perf

Performance Mode

Recommended model tier: smart (opus) - this skill requires complex reasoning

Systematic approach to identifying and fixing performance issues.

Prerequisites

Before starting:

• Identify the specific operation or endpoint that is slow
• Understand what "fast enough" means (target latency, throughput)
• Ensure you can measure performance reproducibly

Workflow

Step 1: Establish Baseline Measurement

Never optimize without data. Measure current performance:

# Node.js - simple timing
time node script.js

# Node.js - CPU profiling
node --cpu-prof script.js
# Creates CPU.*.cpuprofile - analyze in Chrome DevTools

# Go - benchmarks
go test -bench=. -benchmem ./...

# API endpoint (ENDPOINT_URL is the URL under test)
curl -w "@curl-format.txt" -o /dev/null -s "$ENDPOINT_URL"

Record baseline metrics:

• Execution time (p50, p95, p99 if available)
• Memory usage
• Number of operations per second
• Number of I/O operations

Step 2: Identify Hotspots

Find where time is being spent:

# Node.js profiling
node --cpu-prof app.js
# Then load .cpuprofile in Chrome DevTools > Performance

# Go profiling
go test -cpuprofile=cpu.prof -bench=.
go tool pprof -http=:8080 cpu.prof

# Get structural overview of suspect files (signatures + line ranges, not full content)
mcp__plugin_aide_aide__code_outline file="path/to/hotspot.ts"

# Find functions/classes in suspect area by name
mcp__plugin_aide_aide__code_search query="processData" kind="function"

# Find all callers of a hot function
mcp__plugin_aide_aide__code_references symbol="processData"

# Search for expensive patterns in code bodies (Grep is better here)
Grep for ".forEach(", ".map(", ".filter("    # Loop/iteration patterns
Grep for "SELECT", "find(", "query("         # Database queries
Grep for "fetch(", "axios", "http.Get"       # Network calls
Grep for "setTimeout", "setInterval"         # Timers
Grep for "JSON.parse", "JSON.stringify"      # Serialization