Individual skill

vectorization

Design, implement, optimize, and review SIMD code in .NET. USE FOR: vectorizing scalar loops with TensorPrimitives, Vector64/128/256/512, or platform hardware intrinsics; reviewing existing SIMD code, including the generic Vector type, for contract equivalence, tail handling, memory safety, portability, fallbacks, and measured performance. DO NOT USE FOR: performance work unrelated to SIMD or vectorization.

plugins/dotnet-advanced/skills/vectorization/SKILL.md

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Last checked: Nov 9, 2026

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# .NET SIMD vectorization

Produce a portable optimization that preserves the scalar contract, remains memory-safe at every
length, and earns its complexity with measured results. **Read the official
[SIMD and hardware-intrinsics guidance](https://learn.microsoft.com/dotnet/standard/simd) first**
and follow its comprehensive implementation templates. In particular, use its self-contained
per-width dispatch, dedicated small-input handling, loop, and remainder shapes rather than reducing
them to a chain of width checks. This skill supplies the decision rules and validation checks to
apply while changing real code.

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