护测评 网安 / 算法推荐 + 深度合成 + 生成式 AI 备案 网信办 / 数据出境 + 个保法 + 数安法 合规联动 / 变更注销转移跨省迁移 — 不含 通用法律咨询 / 海外域名注册 / 网络营销 SEO / 翻墙 VPN) (China ICP filing & internet content compliance — the full regulatory life-cycle for any website / app / mini-program / IT service that publishes content or runs commercial services targeting mainland-China users; covers (a) 主体备案 + 网站备案 (non-commercial ICP record on beian.miit.gov.cn 经接入商 提交), (b) 域名实名认证 (CNNIC 一级域名 / 注册商核验), (c) 公安备案 (网安部门 30 日内补办), (d) ICP经营许可证 (有偿经营 / 增值电信业务 — 申请门槛极高), (e) APP 备案 (2024-03 工信部令), (f) 等级保护测评 (公安部 GB/T 22239), (g) 算法推荐 / 深度合成 / 生成式 AI 备案 (网信办专项), (h) 数据出境 + 个保法 + 数安法 合规联动, (i) 变更 / 注销 / 转移 / 跨省迁移 工作流; NOT 通用法律咨询, NOT 海外域名注册技术, NOT 网络营销 SEO, NOT 翻墙 / VPN 相关.) Master OS — automated mastery of China ICP filing & internet content compliance — the full regulatory life-cycle for any website / app / mini-program / IT service that publishes content or runs commercial services targeting mainland-China users; covers (a) 主体备案 + 网站备案 (non-commercial ICP record on beian.miit.gov.cn 经接入商 提交), (b) 域名实名认证 (CNNIC 一级域名 / 注册商核验), (c) 公安备案 (网安部门 30 日内补办), (d) ICP经营许可证 (有偿经营 / 增值电信业务 — 申请门槛极高), (e) APP 备案 (2024-03 工信部令), (f) 等级保护测评 (公安部 GB/T 22239), (g) 算法推荐 / 深度合成 / 生成式 AI 备案 (网信办专项), (h) 数据出境 + 个保法 + 数安法 合规联动, (i) 变更 / 注销 / 转移 / 跨省迁移 工作流; NOT 通用法律咨询, NOT 海外域名注册技术, NOT 网络营销 SEO, NOT 翻墙 / VPN 相关.: top builders' mental models, tool stack, current workflows, jargon, and where to keep up. Trigger this skill when the user works on China ICP filing & internet content compliance — the full regulatory life-cycle for any website / app / mini-program / IT service that publishes content or runs commercial services targeting mainland-China users; covers (a) 主体备案 + 网站备案 (non-commercial ICP record on beian.miit.gov.cn 经接入商 提交), (b) 域名实名认证 (CNNIC 一级域名 / 注册商核验), (c) 公安备案 (网安部门 30 日内补办), (d) ICP经营许可证 (有偿经营 / 增值电信业务 — 申请门槛极高), (e) APP 备案 (2024-03 工信部令), (f) 等级保护测评 (公安部 GB/T 22239), (g) 算法推荐 / 深度合成 / 生成式 AI 备案 (网信办专项), (h) 数据出境 + 个保法 + 数安法 合规联动, (i) 变更 / 注销 / 转移 / 跨省迁移 工作流; NOT 通用法律咨询, NOT 海外域名注册技术, NOT 网络营销 SEO, NOT 翻墙 / VPN 相关. problems and wants industry-grade thinking, tool selection, or workflow guidance. 触发词：「ICP备案」「icp备案」「icp filing」「icp license」「中国备案」

日本語の概要は準備中です。原文の説明を表示しています。

Figma 转 Photoshop (PSD) (Figma → Photoshop (PSD) 设计稿转换 (从业者视角) — 把 Figma 设计文件高保真转换为 Photoshop 分层 PSD, 核心是对齐两个工具的概念/渲染差异, 让产出的设计稿视觉一致. 覆盖: (a) 概念映射 — Figma frame / auto-layout / component / variant / constraint / vector-network / boolean / effect (drop·inner shadow / layer·background blur) / mask / blend mode / gradient / text-engine / color 与 Photoshop artboard / group / smart-object / linked / layer-style / shape-layer / path / clipping·layer mask / blend-mode 的对应关系与不可逆损耗点; (b) 转换方法栈 — 插件直出 PSD / 导出图层 (PNG·SVG) 再重组 / SVG·PDF 桥接 / 手工重建 / Figma REST API + Photoshop UXP·ExtendScript 脚本化, 各法在保真度-可编辑性-工时上的权衡与选型; (c) 保真度 QA — 字体回退与缺字处理 / 颜色空间 (sRGB·ICC profile) / 效果参数对齐 / @Nx·DPI 与栅格化 / 像素 diff 验收; (d) 两种交付目标 — 可编辑分层 PSD vs 像素保真扁平 PSD 的方法选择. 主战场 = Figma Community 插件 + Adobe 官方文档/论坛 + 设计交付 (design handoff) 实践 + 设计师社区. 不含: PSD→Figma 反向转换 / Sketch / Adobe XD / 纯前端 code handoff (HTML·CSS) / 印刷 CMYK 专业制版 (仅触及).) Master OS — automated mastery of Figma → Photoshop (PSD) 设计稿转换 (从业者视角) — 把 Figma 设计文件高保真转换为 Photoshop 分层 PSD, 核心是对齐两个工具的概念/渲染差异, 让产出的设计稿视觉一致. 覆盖: (a) 概念映射 — Figma frame / auto-layout / component / variant / constraint / vector-network / boolean / effect (drop·inner shadow / layer·background blur) / mask / blend mode / gradient / text-engine / color 与 Photoshop artboard / group / smart-object / linked / layer-style / shape-layer / path / clipping·layer mask / blend-mode 的对应关系与不可逆损耗点; (b) 转换方法栈 — 插件直出 PSD / 导出图层 (PNG·SVG) 再重组 / SVG·PDF 桥接 / 手工重建 / Figma REST API + Photoshop UXP·ExtendScript 脚本化, 各法在保真度-可编辑性-工时上的权衡与选型; (c) 保真度 QA — 字体回退与缺字处理 / 颜色空间 (sRGB·ICC profile) / 效果参数对齐 / @Nx·DPI 与栅格化 / 像素 diff 验收; (d) 两种交付目标 — 可编辑分层 PSD vs 像素保真扁平 PSD 的方法选择. 主战场 = Figma Community 插件 + Adobe 官方文档/论坛 + 设计交付 (design handoff) 实践 + 设计师社区. 不含: PSD→Figma 反向转换 / Sketch / Adobe XD / 纯前端 code handoff (HTML·CSS) / 印刷 CMYK 专业制版 (仅触及).: top builders' mental models, tool stack, current workflows, jargon, and where to keep up. Trigger this skill when the user works on Figma → Photoshop (PSD) 设计稿转换 (从业者视角) — 把 Figma 设计文件高保真转换为 Photoshop 分层 PSD, 核心是对齐两个工具的概念/渲染差异, 让产出的设计稿视觉一致. 覆盖: (a) 概念映射 — Figma frame / auto-layout / component / variant / constraint / vector-network / boolean / effect (drop·inner shadow / layer·background blur) / mask / blend mode / gradient / text-engine / color 与 Photoshop artboard / group / smart-object / linked / layer-style / shape-layer / path / clipping·layer mask / blend-mode 的对应关系与不可逆损耗点; (b) 转换方法栈 — 插件直出 PSD / 导出图层 (PNG·SVG) 再重组 / SVG·PDF 桥接 / 手工重建 / Figma REST API + Photoshop UXP·ExtendScript 脚本化, 各法在保真度-可编辑性-工时上的权衡与选型; (c) 保真度 QA — 字体回退与缺字处理 / 颜色空间 (sRGB·ICC profile) / 效果参数对齐 / @Nx·DPI 与栅格化 / 像素 diff 验收; (d) 两种交付目标 — 可编辑分层 PSD vs 像素保真扁平 PSD 的方法选择. 主战场 = Figma Community 插件 + Adobe 官方文档/论坛 + 设计交付 (design handoff) 实践 + 设计师社区. 不含: PSD→Figma 反向转换 / Sketch / Adobe XD / 纯前端 code handoff (HTML·CSS) / 印刷 CMYK 专业制版 (仅触及). problems and wants industry-grade thinking, tool selection, or workflow guidance. 触发词：「figma 转 psd」「figma to psd」「figma 转 photoshop」「figma 导出 psd」「设计稿转换」

日本語の概要は準備中です。原文の説明を表示しています。

iOS 应用上架大师 (审核 + ASO + 合规 + 国内/海外多区 + 付费策略) (iOS App Store launch — submission, review, ASO, compliance, post-launch monetization) Master OS — automated mastery of iOS App Store launch — submission, review, ASO, compliance, post-launch monetization: top builders' mental models, tool stack, current workflows, jargon, and where to keep up. Trigger this skill when the user works on iOS App Store launch — submission, review, ASO, compliance, post-launch monetization problems and wants industry-grade thinking, tool selection, or workflow guidance. 触发词：「iOS 上架」「App Store 上架」「App 审核」「App Review」「TestFlight」

日本語の概要は準備中です。原文の説明を表示しています。

Read Bluesky/ATProto without depending on Bluesky's AppView — interactions on a user's posts, thread replies, any repo's records, network-wide backlinks across every lexicon, an account's handle and PDS history, which collections are active network-wide, and layer-by-layer outage diagnosis. Use when bsky.app or the AppView is down or slow, when a Bluesky read returns timeouts or 5xx, when asked who liked/replied/quoted/reposted something, when pulling live Bluesky context cheaply, when reading records straight from a PDS, when asked who references a record or account anywhere on the network, whether an account has changed handle or migrated servers, or which lexicons and non-Bluesky apps are active on atproto. Complements browsing-bluesky, which routes everything through the AppView and fails when it does.

日本語の概要は準備中です。原文の説明を表示しています。

Train and deploy neural networks in distributed E2B sandboxes with Flow Nexus

日本語の概要は準備中です。原文の説明を表示しています。

Applies the nutritional epidemiology and public health frameworks of Frank B. Hu (nutrition epidemiologist, Harvard University). Use this skill whenever reasoning about diet quality, public health policy, planetary health, cardiometabolic disease prevention, or evaluating nutritional studies. Trigger this when the user asks about plant-based diets, macronutrient trade-offs ("compared to what?"), the impact of the food environment, or lifestyle factors for longevity. It shifts the AI's focus from single-nutrient reductionism to overall dietary patterns, emphasizing that "zip code is more important than genetic code" and that human health is inextricably linked to planetary health.

日本語の概要は準備中です。原文の説明を表示しています。

Applies the reasoning style of Kaiming He, computer vision pioneer and creator of ResNet. Use this skill whenever you are designing deep learning architectures, debugging neural network optimization, formulating generative AI problems, or bridging AI with other scientific domains. Trigger this skill for discussions on network depth, weight initialization, residual learning, flow matching, or when reframing discriminative tasks as conditional generation. It emphasizes simplicity in complex visual problems, end-to-end optimization, and viewing AI as a universal language for science.

日本語の概要は準備中です。原文の説明を表示しています。

Train and deploy neural networks in distributed E2B sandboxes with Flow Nexus

日本語の概要は準備中です。原文の説明を表示しています。

Network-engineering audit playbook for the RIPDPI Android app: VpnService, TUN-to-SOCKS, Rust data plane, DNS, TLS/QUIC strategy, relays, and diagnostics. Use for RIPDPI app network reviews or protocol/VPN architecture audits.

日本語の概要は準備中です。原文の説明を表示しています。

Design or audit Kubernetes RBAC — roles, bindings, service accounts, and least-privilege model. Use when asked to "audit Kubernetes RBAC", "apply least privilege in K8s", or "review service account permissions".

日本語の概要は準備中です。原文の説明を表示しています。

Audit an existing Kubernetes cluster — find misconfigurations, security gaps, and resource issues. Use when asked to "audit our Kubernetes cluster", "find K8s misconfigurations", or "check cluster security gaps".

日本語の概要は準備中です。原文の説明を表示しています。

Implements ASP.NET Core rate limiting middleware for API protection. Covers fixed window, sliding window, token bucket, and concurrency limiters with custom policies.

日本語の概要は準備中です。原文の説明を表示しています。

Implements email service using AWS SES for .NET APIs. Designed for applications with HTML template support, placeholder replacement, and Result pattern error handling.

日本語の概要は準備中です。原文の説明を表示しています。

SQL Server database design best practices, naming conventions, indexing strategies, and performance optimization for .NET applications using Microsoft.Data.SqlClient and EF Core.

日本語の概要は準備中です。原文の説明を表示しています。

Configures JWT Bearer authentication for .NET APIs. Includes token generation, validation, refresh tokens, and user context extraction from claims.

日本語の概要は準備中です。原文の説明を表示しています。

PostgreSQL database design best practices, naming conventions, indexing strategies, and performance optimization for .NET applications using Npgsql and EF Core.

日本語の概要は準備中です。原文の説明を表示しています。

Scaffolds a complete .NET solution following Clean Architecture principles with proper layer separation (API, Application, Domain, Infrastructure). Creates project structure, dependency injection setup, and cross-cutting concerns configuration.

日本語の概要は準備中です。原文の説明を表示しています。

Implements the Options pattern for strongly-typed configuration in .NET. Covers IOptions<T>, IOptionsSnapshot<T>, and IOptionsMonitor<T> with validation and reload support.

日本語の概要は準備中です。原文の説明を表示しています。

Generates scheduled background jobs using Quartz.NET. Includes job definitions, triggers, cron scheduling, dependency injection, and persistent job store configuration.

日本語の概要は準備中です。原文の説明を表示しています。

Provides Kubernetes resource management, Helm chart patterns, service mesh configuration, and autoscaling strategies. Covers HPA, VPA, KEDA, operators, security contexts, and namespace isolation. Use when user mentions 'kubernetes', 'k8s', 'helm', 'istio', 'linkerd', 'service mesh', 'HPA', 'VPA', 'KEDA', 'pod security', 'resource quotas', 'operators'.

日本語の概要は準備中です。原文の説明を表示しています。

Sound systems of human language -- phoneme inventories, the International Phonetic Alphabet, articulatory and acoustic phonetics, phonological rules, suprasegmental features (stress, tone, intonation), and ear training for non-native sound perception. Covers place and manner of articulation, voicing contrasts, vowel space, minimal pair analysis, allophonic variation, phonotactic constraints, connected speech phenomena (assimilation, elision, liaison), and prosody across language families. Use when analyzing pronunciation, teaching sound systems, performing phonemic transcription, or diagnosing intelligibility problems in any language.

日本語の概要は準備中です。原文の説明を表示しています。

Analysis of aggregate economic phenomena -- GDP measurement, inflation dynamics, unemployment theory, monetary policy (central banks, interest rates, money supply), fiscal policy (government spending, taxation, debt), and the business cycle. Covers Keynesian, monetarist, and new classical perspectives. Use when analyzing national or global economic conditions, policy debates, economic growth, recessions, or the interaction between monetary and fiscal authorities.

日本語の概要は準備中です。原文の説明を表示しています。