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Nexscope Ozon Russia seller drill-down product list by seller ID. Returns all SKUs under a seller with complete metrics: sales, revenue, price, rating, stock, turnover, lost revenue, supporting multi-dimensional numeric filters, sorting, and currency conversion. Use for store structure analysis, seller bestseller analysis, competitor store benchmarking. Trigger when the user mentions Ozon seller products, Ozon store analysis, Ozon seller drill-down, Ozon seller SKUs, Ozon store bestsellers, Ozon competitor store, Nexscope seller, Ozon seller drill-down, Ozon shop audit, Russian marketplace seller SKUs, Ozon store structure. Also trigger when the intent is to view all products and their sales performance under an Ozon seller ID, even without explicitly mentioning Nexscope.

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。

Break down all competitor ASIN traffic sources under a given keyword -- organic search, SP ads, SB brand ads, SBV video ads, SP recommendations, AC/ER/TR recommendation slots, with support for ASIN filtering, custom date ranges, and new traffic keyword filters. Trigger when the user mentions keyword traffic sources, which competitors are grabbing traffic under this keyword, organic traffic vs paid traffic share, SP ad exposure, brand ad share, SP recommendation slots, recommendation ad/non-ad split, search display analysis, Amazon's Choice or editorial recommendation exposure, keyword competitive landscape, ASIN traffic composition, keyword traffic, traffic structure analysis, search share, ad share, traffic source distribution, Nexscope, traffic analysis, SP recommendation, recommend position breakdown. Even if the user does not explicitly mention "Nexscope", if their need involves analyzing competitor ASIN traffic source distribution under a keyword, 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 Amazon product historical time-series data, including price trends, BSR (Best Sellers Rank) trends, rating changes, seller counts, and monthly sales, supporting any ASIN across multiple Amazon marketplaces. Triggered when users mention price history, price tracking, BSR history, BSR trends, historical pricing, price fluctuations, Nexscope data, rank history, price drop alerts, lightning deal historical prices, Buy Box price trends, coupon prices, FBA/FBM price comparison, seller count changes, rating trends, sales history. Even if users do not explicitly mention "Nexscope" or "time-series data", this skill should be triggered whenever the task involves analyzing Amazon product-level historical data (such as price, rank, or sales trends over time).

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。

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。

Use Nexscope market list capability to filter Amazon niche markets by category dimensions, supporting market size, competition, top concentration, seller structure, new product share, price/rating/margin ranges, and many other criteria for discovering viable markets and evaluating product selection directions. Trigger when the user mentions Amazon market research, niche category research, market opportunity screening, market concentration analysis, new product opportunities, market selection, Nexscope market research, category market research. Even if the user does not explicitly mention "Nexscope", if the need is to screen and evaluate Amazon markets by category dimensions, 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。

Advanced Amazon product search and filtering powered by Nexscope data, supporting multi-dimensional criteria including category, price, monthly sales, keywords, BSR rank, review count, rating, package dimensions, weight, fulfillment type, and more. Trigger when users mention Nexscope product selection, Amazon product search, advanced product selection, BSR filtering, sales rank filtering, keyword-based product discovery, category search, competitor screening, niche product discovery, historical rank filtering, or similar terms. Even if the user does not explicitly mention "Nexscope," trigger this skill whenever the request involves multi-criteria Amazon product search, sales-metric-based filtering, or advanced product selection beyond simple keyword search.

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。

Multi-dimensional Amazon product search and filtering based on Nexscope data, covering 14 marketplaces, with support for historical monthly snapshot lookback. Trigger when the user mentions Nexscope product search, Amazon product filtering, competitor research, category analysis, brand bestsellers, seller analysis, seasonal products, historical snapshot review, product search, monthly sales/revenue, ABA keyword product discovery, price range filtering, new product discovery, multi-condition combined filtering, product search, competitor research, category analysis, brand bestsellers, seller analysis, seasonal products, historical snapshot. Even if the user does not explicitly mention "Nexscope", if their need involves Amazon product search, filtering, comparison, or product exploration by category/brand/seller dimensions, 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 Amazon is latest policy, regulation, and compliance feed. Supports paginated browsing by marketplace and time range (with AI-generated Chinese summaries), and fetching full article body by record ID. Trigger when users mention Amazon policies & regulations, seller compliance announcements, platform rule changes, policy alerts, FBA/fee policy updates, multi-marketplace policy tracking, policy original text, news details, or similar terms. Even if the user does not explicitly mention "policy feed," trigger this skill whenever the request involves Amazon is officially published policies, regulations, and news for sellers, including their full text.

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。

Use for every task that plans, writes, edits, debugs, tests, refactors, reviews, or maintains code. Apply to professional repositories, scripts, apps, services, prototypes, beginner work, vibe coding, and bounded code changes inside large systems. Choose the fewest clear lines, files, paths, states, and abstractions that correctly implement the requested behavior. Do not invent fallbacks, compatibility paths, defensive layers, or future scale without evidence. Do not use for ordinary conversation, emotional support, prose, translation, summarization, social-media content, or general research or technical discussion that is not part of planning, changing, or reviewing code. Never weaken real security, integrity, concurrency, compatibility, privacy, accessibility, legal, or regulatory requirements.

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。

AI-assisted pair programming with multiple modes (driver/navigator/switch), real-time verification, quality monitoring, and comprehensive testing. Supports TDD, debugging, refactoring, and learning sessions. Features automatic role switching, continuous code review, security scanning, and performance optimization with truth-score verification.

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。

Adding edge-case tests, repairing flaky tests, and improving coverage. Use when test gaps need filling or regressions need guarding. Supports JS/TS, Python, Go, Rust, and Java.

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。

Implementing i18n and l10n: extracts hardcoded strings to t() functions, integrates Intl API for date/currency/number formatting, manages translation keys, and adds RTL layout support.

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 a Monte Carlo monitor and recommend config changes to reduce alert noise. Supports metric, custom SQL, validation, table, and agent (metric, evaluation, trajectory, validation) monitors. Fetches the report, identifies patterns, and suggests tuning.

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。

Security audit: supported static findings; qualified profiles add reproduction and repair candidates. (gstack)

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。

Predicts access barriers in a learning task before delivery, given a learner variability profile. Distinguishes between barriers addressable through design and those requiring specialist support.

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。

Analyse text complexity across quantitative, qualitative, and reader-task dimensions with scaffolding recommendations. Use when selecting texts, assessing readability, or planning reading support.

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。

After scaffolded practice, run an unassisted check — a problem with no AI help. Separates what the learner can do with support from what they can do independently. Critical for preventing phantom attainment.

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 a lesson that embeds PERMA wellbeing elements alongside academic learning objectives. Use when planning lessons that intentionally support both content mastery and student flourishing.

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。

Modify a lesson plan using SIOP sheltered instruction principles to support language learners across all four skills. Use when planning lessons for classes that include EAL students.

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 scaffolds supporting student self-regulation across planning, monitoring, and evaluation phases. Use when students struggle to manage their own learning during extended or independent tasks.

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。

Select and sequence reading comprehension strategies matched to a specific text's challenges and demands. Use when students struggle with a text or need targeted reading support.

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。

Script a multi-turn tutoring dialogue with branching responses for anticipated student difficulties. Use when designing AI tutors, chatbot interactions, or structured one-to-one support scripts.

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。