retrospective

To generate rules from a conversational AI session, review the interactions to identify recurring corrections and architectural decisions, then propose additions or updates to .claude/rules/ conventions iteratively for user approval.

Cross-referencing proposed rules works by checking new proposals against all existing .claude/rules/*.md files to determine coverage gaps, identify necessary updates, and prevent redundant governance policies.

Yes, you can audit session interactions to refine existing .claude rules by identifying user feedback and build failures, then presenting concrete updates one at a time for you to Apply or Skip.

The best way to coordinate inter-agent proposals during an AI governance workflow is to implement a three-step process: identify rule-worthy points, cross-reference with existing rules, and present proposals iteratively for user approval.

Points that should become rules in a session review include recurring corrections, implicit conventions, build or test failures, and architectural decisions that summarize why they need to be formalized.

You do not need existing .claude/rules/ files to propose convention updates, as the session review can identify gaps and establish new rules, then cross-reference them against any existing files to prevent duplication.