benchmark
benchmark is a Runtime diagnostics & profiling skill for AI agents, published by SciSharp in the NumSharp repository on GitHub. Skillspace rates benchmark D (66/100) and it passed the safety scan; the rating is based on static analysis only.
NumSharp's NumPy-vs-NumSharp performance harness — the op/dtype/N matrix (C# BenchmarkDotNet vs a warm NumPy process) plus five appended subsystems (nditer, layout, operand, cast, fusion), 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 <op> vs numpy", "run_benchmark.py", "BenchmarkDotNet", "NPY/NS ratio", "perf comparison", "benchmark <op>", "benchmark-report", "history snapshot", "why is my timing 2x slow". Reach for it before quoting any NumSharp-vs-NumPy speed number.
- Grade
- D
- Quality
- 66 / 100
- Safety
- Passed
- GitHub stars
- 1,479
Unproven: rated by static analysis only, not yet compared head-to-head.
This rating describes the version of the repository we crawled; it may have changed since, so go by its current files.
Our review
- Architecture
- 84 / 100
- Maturity
- Structured: ships references or scripts
- Security scan
- No issues found
- License
- Apache-2.0
- Adoption
- Adopted by 0 other repositories, in 0 collections
Not checked yet
- Category is a classifier prediction; no model has read this skill yet
- Not judged on the rubric yet (not a contender in its category)
- No blind task eval yet
- Scope (portable / vendor / repo-internal) not assessed; only the top of each category is read for scope
- Sibling files (scripts, references) not fetched; security scan covers SKILL.md only
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