182 件 ・ 関連度順
概要と使いどころ
Customer success across onboarding, adoption, retention, and expansion. Use when designing onboarding playbooks, calculating health scores, building QBR decks, planning renewal strategies, or identifying expansion opportunities.
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。
Collaboration platform configuration methodology for legal matter sites. Site architecture, workflow identification, dashboard design, data quality governance, and user adoption for SharePoint, Teams, and equivalent platforms. M365 is the reference implementation — outputs are platform-agnostic enough to brief IT or build simple automations without becoming a Power Automate manual. Use when setting up a matter site, identifying workflows to automate, designing reporting dashboards, managing platform data quality, or driving user adoption. Trigger on: 'set up the matter site', 'configure SharePoint', 'build a dashboard', 'what should we automate', 'brief IT on this workflow', 'nobody is using the platform', 'data quality is poor', 'set up Teams channel', 'matter site structure', 'alerts and notifications', 'user training', 'platform governance', 'status dashboard', 'what workflows can we automate', 'matter site template'.
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。
Applies Geoffrey Moore's chasm-crossing strategy for B2B tech products moving from visionary early adopters to pragmatist mainstream. Use when a product has early traction but stalls before mainstream adoption, when planning a beachhead/niche strategy, when designing whole-product offerings, when positioning against established competitors, or when scaling from innovator usage to industry standard. Triggers include 'stuck between early adopters and mainstream', 'we need a beachhead', 'pragmatist customers won't buy', 'how do we go from 10 to 1000 customers'. NOT for PLG/freemium SaaS (Slack, Notion, Cursor), pure consumer apps, two-sided marketplaces, or AI-native products with bottoms-up viral adoption - their dynamics break the visionary-to-pragmatist sequence.
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。
Investigate MCP tool availability, discovery, and selection, including controlled adoption evaluations.
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 an instructor asks for a setup guide, adoption guide, planning guide, implementation checklist, prerequisite check, MATLAB Course Designer course structure plan, Common Cartridge workflow, Simulink starter and solution model planning, LMS import plan, or starter prompt for creating a MATLAB and Simulink enabled course with the matlab-generate-course 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。
Use when an instructor asks for a MATLAB Grader assessment setup guide, adoption guide, pilot plan, course-specific rollout, QTI 3 sharing workflow, or recommended MATLAB Grader assessment-item-generation configuration based on a learning objective, course title, course description, module description, lab description, or assessment goal.
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 an instructor asks for a MATLAB AI tutor setup guide, adoption guide, pilot plan, course-specific rollout, or recommended tutor configuration based on a learning objective, course title, course description, module description, or lab description.
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 adoption strategies and materials to drive design system usage across teams.
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。
Crypto Fundamental Analysis — evaluates project viability, team quality, technology, adoption metrics, and competitive moat with a composite Fundamental Score (0-100)
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 Theory of Planned Behavior to predict behavioral intentions from attitudes, subjective norms, and perceived behavioral control, and identify intervention leverage points. Use this skill when the user needs to predict adoption of a new behavior, diagnose why an intended behavior does not occur, design behavior change campaigns, or when they ask 'why do people not follow through', 'what predicts behavior change', or 'how to increase adoption rates'.".
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, sequencing, or troubleshooting an org-wide migration from Salesforce Classic to Lightning Experience. Covers the LEX Transition Assistant Readiness Check, asset triage matrix (Visualforce, JavaScript buttons, page layouts, Knowledge, email templates, list views, AppExchange), pilot/wave rollout sequencing, end-user adoption telemetry, and cutover criteria. Triggers: 'lightning experience transition', 'classic to lightning migration plan', 'LEX readiness check', 'why are some users still on Classic', 'turning on Lightning for everyone'. NOT for individual asset migrations like a single VF page (use lwc/visualforce-to-lwc-migration), a single JavaScript button (use admin/custom-button-to-action-migration), or Knowledge article migration (use admin/knowledge-classic-to-lightning) — this skill orchestrates the program. NOT for Lightning App Builder page design (use admin/lightning-app-builder-advanced).
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。
Guides the practical path from 'we have no design system' or 'we have a mess' to a real, functional design system. Component inventory, design token extraction, headless UI adoption (Radix, React Aria, Headless UI), migration order of operations, and the assessment-to-rollout pipeline. Activate on 'design system from scratch', 'bootstrap design system', 'no design system', 'design system migration', 'headless UI adoption', 'component audit', 'design token extraction', 'design system strategy'. NOT for building specific components once a system exists (use design-system-creator), not for generating token files/configs (use design-system-generator).
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 and adopt production AI agent infrastructure in 2026. Covers framework selection (LangGraph, CrewAI, AutoGen, MCP), orchestration patterns, evaluation, observability, memory systems, and tool use. Also covers the SOCIAL dimension: how to sell agent infrastructure internally, change management, measuring ROI, building trust in autonomous systems, and scaling adoption across teams. Activate on: "agent infrastructure", "agent framework comparison", "which agent framework", "sell AI tools internally", "agent adoption", "agent observability", "agent evaluation", "MCP architecture", "agentic mesh", "enterprise AI agents", "AI change management", "agent ROI". NOT for: building specific agents (use ai-engineer), designing agent behavior patterns (use agentic-patterns), prompt tuning (use prompt-engineer).
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。
Reviews a proposal, business case, deck or product plan in character as a Chief Product Officer archetype and returns a DRAFT review in the chat with a verdict, findings that cite the exact passage, product and adoption risks, what would change the verdict and five interrogation questions. Use when the user asks to "run a CPO review", "pressure-test this product plan", "what would a CPO say about this", "challenge the user evidence" or "check the roadmap opportunity cost". Do not use for positioning, messaging or launch marketing, use cmo-reviewer instead; for internal staff adoption of a rollout, use frontline-skeptic-reviewer; for what a change asks of paying customers, use customer-advocate-reviewer. Drafts for human review; never approves, authorises or signs off.
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。
Reviews a proposal, business case, deck or rollout plan in character as a Frontline Sceptic archetype, the experienced staff member who will live with the change, and returns a DRAFT review in the chat with a verdict, findings that cite the exact passage, adoption risks, what would change the verdict and five interrogation questions. Use when the user asks to "run a frontline review", "give me the shop-floor view of this rollout", "pressure-test adoption of this plan" or "what will the team actually say about this". Do not use for employee consultation, monitoring or works council preparation, use works-council-reviewer instead; for people strategy or restructuring, use chro-reviewer; for product evidence, use cpo-reviewer. Drafts for human review; never approves, authorises or signs off.
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 working with Codeium — codeium (Windsurf) AI coding assistant management and analytics. Covers team seat management, usage tracking, language analytics, editor adoption metrics, security policy configuration, and enterprise deployment review. Use when managing Codeium/Windsurf licenses, reviewing team adoption, or analyzing AI-assisted coding productivity.
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 en transformation digitale (strategy, change management, technology adoption, maturity assessment)
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 en customer success avancé (onboarding, adoption, expansion, QBRs, NPS, health scores)
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-company language sweep via Quartr full-text search — date the adoption curve of a term/phrase across the global transcript+filing corpus, identify which companies (and which speaker roles) use it and when they started, and cross-reference against vault theses and graph adjacency to flag terms propagating through supply chains the book owns. Use when user says "sweep", "who is talking about X", "language scan", "adoption curve for X", or "is X inflecting".
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 and scale developer-led adoption through ecosystem programs. Use when deciding open vs curated ecosystems, building developer programs, scaling platform adoption, or designing student program pipelines.
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。
Guides the practical path from 'we have no design system' or 'we have a mess' to a real, functional design system. Component inventory, design token extraction, headless UI adoption (Radix, React Aria, Headless UI), migration order of operations, and the assessment-to-rollout pipeline. Activate on 'design system from scratch', 'bootstrap design system', 'no design system', 'design system migration', 'headless UI adoption', 'component audit', 'design token extraction', 'design system strategy'. NOT for building specific components once a system exists (use design-system-creator), not for generating token files/configs (use design-system-generator).
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, evaluate, and adopt agent infrastructure for a named workload. Use for agent runtime/framework selection, agent evaluation or observability plans, memory and tool boundaries, infrastructure cost controls, and organizational adoption. Covers workload-first comparison, risk and recovery planning, held-out evaluation, cost accounting, and staged rollout. Not a vendor ranking or a production certification. NOT for implementing a specific agent behavior (agentic-patterns), building a specific agent (ai-engineer), prompt optimization (prompt-engineer), or conventional ETL workflow design (jury_rig-architect).
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。
新しいスキルを作る前に、ローカル環境やマーケット、GitHub、ウェブから既存候補を探し、用途との一致度や安全性を確認して再利用・拡張・新規作成の判断を助けます。
市場規模の推計、競合比較、投資家や技術の調査を、出典付きでまとめるスキル。事実と推測を分け、反証やリスクも含めて事業判断につながる提案を整理します。