rule-authoring

Cross-tool AI coding rules require unambiguous templates with testable criteria that translate consistently across tools like Cursor, Claude Code, and Codex. This Skill provides a principled approach to authoring rules that are clear, testable, and auditable across multiple environments.

Testable AI coding rules include defined "Did It Work?" criteria for each rule and a structured auditing workflow. This Skill enforces unambiguous rule templates with explicit testing criteria, allowing you to verify rule effectiveness and audit existing rules for ambiguity.

Auditing existing AI rules involves checking for unambiguous structure and defined testable criteria. This Skill applies a principled auditing workflow to evaluate and adapt coding rules, ensuring they are clear and compatible across toolchains like Copilot, Q, and Kiro.

Authoring cross-tool AI rules with this Skill requires no external dependencies or scripts. It satisfies frontmatter requirements for name and description natively, acknowledging optional references and assets while emphasizing safe, well-documented guidance for AI-assisted programming.

Adapting coding rules for new tools requires applying cross-tool compatibility guidance and principled templates. This Skill helps you translate and adjust existing rules to fit new AI toolchains, ensuring they remain unambiguous, testable, and properly documented across projects.

AI coding rules behave inconsistently when they are ambiguous or poorly structured. This Skill solves the problem by providing unambiguous rule templates, defined testable criteria, and cross-tool compatibility guidance to ensure consistent behavior across Cursor, Claude Code, and Codex.