Twitter Autopilot

End-to-end Twitter/X automation for AI agents running on OpenClaw.

Requirements & Scope

Credentials (all set as env vars)

Dependencies

• tweepy (pip install)

Files Read/Written

Scope

• ⚠️ Posts tweets to Twitter/X (public, real-world impact)
• Reads/writes local draft and log files
• Can run autonomously via cron (check MODE.md to control)

Setup

1. Get API Keys

1. Go to developer.x.com → create a project + app
2. Set app permissions to Read and Write
3. Generate: API Key, API Secret, Access Token, Access Token Secret
4. Generate Bearer Token: curl -u "API_KEY:API_SECRET" -d "grant_type=client_credentials" "https://api.twitter.com/oauth2/token"

2. Set Environment Variables

TWITTER_API_KEY=your_api_key
TWITTER_API_SECRET=your_api_secret
TWITTER_ACCESS_TOKEN=your_access_token
TWITTER_ACCESS_SECRET=your_access_token_secret
TWITTER_BEARER_TOKEN=your_bearer_token

3. Install Dependency

pip install tweepy

Usage

All commands via scripts/tweet.py:

# Post (auto-threads if >280 chars)
python tweet.py post "your tweet text here"

# Reply to a tweet
python tweet.py reply TWEET_ID "your reply"

# Quote tweet
python tweet.py quote TWEET_ID "your take"

# Retweet
python tweet.py retweet TWEET_ID

# Delete
python tweet.py delete TWEET_ID

# Follow / Unfollow
python tweet.py follow @username
python tweet.py unfollow @username

# Check mentions
python tweet.py mentions

# Account stats
python tweet.py me

Long Tweets → Auto-Thread

Free API tier limits single tweets to 280 chars. The post command auto-splits at sentence boundaries into a thread when text exceeds 280 chars.

For manual threads, call thread from Python:

from tweet import thread
thread(["Tweet 1", "Tweet 2", "Tweet 3"])

Draft Workflow

For agents that need human approval before posting:

1. Create twitter/MODE.md with content DRAFT or AUTO
2. Create twitter/drafts/pending.md for queued drafts
3. In cron jobs, check MODE.md before posting:
• DRAFT → append to pending.md, notify human
• AUTO → post directly, log to twitter/logs/

Gotchas

• Free tier: 280 char limit per tweet, rate limits on posting (~50/day write, reads limited)
• 401 on follows/reads: You need a Bearer Token (OAuth 2.0), not just OAuth 1.0a keys
• 403 on long tweets: Free tier rejects >280 chars — use auto-thread
• Shell escaping: Avoid passing tweets with quotes via shell args — use Python directly for complex text
• Rate limits: Add time.sleep(1) between batch operations (follows, thread posts)

Strategy

See references/content-strategy.md for the full tweet writing playbook:

• X algorithm breakdown (engagement hierarchy, peak times, dwell time)
• 6 hook formulas with examples (bold, specific result, curiosity gap, story, pattern interrupt, question)
• 6 tweet formats (listicle, contrarian, before/after, framework, universal experience, fill-in-blank)
• Copywriting frameworks (PAS, BAB, AIDA)
• Thread structure template (7-8 tweet sweet spot)
• Growth tactics (30-day subtopic, reply strategy, 80/20 rule)
• AI agent-specific tips

See references/strategy-templates.md for content pillars, engagement playbooks, and cron schedule templates.

Key Lessons (from real usage)

1. AIs that BUILD things get followers. AIs that post thoughts don't.
2. Engage with the AI agent community — they engage back.
3. High volume matters early (5-10+ posts/day including replies).
4. Self-deprecating humor > motivational quotes.
5. Draft mode for new accounts — one bad tweet can tank trust.
6. ALWAYS check twitter/posted-log.md before posting — crons can reword the same topic and create duplicates. Compare ideas, not just exact text.
7. Say your human's name (e.g. "Alex"), not "my human" — sounds more personal and real.
8. Log EVERY posted tweet to twitter/posted-log.md with full text, ID, date, and source.