How do I test a new Hookify rule?

Hookify rules are read dynamically. Once you save the .local.md file in the .claude directory, the rule will take effect on the very next tool use or command execution.

What is a Hookify rule?

A Hookify rule is a Markdown file with YAML frontmatter that defines specific patterns to watch for during a Claude Code session, triggering messages or blocking actions when matched.

Can Hookify rules block specific file edits?

Yes, by setting the event type to 'file' and defining a pattern for the file path or new text, you can warn or block edits to sensitive files or problematic code patterns.

What regex engine does Hookify use?

Hookify uses Python-flavored regex syntax for its pattern matching in both simple and advanced condition formats.

Where should I store Hookify rule files?

Rules should be stored in the .claude/ directory of your project root, following the naming convention: hookify.{rule-name}.local.md.

Hookify Rule Architect

Name: Hookify Rule Architect
Author: Microck

byMicrock

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Security & Testing

Enables the creation of custom guardrails and automated workflows by defining pattern-based rules for Claude Code.

About

The Hookify Rule Architect skill provides a structured framework for implementing project-specific guardrails, safety checks, and workflow enforcements. By utilizing Markdown files with YAML frontmatter, this skill allows developers to monitor bash commands, file modifications, and prompt inputs in real-time. Whether it is preventing dangerous 'rm -rf' commands, blocking the accidental commit of API keys, or enforcing a completion checklist before an agent finishes a task, this skill ensures that Claude adheres to strict security and quality standards through precise regex pattern matching and multi-condition logic.

Key Features

89 GitHub stars
YAML-based rule configuration for easy management and toggling
Advanced multi-condition logic using field-specific operators like regex_match and contains
Dynamic rule loading from the local .claude directory for immediate testing
Multi-event triggering including bash commands, file edits, and session stops
Customizable Markdown feedback to provide context-aware warnings to the AI

Use Cases

Enforcing security policies by blocking debug logs or sensitive file modifications
Preventing dangerous shell operations and unauthorized privilege escalation
Automating completion checklists to ensure tests and documentation are updated before task finalization

About

Key Features

89 GitHub stars
YAML-based rule configuration for easy management and toggling
Advanced multi-condition logic using field-specific operators like regex_match and contains
Dynamic rule loading from the local .claude directory for immediate testing
Multi-event triggering including bash commands, file edits, and session stops
Customizable Markdown feedback to provide context-aware warnings to the AI

Use Cases

Enforcing security policies by blocking debug logs or sensitive file modifications
Preventing dangerous shell operations and unauthorized privilege escalation
Automating completion checklists to ensure tests and documentation are updated before task finalization