'image search' 검색 결과

Image SEO audit — make a site's images discoverable in Google Images and stop them from dragging down Core Web Vitals. Audits alt text quality and coverage, descriptive file names, modern formats (WebP/AVIF), dimensions and compression, responsive srcset, lazy-loading, explicit width/height (CLS), image sitemaps, and ImageObject structured data. Use this skill whenever the user asks about image SEO, alt text, Google Images ranking, image optimization for search, image file size hurting page speed, WebP/AVIF, or image structured data. Trigger on: "image SEO", "alt text", "rank in Google Images", "optimize my images for search", "image alt tags", "WebP", "image compression SEO", "image sitemap", "ImageObject schema", or any image-discoverability / image-performance question. For overall page speed use /seo-analysis; for generating new images use the brand image skills.

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Guides image classification and search with OpenAI's CLIP by comparing images with text descriptions, without additional training. Includes batch processing, image-text matching, and category-based checks for potentially inappropriate images.

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• Classify images using text labels without additional training
• Search an image collection with natural-language descriptions
• Compare how closely images match text descriptions

Searches X (Twitter) for tweets by free-form advanced query and returns a normalized tweet list with text, author profile, engagement counts, media, hashtags, mentions, and cursor for pagination. Use when user mentions X search, Twitter search, scrape tweets by keyword, search X by hashtag, search Twitter by hashtag, find tweets about a topic, search tweets by date range, since until search Twitter, advanced Twitter search, tweets with media, tweets with images, tweets with video, top tweets, latest tweets, X latest tab, X top tab, min_faves, min_retweets, min_replies, filter verified Twitter, filter blue verified, from user Twitter, to user Twitter, mentioning user Twitter, geocode Twitter search, near location Twitter, lang code search Twitter, exclude retweets, exclude replies, collect tweets at scale, bulk tweets export, monitor brand mentions on X, monitor brand mentions on Twitter, competitor mention monitoring, KOL tweet harvesting, twitter keyword scraper, tweet keyword scraper, search X by keyword, search twitter, search x.com, x.com search, twitter advanced search, x advanced search, tweet collection by query, scrape twitter search results. Also applies to time-bounded research, sentiment analysis input gathering, hashtag campaign tracking, geo-targeted tweet collection, and any paginated bulk tweet collection driven by a search query.

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Audit website images for search visibility and loading performance, then prioritize fixes. Check descriptive text, file sizes, and loading settings, convert files to WebP or AVIF, and add creator and copyright metadata.

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• Audit product and article images for SEO issues
• Convert large images to WebP or AVIF
• Fix image loading settings and layout shifts

Walmart keyword search scraper: input a search keyword and page number, navigate to walmart.com search results, extract paginated product listings with itemId, url, title, brand, image, price, wasPrice, rating, reviewCount, availability, seller info, fulfillmentBadge, classType, and shortDescription. Use when user mentions walmart search, walmart keyword search, search walmart products, scrape walmart search results, walmart search scraper, walmart product search, search items on walmart, walmart search by keyword, walmart product listing, get walmart search data, extract walmart products, walmart search results scraper, walmart shop search, walmart catalog search, walmart product list by keyword, walmart browse by keyword. Also applies to price comparison research on walmart, finding walmart product URLs in bulk, monitoring walmart search rankings, collecting walmart product data by category keyword.

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Build image classification and search workflows by comparing images with text descriptions. Covers classification without additional training, similarity scoring, batch processing, and storing image representations in a vector database for retrieval.

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• Classifying images without additional training
• Searching image collections with text descriptions
• Comparing images with candidate descriptions

Perform image-based product search on the 1688 platform. Use an image URL to find visually similar supplier products, returning title, price, minimum order quantity, monthly sales, repurchase rate, trade score, and seller identity. Triggered when users mention 1688 image search, 1688 visual search, find supplier by image, reverse image search 1688, 1688 product sourcing by image, or Alibaba 1688 image search. Even if the user does not explicitly mention "image search," this skill should be triggered whenever a user provides an image URL and wants to find matching or similar products on the 1688 wholesale platform.

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Search Xiaohongshu (XHS / RedNote) notes by keyword with full field extraction including body text, topics/tags, image list URLs, video stream URL, publish timestamp, and all engagement stats (likes, collects, comments, shares). Supports all page filter options: sort order (general, latest, most liked, most commented, most collected), note type (image-text, video), publish time range (within 1 day, 1 week, 6 months), search scope (seen, unseen, followed), and location distance (same city, nearby). Use when user mentions search xiaohongshu notes, xhs keyword search, rednote note search, search xiaohongshu posts, scrape xhs search results, xiaohongshu note discovery, rednote content search, collect xhs notes by keyword, xiaohongshu topic search, xhs note body text, xiaohongshu video notes, xiaohongshu image notes, rednote post filter, xhs search with filters, xiaohongshu full note data, xhs note details from search, extract rednote posts, xiaohongshu content monitoring, xhs search scrape.

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Find public web images from a text query and return image URLs with their source pages. Use the results to select visuals for documents, feeds, or creative references, with checks for image links used in deliverables.

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• Find image candidates for a document
• Collect visual references for a design
• Find remote image URLs for a feed

Build image generation workflows in Python with Stable Diffusion and HuggingFace Diffusers. The guide covers text prompts, transforming existing images, replacing selected regions, controlling composition, applying learned styles, and adjusting memory use during generation.

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• Generating images from text descriptions
• Changing the style of an existing image
• Replacing a selected region of an image

Etsy keyword search scraper: given a search keyword and optional page number, returns paginated product listings with listingId, shopId, title, url, image, salePrice, originalPrice, currency, rating, reviewCount, shopName, isAd, freeShipping, badge from etsy.com search results. Use when user mentions Etsy, etsy.com, Etsy search, search Etsy by keyword, scrape Etsy listings, extract Etsy products, Etsy product search, Etsy marketplace search, find products on Etsy, Etsy handmade search, Etsy vintage search, Etsy craft search, bulk Etsy product export, Etsy price monitoring, Etsy competitor research, Etsy top listings, Etsy bestseller extraction, Etsy sales data, Etsy shop discovery via keyword. Also applies to trend research on handmade or craft niches, competitor keyword ranking on Etsy, sourcing Etsy suppliers by product type, and any paginated bulk product collection driven by a search keyword.

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Perform visual similarity search for design patents using an image URL, with filtering by country, legal status, date ranges, Locarno classification, and assignee. Supports design patent types only (type D). Triggered when users mention patent image search, design patent search, search patent by image, visual patent lookup, patent similarity detection, patent image matching, or design patent infringement check. Even if the user does not explicitly mention "patent image," this skill should be triggered whenever the need involves searching for similar design patents through an image. For utility model patents, use ecommerce.Nexscope-utility-patent-image-search instead.

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Perform image-based visual product search on Amazon across 8 marketplaces. Use an image URL to find visually similar products, with optional Nexscope enrichment for sales data. Triggered when users mention Amazon image search, Amazon visual search, find similar products on Amazon, reverse image lookup Amazon, Amazon search by photo, or competitor image search. Even if the user does not explicitly mention "image search," this skill should be triggered whenever a user provides an image URL and wants to find visually matching or similar products on Amazon.

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Find reference images or photos for documents, pages, and presentations, then save selected files locally. Check that each file opens and report its location, dimensions, source, and stated license so the results are ready to review.

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• Gather reference images
• Find photos for documents or presentations
• Save selected web images locally

Build image captioning, visual question answering, and text-based image search with BLIP-2. The skill guides model selection, batch processing, and memory optimization, with examples for understanding images without training for each specific task.

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• Generate captions for multiple images
• Build a question-answering system for images
• Search an image collection using text

Automate All Images AI operations through Rube MCP, a connection for agent tools. The skill guides tool discovery, connection checks, and execution using the input requirements returned by Composio.

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• Discovering available All Images AI operations
• Checking the service connection before execution
• Running multiple operations in a workflow

Automate All Images AI operations through Rube MCP, a connection for agent tools. The skill guides tool discovery, connection checks, and execution using the input requirements returned by Composio.

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• Discovering available All Images AI operations
• Checking the service connection before execution
• Running multiple operations in a workflow

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.

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• Build a chatbot that answers questions about images
• Write detailed descriptions of photos
• Ask follow-up questions about the same image

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.

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Use when the user wants a research-paper figure Skill Factory: build, patch, package, or use reusable specialized paper-figure-making skills from lawful literature/corpus evidence. Generated skills must use a specialized-skill-first workflow, full-feasible local PDF coverage where available, startup-plan-only first replies, target-paper candidate/final image isolation, mandatory image-embedded visual-structure explanation, mandatory non-target concept/modeling example display for abstract visual decisions, saved subtype/style illustration atlases, ChatGPT web Create image / ChatGPT Images 2.0 rendering, Codex $imagegen-first rendering, sample-image transfer rules, all-step/current-position state footers, and a mandatory first-round diverse candidate board followed by P6b/P6c paper-local best-practice optimization before final prompt construction.

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Classify images using written labels without additional training data, or find images that match a text query. This skill provides CLIP workflows for image-text similarity, batch processing, and storing searchable image representations in vector databases.

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• Classify photos using written category labels
• Find images by describing their content
• Compare images with candidate descriptions

Build image captioning, visual question answering, and text-based image search with BLIP-2. The skill explains model selection, batch processing, and memory-saving settings, with Python examples for connecting image understanding to language models.

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• Generating captions for multiple images
• Building a question-answering system for images
• Searching an image collection with text

This skill helps users automatically extract structured image data from Google Images via BrowserAct API. Agent should proactively apply this skill when users express needs like finding images for specific keywords, gathering product style images for competitors, building visual datasets at scale, scanning visual search results for market research, tracking localized image trends by country, compiling related image thumbnails and links, extracting image titles and source logos, fetching click through URLs from image results, monitoring competitor visual assets, sourcing creative content for specific topics, looking up product pictures in different regions, collecting structured image metadata without opening detail pages.

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Amazon search and category listing scraper: extract product listings from any Amazon search results page, keyword search URL, or category browse page and return per-item cards (asin, title, url, image, price, listPrice, stars, reviewCount, badges, isAmazonChoice, isBestSeller, isSponsored, delivery, boughtInPast, positionIndex) plus pagination state (currentPage, hasNextPage, nextPageUrl, totalResultsApprox). Works across all Amazon regional TLDs (amazon.com, amazon.co.uk, amazon.de, amazon.co.jp, amazon.in, amazon.fr, amazon.ca, amazon.com.au, amazon.it, amazon.es, amazon.com.mx, amazon.com.br, amazon.nl, amazon.se, amazon.sg, amazon.ae, amazon.sa, amazon.pl, amazon.tr, amazon.eg). Use when user mentions Amazon, amazon.com, Amazon SERP, Amazon search results, Amazon keyword search, Amazon category page, Amazon subcategory URL, scrape Amazon listings, extract Amazon search, Amazon product cards, Amazon browse page, Amazon /s URL, Amazon /s?k= URL, Amazon /b/ category URL, Amazon /gp/browse category, Amazon bbn category filter, Amazon rh filter, Amazon search pagination, Amazon 7 pages limit, Amazon subcategory scraper, Amazon keyword scraper, Amazon SERP scraper, Amazon by URL, Amazon by category, Amazon by keyword. Also applies to price monitoring across a keyword or category, competitive listing tracking on Amazon, brand keyword ranking on Amazon, category catalog audits, best-of category discovery from a search, paginated bulk extraction of Amazon search hits, and building ASIN lists from a search.

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