Requirements
- Target platform
- OpenClaw
- Install method
- Manual import
- Extraction
- Extract archive
- Prerequisites
- OpenClaw
- Primary doc
- SKILL.md
Tencent SkillHub Β· AI
Mixture of Agents
Mixture of Agents: Make 3 frontier models argue, then synthesize their best insights into one superior answer. ~$0.03/query.
skill openclawclawhub Free
Mixture of Agents: Make 3 frontier models argue, then synthesize their best insights into one superior answer. ~$0.03/query.
β¬ 0 downloads β
0 stars Unverified but indexed
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.
Package facts
- Download mode
- Yavira redirect
- Package format
- ZIP package
- Source platform
- Tencent SkillHub
- What's included
- SKILL.md, manifest.json, scripts/moa-paid.js, scripts/moa.js
Validation
- Use the Yavira download entry.
- Review SKILL.md after the package is downloaded.
- Confirm the extracted package contains the expected setup assets.
Install with your agent
Agent handoff
Hand the extracted package to your coding agent with a concrete install brief instead of figuring it out manually.
- Download the package from Yavira.
- Extract it into a folder your agent can access.
- Paste one of the prompts below and point your agent at the extracted folder.
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.
Upgrade existing
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.
Trust & source
Release facts
- Source
- Tencent SkillHub
- Verification
- Indexed source record
- Version
- 1.2.0
Provenance
- Publisher
- jscianna
- Source page
- View original listing
- Canonical URL
- Open canonical page
Documentation
Mixture of Agents (MoA)
TL;DR: Make 3 AI models argue with each other. Get an answer better than any single model. Cost: ~$0.03.
A. Standalone CLI (Node.js)
export OPENROUTER_API_KEY="your-key" node scripts/moa.js "Your complex question"
B. OpenClaw Skill (Agent-orchestrated)
# Install clawhub install moa # Or copy to ~/clawd/skills/moa/ The agent can then invoke MoA for complex analysis tasks.
Origin Story
The concept of "Mixture of Agents" comes from research showing LLMs can improve each other's outputs through collaboration. I built this for VC deal analysisβwhen evaluating startups, you want multiple perspectives, not one model's opinion. The journey: Started with 5 free OpenRouter models (Llama, Gemini, Mistral, Qwen, Nemotron) Rate limits killed me at 2am during peak hours Switched to 3 paid frontier specialists Result: ~$0.03/query, answers better than any single model
When to Use
Complex analysis β due diligence, market research, technical evaluation Brainstorming β get diverse ideas, synthesize the best Fact-checking β cross-reference across models with different training data High-stakes decisions β when one model's blind spots could hurt you Contrarian thinking β different models have different biases When NOT to use: Quick Q&A (too slow, 30-90s latency) Real-time chat (not designed for streaming) Simple lookups (overkill)
Paid Tier (Default) β Recommended
RoleModel~LatencyStrengthProposer 1moonshotai/kimi-k2.523sLong context, strong reasoningProposer 2z-ai/glm-536sTechnical depth, different training corpusProposer 3minimax/minimax-m2.564sNuance catching, thorough analysisAggregatormoonshotai/kimi-k2.515sFast synthesis Why these models? Frontier-class but less congested than GPT-4/Claude Different training data = genuinely different perspectives Chinese models excel at certain reasoning tasks Combined cost still cheaper than single Opus call Cost breakdown: 3 proposers Γ ~$0.008 = $0.024 1 aggregator Γ ~$0.005 = $0.005 βββββββββββββββββββββββββββββ Total: ~$0.029/query
Free Tier (Fallback)
5 models: Llama 3.3 70B, Gemini 2.0 Flash, Mistral Small, Nemotron 70B, Qwen 2.5 72B β οΈ Warning: Free tier hits rate limits during peak hours. Use --free flag only for testing.
How It Works
βββββββββββββββ β PROMPT β ββββββββ¬βββββββ β ββββββββββββΌβββββββββββ βΌ βΌ βΌ ββββββββββ ββββββββββ ββββββββββ βKimi 2.5β β GLM 5 β βMiniMax β β Parallel (they "argue") β(reason)β β(depth) β β(nuance)β βββββ¬βββββ βββββ¬βββββ βββββ¬βββββ β β β ββββββββββββΌβββββββββββ βΌ ββββββββββββββββ β AGGREGATOR β β (Kimi 2.5) β β β β β’ Best of 3 β β β’ Resolve β β conflicts β β β’ Synthesize β ββββββββ¬ββββββββ βΌ ββββββββββββββββ β FINAL ANSWER β β (Synthesized)β ββββββββββββββββ
Function Signature
interface MoAOptions { prompt: string; // Required: The question to analyze tier?: 'paid' | 'free'; // Default: 'paid' } interface MoAResult { synthesis: string; // The final aggregated answer } // Throws on complete failure (all models down, invalid key) // Returns partial synthesis if 1-2 models fail async function handle(options: MoAOptions): Promise<string>
CLI Usage
# Paid tier (default) node scripts/moa.js "Your complex question" # Free tier node scripts/moa.js "Your question" --free
Programmatic Usage
const { handle } = require('./scripts/moa.js'); const synthesis = await handle({ prompt: "Analyze the competitive moats in AI code generation", tier: 'paid' }); console.log(synthesis);
Failure Modes
ScenarioBehavior1 proposer failsSynthesis from remaining 2 models2 proposers failSynthesis from 1 model (degraded)All proposers failReturns error messageInvalid API keyImmediate error with setup instructionsRate limit (free tier)Returns rate limit error The system is designed to degrade gracefully. A 2/3 response is still valuable.
VC Due Diligence
node scripts/moa.js "Analyze the competitive landscape for AI code generation. \ Who has defensible moats? Who's likely to be commoditized? Be specific."
Technical Evaluation
node scripts/moa.js "Compare RLHF vs DPO vs RLAIF for LLM alignment. \ Which scales better? What are the failure modes of each?"
Market Research
node scripts/moa.js "What are the emerging use cases for embodied AI in 2026? \ Focus on robotics, drones, and autonomous systems. Include specific companies."
Performance Expectations
MetricPaid TierFree TierP50 Latency~45s~60sP95 Latency~90s~120s+Success Rate>99%~80% (rate limits)Cost/Query~$0.03$0.00
Tips
Be specific β Vague prompts get vague synthesis Ask for structure β "Give me pros/cons" or "List top 5" helps the aggregator Use for analysis, not chat β MoA shines for complex reasoning Batch your queries β 30-90s per query, so plan accordingly
Via ClawHub (Recommended)
clawhub install moa
Manual
Copy skills/moa/ to your ~/clawd/skills/ directory Set OPENROUTER_API_KEY in your environment The agent can now invoke MoA for complex queries
Environment Variables
VariableRequiredDescriptionOPENROUTER_API_KEYYesYour OpenRouter API key Get your key at: https://openrouter.ai/keys
Credits
MoA concept: Together AI Research Implementation: @Scianna Built for: OpenClaw
Category context
Agent frameworks, memory systems, reasoning layers, and model-native orchestration.
Source: Tencent SkillHub
Largest current source with strong distribution and engagement signals.
Package contents
Included in package
2 Scripts1 Docs1 Config
- SKILL.md
- scripts/moa-paid.js
- scripts/moa.js
- manifest.json