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-pixiu-amazon-product-research-differentiationgit 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-pixiu-amazon-product-research-differentiation)<a href="https://agentmods.dev/skills/xjli360/sealeap-amazon-ad-skills/sealeap-pixiu-amazon-product-research-differentiation"><img src="https://agentmods.dev/badge/skills/xjli360/sealeap-amazon-ad-skills/sealeap-pixiu-amazon-product-research-differentiation/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-pixiu-amazon-product-research-differentiation"><img src="https://agentmods.dev/badge/skills/xjli360/sealeap-amazon-ad-skills/sealeap-pixiu-amazon-product-research-differentiation.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 | $0.00083 | $0.01186 |
| Opus 5 | $0.00042 | $0.00593 |
| Sonnet 5 | $0.00017 | $0.00237 |
| Haiku 4.5 | $0.00008 | $0.00119 |
Grade A, and why
sealeap-pixiu-amazon-product-research-differentiation 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 5d 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.
This is a copy
78% identical to sealeap-pixiu-amazon-account-compliance — 62 lines differ, which has more behind it and is treated as the original. This page carries a canonical link to it rather than competing with it.
What it actually says
Amazon 选品与产品开发：差异化、类目与节点、市场机会
目标
围绕差异化、类目与节点、市场机会，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
- 差异化：从未满足问题、工程约束和可验证收益推导差异，不只改颜色或包装。
- 类目与节点：核对 product type、browse node、属性和前台归类，避免把错类流量当广告问题。
- 使用场景：把时间、地点、任务、触发和限制写成可验证的需求链。
- 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。
- 季节性：将需求窗口、备货、排名和广告节奏对齐，并区分事件效应。
- 关键词排名：按固定站点、时间和查询记录排名，结合库存、价格、评价和广告干扰解释变化。
- FBA：核对配送模式、尺寸重量、费率、入仓要求和可追踪状态。
- AI 工作流：把 AI 限定为有输入、证据、审核和回退的可复核流程。
完整的 41 张主题证据卡见 references/topic-cards.md；35 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.
- 5d ago First seen · 77 lines · 83 tokens per session scan A a978b499e3d6
sealeap-pixiu-amazon-product-research-differentiation is a skill published in the GitHub repository xjli360/sealeap-amazon-ad-skills (86 stars, last pushed 6d ago), licensed MIT. It adds 83 tokens to every session and 1,186 once invoked, about $0.0004 per session on Opus 5. A static security scan graded it A with 0 findings. It is 78% identical to sealeap-pixiu-amazon-account-compliance, differing in 62 lines, and is treated as a copy.
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