「aces」の検索結果

Nexscope keyword historical search volume query, returning Amazon keyword exact search volume trends in 7-day periods, covering 10 marketplaces including US, UK, DE, JP, etc. Trigger when the user mentions keyword search volume trends, historical search volume, search popularity changes, keyword seasonality, search volume fluctuations, Nexscope search volume, keyword search volume history, keyword trend, search volume over time, seasonal search volume, keyword popularity trend. Even if the user does not explicitly mention "Nexscope", if their need involves viewing the search volume trend of an Amazon keyword over time, 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。

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。

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。

Query and analyze Amazon ABA (Brand Analytics) search term data, covering 15 marketplaces with nearly 3 years of weekly data. Trigger when the user mentions ABA data, Amazon search term analysis, keyword mining, search ranking trends, market opportunity analysis, seasonal keywords, high-click low-conversion analysis, blue ocean keyword discovery, competitor keyword analysis, ABA data, search term report, keyword mining, search ranking trends, blue ocean keywords, click share, conversion share, seasonal keywords, market opportunity analysis, competitor keywords. Even if the user does not explicitly mention "ABA", if their need involves Amazon search term data and ranking analysis, 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。

Use Nexscope data to find and analyze competitors on Amazon, covering 12 marketplaces, with product metrics including sales, BSR, pricing, ratings, and growth trends. Trigger when the user mentions competitor lookup, competitor analysis, ASIN reverse lookup, competitive product research, finding similar products, market competitor discovery, product benchmarking, competitor sales estimation, analyzing competitor listings, competitor analysis, ASIN reverse lookup, competitor sales, competitor research, Nexscope, market competitor discovery, competitor trends. Even if the user does not explicitly mention "Nexscope" or "competitor lookup", if their need involves discovering and analyzing Amazon competitors by ASIN, keyword, seller name, brand, or category, 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 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。

Nexscope keyword expansion tool that expands a seed keyword into a list of related keywords with search volume, trends, PPC bids, ranking difficulty, and other metrics, covering 10 Amazon marketplaces including US, UK, DE, JP, etc. Trigger when the user mentions keyword expansion, keyword mining, long-tail keyword mining, related keywords, keyword suggestions, keyword discovery, PPC bid research, keyword competition, keyword discovery, Nexscope keywords, keyword expansion, keyword discovery, keyword scout, related keywords, long-tail keywords, keyword suggestions, PPC bid research, keyword competition, seed keyword expansion, keyword mining. Even if the user does not explicitly mention "Nexscope", if their need involves finding more related keywords and their search volume, competition, and other metrics starting from a seed 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。

Design trauma-informed classroom practices that prioritise safety, predictability, connection, and regulation. Use when student behaviour may be trauma-related or the class needs a sensitive approach.

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。

Diagnosis loop for bugs and failing behavior. Use for errors, stack traces, regressions, failed tests, issue-tracker bugs, stuck investigations after failed fixes, or asks to debug/fix a bug.

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。

Code review focused exclusively on over-engineering. Finds what to delete: reinvented standard library, unneeded dependencies, speculative abstractions, dead flexibility. One line per finding: location, what to cut, what replaces it. Use when the user says "review for over-engineering", "what can we delete", "is this over-engineered", "simplify review", or invokes /ponytail-review. Complements correctness-focused review, this one only hunts complexity.

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。

Builds production-quality, accessible, responsive user-facing UIs. Use when building or modifying interfaces and pages, creating components, implementing layouts, meeting WCAG accessibility requirements, managing state, or when the output needs to look and feel production-quality rather than AI-generated.

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。

Capture a forward-looking idea with trigger conditions — surfaces automatically at the right milestone

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。

DeFi Protocol Architect: Mechanism design for AMMs, lending, vaults and derivatives: oracles, liquidations, attack surfaces.

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。

Translate a confirmed mini-program product specification or bounded feature into an implementable architecture covering modules, pages, components, services, canonical state sources, data models, interfaces, permissions, failures, caching, concurrency, idempotency, migration, rollback, external dependencies, and architecture decision records. Use when users ask how to structure a mini program, design data or APIs, divide frontend and cloud responsibilities, evolve an existing system safely, or prepare implementation after product semantics are stable. Preserves product meaning, current repository constraints, user-owned changes, and verifiable acceptance behavior rather than redesigning the product for technical convenience.

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。

Create distinctive, production-grade frontend interfaces with high design quality using Mobile First strategy. Analyzes design systems from screenshots, generates Tailwind CSS + Shadcn UI configurations, and retrofits existing projects. Optimized for Claude Opus 4.5 with structured decision trees and self-verification checkpoints.

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。

Clerk Organizations for B2B SaaS - create multi-tenant apps with org switching, role-based access, verified domains, and enterprise SSO. Use for team workspaces, RBAC, org-based routing, member management.

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 building, debugging, or answering questions about Liveblocks. Liveblocks gives you the building blocks and infrastructure to enable people and AI to work together inside your app, powering realtime collaboration. Liveblocks features include collaboration, rooms, organizations, workspaces, comments, composer, threads, notifications, multiplayer, conflict resolution, realtime presence, avatar stacks, AI collaborators, AI agents, text editors, Tiptap, BlockNote, Lexical, React Flow, Chat SDK. Common components include AiChat, Thread, InboxNotification, Composer, Toolbar (for Lexical Tiptap), FloatingToolbar, FloatingComposer, FloatingThreads, AnchoredThreads. Common hooks include useThreads, useStorage, useMutation, useOthers, useInboxNotifications, useAiChats. Common issues are related to authentication (ID tokens vs access tokens), permissions, room limits, connection errors, user info.

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。

Root cause analysis for bugs and unexpected behavior. Traces errors through code, uses structured reasoning, and hands off to fix when cause is found. Core of the debug↔fix mesh. When the diagnosed cause is a memory leak in a long-running process, escalates to perf for cost-impact framing (leaks drive OOM-restart → cold-start → autoscaler spend, often 20-40% bill inflation).

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。

Specialized git operations — semantic commits, PR descriptions, branch management, conflict resolution guidance. Replaces ad-hoc git commands with a dedicated, convention-aware utility.

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。

Advisory wrap for tool results from untrusted external surfaces. Appends `[QUARANTINE-NOTICE]` to next-turn context after `mcp__*`, `WebFetch`, and `Read` of `**/uploads/**` so prior tool output is treated as data — not directives. Use when the session ingests MCP user-content (Zendesk, Intercom, support tickets), fetched HTML, or operator-uploaded files. Hook fires AFTER ingestion — advisory, not structural.

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。

Vercel AI Elements for workflow UI components. Use when building chat interfaces, displaying tool execution, showing reasoning/thinking, or creating job queues. Triggers on ai-elements, Queue, Confirmation, Tool, Reasoning, Shimmer, Loader, Message, Conversation, PromptInput.

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。