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

Performance Reviewer

Review code for performance issues and run cross-language benchmarks.

Quick Start

Run benchmarks comparing AILANG vs Python vs compiled Go:

# Run all standard benchmarks
.claude/skills/perf-reviewer/scripts/benchmark.sh

# Run specific benchmark
.claude/skills/perf-reviewer/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 wants to compare AILANG vs Python vs Go
• 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 cross-language benchmarks comparing AILANG interpreted, Python, and AILANG compiled to Go.

Available benchmarks: fibonacci, sort, transform, all

Output: Timing comparisons, speedup ratios, and recommendations.

scripts/profile_ailang.sh <file.ail>

Profile an AILANG file with timing breakdown by compilation phase.

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(n²) 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(n²) 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 (Cross-Language Comparison)

Run benchmarks:

# Full benchmark suite
.claude/skills/perf-reviewer/scripts/benchmark.sh all

# Single benchmark
.claude/skills/perf-reviewer/scripts/benchmark.sh fibonacci

Interpret results:

Ratio	Interpretation
Go/AILANG < 0.1x	Compiled Go is 10x+ faster (expected)
Python/AILANG ~ 1x	Similar interpreted performance
AILANG/Go > 10x	Consider compilation for this workload

3. Profiling (Phase Breakdown)

.claude/skills/perf-reviewer/scripts/profile_ailang.sh examples/compute_heavy.ail

Phase timing helps identify:

• Slow parsing -> complex syntax
• Slow type checking -> deep type inference
• Slow evaluation -> algorithmic issues

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/go_patterns.md - Go-specific optimization patterns

Notes

• Compiled AILANG (Go) should be 10-100x faster than interpreted
• Python comparison provides baseline for interpreted languages
• Always profile real workloads, not just microbenchmarks