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ralphinho-rfc-pipeline

Decompose large features into RFC-driven work units with a multi-agent DAG.

1|1|Updated Mar 31, 2026
Search Tags:#workflow#multi-agent#integration-testing#software-engineering#dag#unit-management#rfc-pipeline
One-click install
npx skills add https://github.com/zardusai-cyber/zardus_setup --skill ralphinho-rfc-pipeline-zardusai-cyber
Or copy as Structured Prompt for Agent▼
Please help me install this Agent Skill.
Skill: ralphinho-rfc-pipeline
Source: https://github.com/zardusai-cyber/zardus_setup/tree/main/ecc/skills/ralphinho-rfc-pipeline
Command: npx skills add https://github.com/zardusai-cyber/zardus_setup --skill ralphinho-rfc-pipeline-zardusai-cyber

SYSTEM DOCUMENTATION & REQUIREMENTS

What problem does it solve?

RFC-driven decomposition patterns to manage large features that exceed a single agent pass, enabling independent verification of each work unit.

Core Features & Use Cases

  • RFC intake, DAG decomposition, unit assignment, implementation, validation, and merge integration.
  • Structured unit metadata and a quality pipeline that guides research, implementation, tests, review, and merge-ready reporting.
  • Recovery and merge-queue rules to ensure resilient, auditable feature delivery across multiple agents.

Quick Start

Decompose a feature into RFC-based units and start the multi-agent workflow.

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 a complex software feature into independently verifiable work units?▼

Yes, multi-agent workflows can handle large features that exceed a single agent pass by applying RFC intake and DAG decomposition. The pipeline assigns work units, enforces metadata, and validates implementation across multiple agents.

How do I manage dependencies and risk when coordinating multiple software engineering agents?▼

Manage dependencies and risk by enforcing structured unit metadata that includes id, depends_on, scope, acceptance_tests, risk_level, and rollback_plan. This metadata ensures disciplined coordination and resilient recovery during multi-agent feature delivery.

Does this RFC pipeline enforce merge-queue rules and integration testing?▼

Yes, the RFC pipeline enforces a disciplined merge-queue workflow that includes integration testing and recovery procedures. This ensures that all independently verified work units merge reliably and auditably into the main project.

When should I use a DAG workflow for software engineering tasks?▼

Use a DAG workflow when a software feature is too complex for a single agent pass and requires tiered complexity management. It is needed when you must decompose features into structured units with explicit dependency tracking and acceptance tests.

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