Agent skill

Sealeap Xiezhi Amazon Audience First Product Discovery

by xjli360 in xjli360/sealeap-amazon-skills

Discover Amazon product opportunities by starting with a clearly defined audience and mapping recurring work, life, event, and gifting needs.

MITAuto-check passedProduct & Project Management

Install Sealeap Xiezhi Amazon Audience First Product Discovery

skills CLI
$ npx skills add xjli360/sealeap-amazon-skills --skill sealeap-xiezhi-amazon-audience-first-product-discovery -a claude-code

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

GitHub CLI
$ gh skill install xjli360/sealeap-amazon-skills sealeap-xiezhi-amazon-audience-first-product-discovery --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/xiezhi/sealeap-xiezhi-amazon-audience-first-product-discovery .claude/skills/sealeap-xiezhi-amazon-audience-first-product-discovery && 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-xiezhi-amazon-audience-first-product-discovery
GitHub stars
251
Token cost
~491 tokens
SKILL.md length
85 words
Files
5 (incl. scripts, references)
Skills in repo
179
Repo updated
First seen
Licence
MIT

At a glance

Discover Amazon product opportunities by starting with a clearly defined audience and mapping recurring work, life, event, and gifting needs.

  • Works in 5 steps: 选择人群 → 画场景旅程 → 生成产品簇 → …
  • Product-first searches produce generic red-ocean ideas
  • 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 Audience First Product Discovery is an agent skill from xjli360/sealeap-amazon-skills. Discover Amazon product opportunities by starting with a clearly defined audience and mapping recurring work, life, event, and gifting needs. Use when product-first searches produce generic red-ocean ideas.

Its SKILL.md is about 490 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 sits in Product & Project Management. 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

  • Product-first searches produce generic red-ocean ideas

Example prompts

  • “/sealeap-xiezhi-amazon-audience-first-product-discovery”

Requirements

  • Python 3

Workflow steps

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

  1. 选择人群
  2. 画场景旅程
  3. 生成产品簇
  4. 验证搜索与竞争
  5. 形成产品线

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 Audience First Product Discovery loads about 491 tokens when it runs, and up to ~2.7k if it reads all its reference files. Until then it costs about 65 tokens; SKILL.md has 85 words of instructions outside code blocks.

Always · name and description, kept in context so the agent knows when to use it
~65
When it runs · the whole SKILL.md, loaded when a task matches
~491
With references · SKILL.md plus every file in references/, read only if the agent opens them
~2.7k

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). 85 words, ~491 tokens.

Download SKILL.mdSave it as .claude/skills/sealeap-xiezhi-amazon-audience-first-product-discovery/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-audience-first-product-discovery
description
Discover Amazon product opportunities by starting with a clearly defined audience and mapping recurring work, life, event, and gifting needs. Use when product-first searches produce generic red-ocean ideas.

Amazon 人群优先选品

目标

从身份明确且需求持续的人群出发,把其生活与活动场景转化为一条可验证的产品机会线。

适用任务

  • 围绕职业、爱好、角色或活动人群寻找产品。
  • 把普通产品重组为人群专属方案。
  • 从单个需求扩展可持续产品线。

开始前要拿到

  • 目标人群及其地区、身份和活动周期。
  • 人群的任务、场景、携带物、礼赠和痛点假设。
  • 相关关键词、商品、评论和价格带。
  • 可用供应链能力与组合限制。

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。
  • 不得以刻板印象替代消费者证据。
  • 涉及文化、宗教、职业或受保护群体表达时做本地化和合规审核。

工作流

1. 选择人群

优先选择角色或任务清楚、需求反复出现且具有可搜索表达的人群;分析群体场景,不识别或采集个人身份。

2. 画场景旅程

列出工作、训练、出行、庆祝、收纳、礼赠等阶段,并为每一阶段记录待解决任务。

3. 生成产品簇

从功能、数量组合、结构搭配、包装、身份表达和场景文案提出产品方案。

4. 验证搜索与竞争

确认存在精准人群/场景词,搜索结果与目标需求一致,并重建直接竞品集合。

5. 形成产品线

把通过需求、利润和供应链门槛的方案放入短中长期路线图,而非只选一个单品。

判断标准

  • 人群词必须对应明确场景和持续需求;宽泛身份标签不构成机会。
  • 普通产品只有在数量、结构、表达或服务方案与目标任务强绑定时才产生有效差异。
  • 搜索结果相关性和评论语言要共同证明人群需求。

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

  • 人群画像与场景旅程
  • 需求—产品机会矩阵
  • 精准词与直接竞品表
  • 组合/定位方案
  • 产品线路线图

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-audience-first-product-discovery 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

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Questions about Sealeap Xiezhi Amazon Audience First Product Discovery

What does Sealeap Xiezhi Amazon Audience First Product Discovery do?

Discover Amazon product opportunities by starting with a clearly defined audience and mapping recurring work, life, event, and gifting needs. Sealeap Xiezhi Amazon Audience First Product Discovery is an agent skill from xjli360/sealeap-amazon-skills. Discover Amazon product opportunities by starting with a clearly defined audience and mapping recurring work, life, event, and gifting needs.

When should I use Sealeap Xiezhi Amazon Audience First Product Discovery?

Sealeap Xiezhi Amazon Audience First Product Discovery fits situations like: product-first searches produce generic red-ocean ideas.

How do I install Sealeap Xiezhi Amazon Audience First Product Discovery in Claude Code?

Run `npx skills add xjli360/sealeap-amazon-skills --skill sealeap-xiezhi-amazon-audience-first-product-discovery -a claude-code`. Or copy the skill folder (amazon-skills/douyin/xiezhi/sealeap-xiezhi-amazon-audience-first-product-discovery in xjli360/sealeap-amazon-skills) into .claude/skills/sealeap-xiezhi-amazon-audience-first-product-discovery in your project. Claude Code loads it when a task matches its description.

How do I install Sealeap Xiezhi Amazon Audience First Product Discovery in Codex?

Run `npx skills add xjli360/sealeap-amazon-skills --skill sealeap-xiezhi-amazon-audience-first-product-discovery -a codex`. Or copy the skill folder (amazon-skills/douyin/xiezhi/sealeap-xiezhi-amazon-audience-first-product-discovery in xjli360/sealeap-amazon-skills) into .agents/skills/sealeap-xiezhi-amazon-audience-first-product-discovery in your project. Codex loads it when a task matches its description.

Can I use Sealeap Xiezhi Amazon Audience First Product Discovery 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-audience-first-product-discovery -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-audience-first-product-discovery, .gemini/skills/sealeap-xiezhi-amazon-audience-first-product-discovery, .github/skills/sealeap-xiezhi-amazon-audience-first-product-discovery and .opencode/skills/sealeap-xiezhi-amazon-audience-first-product-discovery in your project.

What does Sealeap Xiezhi Amazon Audience First Product Discovery need to run?

Going by SKILL.md and its folder, Sealeap Xiezhi Amazon Audience First Product Discovery needs Python for the scripts in its folder. Our summary lists: Python 3.

Does Sealeap Xiezhi Amazon Audience First Product Discovery 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 Audience First Product Discovery 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 Audience First Product Discovery use?

Sealeap Xiezhi Amazon Audience First Product Discovery 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 Audience First Product Discovery use?

About 491 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 Audience First Product Discovery?

Skills that share tags, products or a category with Sealeap Xiezhi Amazon Audience First Product Discovery: Ouroboros PM Interview (Q00/ouroboros, 6.2k stars), Produck Feedback To Build (tryproduck/produck-skills, 511 stars), Jira Natural Language Interface (jjmartres/opencode, 133 stars) and Rhesis (rhesis-ai/rhesis, 397 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 Audience First Product Discovery?

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.