perf-benchmark-skill

Linux performance benchmarking skill for coding agents that support Skills. It profiles Python and C workloads, scores a 7-dimension rubric, and keeps algorithmic issues ahead of cache, branch, and ASM tuning.

For repo-agnostic use, pass an explicit --target or --binary.

Installation

npx skills add <skill-source>/perf-benchmark-skill

<skill-source> means the installable source or repository path that hosts this skill.

Scope

• Algorithmic scaling, wall-time stability, CPU efficiency, cache behavior, branch prediction, memory profile, and optional ASM review
• Linux-only, using /proc, /sys, Valgrind, and perf
• SKILL.md is the detailed agent-facing workflow and CLI reference

Pytest benchmark autodiscovery is a convenience for Python repos. For non-pytest entrypoints, use --target or --binary.

Multi-size explicit targets must include {SIZE}.

Full Algorithmic Scaling scoring requires deep or asm because allocation churn comes from massif.

Usage

python scripts/perf_benchmark_pipeline.py \
  --root /path/to/repo \
  --out-dir /tmp/bench \
  --tier deep \
  --target "python -m benchmark_entrypoint {SIZE}" \
  --sizes 10000,100000 \
  --source-prefix path/to/source/ \
  --perf-record

--perf-record is opt-in native hotspot sampling via perf record and perf report. Use it when perf is available and you want flat sampled hotspots in addition to the rubric.

Outputs

• benchmark_report.md: scorecard, findings, prescriptions
• benchmark_summary.json: machine-readable scores and regression data
• tier1/ to tier4/: raw profiler artifacts by depth

More Detail

See SKILL.md for the full workflow, tier behavior, agent guidance, and reference links.

License

MIT