agent-md-refactor

Refactor a bloated CLAUDE.md file by applying progressive disclosure principles, which extract non-essential rules into linked, categorized files to reduce context waste and improve instruction clarity.

Progressive disclosure for agent configuration is a structuring technique that moves topic-specific rules out of a monolithic root instruction file into modular, linked documents, ensuring universal project rules remain concise.

Detect contradictory instructions in AGENS.md by running a refactoring process that identifies conflicting guidelines during its initial analysis phase before extracting and categorizing essential rules.

The best way to organize project-level AI agent instructions is through a five-phase process: contradiction detection, essential extraction, categorization, structural hierarchy creation, and redundant instruction pruning.

You need to split a monolithic agent instruction file when it suffers from context waste, maintenance difficulty, or contradictory guidelines, requiring categorization and structural hierarchy creation to manage AI context effectively.

Yes, refactoring agent instructions works without external dependencies, using a standalone five-phase process to prune redundant instructions and minimize the root configuration file for better AI context management.