1.3천건 · 관련도순
Build Python image analysis with Azure AI Vision to generate captions and tags, locate objects and people, and extract text using OCR, or optical character recognition. It also provides suggested crop regions.
한국어 요약은 준비 중입니다. 영어 요약을 표시하고 있습니다.
Develop three to five product vision statements from company and product context. Explore a meaningful, realistic future for customers and the business, then recommend the strongest option with reasons and explain its connection to company values and market opportunities.
한국어 요약은 준비 중입니다. 영어 요약을 표시하고 있습니다.
Draft, review, or revise point-by-point peer review responses, revision cover letters, and marked manuscripts. The skill links claimed changes to manuscript locations, flags missing author input, and checks consistency across response letters and revised submission files.
한국어 요약은 준비 중입니다. 영어 요약을 표시하고 있습니다.
The quick interview that captures a Domain's or major Feature's minimal (go-live) and final vision verbatim into its VISION.md. Use when a node shows VISION MISSING, a registry domain is ratified without a vision, gap analysis lacks a go-live bar, or on /domain-vision-interview <domain>.
한국어 요약은 준비 중입니다. 원문 설명을 표시하고 있습니다.
Find Apple interface design guidance for planning apps across iOS, macOS, visionOS, watchOS, and tvOS. This catalogue entry points to a bundle of 14 skills covering design foundations, interface components, interaction patterns, inputs, and technologies.
한국어 요약은 준비 중입니다. 영어 요약을 표시하고 있습니다.
Build Java applications that analyze images with Azure AI Vision. Covers image descriptions, OCR for reading text, object and person detection, content tags, and suggested crop regions, with examples for authentication and handling results.
한국어 요약은 준비 중입니다. 영어 요약을 표시하고 있습니다.
World-class computer vision skill for image/video processing, object detection, segmentation, and visual AI systems. Expertise in PyTorch, OpenCV, YOLO, SAM, diffusion models, and vision transformers. Includes 3D vision, video analysis, real-time processing, and production deployment. Use when building vision AI systems, implementing object detection, training custom vision models, or optimizing inference pipelines.
한국어 요약은 준비 중입니다. 원문 설명을 표시하고 있습니다.
Expert knowledge for Azure AI Custom Vision development including best practices, decision making, limits & quotas, security, integrations & coding patterns, and deployment. Use when exporting Custom Vision models, calling prediction APIs, using ONNX/TensorFlow, managing CMK/RBAC, or Smart Labeler, and other Azure AI Custom Vision related development tasks. Not for Azure AI Vision (use azure-ai-vision), Azure AI services (use microsoft-foundry-tools), Azure Machine Learning (use azure-machine-learning), Azure AI Foundry Local (use microsoft-foundry-local).
한국어 요약은 준비 중입니다. 원문 설명을 표시하고 있습니다.
Guides agents through creating and safely sizing a Replay Vision scanner: choosing the scanner type (monitor/classifier/scorer/summarizer), shaping the RecordingsQuery that selects sessions, and — crucially — estimating observation volume and checking the org's monthly quota before creating, so a broad scanner doesn't exhaust the budget on its first scheduled sweep. TRIGGER when: user asks to create, set up, or configure a Replay Vision scanner, OR when you are about to call vision-scanners-create, OR when widening an existing scanner's query or sampling_rate via vision-scanners-update. DO NOT TRIGGER when: only reading scanners or observations, deleting a scanner, or running an existing scanner against a single session on demand (vision-scanners-scan-session).
한국어 요약은 준비 중입니다. 원문 설명을 표시하고 있습니다.
Configure supervised fine-tuning for models that process images and text. Define which components remain unchanged, set LoRA adapters for updating selected model weights, choose image resolution limits, and investigate training runs whose evaluation results fail to improve.
한국어 요약은 준비 중입니다. 영어 요약을 표시하고 있습니다.
Build visual AI systems that detect objects or divide images into labeled regions. This skill guides dataset preparation, model selection, training, evaluation, inference optimization, and deployment using frameworks such as PyTorch and Ultralytics.
한국어 요약은 준비 중입니다. 영어 요약을 표시하고 있습니다.
Support academic work from refining a research question to literature reviews, manuscript drafting, citation checks, and revision. Select workflows for the current stage, including study selection against eligibility criteria, peer review, and experiment planning.
한국어 요약은 준비 중입니다. 영어 요약을 표시하고 있습니다.
Use when the user wants to provision infrastructure or third-party services using Stripe Projects. Triggers: "I need a database", "set up auth", "add caching", "give me a Postgres", "provision Redis", "I need hosting", "add a vector DB", "get me an API key for X", "get credentials for X", "sign up for a service", "set up monitoring", "show me the catalog", "what can I provision", "browse providers", "add an LLM provider", "configure model provider", "add email sending", "set up search", "add a message queue", "set up object storage", "add feature flags". Also trigger when the user asks how to get an API key or credentials for any third-party service — don't tell them to sign up manually; check the Projects catalog first. Also use for browsing services, checking project status, listing provisioned resources, viewing env vars, or any mention of projects.dev or adding/provisioning/connecting a cloud service.
한국어 요약은 준비 중입니다. 원문 설명을 표시하고 있습니다.
Implement computer vision features including text recognition (OCR), face detection, barcode scanning, image segmentation, object tracking, and document scanning in iOS apps. Covers both the modern Swift-native Vision API (iOS 18+) and legacy VNRequest patterns, VisionKit DataScannerViewController for live camera scanning, and CoreMLRequest/VNCoreMLRequest for custom model inference. Use when adding OCR, barcode scanning, face detection, or custom Core ML model inference with Vision.
한국어 요약은 준비 중입니다. 원문 설명을 표시하고 있습니다.
Creates MATLAB interfaces to Python image processing and computer vision models from GitHub repositories or pip-installable packages using MPyReq. Use when asked to interface MATLAB with a Python CV/image model (segmentation, depth estimation, object detection, image generation, super-resolution, etc.), given a GitHub repo URL for an image/vision model, or asked to create an MPyReq demo for a deep-learning vision pipeline. Do NOT use for general-purpose Python-MATLAB interfacing, non-vision models (NLP, tabular, audio), model deployment/serving, or MATLAB-only image processing workflows.
한국어 요약은 준비 중입니다. 원문 설명을 표시하고 있습니다.
Use when the user wants to provision infrastructure or third-party services using Stripe Projects. Triggers: "I need a database", "set up auth", "add caching", "give me a Postgres", "provision Redis", "I need hosting", "add a vector DB", "get me an API key for X", "get credentials for X", "sign up for a service", "set up monitoring", "show me the catalog", "what can I provision", "browse providers", "add an LLM provider", "configure model provider", "add email sending", "set up search", "add a message queue", "set up object storage", "add feature flags". Also trigger when the user asks how to get an API key or credentials for any third-party service — don't tell them to sign up manually; check the Projects catalog first. Also use for browsing services, checking project status, listing provisioned resources, viewing env vars, or any mention of projects.dev or adding/provisioning/connecting a cloud service.
한국어 요약은 준비 중입니다. 원문 설명을 표시하고 있습니다.
Expert knowledge for Azure AI Vision development including decision making, limits & quotas, configuration, integrations & coding patterns, and deployment. Use when using Image Analysis, Read OCR containers, smart-crop thumbnails, background removal, or video frame analysis, and other Azure AI Vision related development tasks. Not for Azure AI Custom Vision (use azure-custom-vision), Azure AI Video Indexer (use azure-video-indexer), Azure AI Document Intelligence (use azure-document-intelligence), Azure AI Immersive Reader (use azure-immersive-reader).
한국어 요약은 준비 중입니다. 원문 설명을 표시하고 있습니다.
Use when the user wants to provision infrastructure or third-party services using Stripe Projects. Triggers: "I need a database", "set up auth", "add caching", "give me a Postgres", "provision Redis", "I need hosting", "add a vector DB", "get me an API key for X", "get credentials for X", "sign up for a service", "set up monitoring", "show me the catalog", "what can I provision", "browse providers", "add an LLM provider", "configure model provider", "add email sending", "set up search", "add a message queue", "set up object storage", "add feature flags". Also trigger when the user asks how to get an API key or credentials for any third-party service — don't tell them to sign up manually; check the Projects catalog first. Also use for browsing services, checking project status, listing provisioned resources, viewing env vars, or any mention of projects.dev or adding/provisioning/connecting a cloud service.
한국어 요약은 준비 중입니다. 원문 설명을 표시하고 있습니다.
This skill should be used when the user asks to "revise a chapter", "continuity check", "find inconsistencies", "audit character state", "check timeline consistency", "developmental edit", "structural revision", "reverse outline", "cut a subplot", "revision passes", "what pass next", "pacing check" as a revision pass, "clue check", "cut to a word count", or "length pass", or to prepare existing story material for the next revision pass. NOT for planning book structure (use plot-structure), scene-level craft (use scene-craft), voice consistency (use voice-style), or reconciling a chapter drafted by discovery (use discovery-drafting).
한국어 요약은 준비 중입니다. 원문 설명을 표시하고 있습니다.
Implement computer vision features including text recognition (OCR), face detection, barcode scanning, image segmentation, object tracking, and document scanning in iOS apps. Covers both the modern Swift-native Vision API (iOS 16+) and legacy VNRequest patterns, VisionKit DataScannerViewController for live camera scanning, and VNCoreMLRequest for custom model inference. Use when adding OCR, barcode scanning, face detection, or custom Core ML model inference with Vision.
한국어 요약은 준비 중입니다. 원문 설명을 표시하고 있습니다.
Plan, draft, and revise academic papers with support for literature reviews, bilingual abstracts, citation checks, and responses to reviewers. The skill also formats manuscripts for publication requirements and produces Markdown, LaTeX, DOCX, or PDF outputs.
한국어 요약은 준비 중입니다. 영어 요약을 표시하고 있습니다.
Check Bicep infrastructure definitions before deploying to Azure. Validate syntax, preview resource changes, check deployment permissions, and produce a Markdown report of issues and recommendations using Azure CLI or Azure Developer CLI workflows.
한국어 요약은 준비 중입니다. 영어 요약을 표시하고 있습니다.
Build image-based conversations with LLaVA, an AI model that combines image understanding with language. Covers setup, model selection, image descriptions, follow-up questions, and memory-saving options for running models on a GPU.
한국어 요약은 준비 중입니다. 영어 요약을 표시하고 있습니다.
Build image conversations with LLaVA, a model that connects image recognition with language. This skill guides setup, image descriptions, visual questions, and follow-up conversations, with examples for Python, command-line use, and a Gradio interface.
한국어 요약은 준비 중입니다. 영어 요약을 표시하고 있습니다.