exp-simd-vectorization
Optimizes hot-path scalar loops in .NET 8+ with cross-platform Vector128/Vector256/Vector512 SIMD intrinsics, or replaces manual math loops with single TensorPrimitives API calls. Covers byte-range validation, character counting, bulk bitwise ops, cross-type conversion, fused multi-array computations, and float/double math operations.
npx skills add dotnet/skills --skill exp-simd-vectorization --agent claude-code
Same command for any agent — swap --agent for codex, cursor, copilot.
Weekly change comes from our own snapshots, not the repository page — it measures attention, not adoption.
# SIMD Vectorization ## Decision Gate 1. **Check `Span<T>` and `MemoryExtensions` first.** If the operation can be expressed using built-in `Span<T>` methods (e.g., `Contains`, `IndexOf`, `CopyTo`, `SequenceEqual`) or `MemoryExtensions`, use them — no additional dependency is needed and the runtime already vectorizes many of these internally. 2. **Check for TensorPrimitives next.** If one or more TensorPrimitives methods cover the operation → use them. If the `.csproj` does NOT already reference `System.Numerics.Tensors`, **add the package**, for example: `<PackageReference Include="System.Numerics.Tensors" />` (or use the versioning approach already used by your solution). Then replace the scalar loop with TP calls and stop. See the full API table below. Compose multiple TP calls when needed (e.g., finding both min and max → `TensorPrimitives.Min(span)` + `TensorPrimitives.Max(span)` as two calls). Do NOT write manual Vector128 code for operations TP already handles. 3. **Scalar loop over contiguous array/span** of `byte`, `sbyte`, `short`, `ushort`, `int`, `uint`, `long`, `ulong`, `nint`, `nuint`, `float`, `double` (and `char` via reinterpretation as `ushort`)? → Implement with e
- Decision Gate
- TensorPrimitives API Reference
- Reductions (span → scalar)
- Element-wise transforms (span → span)
- Two-span operations (a, b → dst)
- Three-span fused operations
- Manual SIMD with Vector128/Vector256/Vector512
- Required imports
- Three-tier dispatch pattern
- Core SIMD operations
- Pattern: Unsigned range check (byte-range validation)
- Pattern: Nibble-lookup counting (character classes, popcount, etc.)
- Pattern: Cross-type conversion (widening chains)
- Trailing elements
What does the exp-simd-vectorization skill do?
Optimizes hot-path scalar loops in .NET 8+ with cross-platform Vector128/Vector256/Vector512 SIMD intrinsics, or replaces manual math loops with single TensorPrimitives API calls. Covers byte-range validation, character counting, bulk bitwise ops, cross-type conversion, fused multi-array computations, and float/double math operations.
How do I install it?
Run `npx skills add dotnet/skills --skill exp-simd-vectorization --agent claude-code` — it drops the skill into your project so the agent can pick it up. Swap the --agent value for codex, cursor or copilot if you use one of those.
Where does this skill come from?
From dotnet/skills, a repository with 4,927 stars. We read it straight from the repository tree rather than a submitted listing, so what you see here is what is actually published.
Is a popular skill a good skill?
Not necessarily. Stars measure attention, not adoption — a repository can trend for a week and be abandoned. That is why we show the weekly change from our own snapshots next to the total, instead of a single flattering number.