Coordinate multiple LLMs through anonymous peer review and synthesis.
npx skills add https://github.com/47network/Sven --skill llm-council-47network
Please help me install this Agent Skill. Skill: llm-council Source: https://github.com/47network/Sven/tree/main/skills/ai-agency/llm-council Command: npx skills add https://github.com/47network/Sven --skill llm-council-47network
Provides governance-aware, high-quality answers by coordinating multi-model deliberation and a structured synthesis process.
Submit a query to the council to receive a synthesized, peer-reviewed answer.
High-intent search queries and answers about installing and using this skill.
Multi-model deliberation improves answer robustness by running parallel queries across multiple LLMs, applying anonymous peer review to rank responses, and using a synthesis stage to finalize the output.
The best way to run anonymous peer review for LLM outputs is to use a configurable council composition that evaluates responses anonymously, ranks them for safety and quality, and synthesizes the final answer.
Yes, you need a runtime LLM provider configured to supply the multiple models required for the council deliberation, peer review, and final synthesis process.
You configure council composition by selecting the participating LLMs and specifying a chairman model responsible for the final synthesis, while the system validates your configuration and returns actionable errors if setup fails.
Yes, you can track token usage and cost across multiple LLMs during deliberation, as the system provides usage statistics and cost-awareness alongside the final synthesized response.
If the council configuration validation fails, the system stops the deliberation process and returns actionable errors to help you correct the setup before retrying the query.
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