perf-reviewer

name	Perf Reviewer
description	Review code for performance issues and run benchmarks. Use when user asks to analyze performance, run benchmarks, or review code for optimization opportunities.

Performance Reviewer

Review code for performance issues and run benchmarks.

Configuration

Environment Variable	Default	Description
BUILD_CMD	make build	Command to build the project
TEST_CMD	make test	Command to run tests
BENCHMARK_CMD	make bench	Command to run benchmarks

Quick Start

# Run benchmarks
scripts/benchmark.sh

# Run specific benchmark
scripts/benchmark.sh fibonacci

# Review code for performance issues
# Just ask: "review this code for performance"

When to Use This Skill

Invoke this skill when:

• User asks to "benchmark" or "compare performance"
• User asks to "review for performance" or "optimize"
• User mentions "slow", "performance", "bottleneck"
• After implementing compute-intensive code
• Before releases to verify no performance regressions

Available Scripts

scripts/benchmark.sh [benchmark_name]

Run benchmarks comparing different implementations or configurations.

Available benchmarks: fibonacci, sum, all

Output: Timing comparisons, speedup ratios, and recommendations.

Workflow

1. Performance Review (Code Analysis)

When reviewing code, check against these principles (see resources/principles.md):

Critical checks:

1. Algorithmic complexity - Is there an O(n log n) solution for O(n^2) code?
2. Batch operations - Can multiple operations be combined?
3. Memory allocation - Are allocations inside hot loops?
4. Data layout - Are frequently-accessed fields colocated?

Quick checklist:

[ ] No O(n^2) where O(n log n) exists
[ ] Batch APIs for repeated operations
[ ] Allocations hoisted outside loops
[ ] Hot paths optimized, edge cases separate
[ ] No unnecessary string formatting in loops

2. Benchmarking

# Full benchmark suite
scripts/benchmark.sh all

# Single benchmark
scripts/benchmark.sh fibonacci

3. Profiling

Use your language's profiling tools to identify actual bottlenecks before optimizing.

Performance Principles Summary

From resources/principles.md:

Principle	Action
Profile First	Measure before optimizing
Algorithms > Micro-opts	O(n) beats optimized O(n^2)
Batch Operations	Amortize overhead
Memory Layout	Cache-friendly structures
Fast Path	Optimize common case
Defer Work	Lazy evaluation
Right-size Data	Appropriate containers

Resources

• resources/principles.md - Full performance principles (Abseil-inspired)
• resources/optimization_patterns.md - Language-specific optimization patterns

Notes

• Always profile real workloads, not just microbenchmarks
• Focus on algorithmic improvements before micro-optimizations
• Document performance characteristics for future reference