twitter-cultivate（推特培养）

Twitter Account Cultivation Skill

Systematic approach to growing Twitter presence based on the open-source algorithm analysis.

Prerequisites

• rnet installed (pip install "rnet>=3.0.0rc20" --pre)
• rnet_twitter.py — the lightweight GraphQL client included in this repo
• Twitter cookies exported to: twitter_cookies.json Format: [{"name": "auth_token", "value": "..."}, {"name": "ct0", "value": "..."}]
• Your Twitter handle configured

Getting Cookies

1. Open Chrome → go to x.com → log in
2. DevTools → Application → Cookies → https://x.com
3. Copy auth_token and ct0 values
4. Save to twitter_cookies.json (see twitter_cookies.example.json)
5. Cookies last ~2 weeks. Refresh when you get 403 errors.

Core Metrics to Track

Workflow: Full Health Check

Step 1: Analyze Account

import asyncio
from rnet_twitter import RnetTwitterClient

async def analyze(username: str):
    client = RnetTwitterClient()
    client.load_cookies("twitter_cookies.json")

    # Get user profile
    user = await client.get_user_by_screen_name(username)
    followers = user.get("followers_count", 0)
    following = user.get("friends_count", 0)
    ratio = following / max(followers, 1)

    # Get recent tweets for content analysis
    tweets = await client.get_user_tweets(user["rest_id"], count=20)

    return {
        "username": username,
        "followers": followers,
        "following": following,
        "ratio": round(ratio, 2),
        "tweet_count": user.get("statuses_count", 0),
        "recent_tweets": len(tweets),
    }

asyncio.run(analyze("YOUR_USERNAME"))

Step 2: Check Shadowban Status

Manual check: shadowban.yuzurisa.com

Step 3: Analyze Following List

Recommends accounts to unfollow based on:

• No tweets in 90+ days (inactive)
• Never interacted with you (no value)
• Low follower count + high following (likely bots)
• No mutual engagement

Step 4: Find Engagement Opportunities

Use search_tweets to find trending conversations:

async def find_opportunities(niche_keywords: list[str]):
    client = RnetTwitterClient()
    client.load_cookies("twitter_cookies.json")

    opportunities = []
    for keyword in niche_keywords:
        tweets = await client.search_tweets(
            f"{keyword} lang:en -filter:replies",
            count=50,
            product="Top"
        )
        # Filter for high-engagement, recent tweets
        for t in tweets:
            if t["favorite_count"] >= 50 and t["reply_count"] < 20:
                opportunities.append(t)

    return sorted(opportunities, key=lambda t: t["favorite_count"], reverse=True)

Step 5: Generate Weekly Report

Compile metrics from Steps 1-4 into a structured report.

Account Health Scoring

Based on Twitter's open-source algorithm:

TweepCred Estimation

Score = PageRank x (1 / max(1, following/followers))

Unfollow Strategy

Priority 1: Inactive Accounts

• No tweets in 90+ days
• Safe to unfollow, no relationship loss

Priority 2: Non-Engagers

• Never liked/replied to your tweets
• One-way relationship

Priority 3: Low-Value Follows

• High following/low followers (bot-like)
• No content in your niche

Execution Plan

Week 1: Unfollow 30 inactive accounts
Week 2: Unfollow 30 non-engagers
Week 3: Unfollow 30 low-value follows
Week 4: Evaluate ratio improvement

Target: Get ratio below 2.0, ideally below 0.6

Content Strategy (Algorithm-Optimized)

Tweet Types by Algorithm Weight

Content Mix

• 40% Value content (insights, tips, frameworks)
• 30% Engagement bait (questions, polls, hot takes)
• 20% Build-in-public (progress updates, wins, losses)
• 10% Promotion (with value attached)

Media Requirements

Every tweet should have ONE of:

• Image (infographic, screenshot, meme)
• Video (< 2:20, hook in first 3 sec)
• Poll
• Thread (7-10 tweets)

NEVER post text-only tweets

Posting Schedule

Optimal Times (General)

First 10 Minutes Protocol

1. Post at optimal time
2. Immediately self-reply with additional insight
3. Reply to ANY comment within 10 minutes
4. Have 2-3 "pod" members ready to RT

Frequency

• Minimum: 1 tweet/day
• Optimal: 3-5 tweets/day
• Gap: 30-60 min between tweets

Engagement Tactics

Reply Strategy (Most Important)

The algorithm gives +75 weight when you reply to replies on your tweets.

Someone comments on your tweet
    |
Reply within 30 minutes (CRITICAL)
    |
Algorithm sees author engagement
    |
Tweet gets boosted to more feeds

Quote Tweet Strategy

Find viral tweet in your niche
    |
Quote with your unique take
    |
Add value, not just "great point"
    |
Post during optimal hours

Thread Formula

1/ Hook (curiosity gap or bold claim)
2-6/ Individual points with specifics
7/ Summary
8/ CTA: Question or "follow for more"

Weekly Routine

Daily (15 min)

• Post 1-3 tweets with media
• Reply to ALL comments on your tweets
• Engage with 5-10 tweets in your niche
• Check notifications and respond

Weekly (Saturday)

• Run full health check
• Review what content performed best
• Unfollow 10-20 low-value accounts
• Plan next week's content themes

Monthly

• Full ratio review (target < 2.0)
• Shadowban check
• Content audit (media %, link %)
• Milestone check (follower goals)

Recovery Plan (Low Follower Count)

Phase 1: Emergency Ratio Fix (Week 1-2)

If your ratio is > 5.0 (following >> followers):

• Unfollow 100+ inactive/non-engaging accounts
• Target: ratio < 5.0 as first milestone

Phase 2: Content Upgrade (Week 2-4)

If you have 0% media tweets:

• Add image/video to EVERY tweet
• Use Canva/Figma for quick graphics
• Screenshot interesting data/insights

Phase 3: Engagement Building (Week 3-6)

• Reply to 20+ tweets/day in your niche
• Quote tweet viral content with your take
• Join relevant Twitter communities
• DM potential collaborators

Phase 4: Consistency (Ongoing)

• 3-5 tweets/day
• Reply to 100% of comments
• Weekly analysis and adjustment