「cta」の検索結果

小红书 AI 自媒体博主 (小红书 AI 自媒体博主 (从业者视角) — 在小红书做『AI 赛道』内容的自媒体博主，重点是**怎么写文案、怎么制作内容**。覆盖: (a) AI 赛道选题 — AI 工具测评 / AI 绘画 / AI 教程与 prompt / AI 副业变现 / AI 资讯解读 / AI 效率，按小红书用户(偏年轻女性 + 泛知识)重构选题; (b) 文案与内容结构(核心) — 封面(第一眼/大字报/对比图)、标题(数字/痛点/好奇缺口/关键词前置)、正文(钩子开头/分点/口语化/emoji/利他价值)、标签与 SEO 关键词、CTA 与互动引导、人设 voice; (c) 内容制作工作流 — 选题(对标爆款/找选题)→标题封面→脚本或正文→制作(图文/口播/录屏/数字人)→标签发布→数据复盘迭代; (d) AI 工具用于内容生产 — 文案(豆包/Kimi/DeepSeek/GPT)、生图(即梦/MJ/可图)、口播数字人、剪辑(剪映)、封面(Canva/创可贴)、选题与 SEO(新红/千瓜/灰豚); (e) 平台机制与起号 — 推荐算法(收藏率/互动率/完播/搜索)、薯条投放、违禁词/限流/避坑、起号与涨粉、垂直度; (f) 变现(次要) — 蒲公英广告 / 知识付费 / 引流私域 / AI 工具带货。学派分歧: 图文 vs 短视频、干货教程 vs 测评种草 vs 资讯搬运、真人出镜 vs 数字人/无人起号、原创 vs 搬运洗稿、AI 全自动批量 vs AI 辅助精做。不含: 通用小红书运营(已有 xiaohongshu-ops skill, 本 skill 聚焦 AI 赛道 + 文案/内容生产)、电商带货全流程、其他平台、AI 工具本身的技术原理。) Master OS — automated mastery of 小红书 AI 自媒体博主 (从业者视角) — 在小红书做『AI 赛道』内容的自媒体博主，重点是**怎么写文案、怎么制作内容**。覆盖: (a) AI 赛道选题 — AI 工具测评 / AI 绘画 / AI 教程与 prompt / AI 副业变现 / AI 资讯解读 / AI 效率，按小红书用户(偏年轻女性 + 泛知识)重构选题; (b) 文案与内容结构(核心) — 封面(第一眼/大字报/对比图)、标题(数字/痛点/好奇缺口/关键词前置)、正文(钩子开头/分点/口语化/emoji/利他价值)、标签与 SEO 关键词、CTA 与互动引导、人设 voice; (c) 内容制作工作流 — 选题(对标爆款/找选题)→标题封面→脚本或正文→制作(图文/口播/录屏/数字人)→标签发布→数据复盘迭代; (d) AI 工具用于内容生产 — 文案(豆包/Kimi/DeepSeek/GPT)、生图(即梦/MJ/可图)、口播数字人、剪辑(剪映)、封面(Canva/创可贴)、选题与 SEO(新红/千瓜/灰豚); (e) 平台机制与起号 — 推荐算法(收藏率/互动率/完播/搜索)、薯条投放、违禁词/限流/避坑、起号与涨粉、垂直度; (f) 变现(次要) — 蒲公英广告 / 知识付费 / 引流私域 / AI 工具带货。学派分歧: 图文 vs 短视频、干货教程 vs 测评种草 vs 资讯搬运、真人出镜 vs 数字人/无人起号、原创 vs 搬运洗稿、AI 全自动批量 vs AI 辅助精做。不含: 通用小红书运营(已有 xiaohongshu-ops skill, 本 skill 聚焦 AI 赛道 + 文案/内容生产)、电商带货全流程、其他平台、AI 工具本身的技术原理。: top builders' mental models, tool stack, current workflows, jargon, and where to keep up. Trigger this skill when the user works on 小红书 AI 自媒体博主 (从业者视角) — 在小红书做『AI 赛道』内容的自媒体博主，重点是**怎么写文案、怎么制作内容**。覆盖: (a) AI 赛道选题 — AI 工具测评 / AI 绘画 / AI 教程与 prompt / AI 副业变现 / AI 资讯解读 / AI 效率，按小红书用户(偏年轻女性 + 泛知识)重构选题; (b) 文案与内容结构(核心) — 封面(第一眼/大字报/对比图)、标题(数字/痛点/好奇缺口/关键词前置)、正文(钩子开头/分点/口语化/emoji/利他价值)、标签与 SEO 关键词、CTA 与互动引导、人设 voice; (c) 内容制作工作流 — 选题(对标爆款/找选题)→标题封面→脚本或正文→制作(图文/口播/录屏/数字人)→标签发布→数据复盘迭代; (d) AI 工具用于内容生产 — 文案(豆包/Kimi/DeepSeek/GPT)、生图(即梦/MJ/可图)、口播数字人、剪辑(剪映)、封面(Canva/创可贴)、选题与 SEO(新红/千瓜/灰豚); (e) 平台机制与起号 — 推荐算法(收藏率/互动率/完播/搜索)、薯条投放、违禁词/限流/避坑、起号与涨粉、垂直度; (f) 变现(次要) — 蒲公英广告 / 知识付费 / 引流私域 / AI 工具带货。学派分歧: 图文 vs 短视频、干货教程 vs 测评种草 vs 资讯搬运、真人出镜 vs 数字人/无人起号、原创 vs 搬运洗稿、AI 全自动批量 vs AI 辅助精做。不含: 通用小红书运营(已有 xiaohongshu-ops skill, 本 skill 聚焦 AI 赛道 + 文案/内容生产)、电商带货全流程、其他平台、AI 工具本身的技术原理。 problems and wants industry-grade thinking, tool selection, or workflow guidance. 触发词：「小红书 ai 博主」「小红书 ai 自媒体」「小红书 ai 赛道」「小红书爆款文案」「小红书 ai 教程」

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。

个人投资理财 (个人投资理财 (Personal Investing & Financial Planning) (从业者/自主投资者视角) — 个人与家庭自主管理投资组合、做长期财务规划的认知操作系统。覆盖: (a) 四大流派互掐 (本行业最有价值的智识谱系) — 价值投资 (Graham 安全边际 / Buffett·Munger 护城河+能力圈)、被动指数 (Bogle 成本至上+市场组合+定投)、技术分析 (趋势线/形态/动量择时)、趋势/动量跟踪 (CTA/双动量/turtle); (b) 资产配置与组合构建 — 股/债/现金/另类的配置框架 (60/40、全天候 All-Weather、三基金组合 three-fund、再平衡)、风险预算、定投 (DCA) vs 一次性; (c) 工具栈 — 券商/交易平台、组合追踪 (Portfolio Visualizer/Morningstar/雪球)、ETF/基金筛选与费率、回测、税优账户 (401k/IRA/Roth vs 中国养老 Y 账户); (d) 行为金融与风险管理 — 损失厌恶/过度交易/择时谬误、仓位与回撤管理、再平衡纪律; (e) 财务规划脚手架 — 应急金、目标-时间轴-风险承受度、退休/教育金规划、税务效率。主战场 = 美股/全球指数 (canon 母语区) + A 股/港股/公募基金 (zh-CN 散户层)。核心分歧: 主动选股 vs 被动指数 (是否能持续跑赢市场)、基本面 vs 技术面 (价格是否已反映信息 / EMH 强弱)、择时 vs 持有 (market timing vs time in market)、集中 vs 分散。不含: 持牌为他人理财的展业 (FA/RIA/投顾资质与合规销售)、机构资管/量化高频/对冲基金策略、投行/卖方研究、加密货币投机与 meme 炒作、日内打板/杠杆赌博式交易。) Master OS — automated mastery of 个人投资理财 (Personal Investing & Financial Planning) (从业者/自主投资者视角) — 个人与家庭自主管理投资组合、做长期财务规划的认知操作系统。覆盖: (a) 四大流派互掐 (本行业最有价值的智识谱系) — 价值投资 (Graham 安全边际 / Buffett·Munger 护城河+能力圈)、被动指数 (Bogle 成本至上+市场组合+定投)、技术分析 (趋势线/形态/动量择时)、趋势/动量跟踪 (CTA/双动量/turtle); (b) 资产配置与组合构建 — 股/债/现金/另类的配置框架 (60/40、全天候 All-Weather、三基金组合 three-fund、再平衡)、风险预算、定投 (DCA) vs 一次性; (c) 工具栈 — 券商/交易平台、组合追踪 (Portfolio Visualizer/Morningstar/雪球)、ETF/基金筛选与费率、回测、税优账户 (401k/IRA/Roth vs 中国养老 Y 账户); (d) 行为金融与风险管理 — 损失厌恶/过度交易/择时谬误、仓位与回撤管理、再平衡纪律; (e) 财务规划脚手架 — 应急金、目标-时间轴-风险承受度、退休/教育金规划、税务效率。主战场 = 美股/全球指数 (canon 母语区) + A 股/港股/公募基金 (zh-CN 散户层)。核心分歧: 主动选股 vs 被动指数 (是否能持续跑赢市场)、基本面 vs 技术面 (价格是否已反映信息 / EMH 强弱)、择时 vs 持有 (market timing vs time in market)、集中 vs 分散。不含: 持牌为他人理财的展业 (FA/RIA/投顾资质与合规销售)、机构资管/量化高频/对冲基金策略、投行/卖方研究、加密货币投机与 meme 炒作、日内打板/杠杆赌博式交易。: top builders' mental models, tool stack, current workflows, jargon, and where to keep up. Trigger this skill when the user works on 个人投资理财 (Personal Investing & Financial Planning) (从业者/自主投资者视角) — 个人与家庭自主管理投资组合、做长期财务规划的认知操作系统。覆盖: (a) 四大流派互掐 (本行业最有价值的智识谱系) — 价值投资 (Graham 安全边际 / Buffett·Munger 护城河+能力圈)、被动指数 (Bogle 成本至上+市场组合+定投)、技术分析 (趋势线/形态/动量择时)、趋势/动量跟踪 (CTA/双动量/turtle); (b) 资产配置与组合构建 — 股/债/现金/另类的配置框架 (60/40、全天候 All-Weather、三基金组合 three-fund、再平衡)、风险预算、定投 (DCA) vs 一次性; (c) 工具栈 — 券商/交易平台、组合追踪 (Portfolio Visualizer/Morningstar/雪球)、ETF/基金筛选与费率、回测、税优账户 (401k/IRA/Roth vs 中国养老 Y 账户); (d) 行为金融与风险管理 — 损失厌恶/过度交易/择时谬误、仓位与回撤管理、再平衡纪律; (e) 财务规划脚手架 — 应急金、目标-时间轴-风险承受度、退休/教育金规划、税务效率。主战场 = 美股/全球指数 (canon 母语区) + A 股/港股/公募基金 (zh-CN 散户层)。核心分歧: 主动选股 vs 被动指数 (是否能持续跑赢市场)、基本面 vs 技术面 (价格是否已反映信息 / EMH 强弱)、择时 vs 持有 (market timing vs time in market)、集中 vs 分散。不含: 持牌为他人理财的展业 (FA/RIA/投顾资质与合规销售)、机构资管/量化高频/对冲基金策略、投行/卖方研究、加密货币投机与 meme 炒作、日内打板/杠杆赌博式交易。 problems and wants industry-grade thinking, tool selection, or workflow guidance. 触发词：「个人投资理财」「个人投资」「理财」「personal investing」「资产配置」

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。

Aural skills development covering interval recognition, chord quality identification, rhythmic and melodic dictation, sight-singing, and progressive pedagogy. Covers the Kodaly method (hand signs, relative solmization, singing-first approach), interval identification strategies, chord quality and inversion recognition, harmonic dictation, solfege systems (fixed-do vs. movable-do), Gordon's Music Learning Theory, Suzuki method principles, and structured difficulty sequencing. Use when developing listening skills, practicing dictation, preparing sight-singing, or designing ear training curricula.

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。

Complex systems and emergent behavior — chaos theory, fractals, neural networks, cellular automata, and computability. Analyzes dynamical systems for chaos via Lyapunov exponents, classifies bifurcations, measures fractal dimension, and models emergent phenomena. Use when analyzing chaotic systems, computing Lyapunov exponents, classifying bifurcations, measuring fractal dimension, modeling neural networks, or studying emergent behavior from simple rules.

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。

Turns raw captures (dictated braindumps in a deliverable's inbox/, or recent macwhspr dictation-log entries) into proposed planning-wiki changes, shown as one unified diff. Classifies each statement as a decision, evidence claim, question, task, or dropped item, traces it to its capture paragraph, tags unconfirmed names, numbers, and citations UNRESOLVED, and writes decisions only with the author's assent. Edits only the planning wiki, never source files. Use when asked to intake, absorb notes, or process the inbox, or after dictating notes from a talk, meeting, or reading session.

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。

Great Expectations, dbt tests, anomaly detection, and data contracts for data quality. Activate on: data quality, data validation, Great Expectations, data contract, anomaly detection, SLA, freshness check, schema validation. NOT for: dbt model structure (use dbt-analytics-engineer), schema evolution (use schema-evolution-manager).

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。

Moves the CARLA spectator camera, frames an actor, or follows one live — third-person/chase, first-person/driver, top-down, or front views, resolving natural-language targets like "the ego" or "the Toyota Prius" to a specific actor. Use when the user asks to "move the camera/spectator", "watch/follow vehicle X", "3rd-person view of the ego", "1st-person view of the prius", "bird's-eye view", or "look at (x,y,z)".

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。

When the user wants to pack 2D rectangular items into bins, optimize 2D cutting patterns, or minimize material waste in 2D packing. Also use when the user mentions "2D packing," "rectangular packing," "sheet packing," "2D bin packing problem," "guillotine cutting," "two-dimensional packing," or "rectangle packing optimization." For 1D problems, see 1d-cutting-stock. For 3D problems, see 3d-bin-packing.

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。

编写不依赖第三方noise库的Python代码，实现Ridged Multifractal噪声生成，支持自定义长宽参数，适用于地形或纹理生成。

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。

Great Expectations, dbt tests, anomaly detection, and data contracts for data quality. Activate on: data quality, data validation, Great Expectations, data contract, anomaly detection, SLA, freshness check, schema validation. NOT for: dbt model structure (use dbt-analytics-engineer), schema evolution (use schema-evolution-manager).

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。

Evaluates and negotiates job offers. Decodes base, bonus, equity, sign-on, level and terms with unknowns kept unknown and each offer in its own currency, benchmarks against checkable pay data, finds true leverage, drafts one counter as email and call script, role-plays the recruiter, and handles the salary-expectations number, deadlines and extensions, "best and final", competing offers, counteroffers and written acceptance or decline. Country-aware (India CTC, 13th-month pay, public-sector scales). Use when the user says "I got an offer of...", "is this offer good", "should I negotiate", "how do I counter", "compare my offers", "what is my equity worth", "they asked my salary expectations", "they want an answer by Friday", "can I ask for more time", "should I accept" or "practice the negotiation call". Uses Reactive Resume MCP tools when connected. For logging the offer, its deadline and withdrawal notes, job-application-manager; for other interview answers, interview-prep.

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。

Write or review UX copy — microcopy, error messages, empty states, CTAs. Trigger with "write copy for", "what should this button say?", "review this error message", or when naming a CTA, wording a confirmation dialog, filling an empty state, or writing onboarding text.

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。

Guided journey from a business with lucky months to a repeatable growth engine that produces a forecast. Orchestrates nine skills phase by phase - one-page-marketing, hundred-million-offers, predictable-revenue, contagious, influence-psychology, crossing-the-chasm, cold-start-problem, lean-analytics, negotiation - asking the user questions at every decision point and recording results in the project docs/ folder (MARKETING.md, OFFER.md, METRICS.md, GROW-BUSINESS-PLAN.md) so the journey resumes across sessions. Use when the user wants to build a marketing and sales system, manufacture predictable pipeline, engineer word-of-mouth and referrals, or says 'revenue is real but lumpy and I need growth that repeats'. If the offer, retention, or operations underneath are broken, run improve-business first; with no paying customers yet, start with create-business; when the product itself must carry the growth loops, use grow-app. For one framework in isolation, invoke that skill directly.

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。

Cultural adaptation for translated content. Run AFTER blog-translate completes. Adjusts brand examples, CTAs, legal references, and formality for the target market (German, French, Japanese, Spanish, etc.). Deep cultural adaptation of translated blog posts. Goes beyond translation to swap brand examples, adapt CTAs, substitute legal references, localize statistic sources where possible, and adjust formality (Sie/du, tu/vous, formal/informal). Built-in profiles for DACH, Francophone, Hispanic, and Japanese markets, plus a custom-locale template. Makes content feel locally authored, not translated. Use when user says "localize blog", "blog localize", "cultural adaptation", "adapt for Germany", "adapt for France", "lokalisieren", "localiser", "adaptar".

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。

Convert physician verbal dictation into structured SOAP notes. Trigger.

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。

Compute statistical power, required sample size, and minimum detectable effect (MDE) for a study design, then write a registry-ready power section. Handles two-arm RCTs (with clustering / ICC and unequal allocation), multiple-arm corrections, and a simulation-based power option for non-standard designs (DiD/event-study, IV, panel). Use when user says "power analysis", "power calculation", "MDE", "minimum detectable effect", "how big a sample do I need", "is my study powered", "power for an RCT", or when /preregister needs a power section for an experiment. Produces a power/MDE table, power curves, and a methods paragraph to paste into a preregistration.

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。

Keep a change small and in the existing idiom - size the plan and weigh smaller alternatives before writing it, reuse the mechanism that already owns the behavior instead of adding a parallel one, and simplify the diff before handoff. Use when planning any change beyond a few lines, before readying a PR, or when reviewing agent-written code for bloat.

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。

Compare job levels (职级) across Chinese technology companies and against international ladders, to judge an offer, a job move or a promotion target: what a level usually means in scope and expectations, how levels roughly line up, and what to ask the recruiter. Use when asked P7 相当于什么级别, 跳槽职级怎么对标, 字节 2-2 对应阿里什么, 这个 offer 的职级合理吗, compare Chinese tech levels, or how does Alibaba P-level map to Google levels. Produces a level comparison built from the person's own ladders and offer details, a scope-and-expectation profile for each level, the negotiation questions to ask, and a clear note on what is approximate.

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。

Build a smooth client-onboarding process that starts projects on the right foot — clear expectations, the info you need, and a professional first impression that prevents problems later. Use when asked to onboard a new client, create an onboarding process, kick off a client project, or client welcome packet. Produces a welcome and kickoff flow, the intake you need to start, expectation-setting on scope/comms/timeline/payment, a kickoff-meeting agenda, and the templates to reuse — so onboarding is consistent, not improvised, and prevents the scope and communication problems that sink projects.

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。

Hume AI Octave TTS and EVI speech-to-speech configs: synthesize speech and manage EVI configs. Trigger phrases: hume, hume ai, octave tts, EVI config.

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。

当你在为《自动化学报》(Acta Automatica Sinica, AAS) 撰写相关工作、文献综述或确立创新性(delta)时调用。给出以差异为先的文献组织法、控制/自动化/模式识别方向代表作的覆盖策略、与最接近工作的对比表、避免"重复前人/无新意"初审退稿的自检，以及综述栏目所需的分类框架与批判性评价方法，帮助在 Acta Automatica Sinica 的中英文双语与综述长文传统下清晰界定本文相对已有研究的增量。

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。

当你在为《自动化学报》(Acta Automatica Sinica, AAS) 撰写或打磨中文稿件体例时调用。覆盖报道性中英文摘要(目的/方法/结果/结论四要素)、中英文关键词、中图分类号(自动化/计算机类 TP 打头)、参考文献 GB/T 7714 顺序编码制著录、量与单位法定计量、控制类公式与图表规范、以及综述/论文/短文三类栏目的体例差异，帮助把控制理论、模式识别、机器人、智能系统方向的中文长文写到符合 Acta Automatica Sinica 的双语传统与编排要求。

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。

当你在为《自动化学报》(Acta Automatica Sinica, AAS) 确定选题、判断稿件是否契合本刊控制与自动化学科定位、以及在综述/论文/短文三类栏目间定位时调用。帮助辨析本刊与兄弟中文刊(计算机学报/软件学报/电子学报/计算机研究与发展/计算机科学)的学科分工，评估创新性与贡献可数性，避免"主题超范围""无新意""重复前人"等初审退稿理由，并把选题打磨到匹配 Acta Automatica Sinica 的控制/模式识别/机器人/智能系统侧重与中英文双语综述长文传统。

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。

当你在需要理解《自动化学报》(Acta Automatica Sinica, AAS) 的双盲三审制审稿流程、各环节时间进度与作者可施加的杠杆时调用。建模编辑初审→双盲外审→退修/复审(多轮)→主编终审的完整链路，说明作者承诺前置、双盲对作者的约束、退修期限与重新送审、状态查询，帮助作者在 Acta Automatica Sinica 的评审各节点做出正确动作、管理预期并避免流程性失误。

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。