rb-project-language

Standardizes domain vocabulary and updates CONTEXT.md for projects.

Updated Jul 2, 2026
One-click install
npx skills add https://github.com/richardmbailey/rb-skills --skill rb-project-language
Or copy as Structured Prompt for Agent
Please help me install this Agent Skill.
Skill: rb-project-language
Source: https://github.com/richardmbailey/rb-skills/tree/main/rb-project-language
Command: npx skills add https://github.com/richardmbailey/rb-skills --skill rb-project-language

SYSTEM DOCUMENTATION & REQUIREMENTS

What problem does it solve?

This skill resolves ambiguity and fragmentation in project documentation by establishing a shared, durable vocabulary and domain-specific context for AI agents and human contributors.

Core Features & Use Cases

  • Vocabulary Standardization: Identifies and defines domain terms, acronyms, and units to ensure consistent communication.
  • Context Management: Updates or creates a central CONTEXT.md file to serve as the single source of truth for project invariants and assumptions.
  • Multi-Agent Alignment: Captures roles, tool boundaries, and handoffs for complex systems involving multiple LLM agents.

Quick Start

Invoke the rb-project-language skill to analyze the current codebase and update the CONTEXT.md file with all identified domain terms and project invariants.

Frequently Asked Questions about rb-project-language

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

FAQPage Schema
How do I standardize project vocabulary and domain context for documentation?

Standardizing project vocabulary involves systematically extracting domain terms, units, and invariants, then updating a central CONTEXT.md file to serve as the single source of truth for alignment.

What is a CONTEXT.md file and when do I need one for domain modeling?

A CONTEXT.md file is a central source of truth that defines domain terms, invariants, and architectural assumptions. You need it when ambiguity threatens alignment in scientific, modeling, or complex software projects.

How can I align multiple AI agents on project terminology and tool boundaries?

Aligning multiple AI agents requires capturing roles, tool boundaries, and handoffs within a shared project vocabulary file. This ensures every agent operates using the same domain definitions and contextual assumptions.

Does this approach work for scientific and modeling projects requiring rigorous variable definitions?

Yes, this approach suits scientific and modeling projects by providing rigorous definitions of variables, units, and domain concepts. It systematically extracts these terms to maintain consistency across all documentation.

What is the best way to resolve ambiguity and fragmentation in project documentation?

The best way to resolve documentation ambiguity is establishing a durable, shared vocabulary file. By defining domain-specific context and acronyms centrally, you eliminate fragmented terminology across human and AI contributors.

When should I not use a centralized vocabulary file for context management?

You should avoid centralized vocabulary files for simple, single-author projects where informal context suffices. It is designed for complex systems requiring rigorous variable definitions, multi-agent alignment, and strict architectural assumptions.