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Analook — 竞品情报分析工具

30 秒看透任何竞品 · AI-powered competitor intelligence for indie hackers & growth teams

License: MIT Website Skills on ClawHub Deployed on Railway

English | 中文 · 🤖 MCP Server · 📖 Docs

🤖 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


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

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

Related MCP server: merch-connector

Table of Contents


🌱 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.


💼 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


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

What's Inside

This repo is the production backend powering analook.com:

  • app.py — FastAPI orchestrator: parallel analysis pipeline, job state, streaming, credit gating

  • modules/ — analysis engines (traffic, social, Product Hunt, growth analysis, AI summary) plus the Supabase client, payment integrations, and the MCP server (mcp_app.py)

  • migrations/ — Supabase SQL migrations (auth/credits/promo codes/attribution)

  • scripts/ — operational tooling: user metrics, attribution reports, EDM campaigns

  • static/ — the web UI served at analook.com

  • tests/ + Dockerfile — regression suite and container build

Stack: FastAPI + Supabase (auth, credits, report storage), deployed via Docker.


🔗 Live Demo

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

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


📦 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


🧠 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.


⚡ Quick Start

git clone https://github.com/Gingiris-1031/Competitor-analysis-tool.git
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


🚀 Deploy Your Own

Production source-of-truth

The production source is the main branch of Gingiris-1031/Competitor-analysis-tool. Before any production deploy, run:

python3 scripts/verify_deploy_source.py

The preflight fails if the checkout is behind origin/main, is on the wrong branch or remote, or is missing tracked SEO/GEO assets such as static/llms.txt, static/robots.txt, and static/sitemap.xml. This prevents an older local clone from silently removing live assets during deployment.

The current production Fly app is declared in fly.toml as competitor-analysis-tool. Run the preflight from the same checkout immediately before fly deploy.

Self-hosting

One-click deploy on Railway:

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).


🔌 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


⭐ Star This Repo

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


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.

Related Playbooks (now on ClawHub):

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


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

git clone https://github.com/Gingiris-1031/Competitor-analysis-tool.git
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。


License

MIT — free to use, modify, and redistribute.

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