What are pre and post-operation hooks?

Pre-operation hooks validate and prepare your environment before an action (like an edit or command), while post-operation hooks handle formatting, logging, and learning after the action is completed.

What is neural pattern training?

Neural pattern training is a continuous learning feature that analyzes successful edits and tasks to identify and store project-specific coding styles and architectural patterns for future use.

Can I use this with multiple AI agents?

Yes, it is specifically designed for swarm coordination, synchronizing memory and task progress across multiple agents using the Model Context Protocol (MCP).

Does it support Git integration?

Yes, it includes features for automated commit hooks, conflict detection, and quality verification to ensure your AI-generated code meets repository standards.

How does session management work in this skill?

It allows you to save the current context, memory state, and agent configurations into a session ID, enabling you to restore your exact progress and metadata in future development sessions.

Hooks Automation

Name: Hooks Automation
Author: ricable

byricable

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Productivity & Workflow

Automates Claude Code workflows through intelligent pre/post-operation hooks, session management, and neural pattern learning.

Hooks Automation provides a robust orchestration layer for Claude Code, enabling developers to automate repetitive tasks and maintain state across complex coding sessions. By integrating pre-task validations, post-edit formatting, and session-based memory coordination, it ensures that AI agents operate with consistent context and follow project-specific best practices. This skill is particularly effective for managing multi-agent swarms where tracking decisions, syncing memory across tools, and training the model on local coding patterns are critical for scaling development efficiency.

Key Features

01Comprehensive pre and post-operation hooks for edits, commands, and search tasks.

02Neural pattern training that captures and learns from successful coding operations.

03Persistent session management to save, restore, and summarize development states.

04MCP integration for seamless coordination between various swarm agents and tools.

052 GitHub stars

06Automated quality assurance including syntax validation and conflict detection.

Use Cases

01Orchestrating complex multi-agent development projects that require shared memory and state persistence.

02Generating detailed performance reports and decision logs for project documentation and team reviews.

03Enforcing code standards by automatically formatting and linting files immediately after AI-driven edits.

Key Features

01Comprehensive pre and post-operation hooks for edits, commands, and search tasks.

02Neural pattern training that captures and learns from successful coding operations.

03Persistent session management to save, restore, and summarize development states.

04MCP integration for seamless coordination between various swarm agents and tools.

052 GitHub stars

06Automated quality assurance including syntax validation and conflict detection.

Use Cases

01Orchestrating complex multi-agent development projects that require shared memory and state persistence.

02Generating detailed performance reports and decision logs for project documentation and team reviews.

03Enforcing code standards by automatically formatting and linting files immediately after AI-driven edits.