「zat」の検索結果

当用户是创业者/管理者/领导者，遇到企业增长瓶颈、转型困难、或者在问"为什么公司越大越创新不了""为什么我们总是错过新机会""公司的问题到底出在哪"时激活。 核心信号：用户在描述组织发展困境，且问题根源指向领导者自身的认知局限而非外部环境。 不适用于：非管理者的个人发展问题、纯粹的市场/竞争分析、具体业务执行问题。

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。

Turborepo monorepo build system guidance. Triggers on: turbo.json, task pipelines, dependsOn, caching, remote cache, the "turbo" CLI, --filter, --affected, CI optimization, environment variables, internal packages, monorepo structure/best practices, and boundaries. Use when user: configures tasks/workflows/pipelines, creates packages, sets up monorepo, shares code between apps, runs changed/affected packages, debugs cache, or has apps/packages directories.

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。

Next.js best practices - file conventions, RSC boundaries, data patterns, async APIs, metadata, error handling, route handlers, image/font optimization, bundling

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。

Director-level AI art prompt creation for Eastern beauty series, including Eastern fantasy / gu feng Chinese beauty character art, classical Eastern beauty, realistic and modern Eastern beauty, Cosplay展会摄影 / official convention cosplay photography with AAA game character campaign and Vogue editorial quality, 古风闺蜜 / AncientFemaleCompanionship two-person ancient Chinese female companionship photography, 东方美学图鉴 / 小红书图鉴 cover and content systems for 四大美人、四大才女、十二花神、东方神女、敦煌飞天、朝代服饰、东方器物、东方神话, 甜系纯欲生活写真 / SweetHomeGirl lifestyle portraits built around realism, feminine charm, romantic feeling, story moment, and natural attraction, new-Chinese-style fashion editorials, magazine portraits, worldbuilding, mythic role design, costume/styling systems, cinematic composition, lighting, negative prompts, and model-specific prompt variants. Use when the user asks for 东方美人, 东方审美, 东方美学图鉴, 小红书图鉴, 图鉴封面, 图鉴正文, 四大美人, 四大才女, 十二花神, 东方神女, 敦煌飞天, 朝代服饰, 东方器物, 东方神话, Cosplay展会摄影, coser, Coser, 漫展摄影, 展会摄影, ChinaJoy, Bilibili World, Tokyo Game Show, 游戏展台, 官方展会摄影, AAA游戏角色Cosplay, 古风闺蜜, AncientFemaleCompanionship, Ancient Female Companionship, 闺阁夜话, 姐妹古风写真, 古代闺阁生活, 灯下梳发, 共读, 夏夜纳凉, 守夜, 江南同行, 湖亭共伞, 山寺同行, 灯会夜游, 柔美, 妩媚, 成熟, 优雅, 高贵, 清冷, 温婉, 英气, 神性, 慵懒, 东方幻想, 古风美女, 古典东方美人, 宋韵, 唐宫, 江南, 洛神, 青瓷, 昆曲, 国风人物, 汉服女子, 写实东方美人, 现代东方审美, 新中式, 东方高级感, SweetHomeGirl, 甜系纯欲生活写真, 甜系纯欲, 甜美女友感, 真实女生, 自然抓拍, 故事感, 女友视角, lifestyle girlfriend portrait, 仙侠/武侠/宫廷/江南/敦煌风 AI 绘画提示词, image prompt optimization, style word expansion, character series concepts, or prompt packs for Midjourney, Stable Diffusion, ComfyUI, DALL-E, Gemini, Seedream, or other image generation tools.

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。

Local SEO audit — "best [cuisine] near me" targeting, Google Business Profile optimization, NAP consistency, citation building, restaurant schema markup

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。

量化专家经验调优库：按「L1 通用专家调优意见 → L2 结构化专家调优意见 → L3 模型专属专家调优意见」三级递进， 输出量化精度调优的完整手段（离群值抑制 / 量化方法·粒度·对称性选型 / 校准集调整 / 敏感层回退 / 模型专属策略）， 每条结论附专家原因分析与专家意见可信度等级。 本 Skill 是专家经验库，负责说明可选离群值抑制算法并询问用户选择哪些算法，以及回答「怎么调、哪些层需要回退及为什么」；不运行离群值抑制 processor，不修改 YAML、不执行量化、不做 EP 检查 / 服务化 / 任务评测。

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。

Interactively gather parameters for `python -m cli.inference.throughput_optimizer`, generate a deployment simulation command, explain assumptions, ask for execution confirmation, then run the simulation and summarize the best parallel strategy. Use when the user wants to evaluate a model on one or more hardware profiles, compare hardware targets, compare aggregation versus disaggregation versus PD ratio optimization, choose TP/EP/MOE-DP search ranges, or obtain recommended concurrency, throughput, TTFT, and TPOT results from throughput modeling.

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。

Analyze Ascend NPU schedule, operator dispatch, operator launch, and Host Bound profiling issues in Ascend profiling data. Use when need to diagnose device Free time, framework/operator dispatch latency, launch latency, PYTORCH_API/CANN_API launch gaps, aclrtSynchronizeStream stalls, task queue behavior, CPU scheduling interference, GC/lock pauses, CPU affinity, or schedule-side optimization actions.

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。

为 msModelSlim 适配器实现逐层量化（按层加载/懒加载）能力。仅在用户明确要求逐层量化或基础适配因 CPU 内存不足无法全量加载权重时使用。该特性为高阶可选项，不是基础适配必需项。

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。

计算算子（如 matmul/GEMM）的 MFU（Machine FLOP Utilization），并给出清晰的公式和推导过程。

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。

Audit and remediate NowStack Mobile security flaws. Use for auth/session abuse, Apple or Stripe payment bypass, Convex authorization, cross-user data leaks, unsafe deep links, R2 uploads, client secrets, SSRF, XSS, or pentest follow-up.

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。

Set up Convex authentication and authorization. Use for Convex Auth, Better Auth, Clerk, Auth0, JWT providers, auth.config.ts, protected functions, and user mapping.

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。

Create a lean, mobile-aware Product Requirements Document for a NowStack Mobile app through interactive discovery, then save it to .agents/plan/PRD.md. Use for "write a PRD", "ns prd", "define the product", "plan my app", or as the first planning step before ns-architecture and ns-tasks. Mobile-flavored — personas, onboarding/paywall flow, platforms (iOS/Android/web), IAP vs subscription monetization, store scope. Feeds ns-init-project (site-config) and the rest of the planning pipeline.

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。

Audits existing landing pages for conversion rate optimization. Scores hero section, CTA placement, social proof, objection handling, and form friction on a 1-10 scale. Use when someone says 'audit my landing page', 'improve conversions', 'why isn't my page converting', 'CRO audit', 'landing page feedback', 'optimize my signup page', 'page review', 'conversion rate', 'bounce rate is high', 'nobody is signing up', or anything about improving an existing page's performance. Even if they just share a URL and say 'what do you think' or 'how can I improve this' — if there's a page involved and conversion matters, this is the skill. Always use this over generic advice when an actual page exists to audit.

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。

Programmatic React video best practices for Remotion. Use this skill whenever the user is writing, debugging, or rendering Remotion code — compositions, useCurrentFrame animations, interpolate, spring, Sequence timing, <Img>/<Video>/<Audio> from @remotion/media, staticFile, calculateMetadata, the Studio preview, `npx remotion render`, captions, subtitles, voiceover, audio visualization, ffmpeg, transitions, light leaks, transparent video, Lottie, 3D scenes, Mapbox flyovers. Triggers especially on HtmlInCanvas / "html in canvas" requests, CRT terminal effects, glitch effects, video shaders, WebGL video, and any request to scaffold a new programmatic React video. Owns `remotion render` and everything about Remotion code. Do NOT trigger for finished MP4 ad assembly from existing slides (that is `/video-content`) or for the inspiration-to-MP4 reference-video pipeline (that is `/cmo-video`).

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。

Trigger when: (1) User wants to create an educational/explainer video, (2) User has a vague concept they want visualized, (3) User mentions "3b1b style" or "explain like 3Blue1Brown", (4) User wants to plan a Manim video or animation sequence, (5) User asks to "compose" or "plan" a math/science visualization. Transforms vague video ideas into detailed scene-by-scene plans (scenes.md). Conducts research, asks clarifying questions about audience/scope/focus, and outputs comprehensive scene specifications ready for implementation with ManimCE or ManimGL. Use this BEFORE writing any Manim code. This skill plans the video; use manimce-best-practices or manimgl-best-practices for implementation.

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, measures, and improves an Answer Engine Optimization program across ChatGPT, Perplexity, Claude, Gemini, Google AI Overviews, and Copilot. Use for AEO, GEO, LLMO, AI search visibility, AI citations, brand mentions, answer-engine monitoring, llms.txt, AI crawler audits, or questions like 'what does AI say about us?' and 'why are competitors cited instead of us?'. Produces a durable evidence report and prioritized gameplan, not a one-shot content checklist.

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。

Knowledge skill that applies behavioral psychology and persuasion principles to any marketing asset. Covers Cialdini's 6 principles, cognitive biases, and ethical persuasion frameworks. This skill is invoked BY other skills to enhance their output. Use it whenever copy needs more psychological weight, when a landing page feels flat, when email sequences lack urgency, or when any marketing asset needs to be more persuasive. Also use when someone says 'make this more convincing', 'add urgency', 'psychological triggers', 'persuasion framework', 'behavioral psychology', 'conversion optimization', 'nudge', 'influence tactics', or 'why isn't this converting'. Make sure to use this whenever ANY marketing copy needs to be more persuasive, even if the user just says 'this feels weak' or 'how do I get more signups', psychology principles likely apply.

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。

Converter 에이전트 v7.0. 승인된 proposal_script.md + visualization_strategy.md를 Gemini 슬라이드 디자인 요청용 proposal_slides.json (v2.0)으로 변환합니다. SPARK-6 구조, S-E-P, 40종 레이아웃, design_intent, gemini_instructions를 포함합니다. 트리거: JSON 변환, convert, proposal_slides.json, Gemini 디자인, 스크립트 변환, 제미나이

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 or review distributed .NET applications with Orleans grains, silos, persistence, streaming, reminders, placement, transactions, serialization, event sourcing, testing, and cloud-native hosting.

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 SmokingGun to find complexity candidates and organize imported optimization evidence.

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 designing or building any terminal user interface — choosing a layout paradigm, keybindings/interaction model, color system, data visualization, or motion. Framework-agnostic universal patterns that work with Ratatui, Ink, Textual, Bubbletea, or any TUI toolkit. For the repo's own pythinker-code TUI, use write-tui instead.

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 AI pricing optimization. TRIGGERS - Use when user needs help with ai-pricing-optimizer related tasks.

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。

Assess organizational AI maturity. TRIGGERS - Use when user needs help with ai-maturity-model related tasks.

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。