Is this skill suitable for large-scale agent topologies?

Yes, it is specifically designed to handle complex environments, including systems with dozens of agents and multiple DuckDB instances.

What are the three waves of expansion?

Wave 1 identifies direct matches (≥0.95 confidence), Wave 2 performs relational discovery via co-occurrence (≥0.75 confidence), and Wave 3 conducts structural analysis of the possibility space (≥0.50 confidence).

What primary data sources does this skill use?

The skill primarily utilizes SUBAGENT_3_PATTERN_DISCOVERY JSON files and the VERS_DUCKLAKE_SCHEMA SQL definition covering 48 agents and 16 repositories.

Can I use this for temporal versioning?

Absolutely. One of its key expansion frontiers is temporal versioning enhancement and mapping patterns to specific temporal clusters.

How does the skill compute world reachability?

It calculates the distance and path between data 'worlds' by applying triangle inequality constraints and identifying intermediate events necessary for traversal.

DuckLake Pattern Explorer & Schema Evolver

Name: DuckLake Pattern Explorer & Schema Evolver
Author: plurigrid

byplurigrid

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데이터베이스 관리

Facilitates progressive pattern discovery and schema evolution for DuckLake databases through wave-based expansion and reachability analysis.

DuckLake Pattern Expansion is a specialized capability designed for sophisticated data exploration and structural analysis within the DuckLake ecosystem. It employs a unique triple-wave methodology—transitioning from high-confidence direct matches to relational discovery and exploratory structural analysis—to uncover hidden relationships and compute reachability between disparate data worlds. This skill is particularly useful for developers managing complex multi-agent topologies, providing a rigorous framework for schema evolution and horizon exploration using categorical models and topological graph analysis.

주요 기능

01Triple-wave pattern expansion methodology with confidence scoring

02Automated world reachability and pathfinding computation

03Multi-agent topology analysis for complex repository structures

04Integration with ACSet morphisms for categorical data modeling

058 GitHub stars

06Relational discovery through semantic co-occurrence analysis

사용 사례

01Mapping and discovering relationships between distributed DuckDB instances

02Calculating feasible synchronization paths between isolated data environments

03Analyzing agent-repo topologies to identify sub-community clusters

What are Skills?·How to Install

Install with 🐟 Skill.Fish

npx skillfish add plurigrid/asi ducklake-pattern-expansion

For use in Claude.ai and ChatGPT

Download Skill