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dependency-analysis

Analyze dependency files to identify CUDA packages and assess Ascend NPU compatibility.

7|Updated Jan 29, 2026
Search Tags:#dependency-analysis#environment-setup#torch_npu#Ascend-NPU#CUDA-compatibility#CANN#requirements.txt
One-click install
npx skills add https://github.com/FeRhodium/ascend-migration --skill dependency-analysis-ferhodium
Or copy as Structured Prompt for Agent▼
Please help me install this Agent Skill.
Skill: dependency-analysis
Source: https://github.com/FeRhodium/ascend-migration/tree/main/skills/dependency-analysis
Command: npx skills add https://github.com/FeRhodium/ascend-migration --skill dependency-analysis-ferhodium

SYSTEM DOCUMENTATION & REQUIREMENTS

What problem does it solve?

This skill analyzes repository dependency configurations to identify CUDA-dependent packages and ensure compatibility with Ascend NPU (torch_npu and CANN). It helps teams prepare environments and avoid runtime incompatibilities.

Core Features & Use Cases

  • CUDA dependency identification: Scans common dependency files (requirements.txt, setup.py, pyproject.toml, environment.yml, Pipfile) to flag CUDA-specific packages.
  • Compatibility assessment: Checks torch_npu and CANN version constraints and suggests safe, NPU-friendly alternatives.
  • Migration planning: Highlights blockers and provides a concrete plan for environment remediation and migration.

Quick Start

Run an analysis on your project by referencing its dependency files, for example: /dependency-analysis:analyze "MyProject" "/path/to/repo"

Frequently Asked Questions about dependency-analysis

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

FAQPage Schema
How do I check if my Python project dependencies are compatible with Ascend NPU?▼

Compatibility assessment validates torch_npu and CANN version constraints against your project dependencies. It checks your dependency files to ensure PyTorch versions align with NPU requirements and highlights any blockers preventing a safe migration to Ascend hardware.

What types of dependency files are supported for CUDA dependency identification?▼

CUDA dependency identification supports scanning requirements.txt, setup.py, pyproject.toml, environment.yml, and Pipfile. This allows the analysis to detect CUDA-dependent packages across various Python environment setup configurations and suggest NPU-friendly alternatives.

Why does my PyTorch environment setup fail when migrating to Ascend NPU?▼

Environment setup fails during Ascend NPU migration due to CUDA-dependent packages and misaligned PyTorch versions. Analyzing your dependency configurations identifies these CUDA-specific blockers and enforces checks for proper torch_npu and CANN version alignment to resolve runtime incompatibilities.

Can I get a migration plan for moving CUDA-dependent packages to an NPU-friendly environment?▼

Yes, migration planning highlights blockers found in your dependency files and provides a concrete plan for environment remediation. It identifies CUDA dependencies and suggests safe, NPU-friendly alternatives to facilitate the transition to Ascend NPU.

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