hooks-automation_skill

This skill automates coordination and learning across Claude Code tasks using intelligent hooks, memory syncing, and MCP integration to accelerate development.
  • Python

1.1k

GitHub Stars

4

Bundled Files

2 months ago

Catalog Refreshed

4 months ago

First Indexed

Readme & install

Copy the install command, review bundled files from the catalogue, and read any extended description pulled from the listing source.

Installation

Preview and clipboard use veilstrat where the catalogue uses aiagentskills.

npx veilstrat add skill openclaw/skills --skill hooks-automation

  • _meta.json285 B
  • configuration.md8.4 KB
  • examples.md7.6 KB
  • SKILL.md4.6 KB

Overview

This skill automates coordination, validation, and continuous learning for development workflows using intelligent hooks and MCP integration. It provides pre/post operation hooks, session lifecycle management, memory coordination, Git integration, and neural pattern training to improve agent-driven tasks. The system focuses on consistent formatting, performance tracking, and adaptive agent orchestration across sessions.

How this skill works

Hooks intercept operations (edits, shell commands, tasks, searches) to validate inputs, assign or spawn agents, and run safety checks before execution. After operations, post-hooks format code, update shared memory, log metrics, and optionally train neural patterns to reinforce successful behaviors. Session hooks persist and restore context, while MCP hooks sync swarm state and agent rosters for coordinated multi-agent workflows.

When to use it

  • Automating pre-commit validations and formatting for multi-language codebases
  • Coordinating multiple AI agents for complex tasks or CI workflows
  • Capturing and synchronizing memory/state across development sessions
  • Improving agent behavior by training neural patterns from successful runs
  • Integrating task metrics and performance analysis into development pipelines

Best practices

  • Initialize hooks at project start and keep configurations under version control
  • Use clear, consistent memory-key namespaces to avoid collisions
  • Enable auto-formatting and language-specific linters for consistent output
  • Continuously train patterns but monitor for drift and validate changes
  • Set sensible timeouts and handle errors with continueOnError to avoid blocking pipelines

Example use cases

  • Pre-task hook auto-spawns specialized agents for implementing an authentication flow and assigns ownership by file type
  • Post-edit hook auto-formats a modified file, stores a memory entry keyed to the feature, and triggers a pattern-training job
  • Session-end hook exports metrics, saves session state, and persists agent rosters to speed up future restores
  • MCP-initialized hook persists swarm configuration and syncs agent capabilities across servers for distributed execution
  • Pre-bash hook vets a destructive command for safety and creates a backup before running it

FAQ

MCP integration is optional. Hooks work locally for validation, formatting, and basic memory, while MCP servers enable distributed agent orchestration and swarm persistence.

How does neural pattern training affect behavior?

Training collects successful operation patterns and updates models to favor those behaviors; monitor training outputs and validate changes to prevent unintended drift.

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