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概要と使いどころ
Remove visible watermarks from user-provided or authorized images, including Gemini sparkle marks, AI-generator corner signatures, and compact high-confidence text watermarks. Use when a user asks to remove, clean, erase, or batch-process visible image watermarks, including requests containing "remove watermark", "去水印", "去除水印", or "水印去除".
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 3D models, textures, reference images, rigging and animation with Meshy, and prepare models for physical 3D printing: white or multicolor prints, Creative Lab products, sizing and slicer handoff. Drives the Meshy CLI; signs in through the browser, no API key required.
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 or edit digital 3D assets with Meshy: models, textures, rigging, animation, and reference images. For physical printing, use meshy-3d-printing.
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。
Compare two images pixel-by-pixel and get a visual diff. Use when the user wants to compare their implementation against a design, spot differences between two screenshots, or verify visual regression.
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 a .glb or .gltf and make it small, correct, and fast to load in a browser, with a measured before/after report. Use when a model is "too big" or "slow to load", when the user asks to "optimize this GLB", "compress this glTF", or "export for web", or when they mention Draco, meshopt, gltfpack, KTX2, Basis, texture VRAM, draw calls, or a Blender / CAD / photogrammetry / Pascal export headed for Three.js, React Three Fiber (drei useGLTF), Babylon.js, or model-viewer. Covers inspection, spec validation, geometry and texture compression, scene-graph cleanup, and the correctness checks (metres, Y-up, node names, animations, PBR fidelity, alpha modes, vertex colors) that must not regress. Not for editing or modelling geometry, authoring or laying out a scene, generating 3D from text or images, converting from FBX/OBJ/CAD, or debugging a runtime frame rate that has nothing to do with asset size.
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 (screenshots, Figma exports, design mockups) to help implement matching UI. Use when the user shares a screenshot, design image, or asks to "match these colors", "extract colors from this image", "implement this design", or "get the color palette".
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 text from images using OCR. Use when the user shares a screenshot and you need to read the text content, copy UI labels, or extract copy from a design mockup.
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 this skill whenever the user wants to create, read, edit, or manipulate Word documents (.docx) or Word templates (.dotx). Triggers include: any mention of 'Word doc', 'word document', '.docx', '.dotx', or requests to produce professional documents with formatting like tables of contents, page numbers, or letterheads. Also use when extracting or reorganizing content from .docx or .dotx files, inserting or replacing images in documents, find-and-replace in Word files, working with tracked changes or comments, or converting content into a polished Word document. If the user asks for a 'report', 'memo', 'letter', 'template', or similar deliverable as a Word or .docx file (to download, email or print), use this skill. However, if they ask for a document, page, report, memo, or notes WITHOUT naming a file format and the session offers a dedicated document or page skill or connector, use that instead. Do NOT use for PDFs, spreadsheets, Google Docs, or coding unrelated to document generation.
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 this skill when a file has been uploaded but its content is NOT in your context — only its path at /mnt/user-data/uploads/ is listed in an uploaded_files block. This skill is a router: it tells you which tool to use for each file type (pdf, docx, xlsx, csv, json, images, archives, ebooks) so you read the right amount the right way instead of blindly running cat on a binary. Triggers: any mention of /mnt/user-data/uploads/, an uploaded_files section, a file_path tag, or a user asking about an uploaded file you have not yet read. Do NOT use this skill if the file content is already visible in your context inside a documents block — you already have it.
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 this skill whenever the user wants to do anything with PDF files. This includes reading or extracting text/tables from PDFs, combining or merging multiple PDFs into one, splitting PDFs apart, rotating pages, adding watermarks, creating new PDFs, filling PDF forms, encrypting/decrypting PDFs, extracting images, and OCR on scanned PDFs to make them searchable. If the user mentions a .pdf file or asks to produce one, use this skill.
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 wants to turn a static scene, character images, aerial map, drawn path, or route-control image into an immersive first-person FPV AI video prompt, especially Seedance/Kling/Runway/Veo style one-shot videos with numbered stop markers, red-line path control, world-map flythroughs, camera route planning, variable character counts, non-human POVs such as drones, pets, robot vacuums, character references, timed interactions, dialogue, spatial audio, and negative constraints.
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 2D orthographic wireframe PNG drawings to 3D Blender models exported as glTF/GLB. Use this skill whenever the user provides wireframe images (technical drawings, line drawings, orthographic views, side/front/back panels) and wants to generate a 3D model, mesh, or .glb file. Triggers on phrases like "convert this wireframe to 3D", "make a 3D model from these drawings", "build a model from this wireframe", "generate GLB from these views", or any image-to-3D-mesh request involving line drawings. Make sure to use this skill even if the user does not explicitly say "wireframe" — also covers "orthographic views", "technical drawings", "line drawings of objects", "front and side views". Requires a configured Blender MCP backend (official or ahujasid) and Python with opencv-python, numpy, scipy installed.
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。
Measure and validate supplied reference images, wireframes, texture atlases, and Blender renders before declaring a reconstruction 1:1. Use when an asset must match a template, when visual feedback says the output is off, when part counts must be exact, or before exporting a brand mascot/logo reconstruction. Pairs with reference-to-3d, contour-to-mesh, orthographic-registration, atlas-uv-fitting, and Blender MCP.
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。
Calibrate Blender materials, lighting, camera crop, emission/glow, color management, and aura/HUD styling against supplied original/reference images using measurable color, brightness, saturation, bbox, and mask statistics. Use when a product must match the lighting/look of a source image or when audits say the product is too bright, desaturated, wrong hue, wrong glow, or wrong silhouette extent.
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。
Segment overlapping visual parts from source images, wireframes, texture atlases, and decals before mesh reconstruction. Use when a mascot/logo/template contains touching or overlapping components and exact structural part masks are needed before contour-to-mesh, UV fitting, or landmark repair.
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 2D orthographic wireframe PNG drawings to 3D Blender models exported as glTF/GLB. Use this skill whenever the user provides wireframe images (technical drawings, line drawings, orthographic views, side/front/back panels) and wants to generate a 3D model, mesh, or .glb file. Triggers on phrases like "convert this wireframe to 3D", "make a 3D model from these drawings", "build a model from this wireframe", "generate GLB from these views", or any image-to-3D-mesh request involving line drawings. Make sure to use this skill even if the user does not explicitly say "wireframe" — also covers "orthographic views", "technical drawings", "line drawings of objects", "front and side views". Requires the Blender MCP addon to be running (port 9876) and Python with opencv-python, numpy, scipy installed.
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。
Detect graphic trademarks in product images by comparing against registered trademark databases across 15 major trademark offices using YOLO-based object detection and visual similarity. Triggered when users mention graphic trademark detection, logo infringement check, trademark similarity analysis, image trademark risk assessment, product image trademark screening, or logo detection. Even if the user does not explicitly say "trademark detection," this skill should be triggered whenever the need involves comparing a product image against registered graphic trademarks or assessing trademark infringement risk.
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。
Nexscope Ozon Russia SKU full detail query for one product ID per call, returning price, discount, Ozon Card price, rating, review count, stock, sales, revenue, revenue potential/lost revenue, listing date, images, and the complete product card. Trigger for Ozon product or SKU detail, price/rating/sales/stock checks, competitor Ozon cards, Nexscope Ozon detail, or Russian marketplace product detail by a known SKU.
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 and extract information from images using multimodal AI recognition. Triggered when users want to analyze, describe, or extract information from an image URL — image recognition, image analysis, image description, visual content understanding, OCR text recognition, or visual Q&A. When a user provides an image URL and asks questions about its visual content, this skill should be triggered even if they do not explicitly say "image recognition."
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 detailed Amazon product information by ASIN, including title, Item Highlights when extractable from raw HTML, images, bullet points, specifications, A+ content, pricing, ratings & reviews, variants, and more. Trigger when users mention Amazon product details, ASIN lookup, Item Highlights, product page data, listing analysis, bullet point extraction, product image retrieval, variant inspection, competitor listing research, price lookup, review breakdown, product specification query, or similar terms. Even if the user does not explicitly say "product details," trigger this skill whenever the request involves retrieving structured data from Amazon product pages via ASIN.
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。
Deepfake detection and media safety — detect AI-generated audio, images, video, and text, trace synthesis sources, analyze media intelligence, and tell people from AI agents on a website (Agent Detection) using direct Resemble AI API calls
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 Docker containers, images, volumes, networks, and Compose stacks — lifecycle ops, debugging, cleanup, and Dockerfile optimization.
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 requests diagrams, flowcharts, sequence diagrams, class diagrams, component diagrams, ER diagrams, architecture charts, or visualizations — including generating a diagram from existing source code, or rendering/extracting PlantUML embedded in a Markdown file to images (e.g. preparing docs for Confluence/Notion). Also use proactively when explaining systems with 3+ components, APIs, data flows, or class hierarchies. Generates .puml files and exports to PNG/SVG via Kroki API (no local install required).
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。
Multi-platform AI image generation via DashScope/Ark/Hunyuan/Zhipu/StepFun plus Grok/OpenAI/Gemini/FLUX (international), specializing in Chinese text rendering and photorealistic 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。