Xjli360 Agent Skills
250 skills published by xjli360 across 1 repository. Together they weigh 2 571 132 tokens — that is what loading all of them at once would cost you in context.
250 skills 2 571 132 tokens total
Diagnose high Amazon Ads ACoS by decomposing CPC, conversion rate, price, query mix, placement mix, and sample sufficiency. Use when the user asks why ACoS is high, whether a keyword has enough clicks, how to estimate CPA or break-even CPC, or whether to move, pause, or continue a target. Return evidence-ranked actions and keep live changes behind approval.
Diagnose Amazon Ads ACOS with reconciled CTR, CPC, CVR, AOV, ROAS, TACOS, placement, search-term, benchmark, attribution, and contribution-margin evidence, then produce a single-variable optimization experiment. Use for ACOS 高低判断, 广告亏损, CTR/CVR/CPC 异常, 盈亏平衡 ACOS, 广告报告诊断, Benchmark 基准, placement 浪费, 搜索词不精准, Listing 转化问题, 广告利润优化, or converting the authorized Amazon Ads metrics course into an account-specific plan. Default to read-only diagnosis and draft; never change live campaigns without explicit human approval.
Build and diagnose a profit-aware Amazon advertising architecture by working backward from stage-level sales and profit goals into inventory, keyword priorities, campaign roles, budgets, and measurable experiments across SP, SB, SBV, SD, keyword targeting, product targeting, and seasonal launch phases. Use for 精品广告架构, 亚马逊广告架构搭建, ASIN 推广计划, 季节性新品预算, 销量利润倒推, 关键词分层/竞争度/SPR/CPR, SP SB SBV SD 组合, 红海类目投放, 广告预算分配, 关键词首页计划, 周复盘, 或根据《如何搭建一个精品的广告架构》形成可审批方案. Default to analysis and draft; do not change live campaigns or claim organic-rank causality without verified account evidence and human approval.
Rebuild an unprofitable Amazon ad account by replacing head-term dependence with a verified keyword universe, root-based broad tests, pre-emptive negatives, exact harvesting, and portfolio-level profit controls. Use when ACoS is extreme, lowering ads kills sales, or a product needs a structured turnaround. Do not promise a fixed turnaround period.
Apply a simple Amazon Ads allocation model that separates discovery from exploitation, tests keyword and placement effects, and reallocates budget toward evidence-backed conversion pockets. Use when the user asks for a clear mental model for Amazon ads, how to find good traffic, how to compare placements, or how to reduce poor traffic. Draft only unless live changes are explicitly approved.
Diagnose and plan Amazon US apparel advertising with an ASIN lifecycle playbook covering long-lifecycle, short-lifecycle, and seasonal products. Use for 美国站服饰广告投放, 女装/内衣/泳装/西装/配饰广告打法, ASIN 生命周期判断, 服饰非标品找词, 广告预算结构, 旺季预热与淡季保温, 主身份/标签/流量池诊断, ACOS 高, 点击高不转化, 大词首页不转化, 断货后重启, SB/SBV/SPV/SD/商品投放组合, 否词与复盘. Default to analysis and draft; do not mutate live campaigns without explicit human approval.
Plan Amazon ASIN product-targeting campaigns by scoring product similarity, demand, truthful conversion advantages, placement hypotheses, budget isolation, and downstream search-term harvesting. Use when the user asks whether a new product can start with ASIN targeting, how to choose competitor ASINs, or when to move discovered queries into exact campaigns. Avoid review manipulation and ranking guarantees.
Diagnose and draft Amazon Canada apparel advertising plans with lifecycle and seasonal timing, English/French search coverage, account evidence, profitability guardrails, and approval-ready experiments. Use for 加拿大站服饰广告, Amazon.ca Coat 外套夹克, Underpants 内衣文胸, 季节性长生命周期, 长生命周期, 新品期成长期成熟期, 广告预算配比, SP/SB/SBV/SD/商品投放, 法语关键词, 旺季预热, 复购再营销, CPC/ROAS/ACOS 异常, or when converting the authorized course material into an account-specific plan. Default to diagnosis and draft; never write live advertising changes without explicit human approval.
Build an Amazon growth strategy that treats tax, product safety, account, IP, and logistics compliance as non-negotiable constraints, then compares niches, price bands, operating models, and marketplaces using weighted economics. Use when the user asks how to find opportunities as compliance costs rise or whether to shift product, model, or marketplace. Reject evasion and infringement strategies.
Design a policy-compliant Amazon review acquisition plan for high-ticket or low-volume products using eligible official programs, neutral Request a Review workflows, product quality, support, and legitimate variations. Use when the user asks how to gain initial reviews without giving away excessive inventory or proposes paid reviews, seed ASINs, cross-market manipulation, or zombie listings. Refuse prohibited tactics.
Design a compliant precision-first Amazon launch that starts with high-intent long-tail demand, validates conversion, and expands toward broader terms without fake orders or review manipulation. Use when the user asks for 白帽 0-1 推新品, a universal launch sequence, keyword-root grouping, or a limited-risk launch framework. Produce a staged plan and require approval before ad changes.
Research and plan Amazon consumer-electronics category growth across wireless, electronics, PC, camera, office products, and musical instruments in North America, Europe, and Japan, with current-demand validation, compliance, logistics, promotion, and lifecycle-ad gates. Use for 消费电子选品, CE品类攻略, wireless/PC/camera/office/musical instruments, AI设备/智能穿戴/耳机, FCC/UL/CE/EPR/EEL/METI, 带电物流, 促销日历, 电子品类生命周期广告, or converting the authorized 2025 guide into an evidence-backed plan. This is not legal advice; all 2025 claims and policies require current official verification.
Improve an Amazon listing's discoverability for conversational shopping assistants through complete structured attributes, factual use cases, evidence-backed content, localization, and compliant customer support signals. Use when the user asks how Rufus, Alexa, or AI shopping recommendations may find a product. Never seed reviews or Q&A, fabricate scenarios, or guarantee recommendation placement.
Turn verified Amazon product and audience evidence into reviewable AI-assisted advertising concepts, copy, image/video briefs, variants, and controlled creative tests. Use for 对话式AI广告素材, Creative Agent, Creative Studio, AI爆款素材, AI视频脚本, 商品广告创意, prompt共创, 素材A/B测试, POE/ABA insight-to-creative, or converting the authorized conversational-AI seller case into a repeatable workflow. Verify current tool availability and policy, keep human fact/brand/compliance review, and never publish generated assets or change live ads without explicit approval.
Diagnose and plan privacy-safe Amazon Marketing Cloud (AMC) analytics and audience activation, especially for European and peak-season accounts, including journey/time-to-conversion analysis, reach/frequency, overlap, new-to-brand, rule-based and lookalike audiences, no-code audience templates, and SP/SB/SD/DSP activation. Use for AMC, 亚马逊营销云, 欧洲站 AMC, 旺季高潜人群, 购物车/浏览未购, 高价值新客, 潮汐人群, 转化路径, 购买周期, 受众竞价加成, or turning the authorized AMC courses into an approval-ready plan. Default to read-only analysis and drafts; never expose user-level data or mutate audiences/campaigns without verified scope and explicit human approval.
Plan and diagnose Amazon Europe multi-market advertising for standard, non-standard, high-ticket, and seasonal products across mature and emerging marketplaces, with localization, logistics, decision-cycle, and profitability gates. Use for 欧洲多站点广告, UK/DE向FR/IT/ES/NL/SE/PL/BE/IE拓站, 标品vs非标品, 欧洲高客单, 返校季, multi-market SP/SB/SD, Pan-European inventory, 本地化素材, or converting the authorized EU posters into a staged experiment. Historical claims and bid/budget examples are source snapshots; verify current marketplace availability and require approval before live changes.
Research and plan Amazon fashion-category growth across the US, Europe, and Japan using trend validation, marketplace-specific selection, brand/store/content tools, promotion economics, inventory routing, and return-reduction gates. Use for 时尚品类选品, 美欧日服饰趋势, fashion opportunity scan, 女装男装童装鞋靴箱包珠宝, 品牌旗舰店与帖子, 促销组合, AWD/FBA库存, 尺码退货, or converting the authorized 2025 fashion guide into a current evidence plan. Route lifecycle campaign execution to the apparel-ads Skills; treat all 2025 trends, product lists, tools, and case figures as snapshots until currently verified.
Triage Amazon FBA inventory that unexpectedly becomes defective or unfulfillable by separating labeling, shipment, Transparency, listing-change, compliance, and system-state causes, then building a factual support case and deadline plan. Use when sellable stock changes to defective, a compliance review clears but stock remains blocked, or auto-removal is approaching. Do not rely on UI loopholes.
Triage loss of the Amazon Featured Offer by checking account health, order defect signals, price competitiveness, offer and fulfillment state, listing classification, unauthorized sellers, and external price evidence before escalating. Use when the Buy Box or purchase button disappears or repeatedly returns. Do not assume sabotage, manipulate feedback, or edit other sellers' data.
Plan and diagnose evidence-based Amazon full-funnel growth across awareness, consideration, conversion, and loyalty without collapsing brand media, retail readiness, and performance ads into one metric. Use for 全流域营销, 全漏斗营销, 品牌出海, media mix, non-linear customer journey, CTV/online video/social/search coordination, new-to-brand, high-ticket decision journeys, brand-plus-performance measurement, or turning the authorized 2025 Ipsos/Amazon study into an account-specific plan. Default to read-only analysis and a test plan; current availability, policy, and live media changes require verification and explicit approval.
Build a human-governed global Amazon growth roadmap that connects AI-assisted insights, product/listing/localization/operations workflows, marketplace sequencing, and a verified opportunity calendar. Use for 亚马逊全球开店趋势, 跨境电商AI转型, AI智能体工作流, 全球站点布局, 节日商机日历, 复活节/樱花季/地球日/墨西哥儿童节, operator-to-decision-maker transition, or converting the authorized 2026 whitepaper and April poster into a measurable plan. Treat trend statistics, cases, dates, tools, and opportunity lists as snapshots; never automate protected business writes without explicit human approval.
Research and plan Amazon home-and-lifestyle category growth across home, home improvement, kitchen, furniture, automotive, lawn and garden, sports, toys, and pets in North America, Europe, and Japan, with demand, fitment/safety, sustainability, logistics, and lifecycle-ad gates. Use for 生活百货选品, 家居/厨房/家具/汽配/花园/运动/玩具/宠物, OHL category, A+ Gen AI, Creator Connections, Climate Pledge Friendly, Amazon Custom, Part Finder/ACES, AWD/SFP, 大件物流, or converting the authorized 2025 guide into an evidence-backed plan. All product lists, programs, policies, and figures are snapshots requiring current validation.
Diagnose and plan Amazon Japan apparel advertising with Japan-specific consumer behavior, seasonality, and ASIN lifecycle playbooks for long-lifecycle, short-lifecycle, and seasonal products. Use for 日本站服饰广告, JP apparel ads, 背包/内衣/泳装投放, ASIN 生命周期判断, 日本站新品冷启动, 品牌推广启动时机, Amazon Points, 日文功能词, 季节性备货与预热, 广告预算结构, ACOS/ROAS 诊断, 复购再营销, 或根据《亚马逊日本站服饰品类广告运营手册》输出可审批的投放方案. Default to analysis and draft; do not mutate live campaigns without explicit human approval.
Sequence Amazon keyword promotion from high-intent long-tail terms to mid-volume and head terms using stage gates, mixed ad formats, profitability checks, and exact harvesting. Use when the user asks which keywords to launch first, why competitor head terms do not convert for a new ASIN, or how to expand traffic without losing relevance. No ranking promises or artificial orders.
Design Amazon keyword-ranking experiments that identify promising terms from relevance, conversion, current organic visibility, and placement performance, then sequence long-tail, mid-tail, and head-term tests. Use when the user asks which keywords to push, what an ad position may reveal about order potential, or why organic rank stalls despite paid orders. Do not equate sponsored placement with organic rank or guarantee movement.
Create a disciplined Amazon keyword-position monitoring system that prioritizes revenue-contributing terms, separates organic and sponsored observations, controls measurement noise, and triggers evidence-based diagnostics. Use when the user asks which keyword ranks to track, how often to track them, or what to do when a core term drops. Do not automate bid changes from a single noisy snapshot.
Turn an Amazon keyword universe into a clean taxonomy and campaign map using relevance, intent, roots, negatives, and evidence from authorized reports. Use when the user asks how to collect keywords, perform word-root analysis, choose auto versus broad versus exact targeting, or prevent broad campaigns from drifting. Produce a draft architecture and never apply ad changes without explicit approval.
Design a scalable Amazon long-tail keyword portfolio with evidence-based query generation, clustering versus single-keyword isolation, portfolio budget caps, automation drafts, sample safeguards, and human approvals. Use when the user asks about hundreds of campaigns, single-keyword structures, low-bid long-tail coverage, bulk sheets, or automated bid rules. Do not create live rules or campaigns without approval.
Audit, diagnose, rewrite, creatively brief, test, and safely prepare updates for Amazon product detail pages using live marketplace and product-type requirements, verified product facts, Brand Analytics, Search Query Performance, Ads search terms, customer feedback, competitor observations, and optional third-party estimates. Use when asked to improve or evaluate Amazon titles, bullets, descriptions, backend search terms, attributes, images, video, A+ Content, Brand Story, variations, indexing, CTR, CVR, return prevention, ad-to-listing relevance, or to prepare an approval-ready Listings Items API PATCH. Default to draft and review; never invent product facts, copy competitors, manipulate reviews, or silently publish changes.
Localize Amazon listings, search terms, advertising copy, images, and video for a target marketplace by combining verified product facts, native-language search evidence, cultural context, policy, and controlled tests. Use for 亚马逊本土化营销, listing翻译, 广告翻译, 多语言关键词, 日德法意西文案, creative translation, keyword localization, 非美国站拓词, culture-to-conversion, or converting the authorized localization course into an approval-ready localization brief. Do not treat literal translation, AI output, or historical tool claims as publishable content; verify current marketplace rules and require native/fact/policy review before release.
Diagnose and design low-bid Amazon Ads discovery experiments that probe residual traffic, minimum viable bids, placements, and harvestable search terms under a portfolio cap. Use when the user asks about 捡漏广告, low-CPC discovery, bid floors, incremental keyword testing, or turning cheap traffic into a durable campaign. Default to analysis and draft changes; never mutate live ads without explicit approval.
Evaluate and improve low-price, high-CPC Amazon products using break-even economics, legitimate bundles or multipacks, long-tail traffic, low-bid discovery, creator channels, and original video ads. Use when a product has thin margin, expensive clicks, or cannot profitably scale Sponsored Products. Respect variation, IP, and creator-program rules and keep execution behind approval.
Triage negative Amazon reviews into policy violations, suspected abuse, and genuine product feedback, then prepare factual official reports and product-remediation actions. Use when the user asks whether a review can be removed, how to report abusive content, or how to respond to a rating decline. Never fabricate evidence, contact reviewers off-platform, or incentivize review changes.
Recover an underperforming Amazon new-product advertising program by diagnosing traffic concentration, sample sufficiency, placement mix, retail readiness, and unit economics in a fixed order. Use when a new ASIN has run for days or weeks with few orders, scattered clicks, or unclear next steps. Build controlled tests and do not increase budget or change live ads without approval.
Diagnose why an Amazon new product cannot gain traction by separating retail-readiness, traffic relevance, click-through, conversion, economics, and feedback quality. Use when a new ASIN is not launching, ads are not converting, or the team is tempted to use fake reviews, inflated reference prices, or artificial orders. Replace unsafe tactics with compliant conversion and advertising work.
Create a quantified Amazon new-product launch plan by translating a sales target into comparable-product benchmarks, keyword economics, budget scenarios, milestones, and stop-loss rules. Use when the user asks for a 新品推广计划, target-order reverse planning, competitor-based launch budget, or GO/NO-GO assessment before launch. Keep competitor estimates labeled and do not execute campaigns without approval.
Build an evidence-based Amazon new-product traffic plan that moves from controlled discovery to stable converting terms and measures organic-rank and organic-order changes without claiming causality. Use when the user asks how a new ASIN can gain organic traffic, how to sequence auto, product targeting, broad, and exact campaigns, or when to taper launch ads. Default to read-only planning and require approval for live changes.
Assess whether an Amazon operator is ready to become an independent seller by modeling cash runway, working-capital cycles, fixed costs, portfolio success probability, transferable capabilities, and ethical boundaries. Use when the user considers leaving a job to start an Amazon business or is struggling after going independent. Provide scenario-based decisions, not motivational guarantees.
Coordinate a careful response to an Amazon patent complaint by preserving notices, verifying jurisdiction and patent status, obtaining qualified counsel, comparing non-infringement, design-around, license, settlement, and validity options, and preparing factual platform submissions. Use when a listing is removed for alleged patent infringement. This is operational triage, not legal advice.
Diagnose why an Amazon keyword converts poorly at top of search but better elsewhere by analyzing placement reports, effective bids, competitor context, and controlled experiments. Use when an exact keyword gains premium placement yet underperforms, when raising bids shifts spend to the wrong placement, or when the user wants a placement test. Never claim exact page-position control.
Filter, interpret, and turn the authorized 2025 Amazon Prime Day advertising insight records into a qualified event plan without averaging incompatible slices or treating historical benchmarks as forecasts. Use for Prime Day/会员日广告规划, 旺季预算, ROAS/DPV/Units/Sales benchmark, marketplace insight lookup, Sponsored Ads/SP/SB/Display mix, event baseline comparison, preheat/peak/tail plan, or querying the included 116-row insight dataset. Default to analysis and draft; verify the current event dates, eligibility, policies, inventory, economics, and account data before any live change.
Research, diagnose, and draft Amazon Ads ASIN and category product-targeting plans that complement keyword targeting, including audience expansion, competitor and category traffic, cross-sell, upsell, self-defense, negative targeting, placement analysis, and single-variable experiments. Use for 商品投放, ASIN 定向, 品类定向, Product Targeting, 关键词引流遇到瓶颈, 关联流量, 互补品/替代品, 竞品详情页抢流量, 自家 ASIN 防御, Best Sellers/New Releases 候选, 自动与手动广告联动, or the local file named 如何提升关键词引流效率. Default to research and draft; verify current marketplace capabilities and never mutate live campaigns without explicit human approval.
Reduce avoidable Amazon returns through root-cause analysis, accurate listing content, packaging and quality fixes, official Product Support features, manuals, support videos, spare-parts workflows, and privacy-safe service operations. Use when customers return products due to setup, usage, missing parts, or delayed support. Never request reviews during support or build an unauthorized customer list.
Audit suspicious Amazon review patterns and proposed review-growth tactics for policy risk, then replace unsafe ideas with official reporting and compliant review programs. Use when the user encounters sudden review spikes, asks about 直评突破, synchronized submissions, paid reviews, review services, or how to investigate competitor review anomalies. Do not reverse-engineer or enable manipulation.
Design a scalable Amazon seller operating system using category specialization, portfolio capacity, operator skill tiers, SOP floors, experiment governance, incentives, and resource allocation. Use when the user asks how a small team can manage more products, improve operator productivity, allocate launches, or balance expert autonomy with standardized processes. Do not repeat anecdotal revenue claims as benchmarks.
Assess whether a team should enter or expand on Amazon using unit economics, cash runway, product-market fit, operational capability, compliance, and staged validation. Use when the user asks whether Amazon is still worth doing, whether a new seller or factory should enter, what product-price band to choose, or how to make a GO/NO-GO decision. Do not promise returns or treat anecdotes as benchmarks.
Respond safely to suspicious Amazon buyer-seller messages, extortion, phishing, coercive orders, and review threats by preserving evidence, avoiding off-platform engagement, securing accounts, and reporting through official channels. Use when a seller receives threatening messages, false service claims, unusual bulk orders, or requests for money or credentials. Do not conceal the seller's own policy violations.
Prepare an evidence-based tax-compliance triage pack for Amazon cross-border sellers by mapping entities, marketplaces, goods flow, customs declarations, invoices, settlements, returns, and inventory reconciliation, then framing questions for qualified tax and customs advisers. Use when users ask about 0110, 1039, 9810, offshore entities, historical gaps, or future compliance. This is not tax or legal advice and must not enable evasion.
Plan, review, diagnose, and safely launch Amazon Sponsored Products video-format ads (SP video/SPV), including eligibility, video briefs, policy checks, ASIN and thumbnail mapping, bid adjustments, measurement, and controlled tests. Use for 商品推广视频, SPV, Sponsored Products video, 静音商品视频, 搜索结果视频素材, 视频竞价加成, 3–5条视频测试, video CTR/CVR/ACOS, or converting the authorized SPV intro, shooting, and syndication materials into an approval-ready plan. Treat all specs and availability as source snapshots until verified in the current marketplace and console; never upload or change live ads without explicit approval.
Diagnose and plan Amazon UK apparel advertising with UK-specific consumer behavior, seasonality, compliance gates, and ASIN lifecycle playbooks for long-lifecycle, short-lifecycle, and seasonal products. Use for 英国站服饰广告, UK apparel ads, 睡衣/泳衣/外套投放, ASIN 生命周期判断, Black Friday/Boxing Day 节奏, UK/EU 尺码, 品牌推广与视频, 季节性预算日历, 退货率与广告利润, ACOS/ROAS 诊断, 复购再营销, 或根据《亚马逊英国站服饰品类广告运营手册》输出可审批的投放方案. Default to analysis and draft; do not mutate live campaigns without explicit human approval.
Design Amazon Ads for legitimate parent-child variations by selecting a hero child, allocating queries by variant attributes, isolating budgets, and monitoring halo effects and inventory. Use when the user asks how to advertise many variants, prevent child ASINs from competing, choose a hero variation, or split keywords by color, size, pack count, or style. Never create invalid variations or execute without approval.
Plan an ad-intensive but policy-compliant Amazon launch that expands indexed and converting keyword coverage while preserving profitability and inventory guardrails. Use when the user asks for a pure white-hat launch, broad keyword coverage, when to move terms into exact campaigns, or how to combine auto, broad, product targeting, and eligible promotions. No review manipulation or ranking guarantees.
Assess a proposed Woot deal or onboarding path using current official eligibility, account ownership, deal economics, inventory, brand rights, operational responsibilities, and compliance risks. Use when the user asks whether to apply for Woot, use a service provider, submit a deal, or treat Woot as a launch channel. Never support account trading, fake reviews, artificial orders, or risk shielding.
Create and quality-control AI-assisted apparel ad concepts from verified product assets, fit and material facts, usage scenarios, rights, and current ad policies. Use before generating or testing fashion creative variants.
Build a privacy-safe Amazon Marketing Cloud audience hypothesis, measurement query plan, and activation experiment. Use when standard audiences are insufficient and the advertiser needs a custom multi-touch or engagement-based segment.
Map Amazon product targets by substitute, complement, own-brand defense, and category discovery using current relevance, price, rating, and performance evidence. Use to structure Sponsored Products, Sponsored Brands, or display product targeting.
Build an Amazon competitor value map from comparable product facts, review and Q&A themes, price, offer, creative, and observable advertising. Use for product, listing, and ad strategy without copying claims or inventing competitor backend data.
Design a goal-led Amazon display advertising plan using contextual, remarketing, in-market, lifestyle, or custom audience tactics. Use when extending beyond search ads while keeping targeting, measurement, and budget expansion controlled.
Turn a verified high-intent Amazon search query into an evidence-backed video storyboard, mobile-first captions, and controlled creative variants. Use for Sponsored Brands video or other eligible video ads.
Build localized, evidence-backed Amazon keyword plans for seasonal peaks across multiple marketplaces. Use when preparing listings and ads for an event while preventing literal translation, shared-budget assumptions, and cross-market data leakage.
Transfer proven Amazon launch knowledge into a new marketplace while revalidating compliance, localization, demand, fees, inventory, and ad eligibility. Use when expanding from a mature marketplace without assuming past success will repeat.
Prepare Amazon ads for a major shopping event with a backward calendar, product tiers, inventory and deal checks, proven keywords, budget guardrails, and post-event recovery. Use before Prime Day, Black Friday, or a regional peak.
Build and test a seasonal Amazon keyword cluster from local shopper intent, event timing, product relevance, and landing-page evidence. Use for holiday, occasion, gifting, or weather-led search opportunities without keyword stuffing.
Manage Amazon ads at the whole-ASIN level before pruning individual targets, separating multi-touch traffic contribution from genuinely irrelevant queries and product-page conversion gaps. Use for 为什么只留出单词后订单更少、自动词移出后变差、整体广告怎么调、词级归因误判.
Design Amazon ad architecture around product searchability, query intent, ASIN substitutability, placements, and evidence quality while separating architecture from conversion root causes. Use for 广告架构怎么搭、有精准词或泛词怎么分、无关键词产品、自动和ASIN投放. Not for blaming architecture for every conversion issue.
Diagnose Amazon ad conversion that declines, stays weak, or fluctuates by separating placement expansion, price-value fit, query relevance, product-page differentiation, and market events. Use for 广告转化越来越差、一直不出单、转化忽高忽低、点击增加但订单不增. Do not use to make live campaign changes without approval.
Review Amazon ad performance as a time sequence and explain how bid, placement, click velocity, conversion, and contribution profit changed after each intervention. Use for 广告日报复盘、调价后为什么变好或变差、点击速度分析、ACOS变化归因. Do not use for isolated one-day judgments.
Optimize Amazon ads in an attribution-safe order: placement allocation first, irrelevant-query controls second, and target-level bid changes last. Use for 先调广告位还是先否词、竞价越调越乱、商品页流量差、搜索词清理. Do not apply negatives mechanically.
Route an Amazon product into precise-attribute, broad-intent, or no-clear-keyword advertising structures based on product truth, search behavior, conversion, and unit economics. Use for 不同产品怎么选广告打法、服装多变体、泛流量品、无明确关键词产品、保守或进攻策略.
Create evidence-backed Amazon image and A+ briefs with AI while preserving the real product's shape, color, scale, included components, and policy compliance. Use for AI做亚马逊商品图、主图差异化、副图和A+规划、评论洞察转视觉、图片测款. Not for fabricating the product.
Turn verified product facts and third-party keyword exports into an auditable AI workflow for filtering queries, selecting ad candidates, drafting listing fields, and preserving evidence for every claim. Use for AI写Listing、关键词表清洗、竞品词过滤、广告词选择、标题五点和后台词草拟. Not for invented claims or direct publishing.
Plan an Amazon new-product traffic mix that limits early audience noise across manual keywords, product targeting, display audiences, and video while testing whether weak conversion is traffic- or product-led. Use for 新品人群标签、用户画像、相同关键词转化不同、展示或视频广告受众、前期控流量.
Validate whether an apparent Amazon blue-ocean opportunity is driven by durable differentiated demand or by price, promotion, review, variation, or off-platform distortions. Use for 蓝海选品、低评论高销量、新品异军突起、差异化机会、真假蓝海. This is a pre-screen, not a sourcing decision.
Plan a profit-oriented Amazon boutique-product operating rhythm from selection gates through precise traffic tests, conversion validation, bid control, and cautious scaling. Use for 精品怎么投广告、低评论新品、追求高投产、什么时候减少或扩大广告. Do not use review manipulation.
Assess an Amazon boutique business model through differentiated demand, pricing room, saturation, tail conversion, market health, capital turnover, and downside controls. Use for 什么是精品、精品选品框架、资金周转、市场健康度、精品是否成立. Do not reduce boutique strategy to premium creative.
Reverse-engineer an Amazon breakout case by testing competing explanations across product innovation, brand, keyword breadth, organic visibility, timing, variants, promotions, returns, and compliance. Use for 爆款案例复盘、为什么突然增长、是不是站外、能不能复制、成功因素拆解.
Control Amazon ad budget and placement as separate levers, diagnosing when click acceleration dilutes conversion and when additional budget can extend profitable traffic. Use for 加预算后转化下降、点击太快、怎么调顶部和商品页、预算不足还是竞价过高.
Infer competitor keyword coverage from observable organic and sponsored positions, shared attributes, query patterns, and product relevance without claiming access to a competitor account. Use for 竞品广告词分析、反查竞品关键词、判断精准词与探索词、找防守或捡漏词. Do not claim unseen campaign settings as facts.
Build a first-pass Amazon competitor traffic network from high-relevance organic and sponsored query observations plus attribute-root exploration. Use for 抢竞品流量、竞品关键词分层、精准和广泛互补、旧版流量网络复盘. Prefer the v2 Skill when full evidence is available.
Build an evidence-gated Amazon competitor traffic network with separate opportunistic exact, contested exact, attribute exploration, automatic discovery, and defense layers. Use for 系统化抢竞品流量、自然强但广告弱的词、竞品核心词、防守词、自动捡漏. Use v1 only for a simpler audit.
Scale Amazon ad traffic only after conversion quality, click velocity, budget continuity, inventory, and contribution economics show that broader reach can be absorbed. Use for 点击突然变多转化下降、要不要加预算、自然排名与广告扩量、维护新品转化.
Diagnose weak Amazon conversion product-first by benchmarking comparable listings, market difficulty, price-value fit, visual differentiation, keyword precision, and placement mix. Use for 产品转化差、广告不出单、价格还是图片问题、词不准还是商品页问题、商品页位置差.
Set Amazon per-SKU inventory and advertising boundaries from realistic sales capacity, cash turnover, conversion, break-even acquisition cost, and portfolio risk. Use for 首批发多少、库存临界点、精铺盈利逻辑、广告花费怎么控、多产品组合风险.
Build testable hypotheses for Amazon keyword relevance and ranking from conversion, clicks, orders, add-to-cart signals, listing semantics, and related-query behavior without claiming a proprietary ranking formula. Use for 关键词权重、精准小词和大词关系、为什么出单后曝光增加、自然排名机制假设.
Turn competitor keyword observations and verified product attributes into separate exact, exploratory, listing, and backend keyword maps with explicit inclusion and exclusion reasons. Use for 写Listing前选词、广告精准词和广泛词、竞品词表过滤、关键词去向映射.
Design an evidence-gated Amazon new-product ad launch with precise keyword selection, small coherent groups, controlled traffic acquisition, placement learning, and profit-aware scaling. Use for 新品广告怎么开、冷启动词怎么选、预算和竞价怎么设、何时降价扩量. Execution remains approval-gated.
Estimate Amazon operating difficulty from comparable-product sales distribution, low-review performance, ad dependence, store constraints, returns, and inventory break-even rather than headline demand alone. Use for 产品好不好运营、评论少能卖多少、该发多少货、头部销量高但新品难做. Do not treat heuristic ratios as guarantees.
Design a compliant Amazon organic-rank experiment around relevant high-converting queries, variant fit, controlled ad support, and incremental-profit checks. Use for 推关键词自然排名、选择推词变体、自然第一后是否停广告、精准词放量. Do not use artificial orders.
Assess paid-versus-organic Amazon order contribution with whole-ASIN economics, cannibalization tests, query relevance, and time-window controls instead of subtracting attributed ad orders mechanically. Use for 广告单挤占自然单、关广告会不会掉单、哪些词可停、自然订单怎么算、整体广告盈利.
Improve Amazon conversion by mining verified customer language, repositioning a product around a defensible benefit, and aligning images, title, bullets, and precise traffic in a controlled test. Use for 主图差异化、评论洞察、标题五点优化、同质化产品重新定位、提升转化.
Find defensible Amazon niches inside competitive categories by combining differentiated product slices, low-review performance, ad-efficiency proxies, and evidence-quality checks. Use for 红海里找蓝海、化妆包等细分类目、低评论销量、广告效率、运营难度. Not for copying or review merging.
Screen an Amazon niche whose demand window, differentiated slice, low-review competition, conservative stocking, and operational follow-through must all align before launch. Use for 季节性蓝海、未来月份选品、趋势下滑、低评论切片、首批库存和后续开发.
Control Amazon spread-model advertising around per-SKU break-even, conservative inventory, precise keyword tests, uniform group logic, and low-touch exception management. Use for 精铺广告打法、广告花费占比、少量备货、多个SKU怎么管、何时淘汰.
Design an Amazon spread-model portfolio that limits per-SKU inventory and ad exposure while aggregating profit across validated non-standardized product opportunities. Use for 精铺选品和运营、多SKU小批量、单品盈利平衡、组合备货. Not for indiscriminate catalog flooding.
Pre-screen Amazon spread-model product ideas by demand timing, market depth, differentiated slices, operating difficulty, conservative sales capacity, and inventory exposure. Use for 精铺选品、低库存多产品模式、首批备货、运营难度快筛. Formal investment decisions require broader research.
Plan a compliant Amazon post-stockout recovery by rebuilding precise traffic, reassessing lost organic and related-product exposure, and scaling only after conversion stabilizes. Use for 补货后转化变差、断货后自然流量掉了、广告重启、受众重新校准. Do not use for review or order manipulation.
Turn a verified buyer concern into a factual, mobile-readable Amazon secondary-image brief. Use when a product needs to explain compatibility, setup, safety, dimensions, or expected results without simulating a review or social-media endorsement.
Translate familiar content patterns into compliant Amazon secondary-image or A+ experiments without copying social interfaces, fabricating testimonials, or changing the main-image rules. Use when listing creative feels generic and needs a controlled engagement test.
Build a fair Amazon creative-team scorecard that separates controllable craft and process quality from shared CTR, conversion, and business outcomes. Use to brief, review, and coach designers without assigning them sole ownership of sales metrics.
Turn Amazon customer evidence into a human-centered product brief covering the job, context, functional and emotional outcomes, alternatives, constraints, and validation. Use before product development or major differentiation decisions.
Design an Amazon image-composition test around one buyer question, truthful product scale, visual hierarchy, mobile readability, and current image rules. Use when an image feels busy or fails to communicate value clearly.
Diagnose Amazon ads with impressions and clicks but low conversion by tracing traffic relevance, placement, offer, reviews, listing expectation match, product fit, inventory, and market change. Use before changing bids or creative.
Design a category-compliant Amazon main-image distinctiveness experiment based on shopper recognition and click-through evidence. Use when competing listings look interchangeable or a visual-similarity feature creates an unverified optimization hypothesis.
Gate an Amazon new-product launch across product-market evidence, compliance, offer, listing, inventory, unit economics, ads, and measurement. Use before treating campaign creation as the launch plan.
Run a controlled Amazon ad-taper experiment after a query reaches stable organic visibility, measuring paid incrementality, total query performance, profit, and rank durability. Use instead of abruptly cutting ads.
Translate verified Amazon product facts into feature-advantage-benefit-proof copy and carefully bounded sensory or emotional language. Use for titles, bullets, images, A+ modules, and ads without unsupported superlatives.
聚焦品牌与备案、知识产权、客服与工单的 Amazon 账户、合规与风险。在需要识别受影响账户与义务，核对通知、证据和期限，形成风险分级、纠正计划与可审核材料时使用；根据当前业务问题选择证据卡，避免加载无关主题。
聚焦点击表现、关键词体系、新品启动的 Amazon 广告诊断与实验。在需要区分流量、零售承接和经济性问题，形成有样本依据的调整建议及受控实验时使用；根据当前业务问题选择证据卡，避免加载无关主题。
聚焦品牌与备案、图片与视频、类目与节点的 Amazon 品牌、内容与增长。在需要将购买任务和品牌事实映射到内容与渠道，形成可验证的创意简报和增长计划时使用；根据当前业务问题选择证据卡，避免加载无关主题。
聚焦评价与口碑、用户画像、新品启动的 Amazon 客户反馈与转化改进。在需要从评论、退货和客服证据定位购买障碍，形成产品或页面改进的优先级和验证计划时使用；根据当前业务问题选择证据卡，避免加载无关主题。
聚焦平台费用、季节性、库存管理的 Amazon FBA、物流与库存。在需要核对可售、在途、交期和费用，量化断货与积压风险，形成可执行的库存处理方案时使用；根据当前业务问题选择证据卡，避免加载无关主题。
聚焦评价与口碑、FBA、发货与入库的 Amazon FBA、物流与库存。在需要核对可售、在途、交期和费用，量化断货与积压风险，形成可执行的库存处理方案时使用；根据当前业务问题选择证据卡，避免加载无关主题。
聚焦供应链、发货与入库、现金流的 Amazon 费用、利润与现金流。在需要统一费用、订单和币种口径，复算贡献利润、资金缺口及压力情景时使用；根据当前业务问题选择证据卡，避免加载无关主题。
聚焦税务与出口、平台费用、季节性的 Amazon 费用、利润与现金流。在需要统一费用、订单和币种口径，复算贡献利润、资金缺口及压力情景时使用；根据当前业务问题选择证据卡，避免加载无关主题。
聚焦五点描述、标题、AI 工作流的 Amazon Listing 与页面操作。在需要定位页面与字段问题，形成有产品事实支撑的修改草案、只读预检和结果核验计划时使用；根据当前业务问题选择证据卡，避免加载无关主题。
聚焦新品启动、市场机会、季节性的 Amazon Listing 与页面操作。在需要定位页面与字段问题，形成有产品事实支撑的修改草案、只读预检和结果核验计划时使用；根据当前业务问题选择证据卡，避免加载无关主题。
聚焦AI 工作流、五点描述、卖家精灵的 Amazon 跨境经营复盘。在需要将经验判断转成可验证的问题，识别适用前提、替代解释和可迁移的经营动作时使用；根据当前业务问题选择证据卡，避免加载无关主题。
聚焦Apify、归因分析、转化率的 Amazon 跨境经营复盘。在需要将经验判断转成可验证的问题，识别适用前提、替代解释和可迁移的经营动作时使用；根据当前业务问题选择证据卡，避免加载无关主题。
聚焦现金流、新品启动、库存管理的 Amazon 跨境经营复盘。在需要将经验判断转成可验证的问题，识别适用前提、替代解释和可迁移的经营动作时使用；根据当前业务问题选择证据卡，避免加载无关主题。
聚焦类目与节点、五点描述、变体关系的 Amazon 跨境经营复盘。在需要将经验判断转成可验证的问题，识别适用前提、替代解释和可迁移的经营动作时使用；根据当前业务问题选择证据卡，避免加载无关主题。
聚焦经营经验的 Amazon 跨境经营复盘。在需要将经验判断转成可验证的问题，识别适用前提、替代解释和可迁移的经营动作时使用；根据当前业务问题选择证据卡，避免加载无关主题。
聚焦站外流量、品牌与备案的 Amazon 跨境经营复盘。在需要将经验判断转成可验证的问题，识别适用前提、替代解释和可迁移的经营动作时使用；根据当前业务问题选择证据卡，避免加载无关主题。
聚焦平台费用、税务与出口、FBA的 Amazon 跨境经营复盘。在需要将经验判断转成可验证的问题，识别适用前提、替代解释和可迁移的经营动作时使用；根据当前业务问题选择证据卡，避免加载无关主题。
聚焦库存管理、Featured Offer、站外流量的 Amazon 跨境经营复盘。在需要将经验判断转成可验证的问题，识别适用前提、替代解释和可迁移的经营动作时使用；根据当前业务问题选择证据卡，避免加载无关主题。
聚焦新品启动、竞价策略、手动广告的 Amazon 跨境经营复盘。在需要将经验判断转成可验证的问题，识别适用前提、替代解释和可迁移的经营动作时使用；根据当前业务问题选择证据卡，避免加载无关主题。
聚焦政策变化、客服与工单、手动广告的 Amazon 跨境经营复盘。在需要将经验判断转成可验证的问题，识别适用前提、替代解释和可迁移的经营动作时使用；根据当前业务问题选择证据卡，避免加载无关主题。
聚焦市场机会、卖家精灵、自动广告的 Amazon 跨境经营复盘。在需要将经验判断转成可验证的问题，识别适用前提、替代解释和可迁移的经营动作时使用；根据当前业务问题选择证据卡，避免加载无关主题。
聚焦利润模型、汇率风险、手动广告的 Amazon 跨境经营复盘。在需要将经验判断转成可验证的问题，识别适用前提、替代解释和可迁移的经营动作时使用；根据当前业务问题选择证据卡，避免加载无关主题。
聚焦评价与口碑、成本结构、账户验证的 Amazon 跨境经营复盘。在需要将经验判断转成可验证的问题，识别适用前提、替代解释和可迁移的经营动作时使用；根据当前业务问题选择证据卡，避免加载无关主题。
聚焦季节性、库存管理、发货与入库的 Amazon 跨境经营复盘。在需要将经验判断转成可验证的问题，识别适用前提、替代解释和可迁移的经营动作时使用；根据当前业务问题选择证据卡，避免加载无关主题。
聚焦季节性、ACOS、转化率的 Amazon 跨境经营复盘。在需要将经验判断转成可验证的问题，识别适用前提、替代解释和可迁移的经营动作时使用；根据当前业务问题选择证据卡，避免加载无关主题。
聚焦发货与入库、账户健康、客服与工单的 Amazon 跨境经营复盘。在需要将经验判断转成可验证的问题，识别适用前提、替代解释和可迁移的经营动作时使用；根据当前业务问题选择证据卡，避免加载无关主题。
聚焦供应链、广告曝光、差异化的 Amazon 跨境经营复盘。在需要将经验判断转成可验证的问题，识别适用前提、替代解释和可迁移的经营动作时使用；根据当前业务问题选择证据卡，避免加载无关主题。
聚焦税务与出口、供应链、发货与入库的 Amazon 跨境经营复盘。在需要将经验判断转成可验证的问题，识别适用前提、替代解释和可迁移的经营动作时使用；根据当前业务问题选择证据卡，避免加载无关主题。
聚焦税务与出口、成本结构、现金流的 Amazon 跨境经营复盘。在需要将经验判断转成可验证的问题，识别适用前提、替代解释和可迁移的经营动作时使用；根据当前业务问题选择证据卡，避免加载无关主题。
聚焦运营工具、卖家精灵、CPC的 Amazon 跨境经营复盘。在需要将经验判断转成可验证的问题，识别适用前提、替代解释和可迁移的经营动作时使用；根据当前业务问题选择证据卡，避免加载无关主题。
聚焦账户验证、用户画像、A+ 页面的 Amazon 跨境经营复盘。在需要将经验判断转成可验证的问题，识别适用前提、替代解释和可迁移的经营动作时使用；根据当前业务问题选择证据卡，避免加载无关主题。
聚焦政策变化、利润模型、季节性的 Amazon 平台政策与异常监控。在需要核对官方规则、时间和适用范围，形成影响清单、应对动作与复核节点时使用；根据当前业务问题选择证据卡，避免加载无关主题。
聚焦现金流、利润模型、供应链的 Amazon 选品与产品开发。在需要验证购买需求、直接竞争、差异化和单位经济，形成进入、有限验证或暂缓的有据判断时使用；根据当前业务问题选择证据卡，避免加载无关主题。
聚焦差异化、类目与节点、市场机会的 Amazon 选品与产品开发。在需要验证购买需求、直接竞争、差异化和单位经济，形成进入、有限验证或暂缓的有据判断时使用；根据当前业务问题选择证据卡，避免加载无关主题。
聚焦FBM、发货与入库、图片与视频的 Amazon 选品与产品开发。在需要验证购买需求、直接竞争、差异化和单位经济，形成进入、有限验证或暂缓的有据判断时使用；根据当前业务问题选择证据卡，避免加载无关主题。
聚焦Listing 诊断、品牌与备案、税务与出口的 Amazon 账户、合规与风险。在需要识别受影响账户与义务，核对通知、证据和期限，形成风险分级、纠正计划与可审核材料时使用；根据当前业务问题选择证据卡，避免加载无关主题。
聚焦评价与口碑、政策变化、客服与工单的 Amazon 账户、合规与风险。在需要识别受影响账户与义务，核对通知、证据和期限，形成风险分级、纠正计划与可审核材料时使用；根据当前业务问题选择证据卡，避免加载无关主题。
聚焦竞价策略、卖家精灵、关键词排名的 Amazon 广告诊断与实验。在需要区分流量、零售承接和经济性问题，形成有样本依据的调整建议及受控实验时使用；根据当前业务问题选择证据卡，避免加载无关主题。
聚焦高客单价、利润模型、成本结构的 Amazon 广告诊断与实验。在需要区分流量、零售承接和经济性问题，形成有样本依据的调整建议及受控实验时使用；根据当前业务问题选择证据卡，避免加载无关主题。
聚焦否定投放、搜索词分析、手动广告的 Amazon 广告诊断与实验。在需要区分流量、零售承接和经济性问题，形成有样本依据的调整建议及受控实验时使用；根据当前业务问题选择证据卡，避免加载无关主题。
聚焦ROAS、政策变化、ACOS的 Amazon 广告诊断与实验。在需要区分流量、零售承接和经济性问题，形成有样本依据的调整建议及受控实验时使用；根据当前业务问题选择证据卡，避免加载无关主题。
聚焦类目与节点、标题、转化率的 Amazon 品牌、内容与增长。在需要将购买任务和品牌事实映射到内容与渠道，形成可验证的创意简报和增长计划时使用；根据当前业务问题选择证据卡，避免加载无关主题。
聚焦成本结构、客服与工单、汇率风险的 Amazon 品牌、内容与增长。在需要将购买任务和品牌事实映射到内容与渠道，形成可验证的创意简报和增长计划时使用；根据当前业务问题选择证据卡，避免加载无关主题。
聚焦经营经验的 Amazon 品牌、内容与增长。在需要将购买任务和品牌事实映射到内容与渠道，形成可验证的创意简报和增长计划时使用；根据当前业务问题选择证据卡，避免加载无关主题。
聚焦图片与视频、运营工具、A+ 页面的 Amazon 客户反馈与转化改进。在需要从评论、退货和客服证据定位购买障碍，形成产品或页面改进的优先级和验证计划时使用；根据当前业务问题选择证据卡，避免加载无关主题。
聚焦季节性、现金流、竞品验证的 Amazon FBA、物流与库存。在需要核对可售、在途、交期和费用，量化断货与积压风险，形成可执行的库存处理方案时使用；根据当前业务问题选择证据卡，避免加载无关主题。
聚焦运营工具、尺寸重量、点击表现的 Amazon FBA、物流与库存。在需要核对可售、在途、交期和费用，量化断货与积压风险，形成可执行的库存处理方案时使用；根据当前业务问题选择证据卡，避免加载无关主题。
聚焦FBA、发货与入库、成本结构的 Amazon 费用、利润与现金流。在需要统一费用、订单和币种口径，复算贡献利润、资金缺口及压力情景时使用；根据当前业务问题选择证据卡，避免加载无关主题。
聚焦尺寸重量、变体关系、品牌与备案的 Amazon Listing 与页面操作。在需要定位页面与字段问题，形成有产品事实支撑的修改草案、只读预检和结果核验计划时使用；根据当前业务问题选择证据卡，避免加载无关主题。
聚焦评价与口碑、五点描述、转化率的 Amazon Listing 与页面操作。在需要定位页面与字段问题，形成有产品事实支撑的修改草案、只读预检和结果核验计划时使用；根据当前业务问题选择证据卡，避免加载无关主题。
聚焦搜索词分析、差异化、关键词排名的 Amazon Listing 与页面操作。在需要定位页面与字段问题，形成有产品事实支撑的修改草案、只读预检和结果核验计划时使用；根据当前业务问题选择证据卡，避免加载无关主题。
聚焦卖家精灵、新品启动、市场机会的 Amazon Listing 与页面操作。在需要定位页面与字段问题，形成有产品事实支撑的修改草案、只读预检和结果核验计划时使用；根据当前业务问题选择证据卡，避免加载无关主题。
聚焦经营经验、FBA、AI 工作流的 Amazon 跨境经营复盘。在需要将经验判断转成可验证的问题，识别适用前提、替代解释和可迁移的经营动作时使用；根据当前业务问题选择证据卡，避免加载无关主题。
聚焦FBA、Listing 诊断、匹配方式的 Amazon 平台政策与异常监控。在需要核对官方规则、时间和适用范围，形成影响清单、应对动作与复核节点时使用；根据当前业务问题选择证据卡，避免加载无关主题。
聚焦市场机会、运营工具、关键词体系的 Amazon 选品与产品开发。在需要验证购买需求、直接竞争、差异化和单位经济，形成进入、有限验证或暂缓的有据判断时使用；根据当前业务问题选择证据卡，避免加载无关主题。
Benchmark Amazon niche ad efficiency by comparing visible advertising breadth, estimated sales, review cohorts, CPC, and conversion economics. Use when deciding whether a product has a simple enough paid-traffic structure for a new entrant.
Create a guarded Amazon Ads launch plan that prioritizes query relevance, listing alignment, conversion evidence, and capped learning spend. Use when a new product has little history and the team wants to improve auction eligibility without assuming a hidden fixed weight score.
Govern AI-assisted Amazon product research by separating automatable evidence work from human commercial judgment. Use when auditing an AI selection workflow, prompt, agent, or Skill that produces product recommendations.
Discover Amazon product opportunities by starting with a clearly defined audience and mapping recurring work, life, event, and gifting needs. Use when product-first searches produce generic red-ocean ideas.
Build a compact Amazon Ads launch keyword set that combines controlled broad discovery with exact intent capture. Use when a new niche product has many candidate terms but limited inventory and cannot afford to test a huge keyword list.
Reposition mature commodity products into differentiated bulk, kit, gifting, or event solutions without unnecessary tooling. Use when a seller has accessible commodity supply but lacks a defensible Amazon use case.
Diagnose weak Amazon conversion in a fixed order from offer and detail-page fundamentals to traffic quality and market ceiling. Use when an FBA product receives clicks but underperforms on orders.
Estimate a defensible pre-launch conversion range from Amazon first-party opportunity data, competitor traffic proxies, and unit economics. Use when a product appears profitable only under an assumed CVR and the team needs a risk-aware launch gate.
Run a fast Amazon opportunity gate using precise-query CPC, defensible conversion scenarios, visible differentiation, and unit economics. Use when deciding whether a product deserves deeper research before spending on samples or inventory.
Use material, feature, audience, occasion, style, and scenario keywords to discover Amazon opportunities across categories. Use when category-first filters are too narrow or the team wants to reuse a supply capability across multiple demand contexts.
Backtrack from a hot Amazon product to the audience, occasion, event, or scenario that created demand, then expand adjacent opportunities. Use when sales charts show what sold but not why it sold.
Plan a compliant long-lived Amazon variation roadmap for products that can legitimately expand by color, size, pattern, or other allowed themes. Use when a team wants recurring niche launches under one valid parent without abusing review sharing.
Match a factory's materials, processes, tooling, MOQ, and quality capabilities to lower-competition Amazon use cases. Use when a manufacturer has supply strength but its standard products face crowded, expensive traffic.
Screen a beginner's first Amazon product for a small, low-review niche with defensible paid-traffic economics. Use when the goal is to learn the FBA loop while protecting capital rather than chasing a large launch.
Evaluate whether a higher-priced Amazon offer creates enough contribution margin to absorb paid traffic and operational risk. Use when comparing low-ticket and bundled or higher-value product concepts.
Validate whether an Amazon niche with dominant old listings still admits low-review new entrants. Use when the first search page looks saturated but the user wants to test for unmet audience, scenario, form, size, or price-segment demand.
Determine whether an Amazon product can economically tolerate the market's CPC and conversion environment before trying to optimize ACoS. Use when ads remain expensive, volume falls after bid cuts, or a team needs a launch feasibility gate.
Screen Amazon niche opportunities for precise demand, low competitive density, conservative paid-traffic profitability, and small-batch inventory fit. Use when the user asks how to find genuinely small blue-ocean markets.
Build an Amazon product, audience, and use-case knowledge map before making product decisions. Use when a beginner lacks product ideas, rejects unfamiliar items too quickly, or needs a disciplined discovery routine.
Run an eight-gate Amazon product selection audit covering discoverability, operating difficulty, economics, differentiation, timing, seasonality, compliance, and inventory. Use when a candidate needs a complete pre-purchase decision review.
Choose a compliant Amazon validation method based on whether the uncertainty is concept acceptance, marketplace conversion, or paid-traffic economics. Use when deciding whether to test an original design, a differentiated mature product, or a proven-market candidate.
Reposition an existing Amazon product resource into a more defensible audience, occasion, or use-case market. Use when a seller asks whether a familiar product can serve different buyers without changing its core manufacturing process.
Operate Amazon Ads for a small-inventory portfolio around a profit boundary rather than rank maximization. Use when campaigns should deliver controlled profitable demand, not force every SKU to scale.
Find defensible micro-niches inside a crowded Amazon category by combining low-review recent winners with precise design, attribute, or use-case terms. Use when a broad category looks saturated but supports visible sub-demand.
Route Amazon product research across six portfolio lanes: niche demand, extensible variations, bulky high-value items, regulated or high-barrier products, seasonal events, and higher-ticket bundles. Use when building a diversified product pipeline around operational fit.
Design a scalable Amazon operating model that converts isolated product wins into repeatable portfolio processes. Use when comparing hero-product and diversified niche strategies or planning growth from a founder-led team to an operating system.
Discover Amazon product opportunities by extracting scenario, audience, activity, and occasion terms from unusual listings. Use when a user has no product inspiration and wants to turn a precise use context into a cross-category candidate set.
Design low-capex Amazon differentiation by repositioning an existing product for a specific audience, occasion, or use case. Use when a seller cannot justify tooling but needs a visible, evidence-backed reason to buy.
Screen Amazon product ideas for low-review seasonal niches with clear query intent, manageable competition, entry timing, and paid-traffic economics. Use when a user needs a fast first-pass candidate list without treating filter thresholds as a final launch decision.
Discover seasonal Amazon micro-niches from accelerating, moderate-volume keywords and then expand from the underlying event scenario. Use when the team wants future demand signals rather than today's bestseller list.
Plan a compliant multi-year lifecycle for seasonal Amazon products using early entry, controlled inventory, valid variations, and year-over-year evidence. Use when a seasonal ASIN should compound learning without review or variation manipulation.
Build a year-round Amazon portfolio calendar around seasonal, holiday, social-event, and bulk-purchase demand. Use when a team wants diversified monthly peaks instead of dependence on one evergreen hero ASIN.
Plan a small-budget, part-time Amazon FBA validation with explicit cost, timeline, product count, and stop-loss assumptions. Use when a beginner wants to test the full operating loop without a large upfront commitment.
Diagnose whether adding Amazon listings is growing a store or merely redistributing a stable order pool. Use when a store adds SKUs but total orders remain flat, old products decline, or the team needs a staged assortment expansion plan.
Apply a tunable low-volume, higher-price, low-review screen to surface unfamiliar Amazon niches for deeper research. Use when the candidate universe is too large and the user needs a fast, explicitly non-final first pass.
聚焦品牌与备案、知识产权、广告曝光的 Amazon 账户、合规与风险。在需要识别受影响账户与义务，核对通知、证据和期限，形成风险分级、纠正计划与可审核材料时使用；根据当前业务问题选择证据卡，避免加载无关主题。
聚焦经营经验的 Amazon 账户、合规与风险。在需要识别受影响账户与义务，核对通知、证据和期限，形成风险分级、纠正计划与可审核材料时使用；根据当前业务问题选择证据卡，避免加载无关主题。
聚焦税务与出口、账户验证、运营工具的 Amazon 账户、合规与风险。在需要识别受影响账户与义务，核对通知、证据和期限，形成风险分级、纠正计划与可审核材料时使用；根据当前业务问题选择证据卡，避免加载无关主题。
聚焦政策变化、点击表现、运营工具的 Amazon 广告诊断与实验。在需要区分流量、零售承接和经济性问题，形成有样本依据的调整建议及受控实验时使用；根据当前业务问题选择证据卡，避免加载无关主题。
聚焦季节性、利润模型、差异化的 Amazon 广告诊断与实验。在需要区分流量、零售承接和经济性问题，形成有样本依据的调整建议及受控实验时使用；根据当前业务问题选择证据卡，避免加载无关主题。
聚焦新品启动、关键词体系、品牌与备案的 Amazon 品牌、内容与增长。在需要将购买任务和品牌事实映射到内容与渠道，形成可验证的创意简报和增长计划时使用；根据当前业务问题选择证据卡，避免加载无关主题。
聚焦利润模型、知识产权、运营工具的 Amazon 品牌、内容与增长。在需要将购买任务和品牌事实映射到内容与渠道，形成可验证的创意简报和增长计划时使用；根据当前业务问题选择证据卡，避免加载无关主题。
聚焦评价与口碑、政策变化、Listing 诊断的 Amazon 品牌、内容与增长。在需要将购买任务和品牌事实映射到内容与渠道，形成可验证的创意简报和增长计划时使用；根据当前业务问题选择证据卡，避免加载无关主题。
聚焦汇率风险、季节性、品牌与备案的 Amazon 客户反馈与转化改进。在需要从评论、退货和客服证据定位购买障碍，形成产品或页面改进的优先级和验证计划时使用；根据当前业务问题选择证据卡，避免加载无关主题。
聚焦评价与口碑、Listing 诊断、AI 工作流的 Amazon 客户反馈与转化改进。在需要从评论、退货和客服证据定位购买障碍，形成产品或页面改进的优先级和验证计划时使用；根据当前业务问题选择证据卡，避免加载无关主题。
聚焦FBA的 Amazon FBA、物流与库存。在需要核对可售、在途、交期和费用，量化断货与积压风险，形成可执行的库存处理方案时使用；根据当前业务问题选择证据卡，避免加载无关主题。
聚焦政策变化、库存管理、平台费用的 Amazon FBA、物流与库存。在需要核对可售、在途、交期和费用，量化断货与积压风险，形成可执行的库存处理方案时使用；根据当前业务问题选择证据卡，避免加载无关主题。
聚焦市场机会、退货与退款、季节性的 Amazon FBA、物流与库存。在需要核对可售、在途、交期和费用，量化断货与积压风险，形成可执行的库存处理方案时使用；根据当前业务问题选择证据卡，避免加载无关主题。
聚焦评价与口碑、FBA、发货与入库的 Amazon FBA、物流与库存。在需要核对可售、在途、交期和费用，量化断货与积压风险，形成可执行的库存处理方案时使用；根据当前业务问题选择证据卡，避免加载无关主题。
聚焦季节性、成本结构、尺寸重量的 Amazon FBA、物流与库存。在需要核对可售、在途、交期和费用，量化断货与积压风险，形成可执行的库存处理方案时使用；根据当前业务问题选择证据卡，避免加载无关主题。
聚焦平台费用、利润模型、政策变化的 Amazon 费用、利润与现金流。在需要统一费用、订单和币种口径，复算贡献利润、资金缺口及压力情景时使用；根据当前业务问题选择证据卡，避免加载无关主题。
聚焦Listing 诊断、政策变化、标题的 Amazon Listing 与页面操作。在需要定位页面与字段问题，形成有产品事实支撑的修改草案、只读预检和结果核验计划时使用；根据当前业务问题选择证据卡，避免加载无关主题。
聚焦市场机会、利润模型、竞品验证的 Amazon Listing 与页面操作。在需要定位页面与字段问题，形成有产品事实支撑的修改草案、只读预检和结果核验计划时使用；根据当前业务问题选择证据卡，避免加载无关主题。
聚焦AI 工作流、用户画像、运营工具的 Amazon 跨境经营复盘。在需要将经验判断转成可验证的问题，识别适用前提、替代解释和可迁移的经营动作时使用；根据当前业务问题选择证据卡，避免加载无关主题。
聚焦A+ 页面的 Amazon 跨境经营复盘。在需要将经验判断转成可验证的问题，识别适用前提、替代解释和可迁移的经营动作时使用；根据当前业务问题选择证据卡，避免加载无关主题。
聚焦经营经验的 Amazon 跨境经营复盘。在需要将经验判断转成可验证的问题，识别适用前提、替代解释和可迁移的经营动作时使用；根据当前业务问题选择证据卡，避免加载无关主题。
聚焦平台费用、税务与出口、发货与入库的 Amazon 跨境经营复盘。在需要将经验判断转成可验证的问题，识别适用前提、替代解释和可迁移的经营动作时使用；根据当前业务问题选择证据卡，避免加载无关主题。
聚焦关键词排名、广告曝光、归因分析的 Amazon 跨境经营复盘。在需要将经验判断转成可验证的问题，识别适用前提、替代解释和可迁移的经营动作时使用；根据当前业务问题选择证据卡，避免加载无关主题。
聚焦市场机会、成本结构、汇率风险的 Amazon 跨境经营复盘。在需要将经验判断转成可验证的问题，识别适用前提、替代解释和可迁移的经营动作时使用；根据当前业务问题选择证据卡，避免加载无关主题。
聚焦退货与退款、品牌与备案、库存管理的 Amazon 跨境经营复盘。在需要将经验判断转成可验证的问题，识别适用前提、替代解释和可迁移的经营动作时使用；根据当前业务问题选择证据卡，避免加载无关主题。
聚焦季节性、政策变化、使用场景的 Amazon 跨境经营复盘。在需要将经验判断转成可验证的问题，识别适用前提、替代解释和可迁移的经营动作时使用；根据当前业务问题选择证据卡，避免加载无关主题。
聚焦供应链、站外流量、知识产权的 Amazon 跨境经营复盘。在需要将经验判断转成可验证的问题，识别适用前提、替代解释和可迁移的经营动作时使用；根据当前业务问题选择证据卡，避免加载无关主题。
聚焦运营工具、政策变化、搜索词分析的 Amazon 跨境经营复盘。在需要将经验判断转成可验证的问题，识别适用前提、替代解释和可迁移的经营动作时使用；根据当前业务问题选择证据卡，避免加载无关主题。
聚焦政策变化、运营工具、类目与节点的 Amazon 平台政策与异常监控。在需要核对官方规则、时间和适用范围，形成影响清单、应对动作与复核节点时使用；根据当前业务问题选择证据卡，避免加载无关主题。
聚焦汇率风险、市场机会、成本结构的 Amazon 选品与产品开发。在需要验证购买需求、直接竞争、差异化和单位经济，形成进入、有限验证或暂缓的有据判断时使用；根据当前业务问题选择证据卡，避免加载无关主题。
聚焦供应链、发货与入库、税务与出口的 Amazon 选品与产品开发。在需要验证购买需求、直接竞争、差异化和单位经济，形成进入、有限验证或暂缓的有据判断时使用；根据当前业务问题选择证据卡，避免加载无关主题。
聚焦FBM、供应链、发货与入库的 Amazon 账户、合规与风险。在需要识别受影响账户与义务，核对通知、证据和期限，形成风险分级、纠正计划与可审核材料时使用；根据当前业务问题选择证据卡，避免加载无关主题。
聚焦AI 工作流、图片与视频、站外流量的 Amazon 广告诊断与实验。在需要区分流量、零售承接和经济性问题，形成有样本依据的调整建议及受控实验时使用；根据当前业务问题选择证据卡，避免加载无关主题。
聚焦品牌与备案、手动广告、自动广告的 Amazon 广告诊断与实验。在需要区分流量、零售承接和经济性问题，形成有样本依据的调整建议及受控实验时使用；根据当前业务问题选择证据卡，避免加载无关主题。
聚焦预算分配、竞价策略、匹配方式的 Amazon 广告诊断与实验。在需要区分流量、零售承接和经济性问题，形成有样本依据的调整建议及受控实验时使用；根据当前业务问题选择证据卡，避免加载无关主题。
聚焦竞品验证、商品投放、关键词体系的 Amazon 广告诊断与实验。在需要区分流量、零售承接和经济性问题，形成有样本依据的调整建议及受控实验时使用；根据当前业务问题选择证据卡，避免加载无关主题。
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聚焦账号关联、自动广告、类目与节点的 Amazon 广告诊断与实验。在需要区分流量、零售承接和经济性问题，形成有样本依据的调整建议及受控实验时使用；根据当前业务问题选择证据卡，避免加载无关主题。
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聚焦SP 广告、新品启动、广告位的 Amazon 广告诊断与实验。在需要区分流量、零售承接和经济性问题，形成有样本依据的调整建议及受控实验时使用；根据当前业务问题选择证据卡，避免加载无关主题。
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聚焦TACOS、利润模型、ACOS的 Amazon 广告诊断与实验。在需要区分流量、零售承接和经济性问题，形成有样本依据的调整建议及受控实验时使用；根据当前业务问题选择证据卡，避免加载无关主题。
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聚焦评价与口碑、新品启动、点击表现的 Amazon 客户反馈与转化改进。在需要从评论、退货和客服证据定位购买障碍，形成产品或页面改进的优先级和验证计划时使用；根据当前业务问题选择证据卡，避免加载无关主题。
聚焦库存管理、发货与入库、点击表现的 Amazon FBA、物流与库存。在需要核对可售、在途、交期和费用，量化断货与积压风险，形成可执行的库存处理方案时使用；根据当前业务问题选择证据卡，避免加载无关主题。
聚焦供应链、利润模型、发货与入库的 Amazon 费用、利润与现金流。在需要统一费用、订单和币种口径，复算贡献利润、资金缺口及压力情景时使用；根据当前业务问题选择证据卡，避免加载无关主题。
聚焦现金流、FBA、发货与入库的 Amazon Listing 与页面操作。在需要定位页面与字段问题，形成有产品事实支撑的修改草案、只读预检和结果核验计划时使用；根据当前业务问题选择证据卡，避免加载无关主题。
聚焦广告位、评价与口碑、关键词体系的 Amazon Listing 与页面操作。在需要定位页面与字段问题，形成有产品事实支撑的修改草案、只读预检和结果核验计划时使用；根据当前业务问题选择证据卡，避免加载无关主题。
聚焦经营经验的 Amazon 跨境经营复盘。在需要将经验判断转成可验证的问题，识别适用前提、替代解释和可迁移的经营动作时使用；根据当前业务问题选择证据卡，避免加载无关主题。
聚焦利润模型、新品启动、供应链的 Amazon 跨境经营复盘。在需要将经验判断转成可验证的问题，识别适用前提、替代解释和可迁移的经营动作时使用；根据当前业务问题选择证据卡，避免加载无关主题。
聚焦关键词体系、卖家精灵、季节性的 Amazon 平台政策与异常监控。在需要核对官方规则、时间和适用范围，形成影响清单、应对动作与复核节点时使用；根据当前业务问题选择证据卡，避免加载无关主题。
聚焦竞品验证、成本结构、关键词体系的 Amazon 选品与产品开发。在需要验证购买需求、直接竞争、差异化和单位经济，形成进入、有限验证或暂缓的有据判断时使用；根据当前业务问题选择证据卡，避免加载无关主题。
聚焦ROAS、季节性、竞价策略的 Amazon 选品与产品开发。在需要验证购买需求、直接竞争、差异化和单位经济，形成进入、有限验证或暂缓的有据判断时使用；根据当前业务问题选择证据卡，避免加载无关主题。