X Tweet Fetcher

Fetch tweets from X/Twitter without authentication. Supports tweet content, reply threads, user timelines, Chinese platforms, and tweet growth tracking.

Feature Overview

Feature	Command	Dependencies
Single tweet	--url <tweet_url>	None (zero deps)
Reply threads	--url <tweet_url> --replies	Camofox
User timeline	--user <username> --limit 300	Camofox
Chinese platforms	fetch_china.py --url <url>	Camofox (except WeChat)
Google search	camofox_search("query")	Camofox
X-Tracker (growth)	tweet_growth_cli.py --add/--run/--report	None (zero deps)

---

Basic Usage (Zero Dependencies)

Fetch a Single Tweet

# JSON output
python3 scripts/fetch_tweet.py --url "https://x.com/user/status/123456"

# Text only (human readable)
python3 scripts/fetch_tweet.py --url "https://x.com/user/status/123456" --text-only

# Pretty JSON
python3 scripts/fetch_tweet.py --url "https://x.com/user/status/123456" --pretty

What It Fetches

Content Type	Support
Regular tweets	✅ Full text + stats
Long tweets (Twitter Blue)	✅ Full text
X Articles (long-form)	✅ Complete article text
Quoted tweets	✅ Included
Stats (likes/RT/views)	✅ Included
Media URLs	✅ Images + videos

---

Advanced Features (Requires Camofox)

⚠️ The following features require Camofox browser service running on localhost:9377. See Camofox Setup below.

Fetch Reply Threads

# Fetch tweet + all replies (including nested replies)
python3 scripts/fetch_tweet.py --url "https://x.com/user/status/123456" --replies

# Text-only mode with replies
python3 scripts/fetch_tweet.py --url "https://x.com/user/status/123456" --replies --text-only

Fetch User Timeline

# Fetch latest tweets from a user (supports pagination, MAX_PAGES=20)
python3 scripts/fetch_tweet.py --user <username> --limit 300

Fetch Chinese Platform Content

# Auto-detects platform from URL
python3 scripts/fetch_china.py --url "https://weibo.com/..."     # Weibo
python3 scripts/fetch_china.py --url "https://bilibili.com/..."  # Bilibili
python3 scripts/fetch_china.py --url "https://csdn.net/..."      # CSDN
python3 scripts/fetch_china.py --url "https://mp.weixin.qq.com/..." # WeChat (no Camofox needed!)

Platform	Status	Notes
WeChat Articles	✅	Uses web_fetch directly, no Camofox
Weibo	✅	Camofox renders JS
Bilibili	✅	Video info + stats
CSDN	✅	Articles + code blocks
Zhihu / Xiaohongshu	⚠️	Needs cookie import for login

Google Search (Zero API Key)

# Python
from scripts.camofox_client import camofox_search
results = camofox_search("your search query")
# Returns: [{"title": "...", "url": "...", "snippet": "..."}, ...]

# CLI
python3 scripts/camofox_client.py "your search query"

Uses Camofox browser to search Google directly. No Brave API key needed, no cost.

---

Camofox Setup

What is Camofox?

Camofox is an anti-detection browser service based on Camoufox (a Firefox fork with C++ level fingerprint masking). It bypasses:

• Cloudflare bot detection
• Browser fingerprinting
• JavaScript challenges

Installation

Option 1: OpenClaw Plugin

openclaw plugins install @askjo/camofox-browser

Option 2: Manual Install

git clone https://github.com/jo-inc/camofox-browser
cd camofox-browser
npm install && npm start

Verify

curl http://localhost:9377/health
# Should return: {"status":"ok"}

REST API

# Create tab
POST http://localhost:9377/tabs
Body: {"userId":"test", "sessionKey":"test", "url":"https://example.com"}

# Get page snapshot
GET http://localhost:9377/tabs/<TAB_ID>/snapshot?userId=test

# Close tab
DELETE http://localhost:9377/tabs/<TAB_ID>?userId=test

---

From Agent Code

from scripts.fetch_tweet import fetch_tweet

result = fetch_tweet("https://x.com/user/status/123456")
tweet = result["tweet"]

# Regular tweet
print(tweet["text"])
print(f"Likes: {tweet['likes']}, Views: {tweet['views']}")

# X Article (long-form)
if tweet.get("is_article"):
    print(tweet["article"]["title"])
    print(tweet["article"]["full_text"])

# Links found in replies
for reply in result.get("replies", []):
    for link in reply.get("links", []):
        print(link)

Output Format

{
  "url": "https://x.com/user/status/123",
  "username": "user",
  "tweet_id": "123",
  "tweet": {
    "text": "Tweet content...",
    "author": "Display Name",
    "screen_name": "username",
    "likes": 100,
    "retweets": 50,
    "bookmarks": 25,
    "views": 10000,
    "replies_count": 30,
    "created_at": "Mon Jan 01 12:00:00 +0000 2026",
    "is_note_tweet": false,
    "is_article": true,
    "article": {
      "title": "Article Title",
      "full_text": "Complete article content...",
      "word_count": 4847
    }
  },
  "replies": [
    {
      "author": "@someone",
      "text": "Reply text...",
      "likes": 5,
      "links": ["https://github.com/..."],
      "thread_replies": [{"text": "Nested reply..."}]
    }
  ]
}

File Structure

x-tweet-fetcher/
├── SKILL.md                    # This file
├── README.md                   # GitHub page with full docs
├── scripts/
│   ├── fetch_tweet.py          # Main fetcher (tweet + replies + timeline)
│   ├── fetch_china.py          # Chinese platform fetcher
│   ├── camofox_client.py       # Camofox REST API client + camofox_search()
│   └── x-profile-analyzer.py   # User profile analysis (AI-powered)
└── CHANGELOG.md

Requirements

• Basic: Python 3.7+, no external packages, no API keys
• Advanced: Camofox running on localhost:9377
• Profile Analyzer: MiniMax M2.5 API key (for AI analysis)

---

X-Tracker: Tweet Growth Monitoring

Track your tweets' growth and detect viral moments — inspired by semiconductor ETCH Endpoint Detection.

Quick Start

# Add a tweet to track
python3 scripts/tweet_growth_cli.py --add "https://x.com/user/status/123" "my launch tweet"

# List all tracked tweets
python3 scripts/tweet_growth_cli.py --list

# Run sampling (new tweets <48h, every 15min)
python3 scripts/tweet_growth_cli.py --run --fast

# Run sampling (all tweets, hourly)
python3 scripts/tweet_growth_cli.py --run --normal

# Generate analysis report
python3 scripts/tweet_growth_cli.py --report 123456789

# Report with topic cross-analysis
python3 scripts/tweet_growth_cli.py --report 123456789 --cross

Cron Integration

# Dual-frequency sampling
*/15 * * * * python3 tweet_growth_cli.py --run --fast    # New tweets (<48h)
0 * * * *    python3 tweet_growth_cli.py --run --normal  # All tweets (hourly)

Detection Algorithm

Component	Method	Purpose
Derivative detection	dV/dt per hour	Spot sudden acceleration
Sliding window	5-sample moving average	Filter noise
Multi-signal fusion	views×1 + likes×1 + bookmarks×1.5 + RT×3	Weighted composite score
Burst confirmation	3 consecutive windows above threshold	Prevent false positives
Surge override	Single window +100%/h	Catch massive spikes
Saturation	6 samples < 2%/h growth	Detect long tail
Propagation	RT-per-1k-views ratio	Influencer vs algorithm driven

Output Example

🎯 Burst detected at 2026-03-14 08:45
   - Growth rate: 156%/h
   - Composite score: +2847 (views +1200, RT +89, likes +234)
   - Propagation: 12.3 RT/1k views (influencer-driven)

---

How It Works

• Basic tweets: FxTwitter public API
• Replies/timelines: Camofox → Nitter (privacy-respecting X frontend)
• Chinese platforms: Camofox renders JS → extracts content
• Google search: Camofox opens Google → parses results
• X-Tracker: FxTwitter API + derivative detection + burst confirmation