Train learning agents on interaction sequences to discover behavioral patterns.
npx skills add https://github.com/plurigrid/asi --skill agent-o-rama
Please help me install this Agent Skill. Skill: agent-o-rama Source: https://github.com/plurigrid/asi/tree/main/skills/agent-o-rama Command: npx skills add https://github.com/plurigrid/asi --skill agent-o-rama
Agent-o-rama trains learning agents on interaction sequences to uncover behavioral patterns, producing models compatible with cognitive-surrogate systems. It supports both traditional temporal learning and a derivational, deterministic generation path for faster pattern discovery.
just agent-train interactions.duckdb --epochs 100
High-intent search queries and answers about installing and using this skill.
Train learning agents on interaction sequences using agent-o-rama to uncover temporal, topic, and network behavioral patterns. The Skill builds predictive models from raw interactions stored in DuckDB, enabling pattern extraction and cross-language integration for pattern discovery and cognitive-surrogate compatibility.
Yes. Agent-o-rama extracts both temporal patterns (time-based behavior) and topic patterns (topic dynamics and transitions) from interaction sequences. It also identifies latent skills within interactions and supports deterministic pattern derivation for faster exploration.
Agent-o-rama requires DuckDB, pandas, and numpy. It uses a DuckDB storage schema for learned_patterns and includes a Python predictor powered by JAX, with support for reproducible results and cross-language integrations in Ruby and Hy.
Use the command `just agent-train interactions.duckdb --epochs 100` to build models that predict next interactions from history. This trains your learning agent on interaction sequences to discover behavioral patterns end-to-end.
Yes. Agent-o-rama supports cross-language integrations including Ruby and Hy alongside Python, enabling pattern discovery and cognitive-surrogate compatibility across multiple interpreter environments.
Agent-o-rama supports both traditional temporal learning for comprehensive pattern discovery and derive-patterns-via-unworld, a deterministic generation path that enables faster pattern exploration without sacrificing accuracy.
secondskyTrain machine learning models with scikit-learn, PyTorch, and TensorFlow.
xmg2024Model Trump-style decision making using cognitive frameworks and communication DNA.
jkitchinSelect and apply Matplotlib, Seaborn, or Plotly for Python visualizations.
tassiovaleFit linear regression and time series models with Python's statsmodels library.
