multi-model-research

Multi-model research orchestrates parallel queries across frontier LLMs (Claude, GPT-5.1, Gemini 3.0 Pro, Perplexity Sonar, Grok 4.1) using an LLM Council pattern. Each model generates independent analysis, then a peer review and synthesis process produces a unified research report with cross-model validation and reduced bias.

Yes. The Skill automates a deterministic workflow that queries multiple models in parallel, applies structured peer review evaluation, and synthesizes findings into a markdown report with citations. Final outputs are saved to Obsidian-backed archives for traceability and reference.

Use the LLM Council pattern with Perplexity Sonar web grounding for current information, citations, and cross-model fact-checking. Parallel queries to diverse frontier LLMs catch gaps and contested interpretations; the chairman synthesis reconciles perspectives into a factually grounded report.

Yes. The Skill requires secure API keys for each frontier LLM and uses configuration-driven routing to direct queries to the appropriate models. Python external API orchestrator and system prompts manage the workflow; dependencies include httpx, pyyaml, and python-dotenv for secure credential handling.

Yes. Multi-model research is designed for complex analyses, factual verification, and contested topics. The peer review and synthesis workflow cross-validates findings across models, surfacing diverse viewpoints and reducing single-model bias while producing a comprehensive, citation-rich final report.

The Skill generates structured markdown reports with citations, JSON outputs for programmatic use, and Obsidian integration for archival and traceability. Reports include executive summaries, cross-model perspectives, and metadata sufficient for downstream analysis or publication workflows.