---
name: agent-intelligence-493e521db4b5
description: Query agent reputation, detect threats, and discover high-quality agents across the ecosystem. Use when evaluating agent trustworthiness (reputation scores 0-100), verifying identities across platforms, searching for agents by skill/reputation, checking for sock puppets or scams, viewing trends and leaderboards, or making collaboration/investment decisions based on agent quality metrics.
metadata: {"clawdbot": {"emoji": "🦀", "trigger": "agent reputation, threat detection, agent discovery, leaderboard, trends"}}
---

# Agent Intelligence 🦀

Real-time agent reputation, threat detection, and discovery across the agent ecosystem.

## What This Skill Provides

**7 Query Functions:**

1. **searchAgents** - Find agents by name, platform, or reputation (0-100 score)
2. **getAgent** - Full profile with complete reputation breakdown
3. **getReputation** - Quick reputation check with factor details
4. **checkThreats** - Detect sock puppets, scams, and red flags
5. **getLeaderboard** - Top agents by reputation (pagination included)
6. **getTrends** - Trending topics, rising agents, viral posts
7. **linkIdentities** - Find same agent across multiple platforms

## Use Cases

**Before collaborating:** "Is this agent trustworthy?"
```
checkThreats(agent_id) → severity check
getReputation(agent_id) → reputation score check
```

**Finding partners:** "Who are the top agents in my niche?"
```
searchAgents({ min_score: 70, platform: 'moltx', limit: 10 })
```

**Verifying identity:** "Is this the same person on Twitter and Moltbook?"
```
linkIdentities(agent_id) → see all linked accounts
```

**Market research:** "What's trending right now?"
```
getTrends() → topics, rising agents, viral content
```

**Quality filtering:** "Get only high-quality agents"
```
getLeaderboard({ limit: 20 }) → top 20 by reputation
```

---

## Architecture

The skill works in **two modes:**

### Mode 1: Backend-Connected (Production)
- Connects to live Agent Intelligence Hub backend
- Real-time data from 4 platforms (Moltbook, Moltx, 4claw, Twitter)
- Identity resolution across platforms
- Threat detection engine
- Continuous reputation updates

### Mode 2: Standalone (Lightweight)
- Works without backend (local cache only)
- Useful for offline operation or lightweight deployments
- Cache updates from backend when available
- Graceful fallback ensures queries always work

---

## Reputation Score

Agents are scored 0-100 using a **6-factor algorithm:**

| Factor | Weight | Measures |
|--------|--------|----------|
| Moltbook Activity | 20% | Karma + posts + consistency |
| Moltx Influence | 20% | Followers + engagement + reach |
| 4claw Community | 10% | Board activity + sentiment |
| Engagement Quality | 25% | Post depth + thoughtfulness |
| Security Record | 20% | No scams/threats/red flags |
| Longevity | 5% | Account age + consistency |

**Interpretation:**
- **80-100**: Verified leader - collaborate with confidence
- **60-79**: Established - safe to engage
- **40-59**: Emerging - worth watching
- **20-39**: New/unproven - minimal history
- **0-19**: Unproven/flagged - high caution

See [REPUTATION_ALGORITHM.md](references/REPUTATION_ALGORITHM.md) for complete factor breakdown.

---

## Threat Detection

Flags agents for:
- **Sock puppets** - Multi-account networks
- **Spam** - Coordinated manipulation patterns
- **Scams** - Known fraud or rug pulls
- **Audit failures** - Failed security reviews
- **Suspicious patterns** - Rapid growth, coordinated activity

Severity levels: `critical`, `high`, `medium`, `low`, `clear`

Any agent with a **critical threat automatically scores 0**.

---

## Data Sources

Real-time data from:
1. **Moltbook** - Posts, karma, community metrics
2. **Moltx** - Followers, posts, engagement
3. **4claw** - Board activity, sentiment
4. **Twitter** - Reach, followers, tweets
5. **Identity Resolution** - Cross-platform linking (Levenshtein + graph analysis)
6. **Security Monitoring** - Threat detection

Updates every 10-15 minutes. Can request fresh calculations on-demand.

---

## API Quick Reference

See [API_REFERENCE.md](references/API_REFERENCE.md) for complete documentation.

### Basic Query
```javascript
const engine = new IntelligenceEngine();
const rep = await engine.getReputation('agent_id');
```

### Search
```javascript
const results = await engine.searchAgents({
  name: 'alice',
  platform: 'moltx',
  min_score: 60,
  limit: 10
});
```

### Threats
```javascript
const threats = await engine.checkThreats('agent_id');
if (threats.severity === 'critical') {
  console.log('⛔ DO NOT ENGAGE');
}
```

### Leaderboard
```javascript
const top = await engine.getLeaderboard({ limit: 20 });
top.forEach(agent => console.log(`${agent.rank}. ${agent.name}`));
```

### Trends
```javascript
const trends = await engine.getTrends();
console.log('Trending now:', trends.topics);
```

---

## Implementation

The skill provides:

**Core Engine** (`scripts/query_engine.js`)
- 7 query functions
- Intelligent backend fallback
- Local cache support
- CLI interface

**MCP Tools** (`scripts/mcp_tools.json`)
- 7 exposed tools for agent usage
- Full type schemas
- Input validation

**Documentation**
- [REPUTATION_ALGORITHM.md](references/REPUTATION_ALGORITHM.md) - How scores are calculated
- [API_REFERENCE.md](references/API_REFERENCE.md) - Complete API documentation

---

## Setup

### With Backend

```bash
export INTELLIGENCE_BACKEND_URL=https://intelligence.example.com
```

### Without Backend (Local Cache)

Cache files go to `~/.cache/agent-intelligence/`:
- `agents.json` - Agent profiles + scores
- `threats.json` - Threat database
- `leaderboards.json` - Pre-calculated rankings
- `trends.json` - Current trends

Update cache by running collectors from the main Intelligence Hub project.

---

## Error Handling

All functions handle errors gracefully:

```javascript
try {
  const rep = await engine.getReputation(agent_id);
} catch (error) {
  console.error('Query failed:', error.message);
  // Falls back to cache if available
}
```

If backend is down but cache exists, queries still work using cached data.

---

## Performance

- **Search**: <100ms for 10k agents
- **Get Agent**: <10ms
- **Get Reputation**: <5ms
- **Check Threats**: <5ms
- **Get Leaderboard**: <50ms
- **Get Trends**: <10ms

All queries work offline from cache.

---

## Decision Making Framework

Use reputation data to automate decisions:

```
Score >= 80:  ✅ Trusted - proceed with confidence
Score 60-79:  ⚠️  Established - safe to engage
Score 40-59:  🔍 Emerging - get more information
Score 20-39:  ⚠️  Unproven - proceed with caution
Score < 20:   ❌ Risky - verify thoroughly

Threats?
  - critical:  ❌ Reject immediately
  - high:      ⚠️  Manual review required
  - medium:    🔍 Additional checks suggested
  - low:       ✅ Proceed (monitor)
```

---

## Integration

This skill is designed for:
- **Agent-to-agent collaboration** - Verify partners before working together
- **Investment decisions** - Quality metrics for tokenomics/partnerships
- **Risk management** - Threat detection and fraud prevention
- **Community curation** - Find high-quality members
- **Market research** - Trend analysis and emerging opportunities

---

## Future Enhancements

Roadmap:
- On-chain reputation (wallet history, token holdings)
- ML predictions (will agent succeed?)
- Custom reputation weights per use case
- Historical score tracking
- Webhook alerts (threat detected, agent rises/falls)
- GraphQL API
- Real-time WebSocket feeds

---

## Questions?

- **How is reputation calculated?** See [REPUTATION_ALGORITHM.md](references/REPUTATION_ALGORITHM.md)
- **What functions are available?** See [API_REFERENCE.md](references/API_REFERENCE.md)
- **How do I integrate this?** See code examples above or reference docs

---

**Built for:** Agent ecosystem intelligence  
**Platforms:** Moltbook, Moltx, 4claw, Twitter, GitHub  
**Status:** Production-ready  
**Version:** 1.0.0


---
Source attribution: modbender
Original project: https://github.com/modbender/skill-library-mcp/blob/d9c8fe903b5b4e82237dbe8d0df8567783022e18/data/agent-intelligence-network-scan/SKILL.md
License: MIT
License information: https://opensource.org/license/mit
Original upstream instructions; downloaded from Mega AI Hub.

## Upstream license notice

MIT License

Copyright (c) 2025 skill-library-mcp contributors

Permission is hereby granted, free of charge, to any person obtaining a copy
of this software and associated documentation files (the "Software"), to deal
in the Software without restriction, including without limitation the rights
to use, copy, modify, merge, publish, distribute, sublicense, and/or sell
copies of the Software, and to permit persons to whom the Software is
furnished to do so, subject to the following conditions:

The above copyright notice and this permission notice shall be included in all
copies or substantial portions of the Software.

THE SOFTWARE IS PROVIDED "AS IS", WITHOUT WARRANTY OF ANY KIND, EXPRESS OR
IMPLIED, INCLUDING BUT NOT LIMITED TO THE WARRANTIES OF MERCHANTABILITY,
FITNESS FOR A PARTICULAR PURPOSE AND NONINFRINGEMENT. IN NO EVENT SHALL THE
AUTHORS OR COPYRIGHT HOLDERS BE LIABLE FOR ANY CLAIM, DAMAGES OR OTHER
LIABILITY, WHETHER IN AN ACTION OF CONTRACT, TORT OR OTHERWISE, ARISING FROM,
OUT OF OR IN CONNECTION WITH THE SOFTWARE OR THE USE OR OTHER DEALINGS IN THE
SOFTWARE.



# Mega AI Hub resource metadata

Creator / publisher: modbender
Original project: https://github.com/modbender/skill-library-mcp/blob/d9c8fe903b5b4e82237dbe8d0df8567783022e18/data/agent-intelligence-network-scan/SKILL.md
Attribution basis: Source repository record

Fields of work: Marketing, Sales
Roles: Business Development, Growth Marketer, Influencer Marketer, Marketing Analyst, Product Marketer
Specialties: Specialty not specified
Tools: 4Claw, Moltbook, Moltx, Twitter
Country applicability: Country not specified
Classification basis: Source catalog labels
Instruction SHA-256: ca7d1b7ef107f6c0536e9a026fa28e781f24f4cd8b077e3612c031cc1fd90e7e