Model Tracking Protocol

About

The Model Tracking Protocol skill provides a standardized framework for managing complex multi-model validation tasks within Claude Code. It enforces a strict pre-launch checklist and real-time tracking system to prevent data loss and incomplete reviews during parallel executions. By creating structured tracking tables and documenting timing, performance, and failure data as it happens, this skill ensures that developers can perform rigorous consensus analysis across different AI models with complete visibility into execution status and potential bottlenecks.

Key Features

• Mandatory pre-launch verification checklist for session initialization
• File-based detection and session logging for persistent tracking
• Automated tracking table generation for real-time model status updates
• Standardized failure documentation for capturing API errors and timeouts
• 196 GitHub stars
• Consensus analysis requirements for comparing multi-model outputs

Use Cases

• Benchmarking the performance and latency of different AI models on specific tasks
• Ensuring reliable error capturing and retry logic during complex orchestration workflows
• Running parallel code reviews across multiple LLM providers to reach a consensus