What are pre-operation hooks in Claude Code?

Pre-operation hooks are automated validation steps that run before Claude modifies files or executes bash commands, ensuring syntax validity, safety, and proper agent assignment.

What is neural pattern training for hooks?

Neural pattern training allows the system to learn from successful coding patterns and search queries, optimizing future operations based on historical development data.

How does this skill handle session persistence?

The skill provides session-start, restore, and end hooks that save current context, export performance metrics, and allow you to resume work exactly where you left off.

Can I use this for multi-agent coordination?

Yes, it includes specialized MCP integration hooks for spawning agents, synchronizing memory namespaces, and orchestrating tasks across a swarm.

Does it support automatic code formatting?

Yes, the post-edit hook can be configured to automatically run formatters like Prettier, Black, or gofmt immediately after Claude finishes a file modification.

Claude Code Hooks Automation

Name: Claude Code Hooks Automation
Author: Microck

byMicrock

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Productivity & Workflow

Automates and coordinates Claude Code workflows through intelligent pre/post-operation hooks and session management.

About

Hooks Automation provides a robust framework for orchestrating complex Claude Code operations using a comprehensive suite of intelligent triggers. By integrating with Model Context Protocol (MCP) tools and implementing neural pattern training, it enables developers to automate agent assignment, enforce code quality through post-edit formatting, and maintain persistent cross-session memory. This skill is essential for developers looking to streamline swarm agent coordination, ensure command safety, and capture performance metrics for continuous workflow optimization.

Key Features

Advanced MCP coordination for synchronizing knowledge across multiple AI agents
Comprehensive session management with context restoration and memory persistence
Intelligent pre-operation validation and auto-assignment of swarm agents
Automated post-operation formatting, linting, and neural pattern training
Built-in command safety checks and resource requirement estimation
89 GitHub stars

Use Cases

Orchestrating multi-agent development tasks with automated state synchronization
Optimizing long-running development sessions through persistent memory and context recovery
Enforcing code standards and safety protocols during AI-assisted refactoring

About

Key Features

Advanced MCP coordination for synchronizing knowledge across multiple AI agents
Comprehensive session management with context restoration and memory persistence
Intelligent pre-operation validation and auto-assignment of swarm agents
Automated post-operation formatting, linting, and neural pattern training
Built-in command safety checks and resource requirement estimation
89 GitHub stars

Use Cases

Orchestrating multi-agent development tasks with automated state synchronization
Optimizing long-running development sessions through persistent memory and context recovery
Enforcing code standards and safety protocols during AI-assisted refactoring