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ralphinho-rfc-pipeline

Decompose large features into verifiable RFC-driven work units.

Updated Mar 31, 2026
Search Tags:#workflow#multi-agent#pipeline#RFC#quality-gates#orchestrate#unit-architecture
One-click install
npx skills add https://github.com/GGEdu/claude-god-mode-template --skill ralphinho-rfc-pipeline-ggedu
Or copy as Structured Prompt for Agent▼
Please help me install this Agent Skill.
Skill: ralphinho-rfc-pipeline
Source: https://github.com/GGEdu/claude-god-mode-template/tree/main/skills/ralphinho-rfc-pipeline
Command: npx skills add https://github.com/GGEdu/claude-god-mode-template --skill ralphinho-rfc-pipeline-ggedu

SYSTEM DOCUMENTATION & REQUIREMENTS

What problem does it solve?

RFC-driven projects often grow too large for a single agent pass, requiring decomposition into verifiable units and a controlled integration path.

Core Features & Use Cases

  • RFC-driven DAG decomposition and unit assignment
  • Independent unit implementation, validation, and merge-ready reporting
  • Supports a quality pipeline with merge queues and recovery guidance

Quick Start

Decompose a large feature into RFC-based work units and execute the end-to-end pipeline from intake to merge-ready verification.

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 RFC-driven features into verifiable work units for multi-agent pipelines?▼

Multi-agent pipelines decompose large RFC-driven features into verifiable work units by assigning structured specs with IDs, dependencies, scope, acceptance tests, risk levels, and rollback plans for independent implementation.

When do I need a multi-agent orchestration pipeline for feature development?▼

You need multi-agent orchestration when RFC-driven feature work grows too large for a single agent pass, requiring decomposition into independent units with controlled integration paths and structured quality gates.

How do I set up a quality pipeline with merge queues and recovery steps for multi-agent workflows?▼

A quality pipeline for multi-agent workflows requires defining merge queues, recovery steps, and final verification processes to guide independent unit implementation from intake to merge-ready validation.

What is the best way to orchestrate multi-agent workflows for large feature integration?▼

The best way to orchestrate multi-agent workflows is decomposing large features into RFC-based work units, enabling independent unit implementation, testing, and integration through a defined quality pipeline.

Can I execute an end-to-end pipeline from intake to merge-ready verification for complex feature work?▼

Yes, you can execute an end-to-end pipeline from intake to merge-ready verification by decomposing large features into RFC-based work units and applying structured quality gates with recovery guidance.

What are the limitations of using single-agent passes for large RFC-driven projects?▼

Single-agent passes cannot handle large RFC-driven projects that require decomposition into verifiable units, independent implementation, testing, and a controlled integration path with merge queues and recovery steps.

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