hookify-rules

To set up custom guardrails, define a YAML-based rule in your .claude directory that uses regex pattern matching to intercept bash commands and trigger a block action. This prevents unauthorized actions during your AI workflow.

Yes, you can prevent sensitive data exposure by creating rules that monitor file modifications for patterns like API keys. When the regex match triggers, the skill automatically warns you or blocks the file operation.

Regex pattern matching intercepts user prompts, bash commands, and file operations by evaluating their text against your defined patterns. If a match occurs, the configured conditional logic executes an alert or block action.

No external dependencies are required to create workflow enforcement rules. You simply need to write structured YAML rule definitions within the .claude directory to specify event triggers, conditions, and actions.

The best way to enforce security practices is defining custom rules that watch for specific file modifications and trigger warnings. You can automatically alert users when sensitive configuration files are changed.

Regex-based rules are limited to matching defined text patterns within bash commands, file operations, and user prompts. They cannot understand semantic intent, meaning complex or obfuscated unauthorized actions might bypass the pattern logic.