「case study」の検索結果

杂志感长文, 含 masthead、hero、figures、pull quote、作者署名

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。

Turn an article, case study, brand narrative, product journey, or long-form story into a cinematic scroll-driven landing page using one of three renderers: scrubbed video, a real-time Three.js world, or semantic HTML/SVG data and typography. Use when the user asks for a scroll world, fly-through landing page, article-to-website transformation, animated planet, data scrollytelling, video-scrubbed page, connected visual journey, or story-led alternative to ordinary stacked 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。

Create or redesign creative-studio, agency, photographer, artist, and portfolio websites where project work leads the story. Use for dark editorial shells, full-bleed campaign media, color-coded case-study chapters, oversized service typography, restrained project reveals, and a decisive contact finale.

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 or redesign enterprise AI, automation, security, and operations product pages that explain system boundaries, approvals, auditability, exceptions, and rollback. Use for dark cinematic heroes, hairline grids, metric pauses, expandable solution rows, case-study evidence, security proof, and qualified demo or waitlist handoffs.

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。

Academic paper writing skill with 12-agent pipeline. v2.5: Style Calibration (learn author's writing voice from past papers) + Writing Quality Check (writing quality checklist for natural prose). Supports IMRaD, literature review, theoretical, case study, policy brief, and conference paper structures. APA 7.0 (default), Chicago, MLA, IEEE, Vancouver citation formats. Bilingual abstracts (zh-TW + EN). Multi-format output (LaTeX, DOCX, PDF, Markdown). Triggers on: write paper, academic paper, paper outline, write abstract, revise paper, check citations, convert to LaTeX, guide my paper, parse reviews, revision roadmap, 寫論文, 學術論文, 論文大綱, 寫摘要, 修改論文, 檢查引用, 引導我寫論文, 帶我規劃論文, 逐章規劃, 論文架構, 審查意見, 修訂路線圖.

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。

⚠️ CRITICAL USER EXPERIENCE-BASED SKILL - ALWAYS CONSULT BEFORE DATA PREPROCESSING ⚠️ Prevents catastrophic errors (88.9% error rate in V1.0 case study) through multi-level feature analysis, data leakage detection, and semantic validation. MANDATORY for: data preprocessing, feature engineering, standardization, normalization, interpolation, missing value handling, feature selection, or ANY data transformation task. Covers grouped time-series, cross-sectional, panel data. Detects: time travel leakage, causal inversion, ID misuse, semantic-numeric fallacies, distribution blindness. User's hard-won lessons from real project failures.

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 text-driven visuals in Markdown: charts, diagrams, cards, architecture and page layouts. Charts and analytical views (bar, line, pie, heatmap, correlation, regression); reliability and operations (latency, incident, throughput, cycle time, OKR, standup, on-call); product and finance (funnel, retention, revenue, budget); process and workflow (approval, BPMN); software design and behaviour (class, state machine, sequence, C4); dependencies and impact (dependency graph, ER, causality); system architecture as HTML (layer stack, wings, zones, topology, service catalogue, request paths); infrastructure (cloud, Kubernetes, ETL, network, security, IAM, compliance); governance and people (ArchiMate, org chart, hiring); knowledge and planning (mind map, roadmap, Gantt, migration); documents (comparison, SWOT, memo, policy, catalogue, case study). Use when a document needs a diagram, chart or card. Figures render live. Not for slide decks or math notation. Not recommended: mermaid / canvas / drawio / dot.

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。

Structure a design presentation for a specific audience and decision. Use when presenting internally. For a portfolio narrative use `case-study`; for the written argument use `design-rationale`.

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 creating radiology educational quizzes, preparing board exam questions, or studying medical imaging cases. Generates interactive quizzes with X-ray, CT, MRI, and ultrasound images for medical education.

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 clear, executable, and auditable inclusion and exclusion criteria for biomedical and clinical research protocols. Always use this skill when a user needs to translate a target population into operational screening rules tied to chart fields, time windows, tests, procedures, prior therapies, exclusions, and reviewable edge cases. Focus on protocol-stage precision, ambiguity reduction, auditability, and screening reproducibility rather than generic study design advice.

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。

Designs complete research plans that integrate clinical variables with multi-omics data from a user-provided biomedical direction. Always use this skill whenever a user wants to design, scope, or structure a study that combines clinical variables with transcriptomics, proteomics, metabolomics, epigenomics, or related omics layers for mechanism interpretation, biomarker development, risk stratification, treatment-response analysis, or translational use. It should define the clinical use case, alignment across data layers, feature-reduction and fusion logic, modeling route, mechanism-interpretation layer, validation ladder, and four workload configurations (Lite / Standard / Advanced / Publication+). Never fabricate datasets, accession numbers, sample counts, metadata completeness, platform coverage, literature references, PMIDs, DOIs, or validation status. Always include the mandatory Dataset Disclaimer immediately before any workflow section that mentions datasets or public resources.

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 defending the research design of an American Educational Research Journal (AERJ) manuscript — quantitative (multilevel, IRT, quasi-experimental, RCT), qualitative (case study, ethnography, interview), or mixed methods. AERJ judges each tradition on its own terms against the AERA reporting standards. Strengthens the design; it does not write code.

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 the study for an Accounting, Organizations and Society (AOS) manuscript — qualitative field studies (access, case logic, interviews, observation), behavioral experiments, surveys, historical/archival inquiry, or theory-laden archival designs, each with the ethics and site-anonymity groundwork done early. Designs the inquiry; it does not run the coding or estimation (aos-data-analysis).

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。

系统化学习材料生成器。给一个新领域/技术/概念，AI 自主调研、搭体系、产出 HTML 学习材料（含骨架/案例/工程化/争议+盲区）。当用户说"我要学 X / 帮我系统拆解 X / 我想吃透 X 这个领域 / 给我整理 X 的全貌 / 深度调研 X 给我系统讲讲 / 把 X 搞透"时触发。不适用于"X 行不行/为什么 Y"（使用可用的调研工具直接研究）、"X 有哪些好玩案例"（用 case-radar 给散点）、"把这堆素材整理成 HTML"（用 readable-output 处理已有素材）、文章写作（用 writing-assistant）、整页界面方案（用 page-solution-design）。

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。

Turn real customer reviews, quotes, and case-study results into polished social-proof videos built in HyperFrames -- single-testimonial spotlights, review-wall montages, and stat-driven proof reels with captions and voiceover, cut for vertical, square, and wide. The highest-converting content most businesses never make.

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 planning how many patients or cases a study needs before data collection (power analysis, IRB justification). Walks a decision tree to the right test and returns reproducible R/Python code and IRB-ready justification text. Analyzing collected data is /analyze-stats.

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。

The art and science of narrative-driven brand communication. This skill transforms brand messages into compelling stories that connect emotionally, build memory, and drive action. In a world of infinite content, story is the only thing that cuts through. Great brands don't sell products—they tell stories. Apple tells the story of creative rebels. Nike tells the story of human potential. Patagonia tells the story of environmental stewardship. This skill teaches you to find, craft, and tell your brand's story across every touchpoint. AI amplifies storytelling capability—generating variations, adapting across channels, maintaining consistency—but the human insight that identifies the true story remains essential. This skill bridges narrative craft with AI execution. Use when "story, storytelling, narrative, brand story, origin story, hero journey, emotional connection, customer story, testimonial, case study, brand film, about us, storytelling, narrative, brand-story, emotional, hero-journey, content" mentioned.

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。

Designs multi-country survey experiments, covering case selection with a stated comparative logic, instrument localization and TRAPD translation, stimulus calibration, per-country power, and pooled versus per-country analysis with measurement-equivalence checks. Use when the user fields the same experiment in several countries, asks which countries to include, how to adapt an instrument without breaking comparability, whether to pool, or how large each country sample must be. Question wording goes to survey-design, reporting to methods-reporting, the study plan to pre-registration-writing.

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 personalized pitch emails to journalists, podcasters, newsletter writers, and analysts using AIDA adapted for media pitching and Ryan Holiday's journalist psychology from "Trust Me I'm Lying." Different from press releases (use /press-release-writer for those). Use when the user asks to pitch a journalist, pitch a reporter, "get coverage in X", media outreach, podcast pitch, analyst briefing request, or wants to land a story. Reads brand voice and positioning from knowledge/. For the press release itself, see press-release-writer. For the underlying customer story, see case-study-writer.

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 press releases for product launches, funding announcements, partnerships, executive hires, and milestones using the Inverted Pyramid (AP journalism) and Ogilvy on Headlines (David Ogilvy, Ogilvy on Advertising). The lede answers Who/What/When/Where/Why in 40 words or fewer. The headline selects the right audience and promises a specific benefit. Use when the user asks for a press release, media release, news announcement, PR for X, launch announcement, funding announcement, or "write a press release". Reads brand voice and positioning from knowledge/. For pitching it to a journalist, see pr-pitch-writer. For the customer story behind it, see case-study-writer.

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 human-sounding, SEO-optimized blog posts for skills-hub.ai. Use when: 'write a blog post', 'draft a post', 'update blog', 'blog about', 'write about'. Supports styles: tutorial, deep_dive, opinion, listicle, case_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。

Research process for comparing frontier harnesses (Claude Code, Codex, OpenClaw, autoresearch, Prime Agent) with the original/upstream implementation and performing GAP analysis before feature implementation. Triggered by "research" ("리서치"), "gap", "frontier", "harness", "case study" ("사례 조사"), "pattern exploration" ("패턴 탐색"), "comparative analysis" ("비교 분석") keywords.

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。

End-to-end playbook for creating a new NV oOS Pro toolkit under addons/pro/includes/tools/<toolkit> — folder layout, service layer, tool class conventions (canonical envelope, two-gate sanitisation, capability flags), ALL seven registration surfaces (central registry, admin Tools section, settings status map, both WP-CLI commands, schedule presets, workflow presets, SA Toolkit Manager), fixture/oracle testing with a standalone smoke harness, and the phpcs gotcha checklist. Use when asked to build a new Pro toolkit, add a toolkit to the plugin, scaffold toolkit tools, register toolkit presets/toggles, or debug why a new toolkit's tools or presets don't appear. First case study: food-beverage (F&B Management, 21 tools, 2026-10-08, 20/20 Check-totals oracle verified). Complements mcp-ai-wpoos-toolkit-audit (hardening existing toolkits) and mcp-ai-wpoos-test-suite (repair).

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。

Per-toolkit hardening audit loop for NV oOS Pro tools. Systematically scans each addons/pro/includes/tools/<toolkit> (and its plugins/nvoos-content-graph-pro mirror) for the four recurring production failure classes — unguarded shell calls that fatal on exec-disabled hosts, calls to nonexistent client methods, string-assuming parsing of provider responses that may be arrays, and provider enum/service mismatches — then fixes, tests (Docker PHPUnit), and validates (phpcs both standards) before moving to the next toolkit. Use when asked to audit a toolkit, review the X tools for similar issues, do the same for the other toolkits, or continue the toolkit audit loop. The first case study (document-generation) found 3 fatal bugs + 1 broken regex.

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。