What is a cognitive surrogate system?

A cognitive surrogate system is an AI model designed to act as a digital representative by learning and mimicking specific behavioral, temporal, and topical interaction patterns from a user or system.

Which programming environments are required for Agent-o-rama?

The skill is multi-interpreter, requiring Python for JAX-based modeling, Ruby for skill discovery logic, and Hy for bidirectional thread learning.

How does Agent-o-rama handle data storage?

It utilizes DuckDB to store learned patterns in a structured format, enabling efficient SQL queries for temporal analysis, network mapping, and pattern confidence scoring.

Does this skill support reproducible machine learning?

Yes, Agent-o-rama uses SPI (Specialized Personal Intelligence) seeds and JAX PRNG keys to ensure that training runs and pattern extractions are deterministic and reproducible.

Agent-o-rama Learning & Pattern Extraction

Name: Agent-o-rama Learning & Pattern Extraction
Author: plurigrid

byplurigrid

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数据科学与机器学习

Trains cognitive agents to extract behavioral, temporal, and network patterns from complex interaction sequences.

Agent-o-rama is a sophisticated Layer 4 skill designed for building cognitive surrogate systems by automating the discovery of patterns within raw interaction data. It utilizes a multi-interpreter architecture—leveraging JAX for high-performance predictive modeling, DuckDB for structured pattern storage, and Ruby for skill discovery—to identify temporal, topic, and network trends. This skill is ideal for developers creating AI agents that need to model user habits, detect emergent capabilities from logs, or synchronize analysis and synthesis through bidirectional learning patterns.

主要功能

01DuckDB-backed storage for temporal, topic, and network patterns

02Deterministic training reproducibility using SPI-integrated seeding

037 GitHub stars

04Bidirectional thread learning for coupled analysis and synthesis

05Automated skill discovery via condensed stack descent algorithms

06Multi-interpreter integration with JAX, Ruby, and Hy (Lisp)

使用场景

01Automating the identification of new functional capabilities within raw interaction logs

02Analyzing community behavior patterns across temporal and topical axes

03Developing AI surrogates that mimic specific user interaction styles

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