Analook — Competitor Intelligence

io.github.Gingiris由 GitHub 帳號 Gingiris 發布v1.0.0更新於 Nov 2, 2026

Competitor intelligence for AI agents — SEO, traffic, social, Product Hunt, pricing, AI insights.

已驗證Streamable HTTP可網頁執行網頁搜尋與抓取資料與分析商業與電商

概覽

AI 產生的概覽

遠端 MCP 伺服器，可對任意產品網址產生 AI 競品情報報告，涵蓋 SEO、流量、社群、Product Hunt、GitHub 與成長策略。

可以做什麼
  • 提交競品分析任務並輪詢任務狀態
  • 以結構化 JSON 或易讀的 Markdown 取得完成的報告
  • 列出你最近的報告，並瀏覽公開報告庫
  • 執行成長稽核，回傳執行摘要、診斷報告與 30 天行動計畫
適用情境
適合獨立開發者驗證市場、成長團隊對標競品、投資人做投前快速調查，或創辦人在擁擠賽道準備發布時，用一份多來源快照取代人工研究。
執行需求
需要遠端 MCP 端點，無須本機安裝或程序。README 的設定範例透過 Authorization Bearer 標頭傳入 Analook 權杖，列出報告的工具需要身分驗證。自行部署開源版本需要 Python，以及 LLM、SEO、擷取與 Product Hunt 服務的 API 金鑰。
安裝前請注意
分析任務會消耗額度，README 建議先瀏覽公開報告以免浪費額度。託管服務會把提交的網址傳送給第三方資料供應商（SEO、流量、社群擷取、Product Hunt、Wayback Machine）與 LLM 服務。列出個人報告需要帳戶權杖，請妥善保管。README 中還夾帶與工具無關的付費顧問推廣。

安裝

在 SourceWeft 中

  1. 開啟 儀表板中的 Analook — Competitor Intelligence，將其新增到工作區。
  2. 為需要使用其工具的對話啟用該服務。

Web executable，透過 Streamable HTTP。 遠端服務在工作區中設定後即可從網頁執行環境執行。

交給代理程式安裝

把這段提示詞貼上到 Claude Code、Codex、Cursor 等代理程式，或 SourceWeft 對話中。代理程式會讀取該伺服器的安裝指南，先向你展示發布者、端點和登入方式，經你同意後再把它新增到你的客戶端。金鑰不會寫進任何命令。

閱讀 https://sourceweft.com/mcp/io.github.Gingiris%2Fanalook/install.md，並按其中說明把這個 MCP 伺服器接入我的客戶端。修改任何設定之前，先告訴我它的發布者、端點和登入方式，等我確認。不要把 API 金鑰寫進命令或我會提交的檔案；問我金鑰應從哪裡取得。

檢視代理程式所依據的安裝指南

其他 MCP 客戶端

把它新增到你客戶端的 mcpServers 設定中。

{
  "mcpServers": {
    "analook-competitor-intelligence": {
      "type": "http",
      "url": "https://www.analook.com/mcp"
    }
  }
}

README

[Analook Logo]

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.

json
{  "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 $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

🌱 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)

SessionPriceBest For
Quick Call (30 min)$150 USDSpecific questions, quick diagnosis
Deep Dive (60 min)$300 USDFull strategy review, detailed roadmap

2. Advisory Retainer

PlanPriceIncludes
Monthly Retainer$1,500 USD/moUp to 5 hours strategic consultation + milestone reviews

3. Playbooks & Templates

PackagePriceContents
Starter Pack$29 USDCore methodology + essential tools
Flagship Bundle$199 USDComplete 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

SignalWhat You Learn
🌐 Website HistoryWhen launched, how fast it grew, Wayback timeline
📈 Traffic & SEOMonthly visits, top channels, keyword gaps
🐦 Twitter / XFollowers, engagement rate, content strategy
🚀 Product HuntLaunch scores, positioning, community response
💡 AI Deep DiveICP, business model, growth flywheel, tactical recs
📡 PropagationPeak traffic events, viral moments, channel breakdown
🧠 Growth StrategyEarly-stage strategy reconstruction from public signals

🔗 Live Demo

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

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

📦 Analysis Modules

ModuleFileDescription
Website Historymodules/website_history.pyWayback Machine CDX API, first-seen date, snapshot timeline
Traffic & SEOmodules/traffic_analysis.pyDataForSEO — monthly visits, channels, top pages, keywords
Social Mediamodules/social.pyApify Twitter scraper — followers, engagement, recent content
Product Huntmodules/producthunt.pyPH GraphQL API — launches, scores, upvotes, reviews
Growth Analysismodules/growth_analysis.pyTraffic peak detection, event correlation, growth stage
AI Summarymodules/ai_summary.py7-section structured analysis via TeamoRouter + DeepSeek fallback
Report Builderapp.pyFastAPI 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

bash
# Source available at https://clawhub.ai/user/gingiris# (Skills published under @gingiris; contact [email protected] for source code access)cd Competitor-analysis-toolpip install -r requirements.txt

Create a .env file:

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

Run locally:

bash
uvicorn app:app --reload --port 8000

Visit http://localhost:8000

🚀 Deploy Your Own

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

ServicePurposeNotes
TeamoRouterPrimary LLMRoutes to best available model
DeepSeekFallback LLMCost-efficient backup
DataForSEOTraffic & SEO dataMonthly visits, channels, keywords
ApifyTwitter/X scrapingapidojo/twitter-profile-scraper actor
ProductHuntLaunch historyPH GraphQL API
Wayback MachineWebsite historyCDX 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 & SEODataForSEO月访问量、流量渠道、关键词矩阵
🐦 Twitter/XApify粉丝数、互动率、内容策略
🚀 Product HuntPH GraphQL发布记录、评分、社区反馈
💡 AI 深度分析TeamoRouterICP 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

bash
# Source: https://clawhub.ai/user/gingiris  (联系 [email protected] 获取源代码访问)cd Competitor-analysis-toolpip 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。

來源：README.md，提交 b01ba9f

工具

8

analyze_competitor

Submit a competitor analysis job. Analyzes a competitor's website across 15+ data sources (SEO, traffic, social, Product Hunt, GitHub, Wayback Machine history, AI-generated insights, etc.) and returns a job_id. Use get_report_status(job_id) to poll and get_report(job_id) to retrieve results when status='completed'. Typical analysis takes 2-5 minutes. Requires authentication (deducts 1 credit from your Analook balance). Args: url: Competitor website URL (e.g. 'https://linear.app' or 'lovable.dev') product_name: Optional product name override (defaults to domain) lang: Report language, 'en' (default) or 'zh' for Chinese output Returns: {job_id: str, status: 'started', poll_url: str} on success {error: str, hint?: str} on auth/validation failure

輸入參數url必填langcontext必填llm_model必填product_nameconversation_id

get_report_status

Poll an analysis job's status. Args: job_id: ID returned from analyze_competitor() Returns: {status: 'running'|'completed'|'failed', progress?: str, report_url?: str}

輸入參數job_id必填context必填llm_model必填conversation_id

get_report

Fetch the full competitor analysis report as structured JSON. Reports contain: website snapshot, Wayback Machine history, SEO/traffic data (DataForSEO), social media presence, Product Hunt launches, GitHub stats, pricing, funding, AI-generated business insights, growth playbooks, and more. Args: job_id: ID from analyze_competitor(); status must be 'completed' Returns: The full report dict (nested structure), or {error} if not found / not ready.

輸入參數job_id必填context必填llm_model必填conversation_id

get_report_markdown

Fetch the competitor analysis report as human-readable Markdown. Suitable for piping into agents that prefer text over structured JSON, or for direct display to end users. Args: job_id: ID from analyze_competitor(); status must be 'completed' Returns: {markdown: str} or {error: str}

輸入參數job_id必填context必填llm_model必填conversation_id

list_my_reports

List your recent competitor analysis reports (up to 50). Requires authentication. Returns a lightweight list (id, url, product_name, created_at, status) — use get_report(job_id) to fetch the full report for any of them. Returns: {reports: [{id, url, product_name, created_at, status}, ...]}

輸入參數context必填llm_model必填conversation_id

run_growth_audit

Run a full Growth Audit — three linked strategic reports for a product. Unlike analyze_competitor (a single 15-signal intelligence snapshot), a Growth Audit produces an Executive Summary + a Diagnosis Report + a 30-day Action Plan, grounded in real channel/tactic playbooks. Best for 'how do I grow THIS product' rather than 'what is this competitor doing'. Takes ~4-6 minutes. Requires authentication and deducts 10 credits. Poll with get_growth_audit(job_id) until status='completed'. Args: url: Product website URL to audit product_name: Optional product name override (defaults to domain) lang: Report language, 'en' (default) or 'zh'

輸入參數url必填langcontext必填llm_model必填product_nameconversation_id

get_growth_audit

Fetch a Growth Audit's three reports (Executive Summary, Diagnosis, Action Plan) as Markdown. Args: job_id: ID from run_growth_audit() (starts with 'ga-') Returns: {status, reports: {executive_summary, diagnosis_report, action_plan}} while running, only {status, progress} is returned.

輸入參數job_id必填context必填llm_model必填conversation_id

browse_public_reports

Browse Analook's public competitor-intelligence report gallery. Returns recently published public reports (product name, domain, category, and a link). No authentication or credits required — a fast way to discover existing analyses before spending a credit on a fresh one. Args: category: Optional filter, e.g. 'AI / Agents', 'Dev Tools', 'Crypto / Web3', 'Marketing / SEO', 'SaaS / Other'

輸入參數context必填categoryllm_model必填conversation_id

版本歷史

1
  1. v1.0.0最新Sep 16, 2026

評分

已滿足適用檢查項的 90%

該評分衡量此收錄條目的完整程度，依據註冊中心條目、端點與公開來源計算得出。它不是對伺服器本身的評價，也不表示伺服器安全或目前可用：可連通性是帶日期的檢查結果，評分也不包含安裝量或使用者評價。

  • 近 30 天內可連通必需25 分
  • 已宣告認證方式必需10 分
  • 已列出工具必需15 分
  • 描述必需15 分
  • 圖示10 分
  • 文件15 分
  • 授權條款10 分

    請在儲存庫或註冊中心條目中宣告授權條款。