Simulate multi-agent debates with Scholar, Logician, Contrarian, and Captain roles.
npx skills add https://github.com/gokulkrishh/skills --skill model-council-gokulkrishh
Please help me install this Agent Skill. Skill: model-council Source: https://github.com/gokulkrishh/skills/tree/main/skills/model-council Command: npx skills add https://github.com/gokulkrishh/skills --skill model-council-gokulkrishh
This Skill tackles complex queries by simulating a debate among specialized AI agents, ensuring a thorough, multi-faceted analysis and a well-reasoned consensus.
Use the model council skill to debate whether to use React Server Components or client-side rendering for a new application.
High-intent search queries and answers about installing and using this skill.
Multi-agent debate analyzes complex architectural decisions by simulating four specialized agents: Scholar, Logician, Contrarian, and Captain. These agents independently analyze your query, challenge assumptions through debate, and synthesize a final consensus to stress-test your technical choices.
A multi-agent debate system for complex queries is a process where specialized AI agents independently analyze a problem, debate their findings, and synthesize a conclusion. It leverages roles like Scholar for research and Contrarian for challenging assumptions to provide well-reasoned consensus.
Yes, you can stress-test software engineering decisions using multiple AI agents. The system deploys a Logician for reasoning and a Contrarian to challenge your assumptions, ensuring a thorough, multi-faceted analysis of critical choices like selecting an application framework.
To get a second opinion on a complex technical problem, you can use a multi-agent debate system. A Captain agent coordinates independent analysis from other specialized agents, debates their perspectives, and synthesizes a comprehensive second opinion.
Yes, the multi-agent debate approach works for evaluating application frameworks. It uses specialized agents to debate pros and cons, considering factors like technical debt, scalability, and maintainability to help you decide between options like React Server Components or client-side rendering.
You should use a multi-agent debate system instead of a standard AI query when facing complex queries that require in-depth analysis. It is ideal for gaining second opinions, stress-testing decisions, and exploring problems from multiple angles to ensure a well-reasoned consensus.
Imad-OuteConduct Phase 0 Gate Reviews with an independent agent for go/no-go decisions.
hxk622Validate incoming code review feedback against the existing codebase.
dnhessOrchestrate structured multi-agent debates to generate Architecture Decision Records.
TitoPrauseeBenchmark Ollama Cloud models for coding subagent delegation compatibility.
