What are ACSets in this context?

ACSets (Attributed Category Sets) are mathematical structures used by this skill to automatically generate robust DuckDB table schemas and constraints from abstract definitions.

What is DuckDB TimeTravel?

It is a Claude Code skill that adds temporal versioning, category-theoretic schema generation, and data snapshot management to DuckDB workflows.

How do I query historical data using this skill?

You can use SQL syntax like 'AT (VERSION => n)' or 'AS OF TIMESTAMP' to query the state of your data at any specific point in history.

Can I compare different versions of my database?

Yes, the skill provides a diffing capability that identifies added, modified, or deleted records and schema changes between any two checkpoints.

What is GF(3) integration used for?

GF(3) integration uses trit-based logic (-1, 0, 1) to verify data conservation and ensure compositional coherence across data streams.

DuckDB TimeTravel & ACSet Schema

Name: DuckDB TimeTravel & ACSet Schema
Author: plurigrid

byplurigrid

•

Database Management

Manages temporal versioning and generates relational schemas from category-theoretic ACSet definitions within DuckDB.

About

duckdb-timetravel is a specialized Claude Code skill designed for advanced database management, enabling temporal versioning and schema generation for DuckDB. It allows developers to perform 'time travel' queries to access historical data states, manage checkpoints for data snapshots, and automatically derive DuckDB schemas from Applied Category Set (ACSet) definitions. By incorporating GF(3) trit logic for data flow coordination, this skill is particularly effective for scientific computing, complex data lineage tracking, and maintaining structural consistency across evolving datasets.

Key Features

Detailed data diffing between database versions and snapshots
Temporal 'time travel' querying using DuckLake-style snapshots
GF(3) color stream tracking for verifiable data evolution
Comprehensive version management including checkpoints and restoration
Automated schema generation from ACSet category definitions
2 GitHub stars

Use Cases

Debugging data pipelines by comparing state changes across specific checkpoints
Generating complex relational schemas from high-level mathematical category definitions
Auditing and tracking historical changes in complex scientific or financial datasets

About

Key Features

Detailed data diffing between database versions and snapshots
Temporal 'time travel' querying using DuckLake-style snapshots
GF(3) color stream tracking for verifiable data evolution
Comprehensive version management including checkpoints and restoration
Automated schema generation from ACSet category definitions
2 GitHub stars

Use Cases

Debugging data pipelines by comparing state changes across specific checkpoints
Generating complex relational schemas from high-level mathematical category definitions
Auditing and tracking historical changes in complex scientific or financial datasets