mz-benchmark
mz-benchmark is a Runtime diagnostics & profiling skill for AI agents, published by MaterializeInc in the materialize repository on GitHub. Skillspace rates mz-benchmark D (57/100) and it passed the safety scan; the rating is based on static analysis only.
Add/modify/debug Materialize perf benchmark scenarios. Three frameworks: Feature Benchmark (single-op micro), Scalability Test (SQL throughput under concurrency), Parallel Benchmark (sustained latency via scenarios.py). Trigger: "benchmark", "feature benchmark", "scalability test", "parallel benchmark", "performance regression", "micro-benchmark", "TPS", "latency test", or edits in feature_benchmark/scenarios/, scalability/workload/workloads/, parallel_benchmark/scenarios.py. Note: measurement, not panic-stress (see mz-parallel-workload).
- Grade
- D
- Quality
- 57 / 100
- Safety
- Passed
- GitHub stars
- 6,334
Unproven: rated by static analysis only, not yet compared head-to-head.
Our two independent classifiers differ on this skill's category, so treat the placement as provisional.
This rating describes the version of the repository we crawled; it may have changed since, so go by its current files.
Our review
- Architecture
- 80 / 100
- Maturity
- Basic: a single SKILL.md
- Security scan
- No issues found
- License
- Not declared. Check the repository before reusing it.
- Adoption
- Adopted by 0 other repositories, in 0 collections
- Scope
- Repo-internal: written for the repository it lives in. Edits Materialize's own benchmark frameworks and scenario files
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
- Sibling files (scripts, references) not fetched; security scan covers SKILL.md only
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