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-amazon-review-manipulation-risk-auditgit 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-amazon-review-manipulation-risk-audit)<a href="https://agentmods.dev/skills/xjli360/sealeap-amazon-ad-skills/sealeap-amazon-review-manipulation-risk-audit"><img src="https://agentmods.dev/badge/skills/xjli360/sealeap-amazon-ad-skills/sealeap-amazon-review-manipulation-risk-audit/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-amazon-review-manipulation-risk-audit"><img src="https://agentmods.dev/badge/skills/xjli360/sealeap-amazon-ad-skills/sealeap-amazon-review-manipulation-risk-audit.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 | $0.00081 | $0.01090 |
| Opus 5.5 | $0.00032 | $0.00436 |
| Sonnet 5.5 | $0.00016 | $0.00218 |
| Haiku 4.5 | $0.00008 | $0.00109 |
Grade A, and why
sealeap-amazon-review-manipulation-risk-audit 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 评论操纵风险审计
目标
识别异常评论只是风险信号还是可验证违规，并把任何绕过式获评诉求转成合规处置与官方获评方案。
适用任务
- 竞品短期出现异常评论，需要做风险判断。
- 团队或服务商提出直评、批量账号、同步提交等方案。
- 需要决定是否向 Amazon 报告可疑评论。
开始前要拿到
- 公开可见评论变化、评论类型、变体结构和站点情况。
- 服务商方案原文、费用、承诺和要求的账号或订单动作。
- 当前 Amazon Customer Reviews policies 与官方报告入口。
缺失的数据要明确列为缺口，并把结论标成事实、估算或假设；不要补造数字。
不可妥协的边界
- 拒绝提供批量账号、同步提交、无购买评论、付费评论或规避检测的步骤。
- 异常模式不是违规定论；不得公开指认买家或竞争对手，也不得捏造证据。
- 不得以测试为名实际下单、操纵评论或访问他人账号。
- 当前 Amazon 官方政策、帮助页、账户资格和后台实际字段优先于本 Skill 中的经验框架；规则可能变化时先核验。
- 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。
- 不输出原素材的创作者身份、账号、链接、视频编号或可反查线索；当前业务证据的官方来源、采集时间和口径仍需保留。
第三方 MCP 数据
只有在本任务确实需要外部市场、竞品、关键词或公开网页证据时，才读取 references/mcp-data-plan.md，并使用 scripts/mcp_research.py。
- 先动态执行 tools/list、search-tools 和 describe，依据实时 inputSchema 构造参数，不照搬历史工具名。
- 凭证只从环境变量读取，不放进命令参数、URL、Skill、结果文件或 Git。
- tools/call 可能计费。调用前展示 Provider、工具名、无密钥参数、预计成本与输出位置，核对已有授权覆盖后才加 --allow-cost；该标志不是费用上限。
- 第三方数据标为估算或代理证据，记录 Provider、工具、无密钥参数、查询时间和原始结果位置；失败一次后记录缺口，不反复消耗额度。
- 脱敏结果用 --output 写到 Skill 包之外的任务私有目录；不假设安装位置受仓库 .gitignore 保护，不把运行结果写入 Skill 包。
工作流
1. 分类风险提议
识别是否涉及报酬、返现、控制内容、非真实变体、账号群或规避系统；命中即标为不可执行。
2. 记录公开信号
保存公开页面、时间范围和变化趋势，只记录可见事实，不收集或曝光无关个人信息。
3. 核对当前政策
优先引用 Amazon 官方评论政策和报告路径，区分明确禁止、需要更多信息和允许行为。
4. 建立替代方案
符合资格时考虑 Vine、Request a Review、改进产品与售后，以及不影响评价倾向的中立沟通。
5. 决定是否报告
只有具备具体可验证材料时才通过官方渠道提交；陈述事实和政策条款，不推断幕后主体。
6. 建立内部控制
记录服务商黑名单、审批要求和员工培训，防止高风险方案被重新包装。
判断标准
- 输出不包含任何可复现评论操纵步骤。
- 证据、推断和未知项清晰分开。
- 替代方案符合当前站点和项目资格。
必须交付的结果
- 评论方案风险分级。
- 公开异常信号与证据缺口。
- 官方政策核对和报告草稿。
- 合规获评替代路径。
结尾列出数据窗口、关键假设、证据缺口、风险和下一步；如包含待执行动作，单独放在“待批准变更”中。
What ships with it
3 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 · 85 lines · 81 tokens per session scan A a1a95b03463a
sealeap-amazon-review-manipulation-risk-audit is a skill published in the GitHub repository xjli360/sealeap-amazon-ad-skills (86 stars, last pushed 1mo ago), licensed MIT. It adds 81 tokens to every session and 1,090 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.
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