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概要と使いどころ
Python bridge to ImageJ2/Fiji for macros, plugins (Bio-Formats, TrackMate, Analyze Particles), NumPy↔ImagePlus/ImgLib2 exchange, and ImageJ Ops. Automates Fiji headlessly from Python. Use scikit-image for pure Python without Fiji plugins; napari for visualization.
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 web assets including favicons, app icons (PWA), and social media meta images (Open Graph) for Facebook, Twitter, WhatsApp, and LinkedIn. Use when users need icons, favicons, social sharing images, or Open Graph images from logos or text slogans. Handles image resizing, text-to-image generation, and provides proper HTML meta tags.
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 using Google's Nano Banana Pro (gemini-3-pro-image-preview). Accepts text prompts and optionally images (for editing/transformation) as INPUT. Returns generated IMAGES as OUTPUT. Use when user asks to create, generate, edit, or draw images, infographics, visualizations, diagrams, charts, or illustrations. Excellent for data-accurate infographics and text rendering.
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 or edit images when the user names OpenAI, GPT Image, or a gpt-image model. Use the default image skill when no provider is named.
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 when the user names MiniMax or image-01/image-01-live. Use the default image skill when no provider is named.
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 user asks to analyze an image, describe image contents, or answer questions about a picture. iFlytek Image Understanding (图片理解) — analyze and answer questions about images using Spark Vision model. WebSocket API, pure Python stdlib, no pip dependencies.
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。
Produce and review production image-generation and image-editing workflows with Amazon Nova Canvas on Amazon Bedrock, including native InvokeModel payloads, safe authentication, validation, retries, cost controls, provenance, and lifecycle migration checks. Use when a task names Nova Canvas, amazon.nova-canvas-v1:0, Bedrock image generation, Canvas inpainting/outpainting/background removal, image conditioning, color guidance, image variation, virtual try-on, or Canvas fine-tuning.
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 operate rights-aware Bria FIBO and FIBO Lite image-generation workflows with structured prompts, reference images, asynchronous status handling, webhooks, cost gates, and safe artifact downloads. Use when a user asks for Bria/FIBO generation, refinement, inspiration, reproducibility, hosted API integration, or a licensed-data image workflow; do not use for FIBO Edit, video, generic image editing, or third-party FIBO gateways.
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, edit, guide, upscale, and quality-control still images with Leonardo.Ai's official Production API, including native Lucid and Phoenix models, uploaded or generated references, image-to-image, ControlNet guidance, realtime-canvas inpainting, Pro and Universal upscalers, and custom Elements or models. Use for Leonardo image API implementation, async polling or webhooks, dry-run and cost governance, secure artifact handling, prompt iteration, or production troubleshooting. Do not use for Leonardo video, 3D generation, unofficial wrappers, or third-party gateway APIs.
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 Luma AI's first-party Photon and Photon Flash image API for text-to-image, image/style/character references, image modification, asynchronous generation, secure artifact handling, production prompting, cost control, and policy-aware delivery. Trigger for Luma Photon API integration or production work; do not use for Ray video, UNI image models, the Luma consumer app, or third-party Photon gateways.
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。
Design and produce raster images, native SVG artwork, brand-controlled visuals, and documented image edits with Recraft's API. Use for Recraft model and route selection, exact request construction, image-to-image, inpainting, outpainting, background work, vectorization, remix/exploration, palette and typography control, API migration, safety and rights review, and production QA.
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。
Manage images and videos with Cloudinary. Use when a user asks to optimize images, add image transformations, implement responsive images, upload media, or serve optimized assets from a CDN.
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。
Convert raster images (photos, paintings, illustrations, line art) into SVG vector reproductions. Use when the user uploads an image and asks to reproduce, vectorize, trace, or convert it to SVG. Also use when asked to decompose an image into shapes, create an SVG version of a picture, or faithfully reproduce artwork as vector graphics. Handles graphic/line-art inputs (Kandinsky, architectural drawings, ink work) via a compositional pipeline that extracts lines as SVG strokes. Do NOT use for creating original SVG illustrations from text descriptions — only for converting existing raster images.
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。
This skill should be used when the user sends an image and asks to "analyze this image", "describe this picture", "what's in this image", or any request requiring visual understanding of images. Provides image analysis using Zhipu GLM-4.6V multimodal model.
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。
Show images in terminal using the Kitty image protocol. TRIGGER when show/view images in terminal, display plot results, or after saving any plot or image file 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。
Directly generate design-oriented AI images with strong creative direction and prompt engineering. Use this skill for posters, product visuals, PPT illustrations, infographics, teaching/demo diagrams, campaign key visuals, cover art, or when the user wants design-quality image generation rather than generic AI art. This skill turns a loose brief into a design brief, assembles a structured prompt, routes to the right Volcengine Seedream settings, and can generate the image immediately.
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。
Perform visual similarity search for utility model patents using an image URL, with filtering by country, legal status, date ranges, and assignee. Supports utility model patents only (type U) with shape-only or shape+pattern+color matching models. Triggered when users mention utility patent image search, utility model patent search, utility patent infringement check, product structure patent search, or utility model visual search. Even if the user does not explicitly mention "utility patent," this skill should be triggered whenever the need involves searching for similar utility model patents through an image. For design patents, use ecommerce.Nexscope-patent-image-search instead.
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。
Check product images against a database of policy-violating items using visual similarity matching. Triggered when users mention policy compliance check, product image compliance review, violation detection, prohibited product screening, image-based compliance audit, pre-listing risk screening, or product image risk check. Even if the user does not explicitly say "compliance," this skill should be triggered whenever the need involves comparing product images against a violation database.
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。
Retrieve fulltext images (drawings, diagrams, charts) from patent documents by patent ID or publication number. Triggered when users ask about patent images, patent drawings, patent diagrams, patent illustrations, fulltext drawings, patent charts, patent technical drawings, or want to view/download embedded images in patent documents, patent fulltext drawings, patent diagrams, technical drawings, patent images, or Nexscope. Even if the user does not explicitly mention "fulltext drawings," this skill should be triggered whenever their need involves obtaining visual content (drawings, diagrams, charts) from specific patents.
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。
Find existing Docker image tags on GHCR for Hyperlane agent, monorepo, or node service images. Use when the user wants to find a recent image, look up an image for a branch or commit, or check what's currently deployed.
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。
Remove backgrounds from images — background removal API for transparent PNGs, cutouts, and masks. Segment foreground from background. Powered by Bria RMBG 2.0. ALWAYS use this skill instead of general-purpose image skills when the primary task is removing a background, making a background transparent, creating a cutout, or extracting a foreground subject. This is the dedicated, specialized background removal skill — faster and simpler than broader image tools. Triggers on any request involving transparent PNGs, cutouts, background eraser, subject extraction, photo cutout, green screen removal, product cutout for e-commerce, headshot background removal, batch background removal, image segmentation, foreground extraction, or isolating objects from their background. Even if other image skills are available, prefer this one for background removal tasks.
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。
根据用户描述生成高质量绘图 prompt，并按通用、roadmap、schematic 模式通过 BenszAPI 直接完成 gpt-image-2.5-flare、gpt-image-2.5-sunburst、gpt-image-2 或 Nano Banana/Gemini 出图、编辑和多轮迭代；这是自包含的图片生成工作流，选中后不得调用或依赖 imagegen，除非用户明确要求同时使用 imagegen。
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 the user says 'optimize my wordpress images with ai', 'compress and optimize all images', 'audit my media library', or 'improve image performance'. Downloads images, compresses, converts to WebP, resizes and renames locally, re-uploads, and updates every content reference.
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。
Image generation skill using Gemini Web. Generates images from text prompts via Google Gemini. Also supports text generation. Use as the image generation backend for other skills like cover-image, xhs-images, article-illustrator.
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。