「video」の検索結果

Amazon product detail page scraper: extract full product data from any open Amazon product detail URL (any /dp/{asin} or /gp/product/{asin} page across all Amazon regional TLDs) — returns asin, url, title, brand, price, listPrice, stars, reviewsCount, starsBreakdown (5/4/3/2/1 star percentages), answeredQuestions, inStock, inStockText, delivery, fastestDelivery, returnPolicy, breadCrumbs, features (bullet points), description, bookDescription, thumbnailImage, highResolutionImages, galleryThumbnails, productOverview (Brand/Model/etc.), attributes (tech spec table), attributesMapped (flat key-value), bestsellerRanks (rank + category + url), variantAttributes (currently selected color/size/style), variantAsins, seller (name + id + url), isAmazonChoice, amazonChoiceText, monthlyPurchaseVolume, hasAPlusContent, hasBrandStory, aiReviewsSummary, reviewsLink, productPageReviews (sample), videosCount, locationText, loadedCountryCode. Works on amazon.com, amazon.co.uk, amazon.de, amazon.co.jp, amazon.fr, amazon.it, amazon.es, amazon.ca, amazon.com.au, amazon.in, 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 product page, Amazon /dp/, Amazon dp URL, Amazon ASIN scraper, Amazon product detail, Amazon PDP, Amazon product data, Amazon product info, Amazon product fields, Amazon product attributes, Amazon full field extraction, Amazon per-ASIN enrichment, Amazon rating breakdown, Amazon stars breakdown, Amazon bestseller rank, Amazon BSR, Amazon variants, Amazon variant ASINs, Amazon color size options, Amazon feature bullets, Amazon A+ content, Amazon brand story, Amazon AI review summary, Amazon bought in past month, Amazon monthly sales volume, Amazon Amazon's Choice badge, Amazon seller info, scrape Amazon product, enrich Amazon ASIN, Amazon ASIN details, Amazon product review data. Also applies to bulk ASIN enrichment from a list of URLs, competitive product research, brand catalog audits, price and stock monitoring per ASIN, and building a normalized product dataset from a list of Amazon URLs.

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Build continuous-shot motion-driven HTML stories from a subject or narrative, including research, story structure, consistent AI stills, Seedance scene and connector clips, interface styling, media normalization, and browser QA. Use for immersive journeys, timelines, product stories, fictional worlds, or other scene-based visual narratives whose camera should flow from beginning to end; not for ordinary autoplay video pages or general web apps.

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Parse and understand an HTTP(S) URL or an authorized local document, audio, or video source through Cue Omni Reader when the Agent has the official Omni MCP tools.

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。

Export a Remotion video

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Preview a Remotion 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。

Create a new Remotion 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。

Comprehensive OSINT methodology for external red-team operations and authorized attack-surface assessments. Covers the 5-stage recon pipeline (seed discovery, asset expansion, enrichment, exposure analysis, reporting), asset-graph discipline with 29 asset types, severity rubric (CRITICAL/HIGH/MEDIUM/LOW/INFO), confidence upgrade workflows, time budgeting, asset-level triage rules, scale-based tactics, identity-fabric mapping (Entra/Okta/ADFS/Google/SAML/M365 Teams+SharePoint+OAuth), API and auth-map methodology, JavaScript deep analysis, mobile attack surface, cloud attack surface, breach×identity correlation, detectability tagging, detection-aware probing (back-off, persona rotation), read-only validator discipline, WAF/CDN bypass + origin discovery, vulnerability prioritization (CVE/EPSS/KEV), phishing infrastructure planning + pretext development, bug bounty submission templates, client deliverable templates with risk translation, threat-actor investigation (incl. RU/CN pivots), cryptocurrency tracing, image/video forensics, chronolocation. Use when planning or executing reconnaissance against authorized targets, mapping an organization's external attack surface, investigating a person/entity, tracing crypto flows, geolocating media, or performing attribution work.

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Repurpose existing content into a native LinkedIn post. Take a tweet, thread, YouTube video, blog, or newsletter and rebuild it for LinkedIn: re-hook before the fold, expand to the 900 to 1300 char sweet spot, add whitespace and a CTA, move links to the first comment, run the humanizer, publish via Publora on approval. Not for writing from scratch (use linkedin-post-writer), not for auditing a draft (use linkedin-humanizer --mode audit).

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。

Transform one long-form piece into multiple platform-specific content derivatives including LinkedIn posts, tweet threads, email snippets, ad hooks, and video scripts while maintaining voice consistency. Use when given a blog post, article, or pillar content to atomize across channels. Platform: Google and Meta.

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。

NYT-newsroom typography, staggered reveal animation, and editorial-grade charts (line, bar, or range band).

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。

Electric split with hand-drawn script — offset panels slide in, display title rises with an outlined word, script strokes itself in.

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。

Luxury-minimal whitespace hero — single word reveals letter by letter, warm-gold hairline, breathing indicators.

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。

Swiss-grid statistic anchor — giant number, red accent, growing bars, black data bar. Rational and editorial.

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A native Remotion data frame — bars grow from zero by real data via spring physics while the figures roll 0→target in sync. The numbers come alive in a way a static HTML chart can't.

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。

Bold colored card on a dark gradient — big section number, nav breadcrumb, orange card sliding in, title rising.

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。

Soft-tech radial node graph as art — frosted rounded card, curved links drawing in, nodes popping outward, gentle float.

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。

Film light leaks, grain, 16:9 letterbox, and large serif type for cinematic openings or chapter cards.

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。

A 1970s European editorial poster in motion — a red rule draws across, a giant tilted figure drops in, a three-line headline rises line-by-line, an italic serif standfirst fades.

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。

Two-panel split with quote as hero — white/blue panels open from center, accent bar grows, quote reveals line by line.

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 writing code that calls the Gemini API for text generation, multi-turn chat, multimodal understanding, image generation, video generation, speech generation (TTS), voice design, voice replication, streaming responses, background research tasks, function calling, structured output, or migrating from the old generateContent API. Covers SDK usage and best practices for Gemini models and agents in Python and TypeScript.

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Use this skill when building real-time, bidirectional streaming applications with the Gemini Live API, or migrating legacy Live models (2.0/2.5/3.1) to Gemini 3.8 Live. Covers WebSocket-based audio/video/text streaming, voice activity detection (VAD), background reasoning (extended thinking), asynchronous function calling, session management, ephemeral tokens, live transcription, and live translation. SDKs covered - google-genai (Python), @google/genai (JavaScript/TypeScript).

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Turn almost any file into Markdown plus metadata — PDF, Office, HTML, email, archives, images, audio/video, 1000+ formats — powered by Apache Tika, either via the tika-app CLI (zero setup, one file) or a running tika-server (curl, warm process, many calls). Leads with rmeta (structured, embedded-item-aware output) as the default operation rather than flat concatenated text, since you can't tell whether a file has embedded content from its extension. Covers metadata-only triage, type and language detection, OCR, and output-size discipline for agent context. Use whenever a task involves reading the content of a file whose format you don't want to hand-parse.

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Audit, diagnose, and safely operate connected TikTok Ads accounts through the NotFair MCP. Use for TikTok advertising, Spark Ads, short-form video ads, campaign or ad-group analysis, spend, conversions, targeting, creative, budgets, bids, creator briefs, campaign setup, or approved TikTok Ads changes.

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Use when the user wants to validate a product idea, find pain points, mine demand signals, discover objections, or gather voice-of-customer language from Reddit, social posts, video transcripts, and comments. Produces evidence-backed product research.

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。