cupynumeric-migration-readiness

cuPyNumeric Migration Readiness Purpose Use this skill BEFORE the migration, not during. Answer one question: which of the user's existing NumPy APIs will scale on cuPyNumeric, and which need refactoring, before they commit engineer-weeks to porting? To answer it: read the source, classify each NumPy idiom by its expected multi-GPU scaling on the Legate/NVIDIA GPU stack, cross-reference the bundled API-support manifest, and produce a structured verdict with per-finding reasoning and recipe pointers. This is a static, read-only assessment. Inspect the user's source with Read , Grep , and Glob . Do not execute the user's code, modify or write files, or print environment variables or secrets. The legate , and cuPyNumeric Doctor commands shown below are suggestions for the user to run — not actions this skill performs. If this skill has never been seen before, head to references/getting-started.md first. When to use this skill Use when the user is about to migrate NumPy code to GPU and asks whether it will scale on cuPyNumeric / GPU, whether they should migrate, which parts will benefit, what must change before porting, or whether the port is worth it — or mentions pre-port assessment, scaling analysis, idiom analysis, GPU refactor planning, or identifying NumPy anti-patterns for GPU. Decline and redirect when the request is not a pre-migration assessment: Post-migration performance / profiling ("already ported, why is it slow?") → point to legate --profile and the upstream profiling and debugging walkthrough. Custom CUDA / kernel authoring ("write/optimize a CUDA kernel") Show more Installs 560 Repository nvidia/skills GitHub Stars 1.3K First Seen May 30, 2026 Security Audits Gen Agent Trust Hub Fail Socket Pass Snyk Pass