oracle

NumSharp's differential-fuzz pipeline — the NumPy 2.4.2 "oracle" that proves every NDIter-backed op is BIT-IDENTICAL to NumPy across the input space. Use this whenever you add or change an np.* op and need fuzz coverage, regenerate the committed corpus, wire an op into OpRegistry, understand or debug the FuzzMatrix gate, triage a divergence (bit-diff / MisalignedRegistry / OpenBugs), or extend dtype/layout/tier coverage. Trigger on: "oracle", "differential fuzz", "fuzz coverage", "FuzzMatrix", "gen_oracle", "OpRegistry", "the corpus", "regenerate the corpus", "bit-exact vs numpy", "why is the fuzz gate failing / red", "add <op> to the fuzz gate", Char/Decimal/index/npy oracle, "shrink a failing case". This is the correctness gate — reach for it before assuming an op is done.

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Repository: SciSharp/NumSharp
Last source activity: October 30, 2026 at 12:35
Detected SKILL.md language: English
Stars: 1,483
Forks: 206

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changelog

NumSharp's release-notes authoring + large-range commit-sweep playbook - how to write a changelog in the house style AND how to work through a few-hundred-commit range without missing or mis-stating anything. Use this whenever you write or refresh a release changelog (`docs/releases/RELEASE_<version>.md`), sweep a commit range for changes not yet in the changelog ("go over the commits and find what's unhandled"), decide whether a commit deserves a line, dedup many commits into one feature line, or verify a changelog is complete AND factually true. Trigger on: "changelog", "release notes", "write up the changelog", "CHANGELOG_STYLE", "RELEASE_x.y.z", "what's new since <version>", "go over the commits in batches", "find unhandled changes", "which commits are missing from the changelog", "sweep the commits", "master..<branch>", "categorize / dedup the commits", "did we cover every commit". Reach for it before hand-writing a changelog from `git log` or claiming a range is "fully covered".

2026-11-08

specialized-path

How to discover, validate, build, and honestly verify a SPECIALIZED path for ANY function — a cheaper route for a recognizable SUBSET of an operation's inputs, sitting behind the general implementation with a gate + fallback so the general case never regresses and semantics stay bit-identical. The cheaper route can be a different KERNEL (SIMD vs scalar, gemv/syrk/stencil), a different KERNEL CONTRACT (one call per block instead of per row), a REPRESENTATION trick (bit-level f16, byte-lane bool, widen-compute-narrow), a different ALGORITHM (hash vs sort, with an observed-growth bailout), a VIEW instead of a copy (O(1) stride tricks), an EARLY EXIT (all-false / empty / k==0 / provably-constant output), a FIXED-COST BYPASS (skip iterator/broadcast setup for the trivial subset), a MEMORY strategy (cpblk, prefetch, write-once, privatized accumulators, retained scratch), a FUSED pass, a composition of EXISTING validated kernels, wrapper-glue removal on a parity-bound op, or a native BACKEND seam. Use this whenever

2026-10-31

benchmark

NumSharp's NumPy-vs-NumSharp performance harness — the op/dtype/N matrix (C# BenchmarkDotNet vs a warm NumPy process), unified Managed/OpenBLAS backend profiles, and six appended subsystems (nditer, layout, operand, cast, fusion, strings), all in the NPY/NS convention. Use this whenever you add a benchmark for an np.* op, wire a C# benchmark to its NumPy twin, run the official suite or a subset, read/interpret the ratio matrix or history snapshots, add a whole subsystem, or debug a suspicious measurement (the Debug-taint 2x pitfall, the InProcessEmit toolchain). Trigger on: "benchmark", "add a benchmark", "how fast is np.add vs numpy", "run_benchmark.py", "BenchmarkDotNet", "NPY/NS ratio", "perf comparison", "benchmark np.add", "benchmark-report", "history snapshot", "why is my timing 2x slow". Reach for it before quoting any NumSharp-vs-NumPy speed number.

2026-10-19

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