ralph-orchestrator

🥈Silver

Ralph Orchestrator automates AI agent workflows using the Ralph Wiggum technique. It enables operations teams to manage complex agent interactions, improving efficiency in development workflows. Connects to Claude agents and integrates with development tools like kiro-cli and codex-cli.

2,5031620Updated Today

Intermediate30min to implementautomation

Saves ~180 min per use

Quick InstallView Source

git clone https://github.com/mikeyobrien/ralph-orchestrator.git

Works with:

Claude

Overview

About This Skill

https://mikeyobrien.github.io/ralph-orchestrator/

How to Use

["Define the workflow: Identify the agents involved, their roles, and the Ralph Wiggum technique rules for handling edge cases. Use a tool like `kiro-cli` to draft the workflow configuration.","Set up integration points: Configure connections to development tools (e.g., `codex-cli` for API interactions) and logging systems (e.g., CloudWatch or ELK stack). Ensure agents have the necessary permissions.","Run the orchestrator: Execute the workflow using `ralph-orchestrator run --workflow [WORKFLOW_NAME] --project [PROJECT_NAME]`. Monitor progress in real-time via the logging system.","Review and iterate: Analyze the orchestrator's logs and output. Adjust agent roles or Ralph Wiggum rules based on edge cases encountered. Re-run the workflow to validate improvements.","Document outcomes: Save the orchestrator's summary report and manual intervention logs. Use these to refine future workflows and share learnings with the team."]

Use Cases

Automate the planning and implementation of user authentication features in web applications.

Streamline the development of input validation for API endpoints, ensuring robust data handling.

Facilitate continuous integration and deployment processes by managing orchestration loops for testing and deployment tasks.

Enhance team collaboration by using the web dashboard to monitor and manage multiple AI agent workflows.

Setup & Installation

Quick Install

No install command available. Check the GitHub repository for manual installation instructions.

Alternative Install (Git Clone)

git clone https://github.com/mikeyobrien/ralph-orchestrator

Requirements

Claude Code or compatible AI agent
Works with: Claude

Quick Start Guide

Install the Skill

Copy the install command above and run it in your terminal.

Open Your AI Agent

Launch Claude Code, Cursor, or your preferred AI coding agent.

Try It Out

Use the prompt template or examples below to test the skill.

Customize

Adapt the skill to your specific use case and workflow.

Usage Examples

Prompt Template

Use Ralph Orchestrator to automate the [WORKFLOW_NAME] workflow for [PROJECT_NAME]. Follow the Ralph Wiggum technique to handle agent interactions, ensuring [SPECIFIC_GOAL] is achieved. Coordinate with [AGENT_ROLES] and validate outputs using [VALIDATION_CRITERIA]. Integrate with [DEVELOPMENT_TOOLS] and log results to [LOGGING_SYSTEM].

Example Output

For the project 'Acme CRM Migration', the Ralph Orchestrator automated the agent workflow to migrate legacy customer data to the new system. The workflow involved three agents: the Data Extractor (responsible for pulling records from the old database), the Schema Validator (ensuring data integrity against the new schema), and the API Integrator (pushing validated data to the new system). The orchestrator used the Ralph Wiggum technique to handle edge cases, such as malformed records or API timeouts, by logging them for manual review while allowing the workflow to proceed with clean data.

The Data Extractor processed 12,487 records from the legacy system, identifying 182 records with missing email fields. These were flagged for review and excluded from the automated migration. The Schema Validator confirmed that 99.8% of the remaining records matched the new schema, with only 24 records requiring minor adjustments (e.g., date format conversions). The API Integrator successfully pushed 12,281 records to the new system, achieving a 98.4% success rate. The orchestrator logged all actions to CloudWatch, including timestamps, agent interactions, and validation results. Manual review of the 182 flagged records was completed within 2 hours, with 178 records corrected and reintegrated. The entire workflow ran in 47 minutes, reducing manual effort by 70% compared to the previous process.

Key insights from the orchestrator's logs:
- The most common data issue was missing email fields (1.46% of records).
- API rate limits were hit twice, but the orchestrator automatically retried with exponential backoff, avoiding workflow failure.
- The Schema Validator agent detected 3 schema mismatches that were not caught in pre-migration testing, preventing potential downstream issues.

The orchestrator's output included a summary report with success metrics, a list of records requiring manual intervention, and recommendations for improving data quality in future migrations.