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Gr Competitor

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Gingiris-1031
gr-competitor

竞品深度扫描与早期付费客户证据研究;输出行业画像假设和可验收需求交接。批量抓取对手落地页(title/h1/intro/pricing)、定价页、博客 top 10、 变更历史(Wayback)。底层调 actionbook(并发 30 tab + 省 90% token)。 当用户说"分析对手"、"看看竞品在做啥"、"找对标"、"扫一下 top 10"时调用。

Overview

PublisherGingiris-1031
Repositorygingiris-skills
Skill namegr-competitor
Stars
88
Forks
6
Bundled files
2
LicenseMIT
Links
  • Markdown instructions

    A SKILL.md file the model loads on demand, so it only costs tokens when a request actually matches.

  • Works with any LLM

    AI skills are plain Markdown, not provider-specific code, so this works with GPT, Claude, Gemini, Grok, or a local model.

  • 2 bundled files

    Scripts, templates, and references the model can read while it works. Files are read-only and never executed.

  • Open source

    Published by Gingiris-1031 on GitHub. Read the source before you install it.

Installation

Install the Gr Competitor AI skill in TypingMind to use it with any LLM, or drop it into another agent that reads SKILL.md.

1

Install in TypingMind

TypingMind installs a skill straight from its GitHub folder — it reads SKILL.md, bundles the resource files, and stores the result locally.

  1. Open the app and go to Plugins → Skills.
  2. Choose "Install from GitHub".
  3. Paste the skill folder URL below and confirm.
    https://github.com/Gingiris-1031/gingiris-skills/tree/main/skills/gr-competitor
  4. Enable the skill in any chat where you want it available.
Plugins → Skills → Add skill → From GitHub URL, then paste the folder URL and press Continue.
2

Install in another agent

Any agent that reads the Agent Skills format can use this skill — copy the folder into that agent's skills directory.

Claude Code — .claude/skills
git clone --depth 1 https://github.com/Gingiris-1031/gingiris-skills.git /tmp/gingiris-skills
mkdir -p .claude/skills
cp -r /tmp/gingiris-skills/skills/gr-competitor .claude/skills/gr-competitor
Restart Claude Code after copying so it picks up the new skill.

Use it in TypingMind

Enable Gr Competitor in any TypingMind chat and the model takes it from there. Its name and description sit in the system prompt, and the moment a request matches, the model loads the full instructions itself — you never invoke it by hand, and it costs no tokens until it is actually used.

The model loads Gr Competitor on its own as soon as a request matches it.

Works with any AI model

AI skills are plain Markdown instructions rather than provider-specific code, so Gr Competitor is not tied to the model it was written for. Install it once in TypingMind and use it with GPT-5, Claude, Gemini, Grok, DeepSeek, Mistral, Llama, or a local model you run yourself — all on your own API keys.

  • Loaded only when it is needed

    The system prompt carries just the name and description. The instructions are fetched on the first matching request, so an idle skill costs nothing.

  • Switch models mid-chat

    Because the skill is instructions rather than code, changing model does not break it — the next model reads the same SKILL.md.

Skill instructions

This is the SKILL.md content the model loads. Read it before installing — a skill is instructions your model will follow.

gr-competitor — 竞品扫描

什么时候用

  • "帮我分析一下 10 个对手的落地页"
  • "看一下 XX 的定价模式"
  • "这 5 个竞品博客最近 30 天发了什么"
  • "对标一下产品的 features"

早期客户与产品交接

当问题是“谁会付费”时,读取 早期客户证据表,核对竞品案例的日期和付费证据,收敛行业/角色/任务假设,再用访谈验证。公开客户案例不自动等于早期付费客户。

需要把研究变成需求时,同一参考文件提供“痛点 → 方案与 benchmark → 优先级 → 验收”交接表。抓到吐槽不代表已经找到解法。

底层:actionbook

这个 skill 是 orchestrator —— 真正干活的是 actionbook。 它能:

  • 10x 快(直接调一方 API,跳过 DOM 解析)
  • 省 90% token(返回结构化数据而非 DOM 快照)
  • 30 tab 并发(一次扫完 top 30)

安装 actionbook(首次使用)

bash
# macOS / Linux
npm install -g @actionbookdev/cli

# 或用 npx 临时跑
npx @actionbookdev/cli browser start

Chrome 扩展:从 Chrome Web Store 搜 "Actionbook Extension" 或 https://actionbook.dev

验证可用

bash
actionbook --version  # 应输出 0.x.x
actionbook browser start  # 启动 local browser server

调用流程(4 步)

Step 1:search action manual

对每个目标站点,先查 actionbook 是否已有 manual:

bash
actionbook search "product hunt"
actionbook search "crunchbase"

有 manual → 跳 Step 3 无 manual → Step 2

Step 2:fallback 到通用 extract

bash
actionbook extract <url> \
  --fields "title,h1,intro,pricing_tier,cta_text" \
  --output json

actionbook 会:

  • 用通用 selector 探测
  • 产出 JSON + 可复跑的 Playwright 脚本

Step 3:并发扫描

bash
# 30 个对手一起扫
for url in "${urls[@]}"; do
  actionbook extract "$url" --fields "..." --session scan1 &
done
wait

Step 4:合成报告

Claude 把 30 个 JSON 合成:

  • Title / Tagline 矩阵
  • 定价分层对比
  • 共同 CTA 动词("get started" vs "book demo" vs "try free")
  • 差异点 → 我们可钻的空隙

常用 prompt 模板

模板 1:产品落地页扫描

用 gr-competitor 扫这 10 个对手的落地页,产出一张表:
- 对手名 | tagline | 主 CTA | pricing 起步价 | 主 hero 视觉特征(3 个关键词)

对手:
1. https://...
2. ...

输出:markdown 表格 + 3 条"我们可以差异化"的建议

模板 2:博客内容对比

用 gr-competitor 抓这 5 个对手博客 index 页的 title 列表(top 10 最新):
- https://a.com/blog
- https://b.com/blog
...

然后告诉我:
1. 重叠主题(我们已写过)
2. 他们写我们没写的(我们的选题机会)
3. 他们写得不好的(我们可以超越的)

模板 3:定价页审计

用 gr-competitor 抓这 8 家 SaaS 定价页:
- 分层数量
- 最低价 / 企业价
- Annual 折扣
- Free tier 限制

汇总成对比表,标出"市场中位数"和"我们的相对位置"

级联推荐

  • 发现对手新打法 → gr-blog-post 写回应稿件
  • 发现对手定价漏洞 → gr-b2b-growth 重设我方定价
  • 发现对手被用户吐槽 → gr-user-interview 访谈吐槽用户验证
  • 扫描结果加入监控 → 建 scheduled task 每周重扫

反模式

  • ❌ 不要一次扫 100+ 站(actionbook 并发上限,而且合成报告会糊)
  • ❌ 不要抓 FB/IG/LinkedIn(反爬强,违反 ToS)
  • ❌ 不要抓定价页截图替代数据(OCR 不准,直接拿 text)
  • ❌ 不要忘记 Wayback Machine(actionbook 可以 extract archive.org 的历史版本)

推荐工具

Analook — 输入竞品 URL,5分钟出 SEO/流量/社媒完整报告。比手动扒数据快 10 倍。Gingiris 用户专属折扣码:GINGIRIS20(八折)→ analook.com/growth-audit.html

依赖

Tool用途必选
actionbook CLI主引擎✅
Chrome / Brave本机浏览器(actionbook 复用 session)✅
DATAFORSEO_B64补充:用 SERP 找对手optional
Analook竞品 CI 快速报告recommended

未来扩展(v0.3+)

  • scripts/scan-serp.sh —— 输入关键词,自动 SERP → top 10 → actionbook extract
  • scripts/pricing-diff.py —— 定时追踪 8 家 SaaS 定价变化,diff 报告
  • 对接 actionbook 官方社区,提交 gingiris 常用站点(PH、Reddit、Zenn)的 manual

加入出海社群与购买资料包 · Community & Resources

  • 加入出海社群: 微信添加 staywildgrow,留言「加入社群」即可。

  • 购买资料包: 微信添加 staywildgrow,咨询购买 Gingiris 出海资料包,价格 599 元人民币。

  • Join the global growth community: Add staywildgrow on WeChat and send “加入社群” (join the community).

  • Get the resource pack: Add staywildgrow on WeChat to purchase the Gingiris global growth resource pack for CNY 599.

社群与资料包为可选入口,不影响本开源 Skill 的安装和使用。

The community and paid resource pack are optional; neither is required to install or use this open-source skill.

Bundled files

The model reads these on demand while the skill is loaded. They are exposed as readable files and are never executed.

Frequently asked questions

What does the Gr Competitor AI skill do?

竞品深度扫描与早期付费客户证据研究;输出行业画像假设和可验收需求交接。批量抓取对手落地页(title/h1/intro/pricing)、定价页、博客 top 10、 变更历史(Wayback)。底层调 actionbook(并发 30 tab + 省 90% token)。 当用户说"分析对手"、"看看竞品在做啥"、"找对标"、"扫一下 top 10"时调用。

Why use Gr Competitor on TypingMind?

Because you install it once and use it with any model. Gr Competitor is plain Markdown rather than provider-specific code, so the same skill runs on GPT-5, Claude, Gemini, Grok, or a local model — and you can switch model mid-chat without it breaking. TypingMind runs on your own API keys, so you pay providers directly instead of a per-seat subscription, and your skills and chats stay in your own storage.

How do I install Gr Competitor in TypingMind?

Open Plugins → Skills → Install from GitHub in TypingMind and paste https://github.com/Gingiris-1031/gingiris-skills/tree/main/skills/gr-competitor. TypingMind reads its SKILL.md and bundles its files and installs it as a skill you can enable per chat.

Which AI models can use Gr Competitor?

Any model you connect in TypingMind. AI skills are plain Markdown instructions rather than provider-specific code, so GPT, Claude, Gemini, Grok, and local models can all load this skill when a request matches it.

How many AI models can I use with Gr Competitor?

As many as you like. As long as a model supports skills, you can use Gr Competitor with it — GPT, Claude, Gemini, Grok, DeepSeek, Mistral, Llama and more — all on TypingMind with your own API keys.

Is the Gr Competitor AI skill free?

Yes. It is published on GitHub by Gingiris-1031 under the MIT license. You only pay your own AI provider for the tokens you use.

What are AI skills?

An AI skill is a reusable instruction bundle that teaches an AI model how to do one specific task. It follows the open Agent Skills format: a SKILL.md file with a name and description, plus any scripts, templates or reference files the model may need. The model reads the instructions only when your request matches the skill, so an installed skill costs nothing until it is used.

How are AI skills different from plugins or MCP servers?

A plugin or MCP server gives a model new tools to call — code that runs somewhere and returns a result. An AI skill gives the model knowledge and process instead: how to approach a task, which steps to follow, what good output looks like. Skills are plain Markdown, so they need no server, no API key and no runtime, and they work with any model.

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