What is GF(3) momentum tracking?

It is a mathematical framework used by this skill to track the balance and velocity of interactions, helping users identify high-drift periods and activity spikes in their workflow.

How does DuckDB-IES aggregate data?

It ingests Claude's JSONL history, GitHub contribution data, and local file manifests into a centralized DuckDB database for high-speed SQL-based querying.

What are the primary data sources supported?

It integrates Claude chat history, GitHub repository metadata, daily contribution counts, and local workspace file indexes.

Can I export the data for other tools?

Yes, the skill includes built-in recipes for exporting unified interactions and flow analysis directly to Parquet files for use in Polars, Python, or R.

DuckDB IES Interactome Analytics

Name: DuckDB IES Interactome Analytics
Author: plurigrid

byplurigrid

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Analytics & Monitoring

Provides unified interaction analytics and GF(3) momentum tracking across Claude history, GitHub activity, and workspace files.

About

DuckDB-IES is a sophisticated analytics layer that aggregates disparate data sources—including Claude chat history, GitHub repositories, and local workspace manifests—into a unified DuckDB database. It employs advanced concepts like GF(3) momentum tracking and deterministic coloring to detect activity drift, cluster topics via regex-based extraction, and analyze cross-source correlations. This skill is essential for developers and researchers looking to track complex interaction patterns and maintain compositional coherence in high-velocity AI-assisted coding environments.

Key Features

GF(3) momentum tracking for interaction velocity and drift detection
Unified analytics for Claude history, GitHub repos, and workspace files
Deterministic fingerprinting and coloring for cross-source correlation
Automated topic clustering using content-based regex extraction
2 GitHub stars
High-performance data exports to Parquet for external scientific analysis

Use Cases

Generating structured Parquet datasets from unstructured development activity
Analyzing interaction patterns between Claude sessions and GitHub contributions
Detecting productivity drift and momentum shifts in development workflows

About

Key Features

GF(3) momentum tracking for interaction velocity and drift detection
Unified analytics for Claude history, GitHub repos, and workspace files
Deterministic fingerprinting and coloring for cross-source correlation
Automated topic clustering using content-based regex extraction
2 GitHub stars
High-performance data exports to Parquet for external scientific analysis

Use Cases

Generating structured Parquet datasets from unstructured development activity
Analyzing interaction patterns between Claude sessions and GitHub contributions
Detecting productivity drift and momentum shifts in development workflows