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Run parallel queries against multiple models and view side-by-side responses in a live dashboard; synthesize consensus and run anonymous model voting.
LLM Council runs parallel queries across multiple AI models and exposes a live web dashboard to compare responses, synthesize a consensus, and collect anonymous model-to-model votes. It includes a lightweight Python server, AI Gateway client code, and a static dashboard (JS) that streams SSE responses for interactive comparison.
Use when you want to compare outputs from different large models (research, prompt testing, multi-model evaluation), when you need a quick visual side-by-side comparison, or when you want to aggregate model opinions into a synthesized answer. Particularly useful for teams evaluating model behavior, running multi-model experiments, or debugging model disagreement.
Best for agents that can run local scripts and expose ports (Claude Code, Codex, agents with shell access) and for human-in-the-loop workflows where a browser dashboard is acceptable.
LLM Council is a multi-model consensus dashboard that queries multiple AI providers in parallel, synthesizes responses, and runs anonymous model-to-model voting. The two scripts (ai_gateway.py client + server.py HTTP server) are well-written pure-stdlib Python with solid error handling. The server timed out during testing as expected (it's a long-running HTTP server). The skill requires an AI_GATEWAY_API_KEY to function fully but is architecturally clean and handles SSE streaming, keepalive pings, and concurrent model queries properly.
Clean skill with no security concerns. Pure stdlib Python, no suspicious network calls, no hardcoded credentials. The hardcoded Origin header (trickle.so) is unusual but not a security issue. Static files (index.html, app.js) referenced in SKILL.md are not included in the scripts dict — they'd need to be cloned from the repo for full functionality.
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