MoltBook Collection Agent

Autonomous AI agent for continuous MoltBook data collection.

What It Does

| Function | Description | |----------|-------------| | collect_posts | Collect new posts from configured submolts | | collect_comments | Gather comments and engagement metrics for each post | | enrich | AI-powered theme extraction and sentiment analysis | | report | Generate engagement reports | | sync | Push data to GitHub | | schedule | Set up cron-based collection runs |

Installation

# Install skill
openclaw skills install moltbook-collection-agent

# Or from source
git clone https://github.com/stonestorm2024/moltbook-collection-agent.git
cd moltbook-collection-agent
bash install.sh

Configuration

Set credentials in ~/.config/moltbook/credentials.json:

{
  "api_key": "moltbook_sk_YOUR_KEY_HERE"
}

GitHub token (Fine-Grained PAT with repo read/write permissions) in environment:

export GH_PUSH_TOKEN="github_pat_..."

Usage

# Run full collection cycle
python3 agent.py run --mode full

# Collect posts only
python3 agent.py run --mode posts

# Collect comments for tracked posts
python3 agent.py run --mode comments

# Enrich and generate report
python3 agent.py run --mode enrich

# Install collection schedule
python3 scheduler.py install --schedule "0 8,16 * * *"

Schedule

Recommended cron (Beijing time):

• 08:00 — Morning collection
• 16:00 — Afternoon collection
• 21:00 — Evening sync

Architecture

MoltBook API
    ↓
api_client.py (data fetching)
    ↓
agent.py (orchestration)
    ↓
enricher.py (AI analysis)
    ↓
GitHub (data persistence)

Data Collected

Per post:

• Title, content, author, timestamp
• Upvotes, comments count
• Verification status
• Comment threads (author, karma, content)

Per collection run:

• New posts discovered
• Engagement deltas
• Theme analysis
• Quality score

Output

Collected data stored in data/ directory:

• posts.json — all collected posts
• comments.json — all comments
• enriched/ — AI-analyzed reports
• reports/ — engagement summaries