What are 'perplexing patterns' in this context?

These are statistical anomalies or contradictions detected during analysis, such as high revert rates in specific languages or unusual message-per-second densities.

How does it handle data normalization?

It uses a Canonical Universal Schema to map platform-specific fields, such as sessionId or first_ts, into a standardized format for cross-platform comparison.

How does the GF(3) conservation status work?

It applies a mathematical hash to thread IDs to determine a 'trit' value (-1, 0, or +1), which helps in monitoring the balance and distribution of thread types across the ecosystem.

What systems does TIDAR Thread Probe support?

TIDAR Thread Probe is designed to analyze and reconcile interaction data across AMP, Claude, Codex, and Warp environments.

TIDAR Thread Probe

Name: TIDAR Thread Probe
Author: plurigrid

byplurigrid

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分析与监控

Analyzes and reconciles AI interaction threads across multiple platforms using topological decomposition and universal schema mapping.

关于

TIDAR Thread Probe provides a sophisticated framework for cross-system thread pattern discovery across AMP, Claude, Codex, and Warp environments. By utilizing ordered locale site semantics and GF(3) trit conservation principles, it identifies shared, pairwise, and unique behaviors within complex datasets of AI agent interactions. This skill is indispensable for developers and researchers who need to maintain data consistency, detect behavioral anomalies, and visualize the underlying structural patterns of multi-agent workflows across diverse interaction surfaces.

主要功能

Universal schema mapping for normalizing diverse AI interaction logs and sessions
High-performance querying integration with DuckDB and JQ for large thread datasets
Cross-system thread pattern discovery across AMP, Claude, Codex, and Warp
Statistical anomaly detection for identifying 'perplexing patterns' in agent behavior
Mathematical state balancing using GF(3) conservation status monitoring
2 GitHub stars

使用场景

Mapping and visualizing agent interaction trajectories using topological locale semantics
Reconciling inconsistent tool-use logs and session metadata across different AI agent environments
Detecting performance anomalies, such as high revert rates or unusual message densities in specific file types

关于

主要功能

Universal schema mapping for normalizing diverse AI interaction logs and sessions
High-performance querying integration with DuckDB and JQ for large thread datasets
Cross-system thread pattern discovery across AMP, Claude, Codex, and Warp
Statistical anomaly detection for identifying 'perplexing patterns' in agent behavior
Mathematical state balancing using GF(3) conservation status monitoring
2 GitHub stars

使用场景

Mapping and visualizing agent interaction trajectories using topological locale semantics
Reconciling inconsistent tool-use logs and session metadata across different AI agent environments
Detecting performance anomalies, such as high revert rates or unusual message densities in specific file types