'generative' 검색 결과

Use this skill when staff already use AI assistants, coding tools or AI features and the company needs rules people will follow, and asks "write our AI usage policy", "can staff use ChatGPT", "generative AI policy", "what data can we put into AI tools", "approved AI tools list", "rules for AI agents and connectors" or "AI literacy for staff". Finds out what people actually use, sets a short list of approved tools with a data traffic light, rules for checking outputs and for agents that act, a fast route to request a new tool, and writes a two-page rule set, the approved tools list and rollout notes.

한국어 요약은 준비 중입니다. 원문 설명을 표시하고 있습니다.

Japanese algorithmic and generative art skill covering traditional pattern generation (wagara), calligraphy simulation, zen garden generators, and mathematical patterns from Japanese art. Triggers on requests for 和柄生成, ジェネラティブアート, アルゴリズミックアート, 伝統紋様パターン, Japanese generative art.

한국어 요약은 준비 중입니다. 원문 설명을 표시하고 있습니다.

Production pipeline for ASCII art video — any format. Converts video/audio/images/generative input into colored ASCII character video output (MP4, GIF, image sequence). Covers: video-to-ASCII conversion, audio-reactive music visualizers, generative ASCII art animations, hybrid video+audio reactive, text/lyrics overlays, real-time terminal rendering. Use when users request: ASCII video, text art video, terminal-style video, character art animation, retro text visualization, audio visualizer in ASCII, converting video to ASCII art, matrix-style effects, or any animated ASCII output.

한국어 요약은 준비 중입니다. 원문 설명을 표시하고 있습니다.

Build generative UI apps with OpenUI and OpenUI Lang — the token-efficient open standard for LLM-generated interfaces. Use when mentioning OpenUI, @openuidev, generative UI, streaming UI from LLMs, component libraries for AI, or replacing json-render/A2UI. Covers scaffolding, defineComponent, system prompts, the Renderer, and debugging OpenUI Lang output.

한국어 요약은 준비 중입니다. 원문 설명을 표시하고 있습니다.

Render mathematically-faithful Turkish marbling (Ebru) and paper-marbling with closed-form coordinate-deformation maps — a drop is a radial outward push, a comb/tine is a decaying shear, all composed as homeomorphisms over colored region boundaries. No fluid solver, runs 60fps on a 2D canvas. Use when building a marbling/Ebru toy or generative-art renderer, implementing drop/comb/wave/swirl deformations, scripting traditional patterns (Battal, Gel-Git, Şal, Bülbül Yuvası), keeping combed edges crisp via adaptive resampling, modeling ox-gall shouldering for crisp ring-in-ring drops, or exporting high-DPI marbled prints. NOT for real fluid / Navier-Stokes simulation, metaball / alpha-blended "smoke" blob effects, raster image-processing filters, or non-marbling generative art.

한국어 요약은 준비 중입니다. 원문 설명을 표시하고 있습니다.

Build, debug, optimize, and deploy ComfyUI workflows for diffusion image, video, and audio generation. Activate on: ComfyUI workflow, ComfyUI custom node, ComfyUI API, ComfyUI Manager, KSampler, FLUX in ComfyUI, Wan 2.2 ComfyUI, Hunyuan video ComfyUI, LTX video ComfyUI, IPAdapter, ControlNet ComfyUI, Kijai wrapper, ComfyDeploy, RunComfy, ComfyUI security, GGUF quantization, TeaCache, Nunchaku, ACE-Step ComfyUI, F5-TTS ComfyUI, subgraphs ComfyUI. NOT for: A1111/Forge/Invoke (different UIs), training pipelines from scratch, non-diffusion ML models, or general image-API integrations (use generative-video-2026 / generative-music-audio / media-gen-deployment).

한국어 요약은 준비 중입니다. 원문 설명을 표시하고 있습니다.

Use when building creative browser demos with @chenglou/pretext — DOM-free text layout for ASCII art, typographic flow around obstacles, text-as-geometry games, kinetic typography, and text-powered generative art. Produces single-file HTML demos by default.

한국어 요약은 준비 중입니다. 원문 설명을 표시하고 있습니다.

참고 영상의 색조와 컷 길이를 측정해 재사용할 스타일 소재로 정리합니다. 색 변환용 LUT, 정지 이미지, 합성 소재와 생성 모델용 명세를 만듭니다.

• 참고 영상의 색조를 소재로 만들 때
• 비슷한 편집 박자를 위해 컷 간격 측정
• 장르별 스타일 소재 모음 제작

여러 단계의 계획을 읽고 바로 붙여 넣을 ECC 명령을 생성합니다. 단계 식별자와 완료 조건을 포함하며, 작업 범위를 전달할 수 없는 명령은 보류로 표시합니다.

• 구현 계획을 명령으로 변환
• 기능 추가·수정·리팩터링 명령 선택
• 지정한 단계의 명령만 준비

영상과 이미지를 컬러 문자 아트로 바꾸고 음악에 반응하는 애니메이션도 제작합니다. 가사와 텍스트, 시각 효과를 더해 MP4, GIF, 연속 이미지로 내보낼 수 있습니다.

• 영상을 컬러 문자 아트로 변환
• 음악에 반응하는 영상 제작
• 가사가 표시되는 영상 제작

Build HTML/CSS pages using visual specifications from 54 established websites, including Stripe, Linear, and Vercel. Templates provide colors, typography, spacing, component styles, and layouts that adapt to different screen sizes.

한국어 요약은 준비 중입니다. 영어 요약을 표시하고 있습니다.

• Creating a landing page inspired by Stripe
• Designing a dashboard inspired by Linear
• Styling a documentation website

언어별 자료에 따라 Claude 앱을 구현하고 유지보수합니다. 공식 SDK 호출, 도구 연동, 모델 이전, 비용 검토와 평가 절차 마련을 다룹니다.

• 앱에 Claude API 연동
• 맞춤 도구를 쓰는 에이전트 구현
• 기존 Claude 연동의 모델 이전

Build Claude applications using guidance for the official Anthropic SDK. Choose an integration approach, implement streaming and tool use, and follow workflows for model migration, prompt audits, evaluation, and cost optimization.

한국어 요약은 준비 중입니다. 영어 요약을 표시하고 있습니다.

• Build a summarization app with Claude API
• Choose how to run an agent with custom tools
• Migrate existing code to a newer Claude model

Audit how a brand appears in AI search answers and identify improvements to its content and website access. Review citation patterns, clear answer structure, supporting evidence, and whether AI systems can retrieve key information.

한국어 요약은 준비 중입니다. 영어 요약을 표시하고 있습니다.

• Check brand citations across AI search services
• Improve existing articles for clearer answers
• Investigate why AI systems cannot read a site

Fit probabilistic models to single-cell measurements for integration across experiments, cell annotation, combined RNA and protein or ATAC analysis, and gene expression comparisons. The skill covers input requirements, training checks, reference mapping, and interpretation of model uncertainty.

한국어 요약은 준비 중입니다. 영어 요약을 표시하고 있습니다.

• Integrating single-cell RNA data across experimental batches
• Assigning cell labels using partial annotations or a reference
• Analyzing RNA with protein or ATAC measurements

Add Application Insights monitoring to browser apps to record page views, clicks, network requests, and errors. Also covers custom measurements and tracing generative AI operations across browser and backend code to investigate behavior and performance.

한국어 요약은 준비 중입니다. 영어 요약을 표시하고 있습니다.

• Monitoring page views and clicks in a browser app
• Recording network requests and application errors
• Linking browser activity to backend traces

Build web apps connecting a CopilotKit React interface to agents hosted in Microsoft Foundry. Covers AG-UI, the communication protocol between them, plus tool approvals, conversation history, shared state, deployment, debugging, and dependency upgrades.

한국어 요약은 준비 중입니다. 영어 요약을 표시하고 있습니다.

• Build a chat interface for a Foundry hosted agent
• Add approval screens for agent tool execution
• Investigate failures across the agent and web interface

Create structured Markdown specifications that AI agents can read and use. Organize requirements, constraints, interfaces, acceptance criteria, and testing plans into a self-contained document with clear definitions and examples.

한국어 요약은 준비 중입니다. 영어 요약을 표시하고 있습니다.

• Writing specifications for AI-assisted implementation
• Defining requirements and acceptance criteria
• Documenting APIs and data contracts

Helps integrate CopilotKit into an app by consulting current documentation and source code. Covers chat interfaces, agent connections, shared state, and troubleshooting, with guidance on checking configuration and avoiding incompatible API versions.

한국어 요약은 준비 중입니다. 영어 요약을 표시하고 있습니다.

• Adding a CopilotKit chat interface to an app
• Connecting an agent framework
• Diagnosing runtime connection problems

Plan user research by matching methods to study goals and explaining participant numbers and confidence. Synthesize tagged observations into recurring findings, distinguish isolated anecdotes, and organize evidence so research results can be found and reused.

한국어 요약은 준비 중입니다. 영어 요약을 표시하고 있습니다.

• Choose a research method for a study goal
• Explain participant numbers and confidence
• Find recurring patterns across participants

Review content for citation in AI search answers. Use checklists covering clear definitions, sourced data, author information, FAQs, and structured data, then plan how to monitor citations, brand mentions, and AI-referred traffic.

한국어 요약은 준비 중입니다. 영어 요약을 표시하고 있습니다.

• Reviewing articles for citation in AI search answers
• Organizing definitions, sources, and FAQs in content
• Checking author details and structured data

Debug json-render interfaces with an inspector panel that shows screen specifications, editable runtime state, action history, and streamed updates. Select a rendered page element to locate its definition and investigate display or interaction problems.

한국어 요약은 준비 중입니다. 영어 요약을 표시하고 있습니다.

• Investigate missing or incorrectly displayed elements
• Inspect and edit application state at runtime
• Review action results and errors

Choose a serving container for models deployed on SageMaker. Match the model type to a compatible image, retrieve its address from the AWS catalog, and check regional availability, CPU or GPU variants, and required host settings.

한국어 요약은 준비 중입니다. 영어 요약을 표시하고 있습니다.

• Choose a container for an LLM deployment
• Select images for embeddings and rerankers
• Check regional image addresses and GPU compatibility

Audit a website for visibility in AI search, including how easily its content can be quoted, technical access, content quality, and structured data. Receive a weighted score out of 100 and a prioritized improvement plan.

한국어 요약은 준비 중입니다. 영어 요약을 표시하고 있습니다.

• Assessing a website’s readiness for AI search
• Finding crawler access and structured data issues
• Reviewing content credibility and ease of quotation