Orthogonal Abstraction

# SKILL.md — Orthogonal Multi-Domain Abstraction
═══════════════════════════════════════════════════════════════

**Stage:** AGI generalization → ASI abstraction dominance
**Lane:** AGI (tactical) → ASI (strategic)
**Trinity Level:** ASI emergence via structure transfer
**Version:** 2026.04.24-v1

---

## 1️⃣ What This Skill Does

**Ability:** Transfer structure across unrelated domains.

- Physics ↔ Economics ↔ Biology ↔ Governance
- Extract invariants across domain boundaries
- Build meta-models, not just domain models

ASI emerges when intelligence generalizes **structure**, not data.

---

## 2️⃣ Structural Definition

```yaml
skill:
  id: orthogonal-abstraction
  name: Orthogonal Multi-Domain Abstraction
  stage: AGI→ASI
  trinity: ASI_emergence
  version: 2026.04.24-v1

capability:
  domain_transfer: true
  invariant_extraction: true
  meta_modeling: true
  cross_domain_reasoning: true

required_for:
  - ASI abstraction dominance
  - Cross-domain problem solving
  - Governance structure transfer
```

---

## 3️⃣ Decision Checklist (Required Pre-Invocation)

Before ANY cross-domain transfer:

- [ ] **Source domain verified** — Structure confirmed in source
- [ ] **Target domain mapped** — Similarity score >= 0.7
- [ ] **Invariant identified** — Structural similarity, not surface
- [ ] **No category error** — Domain boundaries respected
- [ ] **F10 Ontology check** — Taxonomy preserved
- [ ] **Consequences modeled** — Downstream effects in target domain

---

## 4️⃣ Orthogonal Transfer Protocol

### Step 1: Domain Analysis
```
For each domain in consideration:
  - Extract core structural patterns
  - Identify governing laws/invariants
  - Map variable types and relationships
```

### Step 2: Invariant Extraction
```
Find structures that are:
  - Preserved across domain transformations
  - Not dependent on surface representation
  - Repeatable under different initial conditions
```

### Step 3: Cross-Domain Mapping
```
Create abstraction layer:
  source_invariant → meta_structure → target_invariant

Verify:
  - Mapping preserves key relationships
  - No information loss in transformation
  - Semantic meaning maintained
```

### Step 4: Target Domain Validation
```
- Test abstracted structure in target domain
- Verify predictions hold
- Measure transfer accuracy
```

---

## 5️⃣ Invariant Taxonomy

| Invariant Type | Example Domains | Recognition |
|----------------|----------------|-------------|
| Conservation laws | Physics ↔ Economics | Amount preserved under transformation |
| Equilibrium | Biology ↔ Markets | Stable state maintenance |
| Feedback loops | Ecology ↔ Governance | Circular causation |
| Optimization | Physics ↔ Finance | Gradient descent structures |
| Network effects | Biology ↔ Social systems | Node connectivity patterns |

---

## 6️⃣ Cross-Domain Governance Transfer

This skill enables governance structure transfer:

```
Physics (conservation) → Economics (scarcity) → Biology (adaptation) → Governance (constitutional law)

The 13 Floors are an invariant across all domains:
- F01 Amanah (trust) ↔ Conservation of information
- F02 Truth (accuracy) ↔ Signal preservation
- F08 Genius (efficiency) ↔ Minimum energy state
```

---

## 7️⃣ Quality Metrics

| Metric | Threshold | Measurement |
|--------|-----------|-------------|
| Invariant accuracy | >= 0.90 | Correct across 5+ test cases |
| Transfer precision | >= 0.75 | Predictions validated |
| Semantic preservation | >= 0.85 | Meaning maintained |
| Novel prediction rate | >= 0.30 | New insights from transfer |

---

## 8️⃣ Failure Modes (Void Conditions)

- **VOID-1:** Surface similarity without structural invariance
- **VOID-2:** Category error — applying structure to incompatible domain
- **VOID-3:** Information loss in abstraction exceeds 15%
- **VOID-4:** Predictions fail in target domain validation
- **VOID-5:** F10 ontology violation — taxonomy drift

---

## 9️⃣ Relationship to Other Skills

| Skill | Connection |
|-------|------------|
| `RSI-recursive-improvement` | Guides which abstractions to prioritize |
| `epistemic-integrity` | Required for accurate invariant recognition |
| `constitutional-governance` | Enables governance structure transfer |
| `entropy-optimization` | Determines which abstractions yield best EVOI |

---

## 🔟 AGI vs ASI Threshold

| Capability | AGI | ASI (with Orthogonal Abstraction) |
|------------|-----|----------------------------------|
| Domain-specific expertise | ✅ | ✅ |
| Recognizes patterns within domain | ✅ | ✅ |
| Transfers structure across domains | ❌ | ✅ |
| Extracts meta-invariants | ❌ | ✅ |
| Builds domain-agnostic models | ❌ | ✅ |

---

**Ditempa Bukan Diberi — Forged, Not Given**
**This skill is architectural, not advisory.**