llm-council
通过多角度匿名评审降低单一模型盲点，适合复杂决策与提案检验。
会增加算力与延迟成本，使用时注意预算与响应时间。
用五位独立 AI 顾问评议并合成决策建议的审议流程。
把一个问题交给五位独立 AI 顾问各自分析、匿名互评，再由主席合成最终建议，帮你从多角度检验决策并减少偏见风险。适合在需要权衡利弊、有实际取舍或重要决策时做压力测试。基于 Karpathy 的 LLM Council 思路，强调多样性、同行复核和综合结论。
▸ 展开 SKILL.md 英文原文
Run any question, idea, or decision through a council of 5 AI advisors who independently analyze it, peer-review each other anonymously, and synthesize a final verdict. Based on Karpathy's LLM Council methodology. MANDATORY TRIGGERS: 'council this', 'run the council', 'war room this', 'pressure-test this', 'stress-test this', 'debate this'. STRONG TRIGGERS (use when combined with a real decision or tradeoff): 'should I X or Y', 'which option', 'what would you do', 'is this the right move', 'validate this', 'get multiple perspectives', 'I can't decide', 'I'm torn between'. Do NOT trigger on simple yes/no questions, factual lookups, or casual 'should I' without a meaningful tradeoff (e.g. 'should I use markdown' is not a council question). DO trigger when the user presents a genuine decision with stakes, multiple options, and context that suggests they want it pressure-tested from multiple angles.
帮我安装这个 skill：https://raw.githubusercontent.com/ton-anywhere/my-favorite-prompts/main/skills/claude-concil/SKILL.mdcurl -fsSL "https://raw.githubusercontent.com/ton-anywhere/my-favorite-prompts/main/skills/claude-concil/SKILL.md"# LLM Council You ask one AI a question, you get one answer. That answer might be great. It might be mid. You have no way to tell because you only saw one perspective. The council fixes this. It runs your question through 5 independent advisors, each thinking from a fundamentally different angle. Then they review each other's work. Then a chairman synthesizes everything into a final recommendation that tells you where the advisors agree, where they clash, and what you should actually do. This is adapted from Andrej Karpathy's LLM Council. He dispatches queries to multiple models, has them peer-review each other anonymously, then a chairman produces the final answer. We do the same thin