Is this skill suitable for multi-file changes?

Yes, it is specifically designed for 'Level 2' and 'Level 3' complexity tasks, such as multi-file behavior changes or major architectural updates.

How does it handle complex dependencies?

The skill creates a Directed Acyclic Graph (DAG) where each unit identifies its dependencies. It ensures units are only implemented and merged once their prerequisites are fully validated.

What is the Ralphinho RFC Pipeline?

It is a Claude Code skill that uses an RFC (Request for Comments) framework to decompose large programming tasks into smaller, manageable units organized in a dependency graph (DAG).

What happens if a task unit fails?

The pipeline includes a recovery mechanism that removes stalled units from the active queue, takes a snapshot of findings, and regenerates the unit with updated constraints.

Ralphinho RFC Pipeline

Name: Ralphinho RFC Pipeline
Author: saar120

bysaar120

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Productividad y Flujo de Trabajo

Orchestrates complex software development tasks using an RFC-driven Directed Acyclic Graph (DAG) for multi-agent execution.

Ralphinho RFC Pipeline is designed for large-scale development tasks that are too complex for a single AI interaction. Inspired by humanplane-style decomposition, this skill breaks down massive features into independent, verifiable work units organized within a dependency graph. It enforces a strict lifecycle for each unit—from research and implementation to testing and review—and utilizes a merge queue to ensure that integration only occurs when all dependencies and quality gates are satisfied. This is the ideal solution for architectural changes, multi-file refactors, and systematic feature rollouts.

Características Principales

01RFC-driven decomposition of large features into granular work units

020 GitHub stars

03Automated merge queue with rebase requirements and integration testing

04DAG-based task orchestration to manage complex dependencies

05Built-in recovery system for stalled units with snapshotting and retries

06Multi-stage quality pipeline including research, plan, and validation

Casos de Uso

01Executing complex architectural migrations that span multiple files and layers

02Managing dependencies across a team of AI agents to ensure sequential system integrity

03Breaking down monolithic feature requests into manageable implementation sub-tasks

What are Skills?·How to Install

Install with 🐟 Skill.Fish

npx skillfish add saar120/skillgate-registry ralphinho-rfc-pipeline

For use in Claude.ai and ChatGPT

Características Principales

01RFC-driven decomposition of large features into granular work units

020 GitHub stars

03Automated merge queue with rebase requirements and integration testing

04DAG-based task orchestration to manage complex dependencies

05Built-in recovery system for stalled units with snapshotting and retries

06Multi-stage quality pipeline including research, plan, and validation

Casos de Uso

01Executing complex architectural migrations that span multiple files and layers

02Managing dependencies across a team of AI agents to ensure sequential system integrity

03Breaking down monolithic feature requests into manageable implementation sub-tasks

What are Skills?·How to Install

Install with 🐟 Skill.Fish

npx skillfish add saar120/skillgate-registry ralphinho-rfc-pipeline

For use in Claude.ai and ChatGPT