「ept」の検索結果

Kubernetes is the operating system for the cloud. It schedules containers, handles networking, manages storage, and keeps your apps running. But K8s has a steep learning curve - abstractions on abstractions on abstractions. This skill covers the core concepts (Pods, Deployments, Services), production patterns (health checks, resource limits, HPA), and the gotchas that catch everyone (namespace confusion, service discovery, secret management). 2025 reality: You probably don't need to run your own cluster. Use managed K8s (EKS, GKE, AKS) unless you have a very good reason not to. Focus on writing good manifests, not managing etcd. Use when "kubernetes, k8s, kubectl, deployment manifest, helm chart, kustomize, pod, service yaml, ingress, horizontal pod autoscaler, hpa, kubernetes, k8s, containers, orchestration, devops, cloud-native, helm, kustomize" mentioned.

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Git is deceptively simple to learn and incredibly hard to master. The difference between a team that ships and a team that fights merge conflicts all day comes down to workflow discipline. Your git history is documentation - make it tell a story future you can understand. This skill covers branching strategies (trunk-based, gitflow, GitHub flow), commit hygiene, merge vs rebase, conflict resolution, and the commands you actually need. Key insight: most git disasters come from not understanding what you're about to do. 2025 lesson: Trunk-based development with short-lived branches has won. Long-lived feature branches are a code smell. If a branch lives more than a few days, something is wrong with your architecture or process. Use when "git workflow, branching strategy, git merge, git rebase, merge conflict, git commit, trunk-based, gitflow, git history, git revert, git reset, git, version-control, workflow, branching, commits, merge, rebase, collaboration" mentioned.

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Expert at building resilient applications through proper error handling. Covers Result types, error boundaries, try-catch patterns, typed errors, and graceful degradation. Use when "error handling, try catch, error boundary, Result type, exception, error-handling, typescript, react, resilience" mentioned.

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Expert in presenting technical work to non-technical audiences. Covers demo preparation, storytelling for demos, handling failures gracefully, and making work visible and impressive. Understands that perception is reality and great work undemoed is invisible work. Use when "demo, present, show stakeholders, demo day, showcase, walkthrough, show and tell, " mentioned.

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Use when implementing object detection, semantic/instance segmentation, 3D vision, or video understanding - covers YOLO, SAM, depth estimation, and multi-modal visionUse when ", " mentioned.

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The craft of communicating technical concepts clearly to developers. Developer communications isn't marketing—it's about building trust through transparency, accuracy, and genuine utility. The best devrel content helps developers solve real problems. This skill covers technical documentation, developer tutorials, API references, changelog writing, developer blog posts, and developer community engagement. Great developer communications treats developers as peers, not leads to convert. Use when "documentation, docs, tutorial, getting started, API reference, changelog, release notes, developer guide, devrel, developer relations, code examples, SDK docs, README, documentation, devrel, tutorials, api-docs, developer-experience, technical-writing, getting-started, changelogs" mentioned.

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The art and science of creating AI-powered digital presenters, avatars, and synthetic spokespersons. This skill covers HeyGen, Synthesia, D-ID, Tavus, and the emerging landscape of photorealistic AI humans that can speak any script in any language. Digital humans aren't replacing human presenters—they're enabling scale that humans can't achieve. A product demo in 50 languages. Personalized video messages for thousands of customers. 24/7 customer support with a friendly face. Training videos that can be updated without reshoots. The practitioners of this skill understand both the power and the responsibility. They know when digital humans enhance experiences and when they feel uncanny. They navigate the ethics of synthetic media thoughtfully. They create AI presenters that feel helpful, not deceptive. Use when "digital human, AI avatar, AI presenter, HeyGen, Synthesia, D-ID, Tavus, synthetic, talking head AI, AI spokesperson, personalized video, video at scale, multilingual video, AI actor, digital-humans, avatar, ai-presenter, synthesia, heygen, d-id, synthetic-media, personalization, multilingual" mentioned.

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Expert in growing and engaging communities sustainably. Covers acquisition channels, activation strategies, engagement programs, events, content, and retention tactics. Knows that healthy growth is about depth, not just numbers. Use when "community growth, grow community, community engagement, community events, community content, member acquisition, community retention, " mentioned.

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World-class color theory expertise combining the scientific precision of Josef Albers' "Interaction of Color," the systematic thinking of color systems from Pantone and RAL, and the perceptual psychology insights from researchers like Bevil Conway. Color is not just aesthetics - it's communication, emotion, and usability compressed into wavelengths. Great color work is invisible when done right. Users don't notice "nice colors" - they notice when they can't read text, when buttons don't look clickable, when errors don't feel urgent, or when the interface feels "off" without knowing why. Color theory is the science of making the right thing feel obvious. Use when "color theory, color palette, color scheme, color harmony, complementary colors, analogous colors, contrast ratio, dark mode colors, light mode, color tokens, semantic colors, color accessibility, color blindness, color psychology, color system, brand colors, data visualization colors, color, design, accessibility, contrast, dark-mode, theming, tokens, wcag, palette, harmony" mentioned.

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Expert in building 3D experiences for the web - Three.js, React Three Fiber, Spline, WebGL, and interactive 3D scenes. Covers product configurators, 3D portfolios, immersive websites, and bringing depth to web experiences. Use when "3D website, three.js, WebGL, react three fiber, 3D experience, spline, product configurator, " mentioned.

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The systematic discipline of reviewing AI-generated and human-written marketing content for quality, accuracy, consistency, and effectiveness. This isn't proofreading—it's strategic quality control that catches issues before they damage brand, waste spend, or miss opportunities. In an era where AI can generate content at scale, QA becomes the bottleneck— and the moat. Speed without quality is just fast failure. Every piece of content is a brand promise, and QA ensures that promise is kept. Content QA operates at multiple levels: tactical (spelling, grammar), strategic (message alignment), technical (platform compliance), and brand (voice, tone). The best QA catches what the creator couldn't see because they were too close. Use when "review content, QA, quality check, proofread, check copy, content review, brand compliance, verify content, fact check, review before publish, content approval, final review, qa, content-review, quality-assurance, brand-compliance, proofreading, content-quality, review" mentioned.

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Stage and commit with flexible intent parsing. Accepts file/folder scope, free-text description, amend, or any combination.

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Apify Actorization workflow skill. Use this skill when the user needs Actorization converts existing software into reusable serverless applications compatible with the Apify platform. Actors are programs packaged as Docker images that accept well-defined JSON input, perform an action, and optionally produce structured JSON output and the operator should preserve the upstream workflow, copied support files, and provenance before merging or handing off.

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Apify Actorization workflow skill. Use this skill when the user needs Actorization converts existing software into reusable serverless applications compatible with the Apify platform. Actors are programs packaged as Docker images that accept well-defined JSON input, perform an action, and optionally produce structured JSON output and the operator should preserve the upstream workflow, copied support files, and provenance before merging or handing off.

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Bret Taylor 视角. Sierra 联合创始人 / CEO, OpenAI 董事会主席 (2024-), ex-Salesforce co-CEO, Google Maps 联合创造者. B2B SaaS enterprise 派代表 / outcome-based pricing 第一手提倡者 / brand-as-moat 在 agent 时代重新阐述者. 把 monetize-agents 语境从 "卖软件 access" 换成 "卖 outcome — 客户问题解决一次收一次费", 把 product 设计从 "feature breadth" 换成 "vertical depth + measurable outcome + procurement-grade trust". 用途: 当用户问 "agent 该怎么定价 / per-seat 还是 per-outcome / B2B agent 怎么 GTM / enterprise 0 到 1 / vertical 还是 horizontal / design partner 怎么搭 / 一开始要不要融大钱" 类问题时, 用 Bret Taylor 视角先把 "outcome 可不可测量 + vertical 是哪个 + 第一批 design partner 是谁" 盘清楚, 再给定价 / GTM / 融资判断. 当用户提到 "Bret Taylor" "Sierra" "outcome-based pricing" "deflection" "vertical agent" "design partner" "enterprise agent" "B2B agent GTM" "agent 定价" "agent procurement" 时使用. 即使用户只是说 "用 Sierra 思路看一下" 也应触发.

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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

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DevOps 与站点可靠性工程 (SRE) — 平台 / 基础设施 / 可靠性工程师的认知操作系统, 覆盖软件交付 + 运维全生命周期 (CI/CD 与发布工程 trunk-based + 渐进式发布 canary/blue-green/feature flag + GitOps Argo CD/Flux / 基础设施即代码 Terraform/OpenTofu/Pulumi/Ansible + policy-as-code OPA / 容器与编排 Docker/Kubernetes + Helm/Kustomize + service mesh Istio/Linkerd / 可观测性 Prometheus + Loki + OpenTelemetry + Honeycomb + eBPF + RED/USE / SLO-SLI-error budget 与可靠性工程 Google SRE 学科 + 容量规划 + 优雅降级 / 事件管理与 on-call 事件指挥 + PagerDuty + runbook + 无指责复盘 + MTTR / 云平台与 FinOps AWS/GCP/Azure + 成本优化 + 弹性伸缩 / 平台工程与开发者体验 IDP + Backstage + golden path + Team Topologies / DevSecOps 与供应链安全 shift-left + SBOM + SLSA + sigstore + Vault / 韧性与混沌工程 fault injection + game day + 安全科学 / DORA 指标与工程效能 部署频率 + 变更前置时间 + 变更失败率 + Accelerate 研究 / 数据库与有状态运维 schema 迁移 + 备份容灾) — 不含 通用应用开发 / 纯云销售认证速成 / 'DevOps = 跑 Jenkins 的岗位' 窄化误解 / ITIL 工单文化传统运维 (旧范式仅做边界) / 把手工运维 ClickOps 当稳态 (是 toil, 本 skill 核心反模式) (DevOps & Site Reliability Engineering — the cognitive operating system of platform / infrastructure / reliability practitioners who own the full software delivery + operational lifecycle, covering (a) CI/CD & release engineering (build pipelines, trunk-based development, progressive delivery — canary / blue-green / feature flags, GitOps with Argo CD / Flux), (b) Infrastructure as Code (Terraform / OpenTofu, Pulumi, CloudFormation, Ansible, Crossplane — module design, state management, drift, policy-as-code OPA / Sentinel / Checkov), (c) containers & orchestration (Docker / OCI, Kubernetes — scheduling, networking CNI, storage CSI, operators / CRDs, Helm / Kustomize, service mesh Istio / Linkerd), (d) observability (the three pillars + beyond — metrics Prometheus / VictoriaMetrics, logs Loki / ELK, traces OpenTelemetry / Jaeger / Tempo, high-cardinality observability Honeycomb, eBPF, RED / USE methods, SLO-based alerting), (e) SLO / SLI / error budgets & reliability engineering (Google SRE discipline — service level objectives, error budget policy, toil budgets, capacity planning, load shedding, graceful degradation), (f) incident management & on-call (incident command, PagerDuty / Opsgenie, runbooks, blameless postmortems, MTTR / MTTD, error budget burn), (g) cloud platforms & FinOps (AWS / GCP / Azure well-architected, multi-region, cost optimization, autoscaling), (h) platform engineering & developer experience (internal developer platforms, Backstage, golden paths, self-service, Team Topologies), (i) DevSecOps & supply-chain security (shift-left, SAST / DAST, SBOM, SLSA, sigstore / cosign, secrets management Vault), (j) resilience & chaos engineering (chaos experiments, fault injection, game days, resilience engineering / safety science), (k) DORA metrics & engineering effectiveness (deployment frequency, lead time, change failure rate, MTTR, the Accelerate research), (l) databases & stateful operations (schema migrations, backups / DR, replication); NOT generic software development / app feature coding (是 平行学科, DevOps/SRE 关注 delivery + operability 不是 product feature), NOT pure cloud sales / certification cram without operational depth, NOT 'DevOps = a job title that runs Jenkins' 的窄化误解 (DevOps 是 文化 + 实践, SRE 是 Google 对 reliability 的工程化具体实现), NOT ITIL-heavy 传统运维 工单文化 (是 被 DevOps 取代的旧范式, 仅做边界标注), NOT manual ops / ClickOps as a steady state (是 toil, 本 skill 的核心反模式).) Master OS — automated mastery of DevOps & Site Reliability Engineering — the cognitive operating system of platform / infrastructure / reliability practitioners who own the full software delivery + operational lifecycle, covering (a) CI/CD & release engineering (build pipelines, trunk-based development, progressive delivery — canary / blue-green / feature flags, GitOps with Argo CD / Flux), (b) Infrastructure as Code (Terraform / OpenTofu, Pulumi, CloudFormation, Ansible, Crossplane — module design, state management, drift, policy-as-code OPA / Sentinel / Checkov), (c) containers & orchestration (Docker / OCI, Kubernetes — scheduling, networking CNI, storage CSI, operators / CRDs, He

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。

摄影 (摄影 (Photography) — 以静态影像为产出的视觉创作 + 服务行业：从看见 → 用光 → 构图 → 拍摄 → 后期 → 交付的认知操作系统，覆盖商业摄影 (commercial: 产品/广告/电商/美食)、人像摄影 (portrait: 影楼/写真/婚礼/时尚/环境人像)、纪实与街头摄影 (documentary/photojournalism/street)，以及与之贯通的风光、新闻、艺术摄影。面向职业摄影师 / 助理 / 修图师 / 想转职业的资深爱好者 / 服务这行的人 (器材厂商、图库、后期外包、影楼运营)。覆盖: (a) 第一性张力 — **光 (light is the craft — 看光、控光、塑光：硬光/柔光、方向、色温、光比、布光是摄影第一性) ⇄ 器材 (gear — 相机/镜头/灯具迷信, 'GAS' gear-acquisition-syndrome, 'the best camera is the one you have with you' — 器材不是摄影)**; **决定性瞬间/抓取 (decisive moment, Cartier-Bresson, 等待与预判, 纪实/街头不可重来) ⇄ 构建/导演 (constructed/directed, 商业与人像是布光摆姿造光的'制造'而非'捕获')**; **前期/拍对 (get it right in camera, 曝光/白平衡/构图当场拍对) ⇄ 后期/暗房 (post/darkroom, Ansel Adams 'negative is the score, print is the performance' — RAW 冲洗/调色/修图是创作一半)**; **作者性/艺术表达 (vision, personal voice, 'photograph what you feel not what you see') ⇄ 服务性/商业交付 (client brief, 影楼标准化出片, 'shoot the brief not your ego', 摄影是生意)**; **真实/未操纵 (truth, photojournalism ethics, 不过度 PS, 新闻摄影伦理) ⇄ 修饰/理想化 (retouch, 商业人像液化磨皮, 广告的'谎言')**; **决定性技术参数 (曝光三角 aperture/shutter/ISO, 景深, 互易律) ⇄ 看见/眼力 (seeing, 'photography is about seeing not cameras', 构图/光感是 tacit 眼力)**; **胶片/慢/不可见 (film, 慢工, 不可即时回看, 暗房) ⇄ 数码/即时/海量 (digital, chimping, 连拍, 后期无限)**; AI 对摄影的冲击 ('是不是摄影'、生成式填充/换天/AI 修图、真实性危机); (b) 核心工作流 / pipeline (最标准、最易蒸高质量+CLI 化) — 通用链: 看光选光 → 曝光三角决策 (光圈定景深/快门定动静/ISO 兜底) → 测光与曝光 (评价/点测光, 向右曝光 ETTR, 直方图, 区域系统) → 构图 (三分法/引导线/框架/负空间/前景) → 对焦 → 拍摄 → 选片 culling → RAW 后期 (Lightroom/Capture One: 曝光/白平衡/色调曲线/HSL/局部) → 精修 retouch (Photoshop: 频率分离/液化/D&B) → 调色 grading → 输出交付 (色彩管理/sRGB/打印/图库上传); 子流派 SOP: 影棚商业布光 (主光/辅光/轮廓光/背景光, 三点布光, 光质修饰器 softbox/beauty-dish/grid)、婚礼/纪实一天流程、街头扫街、产品/电商台面、人像引导摆姿 posing & direction; (c) 工具栈 — 相机系统 (无反主导: Sony A 系 / Canon R / Nikon Z / 富士 X&GFX / 徕卡 M; 中画幅 Phase One/Hasselblad/GFX 商业; 残幅 vs 全幅 vs 中幅)、镜头 (大三元, 定焦 vs 变焦, 35/50/85 人像三剑客, 微距, 移轴)、灯具 (机顶闪 Godox/Profoto, 影室灯, 常亮 LED, 持续光 vs 闪光)、修饰器 (柔光箱/反光伞/束光筒/雷达罩/旗板)、测光/附件 (测光表, 灰卡, 三脚架, 滤镜 ND/CPL/渐变)、后期软件 (Adobe Lightroom Classic/CC 主导, Capture One 商业/影室, Photoshop 精修, DxO PhotoLab/PureRAW 降噪, Capture One vs Lightroom 之争, 国产: 醒图/泼辣 Polarr/Snapseed 移动端, RawTherapee/Darktable 开源, AI: Lightroom 生成式/Photoshop Generative Fill/Topaz Photo AI/Gigapixel/Luminar Neo)、色彩管理 (X-Rite/Calibrite 校色仪, ICC, 显示器)、图库/交付 (Pixieset/图虫/500px/视觉中国/Getty/Adobe Stock, 客片在线选片); (d) 知识正典 — Ansel Adams 'The Camera / The Negative / The Print' 三部曲 + 区域系统 (Zone System)、Henri Cartier-Bresson 'The Decisive Moment'、Michael Freeman 'The Photographer's Eye' (构图) 'The Photographer's Mind'、Bryan Peterson 'Understanding Exposure' (曝光入门圣经)、Scott Kelby 'Digital Photography Book' 系列 + Lightroom/Photoshop 书、Joe McNally 'The Hot Shoe Diaries / Sketching Light' (闪光灯)、Light Science & Magic (布光科学经典教材)、Roland Barthes 'Camera Lucida' (摄影哲学)、Susan Sontag 'On Photography'、John Szarkowski 'The Photographer's Eye'、National Geographic 摄影指南、Robert Capa / Sebastião Salgado / Steve McCurry (纪实)、Annie Leibovitz / Richard Avedon / Peter Lindbergh (人像/时尚)、Galen Rowell / Ansel Adams (风光); (e) figures/流派 — 风光/系统派 (Ansel Adams 区域系统奠基、Galen Rowell 探险风光、Marc Adamus); 决定性瞬间/纪实街头派 (Henri Cartier-Bresson 奠基、Robert Capa 战地、Sebastião Salgado 社会纪实、Steve McCurry、Vivian Maier、Daido Moriyama、森山大道/荒木经惟 日本派、Vivian Maier); 人像/时尚派 (Richard Avedon、Annie Leibovitz、Peter Lindbergh、Platon、Joey L、Lindsay Adler 时尚布光); 布光/教育派 (Joe McNally、Zack Arias 'one light'、Profoto/strobist David Hobby 'Strobist' 闪光灯启蒙博客、Karl Taylor 商业布光教学); 商业/广告派 (Chase Jarvis 'CreativeLive'创办+'best camera'、Joel Grimes、Tim Wallace 汽车); 后期/修图派 (Pratik Naik、Aaron Nace 'Phlearn'); 街头当代派 (Eric Kim 街头博客、Joel Meyerowitz、Saul Leiter 色彩街头); 中文圈 (李涛 'photoshop教学'、宁思潇潇 器材评测、thomas 'Thomas看看世界' 风光后期、阿刘 风光、668 人像); 哲学/批评 (Susan Sontag、Roland Barthes、John Szarkowski MoMA); (f) 行业话术/黑话 — 曝光三角 exposure triangle / 光圈 aperture f-stop / 快门 shutter speed / 感光度 ISO / 景深 DoF depth of field / 焦段 focal length / 等效 / 全画幅 full-frame vs 残幅 crop / 大三元 / 定焦 prime / 互易律 reciprocity / 向右曝光 ETTR / 直方图 histogram / 高光溢出 clipping / 动态范围 dynamic range / 宽容度 / 白平衡 WB

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。

AIGC 创作工作流 (AIGC 创作工作流 / AIGC Creative Workflow — 以「生成式 AI 视觉创作（图像 + 视频）的工程化创作流水线」为对象的认知操作系统：从「需求/概念（要做什么画面/镜头/风格）→ 模型与工具选型（Midjourney / Stable Diffusion·SDXL·Flux / ComfyUI / 可灵 Kling / 即梦 Jimeng / Runway / Vidu / Sora）→ Prompt 与参数设计（提示词工程、风格描述、负向、种子、CFG/步数/采样器、参考图）→ 可控生成（ControlNet / LoRA / IPAdapter / inpainting / 图生图 / 局部重绘 / 区域控制）→ 批量出图与筛选 → 精修与合成（放大/超分、修手修脸、PS/AE 后期、抠图合成）→ 图生视频/文生视频（首尾帧、运镜、时长、一致性、对口型）→ 商业交付与迭代复现（参数留存、工作流文件复用、版权与可商用判断）」的完整创作-工程-交付决策链。覆盖 (a) 第一性张力 — **可控性/工程化复现（ControlNet/LoRA/ComfyUI 节点工作流/固定种子与参数/可复现可迭代）⇄ 随机性/抽卡玄学（出图随机、prompt 玄学、抽卡刷图、靠运气）**；**审美/创意/艺术指导主导（人的审美判断、概念设计、构图、镜头语言、艺术总监能力——'模型是地板，审美是天花板'）⇄ 工具/模型/参数主导（堆模型能力、堆参数、堆 LoRA、以为换个大模型就行）**；**开源生态/本地可控（Stable Diffusion / ComfyUI / Flux / civitai / liblib 本地部署可定制可控可商用、显卡门槛）⇄ 闭源商业/易用黑盒（Midjourney / 可灵 / 即梦 / Runway 开箱即用但不可精控、按量付费、黑盒）**；**图像生成（T2I/I2I：Midjourney / SDXL / Flux）⇄ 视频生成（T2V/I2V：可灵 / Runway Gen-3 / Vidu / 即梦 / Sora，2024-2025 前沿从图迁移到视频）**；**生产力/商业量产（替代或增强传统设计/插画/电商/广告/影视分镜流程、降本提效、批量）⇄ 纯艺术创作/作者表达（个人风格、实验性、作品集）**；**效率/批量（一次出几十张筛选、商单走量、自动化 pipeline）⇄ 质量/精控（单张精修、商业可用级细节、修手修脸修穿帮）**。(b) 核心工作流 / pipeline（最标准、最易蒸高质量 + CLI 化）— 单图创作链：概念/参考收集（moodboard/风格定位/竞品）→ 模型工具选型（按可控性/风格/成本/可商用判断 MJ vs SD/Flux vs 国产）→ Prompt 与参数设计（主体/风格/光影/镜头/负向词/CFG/采样器/分辨率/种子）→ 首轮出图与方向筛选 → 可控迭代（ControlNet 控构图/姿态/线稿、LoRA 控风格/角色一致性、图生图重绘、inpainting 局部修）→ 放大超分与精修（高清放大/修手修脸/后期调色合成）→ 交付与参数留存（工作流文件/种子/参数复现）。视频创作链：分镜/脚本→首帧出图（图像链产出关键帧）→图生视频（运镜/时长/首尾帧/动态强度）→多镜头一致性（角色/场景/风格统一）→对口型/配音/配乐→剪辑合成→交付。ComfyUI 工程链：节点工作流搭建（加载器/采样/ControlNet/放大/面部修复节点）→ 参数化与复用→批量队列→工作流分享（civitai/openart workflow json）。(c) 工具栈 — 闭源图像（Midjourney v6/v7、DALL·E 3、Adobe Firefly、Ideogram、Recraft）、开源图像与底座（Stable Diffusion 1.5/SDXL/SD3、Flux.1 dev/schnell（Black Forest Labs）、ComfyUI（节点式）、Automatic1111/Forge WebUI、Fooocus）、可控生成插件（ControlNet、LoRA、IPAdapter、AnimateDiff、inpainting/outpainting、ReActor 换脸、面部修复 CodeFormer/GFPGAN、放大 ESRGAN/SUPIR/Topaz）、模型/资源社区（Hugging Face、Civitai、liblib 哩布、吐司 Tusi、openart）、视频生成（可灵 Kling、即梦 Dreamina/Jimeng、Runway Gen-3/Gen-4、Luma Dream Machine、Pika、Vidu、海螺 Hailuo、Sora、Wan 通义万相、MiniMax）、辅助（Magnific 放大、Krea、Photoshop/After Effects 后期、ChatGPT/Claude 写 prompt、reverse prompt 反推）。(d) 知识正典 — AIGC 创作 canon 横跨论文/工程/社区教程：奠基论文（DDPM、Latent Diffusion/Stable Diffusion（Rombach et al）、DiT、ControlNet（Zhang et al）、LoRA、DreamBooth、Textual Inversion、SDEdit、IP-Adapter、视频扩散 SVD/Sora 技术报告）、官方文档与模型卡（Stability AI、Black Forest Labs Flux、ComfyUI 文档、Midjourney docs、可灵/即梦官方教程、Hugging Face diffusers）、社区教程与工作流（ComfyUI 官方示例、civitai 文章、B站/YouTube 创作者长教程、openart workflow）、prompt 工程资源（Midjourney 风格库、提示词指南）。(e) figures/流派 — 模型/工具创造者（Midjourney David Holz、Stability AI Emad/Robin Rombach、Black Forest Labs（Flux，原 SD 团队）、ComfyUI comfyanonymous、ControlNet 张吕敏 Lvmin Zhang、AUTOMATIC1111、可灵/即梦/快手字节团队）、创作者 KOL 与教育者（Midjourney/SD 头部创作者、ComfyUI 工作流大神、AI 视频创作者、国内 B站/抖音 AIGC 教程作者、独立 AI 艺术家）、行业分析与评测（AI 工具评测、生成式 AI 创作生态观察者）。流派分歧：开源可控派（SD/ComfyUI/Flux 本地精控）vs 闭源易用派（MJ/可灵开箱即用）、工程化派（节点工作流/可复现/参数化）vs 玄学抽卡派（prompt 玄学/堆词/抽卡）、审美主导派（'模型是地板审美是天花板'）vs 工具主导派（堆模型堆参数）、图像派 vs 视频派、艺术创作派 vs 商业生产力派、国产工具派（可灵/即梦/liblib）vs 海外工具派（MJ/Flux/Runway）。(f) 行业话术/黑话 — 出图 / 抽卡 / 刷图 / 炼丹（训练 LoRA）/ 喂图 / 垫图（参考图/图生图）/ 控图 / 锁种子 seed / CFG / 采样器 sampler / 步数 steps / 重绘幅度 denoise / 提示词 prompt / 咒语 / 负向 negative / 权重 / tag / 大模型 checkpoint / 底模 / LoRA / ControlNet / 控线稿/openpose/depth / IPAdapter / inpainting 局部重绘 / outpainting 扩图 / 高清放大 / 超分 / 修手 / 崩手 / AI 味 / 风格 / 角色一致性 / 工作流 workflow / 节点 / 跑图 / 队列 / 文生图 T2I / 图生图 I2I / 文生视频 T2V / 图生视频 I2V / 首帧/尾帧 / 运镜 / 动态强度 / 时长 / 对口型 / 一致性 / 可商用 / 训练集 / 过拟合 / 翻车 / 穿帮。(g) 争议/批判 — 「版权与训练数据」（模型训练用未授权作品、生成图版权归属、可商用风险、洗图/伪原创）、「AI 替代创作者就业」（替代画师/插画师/设计师/摄影、行业冲击与转型）、「AI 味与审美趋同」（生成图同质化、套模板、缺乏原创审美）、「玄学 prompt 神话与割韭菜」（'咒语'神秘化、'7天 AIGC 变现/月入过万'付费课割韭菜、抽卡当能力）、「工具快速迭代与淘汰焦虑」（模型月月换、工作流频繁失效、追新疲劳）、「开源 vs 闭源之争」（可控可商用 vs 易用黑盒）、「质量天花板」（修手修脸穿帮、长视频一致性、可控性仍有限）。(h) 大量隐性创作手艺 + 审美 + 工程 + 沟通软技能（prompt 的描述功底与审美词汇、参数与采样的手感、ControlNet/LoRA 的组合控制经验、节点工作流的搭建与调试、批量筛选的审美眼力、修图合成的后期功底、镜头语言与运镜设计、角色/风格一致性的控制、商业需求的翻译与交付、版权与可商用的判断）是高 tacit、靠大量实操跑图 + 审美积累 + 工程调试经验的核心。水分极高（大量'咒语速成/一键变现/AI 绘画割韭菜'营销

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。

Execute programs on a compiled transformer stack machine where every instruction fetch and memory read is a parabolic attention head. Demonstrates that transformer attention + FF layers can implement a working computer. Use when user mentions "llm-as-computer", "lac", "stack machine", "compiled transformer", "percepta", "parabolic attention", "execute program", or asks to run/trace programs on the transformer executor.

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。

In-process semantic search over text files or in-memory strings, using Gemini embeddings via the CF AI Gateway. Use when user wants fuzzy/conceptual search where exact-keyword grep would miss — "sessions discussing regulatory constraints", "code about retry logic", "notes mentioning burnout even if the word isn't there". Complements searching-codebases (regex/AST) and extracting-keywords (YAKE). Do NOT use when an exact string/regex match is what's wanted — grep/rg wins on speed and precision there.

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。

DEPRECATED, renamed to orchestrating-chat-agents. Kept so existing imports of its scripts keep working; every module here loads the same module from orchestrating-chat-agents. Use orchestrating-chat-agents for parallel Claude calls over the API from claude.ai, and dispatching-subagents for fan-out in Claude Code.

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。

Load at the start of any task whose deliverable is prose another person will read — a PR description, commit message, README, doc, postmortem, blog post, issue or review comment, report, release note or essay — before drafting it, whether the result is pushed, posted, published or saved to a file, without being asked. Draft, then run this pass on the draft before handing it over. Also use when someone says "de-claude", "de-slop", "humanize this", "this reads like AI", "make it sound human", or asks for a voice, tone or register edit. Rewrites the constructions that mark prose as model-written (staged reveals, verdict headers, aphoristic closers, "it's not X, it's Y", em-dash drama, forced triads, flat-certainty adverbs) into plain technical prose and checks the rewrite kept every claim. Not for fiction, poetry, code, or quoted text; for a full adversarial review of a deliverable use challenging.

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。

Ranked content search over a text corpus you point it at, using BM25 (via xhluca/bm25s). Corpus-agnostic: cloned repos, project knowledge stores, uploaded files and archives, any local directory. In-memory BM25 index per invocation, with a session-local disk cache for repeat runs against the same corpus. Use for "rank these documents", "search this corpus", "which files are most about X", "find content about Y", or any multi-word concept query against a known body of text where grep would return everything or nothing. Needs a corpus on disk. Not for searching stored memories or prior-session decisions (remembering), not for a named symbol or a file's structure (tree-sitting), and not for a literal string you could grep.

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。