How does the development loop work?

The skill utilizes the Ralph Wiggum development loop to iteratively write code, implement features, and create tests based on your initial prompt until the 'agent implemented' promise is met.

Do I need a Railway account to use this skill?

Yes, if you wish to use the optional deployment stage, you will need a Railway account and the Railway CLI configured on your system.

Can I use this for non-commercial agents?

Absolutely. While the pipeline is optimized for the 'paid-agent' structure and agentic commerce, it is a robust framework for building and deploying any Lucid agent.

Does the pipeline handle code quality?

Yes, the pipeline includes a dedicated Code Review stage that analyzes the implementation for security vulnerabilities and logic errors before any deployment occurs.

What is a Lucid Agent?

Lucid Agents are specialized, autonomous AI agents designed for specific tasks, often utilized within the Daydreams AI ecosystem for agentic commerce and automated workflows.

Paid Agent Pipeline

Name: Paid Agent Pipeline
Author: daydreamsai

bydaydreamsai

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E-commerce Solutions

Automates the end-to-end creation, testing, and deployment of production-ready monetized agents.

About

The Paid Agent Pipeline skill orchestrates a sophisticated multi-stage workflow to build and launch Lucid Agents from scratch. It leverages iterative development loops for feature implementation, performs automated code reviews for quality assurance, executes a dedicated test-refinement phase, and handles final delivery by publishing to GitHub and deploying to Railway. This skill is ideal for developers looking to move from a concept to a live, monetized agent with minimal manual intervention while ensuring high code quality and robust testing coverage.

Key Features

Dedicated test refinement loop to ensure 100% passing test suites
One-click production hosting deployment via Railway integration
Seamless GitHub repository creation and code publishing via gh CLI
Automated code review for security, logic, and best practices
7 GitHub stars
Iterative agent building using the Ralph Wiggum development loop

Use Cases

Building a monetized AI agent for automated GitHub PR analysis
Creating and deploying a paid Lucid agent for specialized commerce tasks
Rapidly prototyping and launching production-ready agentic services

About

Key Features

Dedicated test refinement loop to ensure 100% passing test suites
One-click production hosting deployment via Railway integration
Seamless GitHub repository creation and code publishing via gh CLI
Automated code review for security, logic, and best practices
7 GitHub stars
Iterative agent building using the Ralph Wiggum development loop

Use Cases

Building a monetized AI agent for automated GitHub PR analysis
Creating and deploying a paid Lucid agent for specialized commerce tasks
Rapidly prototyping and launching production-ready agentic services