Context engine builder

A skill for generating a context engine — a structured, multi-layered knowledge base that gives AI agents the complete picture of a design system. The engine encodes seven dimensions of system knowledge (UX, UI, content, accessibility, ethical, technical, and business intelligence) as machine-readable blueprints that agents load, reason over, and apply without requiring implicit knowledge or human interpretation.

Context

A design system is more than a component library. It encodes decisions about user experience patterns, visual language, content voice, accessibility requirements, ethical guardrails, technical constraints, and business rules. These decisions live in different places — Figma files, code repos, wikis, Slack threads, the heads of senior team members — and most of them are invisible to AI agents.

When an AI agent interacts with a design system, it typically receives a narrow slice: component props, maybe a description, perhaps some token values. It does not receive the reasoning behind those components, the constraints that govern their use, or the relationships between design decisions and business outcomes. The result is output that is technically valid but contextually wrong — a login form that uses the right components but ignores the system's established authentication patterns, or a dashboard that follows the grid but violates the system's data visualisation principles.

A context engine front-loads this knowledge. Instead of letting agents discover context through trial and error (or not discover it at all), the engine encodes it as structured data that agents load at the start of a task. The seven blueprints are not arbitrary categories — they represent the seven dimensions of knowledge that, when missing, produce the most common classes of AI-generated design system errors.

The practical output is a set of structured files — one per blueprint — that live alongside the codebase and are consumed by AI agents, MCP servers, and developer tooling. Together they form the machine-readable brain of the design system.

Boundaries

This skill builds context infrastructure — it does not assess system health or score quality (use system-health for that). If the system has no documented components, tokens, or patterns yet, the context engine has nothing to encode; help the team establish foundations first. If only one or two blueprints are needed (a common case — many teams start with the Technical and UI blueprints only), generate those specifically rather than forcing all seven. The engine is modular; partial generation is a feature, not a gap.