llm-councillisted

# 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. This skill applies the same structure inside your agent, using different thinking lenses instead of different models. ## honest capability tiers How independent the advisors really are depends on what your host supports. Detect the tier, run the best available version, and tell the user which tier ran. Never claim more independence than the host actually provided. - **Tier 1, parallel sub-agents** (for example Claude Code with the Task tool): spawn all 5 advisors as parallel sub-agents. Each response is genuinely independent. Run peer review as 5 more parallel sub-agents. This is the full method described below. - **Tier 2, single model, no sub-agents** (for example most chat surfaces): write each advisor's response sequentially, completing one fully before starting the next, staying strictly inside t