ralphinho-rfc-pipeline

Decompose large features into verifiable work units for multi-agent execution.

3|2|Updated Mar 8, 2026
One-click install
npx skills add https://github.com/agentmatters/mullai-bot --skill ralphinho-rfc-pipeline-agentmatters
Or copy as Structured Prompt for Agent
Please help me install this Agent Skill.
Skill: ralphinho-rfc-pipeline
Source: https://github.com/agentmatters/mullai-bot/tree/main/src/Mullai.Skills/Skills/claude-code-everything/ralphinho-rfc-pipeline
Command: npx skills add https://github.com/agentmatters/mullai-bot --skill ralphinho-rfc-pipeline-agentmatters

SYSTEM DOCUMENTATION & REQUIREMENTS

What problem does it solve?

Decomposes large features into independently verifiable work units to enable coordinated multi-agent execution with a structured quality gate pipeline.

Core Features & Use Cases

  • RFC intake, DAG decomposition, unit assignment, unit implementation
  • Unit validation, merge queue, and final system verification
  • Scalable orchestration across multiple agents for complex features

Quick Start

Decompose a large feature into verifiable work units and run them through the RFC quality-pipeline for integrated delivery.

Frequently Asked Questions about ralphinho-rfc-pipeline

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

FAQPage Schema
How do I decompose large software features into independently verifiable work units for multi-agent execution?

Large features are decomposed into independently verifiable work units through a structured pipeline covering RFC intake, DAG decomposition, and unit assignment. This enables coordinated multi-agent execution with explicit dependencies and integration checkpoints for complex software initiatives.

What is RFC-driven DAG decomposition and when do I need it for multi-agent orchestration?

RFC-driven DAG decomposition is a process that breaks down complex software features into a directed acyclic graph of work units. You need it when orchestrating multiple agents on large initiatives that require explicit unit definitions, dependencies, and quality gates.

What's the best way to enforce quality gates across a pipeline of multi-agent software implementation?

The best way to enforce quality gates is through a structured pipeline that applies unit validation, merge queues, and final system verification. This ensures each independently verifiable work unit meets integration checkpoints before progressing through the multi-agent execution workflow.

Can I scale multi-agent orchestration across complex features without explicit dependency management?

Scaling multi-agent orchestration across complex features requires explicit dependency management and integration checkpoints. Without structured DAG decomposition and unit assignment, coordinated execution lacks the verifiable work units needed to maintain quality and track progress through merge queues.

How do I assign implementation units to multiple agents after DAG decomposition?

Implementation units are assigned to multiple agents following DAG decomposition and RFC intake. Each agent receives independently verifiable work units with explicit definitions, enabling parallel execution while maintaining structured dependencies and integration checkpoints throughout the pipeline.

Why does multi-agent feature development need final system verification after unit validation?

Multi-agent feature development needs final system verification because individually validated units must still pass integration checkpoints. Unit validation confirms isolated correctness, but final verification ensures the merged work units function together as a complete, quality-gated software initiative.