ralphinho-rfc-pipeline

Orchestrate multi-agent DAG execution for RFC-driven feature decomposition and integration validation.

1|Updated Apr 6, 2026

Search Tags:#workflow #multi-agent #orchestration #pipeline #rfc #dag

Ready to run

npx skills add https://github.com/vrcms/everything-qwen-code --skill ralphinho-rfc-pipeline-vrcms

💬 Prompt for ChatGPT / Claude Chat▼

Please help me install this Agent Skill.
Skill: ralphinho-rfc-pipeline
Source: https://github.com/vrcms/everything-qwen-code/tree/main/.qwen/skills/ralphinho-rfc-pipeline
Command: npx skills add https://github.com/vrcms/everything-qwen-code --skill ralphinho-rfc-pipeline-vrcms

SYSTEM DOCUMENTATION & REQUIREMENTS

What problem does it solve?

This Skill addresses the challenge of managing large-scale feature development that exceeds the capacity of a single AI pass by breaking work into independently verifiable, manageable units.

Core Features & Use Cases

RFC Decomposition: Automatically breaks down high-level feature requests into a Directed Acyclic Graph (DAG) of work units.
Quality Gates: Enforces strict validation stages including research, implementation, and review for every unit.
Use Case: When building a complex new microservice, use this Skill to coordinate multiple agents to handle database schema design, API implementation, and security testing in a structured, dependency-aware sequence.

Quick Start

Invoke the ralphinho-rfc-pipeline skill to decompose the current feature request into a series of verifiable work units with defined dependencies.

Frequently Asked Questions about ralphinho-rfc-pipeline

High-intent search queries and answers about installing and using this skill.

FAQPage Schema

How do I orchestrate multi-agent workflows for large-scale feature development?▼

You can break down large feature requests by using RFC decomposition to automatically split them into a Directed Acyclic Graph (DAG) of work units. Each unit represents an independently verifiable task with defined dependencies for structured execution.

What is RFC decomposition in a multi-agent pipeline?▼

RFC decomposition automatically breaks down high-level feature requests into a Directed Acyclic Graph (DAG) of manageable work units. This allows complex software development tasks to be divided into independently verifiable units with defined dependencies.

How do I enforce quality gates across multiple agents during software development?▼

Quality gates across multiple agents are enforced through strict validation stages applied to every work unit, including research, implementation, and review phases. The pipeline also implements merge queue logic to ensure system integrity across complex multi-unit workflows.

Can I coordinate multiple agents to build a microservice with database, API, and security tasks?▼

Yes, you can coordinate multiple agents to build a microservice by decomposing the feature into a dependency-aware sequence. This allows separate agents to handle database schema design, API implementation, and security testing in a structured workflow with integration validation.

When do I need a DAG-based pipeline for software engineering tasks?▼

A DAG-based pipeline is needed when managing large-scale feature development that exceeds the capacity of a single AI pass. It breaks work into independently verifiable, manageable units with defined dependencies to maintain system integrity across complex multi-unit workflows.

What are the limitations of using a multi-agent orchestration pipeline for feature development?▼

The pipeline requires features to be decomposable into a DAG of verifiable units and depends on strict quality gates for validation. It is designed for complex, large-scale workflows rather than simple tasks that a single AI pass can complete.