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KanakMalpani
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ralphinho-rfc-pipeline

Coordinate multi-agent feature work via RFC intake and DAG decomposition.

Updated Mar 20, 2026
Search Tags:#multi-agent#orchestration#pipeline#rfc#quality-gates#work-units#merge-queue
One-click install
npx skills add https://github.com/KanakMalpani/General-Private-Skills --skill ralphinho-rfc-pipeline-kanakmalpani
Or copy as Structured Prompt for Agent▼
Please help me install this Agent Skill.
Skill: ralphinho-rfc-pipeline
Source: https://github.com/KanakMalpani/General-Private-Skills/tree/main/skills/ralphinho-rfc-pipeline
Command: npx skills add https://github.com/KanakMalpani/General-Private-Skills --skill ralphinho-rfc-pipeline-kanakmalpani

SYSTEM DOCUMENTATION & REQUIREMENTS

What problem does it solve?

This RFC-driven multi-agent DAG orchestration pattern addresses the challenge of large features that exceed a single agent's capacity, enabling decomposition into independently verifiable work units.

Core Features & Use Cases

  • RFC intake, DAG decomposition, unit assignment, implementation, validation, and merge-queue integration.
  • Quality gates and a structured unit spec (id, depends_on, scope, acceptance_tests, risk_level, rollback_plan).
  • Adaptive recovery and merge-ready reporting for complex software projects.

Quick Start

Outline how to decompose a large feature into verifiable RFC work units and set up the accompanying quality gates and merge rules.

Frequently Asked Questions about ralphinho-rfc-pipeline

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

FAQPage Schema
How do I coordinate large feature work across multiple agents to ensure verifiable outcomes?▼

Multi-agent orchestration coordinates large feature work by decomposing complex features into independent work units using RFC intake and DAG decomposition. It assigns units to agents for implementation and validation, ensuring verifiable outcomes through structured governance.

What is RFC-driven DAG decomposition and when do I need it for software pipelines?▼

RFC-driven DAG decomposition breaks down complex features into independent work units with dependency tracking. You need it when features exceed a single agent's capacity and require structured unit specs, quality gates, and tiered complexity management for verifiable outcomes.

How do I set up quality gates and a merge queue for multi-agent pipelines?▼

Define quality gates using a structured unit spec containing acceptance tests, risk levels, and rollback plans. Integrate a merge queue with adaptive recovery to validate units sequentially, ensuring only merge-ready work units pass final verification in the pipeline.

Can I use multi-agent orchestration for tiered complexity work units in software engineering?▼

Yes, multi-agent orchestration supports tiered complexity (Tier 1-3) for work units in software engineering. It assigns units based on dependency graphs and risk levels, enabling adaptive recovery and merge-ready reporting for complex software projects.

What is the best way to break down complex features into independent work units for agents?▼

Use RFC intake to define a structured unit spec with IDs, dependencies, scope, acceptance tests, risk levels, and rollback plans. DAG decomposition maps dependencies to create independent, verifiable units for agent assignment and implementation.

What are the limitations of multi-agent pipelines for large feature decomposition?▼

Multi-agent pipelines require features decomposable into independent work units with clear dependencies. Limitations arise when features lack verifiable acceptance tests or rollback plans, making adaptive recovery and merge-queue governance difficult to enforce.

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