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Walk the user through four directional axes (tone register, aesthetic philosophy, audience relationship, sensory ambition) and produce a structured aesthetic brief that downstream content, copy, design, and art-direction skills consume as required input. The aesthetic depth layer, distinct from `creative-brief` (operational kickoff: scope, audience, deliverables, constraints). Use when a project needs aesthetic coherence across many small decisions and the user has only a vague feeling, or when multiple downstream aesthetic-producing skills need a shared brief. Triggers on creative direction, aesthetic direction, set the aesthetic, define the visual direction, what's the vibe, what's the tone, the four axes. Does NOT fire for a general kickoff brief (use `creative-brief`), tactical single-piece work, already-documented direction, purely functional output, or locked production-stage work.

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。

Screens a dismissal — proposed or already done — against the unfair dismissal framework for England & Wales, and shows where it is exposed. Structures the qualifying-service question, the automatically-unfair categories (no qualifying period), and a Burchell / Polkey / band-of-reasonable-responses analysis for a solicitor to verify — it does not determine fairness. Surfaces specific procedural risks. Use when the user says 'is this unfair', 'screen this dismissal', 'ordinary unfair dismissal', 'automatic unfair dismissal', or wants a structured fairness review before or after dismissal.

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。

Expert knowledge for Azure Virtual Machines development including troubleshooting, best practices, decision making, architecture & design patterns, limits & quotas, security, configuration, integrations & coding patterns, and deployment. Use when choosing VM/VMSS sizes, configuring disks/GPU/InfiniBand, using ADE/CMK, Trusted Launch, or Oracle/HPC workloads, and other Azure Virtual Machines related development tasks. Not for Azure Data Science Virtual Machines (use azure-data-science-vm), SQL Server on Azure Virtual Machines (use azure-sql-virtual-machines), Azure Virtual Machine Scale Sets (use azure-vm-scalesets), Azure Virtual Desktop (use azure-virtual-desktop).

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。

This skill should be used when working with Firebase projects, including initializing projects, adding Cloud Functions or Firestore collections, debugging emulator issues, or reviewing Firebase code. Triggers on "firebase", "firestore", "cloud functions", "emulator", "firebase auth", "deploy to firebase", "firestore 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。

Acts as a director and previs supervisor for film, video, and AI filmmaking. Turns scripts, prose, briefs, or existing image and video assets into production deliverables — subtext breakdown, beat sheet, director's book, blocking and staging, shot list with coverage, keyframe and storyboard prompts, image-to-video motion prompts, sound and dialogue plans, edit timelines, continuity bibles, and QC repair notes. Carries genre playbooks, a controlled prompt lexicon, a coded failure-diagnosis manual, and capability-first adapters for current video and image models. Optionally applies one named director's style lens — Spielberg, Hitchcock, Kubrick, Kurosawa, Scorsese, Fellini, Bergman, Tarkovsky, Wong Kar-wai, Nolan, Villeneuve, Fincher, Refn, Bi Gan, Zhang Yimou, Hou Hsiao-hsien, Park Chan-wook, Malick, Michael Mann, Coen Brothers. Use for shot planning, blocking, staging, camera movement, visual continuity, storyboard and keyframe design, prompt repair, "in the style of X" direction, and any AI video workflow.

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 this skill for all requirements-related work in a MATLAB MBSE project using the Requirements Toolbox (slreq). Covers creating and populating requirement sets, derivation links, test case requirements, verification coverage, reading and tracing links across requirement sets and models, checking link health, allocating requirements to components (Implement links), and building traceability reports. Trigger when the user asks about slreq API, slreqx files, slmx link files, outLinks/inLinks, traceability matrices, coverage analysis, broken links, or mapping requirements to architecture components. Use proactively for any requirements or traceability task.

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。

侍酒师与葡萄酒 (侍酒师 / 葡萄酒 (Sommelier & Wine) — 葡萄酒鉴赏 + 侍酒服务的职业认知操作系统，从业者/侍酒师/学习者视角。覆盖: (a) 两大认证体系张力 — WSET (Wine & Spirit Education Trust, 英国, 知识+SAT 系统化品鉴方法, Diploma/L4 笔试导向) vs Court of Master Sommeliers (CMS, 美/英, 服务+盲品演绎法+理论三位一体, MS 头衔), 外加 Institute of Masters of Wine (MW, 学术研究+论文) 与 UC Davis 葡萄酒学/葡萄栽培学学院派; (b) 盲品 = 纯 tacit 校准技能 — 演绎品鉴网格 (deductive tasting: 看色/闻香/品味 → 结构线索 calculated structure → 推断品种/产区/年份/酒龄), WSET SAT vs CMS deductive grid 两套方法论; (c) 第一性张力 — codifiable 理论层 (产区/品种/酒法/风土/酿造/葡萄栽培) ⇄ tacit 校准层 (品鉴味觉记忆/盲品/侍酒服务), 这行的核心矛盾是「可背诵的知识」与「不可言传的味觉」之间; (d) 旧世界 vs 新世界框架 — 旧世界以风土/法定产区为组织原则 (AOC/DOCG/QbA 分级法), 新世界以品种为标签; 风土 (terroir) 是核心组织概念; (e) 侍酒服务/酒店层 — 餐厅 floor 的配餐 (food pairing)、醒酒/侍酒温度、酒单与酒窖管理、by-the-glass 与饮品项目经济学、盲品 service exam; (f) 葡萄酒商业层 — 三级分销体系 (美国 three-tier)、定价/配额 (allocation)、期酒 (en primeur)、投资酒 (Liv-ex) 与拍卖; (g) 智识/品味演进 — 帕克百分制 (Robert Parker / Wine Advocate 100-point) vs Jancis Robinson 20 分制散文派; 经典派 vs 现代派酿造; 自然酒/低干预 (natural/minimal-intervention) vs 传统技术派. 学派分歧: WSET 知识体系派 vs CMS 服务盲品派、旧世界风土派 vs 新世界品种/技术派、自然酒派 vs 传统派、百分制点评派 vs 散文叙述派、经典平衡派 vs 浓郁现代派. 不含: 家庭自酿 DIY 套件、纯调酒/鸡尾酒/烈酒、葡萄园重型农机工程、酒精成瘾/公共卫生政策、酒类电商运营纯营销。) Master OS — automated mastery of 侍酒师 / 葡萄酒 (Sommelier & Wine) — 葡萄酒鉴赏 + 侍酒服务的职业认知操作系统，从业者/侍酒师/学习者视角。覆盖: (a) 两大认证体系张力 — WSET (Wine & Spirit Education Trust, 英国, 知识+SAT 系统化品鉴方法, Diploma/L4 笔试导向) vs Court of Master Sommeliers (CMS, 美/英, 服务+盲品演绎法+理论三位一体, MS 头衔), 外加 Institute of Masters of Wine (MW, 学术研究+论文) 与 UC Davis 葡萄酒学/葡萄栽培学学院派; (b) 盲品 = 纯 tacit 校准技能 — 演绎品鉴网格 (deductive tasting: 看色/闻香/品味 → 结构线索 calculated structure → 推断品种/产区/年份/酒龄), WSET SAT vs CMS deductive grid 两套方法论; (c) 第一性张力 — codifiable 理论层 (产区/品种/酒法/风土/酿造/葡萄栽培) ⇄ tacit 校准层 (品鉴味觉记忆/盲品/侍酒服务), 这行的核心矛盾是「可背诵的知识」与「不可言传的味觉」之间; (d) 旧世界 vs 新世界框架 — 旧世界以风土/法定产区为组织原则 (AOC/DOCG/QbA 分级法), 新世界以品种为标签; 风土 (terroir) 是核心组织概念; (e) 侍酒服务/酒店层 — 餐厅 floor 的配餐 (food pairing)、醒酒/侍酒温度、酒单与酒窖管理、by-the-glass 与饮品项目经济学、盲品 service exam; (f) 葡萄酒商业层 — 三级分销体系 (美国 three-tier)、定价/配额 (allocation)、期酒 (en primeur)、投资酒 (Liv-ex) 与拍卖; (g) 智识/品味演进 — 帕克百分制 (Robert Parker / Wine Advocate 100-point) vs Jancis Robinson 20 分制散文派; 经典派 vs 现代派酿造; 自然酒/低干预 (natural/minimal-intervention) vs 传统技术派. 学派分歧: WSET 知识体系派 vs CMS 服务盲品派、旧世界风土派 vs 新世界品种/技术派、自然酒派 vs 传统派、百分制点评派 vs 散文叙述派、经典平衡派 vs 浓郁现代派. 不含: 家庭自酿 DIY 套件、纯调酒/鸡尾酒/烈酒、葡萄园重型农机工程、酒精成瘾/公共卫生政策、酒类电商运营纯营销。: top builders' mental models, tool stack, current workflows, jargon, and where to keep up. Trigger this skill when the user works on 侍酒师 / 葡萄酒 (Sommelier & Wine) — 葡萄酒鉴赏 + 侍酒服务的职业认知操作系统，从业者/侍酒师/学习者视角。覆盖: (a) 两大认证体系张力 — WSET (Wine & Spirit Education Trust, 英国, 知识+SAT 系统化品鉴方法, Diploma/L4 笔试导向) vs Court of Master Sommeliers (CMS, 美/英, 服务+盲品演绎法+理论三位一体, MS 头衔), 外加 Institute of Masters of Wine (MW, 学术研究+论文) 与 UC Davis 葡萄酒学/葡萄栽培学学院派; (b) 盲品 = 纯 tacit 校准技能 — 演绎品鉴网格 (deductive tasting: 看色/闻香/品味 → 结构线索 calculated structure → 推断品种/产区/年份/酒龄), WSET SAT vs CMS deductive grid 两套方法论; (c) 第一性张力 — codifiable 理论层 (产区/品种/酒法/风土/酿造/葡萄栽培) ⇄ tacit 校准层 (品鉴味觉记忆/盲品/侍酒服务), 这行的核心矛盾是「可背诵的知识」与「不可言传的味觉」之间; (d) 旧世界 vs 新世界框架 — 旧世界以风土/法定产区为组织原则 (AOC/DOCG/QbA 分级法), 新世界以品种为标签; 风土 (terroir) 是核心组织概念; (e) 侍酒服务/酒店层 — 餐厅 floor 的配餐 (food pairing)、醒酒/侍酒温度、酒单与酒窖管理、by-the-glass 与饮品项目经济学、盲品 service exam; (f) 葡萄酒商业层 — 三级分销体系 (美国 three-tier)、定价/配额 (allocation)、期酒 (en primeur)、投资酒 (Liv-ex) 与拍卖; (g) 智识/品味演进 — 帕克百分制 (Robert Parker / Wine Advocate 100-point) vs Jancis Robinson 20 分制散文派; 经典派 vs 现代派酿造; 自然酒/低干预 (natural/minimal-intervention) vs 传统技术派. 学派分歧: WSET 知识体系派 vs CMS 服务盲品派、旧世界风土派 vs 新世界品种/技术派、自然酒派 vs 传统派、百分制点评派 vs 散文叙述派、经典平衡派 vs 浓郁现代派. 不含: 家庭自酿 DIY 套件、纯调酒/鸡尾酒/烈酒、葡萄园重型农机工程、酒精成瘾/公共卫生政策、酒类电商运营纯营销。 problems and wants industry-grade thinking, tool selection, or workflow guidance. 触发词：「侍酒师」「sommelier」「葡萄酒」「wine」「盲品」

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。

调香师与香水 (调香师 / 香水 (Perfumery & Fragrance) — 香水创作与香气调配的职业认知操作系统，从业者/调香师/评香师/学习者视角。覆盖: (a) 第一性张力 — codifiable 理论层 (原料知识/香料化学/嗅觉家族分类/accord 和弦/配方结构 %/挥发度金字塔/IFRA 法规) ⇄ tacit 校准层 (嗅觉记忆/闻香训练/「the nose」/创作直觉/盲嗅识别)，这行的核心矛盾是「可背诵的原料与公式」与「不可言传的嗅觉记忆与品味」之间，嗅觉记忆是极致 tacit 技能; (b) 法系传统 vs 分子/合成派 — 法国 Grasse 天然香料 + haute parfumerie 艺术传统 (天然精油/原精/学徒制) vs 现代分子派 (aroma chemicals 香基/captives 专利分子/headspace 顶空捕香/功能香精)，两套对「香水是什么」的世界观; (c) 培养/传承 — ISIPCA (凡尔赛香水学校) + 四大香精公司内部调香学校 (Givaudan/Firmenich(DSM-Firmenich)/IFF/Symrise) + 学徒制 + 「鼻子」tacit 校准; 角色分工: perfumer(parfumeur 调香师) vs evaluator(评香师) vs flavorist(食用香精师); (d) 行业结构 — 四大香精公司寡头 (Givaudan/DSM-Firmenich/IFF/Symrise) + Mane/Robertet/Takasago，为品牌做 B2B 命题创作 (the brief → creation → mods 模型); vs niche/indie 小众独立香水 (artisanal/直营); 天然 (Grasse/精油) vs 合成 (香基/captives) 的成本与可持续之争; (e) 创作层 — 香调金字塔 (top/heart/base notes 前中后调)、accord 和弦、配方 (酒精中 % 浓度)、浓度分级 (parfum/EDP/EDT/EDC)、调香师的「香风琴」orgue、trials/mods 试样迭代、weighing 称量精度; (f) 法规与安全 — IFRA standards (原料限用)、EU 致敏原标签 (26→80+ allergens)、REACH、橡苔/atranol 限用、经典香水 (Mitsouko/Chanel No.5) 因法规被迫重配 (reformulation) 与创作自由之张力; (g) 评价/批评 — Luca Turin & Tania Sanchez (《Perfumes: The Guide》《The Secret of Scent》气味的科学)、香评人、Fragrantica/Basenotes 社区、vintage vs reformulation 之争、niche vs designer 之争。学派分歧: 法系天然传统派 vs 分子合成派、天然纯粹派 vs 合成实用派、haute parfumerie/niche 艺术派 vs mass designer 商业派、「鼻子=艺术家」vs「调香师=香气工程师/技师」、嗅觉科学之争 (Luca Turin 振动理论 vs 主流形状/受体理论)。不含: 香薰蜡烛/家居香氛纯生产营销、芳香疗法健康宣称 (aromatherapy)、纯化妆品配方学 (超出香精范畴)、DIY 家用香氛套件、精油传销/MLM。) Master OS — automated mastery of 调香师 / 香水 (Perfumery & Fragrance) — 香水创作与香气调配的职业认知操作系统，从业者/调香师/评香师/学习者视角。覆盖: (a) 第一性张力 — codifiable 理论层 (原料知识/香料化学/嗅觉家族分类/accord 和弦/配方结构 %/挥发度金字塔/IFRA 法规) ⇄ tacit 校准层 (嗅觉记忆/闻香训练/「the nose」/创作直觉/盲嗅识别)，这行的核心矛盾是「可背诵的原料与公式」与「不可言传的嗅觉记忆与品味」之间，嗅觉记忆是极致 tacit 技能; (b) 法系传统 vs 分子/合成派 — 法国 Grasse 天然香料 + haute parfumerie 艺术传统 (天然精油/原精/学徒制) vs 现代分子派 (aroma chemicals 香基/captives 专利分子/headspace 顶空捕香/功能香精)，两套对「香水是什么」的世界观; (c) 培养/传承 — ISIPCA (凡尔赛香水学校) + 四大香精公司内部调香学校 (Givaudan/Firmenich(DSM-Firmenich)/IFF/Symrise) + 学徒制 + 「鼻子」tacit 校准; 角色分工: perfumer(parfumeur 调香师) vs evaluator(评香师) vs flavorist(食用香精师); (d) 行业结构 — 四大香精公司寡头 (Givaudan/DSM-Firmenich/IFF/Symrise) + Mane/Robertet/Takasago，为品牌做 B2B 命题创作 (the brief → creation → mods 模型); vs niche/indie 小众独立香水 (artisanal/直营); 天然 (Grasse/精油) vs 合成 (香基/captives) 的成本与可持续之争; (e) 创作层 — 香调金字塔 (top/heart/base notes 前中后调)、accord 和弦、配方 (酒精中 % 浓度)、浓度分级 (parfum/EDP/EDT/EDC)、调香师的「香风琴」orgue、trials/mods 试样迭代、weighing 称量精度; (f) 法规与安全 — IFRA standards (原料限用)、EU 致敏原标签 (26→80+ allergens)、REACH、橡苔/atranol 限用、经典香水 (Mitsouko/Chanel No.5) 因法规被迫重配 (reformulation) 与创作自由之张力; (g) 评价/批评 — Luca Turin & Tania Sanchez (《Perfumes: The Guide》《The Secret of Scent》气味的科学)、香评人、Fragrantica/Basenotes 社区、vintage vs reformulation 之争、niche vs designer 之争。学派分歧: 法系天然传统派 vs 分子合成派、天然纯粹派 vs 合成实用派、haute parfumerie/niche 艺术派 vs mass designer 商业派、「鼻子=艺术家」vs「调香师=香气工程师/技师」、嗅觉科学之争 (Luca Turin 振动理论 vs 主流形状/受体理论)。不含: 香薰蜡烛/家居香氛纯生产营销、芳香疗法健康宣称 (aromatherapy)、纯化妆品配方学 (超出香精范畴)、DIY 家用香氛套件、精油传销/MLM。: top builders' mental models, tool stack, current workflows, jargon, and where to keep up. Trigger this skill when the user works on 调香师 / 香水 (Perfumery & Fragrance) — 香水创作与香气调配的职业认知操作系统，从业者/调香师/评香师/学习者视角。覆盖: (a) 第一性张力 — codifiable 理论层 (原料知识/香料化学/嗅觉家族分类/accord 和弦/配方结构 %/挥发度金字塔/IFRA 法规) ⇄ tacit 校准层 (嗅觉记忆/闻香训练/「the nose」/创作直觉/盲嗅识别)，这行的核心矛盾是「可背诵的原料与公式」与「不可言传的嗅觉记忆与品味」之间，嗅觉记忆是极致 tacit 技能; (b) 法系传统 vs 分子/合成派 — 法国 Grasse 天然香料 + haute parfumerie 艺术传统 (天然精油/原精/学徒制) vs 现代分子派 (aroma chemicals 香基/captives 专利分子/headspace 顶空捕香/功能香精)，两套对「香水是什么」的世界观; (c) 培养/传承 — ISIPCA (凡尔赛香水学校) + 四大香精公司内部调香学校 (Givaudan/Firmenich(DSM-Firmenich)/IFF/Symrise) + 学徒制 + 「鼻子」tacit 校准; 角色分工: perfumer(parfumeur 调香师) vs evaluator(评香师) vs flavorist(食用香精师); (d) 行业结构 — 四大香精公司寡头 (Givaudan/DSM-Firmenich/IFF/Symrise) + Mane/Robertet/Takasago，为品牌做 B2B 命题创作 (the brief → creation → mods 模型); vs niche/indie 小众独立香水 (artisanal/直营); 天然 (Grasse/精油) vs 合成 (香基/captives) 的成本与可持续之争; (e) 创作层 — 香调金字塔 (top/heart/base notes 前中后调)、ac

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。

🇺🇸 AI Product / SaaS Go-Global Complete SOP — From competitor research to launch to monetization. A full-cycle playbook covering Phase 0-5 (market validation, positioning, first 100 users, user interviews, beta-to-growth) plus open-source launch, Product Hunt, Reddit, SEO/GEO, conversion, and org principles. **Defaults to dev tools / OSS / B2B SaaS; for 2C products (education, apps, games) the launch channels & metrics differ — see gingiris-seo-geo/references/2c-adaptation.md.** 🇨🇳 AI 产品 / SaaS 企业出海完整 SOP — 从竞品调研到 Launch 到商业化的全流程操作手册。覆盖 Phase 0-5（市场验证、定位、前100用户、用户访谈、Beta转增长）+ 开源发布、Product Hunt、Reddit、SEO/GEO、转化与组织原则。**默认面向开发者工具/开源/B2B SaaS；2C 产品（教育/应用/游戏）的启动渠道与指标不同，见 gingiris-seo-geo/references/2c-adaptation.md。** 🇯🇵 AI製品/SaaS海外展開フルサイクルSOP — 競合調査からローンチ、マネタイズまで。Phase 0-5（市場検証、ポジショニング、最初の100ユーザー、ユーザーインタビュー、ベータから成長）＋オープンソース、Product Hunt、Reddit、SEO/GEO、コンバージョン、組織原則。 🇰🇷 AI 제품/SaaS 글로벌 진출 완전 SOP — 경쟁사 조사부터 런칭, 수익화까지 전 주기 플레이북. Phase 0-5(시장 검증, 포지셔닝, 첫 100명 사용자, 사용자 인터뷰, 베타→성장) + 오픈소스 런칭, Product Hunt, Reddit, SEO/GEO, 전환, 조직 원칙. Triggers: "go global" | "出海" | "overseas expansion" | "GTM" | "cold start" | "launch strategy" | "international expansion" | "海外增长" | "出海SOP" | "product launch overseas" | "海外进出" | "グローバル展開" | "글로벌 진출" | "go-to-market" | "出海打法"

Use whenever the user wants to find, shortlist, vet, or enrich US public-relations and communications agencies — media relations, crisis comms, investor relations (IR), product-launch PR, tech/startup PR, healthcare PR, B2B PR, public affairs, brand reputation, and internal communications. Triggers on "find me a tech PR agency in NY", "shortlist three IR firms for our IPO", "we need crisis comms help for a brand reputation issue", or "pull contact info for these 10 PR firm domains", even when described indirectly (we need press, get us into TechCrunch, manage our brand reputation). Drives the ServiceGraph API (api.servicegraph.co) — a 100k+ US firm catalog filterable by industry, services, location, size, ratings. Defer to find-marketing-agency when scope is broader marketing beyond PR/comms. Skip in-house PR/comms hires, "write me a press release" DIY asks, PR-software comparisons (Cision, Muck Rack), influencer-marketplace questions, non-US firms, individual freelance PR people.

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Use whenever the user wants to find, rank, or shortlist directories and registries where they can submit or list an MCP server (Model Context Protocol server) — to get backlinks, referral traffic, and discovery by agent builders. Triggers on "where do I list my MCP server", "best MCP directories", "MCP registries to submit to", "get my MCP server discovered", or "pull submission details for these MCP-directory domains", even when described indirectly (we built an MCP server, where do we publish it). Drives the ServiceGraph API (api.servicegraph.co) — a catalog of 1,000+ product directories enriched with Domain Rating, backlinks, and organic traffic. Defer to find-ai-directories for general AI-tool / AI-agent / agent-skill listings, and to find-product-directories for general SaaS/software launches. Skip building an MCP server or asking how MCP works (DIY), finding a firm to build one (use find-ai-consultancy / find-software-developer), and MCP link-building *services*.

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Use whenever the user wants to find, shortlist, vet, or enrich US cybersecurity firms — pen-testing/red team, security audits, vCISO, SOC 2 readiness, incident response, managed SOC, IAM, cloud security, and AppSec. Triggers on "find me a pen-testing firm for our SOC 2 audit", "shortlist three vCISO services for our healthcare-tech startup", "we need an incident response retainer", or "pull contact info for these 8 security firm domains", even when described indirectly (we got breached, prepare us for the compliance audit, get us SOC 2 ready). Drives the ServiceGraph API (api.servicegraph.co) — a 100k+ US firm catalog filterable by industry, services, location, size, ratings. Skip in-house security hires, "how do I patch CVE-X" or "configure firewall Y" DIY questions, security-product reviews (CrowdStrike vs SentinelOne, etc.), generic security knowledge questions, consumer/personal security advice, non-US firms, individual freelancers and bug-bounty hunters.

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Use whenever the user wants to find, shortlist, vet, enrich, or research US professional-services firms — law, marketing, consulting, accounting, IT services, architecture, engineering, HR, PR, design, and similar B2B service providers. Triggers on requests like "find me a PPC agency in California", "shortlist three boutique IP law firms", "build a longlist of 50 mid-size IT consultancies", or "here are 12 agency domains — pull contact info and confirm which are US-based", even when the need is described indirectly without naming a category. Drives the ServiceGraph API (api.servicegraph.co) — a 100k+ US firm catalog with filters for industry, services, location, size, ratings, and third-party listings. Skip when the user is asking for personal/consumer services for themselves (an individual's own legal, tax, or medical needs), non-US firms, individual freelancers, retail/ecommerce/SaaS-product companies, recruiting-an-employee tasks, or general web research that doesn't need a structured firm directory.

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 this skill to gather, document, and validate OmniScript flow design requirements before development begins — covering screen layout requirements, branching logic, data source requirements, and user journey mapping. Trigger keywords: OmniScript requirements, OmniScript BA, OmniScript screen design, OmniScript user journey, OmniScript branching requirements. NOT for building the OmniScript or moving logic into an Integration Procedure — use omnistudio/omniscript-design-patterns. NOT for FlexCard requirements — use admin/flexcard-requirements.

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 this skill to elicit, document, and validate CRM Analytics requirements — covering data source mapping (Salesforce object sync vs external connector vs Data Cloud), transformation needs, audience-specific lens or dashboard views, and drill-down path specifications — before any dataset or dashboard is built. Trigger keywords: CRM Analytics requirements, analytics data source mapping, CRM Analytics audience requirements, analytics visualization requirements. NOT for the formula and target behind each KPI — use admin/analytics-kpi-definition. NOT for building the dashboard once requirements are agreed — use admin/analytics-dashboard-design.

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Routing skill — recommends the correct mk:jira-* leaf for any Jira task. Triggers: 'jira', 'jira ticket', ambiguous Jira intent. NOT an executor — every actual operation forks via a leaf skill.

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。

【非推奨】requirement-management に統合された。新規の要件定義には requirement-management を使用すること。既存の docs/sdd/requirements/ を保守・参照する場合にのみ使用する。EARS記法によるMarkdownの要件定義書（ユーザーストーリー・受入基準・非機能要件）を作成・編集する。Do NOT use for 新規プロジェクトの要件定義（requirement-management を使用すること）。

Produces a DRAFT product requirements document from the discovery notes, interview summaries, support themes, analytics notes and stakeholder messages the user provides: problem statement, target users, goals with measures as stated, numbered requirements each with a testable acceptance criterion, non-goals, constraints, dependencies and open questions, with every statement tagged evidenced, assumed or UNKNOWN so each assumption is visible for the product owner to confirm. Use when the user asks to "write a PRD", "draft the product requirements", "turn these discovery notes into requirements", "document what this feature needs to do" or "build the requirements doc for this epic". Do not use for supplier-facing requirements in a tender, use rfp-requirements-pack instead; for a technical design decision, use architecture-decision-record; for the change ticket that ships the work, use change-request-pack. Drafts for human review; never approves, authorises or signs off.

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Trace BIRD routing-daemon behavior through the actual C source tree. Use for implementation questions, suspected upstream bugs, parser/keyword origins, filter instructions, protocol state machines, route attributes, birdc commands, stack traces, or BIRD 2 versus BIRD 3 source comparisons. Prefer a local checkout or exact upstream tag/commit and cite concrete files, functions, and revisions. Route config linting to bird-agent and active incidents to bird-troubleshooting.

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Complete guide for .NET Aspire Testing integration testing framework. Use when testing .NET Aspire distributed applications, configuring AppHost tests, or migrating from Testcontainers to Aspire testing. Covers DistributedApplicationTestingBuilder, container lifecycle management, multi-service orchestration, Respawn configuration, and time testability design. Keywords: aspire testing, .NET Aspire, DistributedApplicationTestingBuilder, AppHost testing, distributed testing, AspireAppFixture, IAsyncLifetime, ContainerLifetime.Session, cloud-native testing, multi-service integration, Aspire.Hosting.Testing, Respawn

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An annotated / redline lo-fi wireframe — a desktop landing/marketing page drawn as flat greyboxes inside a browser chrome frame, overlaid with numbered annotation pins (①②③④⑤) in a single accent color, paired with a right-hand SPEC PANEL that lists each numbered pin with a short engineering / UX note. This is the "wireframe + redline spec" style — clean, flat, low-fidelity, NOT hand-drawn. Use when the brief asks for "annotated wireframe", "redline wireframe", "wireframe with spec", "lo-fi landing wireframe", "low fidelity", "线框图", "标注线框", or "redline".

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Use when an RFP, RFI, RFQ, security questionnaire, vendor questionnaire, or proposal request arrives and the team needs a structured response — parsing multi-section buyer-dictated requirements (MANDATORY vs WEIGHTED vs NICE-TO-HAVE), building a Shipley-method proof-point matrix mapping each requirement to a verifiable proof point, articulating 3-5 win-themes that ladder up across requirements, and producing a Shipley-derived winrate estimate that informs a bid / no-bid / partner-bid recommendation. For Bid Managers, Proposal Leads, Directors of Sales, and Sales Engineers at the response-strategy moment. Surfaces GAP requirements explicitly — never invents claims. NOT free-form proposal narrative authoring, NOT contract redline, NOT marketing collateral.

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Help users chaos test their app with mirrord: inject artificial latency or connection errors into a mirrord session's outgoing traffic via per-session chaos rules managed with the `mirrord chaos` CLI. Use when a user wants to add latency or delay to outgoing connections or a dependency (e.g. a slow database), simulate connection failures (reset, timed out, refused), test app behavior under degraded network conditions, or wire chaos rules into CI test runs. Always use this skill instead of the deprecated _experimental_.latency mirrord config option.

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Helps users run multiple concurrent mirrord sessions from a single mirrord-up.yaml (compose-style multi-service local debugging). Use when the user mentions mirrord up, mirrord-up.yaml, mirrord up init, debugging several related microservices together, or managing multiple mirrord sessions' lifecycle in one command.

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。