本文へ移動
cccskills
無料GitHub で公開

sif-amazon-research

Use this skill for Amazon e-commerce research, market validation, competitor analysis, traffic diagnosis, keyword strategy, advertising review, launch evaluation, and growth optimization using the Sif MCP server and Sif's real marketplace, traffic, keyword, sales, and ad data. Trigger when users mention Sif, Sif MCP, ASIN research, Amazon product opportunity research, keyword opportunity, competitor teardown, traffic drop/root-cause diagnosis, advertising structure review, or whether a product/market is worth entering, launching, scaling, optimizing, or abandoning.

インストール方法を見る

含まれるファイル(4)

  • SKILL.md4.0 KB
  • references/field-notes.md4.2 KB
  • references/research-playbooks.md5.4 KB
  • references/sif-tool-map.md4.2 KB

SKILL.md(原文)

インストールする前に、エージェントに与えられる指示の中身を確認できます。

Sif Amazon Research

Operating rules

Use Sif MCP as the evidence source whenever available. Do not infer final business conclusions from one metric alone.

Before calling tools, identify the task type:

  • Market/opportunity research: keyword or category direction.
  • Market validation: keyword + candidate ASIN viability.
  • Competitor analysis: competitor ASIN growth path and traffic source.
  • Keyword layout: main terms, long tails, root structure, missing terms.
  • Launch evaluation: whether to push a product.
  • Post-launch feedback: continue, optimize, or stop.
  • Growth optimization: expand proven traffic and keywords.
  • Traffic/ad diagnosis: root cause of traffic or ad performance changes.

If the request does not include enough identifiers, ask for the missing ASIN, keywords, country, and time window. Default country to US only when the user does not specify a marketplace.

Core workflow

  1. Define the decision being made: enter / not enter, launch / not launch, scale / optimize / stop, or diagnose root cause.
  2. Collect Sif evidence across the minimum necessary domains:
    • Market demand: keyword demand, history, root trend.
    • Competition: keyword competition, top ASIN concentration, competitor keyword signals.
    • Traffic: ASIN/listing traffic trend, structure, keyword distribution.
    • Sales: sales list/trend, variant performance.
    • Ads: ASIN ad structure, ad trend, campaign contribution, campaign/ad group drill-down.
  3. Cross-check evidence. Separate confirmed findings from hypotheses.
  4. Produce a business decision with confidence, evidence chain, risks, missing data, and next validation actions.

For scenario-specific tool sequences, read references/research-playbooks.md. For tool selection and parameter conventions, read references/sif-tool-map.md. For real-world MCP response quirks, large-output handling, and field interpretation pitfalls, read references/field-notes.md before any full analysis or debugging.

Required output format

For every Sif-based research answer, include:

  1. Decision / conclusion: the direct answer and confidence level.
  2. Problem layer: market demand, competition, traffic, ads, product/listing, or mixed.
  3. Evidence chain: concise bullets linking each claim to Sif data returned by tools.
  4. Business impact: what the evidence means for sales, traffic, ranking, or launch risk.
  5. Priority actions: the top 3 actions, ordered by expected impact and speed.
  6. Do not do yet: actions blocked by missing or weak evidence.
  7. Missing data: 3 data gaps and which conclusion each gap affects.
  8. 7-day validation plan: measurable checks the user can run quickly.

If data is incomplete, say what current data supports, what is missing, and whether the recommendation is only a hypothesis or execution-ready.

Guardrails

  • Do not conclude from ACOS alone, CTR alone, or sales change alone.
  • Do not output a final plan when key Sif data is unavailable; label it as a hypothesis.
  • Do not treat analyze_traffic_anomaly as a generic raw-data tool. Use it only for explicit traffic anomaly/root-cause diagnosis or when the user explicitly asks to diagnose traffic.
  • Preserve any render_footer returned by Sif tools verbatim at the end of the reply.
  • Respect Sif time conventions: week values must be Sunday dates; current week data may be delayed, so use latelyDay=7 for current-week-style checks.
  • Do not expose or repeat MCP secret keys.

レビュー

まだレビューはありません。使ってみた感想をお寄せください。

同じリポジトリのスキル

概要と使いどころ

对亚马逊竞品Listing进行全维度穿透分析,包括文案逻辑、评论分析、关键词分析、市场动态等。分析完成后自动保存为Markdown报告文档到reports/目录。Invoke when user uses /amazon-analyse command with a product ASIN.

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。

liangdabiao/amazon-sorftime-research-MCP-skill9732026年10月9日 更新

亚马逊品类自动化选品分析技能。通过五维评分模型对亚马逊品类进行深度市场调研,生成Markdown分析报告。当用户使用 /category-selection 命令或提出'分析XX品类'、'XX品类市场调研'、'XX品类选品'等需求时触发此技能。支持配置分析数量,默认Top20。

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。

liangdabiao/amazon-sorftime-research-MCP-skill9732026年10月9日 更新

亚马逊关键词深度调研与智能分类分析。基于 Sorftime MCP 数据采集 2000+ 关键词,通过 LLM Agent 按 8 维度智能分类(否定词、品牌词、材质词、场景词、属性词、功能词、核心词、其他),生成 Markdown 报告、CSV 词库和 HTML 仪表板。触发方式:/keyword-research {ASIN} {SITE}

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。

liangdabiao/amazon-sorftime-research-MCP-skill9732026年10月9日 更新

基于Sorftime MCP的深度选品调研。通过LLM Agent执行多维度分析:数据采集→属性标注→交叉分析→竞品VOC→壁垒评估→选品决策评估。交互式执行,输出Markdown报告和Dashboard看板。

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。

liangdabiao/amazon-sorftime-research-MCP-skill9732026年10月9日 更新

对亚马逊商品评论进行深度分析,自动识别产品痛点、分析退货原因,生成改进建议和客服回复模板。Invoke when user uses /review-analysis command with a product ASIN.

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。

liangdabiao/amazon-sorftime-research-MCP-skill9732026年10月9日 更新

基于西柚洞察MCP的亚马逊竞品分析与广告策略工具。提供7大核心工作场景:实时监控广告投放效果、快速找到高性价比流量缺口、快速拆解对标竞对打法、提升新品推广效率、精准拆解竞品流量以及广告策略、透视竞品广告策略和预算、高效搭建关键词库。适用于亚马逊卖家进行竞品分析、广告优化和关键词研究。

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。

liangdabiao/amazon-sorftime-research-MCP-skill9732026年10月9日 更新

liangdabiao のスキルをすべて見る

このスキルの問題を報告する