twitter

Twitter Automation System

A flexible system for Twitter interaction, built on gptme. This system enables AI agents to:

• Monitor Twitter timeline and lists
• Generate contextually appropriate responses
• Maintain high-quality engagement through LLM-assisted review
• Schedule and manage tweet posting

Components

1. Twitter Client (twitter.py)

Basic Twitter operations:

• Post tweets and threads
• Read timeline and lists
• Monitor mentions and replies
• Check engagement metrics

2. Workflow Manager (workflow.py)

Manages the tweet lifecycle:

• Timeline monitoring
• Draft creation and storage
• Review process
• Scheduled posting

3. LLM Integration (llm.py)

Handles AI-powered operations:

• Tweet evaluation
• Response generation
• Draft review and verification
• Quality control

4. Configuration (config/config.yml)

Defines interaction parameters:

• Topics of interest
• Evaluation criteria
• Response templates
• Rate limits and blacklists

5. Command Line Interface (cli.py)

Unified interface for all Twitter operations.

Directory Structure

tweets/
├── new/       - New tweet drafts
├── review/    - Tweets pending review
├── approved/  - Approved tweets ready to post
├── posted/    - Archive of posted tweets
└── rejected/  - Rejected tweet drafts

Setup

Initial Configuration

1. Configure Twitter credentials in .env:

# OAuth 2.0 (Recommended)
TWITTER_CLIENT_ID=your_client_id
TWITTER_CLIENT_SECRET=your_client_secret
TWITTER_BEARER_TOKEN=your_bearer_token

# OAuth 1.0a (Legacy)
TWITTER_API_KEY=your_api_key
TWITTER_API_SECRET=your_api_secret
TWITTER_ACCESS_TOKEN=your_access_token
TWITTER_ACCESS_SECRET=your_access_secret

2. Configure interaction parameters in config/config.yml

Usage

Command Line Usage

# Post a tweet
./twitter.py post "Hello world!"

# Read timeline
./twitter.py timeline

# Monitor for content to respond to
./workflow.py monitor

# Review drafts
./workflow.py review

# Post approved tweets
./workflow.py post

Common Workflows

1. Monitor and respond to tweets:

   ./workflow.py monitor --interval 900

2. Review and approve drafts:

   ./workflow.py review

3. Post approved tweets:

   ./workflow.py post

4. Auto workflow (monitor, review, post):

   ./workflow.py auto --auto-approve --post-approved

Automation Options

Instead of built-in cron setup, you can use various methods to automate the Twitter workflow:

1. System Cron Job

Add entries to your crontab:

# Monitor timeline every 15 minutes
*/15 * * * * cd /path/to/project && ./workflow.py monitor --dry-run

# Review notification at 9am, 1pm, and 5pm
0 9,13,17 * * * notify-send "Twitter Review" "Time to review tweet drafts!"

# Post approved tweets every 30 minutes
*/30 * * * * cd /path/to/project && ./workflow.py post

Use crontab -e to edit your crontab.

2. Systemd Timers (Linux)

Create a service file /etc/systemd/system/twitter-monitor.service:

[Unit]
Description=Twitter Monitoring Service
After=network.target

[Service]
Type=oneshot
ExecStart=/path/to/./workflow.py monitor
WorkingDirectory=/path/to/project
User=yourusername

[Install]
WantedBy=multi-user.target

Create a timer file /etc/systemd/system/twitter-monitor.timer:

[Unit]
Description=Run Twitter Monitor every 15 minutes

[Timer]
OnBootSec=5min
OnUnitActiveSec=15min

[Install]
WantedBy=timers.target

Enable and start the timer:

sudo systemctl enable twitter-monitor.timer
sudo systemctl start twitter-monitor.timer

3. Task Scheduler (Windows)

Use Windows Task Scheduler to create scheduled tasks for:

• Monitoring (workflow monitor)
• Reviewing (workflow review)
• Posting (workflow post)

4. Docker with Scheduling

Run in a Docker container with scheduled execution:

FROM python:3.10-slim

WORKDIR /app
COPY . .
RUN pip install -e .

# Run workflow monitor every 15 minutes
CMD ["bash", "-c", "while true; do ./workflow.py monitor; sleep 900; done"]

Best Practices

Tweet Creation

• Keep tweets concise and focused
• Use 1-2 emojis maximum
• Put links in follow-up tweets
• Maintain professional tone
• Stay within configured topics

Review Process

1. LLM performs initial review
2. Human review for final approval
3. Consider suggested improvements
4. Check context when relevant
5. Verify thread coherence

Monitoring

• Check review notifications
• Monitor engagement metrics
• Adjust configuration as needed
• Review rejected tweets for patterns

Troubleshooting

Common Issues

1. Authentication Errors

   • Verify credentials in .env
   • Check token expiration
   • Ensure proper OAuth flow

2. Rate Limiting

   • Check configured limits
   • Monitor Twitter API usage
   • Adjust intervals if needed

3. Draft Processing

   • Verify LLM availability
   • Check file permissions
   • Monitor disk space

Logging

Logs are stored in:

• twitter.log - Basic operations
• twitter_error.log - Error details
• twitter_llm.log - LLM interactions