agentsclimarketplace

Merch research

Skill vikynofebriputra-creator/Orkas-Awesome-AgentSkills/ecommerce/skills/merch-research

Research e-commerce merchandise opportunities, category viability, price bands, competitors, profit assumptions, sourcing risks, and evidence gaps across Amazon and Chinese commerce platforms.From its SKILL.md

Install
npx -y skills add vikynofebriputra-creator/Orkas-Awesome-AgentSkills --skill merch-research

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SKILL.md

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商品选品研究

用于电商商品的前置研究和选品判断。核心任务是帮助用户判断“这个商品/类目是否值得继续验证”,并把数据来源、估算口径、风险和下一步验证说清楚。

何时使用

  • 用户提供 Amazon keyword、category、ASIN、brand 或竞品材料,希望做类目机会、竞品、价格、评论痛点或选品可行性分析。
  • 用户要研究淘宝、天猫、京东、拼多多、抖音、快手、1688、小红书等国内平台的商品机会。
  • 用户要设计价格对比表、竞品字段、利润测算、采集计划或选品报告。
  • 用户已有手动整理的数据、后台导出或合法 API 结果,希望形成经营判断。

不用于商品页面文案、主图/视频创意、评论 VOC 深度归因、下单采购、广告投放或自动平台抓取。

如何调用

  1. 先确认研究对象:商品、关键词、类目、平台、站点、价格带、目标市场、卖家能力、预算、供应链条件和已有数据。
  2. 判断路径:
    • Amazon 市场/ASIN/类目研究:使用 references/amazon-market-research.md
    • 国内平台选品/价格/供应链研究:使用 references/china-platform-research.md
  3. 明确数据来源:用户上传、平台后台导出、手动摘录、公开页面、授权 API、第三方数据源。来源不足时标为待验证。
  4. 不默认自动抓取平台;如用户要求采集,先说明平台条款、登录、频率、授权和反爬风险。
  5. 若使用 APIClaw,必须检查 APICLAW_API_KEY 和额度;缺失时不请求数据,只说明需要凭证、用途和成本风险。
  6. 进行需求、竞争、价格带、评价壁垒、差异化、利润、供应链、合规和售后风险分析。
  7. 对每个结论标注证据类型:直接数据、推断、建议或待验证。使用 references/data-provenance-and-confidence.md
  8. 输出报告结构时使用 references/product-research-output.md

返回格式

  • 调研目标和数据来源。
  • 市场需求与价格带。
  • 竞争格局、评价壁垒和差异化机会。
  • 利润测算框架和供应链风险。
  • 平台/类目合规与售后风险。
  • 数据来源、置信标签和待验证项。
  • 结论:推荐 / 观察 / 暂缓。
  • 下一步验证计划。

外部依赖

  • 无必需外部依赖。
  • Amazon APIClaw 数据研究需要 APICLAW_API_KEY、账户额度和网络访问。
  • Excel、CSV 或表格文件分析需要当前会话具备文件读取能力。
  • 自动采集平台页面必须另行确认授权、登录状态、平台条款、采集频率和依赖。

限制与已知问题

  • 不承诺销量、利润、排名、广告 ROAS 或经营结果。
  • 不把平台展示销量、BSR、热度、评论数或第三方估算当作绝对事实。
  • 不绕过登录、验证码、反爬、权限或付费 API 限制。
  • 不替用户做最终采购、备货、投放或定价决策。
  • 高风险类目如食品、保健、美妆、儿童、医疗器械、宠物食品和电器,需要更严格证据和人工复核。

What ships with it: 4 files

7.0 KB alongside SKILL.md

Gives 0 of the 12 instructions most research analysis skills give in ~1.1k tokens

Counted across 1,063 of the 1,754 authors here whose files we hold, read 2026-08-07

  • Generate a markdown reportin 32 of 1063, across 23 files
  • Cite each claim's sourcein 30 of 1063, across 15 files
  • Define the ideal customer profilein 20 of 1063, across 2 files
  • Search for companies matching the criteriain 20 of 1063, across 2 files
  • Assign a fit score from one to tenin 20 of 1063, across 2 files
  • Analyze the codebase to understand the productin 19 of 1063, across 1 file
  • Ask clarifying questions about the value propositionin 19 of 1063, across 1 file
  • Look for signals of immediate needin 19 of 1063, across 1 file
  • Identify the target decision maker rolein 19 of 1063, across 1 file
  • Suggest a personalized contact strategyin 19 of 1063, across 1 file
  • Provide conversation starters for outreachin 19 of 1063, across 1 file
  • Format results in a scannable markdown templatein 19 of 1063, across 1 file

Grouped from the skills themselves: near-identical wordings counted once, and counted by distinct author, so one author publishing three of these counts once. Length counted with cl100k_base; the agent that loads this file may tokenize it differently.

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