Orchestrate multiple LLMs through peer review and synthesis.
npx skills add https://github.com/shuntacurosu/llm_council_skill --skill llm-council-shuntacurosu
Please help me install this Agent Skill. Skill: LLM Council Source: https://github.com/shuntacurosu/llm_council_skill/tree/main Command: npx skills add https://github.com/shuntacurosu/llm_council_skill --skill llm-council-shuntacurosu
This Skill reduces weak, single-model answers by coordinating multiple LLMs to independently reason, critique each other, and produce a synthesized final response.
Use the LLM Council skill to analyze my question with multiple models and return the council’s final synthesized answer.
High-intent search queries and answers about installing and using this skill.
Multi-model peer review improves code review by having multiple LLMs independently reason and critique each other's responses. This cross-checking mechanism produces a synthesized final answer, reducing weak single-model outputs for architecture decisions and refactoring tasks.
You orchestrate multiple LLMs by triggering the council skill with your coding question. The system collects independent responses, runs anonymous peer review, and applies chairman synthesis to return a final, robust answer.
Yes, multi-LLM orchestration supports code-change tasks using git worktree isolation. This ensures that model evaluations and refactoring suggestions operate safely within isolated worktrees during the peer review process.
Yes, LLM orchestration requires OpenCode CLI and provides operational visibility through real-time dashboarding, conversation history storage, merge controls, and detailed session logging.
The best way to handle decision support tasks is using a multi-model council approach. Models independently generate opinions, conduct anonymous peer review, and synthesize a final response, ensuring robust cross-verification.
You should avoid using a multi-model council for simple, single-answer queries where cross-checking multiple model perspectives is unnecessary overhead. It is designed for complex tasks like refactoring and architecture decisions.
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