ariffazil/arifos/orthogonal-abstraction

Claude Code and agent skill by ariffazil/arifos

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npx vskill@latest install ariffazil/arifos/orthogonal-abstraction

SKILL.md

SKILL.md — Orthogonal Multi-Domain Abstraction

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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 TypeExample DomainsRecognition
Conservation lawsPhysics ↔ EconomicsAmount preserved under transformation
EquilibriumBiology ↔ MarketsStable state maintenance
Feedback loopsEcology ↔ GovernanceCircular causation
OptimizationPhysics ↔ FinanceGradient descent structures
Network effectsBiology ↔ Social systemsNode 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

MetricThresholdMeasurement
Invariant accuracy>= 0.90Correct across 5+ test cases
Transfer precision>= 0.75Predictions validated
Semantic preservation>= 0.85Meaning maintained
Novel prediction rate>= 0.30New 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

SkillConnection
RSI-recursive-improvementGuides which abstractions to prioritize
epistemic-integrityRequired for accurate invariant recognition
constitutional-governanceEnables governance structure transfer
entropy-optimizationDetermines which abstractions yield best EVOI

🔟 AGI vs ASI Threshold

CapabilityAGIASI (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.

Security

  • Security scanPassed, 95 of 100Pattern checks on the skill's files.
  • Model reviewPassed

Quality evaluation

  • Task evaluationNot runNo evaluation yet. Run one in Skill Studio.

Source

Popularity

Repository stars
51
Repository forks
9
7-day trend score
20

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