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-full-funnel-growthgit 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-full-funnel-growth)<a href="https://agentmods.dev/skills/xjli360/sealeap-amazon-ad-skills/sealeap-amazon-full-funnel-growth"><img src="https://agentmods.dev/badge/skills/xjli360/sealeap-amazon-ad-skills/sealeap-amazon-full-funnel-growth/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-full-funnel-growth"><img src="https://agentmods.dev/badge/skills/xjli360/sealeap-amazon-ad-skills/sealeap-amazon-full-funnel-growth.svg" alt="Reviewed on agentmods" width="80" height="20"></a>- NVIDIA SkillSpector pass
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 | $0.00135 | $0.01390 |
| Opus 5.5 | $0.00054 | $0.00556 |
| Sonnet 5.5 | $0.00027 | $0.00278 |
| Haiku 4.5 | $0.00014 | $0.00139 |
Grade A, and why
sealeap-amazon-full-funnel-growth 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.
How it starts
The opening of the file, as written. The whole thing — 82 lines — stays where its author put it; the contents beside it link to each section on GitHub.
Amazon 全流域增长规划
目标
把“全渠道都投一点”改造成可验证的增长系统：确定业务目标与目标人群，画出非线性决策路径，区分各触点职责，连接零售与品牌信号，并用增量或可归因实验决定下一轮预算，而不是用最后点击代替全程贡献。
先读 references/source-and-guardrails.md。需要消费者旅程与渠道角色时读 references/journey-and-channel-map.md；设计指标和实验时读 references/measurement-and-experiment.md。
核心原则
全流域是“认知 → 考虑 → 转化 → 忠诚”的协同，不等于所有广告产品同时开启。- 消费者路径非线性。搜索、详情页、社媒、在线视频、流媒体电视、达人内容、品牌店和复购触点可能往返出现。
- 电商详情页与品牌店是承接中枢；库存、价格、配送、评价和内容质量不足时，扩大媒体只会放大漏损。
- 每个触点只承担一个首要任务，并拥有匹配的指标。不得用 ACOS 单独评价认知，也不得用曝光量证明销售增量。
- 课程研究数据和 TCL、SOJOS 案例是
SOURCE_SNAPSHOT/TRAINING_CASE，不是当前账户基准、效果承诺或预算配方。 - 默认只读。预算、受众、竞价、投放状态、素材和详情页写入都要逐对象批准。
工作流
1. 锁定问题与作用域
记录 marketplace、品牌、ASIN/品类、日期、币种、客单价、复购周期、库存、价格、渠道、归因窗口和数据更新时间。只选一个主要问题：认知不足、考虑流失、转化断点、复购不足或跨触点重复浪费。
2. 建立证据表
将输入分为：
ACCOUNT_FACT：当前授权账户和站点的可复核数据；CURRENT_POLICY：本轮从官方来源确认的能力、资格与规则；SOURCE_SNAPSHOT：课程研究、历史比例、页面路径和案例；HYPOTHESIS：待验证的消费者或渠道解释；NEEDS_DATA：缺失后不能继续下结论的字段。
不把调查相关性写成当前品牌因果，不把行业比例写成当前账户人群比例。
3. 画决策旅程
至少覆盖：首次发现、主动研究、商品比较、详情页验证、加购/放弃、购买、复购/推荐。对每一步写清：用户问题、现有触点、证据、漏损、下一触点。高客单价商品单独检查更长的研究周期、多次品牌验证和设备切换。
4. 分配触点职责
按 references/journey-and-channel-map.md 给每个渠道分配一个首要任务：扩大合格触达、建立品牌记忆、推动深度访问、捕捉需求、转化或复购。渠道不可用时标 NOT_AVAILABLE，不要替换成同名但不同能力的产品。
5. 先修零售承接
检查 Featured Offer、库存、价格/优惠、配送、评分、退货原因、详情页事实完整性、移动端首屏、视频/A+、品牌店导航和广告承诺一致性。承接失败时先 HOLD_MEDIA_SCALE。
6. 建立指标树
每层保留一个主指标和一组护栏：
| 阶段 | 主指标候选 | 护栏 |
|---|---|---|
| 认知 | 增量触达、视频完成、品牌搜索提升 | 频次、可视成本、无效地域 |
| 考虑 | 品牌店/详情页合格访问、互动、加购 | 跳出、重复触达、价格/库存 |
| 转化 | 增量订单、贡献利润、CVR、ROAS | TACOS、自然替代、退货 |
| 忠诚 | 复购、新客后续价值、交叉购买 | 折扣依赖、联系频次、毛利 |
跨层报告时保留归因窗口与去重规则。
7. 设计一个可归因实验
只改变一个主要变量：人群、渠道组合、创意、频次、落地页或预算之一。固定其它主要变量，设置基线、对照、样本门槛、最大花费、成功/停止线、归因成熟时间和回退。若无法建立实验或合理对照，只能写关联性观察。
8. 审批与复读
若用户要求执行，先展示账户/站点、活动、对象、旧值、新值、预算上限、证据、停止线和回退。批准后只执行明确对象；写后复读状态与实际值。
必须交付
- 业务目标、作用域、证据标签和缺口；
- 一张阶段 × 用户问题 × 触点 × 指标的旅程表；
- 当前最大漏损及其证据强度；
- 零售承接检查与
GO / HOLD； - 一个单变量实验卡；
DRAFT、READY_FOR_REVIEW、APPROVED或HOLD状态。
What ships with it
4 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 · 82 lines · 135 tokens per session scan A 0a8a13123608
sealeap-amazon-full-funnel-growth is a skill published in the GitHub repository xjli360/sealeap-amazon-ad-skills (86 stars, last pushed 1mo ago), licensed MIT. It adds 135 tokens to every session and 1,390 once invoked, about $0.0005 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 2026-09-04.
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