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ralphinho-rfc-pipeline

Decompose large RFCs into ordered work units with dependency tracking.

2|Updated Apr 7, 2026
Search Tags:#software-engineering#quality-gates#feature-decomposition#work-units#dag-orchestration#merge-queue#rfc-pipeline
One-click install
npx skills add https://github.com/Zenobia000/ai-brainstorming --skill ralphinho-rfc-pipeline-zenobia000
Or copy as Structured Prompt for Agent▼
Please help me install this Agent Skill.
Skill: ralphinho-rfc-pipeline
Source: https://github.com/Zenobia000/ai-brainstorming/tree/main/.claude/custom-rule%26skill/skills/ralphinho-rfc-pipeline
Command: npx skills add https://github.com/Zenobia000/ai-brainstorming --skill ralphinho-rfc-pipeline-zenobia000

SYSTEM DOCUMENTATION & REQUIREMENTS

What problem does it solve?

Large features that exceed single-agent execution capacity often result in incomplete, unvalidated work and integration failures. This Skill provides a structured RFC-driven pipeline to break down complex work into manageable, independently verifiable units with built-in quality controls.

Core Features & Use Cases

  • DAG Decomposition: Automatically split large RFCs into ordered work units with clear dependency tracking.
  • Tiered Complexity Handling: Classify work units by risk level (isolated edits, multi-file changes, schema/security modifications) to apply appropriate validation.
  • Merge Queue & Recovery: Enforce merge rules to prevent broken integrations, with automated recovery steps for stalled units.
  • Use Case: A solo engineer developing a new user authentication system can use this Skill to split the work into separate units for database schema updates, API endpoint implementation, and frontend integration, each with custom acceptance tests and rollback plans.

Quick Start

Use the ralphinho-rfc-pipeline skill to decompose the new payment processing RFC into 3 independent work units with dependency tracking and acceptance tests.

Frequently Asked Questions about ralphinho-rfc-pipeline

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

FAQPage Schema
How do I split large feature implementations into verifiable work units?▼

Split large feature implementations by decomposing them into ordered work units using DAG dependency tracking, tiered complexity classification, and automated validation gates to ensure independently verifiable software delivery.

What's the best way to manage cross-team feature dependencies for complex code changes?▼

Manage cross-team feature dependencies using DAG decomposition to automatically order work units with clear dependency tracking, enforcing merge queue rules to prevent broken integrations across multi-file changes.

When do I need quality gates for feature decomposition in software engineering?▼

Apply quality gates for feature decomposition when large features exceed single-agent execution capacity, requiring tiered complexity handling for isolated edits, schema modifications, and security changes with automated failure recovery.

How does a merge queue handle stalled work units during feature delivery?▼

A merge queue handles stalled work units by enforcing merge rules to prevent broken integrations and applying automated recovery steps to resume stalled units in the feature delivery pipeline.

Can I classify work units by risk level for regulated code changes?▼

Classify work units by risk level through tiered complexity handling that distinguishes isolated edits, multi-file changes, and schema or security modifications to apply appropriate validation gates for regulated code.

Why do large features fail during single-agent execution and how to prevent it?▼

Large features fail during single-agent execution due to incomplete validation and integration failures; prevent this with RFC-driven pipeline decomposition into manageable work units with built-in quality controls and rollback plans.

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