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pypto-case-reduction-sum

Sum a 3D tensor along a fixed axis while preserving original dimensions.

6|1|Updated Apr 19, 2026
Search Tags:#kernel#tensor#pypto#reduction#3d-tensor#fixed-dim#dimensionality
One-click install
npx skills add https://github.com/xchang1121/AutoResearch-CC-hook --skill pypto-case-reduction-sum
Or copy as Structured Prompt for Agent▼
Please help me install this Agent Skill.
Skill: pypto-case-reduction-sum
Source: https://github.com/xchang1121/AutoResearch-CC-hook/tree/main/skills/pypto/cases/pypto-case-reduction-sum
Command: npx skills add https://github.com/xchang1121/AutoResearch-CC-hook --skill pypto-case-reduction-sum

SYSTEM DOCUMENTATION & REQUIREMENTS

What problem does it solve?

This skill demonstrates a minimal, dimension-preserving single-axis reduction on a 3D tensor, enabling simple, predictable reductions without reshaping or loops.

Core Features & Use Cases

  • Keeps original tensor dimensions while performing a reduction along a single axis.
  • Uses a minimal, deterministic kernel suitable for fixed input shapes like 16, 256, 256.
  • Enforces a fixed reduction dimension to simplify implementation and testing.

Quick Start

Instantiate and run the fixed-dim reduction example to apply a sum over the specified axis to a 3D tensor and observe the preserved shape.

Frequently Asked Questions about pypto-case-reduction-sum

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

FAQPage Schema
How do I perform a 3D tensor reduction while preserving the original dimensions?▼

3D tensor reduction with preserved dimensions is achieved by summing along a single fixed axis using a minimal kernel, keeping the original tensor shape without reshaping or adding loops.

What is a fixed-dim reduction kernel and when should I use it?▼

A fixed-dim reduction kernel enforces a constant reduction dimension to ensure deterministic behavior. Use it for simple, predictable single-axis reductions on fixed input shapes like 16, 256, 256.

Can I use a minimal kernel for 3D tensor sum operations on variable input shapes?▼

No, this minimal kernel targets fixed input shapes such as 16, 256, 256. It enforces a fixed reduction dimension through a constant, making it unsuitable for dynamic or variable tensor dimensions.

What's the best way to do a single-axis sum on a 3D tensor without reshaping?▼

The best way is to use a minimal kernel that performs a single-axis 3D reduction by summing along the specified dimension, avoiding loops and reshaping while maintaining the original tensor shape.

Why does my 3D tensor reduction kernel enforce a fixed reduction dimension?▼

Enforcing a fixed reduction dimension through a constant in the kernel and forward path simplifies implementation and testing, ensuring deterministic behavior for fixed input shapes like 16, 256, 256.

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