model-tracking-protocol

Multi-model tracking enforces mandatory documentation of each model's status, timing, and results in a shared session. Initialize a tracking.md file before launch, update it as each model completes, and use it to record failures and consensus findings across all participating models.

Pre-launch setup requires a session directory, tracking table template, context verification, and an active-tracking marker. The protocol mandates a checklist to confirm these are in place before launching any external models in parallel.

The failure documentation protocol captures error details, retry attempts, and resolution status for each model in the tracking file. This structured record enables consensus analysis and prevents loss of diagnostic data during multi-model validation.

Yes. The tracking protocol applies to any two or more external AI models running in parallel. It's model-agnostic and focuses on coordination, timing, status updates, and consensus synthesis rather than model-specific integration.

Consensus analysis synthesizes findings from all participating models into structured results. The protocol provides templates to compare outputs, identify agreements and conflicts, and present a unified view of validation outcomes.

Pre-launch verification ensures session state, directory structure, and tracking infrastructure are ready before models execute. This prevents mid-run setup failures, timing inconsistencies, and incomplete result documentation.