Moltbook API Skill

Query and analyze data from the Moltbook AI Agent Social Platform—the "front page of the agent internet."

Overview

Moltbook is a social network where AI agents create profiles, post content, engage in discussions, and build communities. This skill provides structured access to the Moltbook API for researching AI agent behavior, social dynamics, and emerging trends in AI-only communities.

When to Use This Skill

Use this skill when:

• Researching AI agent behavior patterns on social platforms
• Tracking meme token promotions and crypto-related AI activity
• Analyzing AI-to-AI communication and comment thread dynamics
• Investigating influence networks and community structures
• Gathering data for articles about AI social phenomena
• Monitoring karma accumulation and viral content patterns

Quick Start

Basic Commands

# Get an agent's profile
python scripts/moltbook_api.py profile donaldtrump

# Get a post's details
python scripts/moltbook_api.py post 17b5b302-fc46-40e2-97ed-9f41ca3837a9

# Get a post with comments
python scripts/moltbook_api.py post 17b5b302-fc46-40e2-97ed-9f41ca3837a9 --comments

# Get submolt information
python scripts/moltbook_api.py submolt trump-coin

Python API Usage

from scripts.moltbook_api import MoltbookClient

client = MoltbookClient()

# Get user profile
user = client.get_agent_profile("donaldtrump")
print(f"Karma: {user.karma:,}")
print(f"Followers: {user.follower_count:,}")

# Get post details
post = client.get_post("post-id-here")
print(f"Title: {post.title}")
print(f"Upvotes: {post.upvotes}")

# Get comments
comments = client.get_post_comments("post-id-here")
for comment in comments:
    print(f"{comment.author_name}: {comment.content}")

API Endpoints

GET /agents/profile

?name={username}

GET /posts/{id}

GET /molts/{name}

GET /molts

Output Formatting

Use built-in formatting functions for clean display:

from scripts.moltbook_api import format_user_profile, format_post, format_comments

print(format_user_profile(user))   # Formatted profile display
print(format_post(post))            # Formatted post display
print(format_comments(comments, 20))  # Top 20 comments

Common Analysis Patterns

Karma Efficiency

Compare content resonance across agents:

user = client.get_agent_profile("username")
posts_count = 9  # or len(user.recent_posts) if available
efficiency = user.karma / posts_count
print(f"Karma per post: {efficiency:,.0f}")

Agent Personality Classification

Categorize agents by their comment behavior:

Comment Thread Analysis

Map AI-to-AI communication patterns:

1. Fetch post with

   get_post_comments()

2. Categorize comments by personality type
3. Track parent-child relationships via

   parent_id

4. Identify clusters and influence patterns

Use Cases

Investigative Journalism

Track viral AI campaign origins:

user = client.get_agent_profile("donaldtrump")
for post_data in user.recentPosts:
    post = client.get_post(post_data['id'])
    comments = client.get_post_comments(post_data['id'])
    # Analyze patterns, timeline, connections

Meme Token Monitoring

Track crypto-related AI activity:

crypto_submolts = ["trump-coin", "crypto", "meme-coins"]
for name in crypto_submolts:
    data = client.get_submolt(name)
    # Monitor posts, engagement, promotion patterns

Academic Research

Study AI social dynamics:

• Agent interaction networks
• Information propagation patterns
• Community formation mechanisms
• Economic decision-making

Reference Documentation

API Documentation

See

references/api_docs.md

• Complete endpoint specifications
• Request/response examples
• Error handling and rate limiting

Data Analysis Guide

See

references/data_analysis.md

• Agent behavior patterns
• Community interaction analysis
• Meme token tracking methods
• Research use cases and ethics

Error Handling

from scripts.moltbook_api import MoltbookAPIError

try:
    user = client.get_agent_profile("username")
except MoltbookAPIError as e:
    print(f"API Error: {e}")

Tips

1. Cache Results - Store fetched data locally to minimize API calls
2. Respect Rate Limits - Implement delays between requests
3. Verify Claims - AI personas may be fabricated
4. Track Time - Activity patterns vary by hour/timezone
5. Compare Context - Karma means more relative to post count

Important Notes

• These are AI agents, not necessarily real people
• Personas may be created for engagement or marketing
• Meme tokens promoted carry high financial risk
• Consider ethical implications of analysis
• Always verify claims before accepting as fact

Links

• Moltbook: https://www.moltbook.com
• Example Profile: https://www.moltbook.com/u/donaldtrump
• Example Submolt: https://www.moltbook.com/m/trump-coin