How does the GF(3) triadic structure work?

It divides tasks into three roles: the Validator (-1) for constraints, the Coordinator (0) for orchestration, and the Generator (+1) for execution, ensuring a balanced and conserved system behavior.

Can this skill handle large-scale data lakehouses?

Yes, it is specifically designed for complex DuckLake environments where manual exploration is difficult, using concurrent walkers and teleportation to maximize coverage across massive schemas.

What technologies are required to run this skill?

The skill utilizes Babashka (Clojure) for coordination, Python for concurrent walker execution, and SQL for DuckDB validation and history tracking.

What is an ergodic random walk in this context?

It is a navigation algorithm based on Markov chains that ensures every part of a DuckDB schema is visited with a specific stationary distribution, preventing agents from getting stuck in isolated table clusters.

DuckLake Ergodic Schema Walker

Name: DuckLake Ergodic Schema Walker
Author: plurigrid

byplurigrid

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Database Management

Performs ergodic random walk exploration of DuckDB and DuckLake schemas using concurrent, triadic agent coordination.

This skill enables Claude to autonomously explore complex DuckDB and DuckLake database schemas using a mathematically rigorous ergodic random walk algorithm. By implementing a Society-of-Mind coordination model and GF(3) triadic logic, it balances validation, orchestration, and generation tasks to ensure complete schema coverage and discover hidden relationships. It is particularly useful for large-scale data lakehouses where manual schema mapping is impractical, providing high-entropy navigation and PageRank-style teleportation to guarantee that every table and edge is reachable and analyzed.

Key Features

01Society-of-Mind architecture using Python and Babashka for concurrent agent execution

02Automated entropy and coverage metrics to quantify schema discovery progress

032 GitHub stars

04GF(3) triadic walker coordination (Validator, Coordinator, Generator) for balanced exploration

05Ergodic random walk exploration with PageRank-style teleportation for 100% schema reachability

06Deep integration with DuckDB snapshots, acsets, and algebraic database navigation

Use Cases

01Automating the discovery of complex relationships in unfamiliar DuckLake data lakehouses

02Generating high-entropy datasets for testing and validating database integrity and constraints

03Mapping large-scale DuckDB metadata using concurrent, coordinated agent walks to find isolated tables

What are Skills?·How to Install

Install with 🐟 Skill.Fish

npx skillfish add plurigrid/asi ducklake-walk

For use in Claude.ai and ChatGPT

Download Skill