performance-profiler

name	performance-profiler
description	App is slow and you don't know where the bottleneck is
executor	HYBRID
skill_id	engineering.cs-engineering.performance-profiler
status	ADOPTED
security	{"level":"standard","pii":false,"approval_required":false}
anchors	["engineering","performance"]
tier	2
input_schema	[{"name":"code_or_task","type":"string","description":"Code snippet, script, or task description to process","required":true}]
output_schema	[{"name":"result","type":"string","description":"Primary output from performance profiler"}]

Performance Profiler

Tier: POWERFUL
Category: Engineering
Domain: Performance Engineering

---

Overview

Systematic performance profiling for Node.js, Python, and Go applications. Identifies CPU, memory, and I/O bottlenecks; generates flamegraphs; analyzes bundle sizes; optimizes database queries; detects memory leaks; and runs load tests with k6 and Artillery. Always measures before and after.

Core Capabilities

• CPU profiling — flamegraphs for Node.js, py-spy for Python, pprof for Go
• Memory profiling — heap snapshots, leak detection, GC pressure
• Bundle analysis — webpack-bundle-analyzer, Next.js bundle analyzer
• Database optimization — EXPLAIN ANALYZE, slow query log, N+1 detection
• Load testing — k6 scripts, Artillery scenarios, ramp-up patterns
• Before/after measurement — establish baseline, profile, optimize, verify

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When to Use

• App is slow and you don't know where the bottleneck is
• P99 latency exceeds SLA before a release
• Memory usage grows over time (suspected leak)
• Bundle size increased after adding dependencies
• Preparing for a traffic spike (load test before launch)
• Database queries taking >100ms

---

Quick Start

# Analyze a project for performance risk indicators
python3 scripts/performance_profiler.py /path/to/project

# JSON output for CI integration
python3 scripts/performance_profiler.py /path/to/project --json

# Custom large-file threshold
python3 scripts/performance_profiler.py /path/to/project --large-file-threshold-kb 256

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Golden Rule: Measure First

# Establish baseline BEFORE any optimization
# Record: P50, P95, P99 latency | RPS | error rate | memory usage

# Wrong: "I think the N+1 query is slow, let me fix it"
# Right: Profile → confirm bottleneck → fix → measure again → verify improvement

---

Node.js Profiling

→ See references/profiling-recipes.md for details

Before/After Measurement Template

 2026-03-01  
 @username  
 PROJ-123  


[1-2 sentences: what was slow, how was it observed]


[What the profiler revealed]


| Metric | Value |
|--------|-------|
| P50 latency | 480ms |
| P95 latency | 1,240ms |
| P99 latency | 3,100ms |
| RPS @ 50 VUs | 42 |
| Error rate | 0.8% |
| DB queries/req | 23 (N+1) |

Profiler evidence: [link to flamegraph or screenshot]


[What changed — code diff or description]


| Metric | Before | After | Delta |
|--------|--------|-------|-------|
| P50 latency | 480ms | 48ms | -90% |
| P95 latency | 1,240ms | 120ms | -90% |
| P99 latency | 3,100ms | 280ms | -91% |
| RPS @ 50 VUs | 42 | 380 | +804% |
| Error rate | 0.8% | 0% | -100% |
| DB queries/req | 23 | 1 | -96% |


Load test run: [link to k6 output]