AgentSkillsCN

cpp-performance-engineer

通过可量化的证据，对C++性能瓶颈进行剖析、诊断与优化。在分析CPU/内存热点、在变更前后进行基准测试、从基准测试输出中筛选回归问题、改善缓存行为、减少锁竞争、调优编译器标志，或准备性能签核报告时使用此技能。

SKILL.md
--- frontmatter
name: cpp-performance-engineer
description: Profile, diagnose, and optimize C++ performance bottlenecks with measurable evidence. Use when analyzing CPU/memory hotspots, benchmarking before/after changes, triaging regressions from benchmark outputs, improving cache behavior, reducing lock contention, tuning compiler flags, or preparing performance sign-off reports.

C++ Performance Engineer

Use this skill to move C++ performance work from intuition to benchmark-backed decisions.

Workflow

  1. •Establish reproducible baseline.
  • •Capture compiler, flags, CPU environment, thread pinning, and dataset sizes.
  • •Run baseline benchmarks before changing code.
  1. •Identify hotspot class.
  • •Distinguish compute, memory bandwidth, cache misses, branch mispredicts, allocations, and lock contention.
  • •Prioritize hotspots by end-to-end impact, not microbenchmark delta alone.
  1. •Apply targeted optimizations.
  • •Use data-layout and allocation changes for memory-bound paths.
  • •Use algorithmic/branch simplification for compute-bound paths.
  • •Use lock scope reduction, sharding, or lock-free structures for contention.
  • •Keep each optimization isolated and benchmarked.
  1. •Validate with benchmark comparison.
  • •Compare current run against baseline with explicit regression thresholds.
  • •Flag statistically suspicious or high-variance benchmark rows.
  1. •Package performance handoff.
  • •Provide measured deltas, affected files, tradeoffs, and risks.
  • •Include reproducible benchmark commands.

Commands

bash
python3 scripts/compare_benchmark_json.py \
  --baseline <baseline.json> \
  --current <current.json> \
  --metric cpu_time \
  --regression-threshold 5.0

Treat non-zero exits as blocker regressions.

Output Contract

Return:

  1. •Baseline Context: compiler/env assumptions and benchmark scope.
  2. •Findings: top regressions/improvements with measured deltas.
  3. •Optimization Plan: exact code-level changes and expected impact.
  4. •Verification: rerun commands and regression gates.
  5. •Residual Risks: variance, measurement noise, or unresolved bottlenecks.

References

  • •references/workflow.md: detailed profiling and optimization sequence.
  • •references/optimization-playbook.md: hotspot-to-technique mapping.
  • •references/signoff-template.md: concise performance report template.

Execution Rules

  • •Never claim performance gains without before/after measurements.
  • •Keep benchmark environments comparable across runs.
  • •Separate microbenchmark wins from end-to-end impact.
  • •Escalate regressions above threshold as blockers.