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cupynumeric-migration-readiness

Analyze NumPy code against the cuPyNumeric API manifest and output migration verdicts.

Updated Apr 16, 2026
Search Tags:#migration#gpu#numpy#distributed-computing#pre-migration#cupynumeric#legate
One-click install
npx skills add https://github.com/sayalinvidia/sayali-skills-test --skill cupynumeric-migration-readiness
Or copy as Structured Prompt for Agent▼
Please help me install this Agent Skill.
Skill: cupynumeric-migration-readiness
Source: https://github.com/sayalinvidia/sayali-skills-test/tree/main/skills/cupynumeric-migration-readiness
Command: npx skills add https://github.com/sayalinvidia/sayali-skills-test --skill cupynumeric-migration-readiness

SYSTEM DOCUMENTATION & REQUIREMENTS

💡 This Skill includes assets (resource) and references (resource) components.

What problem does it solve?

Pre-migration readiness assessor that reads NumPy source code, cross-references the cuPyNumeric API manifest, and outputs a structured scaling verdict with per-finding pointers and concrete refactor recipes.

Core Features & Use Cases

  • Static analysis against the cuPyNumeric API support manifest to identify scalable idioms and blockers.
  • Produces a verdict with actionable refactor pointers (READY / LIGHT REFACTOR / SIGNIFICANT REFACTOR / NOT RECOMMENDED) and a plan to migrate.
  • Works across common CPU/GPU patterns (stencils, linear algebra, reductions, RNG, and boundary-work from SciPy).

Quick Start

Run this skill on your Python codebase to determine its cuPyNumeric migration readiness with a detailed, recipe-driven verdict.

Frequently Asked Questions about cupynumeric-migration-readiness

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

FAQPage Schema
How do I check if my NumPy code will scale on cuPyNumeric before GPU porting?▼

Run static code analysis on your NumPy source to cross-reference the cuPyNumeric API manifest, producing a structured scaling verdict with per-finding reasoning and concrete refactor pointers.

What NumPy patterns does cuPyNumeric migration readiness cover?▼

The analysis covers stencils, linear algebra, reductions, RNG, and SciPy boundary-work patterns, identifying which idioms scale on cuPyNumeric and flagging blockers that require refactoring.

What does a pre-migration readiness verdict look like for NumPy to GPU porting?▼

The verdict classifies code as READY, LIGHT REFACTOR, SIGNIFICANT REFACTOR, or NOT RECOMMENDED, providing a migration plan with actionable refactor recipes for each finding.

Can I use this to find which NumPy idioms need refactoring for distributed computing?▼

Yes, it identifies NumPy idioms that will not scale on cuPyNumeric and provides concrete refactor pointers to restructure them before attempting a distributed GPU port.

Do I need to install cuPyNumeric or Legate to run a migration readiness check?▼

No, the skill performs static code analysis by reading the source and cross-referencing the cuPyNumeric API manifest, so no runtime installation of cuPyNumeric or Legate is required.

When should I not use automated static analysis for NumPy to cuPyNumeric migration?▼

Static analysis may miss dynamic runtime behaviors or complex inter-dependent operations not visible in the source, so heavily dynamic NumPy codebases may require additional runtime profiling before GPU porting.

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