698 件 ・ 関連度順
概要と使いどころ
Write a LinkedIn post from a raw idea using 21 proven hook formulas, in the user's own voice, humanized so it does not read as AI. Use whenever the user wants a LinkedIn post, a hook, a draft for the feed, "post about X", "turn this into a LinkedIn post", or asks for hook options. Produces three hook options, one full draft, and a copy-ready block that is never published without an explicit yes.
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。
Interactive guided deployment flow for Azure OpenAI models with full customization control. Step-by-step selection of model version, SKU (GlobalStandard/Standard/ProvisionedManaged), capacity, RAI policy (content filter), and advanced options (dynamic quota, priority processing, spillover). USE FOR: custom deployment, customize model deployment, choose version, select SKU, set capacity, configure content filter, RAI policy, deployment options, detailed deployment, advanced deployment, PTU deployment, provisioned throughput. DO NOT USE FOR: quick deployment to optimal region (use preset).
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。
Handle a dispute with a contractor — unfinished work, poor quality, overcharging, or a no-show — with a path that protects your money and your options. Use when asked to deal with a contractor dispute, my contractor did bad work / won't finish / overcharged, or how to get a builder to fix their work. Produces a read on your position (contract, payments, evidence), a firm-but-professional communication and demand path, documentation and payment-leverage guidance, and escalation options (mediation, licensing board, chargeback, small claims) — flagging that it's not legal advice.
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。
Brainstorm evidence-backed options for what to build, or stress-test an idea. Use when: deciding what to build next, comparing options or testing an idea.
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。
Generate, evaluate, and narrow brand concepts during early ideation including positioning territories, naming candidates, mood directions, and narrative angles. Use this skill whenever the user is in the early phase of brand creation, exploring brand directions, brainstorming names, building moodboards, generating positioning options, or trying to choose between multiple brand directions. Triggers on brand ideation, brand concept, naming, brand name, name candidates, positioning, brand positioning, mood board, brand directions, exploring brands, early brand work, brand exploration, brand brainstorm, brand options. Also triggers when the user has multiple half-formed brand ideas and needs help converging on one, even if they do not say 'ideation' explicitly.
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。
Answer prediction questions using market trading data, not opinions. Use when the user asks probability questions about geopolitics, economics, markets, industries, or any topic where real money is being traded on the outcome. Examples: 'What's the probability of WW3?', 'Will there be a recession?', 'Is AI in a bubble?', 'When will the Russia-Ukraine war end?', 'Is it a good time to buy gold?', 'Will SPY drop 5% this month?', 'Is NVDA options premium overpriced?'. The skill reads prices from prediction markets, commodities, equities, options chains, derivatives, yield curves, and currencies, then cross-validates multiple signals to produce a structured probability report.
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。
Full brand naming workflow for founders, agencies, and businesses. Use this skill whenever the user says "help me name this brand", "brand naming", "I need a name for", "name ideas for", "what should I call my brand/company/product", "naming a startup", "brand name suggestions", "help with naming", or shares a brand brief and asks for name options. Also triggers when the user shares existing name options and asks for feedback, evaluation, ranking, or scoring of those names. Auto-detects whether to run the full generation workflow or the evaluation workflow based on what the user provides. Always use this skill for any brand or product naming task — even if the user just casually mentions needing a name.
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。
Retrieves up-to-date documentation, API references, and code examples for any developer technology. Use this skill whenever the user asks about a specific library, framework, SDK, CLI tool, or cloud service — even for well-known ones like React, Next.js, Prisma, Express, Tailwind, Django, or Spring Boot. Your training data may not reflect recent API changes or version updates. Always use for: API syntax questions, configuration options, version migration issues, "how do I" questions mentioning a library name, debugging that involves library-specific behavior, setup instructions, and CLI tool usage. Use even when you think you know the answer — do not rely on training data for API details, signatures, or configuration options as they are frequently outdated. Always verify against current docs. Prefer this over web search for library documentation and API details.
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。
Use the Microsoft.Extensions stack correctly across Generic Host, dependency injection, configuration, logging, options, HttpClientFactory, and other shared infrastructure patterns. USE FOR: wiring dependency injection, configuration, logging, or options; introducing Generic Host patterns into non-web .NET apps; cleaning up service registration, typed HTTP. DO NOT USE FOR: unrelated stacks; generic tasks that do not need this specific guidance. INVOKES: inspect the repository context, edit targeted files, and run relevant build, test, lint, or validation commands when changes are made.
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。
Imperative guidance for the System.Text.Json APIs added in .NET 11: the built-in `JsonNamingPolicy.PascalCase` naming policy, and the strongly-typed generic `JsonSerializerOptions.GetTypeInfo` and `JsonSerializerOptions.TryGetTypeInfo` metadata accessors (generic overloads that return a typed `JsonTypeInfo`). USE ONLY when the user is targeting net11.0 or later and needs PascalCase JSON property or dictionary-key names without writing a custom naming policy, a strongly-typed generic `JsonTypeInfo` instead of the non-generic `JsonTypeInfo`, or a no-throw way to probe whether a type's serialization metadata is resolved. DO NOT USE when the target is earlier than net11.0, the requested behavior uses an established pre-net11 naming policy, or the user explicitly selected another JSON library.
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。
Brainstorm evidence-backed options for what to build, or stress-test an idea. Use when: deciding what to build next, comparing options or testing an idea.
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。
Use when upgrading an existing TypeScript codebase from Inngest SDK v3 to v4, or when fixing mixed v3/v4 API usage. Covers detecting current SDK usage, moving triggers into createFunction options, replacing EventSchemas with eventType/staticSchema, moving serve options to the client, updating realtime imports, rewriting step.invoke string IDs, checkpointing/serverless runtime settings, Connect option changes, and verification.
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。
Get option chain data from Interactive Brokers for equities, ETFs, and futures (FOP), including calls and puts with strikes, bids, asks, volume, implied volatility, and model Greeks. Use when user asks about options using IBKR data, futures options (NQ/ES/CL/GC...), or needs real-time option quotes from their broker. Requires TWS or IB Gateway running locally.
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。
Researches an open-ended question — options, possible solutions, prior art, trade-offs, or how something works — and produces a durable, evidence-backed, adversarially-validated report that recommends an option without committing the team to any artifact. Use when you want to research approaches, weigh options, survey prior art or the state of the art, or understand how something works before committing to a direction. Does not diagnose a bug, failure, or root cause — use investigate. Does not specify a feature — use plan-a-feature. Does not create or update a coding standard — use coding-standard. Does not compare two concrete artifacts for gaps — use gap-analysis. Does not assess an existing module's architecture — use architectural-analysis. Does not capture feedback on Han's own skills — use han-feedback.
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。
options income, covered call strategy, cash secured puts, credit spreads, iron condor, wheel strategy, premium selling, yield enhancement, defined risk income, monthly income options
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。
Protocol for handling ambiguous decisions and missing/conflicting knowledge during code generation, design, and review. Ensures AI surfaces genuine judgment calls with structured options and stops on hallucination risk instead of silently assuming. Use when a decision has multiple valid approaches, when facts are missing or contradictory, when the user asks 'what should we do here?', 'is this a judgment call?', 'should I ask about this?', 'am I guessing here?', 'what are the tradeoffs?', or when deciding between two reasonable architectural or design options. Also composed by molecules to define how judgment calls and clarification requests are surfaced and resolved.
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。
Alpha Vantage — Financial Market Data workflow skill. Use this skill when the user needs Access 20+ years of global financial data: equities, options, forex, crypto, commodities, economic indicators, and 50+ technical indicators and the operator should preserve the upstream workflow, copied support files, and provenance before merging or handing off.
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。
管理咨询 (管理咨询 (Management Consulting) — 战略与管理咨询的职业认知操作系统，从业者/咨询顾问/想入行者/采购咨询服务的客户视角。覆盖: (a) 第一性张力 — 假设驱动 (hypothesis-driven / answer-first / Day-1 answer) 的「先有答案再验证」⇄ 数据驱动 (data-led / bottom-up) 的「先穷尽事实再归纳」，这行的核心张力是「先射箭再画靶 vs 先画靶再射箭」，资深人偏 top-down 假设树减少 boil-the-ocean，但要时刻防 confirmation bias; 以及更深层的张力 — 「卖洞见 (insight / so-what) vs 卖工时 (analysis / 体力)」，「客户影响 (client impact / 落地) vs 智识严谨 (intellectual rigor / 漂亮 deck)」; (b) 方法论正典 (最标准化、最易蒸出高质量) — MECE (相互独立完全穷尽)、issue tree / logic tree (问题树/逻辑树)、hypothesis-driven problem solving (假设驱动)、Pyramid Principle (Minto 金字塔原理: SCQA 情境-冲突-疑问-回答 + 结论先行 + 自上而下 + 归纳/演绎)、80/20 (帕累托/抓大放小)、so-what (所以呢/洞见提炼)、storyline (故事线/ghost deck 鬼影稿)、driver tree / profit tree、2x2 矩阵、value chain、frameworks (Porter 五力/3C/4P/7S/BCG 矩阵/价值链) 既是脚手架也是陷阱 (套框架 vs 真洞见); (c) 行业结构 — MBB (McKinsey/BCG/Bain 战略三巨头) vs Big Four 咨询臂 (Deloitte/PwC Strategy&/EY-Parthenon/KPMG) vs 精品战略所 (Oliver Wyman/Kearney/Roland Berger/L.E.K./Arthur D. Little) vs 专业/职能 boutique (运营/数字化/PE 尽调); up-or-out (非升即走)、leverage model (杠杆模型: 1 个合伙人带多个 EM/咨询师/分析师的金字塔)、partner/principal/EM(项目经理)/associate(咨询师)/analyst(分析师) 职级; (d) 项目交付模型 (the case/study/engagement) — 提案/SOW (scope of work)、problem definition (问题定义/真问题 vs 表象问题)、workplan (工作计划)、hypothesis tree (假设树)、analysis (分析/建模)、synthesis (综合)、recommendation (建议)、implementation (落地); 客户管理 (steering committee 指导委员会、stakeholder 利益相关方、对齐); (e) 产出物 — the deck (PPT 幻灯片是咨询的母语)、executive summary、action title (行动标题/每页一句话结论)、ghost deck (先搭骨架后填数据)、Excel 财务模型、framework 图; (f) 职业/招聘 — case interview 生态 (案例面试: Victor Cheng LOMS/Case in Point/market sizing 市场估算/估算题/fit 行为面)、网申-笔试-案例面、up-or-out、exit options (退出路径: PE/VC/corp dev 企业发展/战略岗/创业/industry); (g) 争议/批判 — 咨询的价值之辩 (「借你的表告诉你时间」「frameworks 是剧场表演 vs 真洞见」Matthew Stewart《The Management Myth》)、McKinsey 的丑闻 (Purdue 阿片类/独裁政府/ICE/利益冲突, Bogdanich & Forsythe《When McKinsey Comes to Town》)、咨询 vs 自建战略部、generalist 通才 vs specialist 专才之争、AI/GenAI 对初级分析师工作的冲击; (h) 流派 — 战略经典 (Porter 定位学派/五力 vs 资源基础观 RBV vs 蓝海战略 Kim&Mauborgne vs 颠覆式创新 Christensen vs Mintzberg 应急战略批判 planning school); 三巨头气质分野 (McKinsey 事实驱动/CEO 信任/通才, BCG 概念/思想领导力/经验曲线/增长-份额矩阵, Bain 结果导向/NPS 净推荐值/PE 关系/与客户深度绑定)。不含: 投行 M&A/财务顾问 (估值/承销超出范畴)、纯 IT 实施/系统集成 (SI 人力外包)、纯审计/税务、纯人力 RPO/猎头、内部战略部 only (虽相关但本 skill 聚焦专业服务咨询的外部视角)。) Master OS — automated mastery of 管理咨询 (Management Consulting) — 战略与管理咨询的职业认知操作系统，从业者/咨询顾问/想入行者/采购咨询服务的客户视角。覆盖: (a) 第一性张力 — 假设驱动 (hypothesis-driven / answer-first / Day-1 answer) 的「先有答案再验证」⇄ 数据驱动 (data-led / bottom-up) 的「先穷尽事实再归纳」，这行的核心张力是「先射箭再画靶 vs 先画靶再射箭」，资深人偏 top-down 假设树减少 boil-the-ocean，但要时刻防 confirmation bias; 以及更深层的张力 — 「卖洞见 (insight / so-what) vs 卖工时 (analysis / 体力)」，「客户影响 (client impact / 落地) vs 智识严谨 (intellectual rigor / 漂亮 deck)」; (b) 方法论正典 (最标准化、最易蒸出高质量) — MECE (相互独立完全穷尽)、issue tree / logic tree (问题树/逻辑树)、hypothesis-driven problem solving (假设驱动)、Pyramid Principle (Minto 金字塔原理: SCQA 情境-冲突-疑问-回答 + 结论先行 + 自上而下 + 归纳/演绎)、80/20 (帕累托/抓大放小)、so-what (所以呢/洞见提炼)、storyline (故事线/ghost deck 鬼影稿)、driver tree / profit tree、2x2 矩阵、value chain、frameworks (Porter 五力/3C/4P/7S/BCG 矩阵/价值链) 既是脚手架也是陷阱 (套框架 vs 真洞见); (c) 行业结构 — MBB (McKinsey/BCG/Bain 战略三巨头) vs Big Four 咨询臂 (Deloitte/PwC Strategy&/EY-Parthenon/KPMG) vs 精品战略所 (Oliver Wyman/Kearney/Roland Berger/L.E.K./Arthur D. Little) vs 专业/职能 boutique (运营/数字化/PE 尽调); up-or-out (非升即走)、leverage model (杠杆模型: 1 个合伙人带多个 EM/咨询师/分析师的金字塔)、partner/principal/EM(项目经理)/associate(咨询师)/analyst(分析师) 职级; (d) 项目交付模型 (the case/study/engagement) — 提案/SOW (scope of work)、problem definition (问题定义/真问题 vs 表象问题)、workplan (工作计划)、hypothesis tree (假设树)、analysis (分析/建模)、synthesis (综合)、recommendation (建议)、implementation (落地); 客户管理 (steering committee 指导委员会、stakeholder 利益相关方、对齐); (e) 产出物 — the deck (PPT 幻灯片是咨询的母语)、executive summary、action title (行动标题/每页一句话结论)、ghost deck (先搭骨架后填数据)、Excel 财务模型、framework 图; (f) 职业/招聘 — case interview 生态 (案例面试: Victor Cheng LOMS/Case in Point/market sizing 市场估算/估算题/fit 行为面)、网申-笔试-案例面、up-or-out、exit options (退出路径: PE/VC/corp dev 企业发展/战略岗/创业/industry); (g) 争议/批判 — 咨询的
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。
Add runtime UI options (select/checkbox/switch/input) to MaaFramework option surfaces such as `assets/interface.json` or `assets/resource/tasks/**/*.json`. Use when adding a user-facing toggle, selector, checkbox, or input; wiring options to `pipeline_override`; aligning option paths with Python `context.get_node_data()` or CustomAction params; or reviewing option behavior across Pipeline JSON and Python.
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。
Builds pricing models for barrier, Asian, lookback, and other path-dependent options with Monte Carlo simulation. Use when pricing exotic options, modeling complex payoffs, or evaluating structured product components.
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。
Prices FX options and exotic structures with Garman-Kohlhagen, local volatility, and stochastic volatility models. Use when pricing FX derivatives, evaluating FX options, or modeling cross-currency products.
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。
Specialized business strategy prompt methodology for Claude. Provides 11 tested approaches (3 identity, 4 reasoning, 4 output formats) for consulting, M&A, corporate strategy, and strategic planning tasks. Use when building prompts for deal evaluation, due diligence, portfolio strategy, business model analysis, board narratives, investment cases, market entry plans, or strategic options assessments. Trigger on: "build a prompt for M&A analysis," "due diligence prompt," "board narrative," "investment case," "market entry strategy," "strategic options," "portfolio strategy prompt," "business model analysis prompt," "consulting-style analysis." Do NOT use for evaluating or scoring existing prompts (use rootnode-prompt-validation if available) or for auditing Claude Projects (use rootnode-project-audit if available).
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。
Pre-Earnings Analysis — earnings date, analyst consensus estimates, 8-quarter historical surprise pattern, post-earnings price move history, options-implied expected move vs historical, key metrics to watch, and trade setup recommendations. Triggered by "trade earnings <TICKER>".
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。
Use when upgrading an existing TypeScript codebase from Inngest SDK v3 to v4, or when fixing mixed v3/v4 API usage. Covers detecting current SDK usage, moving triggers into createFunction options, replacing EventSchemas with eventType/staticSchema, moving serve options to the client, updating realtime imports, rewriting step.invoke string IDs, checkpointing/serverless runtime settings, Connect option changes, and verification.
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。