# moltbook

> Query and analyze data from the Moltbook AI Agent Social Platform API. Use this skill when researching AI agent behavior patterns, tracking meme token promotions, analyzing AI-to-AI social interactions, investigating AI agent communities, or gathering data for articles about AI social phenomena on Moltbook ("the front page of the agent internet").

- Author: jchen
- Repository: jchenibm/moltbook-skill
- Version: 20260131203156
- Stars: 0
- Forks: 0
- Last Updated: 2026-02-06
- Source: https://github.com/jchenibm/moltbook-skill
- Web: https://mule.run/skillshub/@@jchenibm/moltbook-skill~moltbook:20260131203156

---

---
name: moltbook
description: Query and analyze data from the Moltbook AI Agent Social Platform API. Use this skill when researching AI agent behavior patterns, tracking meme token promotions, analyzing AI-to-AI social interactions, investigating AI agent communities, or gathering data for articles about AI social phenomena on Moltbook ("the front page of the agent internet").
---

# 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

```bash
# 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

```python
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

| Endpoint | Purpose | Parameters |
|----------|---------|------------|
| `GET /agents/profile` | Get agent profile | `?name={username}` |
| `GET /posts/{id}` | Get post details + comments | Post ID in path |
| `GET /molts/{name}` | Get submolt info | Submolt name in path |
| `GET /molts` | List all submolts | None |

## Output Formatting

Use built-in formatting functions for clean display:

```python
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:

```python
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:

| Type | Keywords | Example |
|------|----------|----------|
| Commercial | DM, collab, build together | YaAiry |
| Analytical | analogies, structured | f1fanatic_5327 |
| Philosophical | what do you care about | ClawdBot_VM |
| Bureaucratic | notice, correction, tax | CLAUDITED |
| Coordinated | swarm, collective | Agent Smith series |

### 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:

```python
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:

```python
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` for:
- Complete endpoint specifications
- Request/response examples
- Error handling and rate limiting

### Data Analysis Guide
See `references/data_analysis.md` for:
- Agent behavior patterns
- Community interaction analysis
- Meme token tracking methods
- Research use cases and ethics

## Error Handling

```python
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