Agent skill

Sealeap Amazon Asin Targeting Strategy

by xjli360 in xjli360/sealeap-amazon-skills

Plan Amazon ASIN product-targeting campaigns by scoring product similarity, demand, truthful conversion advantages, placement hypotheses, budget isolation, and downstream search-term harvesting.

MITAuto-check passed

Install Sealeap Amazon Asin Targeting Strategy

skills CLI
$ npx skills add xjli360/sealeap-amazon-skills --skill sealeap-amazon-asin-targeting-strategy -a claude-code

Project install by default; add -g for ~/.claude/skills/.

GitHub CLI
$ gh skill install xjli360/sealeap-amazon-skills sealeap-amazon-asin-targeting-strategy --agent claude-code

Project scope by default; add --scope user for a personal install. Needs GitHub CLI 2.90.0 or later (public preview).

Manual copy
$ git clone --depth 1 https://github.com/xjli360/sealeap-amazon-skills.git skills-src && mkdir -p .claude/skills && cp -r skills-src/amazon-skills/douyin/qilin/sealeap-amazon-asin-targeting-strategy .claude/skills/sealeap-amazon-asin-targeting-strategy && rm -rf skills-src

Use ~/.claude/skills/ instead of .claude/skills for a personal install. The folder must contain SKILL.md.

Claude Code skills documentation · loads skills from .claude/skills/

Facts

Skill name
sealeap-amazon-asin-targeting-strategy
GitHub stars
251
Token cost
~518 tokens
SKILL.md length
90 words
Files
4 (incl. scripts, references)
Skills in repo
179
Repo updated
First seen
Licence
MIT

At a glance

Plan Amazon ASIN product-targeting campaigns by scoring product similarity, demand, truthful conversion advantages, placement hypotheses, budget isolation, and downstream search-term harvesting.

  • Works in 6 steps: 建立目标评分 → 控制初始范围 → 提出位置假设 → …
  • The user asks whether a new product can start with ASIN targeting
  • SKILL.md covers 目标, 适用任务, 开始前要拿到 and 不可妥协的边界, plus 4 more sections
  • Runs Python scripts from its folder

What it does

Sealeap Amazon Asin Targeting Strategy is an agent skill from xjli360/sealeap-amazon-skills. Plan Amazon ASIN product-targeting campaigns by scoring product similarity, demand, truthful conversion advantages, placement hypotheses, budget isolation, and downstream search-term harvesting. Use when the user asks whether a new product can start with ASIN targeting, how to choose competitor ASINs, or when to move discovered queries into exact campaigns. Avoid review manipulation and ranking guarantees.

Its SKILL.md is about 520 tokens, which your agent loads only when the skill is triggered. The skill folder holds 6 other files, including scripts and reference files (for example `agents/openai.yaml`, `references/mcp-data-plan.md` and `scripts/mcp_research.py`).

It works with Model Context Protocol. The repository describes itself as: Reusable Agent Skills for Amazon product research, listings, advertising, inventory, and operations. The licence is MIT.

When your agent uses it

  • The user asks whether a new product can start with ASIN targeting
  • How to choose competitor ASINs
  • To move discovered queries into exact campaigns

Example prompts

  • “/sealeap-amazon-asin-targeting-strategy”

Requirements

  • Python 3

Workflow steps

6 steps, taken from the step headings in SKILL.md.

  1. 建立目标评分
  2. 控制初始范围
  3. 提出位置假设
  4. 运行验证
  5. 迁移赢家
  6. 扩大边界

What it can do on your machine

Read from SKILL.md and the folder at commit 497d4b8. It shows what the files ask for, not the result of running them.

  • Tool permissions

    Pre-approves nothing: there is no allowed-tools line, so your agent's usual permission prompts apply.

    From allowed-tools in the SKILL.md frontmatter.

  • Runs code

    Ships 1 file in scripts/ (Python), which the agent can run.

    From the folder's file list and the shell code blocks in SKILL.md.

  • Network

    No URLs in SKILL.md.

    From URLs in SKILL.md, links to its own repository left out.

  • Credentials

    Names no API keys, tokens, secrets or passwords.

    From names ending in _API_KEY, _TOKEN, _SECRET, _KEY or _PASSWORD in SKILL.md.

Context cost

Sealeap Amazon Asin Targeting Strategy loads about 518 tokens when it runs, and up to ~2k if it reads all its reference files. Until then it costs about 112 tokens; SKILL.md has 90 words of instructions outside code blocks.

Always · name and description, kept in context so the agent knows when to use it
~112
When it runs · the whole SKILL.md, loaded when a task matches
~518
With references · SKILL.md plus every file in references/, read only if the agent opens them
~2k

Estimates: characters ÷ 4, the usual rule of thumb; real counts depend on the model's tokenizer. Scripts and assets cost tokens only if the agent reads them.

Safety

Auto-check passed

The automated check found no risky patterns in SKILL.md.

Automated static check — not a guarantee. Review scripts before installing. It scans the text of SKILL.md for risky patterns (piping downloads into a shell, reading credential files, hidden Unicode, destructive commands); the scripts in this folder are not scanned.

SKILL.md

The full file from xjli360/sealeap-amazon-skills at commit 497d4b8, republished under its MIT licence (© xjli360). 90 words, ~518 tokens.

Download SKILL.mdSave it as .claude/skills/sealeap-amazon-asin-targeting-strategy/SKILL.md (or your agent's skills folder). This skill also uses 3 other files; get the full folder from GitHub.
name
sealeap-amazon-asin-targeting-strategy
description
Plan Amazon ASIN product-targeting campaigns by scoring product similarity, demand, truthful conversion advantages, placement hypotheses, budget isolation, and downstream search-term harvesting. Use when the user asks whether a new product can start with ASIN targeting, how to choose competitor ASINs, or when to move discovered queries into exact campaigns. Avoid review manipulation and ranking guarantees.

Amazon ASIN 商品投放策略

目标

用少量高匹配商品目标验证详情页与搜索环境机会,并把被报告证实的查询或目标迁移到独立结构。

适用任务

  • 新品希望用商品投放启动。
  • 从大量竞品 ASIN 中筛选少量高价值目标。
  • 商品投放有订单但预算与目标混杂。

开始前要拿到

  • 候选 ASIN 的品类、规格、用途、价格、评分、配送和销量代理。
  • 本品真实差异化、价格、评分、库存和内容承接力。
  • 商品投放、搜索词和广告位报告。

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

  • 所谓转化优势必须来自真实价格、功能、内容或服务,不得来自虚假评论或人为参考价。
  • 商品投放不能保证复制对方关键词,也不能精确承诺出现在某个自然页码。
  • 不得使用无授权的竞品私密数据。
  • 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. 建立目标评分

按品类相关性、功能相似度、受众重合、需求、价格区间和本品真实优势筛选。

2. 控制初始范围

优先单目标或小同质组,关闭不必要的扩展,确保每个结果可归因。

3. 提出位置假设

根据广告位报告区分商品页面与搜索位置表现;调整基础竞价和位置系数前先计算有效出价。

4. 运行验证

记录点击、CVR、CPA、已购商品、搜索词和利润;无订单时先判断样本和相关性。

5. 迁移赢家

稳定商品目标独立管理;报告中出现且证据充分的高转化查询可迁移到精准关键词广告。

6. 扩大边界

只有首批结果成立后才增加更广目标,并持续检查内部重复与库存。

判断标准

  • 每个候选 ASIN 都有入选或排除理由。
  • 位置结论来自报告,不由主观前台观察单独决定。
  • 迁移查询有实际报告证据。

必须交付的结果

  • ASIN 目标评分与批次。
  • 商品投放结构和竞价假设。
  • 目标或查询迁移规则。
  • 扩量与停止条件。

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。

© xjli360, MIT. Rendered from Markdown: HTML in the file is shown as text, images as links, and headings moved down two levels. Raw file

Files

SKILL.md and 3 other files (scripts, references) in amazon-skills/douyin/qilin/sealeap-amazon-asin-targeting-strategy of xjli360/sealeap-amazon-skills.

  • SKILL.md
  • agents/openai.yaml
  • references/mcp-data-plan.md
  • scripts/mcp_research.py

Open the folder on GitHubat commit 497d4b8

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Questions about Sealeap Amazon Asin Targeting Strategy

What does Sealeap Amazon Asin Targeting Strategy do?

Plan Amazon ASIN product-targeting campaigns by scoring product similarity, demand, truthful conversion advantages, placement hypotheses, budget isolation, and downstream search-term harvesting. Sealeap Amazon Asin Targeting Strategy is an agent skill from xjli360/sealeap-amazon-skills. Plan Amazon ASIN product-targeting campaigns by scoring product similarity, demand, truthful conversion advantages, placement hypotheses, budget isolation, and downstream search-term harvesting.

When should I use Sealeap Amazon Asin Targeting Strategy?

Sealeap Amazon Asin Targeting Strategy fits situations like: the user asks whether a new product can start with ASIN targeting; how to choose competitor ASINs; to move discovered queries into exact campaigns.

How do I install Sealeap Amazon Asin Targeting Strategy in Claude Code?

Run `npx skills add xjli360/sealeap-amazon-skills --skill sealeap-amazon-asin-targeting-strategy -a claude-code`. Or copy the skill folder (amazon-skills/douyin/qilin/sealeap-amazon-asin-targeting-strategy in xjli360/sealeap-amazon-skills) into .claude/skills/sealeap-amazon-asin-targeting-strategy in your project. Claude Code loads it when a task matches its description.

How do I install Sealeap Amazon Asin Targeting Strategy in Codex?

Run `npx skills add xjli360/sealeap-amazon-skills --skill sealeap-amazon-asin-targeting-strategy -a codex`. Or copy the skill folder (amazon-skills/douyin/qilin/sealeap-amazon-asin-targeting-strategy in xjli360/sealeap-amazon-skills) into .agents/skills/sealeap-amazon-asin-targeting-strategy in your project. Codex loads it when a task matches its description.

Can I use Sealeap Amazon Asin Targeting Strategy in Cursor, Gemini CLI or GitHub Copilot?

Cursor, Gemini CLI, GitHub Copilot and OpenCode also load SKILL.md folders. With the skills CLI, run `npx skills add xjli360/sealeap-amazon-skills --skill sealeap-amazon-asin-targeting-strategy -a cursor` (or -a gemini-cli, github-copilot or opencode for the others). To copy it by hand, put the folder in .cursor/skills/sealeap-amazon-asin-targeting-strategy, .gemini/skills/sealeap-amazon-asin-targeting-strategy, .github/skills/sealeap-amazon-asin-targeting-strategy and .opencode/skills/sealeap-amazon-asin-targeting-strategy in your project.

What does Sealeap Amazon Asin Targeting Strategy need to run?

Going by SKILL.md and its folder, Sealeap Amazon Asin Targeting Strategy needs Python for the scripts in its folder. Our summary lists: Python 3.

Does Sealeap Amazon Asin Targeting Strategy access the network?

SKILL.md contains no URLs. Any network use would come from the scripts or tools the agent runs. This is read from the text; nothing was executed.

Is Sealeap Amazon Asin Targeting Strategy safe to install?

Our automated static check of SKILL.md found no risky patterns, such as piping downloads into a shell, reading credential files or hidden Unicode. It is not a guarantee. The check reads SKILL.md only: the scripts in the folder are not scanned, so read them before running anything.

What licence does Sealeap Amazon Asin Targeting Strategy use?

Sealeap Amazon Asin Targeting Strategy is published under the MIT licence (the repository's licence). It allows redistribution, so the full SKILL.md is shown on this page.

How many tokens does Sealeap Amazon Asin Targeting Strategy use?

About 518 tokens (SKILL.md is roughly 2.1k characters). Agents keep only the skill's name and description in context until a task matches; then they load SKILL.md in full. Its references folder adds about 1.5k tokens, read only when the agent opens those files.

What are the alternatives to Sealeap Amazon Asin Targeting Strategy?

Skills that share tags, products or a category with Sealeap Amazon Asin Targeting Strategy: MCP Server Builder (anthropics/skills, 180k stars), MCP Server Builder (shareAI-lab/learn-claude-code, 78k stars), MCP Integration for Plugins (anthropics/claude-plugins-official, 38k stars) and Figma use_figma Plugin API Rules (warpdotdev/warp, 65k stars). The comparison table on this page puts their stars, adoption, token cost, safety result and licence side by side.

Who maintains Sealeap Amazon Asin Targeting Strategy?

xjli360 (a GitHub user) maintains it in xjli360/sealeap-amazon-skills, which has 251 GitHub stars. The repository holds 179 skills in this directory. The repository was last updated on September 28, 2026.

Source: xjli360/sealeap-amazon-skills on GitHub. Facts on this page come from the repository at the commit we read; the author's words are quoted as theirs.