performance-engineering

Name: performance-engineering
Author: ancoleman

52installs59forks

How to install performance-engineering?

ancoleman/ai-design-components/performance-engineering

$npx -y skills add ancoleman/ai-design-components --skill performance-engineering

Installs into the current project.

Summary

When validating system performance under load, identifying bottlenecks through profiling, or optimizing application responsiveness. Covers load testing (k6, Locust), profiling (CPU, memory, I/O), and optimization strategies (caching, query optimization, Core Web Vitals). Use for

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SKILL.md

1	# Performance Engineering
2
3	## Purpose
4
5	Performance engineering encompasses load testing, profiling, and optimization to deliver reliable, scalable systems. This skill provides frameworks for choosing the right performance testing approach (load, stress, soak, spike), profiling techniques to identify bottlenecks (CPU, memory, I/O), and optimization strategies for backend APIs, databases, and frontend applications.
6
7	Use this skill to validate system capacity before launch, detect performance regressions in CI/CD pipelines, identify and resolve bottlenecks through profiling, and optimize application responsiveness across the stack.
8
9	## When to Use This Skill
10
11	Common Triggers:
12	- "Validate API can handle expected traffic"
13	- "Find maximum capacity and breaking points"
14	- "Identify why the application is slow"
15	- "Detect memory leaks or resource exhaustion"
16	- "Optimize Core Web Vitals for SEO"
17	- "Set up performance testing in CI/CD"
18	- "Reduce cloud infrastructure costs"
19
20	Use Cases:
21	- Pre-launch capacity planning and load validation
22	- Post-refactor performance regression testing
23	- Investigating slow response times or high latency
24	- Detecting memory leaks in long-running services
25	- Optimizing database query performance
26	- Validating auto-scaling configuration
27	- Establishing performance SLOs and budgets
28
29	## Performance Testing Types
30
31	### Load Testing
32	Validate system behavior under expected traffic levels.
33
34	When to use: Pre-launch capacity planning, regression testing after refactors, validating auto-scaling.
35
36	### Stress Testing
37	Find system capacity limits and failure modes.
38
39	When to use: Capacity planning, understanding failure behavior, infrastructure sizing decisions.
40
41	### Soak Testing
42	Identify memory leaks, resource exhaustion, and degradation over time.
43
44	When to use: Detecting memory leaks, validating connection pool cleanup, testing long-running batch jobs.
45
46	### Spike Testing
47	Validate system response to sudden traffic spikes.
48
49	When to use: Validating auto-scaling, testing event-driven systems (product launches), ensuring rate limiting works.
50
51	## Quick Decision Framework
52
53	Which test type to use?
54
55	```
56	What am I trying to learn?
57	├─ Can my system handle expected traffic? → LOAD TEST
58	├─ What's the maximum capacity? → STRESS TEST
59	├─ Will it stay stable over time? → SOAK TEST
60	└─ Can it handle traffic spikes? → SPIKE TEST
61	```
62
63	For detailed testing patterns, load scenarios, and interpreting results, see `references/testing-types.md`.
64
65	## Load Testing Quick Starts
66
67	### k6 (JavaScript)
68
69	Installation:
70	```bash
71	brew install k6 # macOS
72	sudo apt-get install k6 # Linux
73	```
74
75	Basic Load Test:
76	```javascript
77	import http from 'k6/http';
78	import { check, sleep } from 'k6';
79
80	export const options = {
81	stages: [
82	{ duration: '30s', target: 20 },
83	{ duration: '1m', target: 20 },
84	{ duration: '30s', target: 0 },
85	],
86	thresholds: {
87	http_req_duration: ['p(95)<500'],
88	http_req_failed: ['rate<0.01'],
89	},
90	};
91
92	export default function () {
93	const res = http.get('https://api.example.com/products');
94	check(res, {
95	'status is 200': (r) => r.status === 200,
96	});
97	sleep(1);
98	}
99	```
100
101	Run: `k6 run script.js`
102
103	For stress, soak, and spike testing examples, see `examples/k6/`.
104
105	### Locust (Python)
106
107	Installation:
108	```bash
109	pip install locust
110	```
111
112	Basic Load Test:
113	```python
114	from locust import HttpUser, task, between
115
116	class WebsiteUser(HttpUser):
117	wait_time = between(1, 3)
118	host = "https://api.example.com"
119
120	@task(3)
121	def view_products(self):
122	self.client.get("/products")
123
124	@task(1)
125	def view_product_detail(self):
126	self.client.get("/products/123")
127	```
128
129	Run: `locust -f locustfile.py --headless -u 100 -r 10 --run-time 10m`
130
131	For REST API testing and data-driven testing, see `examples/locust/`.
132
133	## Profiling Quick Starts
134
135	### When to Profile
136
137	\| Symptom \| Profiling Type \| Tool \|
138	\|---------\|----------------\|------\|
139	\| High CPU (>70%) \| CPU Profiling \| py-spy, pprof, DevTools \|
140	\| Memory growing \| Memory Profiling \| memory_profiler, pprof heap \|
141	\| Slow response, low CPU \| I/O Profiling \| Query logs, pprof block \|
142
143	### Python Profiling
144
145	py-spy (Production-Safe):
146	```bash
147	pip install py-spy
148
149	# Profile running process
150	py-spy record -o profile.svg --pid <PID> --duration 30
151
152	# Top-like view
153	py-spy top --pid <PID>
154	```
155
156	Memory Profiling:
157	```python
158	from memory_profiler import profile
159
160	@profile
161	def my_function():
162	a = [1] * (10 ** 6)
163	return a
164
165	# Run: python -m memory_profiler script.py
166	```
167
168	### Go Profiling
169
170	pprof (Built-in):
171	```go
172	import (
173	"net/http"

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Type

performance-engineering

52installs59forks

View on GitHub

How to install performance-engineering?

ancoleman/ai-design-components/performance-engineering

$npx -y skills add ancoleman/ai-design-components --skill performance-engineering

Installs into the current project.

Summary

Files · 1

View on GitHub

SKILL.md

1	# Performance Engineering
2
3	## Purpose
4
5	Performance engineering encompasses load testing, profiling, and optimization to deliver reliable, scalable systems. This skill provides frameworks for choosing the right performance testing approach (load, stress, soak, spike), profiling techniques to identify bottlenecks (CPU, memory, I/O), and optimization strategies for backend APIs, databases, and frontend applications.
6
7	Use this skill to validate system capacity before launch, detect performance regressions in CI/CD pipelines, identify and resolve bottlenecks through profiling, and optimize application responsiveness across the stack.
8
9	## When to Use This Skill
10
11	Common Triggers:
12	- "Validate API can handle expected traffic"
13	- "Find maximum capacity and breaking points"
14	- "Identify why the application is slow"
15	- "Detect memory leaks or resource exhaustion"
16	- "Optimize Core Web Vitals for SEO"
17	- "Set up performance testing in CI/CD"
18	- "Reduce cloud infrastructure costs"
19
20	Use Cases:
21	- Pre-launch capacity planning and load validation
22	- Post-refactor performance regression testing
23	- Investigating slow response times or high latency
24	- Detecting memory leaks in long-running services
25	- Optimizing database query performance
26	- Validating auto-scaling configuration
27	- Establishing performance SLOs and budgets
28
29	## Performance Testing Types
30
31	### Load Testing
32	Validate system behavior under expected traffic levels.
33
34	When to use: Pre-launch capacity planning, regression testing after refactors, validating auto-scaling.
35
36	### Stress Testing
37	Find system capacity limits and failure modes.
38
39	When to use: Capacity planning, understanding failure behavior, infrastructure sizing decisions.
40
41	### Soak Testing
42	Identify memory leaks, resource exhaustion, and degradation over time.
43
44	When to use: Detecting memory leaks, validating connection pool cleanup, testing long-running batch jobs.
45
46	### Spike Testing
47	Validate system response to sudden traffic spikes.
48
49	When to use: Validating auto-scaling, testing event-driven systems (product launches), ensuring rate limiting works.
50
51	## Quick Decision Framework
52
53	Which test type to use?
54
55	```
56	What am I trying to learn?
57	├─ Can my system handle expected traffic? → LOAD TEST
58	├─ What's the maximum capacity? → STRESS TEST
59	├─ Will it stay stable over time? → SOAK TEST
60	└─ Can it handle traffic spikes? → SPIKE TEST
61	```
62
63	For detailed testing patterns, load scenarios, and interpreting results, see `references/testing-types.md`.
64
65	## Load Testing Quick Starts
66
67	### k6 (JavaScript)
68
69	Installation:
70	```bash
71	brew install k6 # macOS
72	sudo apt-get install k6 # Linux
73	```
74
75	Basic Load Test:
76	```javascript
77	import http from 'k6/http';
78	import { check, sleep } from 'k6';
79
80	export const options = {
81	stages: [
82	{ duration: '30s', target: 20 },
83	{ duration: '1m', target: 20 },
84	{ duration: '30s', target: 0 },
85	],
86	thresholds: {
87	http_req_duration: ['p(95)<500'],
88	http_req_failed: ['rate<0.01'],
89	},
90	};
91
92	export default function () {
93	const res = http.get('https://api.example.com/products');
94	check(res, {
95	'status is 200': (r) => r.status === 200,
96	});
97	sleep(1);
98	}
99	```
100
101	Run: `k6 run script.js`
102
103	For stress, soak, and spike testing examples, see `examples/k6/`.
104
105	### Locust (Python)
106
107	Installation:
108	```bash
109	pip install locust
110	```
111
112	Basic Load Test:
113	```python
114	from locust import HttpUser, task, between
115
116	class WebsiteUser(HttpUser):
117	wait_time = between(1, 3)
118	host = "https://api.example.com"
119
120	@task(3)
121	def view_products(self):
122	self.client.get("/products")
123
124	@task(1)
125	def view_product_detail(self):
126	self.client.get("/products/123")
127	```
128
129	Run: `locust -f locustfile.py --headless -u 100 -r 10 --run-time 10m`
130
131	For REST API testing and data-driven testing, see `examples/locust/`.
132
133	## Profiling Quick Starts
134
135	### When to Profile
136
137	\| Symptom \| Profiling Type \| Tool \|
138	\|---------\|----------------\|------\|
139	\| High CPU (>70%) \| CPU Profiling \| py-spy, pprof, DevTools \|
140	\| Memory growing \| Memory Profiling \| memory_profiler, pprof heap \|
141	\| Slow response, low CPU \| I/O Profiling \| Query logs, pprof block \|
142
143	### Python Profiling
144
145	py-spy (Production-Safe):
146	```bash
147	pip install py-spy
148
149	# Profile running process
150	py-spy record -o profile.svg --pid <PID> --duration 30
151
152	# Top-like view
153	py-spy top --pid <PID>
154	```
155
156	Memory Profiling:
157	```python
158	from memory_profiler import profile
159
160	@profile
161	def my_function():
162	a = [1] * (10 ** 6)
163	return a
164
165	# Run: python -m memory_profiler script.py
166	```
167
168	### Go Profiling
169
170	pprof (Built-in):
171	```go
172	import (
173	"net/http"

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Type