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概要と使いどころ
Create distinctive, production-grade frontend interfaces with high design quality. Use this skill when the user asks to build web components, pages, or applications. Generates creative, polished code that avoids generic AI aesthetics.
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 a UE5 task involves characters in motion: "import FBX from Maya" with a skeleton, "retarget animations", IK Rig, IK Retargeter, feet sinking or floating after retarget, mocap cleanup, Motion Matching, Pose Search, Choosers, GASP, state machine vs motion matching, root motion, offset root bone, foot IK, Control Rig, Animation Blueprint cost, "animation is expensive", budget allocator, URO, MetaHuman LODs, ML Deformer, cloth.
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 a creative task has no direction yet: finding inspiration, exploring looks or styles, proposing concept directions to pick between, building a moodboard or reference board, doing visual research, mining existing assets for a style, or escaping generic AI-looking output. Also when a saved Scenario collection should drive a new batch as art direction. Keywords: inspiration, moodboard, references, art direction, style exploration, concepts, serendipity, ideation, surprise me.
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 generating or editing images with Google's Gemini image models (Nano Banana) on Scenario via MCP: text-to-image, natural-language instruction editing, identity locking or style transfer from reference images, multi-image fusion, pulling stills from a video clip, Google Search grounding, thinking level tuning, 512 to 4K output, or choosing between Flash, Pro, and Lite. Keywords: Gemini 3.1 Flash, Gemini 3.0 Pro, Gemini 3.1 Lite, Nano Banana 2, Nano Banana Pro, txt2img, img2img.
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。
Navigate CNKI search result pages (next/previous/specific page) or change sort order. Use when user wants to see more results or change sorting.
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。
对亚马逊商品评论进行深度分析，自动识别产品痛点、分析退货原因，生成改进建议和客服回复模板。Invoke when user uses /review-analysis command with a product ASIN.
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。
基于西柚洞察MCP的亚马逊竞品分析与广告策略工具。提供7大核心工作场景：实时监控广告投放效果、快速找到高性价比流量缺口、快速拆解对标竞对打法、提升新品推广效率、精准拆解竞品流量以及广告策略、透视竞品广告策略和预算、高效搭建关键词库。适用于亚马逊卖家进行竞品分析、广告优化和关键词研究。
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。
对亚马逊竞品Listing进行全维度穿透分析，包括文案逻辑、评论分析、关键词分析、市场动态等。分析完成后自动保存为Markdown报告文档到reports/目录。Invoke when user uses /amazon-analyse command with a product ASIN.
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。
亚马逊品类自动化选品分析技能。通过五维评分模型对亚马逊品类进行深度市场调研，生成Markdown分析报告。当用户使用 /category-selection 命令或提出'分析XX品类'、'XX品类市场调研'、'XX品类选品'等需求时触发此技能。支持配置分析数量，默认Top20。
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。
Designing first-run product onboarding wizards that get users to the ah-ha moment without overwhelming them. Step architecture, progressive disclosure, escape hatches, completion incentives, drop-off measurement. Honest about tutorial-overload (dump everything upfront), skip-friendly-empty (skipped onboarding leads to abandoned product), and earned-progressive-disclosure (right things at the right moments) patterns. Triggers on onboarding wizard, product onboarding, first-run experience, signup flow, activation flow, FRX, time-to-value, ah-ha moment design. Also triggers when activation rates are low, when users skip onboarding and never return, when onboarding flows are being scoped for the first time, or when audience research shows users not finding key features.
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。
Cross-format content adaptation. Turning one substantial piece into many derivative formats (blog series, email sequences, social posts, webinars, podcasts, video shorts) without losing the original's value or producing AI-slop variants. The discipline of adaptation per medium rather than mass-blast distribution. Triggers on content repurposing, content adaptation, cross-format content, content atomization, content multiplication, content distribution across formats, source-piece-to-derivative, video shorts from blog, email from whitepaper, podcast from article, blog series from research. Also triggers when a flagship piece is shipping but the team has not planned how to extend it across formats, when repurposing is happening but the derivatives feel mass-produced, or when AI-assisted repurposing is producing slop variants of strong source pieces.
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 AI images using Volcengine Seedream model. Supports text-to-image (T2I), image editing (I2I), multi-image fusion, and web-search-based generation. Use this skill when the user wants to create, generate, or edit images.
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。
Jira expert for project setup, workflow design, JQL queries, custom fields, automation rules, dashboards, and reporting. Use for Jira configuration, advanced search, board and dashboard creation, and technical Jira operations.
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。
Structured intelligence dossiers on companies, people, markets, or domains, with source triangulation and fact/inference discipline. Use when preparing a deal-prep dossier, executive briefing, or due-diligence overview.
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。
Market sizing and market structure work — TAM/SAM/SOM built top-down and bottom-up then reconciled, segmentation, demand triangulation, and survey design. Use when sizing a market, writing a sizing memo, or fielding a survey.
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。
Grant writing and proposal architecture: funder fit, proposal structure, budget design, and success-factor scoring. Use when writing a grant proposal, evaluating funder fit, auditing a draft for competitiveness, or planning a budget.
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。
Clinical study operations — protocol structure, endpoint selection, eligibility design, sample-size and power planning, site feasibility, and documentation readiness. Use when planning, auditing, or costing a study.
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。
Product manager toolkit covering RICE prioritization, customer interview analysis, PRDs, and discovery frameworks. Use for feature prioritization, user research synthesis, requirement documentation, or product strategy.
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。
Plan content strategy: content pillars, audience research, topic clustering, funnel mapping, and content audits. Use when deciding what content to create, building a content calendar, planning topic clusters, or finding content gaps.
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。
Product design across UI/UX, design systems, prototyping, and user research. Use when creating user journey maps, building wireframes, defining design tokens and component systems, planning usability tests, or establishing design principles.
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。
Structured data implementation, validation, and optimization. Covers JSON-LD patterns for 20+ schema types, rich snippet eligibility, AI search visibility, Knowledge Graph optimization, and CMS-specific deployment guides.
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。
Qualify and prioritize sales leads against an ICP, score lead lists, and draft personalized outreach hooks. Use when building a target account list, qualifying inbound leads, prepping for outreach, or scoring prospects.
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 content production pipeline from blank page to publish-ready piece: research, briefs, drafting, SEO, readability, and editorial gates. Use when writing blog posts, articles, guides, or long-form content end-to-end at scale.
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。
Plan, write and measure ads inside AI assistants and AI search (ChatGPT Ads, Google AI Overviews and AI Mode, Microsoft Copilot). Use when testing ChatGPT ads, answer-adjacent copy, or incrementality for AI placements.
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。