「choice」の検索結果

Transform the agent into a Cloud Solution Architect following Azure Architecture Center best practices. Use when designing cloud architectures, reviewing system designs, selecting architecture styles, applying cloud design patterns, making technology choices, or conducting Well-Architected Framework reviews.

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Have Codex implement a concrete work order and have Claude independently inspect the final changes. Preserves the explicit Codex builder choice; use claudex-loop for host-based planning and automatic reviewer selection.

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Use when the user asks to "pick a bid strategy", "set a tCPA/tROAS target", or "plan the learning-phase entry"; produces a bid-strategy choice (tCPA / tROAS / max-conversions / manual CPC), the starting target math, a portfolio grouping map, and a learning-phase entry plan. Not for splitting the budget across campaigns — use budget-optimizer; not for in-flight pacing/scale moves — use budget-pacing-monitor; not for scoring the account — use ad-account-auditor. 出价策略/tCPA目标/tROAS/学习期

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Critique a rendered screen's density — cognitive load, content prioritisation, scanning patterns, and progressive disclosure. Use when a screen feels overwhelming. For the underlying choice-count principle, use `hicks-law` (interaction-design).

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Apply Miller's Law — chunk information into groups of about four to fit working memory. Use when grouping fields, menu items, or steps. For reducing the number of choices offered, use `hicks-law`.

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Apply Tesler's Law — every process has irreducible complexity that someone must absorb. Use when deciding whether the product or the user carries it. For reducing apparent choice, use `hicks-law`.

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Apply Hick's Law — decision time grows with the number of simultaneous choices. Use when a screen offers too many options at once. For how many items survive in memory afterwards, use `millers-law`.

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Routes serverless requests to a compute form factor and the skill that owns it. Applies first to Lambda, API Gateway, EventBridge, Step Functions and event-driven work while the compute, orchestration or integration choice is open — API backends, webhooks, jobs, cron, queue/stream consumers, durable workflows, event routing, Step Functions vs Durable Functions, and Pipes vs native event source mappings for queues and streams. Decides Event Functions on-demand vs Managed Instances, Web Functions, MicroVMs, EventBridge rules/Scheduler/Pipes/bus, HTTP vs REST API. Owns shared Lambda references (cold starts, SnapStart, concurrency, event sources, function URLs, throttling, troubleshooting). Defers to the specialized skill once a request commits to one form factor or service, or describes its defining feature, even unnamed (durable checkpointing and automatic step retries, Firecracker sandbox, managed instances, Node.js server). Not for EC2, ECS/EKS/Fargate or Amplify hosting.

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AWS security service suitability, coverage and cost recommendations: WAF/origin overlap, AWS Network Firewall inspection coverage and policy review, VPC endpoint, Transit Gateway/Cloud WAN segmentation and DNS Firewall security-control suitability, certificate renewal ownership and monitoring, incident response triage ownership, centralized security log store (CloudWatch or Security Lake). Also factual configuration, findings, data sources and severity scoring for GuardDuty, Inspector, Security Hub V2/OCSF (Exposure, connectors, automation), Security Hub CSPM/ASFF (standards, controls), Macie, Detective, Security Lake, AWS Security Incident Response membership and case coverage, and Organizations security policies. Applies to advisory security-service choices even when one service is named. Implementation, rule or code authoring, deployment, scans and troubleshooting outside these factual procedures belong to specialist skills. Supports local AWS CLI and AWS MCP.

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Designs, reviews, and debugs DynamoDB data layers from design axioms — enumerates access patterns, chooses partition/sort keys and GSIs, decides single-table vs. multi-table, configures Streams, Global Tables, TTL, vector indexes for similarity search, and zero-ETL integrations to OpenSearch/Redshift/SageMaker Lakehouse, and produces a defensible data-layer design with a monthly cost estimate and optional live validation. Applies whenever a user is designing, reviewing, or refactoring anything backed by DynamoDB — schemas, access patterns, GSIs, single- vs. multi-table choices, Streams consumers, transactional outboxes, Global Tables, zero-ETL pipelines, or storing embeddings and running semantic/vector similarity search with SearchVectors on items already in DynamoDB — even when they don't say "axioms" or "design review." Also applies when debugging hot partitions, throttling, unbounded Scans, LWW conflicts, or surprise bills on DynamoDB workloads.

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Analyze and tune AssemblyAI model choice, audio delivery, polling, Streaming v3 turns, concurrency, and downstream work. Use when pursuing measured latency or throughput goals. Trigger with "tune AssemblyAI" or "AssemblyAI latency".

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Generates images and game sprites through GPT or Grok with guided provider choices, separate saved defaults, automatic cleanup and optional curation. Handles sprite requests, ordinary image generation/editing, standalone image-to-video clips (i2v, animate this still, 그록 영상, 이매진 비디오, 스틸 움직여줘, first/last frame, reference-to-video, 영상 이어붙이기, 영상 편집, extend/edit a clip), clips from a video MCP on the agent (ZCRE, 지크 MCP), chroma removal, animation atlases, video loops, 큐레이션뷰, image candidates, 팔레트 스왑, palette swap, recolor, rig layers, engine exports, repeating backgrounds, projected shadows, motion/contact inspection and optional scene composition from existing assets.

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Turn a topic or finished script into a complete, publish-ready explainer video — led by an AI presenter from an authorized adult presenter image, or performed with no presenter in one of twelve explainer styles (nine visual styles and three narrative ones). Not for promos, ads, or footage montages. Use for new presenter or styled explainer videos and for continuing, revising, captioning, lip-sync repairing, or re-exporting an existing job. Use it whenever the user asks for 数字人口播 / 数字人讲解, for a 讲解视频 / 动画讲解 / 科普视频, asks which styles or templates are available (有哪些风格 / 看看风格 / 风格模板), or names one of the styles: 留白, 信号/科技感, 手帐/手绘, 立体书/纸艺, 波普漫画, 一镜到底/3D, 图纸与注脚, 黑板报, 黏土小城, 推演/动态图解, 案卷/调查复盘, 小剧场/生活场景. Keep model and provider selection capability-based and record the actual choices per job.

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Facilitates deliberate skill development during AI-assisted coding. Offers interactive learning exercises after architectural work (new files, schema changes, refactors). Use when completing features, making design decisions, or when user asks to understand code better. Supports the user's stated goal of understanding design choices as learning opportunities.

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Design data systems by understanding storage engines, replication, partitioning, transactions, and consistency models. Use when the user mentions "database choice", "which database should I use", "SQL or NoSQL", "replication lag", "partitioning strategy", "consistency vs availability", "stream processing", "ACID transactions", "eventual consistency", "my queries are slow at scale", or "data is inconsistent across replicas". Also trigger when choosing a datastore, designing data pipelines, or debugging distributed-system consistency issues. Covers data models, batch/stream processing, and distributed consensus. For system design, see system-design. For resilience, see release-it.

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Use when a OpenChatCut video editing or creation workflow needs export, render, download, share, final delivery, subtitle-file export, render choice, local-only asset handling, or export fallback explanation.

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Use when the agent should ask the user for structured input with an in-chat form, including single-select, multi-select, text fields, style pickers, or voice audition choices.

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Use with the dstack skill for model-serving work when the image, serving command, resources, backend/fleet choice, or service behavior is not proven. Guides task-first prototyping on real hardware, choosing fleets/backends that can reuse idle instances and caches, checking vLLM/SGLang sources, and verifying the final dstack service with a model request.

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When the user wants to choose, change, or evaluate their App Store / Google Play category and subcategory — including primary vs secondary category trade-offs, chart-rank competitive analysis, category-driven discoverability, and how category choice affects featuring eligibility. Use when the user mentions "which category", "App Store category", "primary category", "secondary category", "change my category", "Health & Fitness vs Lifestyle", "Productivity vs Utilities", "rank higher in a smaller category", "category chart", "subcategory", "Play Store category", or "should I switch categories". For full ASO health beyond category, see aso-audit. For competitor analysis within the chosen category, see competitor-analysis. For chart movements within categories, see market-movers.

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When the user wants to design, test, or optimize their app's paywall — layout, copy, pricing display, trial offers, plan structure, hard vs soft paywall, paywall placement, or paywall A/B tests. Use when the user mentions "paywall", "paywall design", "paywall conversion", "trial-to-paid", "soft paywall", "hard paywall", "paywall A/B test", "paywall copy", "plan picker", "annual vs monthly display", "best paywall", "RevenueCat paywall", "Superwall", "Adapty", or "my paywall isn't converting". For overall pricing strategy and monetization model choice, see monetization-strategy. For trial nurture, dunning, and churn, see subscription-lifecycle. For where in the onboarding the paywall fires, see onboarding-optimization.

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Generate A2UI v0.8 surfaces for the OpenClaw Windows tray node, which renders A2UI natively in WinUI/XAML (not a WebView). Read this when pushing canvas.a2ui.push/canvas.a2ui.pushJSONL to a Windows node - covers the 18 supported components (Row, Column, List, Card, Tabs, Modal, Divider, Text, Image, Icon, Video, AudioPlayer, Button, CheckBox, TextField, DateTimeInput, MultipleChoice, Slider), the icon-name enum, the A2UIValue tagged union, JSONL envelope shapes, and action.context security / dataBinding rules. Catalog-strict: components outside this list render as the fallback Unknown placeholder.

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Owns architecture, engineering delivery, infrastructure, data platform, and internal systems. Use this for build-versus-buy calls, technology selection, architectural direction, engineering capacity and delivery risk, technical debt tradeoffs, platform and tooling decisions, or when a technical choice has business consequences that need stating in business terms. Also use to judge whether a technical plan is sound before it is committed to.

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Owns the security posture of the organization — architecture, program strategy, risk acceptance, incident command, and the authority to stop work that creates unacceptable exposure. Use this for a security strategy or program decision, when a technical choice creates security risk that needs a verdict, when deciding whether to accept or block a risk, when standing up a security function, or when security and delivery priorities conflict and someone has to decide.

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Applies decision science and cognitive bias research to marketing and product decisions — how people actually choose under uncertainty, and how framing, defaults, sequencing, and social context change behavior. Use this to diagnose why a well-argued offer is not converting, to structure choices and pricing presentation, to design an experience around how attention and memory work, or to pressure-test whether a persuasion tactic is legitimate or manipulative.

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。