pipeio-flow-create

Name: pipeio-flow-create
Availability: InStock
Author: arashshahidi1997

Scaffold a new pipeline flow with config, Snakefile, and datalad subdataset.

1|Updated Mar 9, 2026

Search Tags:#workflow #config #pipeline #snakemake #pipeio #flow-creation #datalad

Ready to run

npx skills add https://github.com/arashshahidi1997/projio --skill pipeio-flow-create

💬 Prompt for ChatGPT / Claude Chat▼

Please help me install this Agent Skill.
Skill: pipeio-flow-create
Source: https://github.com/arashshahidi1997/projio/tree/main/docs/prompts/skills/pipeio-flow-create
Command: npx skills add https://github.com/arashshahidi1997/projio --skill pipeio-flow-create

SYSTEM DOCUMENTATION & REQUIREMENTS

What problem does it solve?

Guides developers to create a new pipeline flow from scratch by surveying existing patterns and scaffolding the project.

Core Features & Use Cases

Survey existing flows and scaffold a new flow directory under code/pipelines/{FLOW_NAME}/ with defaults
Write a complete config.yml and Snakefile aligned to pipeio conventions
Create and register necessary datalad subdatasets, then run dry-run verification

Quick Start

Initiate a new flow with pipeio_flow_new(flow=FLOW_NAME), then immediately register it with pipeio_registry_scan to make it discoverable.

Frequently Asked Questions about pipeio-flow-create

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

FAQPage Schema

How do I create a new Snakemake pipeline from scratch?▼

Scaffolding a new pipeline flow involves surveying existing patterns, creating a flow directory with defaults, writing a config.yml and Snakefile aligned to conventions, and setting up datalad subdatasets for reproducible data management.

How do I set up datalad subdatasets for a Snakemake workflow?▼

You can set up datalad subdatasets within a pipeline flow by creating and registering them during the scaffolding process, ensuring your data inputs and outputs are version-controlled and discoverable across the pipeline.

What is the best way to structure a Snakemake config.yml for a new pipeline?▼

The best way to structure a config.yml for a new pipeline is to follow established pipeio conventions, using scaffolding templates that provide required defaults and ensure the configuration aligns with the Snakefile for discoverability.

Can I use datalad with Snakemake for reproducible pipeline workflows?▼

Yes, you can use datalad with Snakemake by creating and registering datalad subdatasets within your pipeline flow, which provides version-controlled data management and ensures end-to-end reproducibility for your workflow.

How do I verify a Snakemake pipeline after creating the flow?▼

You verify a newly created Snakemake pipeline by running a dry-run verification immediately after scaffolding the project and writing the Snakefile, ensuring all configurations and scripts are correctly aligned before execution.

What are the limitations of scaffolding pipeline flows with existing patterns?▼

A limitation is that scaffolding relies on surveying existing patterns, meaning a lack of established flows to reference may hinder generating accurate defaults, and manual adjustments to the Snakefile or scripts might be needed for non-standard configurations.