MCP Server Builder
anthropics/skills
Guides the design and implementation of Model Context Protocol servers in TypeScript or Python, from tool naming and error messages to evaluation.
Design Amazon keyword-ranking experiments that identify promising terms from relevance, conversion, current organic visibility, and placement performance, then sequence long-tail, mid-tail, and…
MITAuto-check passed
Install Sealeap Amazon Keyword Ranking Experiment
$ npx skills add xjli360/sealeap-amazon-skills --skill sealeap-amazon-keyword-ranking-experiment -a claude-codeProject install by default; add -g for ~/.claude/skills/.
$ gh skill install xjli360/sealeap-amazon-skills sealeap-amazon-keyword-ranking-experiment --agent claude-codeProject scope by default; add --scope user for a personal install. Needs GitHub CLI 2.90.0 or later (public preview).
$ 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-keyword-ranking-experiment .claude/skills/sealeap-amazon-keyword-ranking-experiment && rm -rf skills-srcUse ~/.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/
Install the "sealeap-amazon-keyword-ranking-experiment" agent skill from https://github.com/xjli360/sealeap-amazon-skills/tree/main/amazon-skills/douyin/qilin/sealeap-amazon-keyword-ranking-experiment into .claude/skills/sealeap-amazon-keyword-ranking-experiment/ in this project. Copy the whole folder (SKILL.md and every file beside it), keep the folder name "sealeap-amazon-keyword-ranking-experiment", then confirm the skill loads.Claude Code copies the folder itself, the same result as the manual copy. Check what it changed before you commit it.
$skill-installer install https://github.com/xjli360/sealeap-amazon-skills/tree/main/amazon-skills/douyin/qilin/sealeap-amazon-keyword-ranking-experimentType this inside Codex. $skill-installer <name> installs a curated skill from openai/skills. The installer writes to $CODEX_HOME/skills (default ~/.codex/skills). Restart Codex if the skill does not show up.
$ npx skills add xjli360/sealeap-amazon-skills --skill sealeap-amazon-keyword-ranking-experiment -a codexProject install goes to .agents/skills/; add -g for ~/.codex/skills/.
$ gh skill install xjli360/sealeap-amazon-skills sealeap-amazon-keyword-ranking-experiment --agent codexProject scope by default (.agents/skills/); add --scope user for a personal install.
$ git clone --depth 1 https://github.com/xjli360/sealeap-amazon-skills.git skills-src && mkdir -p .agents/skills && cp -r skills-src/amazon-skills/douyin/qilin/sealeap-amazon-keyword-ranking-experiment .agents/skills/sealeap-amazon-keyword-ranking-experiment && rm -rf skills-srcUse ~/.agents/skills/ instead of .agents/skills for a personal install.
Codex skills documentation · loads skills from .agents/skills/
Install the "sealeap-amazon-keyword-ranking-experiment" agent skill from https://github.com/xjli360/sealeap-amazon-skills/tree/main/amazon-skills/douyin/qilin/sealeap-amazon-keyword-ranking-experiment into .agents/skills/sealeap-amazon-keyword-ranking-experiment/ in this project. Copy the whole folder (SKILL.md and every file beside it), keep the folder name "sealeap-amazon-keyword-ranking-experiment", then confirm the skill loads.Codex copies the folder itself, the same result as the manual copy. Check what it changed before you commit it.
$ npx skills add xjli360/sealeap-amazon-skills --skill sealeap-amazon-keyword-ranking-experiment -a cursorProject install goes to .agents/skills/; add -g for ~/.cursor/skills/.
$ gh skill install xjli360/sealeap-amazon-skills sealeap-amazon-keyword-ranking-experiment --agent cursorProject scope by default (.agents/skills/); add --scope user for a personal install.
$ git clone --depth 1 https://github.com/xjli360/sealeap-amazon-skills.git skills-src && mkdir -p .cursor/skills && cp -r skills-src/amazon-skills/douyin/qilin/sealeap-amazon-keyword-ranking-experiment .cursor/skills/sealeap-amazon-keyword-ranking-experiment && rm -rf skills-srcUse ~/.cursor/skills/ instead of .cursor/skills for a personal install.
Cursor skills documentation · loads skills from .cursor/skills/, .agents/skills/, .claude/skills/, .codex/skills/
Install the "sealeap-amazon-keyword-ranking-experiment" agent skill from https://github.com/xjli360/sealeap-amazon-skills/tree/main/amazon-skills/douyin/qilin/sealeap-amazon-keyword-ranking-experiment into .cursor/skills/sealeap-amazon-keyword-ranking-experiment/ in this project. Copy the whole folder (SKILL.md and every file beside it), keep the folder name "sealeap-amazon-keyword-ranking-experiment", then confirm the skill loads.Cursor copies the folder itself, the same result as the manual copy. Check what it changed before you commit it.
$ gemini skills install https://github.com/xjli360/sealeap-amazon-skills.git --path amazon-skills/douyin/qilin/sealeap-amazon-keyword-ranking-experiment--scope user (default) or --scope workspace; --path is the subfolder of the repo that holds the skill; --consent skips the security confirmation prompt.
$ npx skills add xjli360/sealeap-amazon-skills --skill sealeap-amazon-keyword-ranking-experiment -a gemini-cliProject install goes to .agents/skills/; add -g for ~/.gemini/skills/.
$ gh skill install xjli360/sealeap-amazon-skills sealeap-amazon-keyword-ranking-experiment --agent gemini-cliProject scope by default (.agents/skills/); add --scope user for a personal install.
$ git clone --depth 1 https://github.com/xjli360/sealeap-amazon-skills.git skills-src && mkdir -p .gemini/skills && cp -r skills-src/amazon-skills/douyin/qilin/sealeap-amazon-keyword-ranking-experiment .gemini/skills/sealeap-amazon-keyword-ranking-experiment && rm -rf skills-srcUse ~/.gemini/skills/ instead of .gemini/skills for a personal install, then run /skills reload.
Gemini CLI skills documentation · loads skills from .gemini/skills/, .agents/skills/
Install the "sealeap-amazon-keyword-ranking-experiment" agent skill from https://github.com/xjli360/sealeap-amazon-skills/tree/main/amazon-skills/douyin/qilin/sealeap-amazon-keyword-ranking-experiment into .gemini/skills/sealeap-amazon-keyword-ranking-experiment/ in this project. Copy the whole folder (SKILL.md and every file beside it), keep the folder name "sealeap-amazon-keyword-ranking-experiment", then confirm the skill loads.Gemini CLI copies the folder itself, the same result as the manual copy. Check what it changed before you commit it.
$ gh skill install xjli360/sealeap-amazon-skills sealeap-amazon-keyword-ranking-experimentInstalls for Copilot at project scope by default; add --scope user for a personal install. Preview a skill first with gh skill preview. Needs GitHub CLI 2.90.0 or later (public preview).
$ npx skills add xjli360/sealeap-amazon-skills --skill sealeap-amazon-keyword-ranking-experiment -a github-copilotProject install goes to .agents/skills/; add -g for ~/.copilot/skills/.
$ git clone --depth 1 https://github.com/xjli360/sealeap-amazon-skills.git skills-src && mkdir -p .github/skills && cp -r skills-src/amazon-skills/douyin/qilin/sealeap-amazon-keyword-ranking-experiment .github/skills/sealeap-amazon-keyword-ranking-experiment && rm -rf skills-srcUse ~/.copilot/skills/ instead of .github/skills for a personal install. Commit .github/skills so cloud agent and code review can use it.
GitHub Copilot skills documentation · loads skills from .github/skills/, .claude/skills/, .agents/skills/
Install the "sealeap-amazon-keyword-ranking-experiment" agent skill from https://github.com/xjli360/sealeap-amazon-skills/tree/main/amazon-skills/douyin/qilin/sealeap-amazon-keyword-ranking-experiment into .github/skills/sealeap-amazon-keyword-ranking-experiment/ in this project. Copy the whole folder (SKILL.md and every file beside it), keep the folder name "sealeap-amazon-keyword-ranking-experiment", then confirm the skill loads.GitHub Copilot copies the folder itself, the same result as the manual copy. Check what it changed before you commit it.
$ npx skills add xjli360/sealeap-amazon-skills --skill sealeap-amazon-keyword-ranking-experiment -a opencodeOpenCode documents no install command of its own. Project install goes to .agents/skills/; add -g for ~/.config/opencode/skills/.
$ gh skill install xjli360/sealeap-amazon-skills sealeap-amazon-keyword-ranking-experiment --agent opencodeProject scope by default (.agents/skills/); add --scope user for a personal install.
$ git clone --depth 1 https://github.com/xjli360/sealeap-amazon-skills.git skills-src && mkdir -p .opencode/skills && cp -r skills-src/amazon-skills/douyin/qilin/sealeap-amazon-keyword-ranking-experiment .opencode/skills/sealeap-amazon-keyword-ranking-experiment && rm -rf skills-srcUse ~/.config/opencode/skills/ instead of .opencode/skills for a personal install.
OpenCode skills documentation · loads skills from .opencode/skills/, .claude/skills/, .agents/skills/
Install the "sealeap-amazon-keyword-ranking-experiment" agent skill from https://github.com/xjli360/sealeap-amazon-skills/tree/main/amazon-skills/douyin/qilin/sealeap-amazon-keyword-ranking-experiment into .opencode/skills/sealeap-amazon-keyword-ranking-experiment/ in this project. Copy the whole folder (SKILL.md and every file beside it), keep the folder name "sealeap-amazon-keyword-ranking-experiment", then confirm the skill loads.OpenCode copies the folder itself, the same result as the manual copy. Check what it changed before you commit it.
Facts
- Skill name
sealeap-amazon-keyword-ranking-experiment- GitHub stars
- 251
- Token cost
- ~535 tokens
- SKILL.md length
- 90 words
- Files
- 4 (incl. scripts, references)
- Skills in repo
- 179
- Repo updated
- First seen
- Licence
- MIT
At a glance
Design Amazon keyword-ranking experiments that identify promising terms from relevance, conversion, current organic visibility, and placement performance, then sequence long-tail, mid-tail, and…
- Works in 6 steps: 筛选候选词 → 设定实验位置假设 → 估算订单潜力 → …
- The user asks which keywords to push
- 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 Keyword Ranking Experiment is an agent skill from xjli360/sealeap-amazon-skills. Design Amazon keyword-ranking experiments that identify promising terms from relevance, conversion, current organic visibility, and placement performance, then sequence long-tail, mid-tail, and head-term tests. Use when the user asks which keywords to push, what an ad position may reveal about order potential, or why organic rank stalls despite paid orders. Do not equate sponsored placement with organic rank or guarantee movement.
Its SKILL.md is about 540 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 which keywords to push
- What an ad position may reveal about order potential
- Why organic rank stalls despite paid orders
Example prompts
- “/sealeap-amazon-keyword-ranking-experiment”
Requirements
- Python 3
Workflow steps
6 steps, taken from the step headings in SKILL.md.
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 Keyword Ranking Experiment loads about 535 tokens when it runs, and up to ~2.1k if it reads all its reference files. Until then it costs about 119 tokens; SKILL.md has 90 words of instructions outside code blocks.
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, ~535 tokens.
Download SKILL.mdSave it as
.claude/skills/sealeap-amazon-keyword-ranking-experiment/SKILL.md (or your agent's skills folder). This skill also uses 3 other files; get the full folder from GitHub.- name
- sealeap-amazon-keyword-ranking-experiment
- description
- Design Amazon keyword-ranking experiments that identify promising terms from relevance, conversion, current organic visibility, and placement performance, then sequence long-tail, mid-tail, and head-term tests. Use when the user asks which keywords to push, what an ad position may reveal about order potential, or why organic rank stalls despite paid orders. Do not equate sponsored placement with organic rank or guarantee movement.
Amazon 关键词排名实验
目标
用广告实验筛选与商品最匹配、在合理位置能产生利润的关键词，再按证据逐级扩大，而不是把排名当作可直接购买的结果。
适用任务
- 从已收录词中选择优先推进对象。
- 估算某查询在不同广告位的订单潜力。
- 广告有订单但自然位置停滞。
开始前要拿到
- 关键词相关性、查询报告、自然位置历史和广告位表现。
- CTR、CVR、CPC、CPA、价格、库存和贡献利润。
- 竞品环境、促销和 Listing 变化时间线。
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
- Sponsored 位置与 organic 位置的曝光和点击机制不同，不能直接等同。
- 自然排名由 Amazon 系统决定；不承诺固定订单量或固定时间换取位置。
- 不得使用分时出价、广告或其他方法配合虚假订单和人为转化。
- 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. 筛选候选词
从真实相关、已有转化、自然可见度较好或广告效率较高的词中建立候选集，不只按当前名次。
2. 设定实验位置假设
选择一个或多个广告位类别，固定商品页、价格和预算，明确不是精确页码控制。
3. 估算订单潜力
比较各位置的曝光、CTR、CVR、CPA 和贡献利润，形成区间而非把广告订单直接当作未来自然订单。
4. 先推高把握词
优先给高意图长尾或中部词稳定预算；达到利润和样本门槛后才扩大。
5. 诊断自然停滞
检查相对转化、点击、库存、价格、竞争和词根相关性；不要仅靠更高竞价追自然位。
6. 迭代到核心词
当多个相关词形成稳定基本盘后，小规模测试核心词，并保留止损和退出路径。
判断标准
- 订单潜力以区间和假设呈现。
- 广告与自然数据分别展示。
- 候选词排序同时考虑利润和相关性。
必须交付的结果
- 候选词评分表。
- 广告位实验与预算护栏。
- 长尾、中部、核心词的推进顺序。
- 自然停滞诊断和下一步建议。
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-keyword-ranking-experiment 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
Compare with similar skills
Sealeap Amazon Keyword Ranking Experiment next to the 5 skills that share the most tags, products or categories with it. Stars are the repository's; “used in” counts other GitHub owners with a copy.
| Skill | Stars | Used in | Tokens | Auto-check | Licence | Repo updated |
|---|---|---|---|---|---|---|
| Sealeap Amazon Keyword Ranking Experiment this skillxjli360/sealeap-amazon-skills | 251 | — | ~535 | Automated safety check: Pass | MIT | |
| MCP Server Builderanthropics/skills | 180k | 63 repos | ~2.3k | Automated safety check: Pass | Apache-2.0 | |
| MCP Server BuildershareAI-lab/learn-claude-code | 78k | 4 repos | ~1.2k | Automated safety check: Pass | MIT | |
| MCP Integration for Pluginsanthropics/claude-plugins-official | 38k | 11 repos | ~3.1k | Automated safety check: Pass | Apache-2.0 | |
| Figma use_figma Plugin API Ruleswarpdotdev/warp | 65k | 4 repos | ~4.4k | Automated safety check: Pass | AGPL-3.0 | |
| Stitch to Remotion Walkthrough Videosgoogle-labs-code/stitch-skills | 8.5k | 6 repos | ~3.2k | Automated safety check: Notes | Apache-2.0 |
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Related
Works with
Questions about Sealeap Amazon Keyword Ranking Experiment
What does Sealeap Amazon Keyword Ranking Experiment do?
Design Amazon keyword-ranking experiments that identify promising terms from relevance, conversion, current organic visibility, and placement performance, then sequence long-tail, mid-tail, and…. Sealeap Amazon Keyword Ranking Experiment is an agent skill from xjli360/sealeap-amazon-skills. Design Amazon keyword-ranking experiments that identify promising terms from relevance, conversion, current organic visibility, and placement performance, then sequence long-tail, mid-tail, and head-term tests.
When should I use Sealeap Amazon Keyword Ranking Experiment?
Sealeap Amazon Keyword Ranking Experiment fits situations like: the user asks which keywords to push; what an ad position may reveal about order potential; why organic rank stalls despite paid orders.
How do I install Sealeap Amazon Keyword Ranking Experiment in Claude Code?
Run `npx skills add xjli360/sealeap-amazon-skills --skill sealeap-amazon-keyword-ranking-experiment -a claude-code`. Or copy the skill folder (amazon-skills/douyin/qilin/sealeap-amazon-keyword-ranking-experiment in xjli360/sealeap-amazon-skills) into .claude/skills/sealeap-amazon-keyword-ranking-experiment in your project. Claude Code loads it when a task matches its description.
How do I install Sealeap Amazon Keyword Ranking Experiment in Codex?
Run `npx skills add xjli360/sealeap-amazon-skills --skill sealeap-amazon-keyword-ranking-experiment -a codex`. Or copy the skill folder (amazon-skills/douyin/qilin/sealeap-amazon-keyword-ranking-experiment in xjli360/sealeap-amazon-skills) into .agents/skills/sealeap-amazon-keyword-ranking-experiment in your project. Codex loads it when a task matches its description.
Can I use Sealeap Amazon Keyword Ranking Experiment 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-keyword-ranking-experiment -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-keyword-ranking-experiment, .gemini/skills/sealeap-amazon-keyword-ranking-experiment, .github/skills/sealeap-amazon-keyword-ranking-experiment and .opencode/skills/sealeap-amazon-keyword-ranking-experiment in your project.
What does Sealeap Amazon Keyword Ranking Experiment need to run?
Going by SKILL.md and its folder, Sealeap Amazon Keyword Ranking Experiment needs Python for the scripts in its folder. Our summary lists: Python 3.
Does Sealeap Amazon Keyword Ranking Experiment 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 Keyword Ranking Experiment 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 Keyword Ranking Experiment use?
Sealeap Amazon Keyword Ranking Experiment 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 Keyword Ranking Experiment use?
About 535 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 Keyword Ranking Experiment?
Skills that share tags, products or a category with Sealeap Amazon Keyword Ranking Experiment: 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 Keyword Ranking Experiment?
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.