Consensus

About

Consensus enables a "model chorus" workflow that queries multiple AI providers in parallel, allowing for cross-validation and more reliable decision-making. By executing several models at once, users can choose from five synthesis strategies—such as majority voting, weighted detail, or structured comparison—to ensure that the final output is accurate and comprehensive. This skill is essential for high-stakes tasks, factual verification, and architectural reviews where a single model's perspective may be limited or prone to hallucinations, providing a robust framework for reliability through model agreement.

Key Features

• Five configurable synthesis strategies including majority and weighted
• Per-provider timeout and error handling with graceful degradation
• Automatic provider failover based on configurable priority lists
• Contextual file support to provide identical data to all models
• Parallel multi-model execution for fast, simultaneous responses
• 0 GitHub stars

Use Cases

• Building reliable workflows with automatic failover if a primary model fails
• Fact-checking and cross-verifying information or calculations across multiple LLMs
• Gathering diverse perspectives for complex architectural or technical decisions