「nes」の検索結果

Für Fitnessstudio AGB: ordnet Norm, Beweislast und Gegenargument; Ergebnis: Prüfprodukt mit Risiko und nächstem Schritt.

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。

Für Business Summary AGB: ordnet Norm, Beweislast und Gegenargument; Ergebnis: Prüfprodukt mit Risiko und nächstem Schritt.

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。

The screenwriting business and career (编剧行业与生意经) — merged from Diamond & Weissman's Bulletproof (buyer's-eye workflow, three Cs, one-page nine-step, set pieces, submission, notes, resilience), Eric Bork's The Idea (PROBLEM seven-element idea test, 60/30/10, television differences), Neill Hicks (market four entities, six criteria, WGA, agents, option, credit arbitration, gross vs net, step deals), Richard Walter (query letter, agents, package, option, spec, release, copyright, guild, gatekeepers, professionalism, 66 principles), Wendy Henson (breaking in), Blake Snyder (logline pitch, genre, agents), Julian Hoxter (industry terms), Mei Feng's Oscar analyses, and the Hollywood masterclass (pitching, notes, credits, TV vs film). Use when evaluating whether an idea is sellable, writing a logline/query/pitch, preparing a submission, handling notes and rewrites, understanding options/credits/WGA/agents/net profit, choosing between film and TV, or building a career strategy.

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Project orchestrator for the screenwriting skill set (剧本项目主线调度 / 状态存档 / story bible) — a meta-skill that adds no new craft knowledge but routes a screenplay or stage-play project through stages (premise → structure → character → scenes → draft → revision → submission) and a TV / streaming series project (电视剧 / 剧集 / pilot / 一季) through a separate series stage table (engine → character network & season arc → documents/bible → pilot structure → break story & outline → draft → submission), names which sw-* skill to invoke at each stage, defines each stage's deliverable and advisory exit check, and keeps all project state in a story-bible.md file so work can resume across sessions. Use when starting a new script project, resuming one ("continue my screenplay", "where were we"), when the user asks "what should I do next" on a script, when converting a vague idea into a full development pipeline, or when the other sw-* skills are firing individually and the overall process needs sequencing.

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From an idea to a sellable series design; series engine / franchise, story pattern, character webs that keep generating conflict, pilot types, and the documents that sell them (剧集引擎/franchise/故事模式/人物网/试播集类型/剧集圣经/跳板/提案). Merged from Rabkin's four-element franchise and story landmines, Landau's tacit contract and pitch recipe, Oberg's series types and bible specs, Douglas's franchise-plus-springboard and bible tiers, Goldberg & Rabkin's 100-episode test, Blum's concept/presentation/bible ladder, Miller's ten-component treatment, Calvisi's Compelling Crisis, Smith's series format, plus engines read off Sopranos, Fleabag and Succession. Use when turning an idea into a series, testing whether a premise has legs, choosing premise vs typical-episode pilot, designing a sustainable character web, writing a logline, pitch document, bible or treatment, picking a series type, adapting IP into a series, or diagnosing a project that feels "more like a movie than a series".

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Case studies of television series writing (剧集案例库) from primary texts; Sorkin's six West Wing teleplays (act-page tables, eight act-out types); Chase's five Sopranos scripts (the 35-beat A/B/C/D recipe, what a pilot refuses to explain); Fellowes's annotated Downton Abbey season two (19 storylines, 419 footnotes); Waller-Bridge's Fleabag Scriptures (the six-episode half-hour template); Calvisi's eight minute-by-minute pilot beat sheets (Breaking Bad, Mad Men, Game of Thrones and more); Landau's 47-series structure appendix; Miller's Hannibal and HTGAWM breakdowns; Goldberg & Rabkin's beat sheets; Sakamoto Yuji's We Made a Beautiful Bouquet (a feature) and Noh Hee-kyung's The Most Beautiful Goodbye. Use when you want a worked example of how an episode or pilot is built, need real act lengths, scene counts or storyline grids to imitate, are breaking a multi-thread ensemble episode, designing a bottle episode or a finale, writing a relationship or family drama, or ask 某一集是怎么写的 / 这部剧的结构表.

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Review UI code for Web Interface Guidelines compliance. Use when asked to "review my UI", "check accessibility", "audit design", "review UX", or "check my site against best practices".

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Comprehensive adversarial audit of a theory, proof, math/econ paper, codebase, or set of claims — decompose into components, fan out independent skeptics that must return CONCRETE defects, adjudicate every finding with a separate judge, fix all confirmed defects, then re-verify. Use when correctness must be bulletproof and single-pass or round-by-round review is too slow and too shallow. Invoke for "audit this rigorously", "find ALL the bugs/gaps", "make this rock solid", "converge faster on correctness".

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Create Ian Xiaohei-style HTML/SVG motion illustrations for Chinese articles, scripts, storyboard scenes, workflow explanations, concept metaphors, and short video visual aids. Use when the user asks for 小黑 SVG、漫画感 HTML、分层 SVG 动效、把小黑生图做成 HTML/SVG、可编辑矢量动效、口播流程动画、手绘漫画动效、正文配图动画, or wants a Xiaohei illustration style adapted into controllable HTML/SVG/GSAP output instead of raster image generation.

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dontbesilent 商业工具箱主入口。双模式：任务前路由（你的问题该用哪个 skill）+ 任务后导航（刚做完诊断，下一步该干什么）。 触发方式：/dbs、/商业、「帮我看看」、「下一步怎么走」 Main entry point for dontbesilent business toolkit. Dual mode: pre-task routing + post-task navigation. Trigger: /dbs, "help me with my business", "what's next"

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AI DevKit · Check local AI DevKit readiness and run setup only when status shows onboarding or harness integration gaps.

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Write Unity 6.3 LTS C# gameplay scripts: the MonoBehaviour lifecycle (Awake/OnEnable/Start/Update/FixedUpdate/LateUpdate), GameObject and component access, coroutines, and Inspector serialization. Use when creating or editing .cs scripts in a Unity project, or when the user mentions MonoBehaviour, Start/Update, GetComponent, SerializeField, coroutines, or "Unity script".

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Pre-deployment validation for Azure readiness. Run deep checks on configuration, infrastructure (Bicep or Terraform), RBAC role assignments, managed identity permissions, and prerequisites before deploying. WHEN: validate my app, check deployment readiness, run preflight checks, verify configuration, check if ready to deploy, validate azure.yaml, validate Bicep, test before deploying, troubleshoot deployment errors, validate Azure Functions, validate function app, validate serverless deployment, verify RBAC roles, check role assignments, review managed identity permissions, what-if analysis, validate Container Apps deployment.

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Build, deploy, evaluate, optimize, fine-tune, and manage Microsoft Foundry agents, models, and resources end to end. USE FOR: foundry, azd ai agent, azd provision/deploy, hosted agent scaffold/develop/run/deploy/troubleshoot, prompt agent create, managed harness agent create, GitHub Copilot harness agent, create agent, update agent, add tool to agent, invoke agent, agent insights, evaluate agent, batch eval, continuous eval, continuous monitoring, agent CI/CD, optimize prompt, improve prompt, optimize agent instructions, Agent Optimizer scaffold, dataset curation from traces, deploy model, model fine-tuning (SFT/DPO/RFT), Foundry project, RBAC, role assignment, permissions, quota, capacity, region, deployment failure, AI Services, create Foundry resource, knowledge index, customize deployment, onboard, availability, training-data, grader, distillation, large file upload. DO NOT USE FOR: Azure Functions, App Service, general Azure deploy (use azure-deploy), general Azure prep (use azure-prepare).

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Assess Kubernetes workloads and cluster configuration for AKS Automatic compatibility. Identifies incompatibilities, generates fixes, and guides migration from AKS Standard to AKS Automatic. WHEN: migrate to AKS Automatic, check AKS Automatic readiness, validate manifests for Automatic, assess cluster for Automatic compatibility, fix deployment for Automatic compatibility, identify AKS Automatic migration blockers, is my cluster ready for AKS Automatic.

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Plan, deploy, operate, and troubleshoot multi-rack (rack scale) deployments of Azure Local — preintegrated racks scaling to hundreds of machines, built on Network Fabric Controller, Cluster Manager, SAN storage, and managed network fabric. Use for the Microsoft.NetworkCloud and Microsoft.ManagedNetworkFabric control plane. NOT for standard 1-16 node Azure Local, and NOT for rack-aware clusters (two racks as availability zones, up to 8 nodes, synchronous replication) — those are standard scale. WHEN: multi-rack, rack scale Azure Local, aggregation rack, compute rack, Network Fabric Controller, NFC, Cluster Manager, network fabric, isolation domain, az networkcloud, az networkfabric, multi-rack logical network, multi-rack Arc VM.

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End-to-end orchestrator: from a business idea, app idea, or existing app to running Azure deployment with cost estimates and pre-deploy approval. Analyzes your app, auto-detects the right Azure services, scaffolds infrastructure code, and deploys — tailored to your app, not a template. Handles moving existing apps to Azure without rewriting or with minimal changes. WHEN: bring your app to Azure, plan my app, cost to run, is my code ready to deploy, deploy my app to the cloud, deploy all my services, what Azure services do I need, plan my Azure deployment, deploy my new app to Azure, one-click deploy, I have an app and want it on Azure, migrate my app to Azure, help me get started, build an app, no code yet, starter project. DO NOT USE FOR: use azd for deployment(use azure-deploy), optimizing existing costs (use cost-optimization), code readiness checks only (use azure-app-onboard-prereq).

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。

Roast a pitch or press release with the eye of a veteran PR director. Honest, sharp, constructive — never cruel for its own sake.

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。

Gate a prompt universe for schema and provenance completeness, target or campaign contamination, evidence entailment, naturalness, one-concept clarity, architecture consistency, aided status, answer leakage, and semantic duplicates. Use after realistic prompt generation and before human panel selection.

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Research any company, product, or service from a URL plus description and build a comprehensive, evidence-bound AEO/GEO/AI-visibility prompt panel across buyer jobs, information acts, journey states, B0-B5 proximity, aided status, roles, locales, variants, partitions, surfaces, and measurement lanes. Use when a user wants prompts to track in ChatGPT, Claude, Gemini, Perplexity, AI search, or answer engines; wants an AI-visibility measurement design; or needs a versioned panel rather than an SEO keyword list.

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Experiment design reference for problem-class framing, Minimum Viable Experiment coaching, hypothesis formation, vetting and red flags, and experiment readiness. Use when translating a stated business outcome into candidate data-science problem classes, or when framing, vetting, scoping, or evaluating an experiment of any kind, including data feasibility, architecture, LLM, performance, use-case, UX, prototyping, and hardware experiments.

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Create or assess an evidence-grounded, falsifiable outcome hypothesis: a testable prediction of what measurable business result will change, for whom, by when, and how leading and lagging indicators will prove or disprove it. Use when framing measurable outcomes, turning an MVP, POC, feature, or technical initiative into a beneficiary result, defining targets and indicators, or judging whether evidence is strong enough to invest. Also applies to business outcome hypotheses, value hypotheses, and outcome statements.

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Performance, load, and reliability (SLO/SRE) planning for production readiness. Use when defining service level objectives, load characterization, capacity, latency budgets, stress/soak/spike test plans, false-positive baselines, and reliability targets. USE FOR: SLO/SLA definition, load testing plan, performance budget, capacity planning, reliability/SRE backlog, latency targets, error-budget policy. DO NOT USE FOR: executing load tests (use Azure Load Testing tooling), security threat modeling, RAI assessment, privacy/compliance planning, or authoring/restating PRD requirements (cite the PRD's existing NFR/FR ids instead).

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Combined codebase health dashboard that scores a project across all four quality dimensions — PR quality, architecture, tech debt, and test quality — in a single pass, drawing on twelve classic engineering books. Triggers when: user wants an overall quality assessment, asks "how healthy is this codebase?", "run all the checks", "I need a health score before the release", or wants to onboard a new team with a quality overview. Do NOT trigger for: server health checks, HTTP health endpoints, Kubernetes liveness/readiness probes, database health, or application uptime. Also do not trigger when the user specifically requests only one dimension — use the corresponding focused skill instead (brooks-review / brooks-audit / brooks-debt / brooks-test).

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。