When should I use the Model Tracking Protocol?

You should use this protocol whenever you are running two or more external AI models in parallel for validation, code review, or consensus-based decision making.

What is the purpose of the pre-launch checklist?

The checklist ensures that session directories, tracking tables, and timing arrays are initialized before any model calls are made, preventing data loss and ensuring accurate statistics.

Can I use this for performance benchmarking?

Absolutely. The protocol tracks start times, end times, and durations for every model, making it an ideal tool for benchmarking AI provider performance.

How does this skill handle model failures?

It provides a specific Failure Documentation Protocol that logs the model name, error type (e.g., timeout, API error), and retry status to ensure the final report is complete.

Does this skill help with consensus analysis?

Yes, it includes a Consensus Analysis Requirement section with templates to compare findings across all models to identify agreement or discrepancies.

Model Tracking Protocol

Name: Model Tracking Protocol
Author: involvex

byinvolvex

分析と監視

Standardizes multi-model validation by enforcing structured tracking, performance logging, and consensus analysis.

概要

The Model Tracking Protocol is a mandatory framework designed for Claude Code to manage complex workflows involving two or more external AI models. It solves the common problem of fragmented results and lost data during parallel execution by enforcing a strict pre-launch checklist and real-time status updates. This skill provides structured templates for session management, failure documentation, and consensus analysis, ensuring that every multi-model review is comprehensive, measurable, and easy to audit.

主な機能

Real-time tracking tables for monitoring parallel model progress
0 GitHub stars
Automated session directory management and performance logging
Mandatory pre-launch verification checklist for session initialization
Standardized failure documentation for API errors and timeouts
Built-in consensus analysis templates to compare model findings

ユースケース

Performing parallel code reviews across multiple LLM providers for higher accuracy
Benchmarking speed and quality metrics across different AI models
Debugging complex orchestration tasks with structured error and status logs

概要

主な機能

Real-time tracking tables for monitoring parallel model progress
0 GitHub stars
Automated session directory management and performance logging
Mandatory pre-launch verification checklist for session initialization
Standardized failure documentation for API errors and timeouts
Built-in consensus analysis templates to compare model findings

ユースケース

Performing parallel code reviews across multiple LLM providers for higher accuracy
Benchmarking speed and quality metrics across different AI models
Debugging complex orchestration tasks with structured error and status logs