llm-council

Query multiple LLM backends in parallel and aggregate outputs for cross-model analysis.

1|Updated May 11, 2026

Search Tags:#llm #evaluation #parallel #synthesis #council #cross-reference #model-ensemble

Ready to run

npx skills add https://github.com/zeropoint-foundation/ironclaw --skill llm-council-zeropoint-foundation

💬 Prompt for ChatGPT / Claude Chat▼

Please help me install this Agent Skill.
Skill: llm-council
Source: https://github.com/zeropoint-foundation/ironclaw/tree/main/skills/llm-council
Command: npx skills add https://github.com/zeropoint-foundation/ironclaw --skill llm-council-zeropoint-foundation

SYSTEM DOCUMENTATION & REQUIREMENTS

What problem does it solve?

Query multiple LLM models in parallel and cross-reference their responses to improve accuracy, coverage, and reliability.

Core Features & Use Cases

Parallel model polling: send the same prompt to several backends simultaneously and gather responses.
Per-call model overrides: override the configured model for a single prompt without altering global settings.
Cross-vendor support: compare outputs across providers and routing backends (e.g., NEAR AI aggregator) for diverse perspectives.
Synthesis & citation: synthesize model opinions into a balanced answer and clearly attribute each insight to its source.

Quick Start

Ask the AI to run a parallel council on a question to compare model responses.

Frequently Asked Questions about llm-council

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

FAQPage Schema

How do I query multiple LLM models in parallel to compare their responses?▼

To query multiple LLM models in parallel, you can use a parallel council to send the same prompt to several backends simultaneously, gather responses, and synthesize the outputs for cross-model analysis.

What is cross-referencing LLM outputs used for in model evaluation?▼

Cross-referencing LLM outputs compares reasoning styles across vendors to validate answers, obtain diverse opinions, and improve accuracy, coverage, and reliability in research or decision-support workflows.

Can I override the configured LLM backend for a single prompt without changing global settings?▼

Yes, you can use per-call model overrides to change the configured backend for a single prompt without altering global settings, allowing targeted cross-vendor comparison within a parallel polling workflow.

How do I synthesize and cite responses from different AI vendors?▼

To synthesize and cite vendor responses, the structured workflow aggregates parallel model outputs into a balanced answer and clearly attributes each insight to its specific source backend.

Do I need a routing backend to compare outputs across different LLM providers?▼

Yes, a configured backend is required to enable cross-vendor support, allowing you to route prompts to different providers and compare diverse perspectives across multiple models.