「image」の検索結果

AI 图片生成器 — 基于 gpt-image-2 模型，支持文生图与图生图，开箱即用。

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。

AI image generation: Gemini and Nano Banana backends; single/series/batch workflows with prompt-to-disk.

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。

FFmpeg-based video creation from image and audio.

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。

Apple's open-source `container` CLI to build, run, and manage OCI/Linux containers as lightweight per-container VMs on Apple-silicon macOS — no Docker daemon required. Use when the user mentions the `container` CLI, "apple container", running or building containers on macOS without Docker/Podman, `container run`, `container build`, `container images`, `container system start`, pushing/pulling images to a registry, or container networking on macOS. This is Apple's `container` tool specifically (repo `apple/container`), NOT Docker, Podman, containerd, nerdctl, or Kubernetes — the CLI is Docker-like but is a different tool.

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。

Analyze images, screenshots, diagrams, and visual content - Use when you need to understand visual content like screenshots, architecture diagrams, UI mockups, or error screenshots.

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。

Execute Python code in a safe sandboxed environment via [inference.sh](https://inference.sh). Pre-installed: NumPy, Pandas, Matplotlib, requests, BeautifulSoup, Selenium, Playwright, MoviePy, Pillow, OpenCV, trimesh, and 100+ more libraries. Use for: data processing, web scraping, image manipulation, video creation, 3D model processing, PDF generation, API calls, automation scripts. Triggers: python, execute code, run script, web scraping, data analysis, image processing, video editing, 3D models, automation, pandas, matplotlib

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。

Audit container images, Dockerfiles, and Kubernetes manifests for misconfigurations, excessive privileges, exposed secrets, and runtime risks. Use when the user mentions 'container security,' 'Docker security,' 'Dockerfile audit,' 'Kubernetes security,' 'K8s security,' 'pod security,' 'container hardening,' 'kubectl audit,' 'image scanning,' 'distroless,' 'rootless containers,' 'pod security policy,' 'pod security standards,' 'PSS,' 'network policy,' 'OPA Gatekeeper,' 'Kyverno,' 'runtime security,' or needs to review container or orchestration security.

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。

Run registered ComfyUI workflows through the fast comfyui-skill CLI, and use the official local Comfy MCP for live template, node, model, validation, and orchestration capabilities. **Use this Skill when:** (1) The user requests to "generate an image", "draw a picture", or "execute a ComfyUI workflow". (2) The user has specific stylistic, character, or scene requirements for image generation. (3) The user asks you to import, register, sync, or configure saved ComfyUI workflows for later reuse.

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 600x600 Instagram-style card news series automatically with optional background images. User provides topic, colors, and optional images - Claude generates content and creates multiple cards with proper text wrapping.

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。

eLife figure preparation: file formats (TIFF/EPS/PDF), striking image requirements (1800x900 px), figure supplement naming, and image screening policy treating selective enhancement as misconduct.

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。

Register, segment, filter, resample 3D medical images (MRI, CT, microscopy) via SimpleITK Python; DICOM, NIfTI, multi-modal. Rigid/affine/deformable registration, threshold/region-growing segmentation, Gaussian/morph filtering, label stats, format conversion. Use to align volumes across timepoints/modalities, segment fluorescence, or convert DICOM→NIfTI.

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。

Comprehensive best practices for robot perception systems covering cameras, LiDARs, depth sensors, IMUs, and multi-sensor setups. Use this skill when working with RGB image processing, depth maps, point clouds, sensor calibration (intrinsic, extrinsic, hand-eye), object detection, semantic segmentation, 3D reconstruction, visual servoing, or perception pipeline optimization. Trigger whenever the user mentions OpenCV, Open3D, PCL, RealSense, ZED, OAK-D, camera calibration, AprilTags, ArUco markers, stereo vision, RGBD, point cloud filtering, ICP registration, coordinate transforms, camera intrinsics, distortion correction, image undistortion, sensor streaming, frame synchronization, or any computer vision task in a robotics context. Also covers multi-camera rigs, time synchronization across sensors, perception latency budgets, and production deployment of perception 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。

把一句话需求转成可复现、可验收视频工程的乔木智能剪辑导演。Use when the user asks to create, plan, edit, remix, explain, narrate, subtitle, animate, composite, or render a video—including one-line requests such as “制作一个科普视频：介绍 LLM 中的 RL”. Builds English-learning movie mixes, person profiles, explainers, code-drawn motion graphics (UI morph loops, showreels, kinetic-type promos, line-art/pixel/Three.js animation, lyric MVs), product launch videos, cinematic shorts, social clips, course/PPT videos, AI image/video/TTS/music-assisted stories, stock-footage stories, or multi-source montage. Routes across 33台词, ClipSeek/open media, local files, ListenHub, image generation, HTML/SVG/Canvas motion graphics with beat-grid storyboards and determinism probes, Manim, ffmpeg-full, captions, sound design, frame-0 video covers with YouTube thumbnails, and release checks.

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。

Provides patterns to deploy ECS tasks and services with GitHub Actions CI/CD. Use when building Docker images, pushing to ECR, updating ECS task definitions, deploying ECS services, integrating with CloudFormation stacks, configuring AWS OIDC authentication for GitHub Actions, and implementing production-ready container deployment pipelines. Supports ECS deployments with proper security (OIDC or IAM keys), multi-environment support, blue/green deployments, ECR private repositories with image scanning, and CloudFormation infrastructure updates.

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 Next.js performance optimization skill covering Core Web Vitals, image/font optimization, caching strategies, streaming, bundle optimization, and Server Components best practices. Use when optimizing Next.js applications for Core Web Vitals (LCP, INP, CLS), implementing next/image and next/font, configuring caching with unstable_cache and revalidateTag, converting Client Components to Server Components, implementing Suspense streaming, or analyzing and reducing bundle size. Supports Next.js 16 + React 19 patterns.

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。

Guidelines for handling image files

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。

Extract data from construction images using AI Vision. Analyze site photos, scanned documents, drawings.

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。

Generate images and visualizations from Revit/IFC files without BIM software. Python-based noBIM tool for batch processing.

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。

Turns a screenshot, mockup, Figma export, or photo of a UI into working front-end code - React + Tailwind CSS v4 by default, or Next.js, Vue, or plain HTML/CSS - matching layout, spacing, colors, and typography, then renders the result and compares it against the image to close visual gaps. Use whenever the user shares an image of a website, app screen, dashboard, component, or wireframe and wants it built, cloned, recreated, or "made real", or says "code this up", "build this design", or "match this screenshot".

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。

Extract color palettes from images, websites, or designs. Identifies dominant colors, generates complementary schemes, and exports in multiple formats (HEX, RGB, HSL, Tailwind, CSS variables). Use when users need color schemes from images, brand colors, or design system palettes.

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。

バナー/図表/スライド/画像の生成・編集を行うサブエージェント。 Gemini Image Generation APIを使用して各種メディアを生成する。 「バナーを作って」「図表を生成」「スライドを作成」「画像を生成」等のリクエストで発動。

Gemini Image Generation API でテキストから図解・インフォグラフィック・ダイアグラムを生成するスキル。 「図を作って」「インフォグラフィック生成」「プロセスを図解して」等のリクエストで発動。

Routes DreamOmni2 image generation, image editing, and Gradio demo workflows.

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 DeepDanbooru for anime-style image tag estimation, project and Danbooru-style dataset preparation, TensorFlow training, image evaluation, TFLite conversion, and experimental Grad-CAM workflows.

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。