advisors

To prompt multiple Hermes models for parallel review, use 'hermes chat -q' to query different models simultaneously. The skill composes these independent perspectives into a synthesized analysis, allowing you to tackle complex problems through multi-model consensus.

Adversarial meta-review is a technique that identifies potential errors in a model consensus. By dispatching adversarial reviews against synthesized findings, it critically evaluates the initial analysis to ensure high-stakes decisions are thoroughly vetted for flaws.

Yes, you can use iterative refinement to improve A/B comparisons with Hermes models. The skill enables sequential review chains where you iteratively prompt models, refining the comparison between different analytical approaches to achieve higher accuracy.

Yes, this skill requires a Hermes agent with skills, tools, and multi-turn reasoning capabilities. It depends on the 'hermes-agent' dependency to properly execute 'hermes chat -q' commands and manage the underlying data channel architecture.

The data channel architecture prevents context pollution by separating data from the controller's context. This design improves processing efficiency by ensuring that parallel and sequential review chains operate on isolated data streams without cross-contamination.