ralphinho-rfc-pipeline

Decompose large features into RFC-driven work units with dependency graphs.

Updated May 9, 2026
One-click install
npx skills add https://github.com/RambleRainbow/jd --skill ralphinho-rfc-pipeline-ramblerainbow
Or copy as Structured Prompt for Agent
Please help me install this Agent Skill.
Skill: ralphinho-rfc-pipeline
Source: https://github.com/RambleRainbow/jd/tree/main/.claude/skills/ralphinho-rfc-pipeline
Command: npx skills add https://github.com/RambleRainbow/jd --skill ralphinho-rfc-pipeline-ramblerainbow

SYSTEM DOCUMENTATION & REQUIREMENTS

What problem does it solve?

This Skill solves the problem of managing large, complex features that are too big for a single agent to handle reliably, which often leads to fragmented work, integration failures, and unclear accountability for deliverables.

Core Features & Use Cases

  • RFC-Driven DAG Decomposition: Break large features into independently verifiable work units with clear dependency graphs and defined acceptance criteria.
  • Quality-Gated Workflow: Enforce research, implementation, testing, and review steps for each work unit to catch issues early and ensure quality.
  • Safe Merge Queue Integration: Manage unit merges with rebasing, integration test re-runs, and dependency failure checks to avoid broken integration branches.
  • Use Case: When implementing a multi-component feature like a new payment processing system, use this Skill to split work into schema updates, API development, frontend integration, and security testing units, each with rollback plans and risk ratings.

Quick Start

Use the ralphinho-rfc-pipeline skill to decompose the new user notification system feature into tracked work units with dependency graphs and quality gates.

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 large feature development that is too complex for a single agent to execute reliably?

Multi-agent DAG orchestration manages large feature development by decomposing the work into independently verifiable work units with clear dependency graphs and defined acceptance criteria. This prevents fragmented work and integration failures.

What is RFC-driven feature decomposition for multi-agent workflows?

RFC-driven feature decomposition is the process of breaking down large software features into smaller, tracked work units. Each unit receives structured specifications, dependency tracking, and per-unit quality gates to ensure reliable cross-component delivery.

How do I enforce quality gates and integration tests in a multi-agent merge queue?

You enforce quality gates in a multi-agent merge queue by requiring research, implementation, testing, and review steps for each work unit. Safe merge queue integration manages rebasing, re-runs integration tests, and checks for dependency failures to avoid broken branches.

Can I use DAG orchestration to split a multi-component feature into schema, API, and frontend work units?

Yes, DAG orchestration is designed for multi-component features like payment processing systems. You can split work into schema updates, API development, frontend integration, and security testing units, each equipped with rollback plans and risk ratings.

How do I recover stalled work units in a feature pipeline?

Stalled unit recovery is handled through structured work unit specification and dependency tracking within the pipeline. This allows you to identify blocked units, enforce integration test compliance, and resume end-to-end system verification for production-grade features.

When should I avoid single-agent workflows and switch to an RFC pipeline for software engineering tasks?

You should switch to an RFC pipeline when a feature is too large for a single agent, causing fragmented work and unclear accountability. If your project requires cross-component delivery with dependency tracking and end-to-end verification, DAG orchestration is necessary.