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.
Agent skill
Sealeap Xiezhi Amazon Scenario Led Differentiation
Design low-capex Amazon differentiation by repositioning an existing product for a specific audience, occasion, or use case.
MITAuto-check passed
Install Sealeap Xiezhi Amazon Scenario Led Differentiation
$ npx skills add xjli360/sealeap-amazon-skills --skill sealeap-xiezhi-amazon-scenario-led-differentiation -a claude-codeProject install by default; add -g for ~/.claude/skills/.
$ gh skill install xjli360/sealeap-amazon-skills sealeap-xiezhi-amazon-scenario-led-differentiation --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/xiezhi/sealeap-xiezhi-amazon-scenario-led-differentiation .claude/skills/sealeap-xiezhi-amazon-scenario-led-differentiation && 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-xiezhi-amazon-scenario-led-differentiation" agent skill from https://github.com/xjli360/sealeap-amazon-skills/tree/main/amazon-skills/douyin/xiezhi/sealeap-xiezhi-amazon-scenario-led-differentiation into .claude/skills/sealeap-xiezhi-amazon-scenario-led-differentiation/ in this project. Copy the whole folder (SKILL.md and every file beside it), keep the folder name "sealeap-xiezhi-amazon-scenario-led-differentiation", 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/xiezhi/sealeap-xiezhi-amazon-scenario-led-differentiationType 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-xiezhi-amazon-scenario-led-differentiation -a codexProject install goes to .agents/skills/; add -g for ~/.codex/skills/.
$ gh skill install xjli360/sealeap-amazon-skills sealeap-xiezhi-amazon-scenario-led-differentiation --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/xiezhi/sealeap-xiezhi-amazon-scenario-led-differentiation .agents/skills/sealeap-xiezhi-amazon-scenario-led-differentiation && 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-xiezhi-amazon-scenario-led-differentiation" agent skill from https://github.com/xjli360/sealeap-amazon-skills/tree/main/amazon-skills/douyin/xiezhi/sealeap-xiezhi-amazon-scenario-led-differentiation into .agents/skills/sealeap-xiezhi-amazon-scenario-led-differentiation/ in this project. Copy the whole folder (SKILL.md and every file beside it), keep the folder name "sealeap-xiezhi-amazon-scenario-led-differentiation", 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-xiezhi-amazon-scenario-led-differentiation -a cursorProject install goes to .agents/skills/; add -g for ~/.cursor/skills/.
$ gh skill install xjli360/sealeap-amazon-skills sealeap-xiezhi-amazon-scenario-led-differentiation --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/xiezhi/sealeap-xiezhi-amazon-scenario-led-differentiation .cursor/skills/sealeap-xiezhi-amazon-scenario-led-differentiation && 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-xiezhi-amazon-scenario-led-differentiation" agent skill from https://github.com/xjli360/sealeap-amazon-skills/tree/main/amazon-skills/douyin/xiezhi/sealeap-xiezhi-amazon-scenario-led-differentiation into .cursor/skills/sealeap-xiezhi-amazon-scenario-led-differentiation/ in this project. Copy the whole folder (SKILL.md and every file beside it), keep the folder name "sealeap-xiezhi-amazon-scenario-led-differentiation", 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/xiezhi/sealeap-xiezhi-amazon-scenario-led-differentiation--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-xiezhi-amazon-scenario-led-differentiation -a gemini-cliProject install goes to .agents/skills/; add -g for ~/.gemini/skills/.
$ gh skill install xjli360/sealeap-amazon-skills sealeap-xiezhi-amazon-scenario-led-differentiation --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/xiezhi/sealeap-xiezhi-amazon-scenario-led-differentiation .gemini/skills/sealeap-xiezhi-amazon-scenario-led-differentiation && 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-xiezhi-amazon-scenario-led-differentiation" agent skill from https://github.com/xjli360/sealeap-amazon-skills/tree/main/amazon-skills/douyin/xiezhi/sealeap-xiezhi-amazon-scenario-led-differentiation into .gemini/skills/sealeap-xiezhi-amazon-scenario-led-differentiation/ in this project. Copy the whole folder (SKILL.md and every file beside it), keep the folder name "sealeap-xiezhi-amazon-scenario-led-differentiation", 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-xiezhi-amazon-scenario-led-differentiationInstalls 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-xiezhi-amazon-scenario-led-differentiation -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/xiezhi/sealeap-xiezhi-amazon-scenario-led-differentiation .github/skills/sealeap-xiezhi-amazon-scenario-led-differentiation && 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-xiezhi-amazon-scenario-led-differentiation" agent skill from https://github.com/xjli360/sealeap-amazon-skills/tree/main/amazon-skills/douyin/xiezhi/sealeap-xiezhi-amazon-scenario-led-differentiation into .github/skills/sealeap-xiezhi-amazon-scenario-led-differentiation/ in this project. Copy the whole folder (SKILL.md and every file beside it), keep the folder name "sealeap-xiezhi-amazon-scenario-led-differentiation", 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-xiezhi-amazon-scenario-led-differentiation -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-xiezhi-amazon-scenario-led-differentiation --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/xiezhi/sealeap-xiezhi-amazon-scenario-led-differentiation .opencode/skills/sealeap-xiezhi-amazon-scenario-led-differentiation && 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-xiezhi-amazon-scenario-led-differentiation" agent skill from https://github.com/xjli360/sealeap-amazon-skills/tree/main/amazon-skills/douyin/xiezhi/sealeap-xiezhi-amazon-scenario-led-differentiation into .opencode/skills/sealeap-xiezhi-amazon-scenario-led-differentiation/ in this project. Copy the whole folder (SKILL.md and every file beside it), keep the folder name "sealeap-xiezhi-amazon-scenario-led-differentiation", 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-xiezhi-amazon-scenario-led-differentiation- GitHub stars
- 251
- Token cost
- ~506 tokens
- SKILL.md length
- 89 words
- Files
- 5 (incl. scripts, references)
- Skills in repo
- 179
- Repo updated
- First seen
- Licence
- MIT
At a glance
Design low-capex Amazon differentiation by repositioning an existing product for a specific audience, occasion, or use case.
- Works in 6 steps: 定义自身边界 → 寻找迁移场景 → 验证真实适配 → …
- A seller cannot justify tooling but needs a visible
- 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 Xiezhi Amazon Scenario Led Differentiation is an agent skill from xjli360/sealeap-amazon-skills. Design low-capex Amazon differentiation by repositioning an existing product for a specific audience, occasion, or use case. Use when a seller cannot justify tooling but needs a visible, evidence-backed reason to buy.
Its SKILL.md is about 510 tokens, which your agent loads only when the skill is triggered. The skill folder holds 7 other files, including scripts and reference files (for example `agents/openai.yaml`, `references/mcp-data-plan.md` and `references/playbook.md`).
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
- A seller cannot justify tooling but needs a visible
- Evidence-backed reason to buy
Example prompts
- “/sealeap-xiezhi-amazon-scenario-led-differentiation”
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 Xiezhi Amazon Scenario Led Differentiation loads about 506 tokens when it runs, and up to ~2.7k if it reads all its reference files. Until then it costs about 67 tokens; SKILL.md has 89 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). 89 words, ~506 tokens.
Download SKILL.mdSave it as
.claude/skills/sealeap-xiezhi-amazon-scenario-led-differentiation/SKILL.md (or your agent's skills folder). This skill also uses 4 other files; get the full folder from GitHub.- name
- sealeap-xiezhi-amazon-scenario-led-differentiation
- description
- Design low-capex Amazon differentiation by repositioning an existing product for a specific audience, occasion, or use case. Use when a seller cannot justify tooling but needs a visible, evidence-backed reason to buy.
Amazon 场景驱动差异化
目标
在不盲目开模的前提下，用真实人群与使用任务重定义产品，使关键词、竞品、页面表达和购买理由同步改变。
适用任务
- 为成熟产品寻找新使用对象或场景。
- 区分有效差异化与仅仅做得不同。
- 设计低投入、搜索页可感知的定位方案。
开始前要拿到
- 现有产品事实、结构限制和供应能力。
- 目标人群、活动、场景与未满足任务证据。
- 原赛道和新赛道的关键词、竞品、价格和评论。
- 包装、图片、文案与合规可修改范围。
缺少字段时列出证据缺口，并把相关结论标为 FACT、ESTIMATE、ASSUMPTION 或 UNKNOWN；不要补造数据。
不可妥协的边界
- 第三方数据均为估算或代理证据；Amazon 一方报告、后台实时字段和产品事实优先。
- 经验阈值只能作为可调起点，必须展示敏感性分析，不能写成 Amazon 官方规则。
- 不得捏造销量、搜索量、CPC、CVR、成本、认证、产品属性或消费者需求。
- 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。
- 不输出或保存素材来源身份、账号、链接、作品编号、互动数据、原始话术或其他可反查来源的线索。
- 不得仅通过文案声称未经验证的用途。
- 涉及儿童、食品、医疗、承重、电气等用途时先完成适用测试与合规。
工作流
1. 定义自身边界
先明确资金、运营能力、MOQ、开发周期和可承受试错，再决定差异化深度。
2. 寻找迁移场景
从同一产品可能服务的地点、对象、活动、礼赠和特殊任务中提出候选定位。
3. 验证真实适配
用评论、关键词与使用流程证明产品确实适合新场景，不能只替换标题或图片。
4. 重建竞争
用新场景的精准词识别直接竞品、价格带、评论门槛和流量成本。
5. 设计可见表达
确保差异能在主图、标题前段、数量、组合或包装中被目标消费者快速理解。
6. 小批验证
完成 IP、安全与政策核查后，以小批库存和单变量页面/广告实验验证新定位。
判断标准
- 有效差异化必须同时具备真实需求、产品适配、消费者可感知和经济可行。
- 低成本定位差异化优先于高投入开模，但不是所有产品都能安全迁移场景。
- 差评共性可提示基础缺陷，低频场景需额外证据后才能立项。
第三方 MCP 数据
需要外部关键词、竞品、评论或公开网页证据时，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。 - 凭证只从环境变量读取，不进入参数、URL、Skill、终端输出或 Git。
- 可能计费的
tools/call先展示 Provider、工具、无密钥参数、预计成本与输出位置，核对已有授权；仅在授权覆盖本次范围时使用--allow-cost，该标志不是费用上限。 - 脱敏结果用
--output写入 Skill 包之外的任务私有目录；不假设安装位置受仓库.gitignore保护。第三方数据标为估算或代理证据。 - 失败一次后记录缺口，不以重复付费重试掩盖不可用状态。
必须交付的结果
- 能力与风险边界
- 候选场景地图
- 新旧竞争集对比
- 搜索页表达草案
- 小批验证与止损计划
结尾列出站点、数据窗口、证据来源、关键假设、缺口、风险、下一步和所有待批准动作。证据不足时写 HOLD，不得包装成可直接执行。
执行细节、证据字段和质量检查见 references/playbook.md。
© 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 4 other files (scripts, references) in amazon-skills/douyin/xiezhi/sealeap-xiezhi-amazon-scenario-led-differentiation of xjli360/sealeap-amazon-skills.
- SKILL.md
- agents/openai.yaml
- references/mcp-data-plan.md
- references/playbook.md
- scripts/mcp_research.py
Open the folder on GitHubat commit 497d4b8
Compare with similar skills
Sealeap Xiezhi Amazon Scenario Led Differentiation 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 Xiezhi Amazon Scenario Led Differentiation this skillxjli360/sealeap-amazon-skills | 251 | — | ~506 | 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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Questions about Sealeap Xiezhi Amazon Scenario Led Differentiation
What does Sealeap Xiezhi Amazon Scenario Led Differentiation do?
Design low-capex Amazon differentiation by repositioning an existing product for a specific audience, occasion, or use case. Sealeap Xiezhi Amazon Scenario Led Differentiation is an agent skill from xjli360/sealeap-amazon-skills. Design low-capex Amazon differentiation by repositioning an existing product for a specific audience, occasion, or use case.
When should I use Sealeap Xiezhi Amazon Scenario Led Differentiation?
Sealeap Xiezhi Amazon Scenario Led Differentiation fits situations like: A seller cannot justify tooling but needs a visible; evidence-backed reason to buy.
How do I install Sealeap Xiezhi Amazon Scenario Led Differentiation in Claude Code?
Run `npx skills add xjli360/sealeap-amazon-skills --skill sealeap-xiezhi-amazon-scenario-led-differentiation -a claude-code`. Or copy the skill folder (amazon-skills/douyin/xiezhi/sealeap-xiezhi-amazon-scenario-led-differentiation in xjli360/sealeap-amazon-skills) into .claude/skills/sealeap-xiezhi-amazon-scenario-led-differentiation in your project. Claude Code loads it when a task matches its description.
How do I install Sealeap Xiezhi Amazon Scenario Led Differentiation in Codex?
Run `npx skills add xjli360/sealeap-amazon-skills --skill sealeap-xiezhi-amazon-scenario-led-differentiation -a codex`. Or copy the skill folder (amazon-skills/douyin/xiezhi/sealeap-xiezhi-amazon-scenario-led-differentiation in xjli360/sealeap-amazon-skills) into .agents/skills/sealeap-xiezhi-amazon-scenario-led-differentiation in your project. Codex loads it when a task matches its description.
Can I use Sealeap Xiezhi Amazon Scenario Led Differentiation 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-xiezhi-amazon-scenario-led-differentiation -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-xiezhi-amazon-scenario-led-differentiation, .gemini/skills/sealeap-xiezhi-amazon-scenario-led-differentiation, .github/skills/sealeap-xiezhi-amazon-scenario-led-differentiation and .opencode/skills/sealeap-xiezhi-amazon-scenario-led-differentiation in your project.
What does Sealeap Xiezhi Amazon Scenario Led Differentiation need to run?
Going by SKILL.md and its folder, Sealeap Xiezhi Amazon Scenario Led Differentiation needs Python for the scripts in its folder. Our summary lists: Python 3.
Does Sealeap Xiezhi Amazon Scenario Led Differentiation 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 Xiezhi Amazon Scenario Led Differentiation 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 Xiezhi Amazon Scenario Led Differentiation use?
Sealeap Xiezhi Amazon Scenario Led Differentiation 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 Xiezhi Amazon Scenario Led Differentiation use?
About 506 tokens (SKILL.md is roughly 2k 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 2.2k tokens, read only when the agent opens those files.
What are the alternatives to Sealeap Xiezhi Amazon Scenario Led Differentiation?
Skills that share tags, products or a category with Sealeap Xiezhi Amazon Scenario Led Differentiation: 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 Xiezhi Amazon Scenario Led Differentiation?
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.