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概要と使いどころ
Erstellt aus Geschäftsmodell, Altbedingungen, Preislisten und Beschwerden vollständige passende AGB samt Einführungstexten. Steuert zehn Fachskills gezielt, fragt entscheidende Lücken nach und prüft aktuelle Normen und Rechtsprechung.
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。
Entwirft oder überarbeitet AGB-Klauseln für ein konkretes Regelungsziel. Wählt konservative, ausgewogene oder verwenderorientierte Gestaltung und berücksichtigt B2C/B2B; liefert ausformulierte Fassung, Begründung und Rückfallposition.
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。
Worked full-length scripts of banqiang-system Chinese opera with their creation records (板腔体全本语料库). Primary texts; 翁偶虹《锁麟囊》 (the revised score edition, 65 arias, 15 self-account-vs-score checks), 汪曾祺等《沙家浜》 (the 1965 text, not the 1970 one; 36 arias, a 12-row two-version diff), 田汉《白蛇传》 (the 1955 book, not the web revision; 16 scenes, 王瑶卿说腔五条), 杨兰春《朝阳沟》 (1978 revival, 50 arias, 口语入唱, per-scene sung/spoken ratios), 魏明伦《潘金莲》 (跨时空, missing pages), 罗怀臻's 越剧《李清照》《真假驸马》, 淮剧《金龙与蜉蝣》, 甬剧《典妻》; plus index tables of 翁偶虹's 60 创作记 and 范钧宏's seven 改编札记. Use when you want to see how a 唱工戏 lays out its 板式, need real scene counts, aria counts and 辙口 to imitate, want the road from raw material to 审腔, are writing a 现代戏 or colloquial lyrics, or compare one author across two声腔 systems. Method is in sw-chinese-opera-banqiang; this skill holds the worked scripts and their creation records.
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。
Case studies of television series writing (剧集案例库) from primary texts; Sorkin's six West Wing teleplays (act-page tables, eight act-out types); Chase's five Sopranos scripts (the 35-beat A/B/C/D recipe, what a pilot refuses to explain); Fellowes's annotated Downton Abbey season two (19 storylines, 419 footnotes); Waller-Bridge's Fleabag Scriptures (the six-episode half-hour template); Calvisi's eight minute-by-minute pilot beat sheets (Breaking Bad, Mad Men, Game of Thrones and more); Landau's 47-series structure appendix; Miller's Hannibal and HTGAWM breakdowns; Goldberg & Rabkin's beat sheets; Sakamoto Yuji's We Made a Beautiful Bouquet (a feature) and Noh Hee-kyung's The Most Beautiful Goodbye. Use when you want a worked example of how an episode or pilot is built, need real act lengths, scene counts or storyline grids to imitate, are breaking a multi-thread ensemble episode, designing a bottle episode or a finale, writing a relationship or family drama, or ask 某一集是怎么写的 / 这部剧的结构表.
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。
Episode and season structure for TV series (剧集单集与季的结构) — teasers and cold opens, broadcast four/five/six-act grids with page anchors, three tests for finding the invisible acts in streaming scripts, act-out and cliffhanger taxonomies, Oberg's four information tools, A/B/C/runner weaving and scene-count ratios, the Calvisi pilot beat sheet, five-part scene structure, and season shape (tentpoles, bottle and container episodes, two-parters, finales). Merged from Calvisi, Douglas, Oberg, Landau, Goldberg & Rabkin, Miller, Rabkin and Blum, plus act lengths measured from published West Wing, Succession, Sopranos, Downton and Fleabag scripts. Use when breaking an episode into acts, deciding how many acts a script needs, placing act outs and cliffhangers, designing a teaser, weaving A/B/C stories and runners, beating out a one-hour pilot, auditing an episode that has no shape, reverse-engineering a show's structure, or planning a season's episode order (分幕、出幕、悬念结局、季弧、瓶子集).
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。
Half-hour television comedy craft (半小时情景喜剧/笑点力学/困境机器/多机位单机位格式) — merged from Evan S. Smith's Writing Television Sitcoms (premise-driven comedy in three levels, nine predicaments, six character mixes, setup/punchline mechanics, toppers, running gags, ten comedic nuances, format rules), Neil Landau's sitcom seven-step formula and comedy-room models, Richard Blum's act-length and set-count parameters, Phoebe Waller-Bridge's Fleabag scripts (direct address as a season-long device, six-episode skeleton), Pamela Douglas on dramedy, Kam Miller on complementary comic traits, and Jesse Armstrong on jokes carrying tragedy. Use when writing or fixing a sitcom episode or spec, raising joke density per page, designing a comic predicament that generates stories, choosing between multi-camera and single-camera format and page counts, structuring cold open/acts/tag, writing a half-hour pilot, running a comedy punch-up, or making a drama funny without shifting genre.
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 production subtitle artifacts from the final narration audio or final merged video using Volcengine Doubao ASR word timestamps. Use for local IndexTTS2 videos, Xiaohei page videos, talking-head delivery subtitles, SRT/VTT export, or whenever subtitle timing must match the final audio. Do not use script-length interpolation for final deliverables.
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。
Capture a user's real writing voice from 5-20 prior samples, store a local voice.yaml fingerprint, and enforce it on newsjack drafts so AI tells disappear. Measures voice with named stylometry lenses (Burrows's Delta function-word vector, MATTR lexical diversity, sentence-length burstiness, Biber Dimension-1 register, opener-POS profile, punctuation rates) and gates drafts against the fingerprint as bands, not vibes.
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。
Self-coaching analysis of your own behavior across meetings — talk-time ratio, filler words, hedging language, monologue length, energy patterns, and (when meetings are tagged via /minutes-tag) what your behavior in winning meetings looks like vs losing ones. Use this whenever the user says "how did I do", "review my last meeting", "mirror", "self-review", "show my patterns", "coach me", "where am I weak", "talk time", "am I improving", "what do I do in meetings I win", "feedback on me", or asks for any kind of personal feedback on their own meeting behavior. This is the rare skill that gives the user a mirror to their own habits — surface it whenever they show curiosity about their own performance, even if they don't use the word "mirror".
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。
Routes finance: Bigdata/RavenPack, US fundamentals/yfinance, A-share news/policy, sector Top N/公告, Gangtise/岗底斯 setup. Reads one bundled specialist. Pharma reporting and general argument review keep their own direct entries.
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。
Transforms vague prompts or feature requests into precise, testable specifications using EARS (Easy Approach to Requirements Syntax) grounded in relevant domain theories (GTD, BJ Fogg, Gestalt). Use when requirements lack triggers or measurable outcomes, or the user asks to "optimize my prompt" / "improve this requirement" / "make this more specific".
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。
Apply the CV conventions of the country where the job is: length, photo, personal details, date and name order, spelling, paper size and the documents expected alongside. Use when asked what a CV looks like in Germany, Japan, China, the UK, the US, France, India, the Gulf or Australia, should I put a photo on my CV, convert my US resume to a UK CV, or make a 简历, 履歴書 or Lebenslauf. Produces the country rules as a checklist, the CV converted to them, and the personal details that must be removed or added, with the legal reason where there is one.
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 a one-page CV for management consulting, investment banking or private equity, in the dense format those screeners expect: education first, quantified bullets, leadership and interests that show drive. Use when asked to write my consulting CV, banking resume, PE CV, make my CV for McKinsey, BCG, Bain, Goldman or a boutique bank, or fit my experience on one page for finance. Produces the one-page CV, a bullet-by-bullet strength check, and the three lines a screener will read first.
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 asked 公开课怎么准备, 帮我写说课稿, 优质课比赛, 评优课, 教研课, 汇报课, 青年教师赛课, 试讲, or prepare an observed lesson in a Chinese school. Produces a 公开课 preparation pack in Chinese: a 说课稿 (说教材, 说学情, 说教学目标, 说重难点, 说教法学法, 说教学过程, 说板书) timed to the required length, the lesson's highlights (亮点) and how observers will see them, a rehearsal and 磨课 plan, contingency plans, and answers to likely 评课 questions.
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 asked 帮我写期末评语, 学生评语, 班主任评语, 综合素质评价评语, 素质报告单评语, or write end-of-term comments for a class in China. Produces individual comments in Chinese, warm and specific to each student, in the second person, built from the teacher's notes: a real strength with an example, one growth area framed as a next step, and encouragement, with no labels, no comparison between students and no repeated stock phrases across the class.
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。
Design a 360-degree feedback survey or write a structured 360 feedback report. Use when asked to build a 360 feedback process, write 360 feedback for a colleague, design a feedback survey, or produce a feedback report. Produces either a complete survey instrument with rating scales and open-ended questions, or a structured narrative feedback report with themes, strengths, and development areas.
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。
Optimize an article for Answer Engine Optimization (AEO) so AI engines like ChatGPT, Perplexity, and Claude can extract, quote, and cite it. Use when asked to AEO-optimize, make content AI-readable, improve AI citation chances, or adapt an article for answer engines. Produces an AEO-optimised rewrite with question headings, 50–80 word answer capsules, a paragraph-length audit, and flagged trust signals.
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。
Help users master the art of facilitating productive, decision-oriented meetings that unblock teams, strengthen interpersonal bonds, and accelerate shipping velocity.
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 and edit React Native Storybook stories using Component Story Format (CSF). Use when writing .stories.tsx files, adding stories to React Native components, configuring Storybook addons (controls, actions, backgrounds, notes), setting up argTypes, decorators, parameters, or working with portable stories for testing. Applies to any task involving @storybook/react-native story authoring.
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 what prompts people ask AI engines (ChatGPT, Gemini, Perplexity, Claude) about a product category and produces a prompts.csv artifact — a prioritized, strictly-schema'd list of the queries where the brand should be cited. Feeds the monitor 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。
Creates data visualizations (charts, graphs, tables) optimized for AI engine parsing and citation. Produces inline SVG/HTML with text summaries, data tables, and JSON-LD so AI engines can quote the data.
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。
Takes existing content markdown files and builds production-final resource center pages on client websites using their existing tech stack and design system. Output is live-ready — no review pass, no placeholder content, no prototype styling. Implements hub pages, section listings, article pages, and cross-linking for Blog, Guides, Learn, and Comparisons sections.
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。
Writes product-led content pages optimized for both search engines and AI engine citations. Produces markdown files with frontmatter, following page-type frameworks (comparison, guide, use case, learn, trust) with verified sources and no fabricated data.
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。
Finds high-value SEO and GEO keywords using web search, AI analysis, and optionally paid tools like Ahrefs or Semrush. Produces a validated keywords.csv file with a fixed schema for downstream pipeline consumption.
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。