「across」の検索結果

Mercado Libre product selection data query and analysis via the Nexscope gateway, covering 24 tools for products, catalog, keywords, categories, trends, sellers, reviews, exchange rates, and plan usage across Mexico, Brazil, Argentina, Chile, and Colombia sites. Triggered by: Mercado Libre, MercadoLibre, product selection, product search, category trends, hot keywords, reverse traffic keywords, seller search, review search, exchange rate, plan usage, Lanjing, Mercado 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。

Search and browse product listings across multiple eBay international sites. Trigger when user mentions eBay product search, eBay listing query, eBay price comparison, eBay market browsing, eBay sold items, eBay auction search, eBay product research, eBay products, eBay pricing, eBay competitors, eBay auctions, eBay market analysis — even if "eBay" is not explicitly mentioned, as long as the need involves searching products on eBay, comparing eBay prices, finding sold listings, or analyzing eBay market data.

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。

Nexscope keyword Share of Voice analysis, returning brand visibility share across the first 3 pages of Amazon search results (organic/ad/combined), 30-day exact search volume, median PPC bid, and TOP3 ASIN click and conversion data, covering 10 marketplaces. Trigger when the user mentions brand market share, brand voice share, search result brand distribution, Share of Voice, SOV analysis, brand competitive landscape, ad slot share, organic ranking brand share, PPC bid analysis, brand monopoly analysis, keyword share of voice, brand visibility, organic vs sponsored share, brand dominance, PPC bid analysis, search result brand distribution, competitive landscape, weighted SOV, top ASIN clicks conversions. Even if the user does not explicitly mention "Share of Voice" or "SOV", if their need involves analyzing brand market share or competitive landscape in Amazon keyword search results, 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。

Perform image-based visual product search on Amazon across 8 marketplaces. Use an image URL to find visually similar products, with optional Nexscope enrichment for sales data. Triggered when users mention Amazon image search, Amazon visual search, find similar products on Amazon, reverse image lookup Amazon, Amazon search by photo, or competitor image search. Even if the user does not explicitly mention "image search," this skill should be triggered whenever a user provides an image URL and wants to find visually matching or similar products on Amazon.

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。

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。

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。

Promote a validated pattern into a reusable Skill in AgentDB's skill library. Use when the same approach has worked 3+ times across episodes, or when the user explicitly says "make this a skill" / "save this as reusable".

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。

Orchestrating migrations, upgrades, and modernization across frameworks, libraries, APIs, databases, and dependencies. Generates codemods, applies Strangler Fig, verifies equivalence, plans rollback.

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。

Analyzing pre-change impact across vertical (dependency chains, files) and horizontal (pattern consistency, naming) dimensions. Use to estimate blast radius before a refactor. No 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。

Controlling combinatorial explosion across multi-dimensional axes: minimum coverage sets, execution plans, test/deploy/UX/risk prioritization. Use when scoping multi-axis combinations.

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。

Orchestrating design-to-implementation pipelines (code to visual to code closed loop), persisting a project design system across agents. Not for a single prototype (Forge) or direction only (Vision).

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 whenever you learn something important during a conversation: a key architectural decision, a project-specific convention, a bug root-cause, a 3rd-party API quirk, or any fact that would help a future agent avoid re-doing the same research. Writes structured entries to the project's knowledge base so that context persists across conversations and agents.

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。

Guide users from coverage analysis to monitor creation. USE WHEN user asks what should I monitor, where are my gaps, improve coverage, or wants a systematic approach to monitoring across their data estate.

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 debugging with persistent state across context resets

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。

Lifts the task's scope for one task. The session, its reviews and fix-the-class then look across the whole codebase and every repo that shares its code, data or vendors, and every problem found anywhere is handled by the usual rules. Runs only when the user types 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。

Design, preview, implement, and assess mini-program interfaces against an approved prototype, screenshot, visual reference, design system, or bounded UI request while preserving product semantics and existing accepted behavior. Use when users ask to reproduce a mini-program screen, refine visual hierarchy, adapt layouts across device sizes, handle safe areas or keyboards, inspect touch targets, scrolling, media, or gesture conflicts, compare an implementation with a reference, or prepare a device-verification matrix. Separates proposed visual previews, user approval, source integration, local checks, and real-device evidence; never invents product functionality for visual completeness or claims device validation without device-specific proof.

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。

Govern mini-program release readiness and evidence across source branches, semantic versions, build artifacts, test flags, debug paths, sensitive data, permissions, privacy declarations, platform uploads, review submissions, staged versions, production releases, and rollback plans. Use when users ask for a release checklist, packaging, export, version bump, upload readiness, submission readiness, launch status, rollback preparation, or a precise judgment of what has and has not shipped. Defaults to read-only preflight, requires separate explicit authorization for every external state change, and never treats a Git push, successful build, platform upload, review submission, approval, or production publication as interchangeable.

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。

Verify mini-program implementations and fixes with risk-calibrated evidence across static checks, unit tests, integration tests, state matrices, simulators, real devices, cloud environments, and release artifacts. Use when users ask to test, validate, accept, regression-check, quality-check, confirm readiness, or determine whether a mini-program feature is actually complete. Binds results to a source and build fingerprint, records commands and observable evidence, separates passed, failed, blocked, and not-run layers, prioritizes the next highest-information check, and never converts local success into device, cloud, release, or formal acceptance claims.

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。

Apply evidence-first engineering discipline to any agent-built software project: fact discovery before action, evidence-calibrated status reporting, explicit change boundaries and stage gates, controlled confirmation for risky or external actions, sensitive-information redaction for anything shared publicly, and resumable continuity after interruptions. Use when users ask an agent to take over an existing codebase, deliver a feature across stages, verify whether work is actually complete, judge release readiness, or keep conclusions honest about what is proven versus assumed. This foundation skill is domain-neutral; vertical suites (for example mini-program engineering) build on it by adding domain facts and platform rules.

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。

Map a scoped topic across three coexisting horizons — current system under strain (H1), preferred future (H3), and transition innovations sorted as H2+ or H2- — to understand change dynamics and identify responsible next actions.

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。