「pet」の検索結果

Simulates a real user searching on Amazon's storefront to get real-time keyword ranking and search results page data. Use when the user mentions Amazon product search, search result scraping, keyword ranking on search pages, ASIN ranking position check, competitor discovery, search page price comparison, sponsored product analysis, new product monitoring, or storefront search simulation. Even if the user does not explicitly mention "search simulation", trigger this skill whenever their need involves real-time Amazon search results, product ranking data, or storefront SERP analysis.

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 Nexscope market statistics capability to output a market statistics dashboard by category node, including top listing average rating, average price, BSR, sales, seller count, and new product related metrics, suitable for quickly assessing the market quality and competitive landscape of a category. Trigger when the user mentions category market statistics, market selection dashboard, market fundamentals assessment, node market quality, top product statistics, Nexscope market statistics, category statistics. Even if the user does not explicitly mention "Nexscope", if the need is to view aggregated statistical results by category node, 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。

Use Nexscope data to search and filter Amazon products, supporting multi-dimensional criteria including price, monthly sales, BSR ranking, gross margin, ratings, fulfillment method, badges, seller origin, and more across multiple Amazon marketplaces. Trigger when the user mentions Amazon product selection research, product filtering, sales filtering, product discovery, BSR analysis, niche product discovery, competitor analysis, market opportunity assessment, product-level market size estimation, gross margin screening, Nexscope product selection, Amazon product selection, sales filtering, BSR analysis, profit screening, market analysis, product selection tool. Even if the user does not explicitly mention "Nexscope", if their need involves filtering and analyzing Amazon product-level data for product selection, 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。

Design learning tasks built on Ubuntu philosophy emphasising collective knowledge-building and mutual responsibility. Use when fostering collaboration that values community over individual competition.

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。

Anchor a multidisciplinary unit in a real-world phenomenon that requires multiple subject lenses to understand. Use when designing integrated or phenomenon-based units across disciplines.

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。

Diagnose motivation problems in a task using self-determination theory and redesign for autonomy, competence, and relatedness. Use when students are disengaged, resistant, or going through the motions.

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。

Track performance across sessions and reduce scaffolding as competence grows. Makes fading visible — the learner knows when scaffolds are removed and why. Use for sustained learning engagement where independence is the goal.

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。

Identify and navigate genuine dilemmas in curriculum, school, or community contexts — tensions between competing goods that cannot be solved, only navigated. Produces a structured dilemma map with both poles named and both/and possibilities.

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。

Explain and configure individual spacing algorithms using student performance data and forgetting curves. Use when personalising retention schedules in adaptive learning platforms.

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。

Generate a criterion-referenced rubric with descriptive performance levels for a task or objective. Use for marking guides and general curriculum contexts. For Manning programmes where Competent = success, use coherent-rubric-logic-builder instead.

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。

When the user wants help creating, scheduling, or optimizing social media content for LinkedIn, Twitter/X, Instagram, TikTok, Facebook, or other platforms, or wants to do social listening and engagement triage. Also use when the user mentions 'LinkedIn post,' 'Twitter thread,' 'social media,' 'content calendar,' 'social scheduling,' 'engagement,' 'viral content,' 'what should I post,' 'repurpose this content,' 'tweet ideas,' 'LinkedIn carousel,' 'social media strategy,' 'grow my following,' 'TikTok video,' 'Reels,' 'Shorts,' 'video script,' 'video hook,' 'short-form video,' 'create a reel,' 'social listening,' 'brand mentions,' 'competitor monitoring,' 'top posts to comment on,' or 'find people asking for.' Use this for social media content creation, repurposing, scheduling, short-form video scripting, and social listening. For broader content strategy, see content-strategy. For paid ads, see ad-creative. For earned media, see public-relations.

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。

When the user wants to add, fix, or optimize schema markup and structured data on their site. Also use when the user mentions "schema markup," "structured data," "JSON-LD," "rich snippets," "schema.org," "FAQ schema," "product schema," "review schema," "breadcrumb schema," "Google rich results," "knowledge panel," "star ratings in search," or "add structured data." Use this whenever someone wants their pages to show enhanced results in Google. For broader SEO issues, see seo-audit. For AI search optimization, see ai-seo.

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。

When the user wants to audit or optimize an App Store or Google Play listing. Also use when the user mentions 'ASO audit,' 'app store optimization,' 'optimize my app listing,' 'improve app visibility,' 'app store ranking,' 'audit my listing,' 'why aren't people downloading my app,' 'improve my app conversion,' 'keyword optimization for app,' or 'compare my app to competitors.' Use when the user shares an App Store or Google Play URL and wants to improve 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。

When the user wants to conduct, analyze, or synthesize customer research. Use when the user mentions "customer research," "ICP research," "talk to customers," "analyze transcripts," "customer interviews," "survey analysis," "support ticket analysis," "voice of customer," "VOC," "build personas," "customer personas," "jobs to be done," "JTBD," "what do customers say," "what are customers struggling with," "Reddit mining," "G2 reviews," "review mining," "digital watering holes," "community research," "forum research," "competitor reviews," "customer sentiment," or "find out why customers churn/convert/buy." Use for both analyzing existing research assets AND gathering new research from online sources. For writing copy informed by research, see copywriting. For acting on research to improve pages, see cro.

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。

Business analyst for market research, competitive analysis, and requirements. Use when the user asks to talk to Mary or requests the business analyst

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。

Diagnose and fix mini-program failures through reproducible, evidence-driven root-cause analysis across source, state, asynchronous timing, caches, mock data, build versions, platform behavior, devices, cloud calls, and external services. Use when users report white screens, crashes, stale or jumping values, missing data, intermittent behavior, permission failures, performance stalls, device-only defects, API or cloud-function errors, source-versus-build mismatches, or regressions whose cause is not yet established. Freezes the observation environment, ranks competing hypotheses, runs discriminating checks, creates a failing regression test before repair, covers analogous states, and never hides a known failure with longer waits, swallowed errors, forced success, or cosmetic patches.

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。

Write and rewrite Turkish so it reads as native Turkish rather than translated English. Use for Turkish marketing copy, product and UI text, localization, documentation, support, legal or security prose, and reviews of AI-written Turkish. Detect translationese, copied clause structure, nominalization, wrong case and collocations, generic helper verbs, SaaS and sales calques, jargon leakage, forced slogans, corporate abstraction, cross-sector template residue, unsupported operational claims, and page-level repetition.

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。

Conduct market research on competition and customers. Use when the user says they need market 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。

Scan market trends, competitor activity, and emerging patterns. Monitors Product Hunt, GitHub Trending, HackerNews, and social platforms.

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。

Web search and external knowledge lookup. Gathers data on technologies, libraries, best practices, and competitor solutions.

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 interpreting research findings, evaluating scientific evidence, analyzing mechanisms, comparing competing hypotheses, designing experiments, or constructing scientific arguments.

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 the Semantic Scholar academic corpus through AI2's Asta MCP server: keyword or title search with venue and date filters, who-cited-a-paper traversal, author profiles and publications, DOI/arXiv/PMID lookup, and ~500-word full-text snippets for evidence, with citation counts and h-index. Use when the user asks for papers on a topic, who cited a paper, an author's work, recent literature since a date, or a venue-scoped paper list. Requires the Asta MCP server.

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 interpreting biological research findings, evaluating life science evidence, analyzing molecular or cellular mechanisms, comparing competing biological hypotheses, designing or critiquing experiments in biology, genetics, genomics, cell biology, immunology, neuroscience, ecology, or any life science domain. Triggers on questions about gene function, pathways, phenotypes, GWAS hits, single-cell data, animal models, clinical translation, evolutionary arguments, or any biology/life science reasoning task.

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。

Design and execute a 14-day GitHub repository growth sprint using README conversion, competitor-window research, Reddit, Hacker News, launch directories, community activation, and contributor loops. Use when launching an open-source repository, diagnosing weak star conversion, or building sustained GitHub discovery without buying stars.

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。