Agent skill · NVIDIA
cupynumeric-migration-readiness
Pre-migration readiness assessor for porting NumPy to cuPyNumeric. Use BEFORE substantial porting work begins when the user asks whether code will scale on GPU, whether they should migrate to cuPyNumeric, which NumPy patterns transfer cleanly, what must be refactored before porting, or mentions pre-port assessment, scaling analysis, or refactor planning. Inspect the user's source code, look up NumPy usage, cross-reference the cuPyNumeric API support manifest, and distinguish distributed-scaling-friendly patterns from blockers such as unsupported APIs, scalar synchronization, host round-trips, Python/object-heavy control flow, shape/data-dependent branching, and in-place mutation hazards. Produce a verdict of READY, LIGHT REFACTOR, SIGNIFICANT REFACTOR, or NOT RECOMMENDED, with concrete refactor pointers.
What it needs
About 9k tokens when loaded.
What this skill does
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") A graph / sparse / ML / NLP workload that the user is asking to migrate is still in scope: assess it and return NOT RECOMMENDED via Gate 4. That is a verdict, not a decline. Instructions Run all five steps below, in order. Read the user's code and reason about it semantically; do not emit a one-shot prose verdict. Step 1 — Gather context Elicit before scanning code. …
How to use it
Reference it in AdaL, Claude Code, Cursor or any coding agent — nothing to install:
@skills NVIDIA/cupynumeric-migration-readiness