Twitter Auto Engage

SKILL.md

---
name: twitter-auto-engage
description: 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.
homepage: https://canlah.ai
metadata: {"requires": ["python3", "openai", "rnet_twitter"], "env": ["OPENAI_API_KEY", "TWITTER_COOKIE_PATH"], "tags": ["twitter", "social-media", "engagement", "automation", "openai", "gpt"]}
---

# Twitter Auto-Engage

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

```bash
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

```bash
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](#obtaining-twitter-cookies) below.

## Usage

```bash
# 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:

```python
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:

```python
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:

```json
{
  "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:

```json
[
  {
    "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.

---

## Author

**[Canlah AI](https://canlah.ai)** — Run performance marketing without breaking your brand.

- GitHub: [github.com/PHY041](https://github.com/PHY041)
- All Skills: [clawhub.ai/PHY041](https://clawhub.ai/PHY041)