GitHub user
Agent skills by xjli360
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- 179
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- 1
Repositories by xjli360
Skills by xjli360, ranked
Ranked by score. Sort bymost stars,trending,newest,recently updated
| # | Skill | Repository | Stars | Used in | Tokens | Auto-check | Licence | Updated |
|---|---|---|---|---|---|---|---|---|
| 1 | 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… | xjli360/ | 251 | — | ~1.2k | Automated safety check: Pass | MIT | 13 days ago |
| 2 | Diagnose and draft Amazon Canada apparel advertising plans with lifecycle and seasonal timing, English/French search coverage, account evidence, profitability guardrails, and approval-ready… | xjli360/ | 251 | — | ~1.2k | Automated safety check: Pass | MIT | 13 days ago |
| 3 | 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… | xjli360/ | 251 | — | ~1.4k | Automated safety check: Pass | MIT | 13 days ago |
| 4 | 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… | xjli360/ | 251 | — | ~591 | Automated safety check: Pass | MIT | 13 days ago |
| 5 | 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… | xjli360/ | 251 | — | ~1.2k | Automated safety check: Pass | MIT | 13 days ago |
| 6 | 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 |
| 7 | 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 |
| 8 | Diagnose and plan Amazon US apparel advertising with an ASIN lifecycle playbook covering long-lifecycle, short-lifecycle, and seasonal products. | xjli360/ | 251 | — | ~1.9k | Automated safety check: Pass | MIT | 13 days ago |
| 9 | 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 |
| 10 | 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 |
| 11 | 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 |
| 12 | 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 |
| 13 | 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 |
| 14 | 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 |
| 15 | 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 |
| 16 | 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 |
| 17 | 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 |
| 18 | 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 |
| 19 | 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 |
| 20 | 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 |
| 21 | 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 |
| 22 | 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 |
| 23 | 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 |
| 24 | 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 |
| 25 | 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 |
| 26 | 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 |
| 27 | 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 |
| 28 | 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 |
| 29 | 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 |
| 30 | 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 |
| 31 | 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 |
| 32 | 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 |
| 33 | 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 |
| 34 | 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 |
| 35 | 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 |
| 36 | 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 |
| 37 | 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 |
| 38 | 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 |
| 39 | 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 |
| 40 | 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 |
| 41 | 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 |
| 42 | 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 |
| 43 | 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 |
| 44 | 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 |
| 45 | 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 |
| 46 | 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 |
| 47 | 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 |
| 48 | 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 |
Questions, answered from the data.
What is the best skill by xjli360?
Sealeap Amazon Acos Diagnostics from xjli360/sealeap-amazon-skills ranks first of the 179 skills by xjli360 listed here, with the highest score: its repository has 251 GitHub stars, its SKILL.md loads about 1.2k tokens and it passes the automated safety check with no findings. Next come Sealeap Amazon Ca Apparel Ads and Sealeap Amazon Listing Optimizer.
Are xjli360's skills official?
None yet. All 179 skills by xjli360 listed here come from community repositories; a skill counts as official when the product's own GitHub organization publishes it.
How are these skills ranked?
By Skill Navigator score, which combines the GitHub stars of the skill's repository (shared across that repo's skills and discounted for large collections), how many other GitHub owners carry a copy of the skill, and automated SKILL.md quality checks, minus penalties for safety-check warnings and for each further skill from the same repository. Skills that fail the safety check are not listed.