What kind of neural training does this skill perform?

The skill learns from successful coding patterns and implementation decisions, storing them to improve the relevance and accuracy of future AI-generated code.

What is a hook in the context of Claude Code?

A hook is an automated command that executes before or after specific operations (like file edits or terminal commands) to validate, format, or record data without manual intervention.

How does memory coordination help swarm agents?

It synchronizes knowledge and context across different AI agents, ensuring that all agents in a swarm have access to the same shared state and decision history.

Do I need the Claude Flow CLI to use this skill?

Yes, this skill relies on the Claude Flow CLI to initialize and manage the hook configurations within your local development environment.

Hooks Automation

Name: Hooks Automation
Author: Activer007

byActiver007

Productivity & Workflow

Automates and orchestrates Claude Code development workflows through intelligent pre- and post-operation hooks and MCP integration.

About

Hooks Automation provides a robust framework for managing complex AI development cycles by intercepting and enhancing Claude Code operations. It automatically assigns agents, validates code syntax, maintains persistent session memory, and applies neural pattern training to improve AI performance over time. By coordinating swarm agents and integrating seamlessly with Git and MCP tools, it transforms individual tasks into a cohesive, learning-capable development ecosystem that ensures code quality and context continuity.

Key Features

Intelligent Pre-Operation Validation: Automates agent assignment and syntax checks before code modifications.
Automated Git Integration: Streamlines version control with quality-verified commit hooks and safety checks.
Post-Operation Pattern Learning: Automatically formats code and trains neural patterns from successful edits.
Cross-Session Memory Persistence: Synchronizes and restores context across multiple development sessions.
Swarm Agent Coordination: Orchestrates multiple AI agents through unified MCP integration and state management.
0 GitHub stars

Use Cases

Orchestrating complex multi-agent tasks that require synchronized state and cross-session memory.
Automating quality assurance by running pre-edit syntax checks and post-edit formatting automatically.
Maintaining architectural consistency across large-scale refactors using shared neural patterns.

About

Key Features

Intelligent Pre-Operation Validation: Automates agent assignment and syntax checks before code modifications.
Automated Git Integration: Streamlines version control with quality-verified commit hooks and safety checks.
Post-Operation Pattern Learning: Automatically formats code and trains neural patterns from successful edits.
Cross-Session Memory Persistence: Synchronizes and restores context across multiple development sessions.
Swarm Agent Coordination: Orchestrates multiple AI agents through unified MCP integration and state management.
0 GitHub stars

Use Cases

Orchestrating complex multi-agent tasks that require synchronized state and cross-session memory.
Automating quality assurance by running pre-edit syntax checks and post-edit formatting automatically.
Maintaining architectural consistency across large-scale refactors using shared neural patterns.