Coordinate five AI advisors to reason and synthesize a decision.
npx skills add https://github.com/MarGustavoFring/llm-council --skill llm-council-margustavofring
Please help me install this Agent Skill. Skill: llm-council Source: https://github.com/MarGustavoFring/llm-council/tree/main Command: npx skills add https://github.com/MarGustavoFring/llm-council --skill llm-council-margustavofring
The LLM Council skill helps users make high-stakes decisions by getting diverse perspectives from five AI advisors, reducing biases and promoting thorough analysis.
Run the council on your decision by typing 'council this' or 'run the council on [your question]'.
High-intent search queries and answers about installing and using this skill.
An AI council improves decision-making by deploying five independent AI advisors that reason from different methods to analyze your question. They conduct anonymous peer reviews to critique each other, ensuring biases are reduced and easy agreements are scrutinized before synthesizing a final actionable verdict.
No, you cannot use this collaborative AI decision support without an environment capable of running multiple sub-agents or supporting multi-model input. The council requires multiple AI advisors to reason independently and perform anonymous peer reviews to stress-test consensus effectively.
To get diverse AI perspectives, you initiate the council by typing 'council this' or 'run the council on [your question]'. Five AI advisors will independently analyze the decision, critique each other's reasoning anonymously, and a chairman will aggregate the advice into a single actionable verdict.
You should use multiple AI advisors for high-stakes, judgment-based decisions where diverse perspectives are valuable. The council structure uncovers blind spots and provides comprehensive analysis by forcing anonymous peer review and consensus stress-testing, which a single prompt cannot replicate.
The primary limitation is the technical requirement: your AI environment must support running multiple sub-agents or multi-model input. Additionally, the structured process of five advisors conducting peer reviews and consensus stress-tests may be excessive for low-stakes or purely factual queries.
AradotsoBuild self-improving AI agents through iterative meta-learning loops.
iulianditaProvides dual-lens adversarial and constructive analysis for evaluating strategic plans and risks.
dadbodgeoffRecord AI generation provenance with decision factors, data lineage, and reasoning chains.
Agent-Engineer-MasterPerforms three-phase decision analysis to surface tail-distribution insights.
