「tradeoff」の検索結果

Sizes models and token budgets using Kaplan/Chinchilla scaling laws. Use when reasoning about compute-optimal N and D, tokens-per-parameter ratios, or over-training tradeoffs.

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CQRS（Command Query Responsibility Segregation）における一貫性・可用性・スケーラビリティの トレードオフ分析と設計判断を支援する。CAP定理に基づくCQRSの評価軸、イベントソーシングとの 組み合わせによる影響、読み書きモデルの分離戦略を提供する。アーキテクチャ設計、技術選定、 既存システムのCQRS導入検討時に使用。 対象言語: 言語非依存（Java, Kotlin, Scala, TypeScript, Go, Rust, Python等すべて）。 トリガー：「CQRSを採用すべきか」「読み書き分離の設計」「結果整合性の判断」 「イベントソーシングを組み合わせるべきか」「CQRSのトレードオフ」「CQRSの可用性」 「CQRSのスケーラビリティ」「書き込みモデルと読み込みモデルの分離」 といったCQRS設計判断関連リクエストで起動。

Build a product roadmap with sequenced bets and explicit tradeoffs. Use when asked to "build a roadmap", "prioritize the backlog strategically", "what do we build next quarter", "sequence our bets", "what should we focus on", or "product strategy for the next N months".

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Supervised and unsupervised learning, bias-variance tradeoff, cross-validation, decision trees, ensemble methods, neural network fundamentals, and the practitioner's workflow from problem framing through deployment. Covers classification, regression, clustering, dimensionality reduction, regularization, hyperparameter tuning, and evaluation metrics. Use when building predictive models, selecting algorithms, or understanding the machine learning pipeline.

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用户要找对标账号/分析竞品爆款/找差异化选题时;或问"怎么做AI工具赛道的差异化""how to find benchmarks / differentiate"时。按5步走:找3-5个低粉高爆对标(搜索from:xxx min_faves:10)→分析90天爆款抓隐性需求→学互动策略→拆变现模式→三公式变形(场景细化/人群聚焦/观点反转)出差异。不适用于:已有定位日常发内容(→x-short-content-craft)、纠结选IP还是泛流量(→x-positioning-tradeoff)、检查草稿(→x-five-piece-checklist)、纯信息查询。

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规划变现路径/判断够不够格开创作者收益/决定卖什么产品时使用。三层金字塔定位：底层流量型(商单+广告分成,需500蓝V+3月5M曝光)、中层产品型(课程/付费专栏/带货)、顶层服务型(咨询/高端社群,利润率>80%)。IP路线可叠三层,泛流量卡底层。Trigger："怎么变现""够不够格开收益""卖什么产品""monetize""creator revenue""what to sell"。不适用：没粉丝的冷启动(→x-cold-start-playbook)、选IP还是泛流量(→x-positioning-tradeoff)、开蓝V/装修(→x-account-setup)。

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用户审计自己的内容结构占比、判断单条帖子传播上限、或困惑"发了感叹/观点帖没人理"时调用。 关键 trigger: "内容原型/content archetype""哪种内容容易爆/which content goes viral" "传播上限/viral ceiling""普通表达型/资源入口型/工具教程型/AI工具发现型" "为什么没人互动/why no engagement""内容方向有问题/content direction wrong"。 不适用于: 已确定写某种短内容类型并需要组织结构(用 x-short-content-craft)、 检查帖子五件套完备性(用 x-five-piece-checklist)、做账号定位取舍(用 x-positioning-tradeoff)。

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账号定位阶段做战略选择时用：用不可能三角（内容深度/传播力/变现力不可兼得）决定放弃哪个角，选IP路线（深度+变现，抗周期/高商业化/复利）或泛流量路线（传播力，但转化差不值钱）。Trigger："追粉丝还是做IP""IP还是泛流量""定位怎么选""深度和传播怎么权衡""泛流量不值钱吗""IP vs generic traffic""build IP or chase followers"。不适用：已有定位后的内容生产/选题（→内容创作类skill）；已有粉丝问怎么变现（→x-monetization-pyramid）；找对标账号（→x-benchmark-research）。

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Use for 'why does X work this way', 'why we picked Y', design rationale, regressions, postmortems, or data-backed thresholds. Discovers available MCPs and queries each evidence category (source control, issue tracker, long-form docs, real-time chat, infrastructure observability, error tracking, product analytics warehouse) in parallel, then returns a cited read on decisions and tradeoffs. Use how for runtime behavior.

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Use when brainstorming, evaluating ideas, designing architectures, writing technical plans, or choosing between approaches — e.g. "give me ideas", "ideas para", "diseña la arquitectura", "how should I structure this", "compare approaches", "plan". Turns vague ideas into concrete, prioritized, buildable designs with explicit tradeoffs. Use before writing code, not after.

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Build a white-labeled, client-facing intelligence dashboard — a live, branded reporting surface an agency stands up per client instead of sending static reports. Covers brand auto-extraction from the client's URL, a three-tier build (Basic/Standard/Advanced), an onboarding feature wizard, a 10-page inventory, public-access tradeoffs, and the retention plays that make the dashboard sticky. Use when "client dashboard", "client intelligence dashboard", "white label dashboard", "client portal", "branded dashboard for my client", "agency dashboard", "build my client a dashboard", "give the client a live view instead of a report", or any request to stand up a durable, client-facing reporting surface.

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The Mileva Method (CRISP) — Phase 2: Results. Outcome alignment, stakeholder register, baseline measurement, success metrics, tradeoff negotiation. Use after problem definition is complete. Triggers on "results", "phase 2", "define success", "success metrics", "what does done look like", or after Phase C exit checklist is complete.

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Develop evidence-backed structural and architectural security hardening proposals from vulnerability disclosures, supplied findings, incident or assessment documents, source code, or a completed Codex Security scan. Use when a user asks for systemic improvements, alternatives beyond per-finding patches, before-and-after security architecture views, engineering tradeoff analysis, or an implementation-ready plan for a selected hardening option. Also use automatically after a Codex Security scan with reportable findings when the top-level scan workflow requests final-report hardening guidance.

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Excellence-first strategic decision support. This skill should be used when users need to choose between options, pressure-test a plan, evaluate risk, or make a specific decision — across security, product, growth, operations, org design, and finance. Use when there is a decision with tradeoffs to score and a recommendation to make. For pure research ("what does the best in the world do about X?"), use best-in-world-research instead.

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Model selection strategy and routing. Use when choosing between models for different task types, subagent configurations, or optimizing token cost vs quality tradeoffs.

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Use when Codex must make a high-quality judgment on any complex problem, not only technical problems. Trigger when a question has high complexity, uncertainty, cost of being wrong, long-term impact, value tradeoffs, possible structural causes, or when the user asks for 全面思考, 深度理解, 系统性分析, 帮我想清楚, 从根上看, 这件事到底怎么看, 方法论, 复杂问题拆解, 系统方案, 架构判断, 策略判断, or 根因分析. Also trigger for project and engineering high-risk scenarios such as multi-module, multi-environment, multi-data-source, multi-state, financial truth, live trading, ledger, revenue, customer assets, order governance, broker interfaces, online availability, major solution selection, and system closure.

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Run a read-only adversarial Kimi challenge review that questions assumptions, design choices, and tradeoffs. Use when the user wants pushback on whether the approach is right, not a defect-only review.

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Apply ant colony optimization and foraging theory to resource search, exploration-exploitation tradeoffs, and distributed discovery. Covers scout deployment, trail reinforcement, diminishing returns detection, and adaptive foraging strategy selection. Use when searching a large solution space where brute-force enumeration is impractical, balancing investment between exploring new approaches and deepening known good ones, optimizing resource allocation across uncertain opportunities, or diagnosing premature convergence on local optima.

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Fit cognitive drift-diffusion models (Ratcliff DDM) to reaction time and accuracy data with parameter estimation (drift rate, boundary separation, non-decision time), model comparison, and parameter recovery validation. Use when modeling binary decision-making with reaction time data, estimating cognitive parameters from experimental data, comparing sequential sampling model variants, or decomposing speed-accuracy tradeoff effects into latent cognitive components.

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Bring how named experts would think about the question. Use when the user wants expert thinking on a tradeoff, choice, or stuck problem — not a verdict.

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Use when analyzing warp divergence, memory coalescing, shared memory bank conflicts, cache behavior, atomics, or occupancy tradeoffs on NVIDIA and AMD GPUs. Not for tool commands: use cuda-profiling.

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Before any material decision — code chunk, plan, strategy, design, scope cut, push to GitHub — convene a war-room. The session driver takes the CEO seat: frame the tradeoff, summon specialist seats from whatever agent roster the user has built, dispatch a cross-family voice via `ato dispatch` so priors actually disagree, decide. A failure-mode filter (wrong assumptions / overcomplexity / orthogonal edits / imperative-over-declarative — Karpathy's four are one good default, swap in your own) runs on every dispatch. Place in the v2.16 stack: war-rooms DECIDE before code starts; `ato-mission` EXECUTES the work between decisions (multi-step, goal-driven, persisted across days); `ato-review` VERIFIES the resulting commits. Use a war-room for the design verdict, hand the verdict to a Mission, review the merged result. Fires before: sending a code draft to the user as final, opening a PR, pushing to a remote-tracking branch, committing >50 LOC of behavior change, or delivering a plan or strategic recommendation as the final answer.

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Use when pricing, packaging, plan structure, or monetization tradeoffs need to be evaluated against positioning, conversion, and margin.

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Use when important product, technical, or founder decisions need to be captured as durable context with rationale, tradeoffs, and follow-up implications.

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。