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haipipe-nn

Coordinate the haipipe-nn four-layer workflow for NN pipeline development.

1|Updated Feb 16, 2026
Search Tags:#registry#ml-pipeline#tuner#model-registry#yaml-templates#haipipe-nn#model-packing
One-click install
npx skills add https://github.com/jluo41/Tools --skill haipipe-nn
Or copy as Structured Prompt for Agent▼
Please help me install this Agent Skill.
Skill: haipipe-nn
Source: https://github.com/jluo41/Tools/tree/main/plugins/research/skills/haipipe-nn
Command: npx skills add https://github.com/jluo41/Tools --skill haipipe-nn

SYSTEM DOCUMENTATION & REQUIREMENTS

What problem does it solve?

Coordinate the haipipe-nn four-layer workflow (Algorithm, Tuner, Instance, ModelSet) to unify and accelerate NN pipeline development across model families.

Core Features & Use Cases

  • Unified four-layer framework that separates algorithm, tuning, orchestration, and packaging.
  • Registry-driven loading and YAML-driven configuration to enable end-to-end training, evaluation, and inference.
  • Use cases include reviewing, generating, and testing any NN pipeline code, and tracking model status via dashboards.

Quick Start

Provide a concrete end-to-end haipipe-nn example that goes from YAML config to a packaged ModelInstance_Set.

Frequently Asked Questions about haipipe-nn

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

FAQPage Schema
How do I coordinate an end-to-end neural network pipeline across multiple model families?▼

Coordinate neural network pipelines by applying a four-layer workflow spanning Algorithm, Tuner, Instance, and ModelSet to unify training and packaging across model families like tsforecast, tefm, and mlpredictor.

How do I use YAML templates to configure and track neural network model registries?▼

YAML templates drive configuration for neural network model registries by enforcing canonical interfaces, enabling registry-driven loading for end-to-end training, evaluation, and inference tasks.

What is the best way to separate algorithm logic from hyperparameter tuning in machine learning pipelines?▼

Separate algorithm logic from tuning by adopting a four-layer framework that isolates the Algorithm, Tuner, Instance, and ModelSet layers, accelerating development across diverse neural network model families.

Can I review, generate, and test neural network pipeline code across different model families?▼

You can review, generate, and test neural network pipeline code across model families like tsforecast, tefm, and mlpredictor by enforcing canonical interfaces within the four-layer workflow.

How do I package a trained neural network model instance from a YAML configuration?▼

Package a trained neural network model instance by loading a YAML configuration through the registry-driven four-layer workflow, progressing from the Algorithm layer to output a packaged ModelInstance_Set.

Does this neural network pipeline workflow require any external dependencies or components?▼

This neural network pipeline workflow requires no external dependencies or components, relying entirely on its internal four-layer framework, canonical interfaces, and YAML templates to function.

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