「search skill」の検索結果

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。

Nexscope Product Database multi-condition filtering. Filter Amazon products by category, price, sales volume, revenue, reviews, rating, weight, BSR rank, LQS, seller type, and more across 10 marketplaces. Trigger when users mention Amazon product selection, product database filtering, BSR rank filtering, category-based product discovery, high-rating low-competition products, FBA product search, Amazon product discovery, or similar terms. Even if the user does not explicitly mention "Nexscope" or "product database," trigger this skill whenever the request involves filtering Amazon products by multiple criteria or discovering potential products.

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。

Initiate natural language Q&A through Amazon's storefront Alexa shopping assistant to get shopping guidance answers, recommended product groups, ASIN lists, and follow-up questions. Each call supports only 1 prompt; for follow-ups, the agent must summarize context and concatenate a new question for a new request. A url can be used to supplement Amazon page context. Trigger when the user mentions Amazon Alexa, Alexa shopping assistant, Amazon smart assistant, AI shopping guide, conversational product selection, natural language shopping, Amazon chat Q&A, Amazon Alexa shopping, conversational shopping, AI shopping assistant, follow-up questions, product recommendation conversation, context follow-ups. Even if the user does not explicitly mention "Alexa", if their need is to "ask for product recommendations on Amazon using natural language", this skill should also be triggered.

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。

Query 1688 product bestseller billboard data for sourcing discovery and wholesale product research. Trigger when the user mentions 1688 product rankings, 1688 bestseller lists, wholesale hot products, domestic sourcing rankings, dropshipping product selection, 1688 trending products, bulk purchase popular items, supplier product rankings, 1688 billboard, 1688 bestsellers, sourcing rankings, wholesale hot products, trending product rankings, supplier rankings, 1688 trends. Even if the user does not explicitly mention "1688 billboard", if their need involves discovering hot-selling wholesale products or sourcing opportunities on the 1688 platform, this skill should also be triggered.

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。

Route Amazon product-selection work across market, keyword, competitor, trend, review, and profitability capabilities. 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。

Fetch and analyze Amazon product reviews by ASIN, supporting 15 marketplaces (including US) with star rating filtering. Triggered when users mention Amazon reviews, US reviews, product reviews, buyer complaints, negative reviews, positive reviews, star ratings, review analysis, review sentiment, product improvement suggestions, Vine reviews, verified purchase reviews, competitor review research, Amazon reviews, US reviews, Amazon.com reviews, product feedback, negative review analysis, positive review analysis, star rating filter, review sentiment analysis, product improvement insights, Vine reviews, competitor reviews, customer feedback. Even if users do not explicitly say "reviews", this skill should be triggered whenever the task involves reading, filtering, or analyzing Amazon product customer reviews.

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。

Close the learning loop — record reward signal for an action AgentDB suggested. Use after using anything from agentdb_pattern_search / reflexion_recall / skill_search / learning_route. The bandit needs the signal to improve.

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。

Designing search engines and vector DBs for full-text, vector, and hybrid retrieval, including permission-aware retrieval for multi-tenant or per-role corpora. Use for search design, index optimization, the RAG retrieval layer, or deciding where ACL filtering belongs in the query path.

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。

Conducting user research: interview guides, usability test plans, qualitative analysis, persona creation, journey mapping. Use when research design or analysis is needed; complements Echo.

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。

Triggers when researching competitive or professional positioning: market intelligence, engineer brands, profiles, and content strategy. Research and strategy only — not code.

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。

Plan how to build with You.com. Use when adding web search, content extraction, or research to an app or agent: finds the right You.com API, MCP server, SDK, or docs path (for example Vercel AI SDK, LangChain, Python SDK, or REST) through the `you-discover` tool and Docs 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。

Medium-native long-form research, author-assistance, writing, editing, review, topic discovery, publication matching, and packaging workflow. Use when creating, outlining, researching, revising, auditing, or packaging Medium stories, essays, tutorials, reviews, explainers, comparisons, case studies, publication submissions, Medium 长文, Medium 选题, Medium Topics, or Medium publishing packs that need original author perspective, narrative craft, relevant topics, responsible AI disclosure, and reader-centered distribution readiness.

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。

LinkedIn-native long-form article and newsletter writing workflow for LinkedIn and Google-to-LinkedIn topic discovery, business-depth research, professional thought leadership, evidence-led drafting, final humanization, discussion design, SEO settings, auditing, and publish-ready packaging. Use when creating, outlining, researching, enriching, rewriting, humanizing, auditing, or packaging LinkedIn Articles, LinkedIn newsletters, LinkedIn long-form posts, LinkedIn thought leadership, LinkedIn B2B articles, LinkedIn 长文, LinkedIn 专栏, LinkedIn 话题调研, LinkedIn 商务内容, 去 AI 化编辑, or LinkedIn 发布包.

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 whenever the user wants to interact with Figshare - searching public datasets/articles, downloading Figshare files, listing their own articles/collections/projects, creating or updating articles, or uploading files (including large multi-part uploads) via the Figshare v2 REST API. Trigger on mentions of "figshare", figshare DOIs (10.6084/m9.figshare.*), figshare.com URLs, or phrases like "upload my dataset to figshare", "publish to figshare", "get figshare 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。

Systematic backlink building for indie founders. Covers 5 channel types ranked by GEO + SEO ROI: Wikipedia (entities), authoritative media (PR), industry reviews (G2/Capterra/Product Hunt), Reddit/Quora discussions, and HARO/Featured.com expert quotes. Designed for 0→1 sites where domain authority is the bottleneck. Aligns with 2026 Google/Bing GEO guidance: backlinks are the strongest brand-authority signal AI search engines use to decide citations. Defaults to dev/B2B link sources; for 2C products (education, consumer apps) authority links differ — see gingiris-seo-geo/references/2c-adaptation.md.

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。

Reconstruct an open-source project's growth channels, key content, and stage-by-stage attribution from GitHub history and public web evidence. Use this skill when asked to research GitHub star growth, identify launch and amplification channels, explain viral milestones, find representative posts or videos, compare Product Hunt/Show HN/Reddit/KOL impact, or produce an evidence-backed OSS growth attribution report.

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。

Facilitate a full design-thinking engagement (Empathize, Define, Ideate, Prototype, Test) grounded in real user evidence. Use whenever the user wants to understand users deeply and design a solution for them: "run design thinking", "understand our users", "design user interviews", "synthesize these interview notes", "build personas", "write How-Might-We questions", "prototype this concept", "design a usability test", "our users are churning and we don't know why", in any language ("tư duy thiết kế", "nghiên cứu người dùng", "phỏng vấn khách hàng", "デザイン思考", "设计思维"), even when they never say "design thinking". Also use when the user drops raw interview notes, transcripts, or survey exports and wants insights, personas, or product decisions. Not for "is there a market for X" desk questions (market-researcher).

Quellenbasierte Profile einzelner Wettbewerber aus öffentlichen Informationen erstellen. Nutze diesen Skill, wenn Positionierung, Zielgruppe, Produkt, Preise, Organisation und Belegbehauptungen eines Anbieters zusammengetragen werden sollen, als Kurzbrief, fokussiertes Profil oder vertiefte Recherche. Trennt Tatsache, Anbieterangabe, Schätzung und Hypothese in einem Claim-Ledger, hält Quellen mit Datum fest und beachtet Datenschutz, Datenbankrechte, Urheberrecht und Geschäftsgeheimnisse. Kein Scraping über das Freigegebene hinaus, keine Personendossiers. Für vergleichende Seiten: competitors.

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。

Technische, inhaltliche und internationale SEO-Probleme für Deutschland und den DACH-Raum prüfen und priorisieren. Nutze diesen Skill bei Ranking- oder Trafficeinbrüchen, bei Problemen mit Crawling, Indexierung, Rendering, Canonicals, Weiterleitungen, Ladezeit, interner Verlinkung, hreflang oder lokaler Sichtbarkeit, vor und nach Migrationen und Relaunches, und wenn Befunde aus der Search Console eingeordnet werden sollen. Liefert reproduzierbare Evidenz statt Vermutungen, trennt Beobachtung von Hypothese und bleibt lesend: produktive Änderungen brauchen eine gesonderte Freigabe.

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。

Financial services advisory skill for consulting engagements. Use when preparing research briefs on banking, insurance, capital markets, or wealth management, building performance benchmarking frameworks, regulatory impact assessments, or stakeholder materials for CFOs, CROs, and technology leaders.

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Personal document management skill using Paperless-ngx. Use when: (1) Ingesting and filing new documents, (2) Searching for existing documents, (3) Applying tags and correspondents, (4) Managing retention policies, (5) Integrating documents with other personal workflows (tax prep, medical records, insurance claims). Tools: paperless-mcp for all document operations.

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。

Research advisor for ACT business setup — Pty Ltd registration, accountants, insurance, R&D tax incentive, family trusts, compliance, and migration. Use when the user asks about any business setup topic, costs, providers, or next steps.

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Cheminformatics toolkit catalog for small molecules. Covers RDKit/OpenChemLib calculations, descriptors, fingerprints, similarity/substructure search, R-group, MMP, scaffold tree, chemical space, MPO, reactions, and ADMET predictions. Open this skill when the user asks about computing molecular properties, comparing/searching molecules, or predicting ADMET/tox.

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。

Extracts paper recommendations from social-media posts and online articles (小红书 / Xiaohongshu / RedNote, 微信公众号 / WeChat Official Accounts, Twitter / X threads, Reddit posts, Bilibili videos, blog posts, newsletters, Jina Reader URLs), identifies the underlying academic papers, locates the authoritative original sources (arXiv, conference proceedings, DOI), and triages relevance to the user's research before any library action. Use when the user forwards a social-media link, screenshot, or article that mentions a paper / method / model, asks to "find the original paper" from a blog or thread, shares a 调研贴 / 论文推荐 / paper recommendation post, or wants to evaluate whether a buzz-worthy paper is worth reading before adding it to Zotero.

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。