「apps」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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Expert in Expo for React Native development. Covers Expo Router, EAS Build and Submit, development builds, native module integration, and production deployment. Knows how to build cross-platform mobile apps efficiently while maintaining access to native capabilities. Use when "expo, react native, mobile app, eas build, expo router, ios android, expo, react-native, mobile, ios, android, eas, cross-platform" mentioned.

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Master the AI tools that automate repetitive work and connect your tools. From simple Zapier integrations to complex AI-powered workflows, reclaim hours every week. Use when "automation, workflow, integrate tools, connect apps, zapier, no-code, automate process, rpa, automation, workflow, integration, no-code, zapier, make, rpa" mentioned.

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Browser automation powers web testing, scraping, and AI agent interactions. The difference between a flaky script and a reliable system comes down to understanding selectors, waiting strategies, and anti-detection patterns. This skill covers Playwright (recommended) and Puppeteer, with patterns for testing, scraping, and agentic browser control. Key insight: Playwright won the framework war. Unless you need Puppeteer's stealth ecosystem or are Chrome-only, Playwright is the better choice in 2025. Critical distinction: Testing automation (predictable apps you control) vs scraping/agent automation (unpredictable sites that fight back). Different problems, different solutions. Use when "playwright, puppeteer, browser automation, headless, web scraping, e2e test, end-to-end, selenium, chromium, browser test, page.click, locator, playwright, puppeteer, browser, automation, testing, scraping, headless, e2e, selenium, web-testing" mentioned.

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Angular Migration workflow skill. Use this skill when the user needs Master AngularJS to Angular migration, including hybrid apps, component conversion, dependency injection changes, and routing migration and the operator should preserve the upstream workflow, copied support files, and provenance before merging or handing off.

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d3k assistant for debugging web apps

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Tauri v2+ cross-platform app development with Rust backend. Use when configuring tauri.conf.json, implementing Rust commands (#[tauri::command]), setting up IPC patterns (invoke, emit, channels), configuring permissions/capabilities, troubleshooting build issues, or deploying desktop/mobile apps. Triggers on Tauri, src-tauri, invoke, emit, capabilities.json.

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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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Generate behavioral/feature documentation for web apps using code-first analysis. Reads source code via tree-sitting to produce _FEATURES.md, with optional visual verification via browser automation. Companion to tree-sitting. Use when documenting app behavior, creating feature inventories, generating behavioral ground truth for agents, or before modifying UI code. Triggers on "map features", "document app behavior", "feature inventory", "what does this app do".

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Specialized Preact development skill for standards-based web applications with native-first architecture and minimal dependency footprint. Use when building Preact projects, particularly those involving data visualization, interactive applications, single-page apps with HTM syntax, Web Components integration, CSV/JSON data parsing, WebGL shader visualizations, or zero-build solutions with vendored ESM imports.

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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.

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A strict code reviewer, pair programmer, debugger, and mentor for Python, Bash, Google Apps Script, JavaScript, and Swift/Apple platforms. Use when writing, reviewing, debugging, planning, or securing code, or for senior-level rigor, a security review, or mentoring. Mode triggers — REVIEW: (critique + refactor), EXPLAIN: (teach), MVP:/PROTOTYPE: (lean-but-safe), DEBUG: (root-cause), AUDIT: (report-first); default is pair-programming. Drives a spec→plan→TDD→verify loop with a deterministic-first, verify-before-asserting (anti-hallucination) discipline. Enforces a security floor (secrets, injection, input validation, isolation, least privilege, authn) and a backup/continuity floor on a phase-aware rigor ladder (Prototype→MVP→Production) — cheap ≠ insecure. Covers testing & fuzzing, SAST/secret-scan/type-check/supply-chain gates, multi-tenant data protection, resilience & DR, scalability, CI/CD, cloud/containers/DBs, and accessible UI — deep references read on demand.

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Craft-first interface design for dashboards, admin panels, SaaS apps, tools, settings pages, data interfaces, and interactive products. Use when designing, building, reviewing, auditing, or refining product UI where visual craft, layout hierarchy, tokens, states, visual direction, or design-system consistency matter. Not for marketing pages, landing pages, campaigns, or brand-only work.

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Generate App Store screenshots and header/search images for iPhone, iPad and Mac from a codebase or landing page, or re-render a set. Use when preparing a submission. Don't use for ASO keywords, review audits, icons, videos, or Google Play.

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Build production-quality CLIs with language detection and a five-step approval-gated workflow. Use when wrapping an existing module or app. Don't use for GUI/TUI apps, web APIs, or one-off shell scripts.

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Manage AI agents in tmux: spawn sessions, send messages, wait, capture replies, inspect fleets, and tear down safely. Use for tmux-hosted CLI agents. Don't use for SSH, GNU screen, or GUI apps.

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Applies the mental models and frameworks of Andrej Karpathy (deep learning, former Director of AI at Tesla, founding member of OpenAI, Eureka Labs). Use this skill whenever you are helping the user build neural networks from scratch, debug deep learning pipelines, evaluate AI agent workflows, design LLM apps, or navigate the transition to Software 3.0 (vibe coding). It is highly relevant for pedagogy (untangling complex knowledge), assessing AI capabilities vs. limitations (jagged intelligence, tokenization limits), and architectural decisions (end-to-end optimization vs. complex pipelines). Reach for this whenever discussing LLM training, autonomous systems, or AI-assisted coding.

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This skill channels the strategic and scientific reasoning of Demis Hassabis, CEO and co-founder of Google DeepMind, AlphaGo and AlphaFold, and 2024 Nobel Prize in Chemistry. Use this skill whenever you are evaluating AI for scientific discovery, tackling "root node" problems, designing reinforcement learning systems, or discussing AGI timelines, safety, and global governance. Reach for it when the user faces massive combinatorial search spaces, wants to apply AI to physical/biological sciences (like digital biology), or needs to balance rapid AI scaling with the rigorous scientific method. Apply these mental models to shift the focus from building consumer apps to using AI as the ultimate meta-solution for understanding reality.

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Suede AI CI and branch-protection wiring for any repo and any stack: path-aware jobs, a single aggregator required check that cannot deadlock, lockfile hygiene, runtime pinning from the repo, and the exact branch-protection settings. Use when asked to set up CI, protect main, make CI block a bad merge, fix a required check that hangs pending forever, or repair duplicate or misfiring pipelines. Detects the repo's real apps, package managers, and runtime versions first; emits workflow files and settings, never pushes or flips protection itself. NOT FOR: reviewing or grading the change the gate is failing on (use suede-code); designing the AI eval cases to wire in (use suede-ai-eval); branch and worktree hygiene (a private Suede Labs companion, not in this pack).

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Review code for quality, security, performance, and maintainability. Use when reviewing PRs, auditing a codebase, or refactoring. Covers TypeScript, Node.js, infrastructure-as-code, and full-stack web apps.

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Browser automation CLI for AI agents. Use when the user needs to interact with websites, including navigating pages, filling forms, clicking buttons, taking screenshots, extracting data, testing web apps, or automating any browser task. Triggers include requests to "open a website", "fill out a form", "click a button", "take a screenshot", "scrape data from a page", "test this web app", "login to a site", "automate browser actions", or any task requiring programmatic web interaction.

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Inspect, stream, and explicitly adjust live Android UI tweak values or invoke app-owned actions through Snap-O. Use when a request mentions Snap-O Tweaks, tweak-enabled Android apps, runtime UI parameters, Compose tweak or action controls, tweak discovery, previously adjusted or currently inactive tweaks, applying all user-made tweak changes, typed values, defaults or resets, tweak batches, explicitly registered actions, the Tweaks REST/SSE API, or selecting between CLI, macOS tool, in-app overlay, and custom tweak interfaces.

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Neo4j Visualization Library (NVL) — framework-agnostic graph rendering for the browser. Covers @neo4j-nvl/base (NVL class, nodes/relationships, Canvas vs WebGL renderer), @neo4j-nvl/interaction-handlers (ZoomInteraction, PanInteraction, DragNodeInteraction, ClickInteraction, HoverInteraction, BoxSelectInteraction, LassoInteraction, KeyboardInteraction), and @neo4j-nvl/react (InteractiveNvlWrapper, BasicNvlWrapper, StaticPictureWrapper). Use when rendering a Neo4j graph in a browser, feeding driver results through nvlResultTransformer, choosing Canvas vs WebGL, wiring node/relationship click/hover/drag handlers, or embedding NVL in React, Vite, or vanilla JS apps. Does NOT handle Cypher query authoring — use neo4j-cypher-skill. Does NOT handle driver lifecycle, sessions, or executeQuery setup — use neo4j-driver-javascript-skill. Does NOT handle GraphVisualization/Needle default embed — use @neo4j-ndl/react.

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Build production Flutter apps with Clean Architecture, Riverpod (preferred over Bloc/Provider), GoRouter navigation, Impeller rendering engine, Dart 3.7+ patterns, platform channels via Pigeon, and App Store/Play Store deployment.

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