role-protocol-architecture

Standardize AI agent role policies in SKILL.md files with resolvers.

22|7|Updated Feb 23, 2026

Search Tags:#prompt engineering #AI architecture #agent development #protocol design #policy management #role policy

Ready to run

npx skills add https://github.com/berrzebb/SoulFlow-Orchestrator --skill role-protocol-architecture

💬 Prompt for ChatGPT / Claude Chat▼

Please help me install this Agent Skill.
Skill: role-protocol-architecture
Source: https://github.com/berrzebb/SoulFlow-Orchestrator/tree/main/src/skills/role-protocol-architecture
Command: npx skills add https://github.com/berrzebb/SoulFlow-Orchestrator --skill role-protocol-architecture

SYSTEM DOCUMENTATION & REQUIREMENTS

💡 This Skill includes references (resource) components.

What problem does it solve?

This Skill addresses the challenge of maintaining consistent and structured role policies across different parts of an AI system, ensuring that role definitions are a single source of truth and are interpreted uniformly by various components like workflow generators and agent UIs.

Core Features & Use Cases

Centralized Role Definition: Manages role policies exclusively within src/skills/roles/*/SKILL.md.
Policy Interpretation: Uses RolePolicyResolver, ProtocolResolver, and PromptProfileCompiler to structure and interpret role policies.
Separation of Concerns: Distinguishes between role baselines and user/persona overrides.
Use Case: When developing a new AI agent that requires specific role-based behaviors, this Skill ensures that the role's capabilities and constraints are clearly defined and consistently applied across the agent's runtime and any associated UI elements for configuration.

Quick Start

Use the role-protocol-architecture skill to standardize role metadata for the 'customer-support-agent' role.

Frequently Asked Questions about role-protocol-architecture

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

FAQPage Schema

How do I standardize role policies for AI agents to ensure consistent behavior across runtime and UI components?▼

Standardize role policies for AI agents by maintaining `src/skills/roles/*/SKILL.md` as a single source of truth, ensuring role definitions are interpreted uniformly by workflow generators and agent UIs.

How do I separate role baselines from persona overrides when developing AI agents?▼

Separate role baselines from persona and task overrides using `RolePolicyResolver` and `PromptProfileCompiler` to structure role policies, preventing user configurations from conflicting with core agent capabilities.

What is the best way to structure role policies for consistent application in agent workflow generation?▼

The best way to structure role policies is through protocol design using `RolePolicyResolver`, `ProtocolResolver`, and `PromptProfileCompiler` to define capabilities and constraints for consistent workflow generation.

Do I need a specific file structure to manage centralized role definitions for prompt engineering?▼

Yes, managing centralized role definitions requires the `src/skills/roles/*/SKILL.md` file structure to act as the source of truth for role policies across different parts of your AI system.

Why does my AI agent apply inconsistent role policies across different workflow components?▼

Inconsistent role policies occur when definitions are scattered; using a centralized `SKILL.md` source with a `RolePolicyResolver` interprets role baselines uniformly across all components.

Can I use this role policy architecture to manage configurations for a new customer support agent?▼

Yes, you can use this role-protocol-architecture to standardize role metadata for a customer-support-agent, ensuring its capabilities and constraints are clearly defined and consistently applied.