rules-normalize

To convert project conventions into machine-readable rules, use a normalization process that transforms scattered standards like linter settings into formal rule files. This ensures consistency by requiring explicit condition, scope, and enforcement level definitions for each generated rule.

Convention normalization for AI agent governance is the process of transforming implicit project standards into explicit, machine-readable rule files. It ensures AI agents follow established project conventions by formally documenting settings and style guides with defined enforcement levels.

To bootstrap governance rules from existing CI configurations, analyze your current configurations and code style guides, then generate formal `.rules.md` files. This process normalizes scattered standards into governed rule files for AI agents to follow.

You can ensure rule integrity when modifying existing project conventions by requiring explicit condition, scope, and enforcement level definitions. This normalization process guarantees that new or modified rule files adhere to a standard format and do not conflict with existing rules.

Yes, convention normalization requires explicit enforcement level definitions for each rule, along with defined conditions and scopes. This strict requirement ensures rule integrity and prevents conflicts when creating or modifying governed rule files.

You need to normalize scattered project standards into rule files during project bootstrapping, when creating new rules, or when modifying existing rules. This process converts implicit conventions into governed rule files, ensuring consistency and integrity across the project.