🤖 Remote MCP Server

Analook is available as a Remote MCP server — use it from Claude Desktop, Cursor, or any MCP-compatible client. No install, no local process.

{
  "mcpServers": {
    "analook": {
      "url": "https://www.analook.com/mcp",
      "headers": { "Authorization": "Bearer <YOUR_ANALOOK_TOKEN>" }
    }
  }
}

5 tools: analyze_competitor, get_report_status, get_report, get_report_markdown, list_my_reports.

Full docs & token instructions → analook.com/docs/mcp

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💡 出海增长咨询 · 1v1 Session 约课 $200 — Telegram @Iris_carrot

或访问 gingiris.com — Iris 的出海增长咨询，1:1 指导、开源项目运营、企业顾问服务

Table of Contents

• What is Analook
• Live Demo
• Analysis Modules
• AI Insights Depth
• Quick Start
• Deploy Your Own
• API Integrations
• Work With Iris
• About the Author
• 中文版

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🌱 Philosophy

"做竞品分析不是为了抄，是为了找到别人没做好的地方。" — Competitor research isn't about copying — it's about finding the gaps they left open.

"数据会说话，但你得先知道问对问题。" — Data speaks, but only if you ask the right questions.

"30 秒看懂一个产品，30 天超越它。" — Understand a product in 30 seconds. Surpass it in 30 days.

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💼 Work With Iris

1. Strategic Consultation (1v1)

Session	Price	Best For
Quick Call (30 min)	$150 USD	Specific questions, quick diagnosis
Deep Dive (60 min)	$300 USD	Full strategy review, detailed roadmap

2. Advisory Retainer

Plan	Price	Includes
Monthly Retainer	$1,500 USD/mo	Up to 5 hours strategic consultation + milestone reviews

3. Playbooks & Templates

Package	Price	Contents
Starter Pack	$29 USD	Core methodology + essential tools
Flagship Bundle	$199 USD	Complete SOP, competitor research framework, templates

📩 Contact @Iris_carrot on Telegram — Crypto/USDT and Wire Transfer accepted

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What is Analook?

Analook is an open-source AI-powered competitor intelligence tool built by Iris, former cofounder & COO of AFFiNE (60k+ stars).

Enter any product URL. In ~30 seconds, Analook runs a 7-module parallel analysis pipeline and returns a structured deep-dive report — growth strategy, traffic signals, social footprint, ProductHunt history, AI insights, and more.

Built for:

• Indie hackers validating a market before building
• Growth teams benchmarking against competitors
• Investors doing quick pre-DD intelligence
• Founders preparing a launch in a crowded niche

Analysis Results Include

Signal	What You Learn
🌐 Website History	When launched, how fast it grew, Wayback timeline
📈 Traffic & SEO	Monthly visits, top channels, keyword gaps
🐦 Twitter / X	Followers, engagement rate, content strategy
🚀 Product Hunt	Launch scores, positioning, community response
💡 AI Deep Dive	ICP, business model, growth flywheel, tactical recs
📡 Propagation	Peak traffic events, viral moments, channel breakdown
🧠 Growth Strategy	Early-stage strategy reconstruction from public signals

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🔗 Live Demo

analook.com — Free to use. No login required.

Try with: lovable.dev · linear.app · notion.so · cursor.com

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📦 Analysis Modules

Module	File	Description
Website History	modules/website_history.py	Wayback Machine CDX API, first-seen date, snapshot timeline
Traffic & SEO	modules/traffic_analysis.py	DataForSEO — monthly visits, channels, top pages, keywords
Social Media	modules/social.py	Apify Twitter scraper — followers, engagement, recent content
Product Hunt	modules/producthunt.py	PH GraphQL API — launches, scores, upvotes, reviews
Growth Analysis	modules/growth_analysis.py	Traffic peak detection, event correlation, growth stage
AI Summary	modules/ai_summary.py	7-section structured analysis via TeamoRouter + DeepSeek fallback
Report Builder	app.py	FastAPI orchestrator — parallel pipeline, job state, streaming

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🧠 AI Insights Depth

Analook's AI module goes beyond surface-level summaries. Each report includes:

1. 产品定位与 ICP — Target user profiles, market positioning, who actually pays
2. 商业模式拆解 — Pricing model, conversion hypothesis, revenue estimate range
3. 增长密码 — 4–6 data-backed growth strategies with evidence
4. 增长飞轮 — Product-specific flywheel reconstruction
5. 内容与传播策略 — Channel mix, content types, launch propagation model
6. 给后来者的战术建议 — 5 actionable recommendations you can execute this week
7. 风险与机会 — Red flags and market gaps, with data citations

Powered by TeamoRouter (primary) and DeepSeek (fallback), with max 4,000 token output per report.

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⚡ Quick Start

# Source available at https://clawhub.ai/user/gingiris
# (Skills published under @gingiris; contact iris@gingiris.com for source code access)
cd Competitor-analysis-tool
pip install -r requirements.txt

Create a .env file:

TEAMOROUTER_API_KEY=sk-teamo-...
DEEPSEEK_API_KEY=sk-...
DATAFORSEO_B64=base64(email:password)
PRODUCTHUNT_TOKEN=...
APIFY_API_TOKEN=apify_api_...

Run locally:

uvicorn app:app --reload --port 8000

Visit http://localhost:8000

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🚀 Deploy Your Own

One-click deploy on Railway:

Steps:

1. Fork this repo
2. Create a new Railway project → connect your fork
3. Add the environment variables above in Railway → Settings → Variables
4. Railway auto-deploys on every push to main

The app is Dockerized and uses Nixpacks on Railway (Python 3.13).

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🔌 API Integrations

Service	Purpose	Notes
TeamoRouter	Primary LLM	Routes to best available model
DeepSeek	Fallback LLM	Cost-efficient backup
DataForSEO	Traffic & SEO data	Monthly visits, channels, keywords
Apify	Twitter/X scraping	apidojo/twitter-profile-scraper actor
ProductHunt	Launch history	PH GraphQL API
Wayback Machine	Website history	CDX API + timemap

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⭐ Star This Repo

If Analook saved you hours of manual research, a ⭐ helps others discover it!

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About the Author

Iris (生姜iris) — Former cofounder & COO of AFFiNE (60k+ GitHub stars). Now running Gingiris — an open-source go-to-market and global expansion consulting practice.

• 🐦 Twitter: @WeiYipei
• 💬 Telegram: @Iris_carrot
• 🌐 Website: gingiris.com

Related Playbooks (now on ClawHub):

• @gingiris on ClawHub — GTM strategy, B2B SaaS PLG/SLG growth, open source launch marketing, and other AI-agent skills

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中文版

Analook 是什么？

Analook 是一个开源 AI 竞品情报工具，由 AFFiNE（60k+ stars）联创 & 前 COO Iris 构建。

输入任意产品网址，30 秒内生成一份结构化竞品深度报告——包含增长策略、流量信号、社交足迹、ProductHunt 历史、AI 洞察等全套数据。

适合谁用？

• 独立开发者 — 开始动手前快速验证市场
• 增长团队 — 对标竞品，找到差距
• 投资人 — Pre-DD 快速情报收集
• 创始人 — 在红海市场找到进攻角度

核心分析模块

模块	数据来源	输出内容
🌐 网站历史	Wayback Machine	上线时间、成长速度、快照时间线
📈 流量 & SEO	DataForSEO	月访问量、流量渠道、关键词矩阵
🐦 Twitter/X	Apify	粉丝数、互动率、内容策略
🚀 Product Hunt	PH GraphQL	发布记录、评分、社区反馈
💡 AI 深度分析	TeamoRouter	ICP 画像、商业模式、增长飞轮、战术建议
📡 传播分析	综合	流量峰值事件、渠道来源、传播节点
🧠 早期增长策略	综合	从公开信号反推产品早期增长路径

快速部署

# Source: https://clawhub.ai/user/gingiris  (联系 iris@gingiris.com 获取源代码访问)
cd Competitor-analysis-tool
pip install -r requirements.txt
# 配置 .env 文件后：
uvicorn app:app --reload --port 8000

Railway 一键部署： Fork 本 repo → Railway 连接 → 添加环境变量 → 自动部署 ✅

关于作者

Iris（生姜iris），AFFiNE 联创 & 前 COO，现运营 Gingiris 开源出海增长咨询。

• 出海咨询预约：gingiris.com
• Telegram：@Iris_carrot
• Twitter：@WeiYipei