context-layer-architect

Build context layers by auditing existing files, identifying semantic boundaries, and structuring CLAUDE.md or AGENTS.md hierarchies. This process captures tribal knowledge and applies progressive disclosure so AI agents load only necessary information, optimizing token usage and operational efficiency.

Progressive disclosure structures context nodes hierarchically so AI agents load only the information needed for a specific task. This approach improves the signal-to-noise ratio, reduces token consumption, and ensures agents navigate complex codebases efficiently without overwhelming their context windows.

Capture tribal knowledge by conducting deep user interviews and synthesizing findings into structured context files. This extracts hidden architectural decisions, historical constraints, and operational knowledge, transforming undocumented practices into accessible nodes within the codebase hierarchy.

Yes, you can audit existing context files to analyze their quality and coverage. The process identifies semantic gaps in your current architecture, evaluating whether the context layer accurately reflects the codebase boundaries and provides sufficient guidance for AI agents.

Structure context nodes by identifying semantic boundaries within the codebase and generating a node hierarchy. This organization supports parallel exploration and creates a structured context layer that significantly improves agent performance, reduces errors, and optimizes signal-to-noise ratio.