SKILL.md

$27

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](#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 &#x26;&#x26; 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

x-tweet-fetcher