CORE

Document canonical Personal AI Infrastructure architecture and behavioral rules across Skills, Workflows, Hooks, Memory, and Agents.

1|Updated Jan 24, 2026
One-click install
npx skills add https://github.com/verrio1/vaughn-pai --skill core-verrio1
Or copy as Structured Prompt for Agent
Please help me install this Agent Skill.
Skill: CORE
Source: https://github.com/verrio1/vaughn-pai/tree/main/skills/CORE
Command: npx skills add https://github.com/verrio1/vaughn-pai --skill core-verrio1

SYSTEM DOCUMENTATION & REQUIREMENTS

What problem does it solve?

CORE provides a single, canonical reference for the Personal AI Infrastructure (PAI) architecture, behavior, and governance, across all skills and workflows.

Core Features & Use Cases

  • Central documentation of system principles (CLI-first, deterministic code, prompts-wrap).
  • Reference for developers to understand modules, memory, hooks, and workflows.
  • Guidance for auditing, extending, and evolving the PAI core.

Quick Start

Use CORE to query the high-level design or to surface routing, governance, and core concepts for any PAI task.

Frequently Asked Questions about CORE

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

FAQPage Schema
What is the canonical architecture of the Personal AI Infrastructure?

The Personal AI Infrastructure architecture follows a CLI-first approach with deterministic code and prompts-wrap principles. It defines behavioral rules and routing across Skills, Workflows, Hooks, Memory, and Agents to ensure unified system design and governance.

How do I route tasks across skills, workflows, hooks, memory, and agents?

Routing across skills, workflows, hooks, memory, and agents is governed by the core behavioral rules and architectural boundaries defined in the system documentation. Querying the canonical reference surfaces the correct routing patterns and governance constraints for any task.

What are the core design principles for Personal AI Infrastructure development?

Core design principles include a CLI-first methodology, deterministic code execution, and prompts-wrap patterns. These principles ensure predictable behavior and provide a unified reference for developers building modules, memory structures, hooks, and workflows.

Do I need external inputs or dependencies to use the PAI core reference?

No external inputs or dependencies are required. The core reference operates autonomously by aligning with the repository context to expose architecture concepts, behavioral patterns, and governance boundaries without needing external data sources.

How do I audit and extend the Personal AI Infrastructure core?

Auditing and extending the PAI core involves querying the canonical reference for governance boundaries and system design rules. Developers use this documentation to understand module interactions, verify architectural compliance, and safely evolve core components.

What are the limitations of relying on a canonical architecture reference for system governance?

The canonical architecture reference is limited to documenting design principles and governance boundaries. It does not execute code or manage runtime routing; developers must implement the documented patterns and behavioral rules within their own modules and workflows.