「image search」の検索結果

Use when the user needs engineering or research-paper figures: system architecture diagrams, algorithm workflows, hardware schematics, benchmark charts, ablation plots, figure briefs, redraws, image edits, or exact publication-style plots from numeric data. Covers Gemini, Nano Banana, OpenAI image models, and local plotting workflows.

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Create, inspect, convert, apply, replace, verify, troubleshoot, update, publish, or restore reversible CodeDrobe themes for supported Chromium/Electron AI desktop apps, including OpenAI Codex, Tencent WorkBuddy, QoderWork (CN/global), and TRAE SOLO (global/CN). Use when a user asks to turn an attached or local reference image into a Codex, WorkBuddy, QoderWork, or TRAE SOLO skin, reproduce a visual style with native UI, build a multi-app theme, work with .codedrobe-theme packages or theme images, analyze a live CDP DOM snapshot, repair DOM compatibility failures, use a custom port or installation path, capture verification screenshots, search the CodeDrobe store or install a store theme by name, publish or submit a theme to the CodeDrobe store, or migrate a legacy .codex-theme file.

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 an agent needs production image or video understanding with Amazon Rekognition: labels, objects, scenes, OCR, moderation, image properties, Custom Labels or moderation adapters, stored-video analysis, conditional streaming-video workflows for existing eligible accounts, searchable media libraries, confidence evaluation, S3/IAM/event architecture, privacy, biometric consent, cost control, lifecycle management, and 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。

Ultimate MCP server for Civitai — search models, browse top images with prompts, download LoRAs/Checkpoints, analyze trends. Use when user asks about AI models, LoRAs, checkpoints, Stable Diffusion, Flux, image generation prompts, or Civitai.

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。

SERP-informed outline generation with H2/H3 heading hierarchy, competitive content gap analysis, section-by-section word count targets, chart and image placement markers, FAQ question planning, and internal linking zones. Skeleton only: structure, H2/H3 hierarchy, word counts, FAQ slots. Use blog-brief instead if you need full competitive analysis, statistics research, and image suggestions. Lighter than a full content brief, generates article skeleton and structure only, ready for /blog write to consume. Use when user says "outline", "blog outline", "content outline", "structure blog", "plan sections", "article skeleton", "heading structure", "SERP analysis", "competitive outline", "plan article".

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 a short motion-graphics video ad — a 15s Facebook/Instagram/TikTok spot — as a rendered MP4. Researches the destination landing page so every on-screen claim matches it, pulls real product imagery off the live site, animates one paused GSAP timeline in a single HTML page, and renders it frame-accurately through Playwright and ffmpeg. Use this whenever the user wants a video ad, promo video, animated ad, "an ad for my product", Meta/TikTok/Reels creative in video form, a 9:16 or 1:1 cut of an existing spot, or a re-render of an earlier ad with a different hook, hero or CTA — including when they never say the words "motion graphics". Not for animating UI inside an app, not for static image ads, and not for long-form or voiceover-led video.

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 design patent infringement risks by comparing a product image against a global design patent database across 25+ jurisdictions. Triggered when users mention design patent detection, patent infringement check, patent risk analysis, TRO case lookup, design patent search, product design patent screen, or visual patent similarity. Even if the user does not explicitly say "design patent," this skill should be triggered whenever the need involves checking whether a product image may infringe existing design 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 visual or keyword-derived competitors from a product image or URL across supported marketplaces. Use this skill when the user asks for this exact ecommerce workflow, analysis, data lookup, or deliverable.

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。

See, Understand, Act on video and audio. See- ingest from local files, URLs, RTSP/live feeds, or live record desktop; return realtime context and playable stream links. Understand- extract frames, build visual/semantic/temporal indexes, and search moments with timestamps and auto-clips. Act- transcode and normalize (codec, fps, resolution, aspect ratio), perform timeline edits (subtitles, text/image overlays, branding, audio overlays, dubbing, translation), generate media assets (image, audio, video), and create real time alerts for events from live streams or desktop capture.

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Build and query a local Markdown knowledge base ("vault"). TWO functions — (1) CONVERT raw files (PDF, Word/docx, PowerPoint/pptx, Excel/xlsx, csv/tsv, images, html, md/txt, json/yaml/code, audio/video) into clean Markdown with retrieval-friendly frontmatter; local-first (pandoc / python-pptx / openpyxl / pymupdf4llm / whisper), with cloud OCR (MinerU) only as a fallback. (2) ANSWER questions over the resulting vault with retrieval discipline — self-monitor coverage, flag missing/lossy content, and propose Maps-of-Content (MOCs). Triggers: "build/sync my local knowledge base", "convert these files to markdown for AI", "整理我的资料库", "把文件转成 md 给 AI 读", "本地知识库", "读我的本地 vault 回答", "这个主题我的资料里怎么说". Not for: one-off web research, or files that are already in a single doc you can read directly.

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 coding skill for the full MapTiler platform — Cloud REST APIs, MapTiler SDK JS (built on MapLibre GL JS), native mobile SDKs, on-premise infrastructure, and vector tile schemas. USE WHEN the user wants to add a map to a web or mobile app, show locations or routes, display geographic data, build a store locator, create data visualizations on maps, add geocoding or address search, do reverse geocoding, look up elevation, do IP geolocation, fetch weather data, transform coordinates between CRS/EPSG, embed static map images, work with vector tilesets (Planet v4, Buildings, Contours, Outdoor, Ocean, Landcover, Cadastre), write MapLibre style expressions or data-driven styling, render 3D terrain, globe view, or 3D buildings extrusion, use satellite imagery, add markers, popups, heatmaps, or clustering, integrate maps in React, Vue, Svelte, Angular, Next.js, Leaflet, OpenLayers, Cesium, or deck.gl, build native maps on iOS (Swift), Android (Kotlin), Flutter, or React Native, self-host with MapTiler Server or generate tiles with MapTiler Engine CLI, or upload/manage tilesets via the Admin API. Also USE WHEN the user mentions MapLibre GL JS — the SDK extends MapLibre with built-in cloud services, helpers, and session billing.

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Integrates Amazon Location Service APIs for AWS applications. Use this skill when users want to add maps (interactive MapLibre or static images); geocode addresses to coordinates or reverse geocode coordinates to addresses; calculate routes, travel times, or service areas; find places and businesses through text search, nearby search, or autocomplete suggestions; retrieve detailed place information including hours, contacts, and addresses; monitor geographical boundaries with geofences; or track device locations. Covers authentication, SDK integration, and all Amazon Location Service capabilities.

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。

Reads, searches and counts Claude Code session transcripts (the .jsonl files under ~/.claude/projects, subagents too) with the csift command-line tool, and ships hooks that re-inject compacted turns or record pending questions. Use when asked what a past or running session said, did, edited, spent or is doing now; when about to grep, jq or script over a transcript jsonl (a type=="user" filter overcounts the person's messages), count how many messages the person typed, tool calls or tokens, or claim something was never done or decided; to rebuild a lost file or plan from what a session wrote, extract pasted images, restore turns a compaction summary lost, find a subagent's own id when CLAUDE_CODE_SESSION_ID names the parent, wait on or message another session or subagent; or when csift prints "unknown label selector", "show needs an address", "looks like a session/agent id, not a project path" or "0 matches — a DEFINITIVE absence". Not for semantic or embedding search: csift matches regular expressions only.

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。

End-to-end AI website generation pipeline. Claude Opus 4.8 emits Bolt-style <boltArtifact> envelopes (multi-file, plan-first) that customize Vite+React+Tailwind templates from pre-researched business data. Pre-research via APIs, media acquisition, brand extraction, visual inspection via GPT Image 2 vision, R2 upload (per-file content-type by extension), D1 status updates. Supports all business types: SaaS, portfolio, non-profit, restaurant, salon, medical, legal, retail, tech.

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。

SERP-informed outline generation with H2/H3 heading hierarchy, competitive content gap analysis, section-by-section word count targets, chart and image placement markers, optional FAQ question planning, and internal linking zones. Skeleton only: structure, H2/H3 hierarchy, word counts, optional FAQ slots. Use blog-brief instead if you need full competitive analysis, statistics research, and image suggestions. Lighter than a full content brief, generates article skeleton and structure only, ready for /blog write to consume. Use when user says "outline", "blog outline", "content outline", "structure blog", "plan sections", "article skeleton", "heading structure", "SERP analysis", "competitive outline", "plan article".

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 ad creatives (images and videos) extracted from competitor research. Use when given a directory of ad images, video files, or transcripts to evaluate ad quality, score visual and messaging effectiveness, assign a scale score for viral/engagement potential, and generate a cross-creative pattern summary. Triggered by requests like "analyze these ads", "score these creatives", "what hooks are competitors using", "evaluate the ad library", "give me a scale score", "analyze the ad folder", or "what's working in these ads".

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。

Automate All Images AI tasks via Rube MCP (Composio). Always search tools first for current schemas.

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。

Autonomous experiment loop that optimizes any file by a measurable metric. Inspired by Karpathy's autoresearch. The agent edits a target file, runs a fixed evaluation, keeps improvements (git commit), discards failures (git reset), and loops indefinitely. Use when: user wants to optimize code speed, reduce bundle/image size, improve test pass rate, optimize prompts, improve content quality (headlines, copy, CTR), or run any measurable improvement loop. Requires: a target file, an evaluation command that outputs a metric, and a git repo.

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。

Automate All Images AI tasks via Rube MCP (Composio). Always search tools first for current schemas.

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-level biomedical imaging covering X-ray, CT, MRI, ultrasound, nuclear medicine, optical imaging, and image processing for clinical and research applications.

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。

OCR scanned PDFs and image files into searchable text using pdftoppm + tesseract (or ocrmypdf if available). Load when about to OCR a scanned PDF or image, or when extracted PDF text is empty/garbled.

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。

- End-to-end workflow to establish where and when a photo or video was captured and whether it is authentic — evidentiary handling, metadata extraction, reverse image search for provenance, visual geolocation, chronolocation from shadows, and manipulation… Use when asked to verify where an image was taken, confirm or refute a claimed location or date, or authenticate media before relying on 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。

Automate All Images AI tasks via Rube MCP (Composio). Always search tools first for current schemas.

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。