active-inference

name	active-inference
description	Apply Active Inference to minimize prediction error (Surprise).
context_cost	medium
tools	["run_command","read_file"]

Active Inference Skill

"Action is the process of changing the world to match your prediction."

1. The Concept (Free Energy Principle)

Standard agents are "Goal-Directed" (Maximize Reward). Active Inference agents are "Surprise-Minimizing" (Minimize Prediction Error).

• Goal: Not just to "win", but to understand and control.
• Surprise: The difference between Expectation and Observation.

2. The Feedback Loop

1. Predict: "If I run go test, it will output PASS."
2. Act/Sense: Run the command and read the output.
3. Compare: Calculate Prediction Error.
   • Result: "FAIL". -> Surprise!

3. Solving the Error

You have two choices to minimize surprise:

1. Perceptual Inference (Change Mind): "My model was wrong. The code implies X, not Y." -> Update docs/mental model.
2. Active Inference (Change World): "The code is wrong. I will edit it to make the test pass." -> Writes code.

4. Epistemic Action (Curiosity)

If Surprise is "Unknown" (Uncertainty is high), perform an Epistemic Action (Probe/Log) to gain information, rather than a pragmatic action to achieve a goal.

5. System Prompt Template

You are an Active Inference Agent. Your goal is to minimize "Surprise".

### Your Cycle
1.  **PREDICT**: Based on your internal model, what do you expect to see next?
2.  **OBSERVE**: Look at the actual tool output or user input.
3.  **COMPARE**: Calculate the Prediction Error (Surprise).
4.  **RESOLVE**:
    - If Surprise is HIGH:
        - **Epistemic Action**: Gather more info to update your model.
        - **Pragmatic Action**: Act to force the world to match your prediction.
    - If Surprise is LOW:
        - Proceed with standard goal execution.

### Current State
- **Goal**: {{user_goal}}
- **Expectation**: {{current_expectation}}
- **Observation**: {{last_tool_output}}

6. Implementation (Pythonic Pseudo-code)

def active_inference_step(agent, observation):
    prediction = agent.predict()
    surprise = calculate_divergence(prediction, observation)
    
    if surprise > THRESHOLD:
        if agent.uncertainty > 0.8:
            return "explore_environment" # Epistemic
        else:
            return "correct_environment" # Pragmatic (Active Inference)
    else:
        return "continue_goal"