Tencent SkillHub · Developer Tools

Agent Intelligence Network Scan

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.

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High Signal

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Install for OpenClaw

Quick setup

Download the package from Yavira.
Extract the archive and review SKILL.md first.
Import or place the package into your OpenClaw setup.

Requirements

Target platform: OpenClaw
Install method: Manual import
Extraction: Extract archive
Prerequisites: OpenClaw
Primary doc: SKILL.md

Package facts

Download mode: Yavira redirect
Package format: ZIP package
Source platform: Tencent SkillHub
What's included: SKILL.md, references/API_REFERENCE.md, references/REPUTATION_ALGORITHM.md

Validation

Use the Yavira download entry.
Review SKILL.md after the package is downloaded.
Confirm the extracted package contains the expected setup assets.

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Hand the extracted package to your coding agent with a concrete install brief instead of figuring it out manually.

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New install

I downloaded a skill package from Yavira. Read SKILL.md from the extracted folder and install it by following the included instructions. Tell me what you changed and call out any manual steps you could not complete.

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I downloaded an updated skill package from Yavira. Read SKILL.md from the extracted folder, compare it with my current installation, and upgrade it while preserving any custom configuration unless the package docs explicitly say otherwise. Summarize what changed and any follow-up checks I should run.

Open Send to Agent page Open JSON manifest Open Markdown brief

Trust & source

Release facts

Source: Tencent SkillHub
Verification: Indexed source record
Version: 0.1.0

Provenance

Publisher: LvcidPsyche
Source page: View original listing
Canonical URL: Open canonical page

Documentation

ClawHub primary doc Primary doc: SKILL.md 24 sections Open source page

Agent Intelligence 🦀

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

What This Skill Provides

7 Query Functions: searchAgents - Find agents by name, platform, or reputation (0-100 score) getAgent - Full profile with complete reputation breakdown getReputation - Quick reputation check with factor details checkThreats - Detect sock puppets, scams, and red flags getLeaderboard - Top agents by reputation (pagination included) getTrends - Trending topics, rising agents, viral posts 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: FactorWeightMeasuresMoltbook Activity20%Karma + posts + consistencyMoltx Influence20%Followers + engagement + reach4claw Community10%Board activity + sentimentEngagement Quality25%Post depth + thoughtfulnessSecurity Record20%No scams/threats/red flagsLongevity5%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 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: Moltbook - Posts, karma, community metrics Moltx - Followers, posts, engagement 4claw - Board activity, sentiment Twitter - Reach, followers, tweets Identity Resolution - Cross-platform linking (Levenshtein + graph analysis) Security Monitoring - Threat detection Updates every 10-15 minutes. Can request fresh calculations on-demand.

API Quick Reference

See API_REFERENCE.md for complete documentation.

Basic Query

const engine = new IntelligenceEngine(); const rep = await engine.getReputation('agent_id');

Search

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

Threats

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

Leaderboard

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

Trends

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 - How scores are calculated API_REFERENCE.md - Complete API documentation

With Backend

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: 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 What functions are available? See 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

Category context

Code helpers, APIs, CLIs, browser automation, testing, and developer operations.

Source: Tencent SkillHub

Largest current source with strong distribution and engagement signals.

Package contents

Included in package

3 Docs

SKILL.md Primary doc
references/API_REFERENCE.md Docs
references/REPUTATION_ALGORITHM.md Docs