Getting it into your agent
One page per mod, every tool's command on it. A separate URL per tool would split the same page into five that compete with each other.
npx skills add xjli360/sealeap-amazon-ad-skills --skill sealeap-qinglong-amazon-product-researchgit clone --depth 1 https://github.com/xjli360/sealeap-amazon-ad-skillsWrote this? Show the measurements
A badge with what this costs and how it scanned, read live from this page, so it follows the numbers instead of freezing them. Markdown for a README, HTML for a documentation site or a project page.
[](https://agentmods.dev/skills/xjli360/sealeap-amazon-ad-skills/sealeap-qinglong-amazon-product-research)<a href="https://agentmods.dev/skills/xjli360/sealeap-amazon-ad-skills/sealeap-qinglong-amazon-product-research"><img src="https://agentmods.dev/badge/skills/xjli360/sealeap-amazon-ad-skills/sealeap-qinglong-amazon-product-research/github.svg" alt="Measured on agentmods" height="20"></a>Or the 80×15 button, for a site that already has a row of RSS and ATOM ones. Only the verdict fits; the numbers stay here.
<a href="https://agentmods.dev/skills/xjli360/sealeap-amazon-ad-skills/sealeap-qinglong-amazon-product-research"><img src="https://agentmods.dev/badge/skills/xjli360/sealeap-amazon-ad-skills/sealeap-qinglong-amazon-product-research.svg" alt="Reviewed on agentmods" width="80" height="20"></a>What it costs to keep this loaded
Counted locally with the o200k_base tokenizer, which is exact for GPT models; Claude uses its own tokenizer and its counts differ. Treat this as one consistent yardstick across the catalogue rather than a bill. Prices are per million input tokens.
| Model | Per session | Once invoked |
|---|---|---|
| Fable 5.1 | $0.00076 | $0.01169 |
| Opus 5.5 | $0.00030 | $0.00468 |
| Sonnet 5.5 | $0.00015 | $0.00234 |
| Haiku 4.5 | $0.00008 | $0.00117 |
Grade A, and why
sealeap-qinglong-amazon-product-research scanned grade A with 0 findings against 26 rules in 11 categories — prompt injection, anti-refusal, data exfiltration, privilege escalation, supply chain, agent snooping, system-prompt leakage, SSRF and excessive agency — measured 1mo ago.
A static scan of the body, not an audit. Every finding is printed with the line that produced it so you can judge whether it matters here. A mod is markdown that instructs an agent; that is exactly why what it instructs is worth reading.
Nothing flagged
None of the 26 patterns this scan looks for appear in this file: no shell pipes, no recursive deletes, no credential paths, no hidden text, no instruction-override or anti-refusal phrasing, no agent-config snooping. That is not a guarantee, it is the absence of the things that are checkable.
What it actually says
Amazon 选品与产品开发：市场机会、运营工具、关键词体系
目标
围绕市场机会、运营工具、关键词体系，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。
使用范围
- 优先处理选品与产品开发任务；从专项证据卡选择与当前对象和问题直接相关的主题。
- 集合名称用于维护文件归属，不限制用户要求的交叉验证。其他来源与业务域的证据须分别标注，再按同口径比较。
- 本 Skill 可独立使用，不依赖仓库中的私有语义稿。保留去标识化边界，不恢复原素材身份或逐条映射。
适用任务
- 机会卡
- 竞品与 VOC 证据表
- 差异化规格
开始前要拿到
- 目标站点、类目、价格带和风险偏好。
- 需求、关键词、直接竞品、评论与上架时间。
- 采购、物流、平台费、广告和退货成本。
- 产品事实、供应链、IP 与合规约束。
缺少字段时明确标为 UNKNOWN 或 NEEDS_EVIDENCE，不要补造数据。
不可妥协的边界
- 本 Skill 来自去标识化语义转译，不保留或推断素材来源身份，也不把素材观点冒充 Amazon 当前政策。
- 产品事实、账户事实和 Amazon 一方报告优先；第三方数据必须标明 Provider、站点、日期、样本和估算口径。
- 不捏造销量、搜索量、成本、合规状态、产品属性、评论、平台通知或执行结果。
- 默认提供诊断或草案。写入前展示对象、旧值、新值、影响、停止线与回退，核对用户已有授权是否覆盖对象、动作与预算；范围已明确授权时继续执行并回读核验，只有未覆盖或扩大的范围才请求批准。
- 平台规则、费率、界面和资格会变化；执行前复核当前官方文档与后台状态。
工作流
- 定义购买对象、场景、核心任务和直接竞品边界。
- 用需求、竞争、评论和新进入者证据验证市场，而非只看热度。
- 从差评、退货和使用障碍提炼可工程化改进。
- 统一规格核算落地成本、广告承受力和贡献利润。
- 通过样品、合规、IP 和小批量测试后再形成 GO/NO-GO。
专项路由
- 市场机会：先验证需求、直接竞争、价格带和进入门槛，再讨论开发。
- 运营工具：先核对工具的数据来源、权限、费用和导出字段，再决定是否接入。
- 关键词体系：按品类、属性、人群、场景和问题收益组织词库，并记录来源与相关性。
- 卖家精灵：先发现实时 MCP schema，再以第三方估算口径获取关键词、竞品或评论证据。
- 点击表现：按搜索结果与广告位拆分点击数据，区分素材、价格、承诺和流量相关性问题。
- 竞品验证：按相同购买对象、场景、功能和价格带定义直接竞品，并识别异常样本。
- FBA：核对配送模式、尺寸重量、费率、入仓要求和可追踪状态。
- Listing 诊断：从合规、可发现性、点击和转化四层审计当前详情页。
完整的 26 张主题证据卡见 references/topic-cards.md；4 种原有组合见 references/scenario-patterns.md。
第三方数据（可选）
只有在用户数据或 Amazon 一方报告不足时，才按 references/mcp-data-plan.md 发现实时 schema、执行 dry-run，并在可能计费的调用前核对具体请求与预算授权；已有授权覆盖时不重复索取。凭证通过环境变量注入，结果保存到 Skill 包之外的任务私有目录。
必须交付的结果
- 机会卡
- 竞品与 VOC 证据表
- 差异化规格
- 单位经济模型
- 样品与上市门槛
结尾列出站点、时间窗、数据口径、证据、假设、缺口、风险、下一步和所有待批准动作。证据不足时写 HOLD，不得包装成可直接执行。
执行细节、证据字段和质量检查见 references/playbook.md。
What ships with it
6 files beside SKILL.md in the same directory: the scripts, references and assets a skill reads on demand. Not counted in the per-session cost; read them before you install if any of them is executable.
What this file has done since we first saw it
Hashed on every crawl. A supply-chain change to an agent config is a question of when, not whether, so the history is kept rather than the latest state alone.
- 1mo ago First seen · 77 lines · 76 tokens per session scan A 9dcdcac3af4b
sealeap-qinglong-amazon-product-research is a skill published in the GitHub repository xjli360/sealeap-amazon-ad-skills (86 stars, last pushed 1mo ago), licensed MIT. It adds 76 tokens to every session and 1,169 once invoked, about $0.0003 per session on Opus 5.5. A static security scan graded it A with 0 findings. No closer match exists in the catalogue, so it is treated as the original; first seen 2026-09-07.
Other skills, from other repositories
zach-search-term-analyzer
An analyzer for Amazon Brand Analytics Top Search Terms reports, which show popular searches across Amazon and how clicks and conversions are distributed among products.
ecom-image2
An image-generation workflow for creating e-commerce visuals with GPT-Image-2, such as product images, banners, social posts, and infographics.
sorftime-seller-agent
Sorftime Seller Agent — Expert-level cross-border e-commerce data analysis and product sourcing intelligence for Amazon, Walmart, TikTok Shop, 1688, Shopee, and TEMU sellers (plus Reddit social-listening queries). A single skill that turns any MCP-enabled AI agent (Claude Code, OpenClaw, Cursor, Copilot) into a…
ai-model-expert-1688-ecommerce-video-generation-editing
An AI tool workflow for creating and editing 1688 e-commerce videos from text, images, videos, or audio. 1688 is a Chinese online wholesale marketplace.
302-ai-alternative-ai-hive
A migration guide for teams moving between AI model API platforms, especially for image and video generation. It helps review an existing OpenAI-compatible integration and plan testing, routing, and fallback options.
ai-hive-agentic-ocr-targeted-evidence
A document-search workflow that first finds likely pages and then uses OCR, or software that reads text from images, on only the relevant scans, tables, or small text.