Does this skill support reproducible AI training?

Yes, it incorporates SPI seeds within its JAX and DuckDB implementations to ensure that learning processes and pattern generations are deterministic and reproducible.

What are 'Trit' values in the context of this skill?

Trit values represent the skill's role in a GF(3) triad; Agent-o-rama is a +1 (Generator), which produces learned patterns to be consumed by other layers.

Which programming languages are used in this skill?

The skill uses a multi-interpreter approach including Python (JAX) for prediction, Ruby for skill discovery, Hy for bidirectional learning, and SQL (DuckDB) for pattern storage.

What is the primary function of Agent-o-rama?

Agent-o-rama is designed to train learning agents on interaction sequences to discover behavioral, temporal, and network patterns for use in cognitive surrogate systems.

How does Agent-o-rama store learned data?

It utilizes DuckDB to create structured tables for learned patterns, including fields for pattern type, confidence scores, and SPI seeds for reproducibility.

Agent-o-Rama Learning Engine

Name: Agent-o-Rama Learning Engine
Author: plurigrid

byplurigrid

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Ciencia de Datos y ML

Extracts complex behavioral patterns and trains predictive learning agents from interaction sequences using multi-interpreter analysis.

Agent-o-rama is a sophisticated pattern extraction and learning skill designed for cognitive surrogate systems. It operates at 'Layer 4' of the learning stack, transforming raw interaction data into structured behavioral models. By leveraging a multi-language approach—using JAX for high-performance predictive modeling, Ruby for skill discovery, and Hy for bidirectional thread learning—it enables Claude to identify temporal, topic, and network patterns. This skill is essential for developers building AI agents that need to mimic, predict, or analyze human-like interaction sequences with mathematical precision.

Características Principales

01Bidirectional learning coupling thread analysis with synthesis

02Automated skill discovery with coherence thresholding

03Multi-interpreter integration across Python (JAX), Ruby, Hy, and SQL

04Deterministic seeding for reproducible machine learning models

05Temporal and network pattern extraction via DuckDB

067 GitHub stars

Casos de Uso

01Discovering latent skills and recurring topics in large communication datasets

02Training cognitive surrogates to mimic specific interaction styles

03Predicting future interaction sequences based on historical temporal patterns

What are Skills?·How to Install

Install with 🐟 Skill.Fish

npx skillfish add plurigrid/asi agent-o-rama

For use in Claude.ai and ChatGPT

Download Skill