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.
Benchmark Amazon niche ad efficiency by comparing visible advertising breadth, estimated sales, review cohorts, CPC, and conversion economics.
MITAuto-check passed
Install Sealeap Xiezhi Amazon Ad Efficiency Benchmark
$ npx skills add xjli360/sealeap-amazon-skills --skill sealeap-xiezhi-amazon-ad-efficiency-benchmark -a claude-codeProject install by default; add -g for ~/.claude/skills/.
$ gh skill install xjli360/sealeap-amazon-skills sealeap-xiezhi-amazon-ad-efficiency-benchmark --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-ad-efficiency-benchmark .claude/skills/sealeap-xiezhi-amazon-ad-efficiency-benchmark && 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-ad-efficiency-benchmark" agent skill from https://github.com/xjli360/sealeap-amazon-skills/tree/main/amazon-skills/douyin/xiezhi/sealeap-xiezhi-amazon-ad-efficiency-benchmark into .claude/skills/sealeap-xiezhi-amazon-ad-efficiency-benchmark/ in this project. Copy the whole folder (SKILL.md and every file beside it), keep the folder name "sealeap-xiezhi-amazon-ad-efficiency-benchmark", 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-ad-efficiency-benchmarkType 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-ad-efficiency-benchmark -a codexProject install goes to .agents/skills/; add -g for ~/.codex/skills/.
$ gh skill install xjli360/sealeap-amazon-skills sealeap-xiezhi-amazon-ad-efficiency-benchmark --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-ad-efficiency-benchmark .agents/skills/sealeap-xiezhi-amazon-ad-efficiency-benchmark && 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-ad-efficiency-benchmark" agent skill from https://github.com/xjli360/sealeap-amazon-skills/tree/main/amazon-skills/douyin/xiezhi/sealeap-xiezhi-amazon-ad-efficiency-benchmark into .agents/skills/sealeap-xiezhi-amazon-ad-efficiency-benchmark/ in this project. Copy the whole folder (SKILL.md and every file beside it), keep the folder name "sealeap-xiezhi-amazon-ad-efficiency-benchmark", 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-ad-efficiency-benchmark -a cursorProject install goes to .agents/skills/; add -g for ~/.cursor/skills/.
$ gh skill install xjli360/sealeap-amazon-skills sealeap-xiezhi-amazon-ad-efficiency-benchmark --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-ad-efficiency-benchmark .cursor/skills/sealeap-xiezhi-amazon-ad-efficiency-benchmark && 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-ad-efficiency-benchmark" agent skill from https://github.com/xjli360/sealeap-amazon-skills/tree/main/amazon-skills/douyin/xiezhi/sealeap-xiezhi-amazon-ad-efficiency-benchmark into .cursor/skills/sealeap-xiezhi-amazon-ad-efficiency-benchmark/ in this project. Copy the whole folder (SKILL.md and every file beside it), keep the folder name "sealeap-xiezhi-amazon-ad-efficiency-benchmark", 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-ad-efficiency-benchmark--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-ad-efficiency-benchmark -a gemini-cliProject install goes to .agents/skills/; add -g for ~/.gemini/skills/.
$ gh skill install xjli360/sealeap-amazon-skills sealeap-xiezhi-amazon-ad-efficiency-benchmark --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-ad-efficiency-benchmark .gemini/skills/sealeap-xiezhi-amazon-ad-efficiency-benchmark && 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-ad-efficiency-benchmark" agent skill from https://github.com/xjli360/sealeap-amazon-skills/tree/main/amazon-skills/douyin/xiezhi/sealeap-xiezhi-amazon-ad-efficiency-benchmark into .gemini/skills/sealeap-xiezhi-amazon-ad-efficiency-benchmark/ in this project. Copy the whole folder (SKILL.md and every file beside it), keep the folder name "sealeap-xiezhi-amazon-ad-efficiency-benchmark", 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-ad-efficiency-benchmarkInstalls 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-ad-efficiency-benchmark -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-ad-efficiency-benchmark .github/skills/sealeap-xiezhi-amazon-ad-efficiency-benchmark && 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-ad-efficiency-benchmark" agent skill from https://github.com/xjli360/sealeap-amazon-skills/tree/main/amazon-skills/douyin/xiezhi/sealeap-xiezhi-amazon-ad-efficiency-benchmark into .github/skills/sealeap-xiezhi-amazon-ad-efficiency-benchmark/ in this project. Copy the whole folder (SKILL.md and every file beside it), keep the folder name "sealeap-xiezhi-amazon-ad-efficiency-benchmark", 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-ad-efficiency-benchmark -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-ad-efficiency-benchmark --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-ad-efficiency-benchmark .opencode/skills/sealeap-xiezhi-amazon-ad-efficiency-benchmark && 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-ad-efficiency-benchmark" agent skill from https://github.com/xjli360/sealeap-amazon-skills/tree/main/amazon-skills/douyin/xiezhi/sealeap-xiezhi-amazon-ad-efficiency-benchmark into .opencode/skills/sealeap-xiezhi-amazon-ad-efficiency-benchmark/ in this project. Copy the whole folder (SKILL.md and every file beside it), keep the folder name "sealeap-xiezhi-amazon-ad-efficiency-benchmark", 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-ad-efficiency-benchmark- GitHub stars
- 251
- Token cost
- ~525 tokens
- SKILL.md length
- 98 words
- Files
- 5 (incl. scripts, references)
- Skills in repo
- 179
- Repo updated
- First seen
- Licence
- MIT
At a glance
Benchmark Amazon niche ad efficiency by comparing visible advertising breadth, estimated sales, review cohorts, CPC, and conversion economics.
- Works in 6 steps: 定义竞品与口径 → 收集广告代理 → 计算相对效率 → …
- Deciding whether a product has a simple enough paid-traffic structure for a new entrant
- 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 Ad Efficiency Benchmark is an agent skill from xjli360/sealeap-amazon-skills. Benchmark Amazon niche ad efficiency by comparing visible advertising breadth, estimated sales, review cohorts, CPC, and conversion economics. Use when deciding whether a product has a simple enough paid-traffic structure for a new entrant.
Its SKILL.md is about 530 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
- Deciding whether a product has a simple enough paid-traffic structure for a new entrant
Example prompts
- “/sealeap-xiezhi-amazon-ad-efficiency-benchmark”
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 Ad Efficiency Benchmark loads about 525 tokens when it runs, and up to ~2.9k if it reads all its reference files. Until then it costs about 72 tokens; SKILL.md has 98 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). 98 words, ~525 tokens.
Download SKILL.mdSave it as
.claude/skills/sealeap-xiezhi-amazon-ad-efficiency-benchmark/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-ad-efficiency-benchmark
- description
- Benchmark Amazon niche ad efficiency by comparing visible advertising breadth, estimated sales, review cohorts, CPC, and conversion economics. Use when deciding whether a product has a simple enough paid-traffic structure for a new entrant.
Amazon 广告效率竞品基准
目标
用同口径竞品组比较广告覆盖与销量代理，识别低评论样本是否能靠少量精准流量正常出单，并把结论限定为代理指标。
适用任务
- 评估细分市场广告架构复杂度。
- 比较低评论和高评论竞品的广告效率。
- 识别由真实差异化带来的异常高效样本。
开始前要拿到
- 一个经过验证的精准词及其搜索结果。
- 直接竞品的父子体口径销量、评论与可见广告词。
- 关键词 CPC、匹配类型、广告位和时间窗。
- 候选产品售价、贡献毛利和 CVR 情景。
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。
- 不得把第三方抓到的广告词当作完整账户事实。
- 不得将无法解释的离群样本作为可复制标杆。
工作流
1. 定义竞品与口径
锁定相同任务、属性和价格带商品，统一父体/子体、销量与广告词统计口径。
2. 收集广告代理
统计可见的商品/品牌广告词或其他覆盖代理，并记录工具覆盖不足和时间点。
3. 计算相对效率
用销量代理/广告词数等指标比较同市场样本，不把该比率当作真实广告产出。
4. 按评论分层
比较低评论组与成熟组的分布，寻找多条正常低评论高效样本，而非只看离群点。
5. 解释差异
检查高效样本是否由精准属性、可见差异、价格、评分、变体或页面承接解释。
6. 连接单位经济
把精准词 CPC 与 CVR 区间带入保本计算，决定是否值得继续。
判断标准
- 广告效率代理 = 销量代理 / 可见广告词数；可见词数为零或缺失时记为不可计算，不能判成无限高效。只在工具覆盖、对象与窗口可比时比较，不代表真实广告 ROI。
- 低评论组接近成熟组是友好信号，不等于新品一定转化相同。
- 可见广告词少可能来自抓取遗漏、预算变化或季节时点，必须报告缺口。
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
- 竞品口径说明
- 广告覆盖与销量代理表
- 评论分层分布
- 高效样本归因
- 经济性与深研结论
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-ad-efficiency-benchmark 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 Ad Efficiency Benchmark 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 Ad Efficiency Benchmark this skillxjli360/sealeap-amazon-skills | 251 | — | ~525 | 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 Ad Efficiency Benchmark
What does Sealeap Xiezhi Amazon Ad Efficiency Benchmark do?
Benchmark Amazon niche ad efficiency by comparing visible advertising breadth, estimated sales, review cohorts, CPC, and conversion economics. Sealeap Xiezhi Amazon Ad Efficiency Benchmark is an agent skill from xjli360/sealeap-amazon-skills. Benchmark Amazon niche ad efficiency by comparing visible advertising breadth, estimated sales, review cohorts, CPC, and conversion economics.
When should I use Sealeap Xiezhi Amazon Ad Efficiency Benchmark?
Sealeap Xiezhi Amazon Ad Efficiency Benchmark fits situations like: deciding whether a product has a simple enough paid-traffic structure for a new entrant.
How do I install Sealeap Xiezhi Amazon Ad Efficiency Benchmark in Claude Code?
Run `npx skills add xjli360/sealeap-amazon-skills --skill sealeap-xiezhi-amazon-ad-efficiency-benchmark -a claude-code`. Or copy the skill folder (amazon-skills/douyin/xiezhi/sealeap-xiezhi-amazon-ad-efficiency-benchmark in xjli360/sealeap-amazon-skills) into .claude/skills/sealeap-xiezhi-amazon-ad-efficiency-benchmark in your project. Claude Code loads it when a task matches its description.
How do I install Sealeap Xiezhi Amazon Ad Efficiency Benchmark in Codex?
Run `npx skills add xjli360/sealeap-amazon-skills --skill sealeap-xiezhi-amazon-ad-efficiency-benchmark -a codex`. Or copy the skill folder (amazon-skills/douyin/xiezhi/sealeap-xiezhi-amazon-ad-efficiency-benchmark in xjli360/sealeap-amazon-skills) into .agents/skills/sealeap-xiezhi-amazon-ad-efficiency-benchmark in your project. Codex loads it when a task matches its description.
Can I use Sealeap Xiezhi Amazon Ad Efficiency Benchmark 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-ad-efficiency-benchmark -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-ad-efficiency-benchmark, .gemini/skills/sealeap-xiezhi-amazon-ad-efficiency-benchmark, .github/skills/sealeap-xiezhi-amazon-ad-efficiency-benchmark and .opencode/skills/sealeap-xiezhi-amazon-ad-efficiency-benchmark in your project.
What does Sealeap Xiezhi Amazon Ad Efficiency Benchmark need to run?
Going by SKILL.md and its folder, Sealeap Xiezhi Amazon Ad Efficiency Benchmark needs Python for the scripts in its folder. Our summary lists: Python 3.
Does Sealeap Xiezhi Amazon Ad Efficiency Benchmark 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 Ad Efficiency Benchmark 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 Ad Efficiency Benchmark use?
Sealeap Xiezhi Amazon Ad Efficiency Benchmark 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 Ad Efficiency Benchmark use?
About 525 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 2.4k tokens, read only when the agent opens those files.
What are the alternatives to Sealeap Xiezhi Amazon Ad Efficiency Benchmark?
Skills that share tags, products or a category with Sealeap Xiezhi Amazon Ad Efficiency Benchmark: 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 Ad Efficiency Benchmark?
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.