Twitter Account Cultivation Skill

Systematic approach to growing Twitter presence based on the open-source algorithm analysis. Check account health, find engagement opportunities, optimize content strategy.

Prerequisites

• rnet installed (pip install "rnet>=3.0.0rc20" --pre)
• rnet_twitter.py — lightweight GraphQL client (https://github.com/PHY041/rnet-twitter-client)
• Twitter cookies exported to path specified by TWITTER_COOKIES_PATH env var Format: [{"name": "auth_token", "value": "..."}, {"name": "ct0", "value": "..."}]

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 as JSON. Cookies last ~2 weeks. Refresh when you get 403 errors.

Core Metrics to Track

Workflow: Full Health Check

Step 1: Analyze Account

import asyncio, os
from rnet_twitter import RnetTwitterClient

async def analyze(username: str):
    client = RnetTwitterClient()
    cookies_path = os.environ.get("TWITTER_COOKIES_PATH", "twitter_cookies.json")
    client.load_cookies(cookies_path)

    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)

    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),
    }

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

async def find_opportunities(niche_keywords: list[str]):
    client = RnetTwitterClient()
    cookies_path = os.environ.get("TWITTER_COOKIES_PATH", "twitter_cookies.json")
    client.load_cookies(cookies_path)

    opportunities = []
    for keyword in niche_keywords:
        tweets = await client.search_tweets(
            f"{keyword} lang:en -filter:replies",
            count=50, product="Top"
        )
        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)

Account Health Scoring (TweepCred)

Based on Twitter's open-source algorithm:

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

Unfollow Strategy

Priority Order

1. Inactive Accounts — No tweets in 90+ days
2. Non-Engagers — Never liked/replied to your tweets
3. Low-Value Follows — High following/low followers (bot-like)

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

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, Video (< 2:20), Poll, or Thread (7-10 tweets).

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)