model-coordination

Coordinate multiple AI models to analyze trading scenarios and aggregate decisions.

1|1|Updated Nov 8, 2025

Search Tags:#multi-model #trading #OpenRouter #analyze #model coordination #TradingAgents #ModelChat

Ready to run

npx skills add https://github.com/AkhilGurrapu/kubera --skill model-coordination

💬 Prompt for ChatGPT / Claude Chat▼

Please help me install this Agent Skill.
Skill: model-coordination
Source: https://github.com/AkhilGurrapu/kubera/tree/main/.claude/skills/model-coordination
Command: npx skills add https://github.com/AkhilGurrapu/kubera --skill model-coordination

SYSTEM DOCUMENTATION & REQUIREMENTS

What problem does it solve?

This skill coordinates multiple AI models to analyze trading scenarios and generate a consolidated decision.

Core Features & Use Cases

Multi-model orchestration: Run enabled models in parallel to evaluate the same ticker and date.
Consensus aggregation: Compute majority decisions, confidence, and identify outliers for auditing.
Auditability & transparency: Log complete model reasoning into ModelChat for review and compliance.
Performance monitoring: Maintain per-model portfolios and a leaderboard to compare results over time.

Quick Start

To begin, configure which models are enabled in the config, supply the ticker/date, and run the model-coordination workflow. The system will execute models in parallel, aggregate decisions, and present a consolidated result along with a transparent reasoning trace.

Frequently Asked Questions about model-coordination

High-intent search queries and answers about installing and using this skill.

FAQPage Schema

How do I coordinate multiple AI models to analyze a trading scenario?▼

Multi-model coordination runs enabled AI models in parallel on the same ticker and date, aggregating their individual analyses into a single consolidated trading decision with transparent reasoning logs.

What is consensus aggregation in multi-model trading analysis?▼

Consensus aggregation computes majority trading decisions across multiple AI models, calculates overall confidence levels, and identifies outlier analyses to provide an auditable, consolidated trading recommendation.

Do I need an OpenRouter API key to run multi-model trading analysis?▼

Yes, multi-model trading analysis relies on OpenRouter for model access and the TradingAgents framework, requiring configured API access to execute enabled models in parallel and aggregate their decisions.

How can I track and compare AI model performance for trading decisions over time?▼

Model performance tracking maintains per-model portfolios and a leaderboard, comparing individual model outcomes against the consensus aggregation over time to identify the most accurate trading analysis models.

How does multi-model trading analysis ensure transparency and auditability?▼

Transparent trading analysis logs complete reasoning traces from every enabled AI model into ModelChat, allowing compliance teams to review individual model logic and the final consensus decision.

What's the best way to configure which AI models analyze my trading data?▼

Configuring multi-model trading analysis involves specifying which models are enabled in the config file, then supplying the target ticker and date to execute the parallel model workflow and generate a consolidated result.