pre-agent-ontology

Define a multi-layer ontology for emergent agents using GF(3) invariants and gluing checks.

60|13|Updated Dec 22, 2025
One-click install
npx skills add https://github.com/plurigrid/asi --skill pre-agent-ontology
Or copy as Structured Prompt for Agent
Please help me install this Agent Skill.
Skill: pre-agent-ontology
Source: https://github.com/plurigrid/asi/tree/main/skills/pre-agent-ontology
Command: npx skills add https://github.com/plurigrid/asi --skill pre-agent-ontology

SYSTEM DOCUMENTATION & REQUIREMENTS

What problem does it solve?

Foundational, multi-layer ontology for agent-o-rama, enabling derivations, gluing, and observational coherence across agents.

Core Features & Use Cases

  • 5-layer hierarchy: from pre-ontological primitives to emergent agents
  • GF(3) invariants: trit conservation across triads
  • Gluing & cohomology: coherence checks across layers

Quick Start

Review the Layer 0 primitives and Layer 2 gluing rules to classify a new agent's derivation.

Frequently Asked Questions about pre-agent-ontology

High-intent search queries and answers about installing and using this skill.

FAQPage Schema
How do I define and construct agents using a multi-layer ontology?

A multi-layer ontology provides a formal framework for constructing emergent agents from foundational primitives through derivations, stalks, and sections across five hierarchical layers. This approach ensures deterministic, order-independent derivations with coherence verification at each layer.

What are GF(3) invariants and how do they verify agent coherence?

GF(3) invariants enforce trit conservation across triadic structures within the ontology, acting as deterministic checks that preserve coherence during derivations. These arithmetic constraints ensure that agent emergence maintains structural integrity across the hierarchy.

How do gluing and cohomology checks work in agent derivation?

Gluing verifies observational coherence when combining agent components across layers, while cohomology checks validate that derivations remain consistent and order-independent. Together they ensure seamless integration from Layer 0 primitives to emergent agents.

What's the difference between deterministic seed-based derivations and other agent construction methods?

Deterministic seed-based derivations eliminate ambiguity by producing identical agents from the same seed across repeated runs, ensuring reproducibility and enabling systematic agent-o-rama experiments without non-deterministic artifacts.

Can I use this ontology to classify existing agent designs?

Yes. By reviewing Layer 0 primitives and Layer 2 gluing rules, you can classify any existing agent's derivation path within the five-layer hierarchy and verify its coherence through GF(3) invariant and cohomology checks.

What prerequisites do I need before applying this ontology to agent design?

You need familiarity with formal ontology concepts, multi-layer hierarchies, and GF(3) arithmetic. Understanding derivation mechanics and cohomology verification enables effective application across agent-o-rama experimental frameworks.