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-negative-review-responsegit 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-negative-review-response)<a href="https://agentmods.dev/skills/xjli360/sealeap-amazon-ad-skills/sealeap-amazon-negative-review-response"><img src="https://agentmods.dev/badge/skills/xjli360/sealeap-amazon-ad-skills/sealeap-amazon-negative-review-response/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-negative-review-response"><img src="https://agentmods.dev/badge/skills/xjli360/sealeap-amazon-ad-skills/sealeap-amazon-negative-review-response.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 | $0.00079 | $0.01081 |
| Opus 5 | $0.00039 | $0.00541 |
| Sonnet 5 | $0.00016 | $0.00216 |
| Haiku 4.5 | $0.00008 | $0.00108 |
Grade A, and why
sealeap-amazon-negative-review-response 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.
What it actually says
Amazon 差评合规处置
目标
只对明确违反社区准则的内容走官方报告，对真实差评回到产品和售后修复，并保留完整证据链。
适用任务
- 判断某条差评是否符合删除或报告条件。
- 怀疑恶意攻击但证据不足。
- 建立差评预警、分流和产品闭环。
开始前要拿到
- 评论原文、公开页面、时间、关联 ASIN 和可见上下文。
- 当前 Amazon Community Guidelines 与官方报告入口。
- 退货原因、客服工单、批次和质量记录。
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
- 不得捏造职业差评师、竞争对手攻击或买家身份；相似表达只算线索。
- 不得站外联系评论者、施压、补偿换改评或委托服务商磨掉差评。
- 真实且合规的负面体验不能因影响评分而要求删除。
- 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. 保存原始证据
记录完整评论、页面、时间和 ASIN，不截取会改变语义的片段，也不扩散个人信息。
2. 按准则分类
逐条比对辱骂、个人信息、促销内容、非商品反馈等当前规则，列出匹配条款和不确定点。
3. 评估异常模式
查看公开可验证的重复、集中时间或跨商品模式，但把它标为风险信号而非主体归因。
4. 选择官方路径
违规内容通过官方报告或支持渠道提交；用事实、链接和条款写简洁材料，不夸大。
5. 处理真实差评
把问题映射到设计、包装、说明、质检、变体或售后，确定负责人和验证指标。
6. 建立闭环
按主题跟踪差评率、退货率和修复后变化，重复问题升级为批次或产品决策。
判断标准
- 每条删除请求都有具体政策依据。
- 报告材料区分事实、推断和未知。
- 真实问题有产品或服务纠正措施。
必须交付的结果
- 评论合规分类表。
- 官方报告草稿与证据附件清单。
- 不可删除评论的产品修复计划。
- 差评主题预警看板字段。
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.
- 5d ago First seen · 85 lines · 79 tokens per session scan A 626e4a094d27
sealeap-amazon-negative-review-response is a skill published in the GitHub repository xjli360/sealeap-amazon-ad-skills (86 stars, last pushed 6d ago), licensed MIT. It adds 79 tokens to every session and 1,081 once invoked, about $0.0004 per session on Opus 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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