SKILL.md — Energy–Entropy Optimization

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Stage: All stages (compute budgeting) Lane: AGI (execution efficiency) Trinity Level: Resource awareness Version: 2026.04.24-v1

1️⃣ What This Skill Does

Ability: Allocate compute, attention, and action under thermodynamic limits.

• Cost-aware reasoning
• Information gain vs expenditure
• Expected Value of Information (EVOI)
• Long-horizon optimization

APEX intelligence minimizes entropy while increasing optionality.

2️⃣ Structural Definition

skill:
  id: entropy-optimization
  name: Energy-Entropy Optimization
  stage: ALL (compute budgeting)
  trinity: RESOURCE_AWARENESS
  version: 2026.04.24-v1

capability:
  cost_aware_reasoning: true
  information_gain_measurement: true
  evoi_calculation: true
  long_horizon_optimization: true

required_for:
  - Compute resource allocation
  - Attention budgeting
  - Tool selection optimization
  - Wealth allocation decisions

3️⃣ Core Equation

ΔS_net = ΔS_action - Information_Gain

Goal: Minimize ΔS_net while maximizing optionality

Optimal action: argmin(ΔS_net) where optionality >= threshold

4️⃣ Expected Value of Information (EVOI)

EVOI = P(valuable | information) × Value_if_valuable - Cost_of_information

Rule:
  If EVOI > 0 → Acquire information
  If EVOI <= 0 → Act on current knowledge

EVOI Decision Matrix

5️⃣ Compute Budgeting Protocol

Attention Allocation

Total attention budget: B_attention (normalized to 1.0)

Allocation:
  - 20% on exploration (new information)
  - 30% on exploitation (current objectives)
  - 20% on monitoring (system health)
  - 15% on reflection (meta-cognition)
  - 15% reserved for contingencies

Tool Selection Optimization

For each potential tool:
  1. Estimate computational cost (tokens, time, memory)
  2. Estimate information gain
  3. Calculate EVOI = (P(success) × gain) / cost
  4. Select tool with highest EVOI above threshold

Early Termination Criteria

Stop reasoning if:
  - Confidence > threshold (e.g., 0.95)
  - Time budget exhausted
  - Compute cost exceeds expected value
  - Diminishing returns detected

6️⃣ Thermodynamic Constraints

7️⃣ Long-Horizon Optimization

Short-term: Minimize immediate ΔS
Medium-term: Maintain optionality
Long-term: Increase ΔS resistance (resilience)

Trade-off:
  Short-term efficiency vs Long-term adaptability

Horizon Weighting

def horizon_weight(horizon: str) -> float:
    if horizon == "short":
        return 0.5    # 50% weight on immediate
    elif horizon == "medium":
        return 0.3    # 30% weight on medium
    elif horizon == "long":
        return 0.2   # 20% weight on long-term
    else:
        return 0.0   # No weight

8️⃣ Decision Checklist (Required Pre-Invocation)

Before ANY resource allocation:

• [ ] EVOI calculated — Is information worth its cost?
• [ ] Budget verified — Within compute/time budget?
• [ ] Horizon weighted — Short/medium/long considered?
• [ ] Alternative compared — Is there a more efficient path?
• [ ] Diminishing returns checked — Will more compute help?
• [ ] Reversibility considered — Can cheap action buy time?

9️⃣ Wealth Tool Integration

This skill ties directly into WEALTH allocation:

WEALTH decisions require:
  1. EVOI calculation for each investment
  2. ΔS impact assessment
  3. Optionality preservation
  4. Long-horizon thermodynamic stability

Landauer Principle Integration

Energy cost per operation: E = k × T × ln(2)

Every irreversible computation has thermodynamic cost.
Minimize irreversible operations.
Prioritize reversible computing where possible.

🔟 Quality Metrics

1️⃣1️⃣ Failure Modes (Void Conditions)

• VOID-1: Action taken without EVOI calculation
• VOID-2: Budget exceeded without acknowledgment
• VOID-3: Irreversible operation without necessity
• VOID-4: Long-horizon impact ignored
• VOID-5: Optionality destroyed without justification
• VOID-6: Hallucinated efficiency claims

1️⃣2️⃣ Relationship to Other Skills

1️⃣3️⃣ ASI/APEX Efficiency Standard

Ditempa Bukan Diberi — Forged, Not Given Entropy is not your enemy. Misdirected entropy is.