Twitter Auto Engage

Automated Twitter/X engagement script that scans a curated pool of target accounts, generates authentic GPT-powered replies, and posts them via the Twitter GraphQL API. Use when you want to run a scheduled engagement routine that builds presence through high-quality, non-sycophantic replies to thought leaders in your niche.

Automated Twitter/X engagement system that scans a pool of target accounts, selects their highest-engagement recent tweets, generates context-aware replies using GPT, and posts them with human-like timing. Built for founders and builders who want to grow through genuine technical conversation, not follower-baiting.

Requirements

pip install openai
# rnet_twitter — async Twitter GraphQL client (see rnet_twitter.py)

You also need:

• A valid Twitter/X session cookie file (twitter_cookies.json)
• An OpenAI API key

Setup

export OPENAI_API_KEY="your_openai_api_key"
export TWITTER_COOKIE_PATH="/path/to/twitter_cookies.json"

The script reads cookies from a JSON file exported from a logged-in browser session. See Obtaining Twitter Cookies below.

Usage

# Run a single engagement session (5 replies max)
python auto_engage.py

# Schedule 4x daily via cron (recommended)
# Morning, midday, afternoon, evening sessions
0 8,12,16,21 * * * cd {skillDir} && python auto_engage.py >> logs/engage.log 2>&1

Configuration

Edit the following constants at the top of the script to customize behavior:

Constant	Default	Description
TARGETS_PER_RUN	20	Accounts to check each session
MAX_REPLIES_PER_RUN	5	Maximum replies to post per session
RUN_PROBABILITY	0.85	Randomized skip chance (human variability)
MIN_REPLY_LENGTH	80	Minimum character count for a reply
MAX_REPLY_LENGTH	260	Maximum character count for a reply

Target Account Pool

Organize accounts into named categories with per-session quotas. The script randomly samples TARGETS_PER_RUN accounts from the full pool each session.

Example structure:

TARGET_POOL = {
    "ai_builders": [
        "simonw",     # Example: ML tools, Datasette
        "swyx",       # Example: AI engineering
    ],
    "indie_builders": [
        "dvassallo",  # Example: indie hacker
        "tdinh_me",   # Example: bootstrapped SaaS
    ],
    "marketing": [
        "harrydry",   # Example: Marketing Examples
        "wes_kao",    # Example: content strategy
    ],
}

CATEGORY_QUOTAS = {
    "ai_builders":   2,  # replies per session
    "indie_builders": 2,
    "marketing":     1,
}

Replace handles with accounts relevant to your niche and goals.

Reply Generation

Replies are generated by GPT using a structured voice prompt. The system enforces:

Voice Rules (Configurable)

• Direct, clear sentences — no filler words
• Specific over vague: numbers, tool names, concrete observations
• Intellectually curious tone — genuine questions, not rhetorical
• Comfortable acknowledging uncertainty or failure

Hard Bans (Built-in Filters)

The generated reply is rejected and skipped if it contains:

• Sycophantic openers: "Great post", "Love this", "So true", "Spot on", etc.
• Casual slang: lol, damn, wild, ngl, fr, bro, lowkey, fire, bussin, no cap
• Emojis (any Unicode > U+1F600)
• Exclamation marks (configurable tolerance)
• Corporate speak: leverage, synergy, paradigm, game-changer
• Self-promotion or product name-dropping

Three Reply Archetypes

GPT is prompted to pick ONE of:

A) Micro-insight + Question Share a specific observation from your own experience, then ask something you genuinely want answered.

"Ran into this building our brand tool — embeddings drift ~15% after 3 months without retraining. How often are you recalibrating?"

B) Respectful Challenge Politely push back with data or a counterexample, then invite their perspective.

"Counterpoint: Stripe's docs are famously good but they still need a sales team for enterprise. Isn't the real question where self-serve stops working?"

C) Pattern Recognition Connect their point to something non-obvious from a different domain.

"This mirrors what happened in recommendation systems — Netflix found that optimizing for clicks killed retention. The proxy metric trap is everywhere. What's your equivalent of 'watch time'?"

Customizing Your Voice

Edit the USER_CONTEXT variable (or equivalent prompt section) to describe your background:

USER_CONTEXT = """You are writing a Twitter reply as YOUR_NAME.

BACKGROUND:
- Brief description of who you are
- What you're building
- Genuine interests relevant to the accounts you target

VOICE:
- [Your preferred communication style]
- [Specific things to include or avoid]
"""

The more specific and authentic your context, the better the replies.

State Management

The script maintains auto_reply_state.json to track replied tweet IDs, preventing duplicate replies across sessions:

{
  "replied_tweets": ["tweet_id_1", "tweet_id_2"],
  "last_updated": "2026-03-06T09:30:00"
}

State is capped at the last 100 tweet IDs to prevent unbounded growth.

Engagement Logic

For each selected account the script:

1. Fetches the 10 most recent original tweets (excluding retweets and @-replies)
2. Filters out tweet IDs already replied to
3. Selects the tweet with highest combined engagement (likes + replies)
4. Generates a reply via GPT — if GPT outputs SKIP, the tweet is marked seen and skipped
5. Likes the tweet first (pre-reply like is an algorithmic signal)
6. Posts the reply
7. Waits 3-6 seconds (randomized human-like delay) before moving to the next account

Output

Each session prints a summary and appends to your log. When called by a bot framework, the script outputs a JSON block after the ---JSON_OUTPUT--- separator for programmatic consumption:

[
  {
    "target": "simonw",
    "tweet": "LLMs are increasingly being used for...",
    "reply": "The retrieval side of this is underrated...",
    "url": "https://x.com/simonw/status/...",
    "liked": true
  }
]

Obtaining Twitter Cookies

1. Log in to Twitter/X in Chrome or Firefox
2. Open DevTools > Application > Cookies > https://x.com
3. Export the cookie values to a JSON file in the format expected by rnet_twitter.py
4. Store the file at the path referenced by TWITTER_COOKIE_PATH

Do not commit cookie files to version control.

Rate Limiting Guidelines

• 5 replies per session, 4 sessions per day = 20 replies/day maximum
• 85% probability of running each session adds natural variability
• 3-6 second delay between each reply action
• 1-2 second delay between like and reply on the same tweet

Twitter's informal limits for reply actions are not publicly documented, but staying under 50 replies/day and spacing them across multiple sessions avoids most friction.

Troubleshooting

Reply not posting: Verify your cookie file is fresh. Twitter sessions expire — re-export cookies from your browser.

GPT always outputs SKIP: Your USER_CONTEXT may not align with the selected accounts. Ensure your background gives GPT enough to work with for the topic domain.

Too many sycophancy rejections: GPT may default to affirmative openers. Increase the system prompt emphasis on the ban list or lower the temperature slightly.

Account flagged / rate limited by Twitter: Reduce MAX_REPLIES_PER_RUN to 3 and RUN_PROBABILITY to 0.6 for a more conservative cadence.

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Author

Canlah AI — Run performance marketing without breaking your brand.

• GitHub: github.com/PHY041
• All Skills: clawhub.ai/PHY041