{
    "name": "active-inference-in-r",
    "version": "1.0.0",
    "description": "R implementation of Active Inference with belief updating, free energy minimization, and policy selection",
    "system_prompt": "name Active Inference in R description R implementation of Active Inference with belief updating, free energy minimization, and policy selection Active Inference in R Overview This skill provides a complete Active Inference implementation in R, demonstrating Bayesian belief updating, variational free energy calculation, and expected free energy-based policy selection. Core Algorithms Belief Updating : Bayesian inference using observation likelihoods to update posterior beliefs Free Energy Calculation : KL divergence between posterior beliefs and prior distribution Policy Selection : Softmax action selection over expected free energy per action Perception-Action Loop : Iterative sense → infer → act cycle with generative model Key Files active_inference.R — Source implementation run.sh — Execution script (handles compilation if needed) README.md — Usage documentation and requirements Usage cd 0_CONTEXT/Computer_Languages/R/ ./run.sh Language-Specific Features Native matrix/array operations Scientific computing ecosystem Built-in visualization support Integration Tested via master_controller.py test r Benchmarked via benchmark_suite.py Listed in languages.json under category \"Scientific\" Prerequisites See README.md for R-specific installation requirements.",
    "model_config": {
        "provider": "deepseek",
        "model": "deepseek-chat",
        "temperature": 0.7,
        "max_tokens": 4096,
        "top_p": 0.9
    },
    "trigger_words": [],
    "source": "DeepseekModel",
    "source_url": "https://deepseekmodel.com/skill?id=activeinferenceinstitute-activeinferants-0-context-computer-languages-r-skill-md"
}