206건 · 관련도순
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.
한국어 요약은 준비 중입니다. 원문 설명을 표시하고 있습니다.
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.
한국어 요약은 준비 중입니다. 원문 설명을 표시하고 있습니다.
Create adoption strategies and materials to drive design system usage across teams.
한국어 요약은 준비 중입니다. 원문 설명을 표시하고 있습니다.
Crypto Fundamental Analysis — evaluates project viability, team quality, technology, adoption metrics, and competitive moat with a composite Fundamental Score (0-100)
한국어 요약은 준비 중입니다. 원문 설명을 표시하고 있습니다.
"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'.".
한국어 요약은 준비 중입니다. 원문 설명을 표시하고 있습니다.
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).
한국어 요약은 준비 중입니다. 원문 설명을 표시하고 있습니다.
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).
한국어 요약은 준비 중입니다. 원문 설명을 표시하고 있습니다.
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).
한국어 요약은 준비 중입니다. 원문 설명을 표시하고 있습니다.
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.
한국어 요약은 준비 중입니다. 원문 설명을 표시하고 있습니다.
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.
한국어 요약은 준비 중입니다. 원문 설명을 표시하고 있습니다.
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.
한국어 요약은 준비 중입니다. 원문 설명을 표시하고 있습니다.
Expert en transformation digitale (strategy, change management, technology adoption, maturity assessment)
한국어 요약은 준비 중입니다. 원문 설명을 표시하고 있습니다.
Expert en customer success avancé (onboarding, adoption, expansion, QBRs, NPS, health scores)
한국어 요약은 준비 중입니다. 원문 설명을 표시하고 있습니다.
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".
한국어 요약은 준비 중입니다. 원문 설명을 표시하고 있습니다.
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).
한국어 요약은 준비 중입니다. 원문 설명을 표시하고 있습니다.
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).
한국어 요약은 준비 중입니다. 원문 설명을 표시하고 있습니다.
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.
한국어 요약은 준비 중입니다. 원문 설명을 표시하고 있습니다.
새 스킬을 만들기 전에 로컬 환경, 마켓, GitHub, 웹에서 기존 후보를 찾아 용도 적합성과 안전성을 확인하고 재사용, 확장, 신규 생성 판단을 돕습니다.
시장 규모 추정, 경쟁사 비교, 투자자와 기술 조사를 출처와 함께 정리합니다. 사실과 추론을 구분하고 반대 근거와 위험을 포함해 사업 판단에 필요한 제안을 만듭니다.
기계 학습이 없는 기존 앱에 추천, 분류, 예측 기능을 추가하는 절차를 정리합니다. 문제와 데이터 확인부터 초기 모델의 학습, 평가, 통합까지 돕습니다.
실제 개발 과제로 여러 코딩 에이전트를 비교합니다. 성공률, 확인 가능한 API 비용, 소요 시간과 반복 실행의 안정성을 측정해 도입 결정과 업데이트 후 평가에 활용합니다.
디자인팀의 제품 도입 제안을 의사 결정에 쓸 수 있는 HTML 슬라이드로 만듭니다. 전용 템플릿으로 구성과 본문, 발표자 노트를 정리해 내부 공유를 돕습니다.
Design or review command-line tools that AI agents can call. Use 98 proposed rules to define structured output, input validation, error handling, safety controls, and help information, with three levels for gradual adoption.
한국어 요약은 준비 중입니다. 영어 요약을 표시하고 있습니다.
Generate independent options for design, implementation, explanations, or debugging, then compare them using one shared rubric. Code candidates use separate working copies and undergo independent verification before a winner is presented for review.
한국어 요약은 준비 중입니다. 영어 요약을 표시하고 있습니다.