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Model Context Protocol · By xjli360
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| # | Skill | Repository | Stars | Used in | Tokens | Auto-check | Licence | Updated |
|---|---|---|---|---|---|---|---|---|
| 1 | Diagnose high Amazon Ads ACoS by decomposing CPC, conversion rate, price, query mix, placement mix, and sample sufficiency. | xjli360/ | 251 | — | ~552 | Automated safety check: Pass | MIT | 13 days ago |
| 2 | 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… | xjli360/ | 251 | — | ~491 | Automated safety check: Pass | MIT | 13 days ago |
| 3 | Plan Amazon ASIN product-targeting campaigns by scoring product similarity, demand, truthful conversion advantages, placement hypotheses, budget isolation, and downstream search-term harvesting. | xjli360/ | 251 | — | ~518 | Automated safety check: Pass | MIT | 13 days ago |
| 4 | 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… | xjli360/ | 251 | — | ~523 | Automated safety check: Pass | MIT | 13 days ago |
| 5 | 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. | xjli360/ | 251 | — | ~530 | Automated safety check: Pass | MIT | 13 days ago |
| 6 | Map verified product facts to customer shopping missions, structured attributes and localized Amazon listing content, then design observable conversational-shopping tests. | xjli360/ | 251 | — | ~541 | Automated safety check: Pass | MIT | 13 days ago |
| 7 | 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… | xjli360/ | 251 | — | ~533 | Automated safety check: Pass | MIT | 13 days ago |
| 8 | 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. | xjli360/ | 251 | — | ~503 | Automated safety check: Pass | MIT | 13 days ago |
| 9 | Create a disciplined Amazon keyword-position monitoring system that prioritizes revenue-contributing terms, separates organic and sponsored observations, controls measurement noise, and triggers… | xjli360/ | 251 | — | ~511 | Automated safety check: Pass | MIT | 13 days ago |
| 10 | 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… | xjli360/ | 251 | — | ~535 | Automated safety check: Pass | MIT | 13 days ago |
| 11 | Turn an Amazon keyword universe into a clean taxonomy and campaign map using relevance, intent, roots, negatives, and evidence from authorized reports. | xjli360/ | 251 | — | ~546 | Automated safety check: Pass | MIT | 13 days ago |
| 12 | 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… | xjli360/ | 251 | — | ~524 | Automated safety check: Pass | MIT | 13 days ago |
| 13 | 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. | xjli360/ | 251 | — | ~577 | Automated safety check: Pass | MIT | 13 days ago |
| 14 | 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. | xjli360/ | 251 | — | ~529 | Automated safety check: Pass | MIT | 13 days ago |
| 15 | Triage negative Amazon reviews into policy violations, suspected abuse, and genuine product feedback, then prepare factual official reports and product-remediation actions. | xjli360/ | 251 | — | ~515 | Automated safety check: Pass | MIT | 13 days ago |
| 16 | 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. | xjli360/ | 251 | — | ~518 | Automated safety check: Pass | MIT | 13 days ago |
| 17 | Diagnose why an Amazon new product cannot gain traction by separating retail-readiness, traffic relevance, click-through, conversion, economics, and feedback quality. | xjli360/ | 251 | — | ~524 | Automated safety check: Pass | MIT | 13 days ago |
| 18 | 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. | xjli360/ | 251 | — | ~548 | Automated safety check: Pass | MIT | 13 days ago |
| 19 | 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. | xjli360/ | 251 | — | ~565 | Automated safety check: Pass | MIT | 13 days ago |
| 20 | 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. | xjli360/ | 251 | — | ~509 | Automated safety check: Pass | MIT | 13 days ago |
| 21 | 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… | xjli360/ | 251 | — | ~531 | Automated safety check: Pass | MIT | 13 days ago |
| 22 | Audit suspicious Amazon review patterns and proposed review-growth tactics for policy risk, then replace unsafe ideas with official reporting and compliant review programs. | xjli360/ | 251 | — | ~535 | Automated safety check: Pass | MIT | 13 days ago |
| 23 | Assess whether a team should enter or expand on Amazon using unit economics, cash runway, product-market fit, operational capability, compliance, and staged validation. | xjli360/ | 251 | — | ~533 | Automated safety check: Pass | MIT | 13 days ago |
| 24 | 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. | xjli360/ | 251 | — | ~528 | Automated safety check: Pass | MIT | 13 days ago |
| 25 | Plan an ad-intensive but policy-compliant Amazon launch that expands indexed and converting keyword coverage while preserving profitability and inventory guardrails. | xjli360/ | 251 | — | ~531 | Automated safety check: Pass | MIT | 13 days ago |
| 26 | Design Amazon ad architecture around product searchability, query intent, ASIN substitutability, placements, and evidence quality while separating architecture from conversion root causes. | xjli360/ | 251 | — | ~566 | Automated safety check: Pass | MIT | 13 days ago |
| 27 | 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. | xjli360/ | 251 | — | ~578 | Automated safety check: Pass | MIT | 13 days ago |
| 28 | Optimize Amazon ads in an attribution-safe order: placement allocation first, irrelevant-query controls second, and target-level bid changes last. | xjli360/ | 251 | — | ~541 | Automated safety check: Pass | MIT | 13 days ago |
| 29 | 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. | xjli360/ | 251 | — | ~553 | Automated safety check: Pass | MIT | 13 days ago |
| 30 | Create evidence-backed Amazon image and A+ briefs with AI while preserving the real product's shape, color, scale, included components, and policy compliance. | xjli360/ | 251 | — | ~551 | Automated safety check: Pass | MIT | 13 days ago |
| 31 | 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… | xjli360/ | 251 | — | ~573 | Automated safety check: Pass | MIT | 13 days ago |
| 32 | Validate whether an apparent Amazon blue-ocean opportunity is driven by durable differentiated demand or by price, promotion, review, variation, or off-platform distortions. | xjli360/ | 251 | — | ~546 | Automated safety check: Pass | MIT | 13 days ago |
| 33 | Plan a profit-oriented Amazon boutique-product operating rhythm from selection gates through precise traffic tests, conversion validation, bid control, and cautious scaling. | xjli360/ | 251 | — | ~552 | Automated safety check: Pass | MIT | 13 days ago |
| 34 | Assess an Amazon boutique business model through differentiated demand, pricing room, saturation, tail conversion, market health, capital turnover, and downside controls. | xjli360/ | 251 | — | ~538 | Automated safety check: Pass | MIT | 13 days ago |
| 35 | Reverse-engineer an Amazon breakout case by testing competing explanations across product innovation, brand, keyword breadth, organic visibility, timing, variants, promotions, returns, and compliance. | xjli360/ | 251 | — | ~540 | Automated safety check: Pass | MIT | 13 days ago |
| 36 | Infer competitor keyword coverage from observable organic and sponsored positions, shared attributes, query patterns, and product relevance without claiming access to a competitor account. | xjli360/ | 251 | — | ~556 | Automated safety check: Pass | MIT | 13 days ago |
| 37 | Build a first-pass Amazon competitor traffic network from high-relevance organic and sponsored query observations plus attribute-root exploration. | xjli360/ | 251 | — | ~531 | Automated safety check: Pass | MIT | 13 days ago |
| 38 | Build an evidence-gated Amazon competitor traffic network with separate opportunistic exact, contested exact, attribute exploration, automatic discovery, and defense layers. | xjli360/ | 251 | — | ~554 | Automated safety check: Pass | MIT | 13 days ago |
| 39 | Diagnose weak Amazon conversion product-first by benchmarking comparable listings, market difficulty, price-value fit, visual differentiation, keyword precision, and placement mix. | xjli360/ | 251 | — | ~537 | Automated safety check: Pass | MIT | 13 days ago |
| 40 | Set Amazon per-SKU inventory and advertising boundaries from realistic sales capacity, cash turnover, conversion, break-even acquisition cost, and portfolio risk. | xjli360/ | 251 | — | ~524 | Automated safety check: Pass | MIT | 13 days ago |
| 41 | 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… | xjli360/ | 251 | — | ~549 | Automated safety check: Pass | MIT | 13 days ago |
| 42 | Turn competitor keyword observations and verified product attributes into separate exact, exploratory, listing, and backend keyword maps with explicit inclusion and exclusion reasons. | xjli360/ | 251 | — | ~540 | Automated safety check: Pass | MIT | 13 days ago |
| 43 | 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. | xjli360/ | 251 | — | ~562 | Automated safety check: Pass | MIT | 13 days ago |
| 44 | Estimate Amazon operating difficulty from comparable-product sales distribution, low-review performance, ad dependence, store constraints, returns, and inventory break-even rather than headline… | xjli360/ | 251 | — | ~560 | Automated safety check: Pass | MIT | 13 days ago |
| 45 | Design a compliant Amazon organic-rank experiment around relevant high-converting queries, variant fit, controlled ad support, and incremental-profit checks. | xjli360/ | 251 | — | ~529 | Automated safety check: Pass | MIT | 13 days ago |
| 46 | 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… | xjli360/ | 251 | — | ~560 | Automated safety check: Pass | MIT | 13 days ago |
| 47 | 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. | xjli360/ | 251 | — | ~547 | Automated safety check: Pass | MIT | 13 days ago |
| 48 | Find defensible Amazon niches inside competitive categories by combining differentiated product slices, low-review performance, ad-efficiency proxies, and evidence-quality checks. | xjli360/ | 251 | — | ~543 | Automated safety check: Pass | MIT | 13 days ago |
