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ralphinho-rfc-pipeline

Automate RFC-driven multi-agent DAGs for feature delivery with quality gates.

Updated Apr 6, 2026
Search Tags:#software development#multi-agent#feature delivery#RFC#DAG
One-click install
npx skills add https://github.com/thangvawn/agent_financial --skill ralphinho-rfc-pipeline-thangvawn
Or copy as Structured Prompt for Agent▼
Please help me install this Agent Skill.
Skill: ralphinho-rfc-pipeline
Source: https://github.com/thangvawn/agent_financial/tree/main/.cursor/skills/ralphinho-rfc-pipeline
Command: npx skills add https://github.com/thangvawn/agent_financial --skill ralphinho-rfc-pipeline-thangvawn

SYSTEM DOCUMENTATION & REQUIREMENTS

💡 This Skill requires python, and includes scripts (resource) components.

What problem does it solve?

This Skill addresses the challenge of managing large features that require multi-agent orchestration, ensuring efficient delivery with quality gates and merge queues.

Core Features & Use Cases

  • RFC-Driven Execution: Automates the execution of RFCs (Request for Comments) with a structured multi-agent DAG pattern.
  • Quality Gates & Merge Queues: Implements quality gates and merge queues to maintain code integrity and streamline integration.
  • Work Unit Orchestration: Orchestrate work units for independent verification, enhancing modularity and maintainability.
  • Use Case: Ideal for complex software development projects where features are too large for a single agent and require a structured, verifiable workflow.

Quick Start

Use the ralphinho-rfc-pipeline skill to initiate the RFC-driven execution of a new feature with the command 'start_feature "Feature Name" --RFC "RFC ID" --scope "Feature Scope"'.

Frequently Asked Questions about ralphinho-rfc-pipeline

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

FAQPage Schema
How do I automate RFC-driven multi-agent execution for feature delivery?▼

Automate RFC-driven multi-agent execution by decomposing large features into independently verifiable work units orchestrated as a DAG. This structured workflow enforces quality gates and manages merge queues to maintain code integrity throughout software development.

What is the best way to orchestrate multi-agent DAGs for complex software features?▼

Orchestrating multi-agent DAGs for complex software features involves decomposing them into independently verifiable work units. This modular approach enhances maintainability and streamlines integration through automated quality gates and merge queues.

Do I need Python to manage multi-agent DAG workflows with quality gates?▼

Yes, you need Python installed to manage multi-agent DAG workflows with quality gates. Python is required for executing the scripts that handle workflow management, work unit orchestration, and merge queue automation.

When should I use a multi-agent DAG pipeline for software development?▼

Use a multi-agent DAG pipeline for software development when features are too large for a single agent and require a structured, verifiable workflow. It ensures efficient delivery by handling work unit decomposition and integration.

How does work unit decomposition improve multi-agent feature delivery?▼

Work unit decomposition improves multi-agent feature delivery by breaking large features into independently verifiable modules. This DAG orchestration enhances modularity and maintainability while streamlining integration through automated merge queues.

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