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haipipe-nn-0-overview

Outline the 4-layer NN pipeline architecture and YAML model templates.

Updated Feb 16, 2026
Search Tags:#architecture#configuration#time series#pipeline#model registry#neural network
One-click install
npx skills add https://github.com/jluo41/research-skills --skill haipipe-nn-0-overview
Or copy as Structured Prompt for Agent▼
Please help me install this Agent Skill.
Skill: haipipe-nn-0-overview
Source: https://github.com/jluo41/research-skills/tree/main/skills/haipipe-nn-0-overview
Command: npx skills add https://github.com/jluo41/research-skills --skill haipipe-nn-0-overview

SYSTEM DOCUMENTATION & REQUIREMENTS

What problem does it solve?

This Skill provides a comprehensive overview and decision-making guide for the Neural Network (NN) pipeline, simplifying the process of understanding its architecture, integrating new models, and navigating the existing model registry.

Core Features & Use Cases

  • Architecture Visualization: Understand the 4-layer separation (Algorithm, Tuner, Instance, ModelSet) crucial for development.
  • Decision Guidance: Follow a clear decision tree for integrating new algorithms based on data format, tuner needs, and serialization.
  • Model Registry & Templates: Access a complete list of registered models and YAML configuration templates for various use cases like time-series forecasting and treatment effect estimation.
  • Use Case: A data scientist needs to add a new custom forecasting model to the pipeline. They can use this Skill to understand the required structure, follow the checklist for creating the Tuner and Instance layers, and then use the provided YAML template to configure and run the new model through the pipeline.

Quick Start

Use the haipipe-nn-0-overview skill to understand the 4-layer architecture of the NN pipeline.

Frequently Asked Questions about haipipe-nn-0-overview

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

FAQPage Schema
How do I add a new neural network model to a time-series forecasting pipeline?▼

A neural network pipeline uses a 4-layer separation principle consisting of the Algorithm, Tuner, Instance, and ModelSet layers to organize code for development and maintainability.

What is the 4-layer separation principle for neural network pipeline architecture?▼

The 4-layer separation principle for neural network pipeline architecture divides code organization into Algorithm, Tuner, Instance, and ModelSet layers to ensure strict separation of concerns and maintainability.

How do I configure a treatment effect estimation model using YAML?▼

To add a new neural network model to a time-series forecasting pipeline, follow the provided decision tree and step-by-step checklist to create the required Tuner and Instance layers, then use a YAML configuration template to run the model.

How do I navigate the model registry for existing neural network algorithms?▼

You configure a treatment effect estimation model using YAML by applying the provided configuration templates from the model registry, which outline the necessary parameters for the neural network pipeline.

What decision framework should I use when integrating a new algorithm into a neural network pipeline?▼

You navigate the model registry for existing neural network algorithms by using the architectural overview guide, which provides a complete list of registered models and their specific use cases.

Are there YAML templates available for time-series forecasting in a neural network pipeline?▼

When integrating a new algorithm into a neural network pipeline, use the decision-making framework that evaluates data format, tuner needs, and serialization requirements to determine the correct integration path.

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