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performance-testing Use for load, stress, and scalability testing of applications and APIs. Covers k6 (Grafana), JMeter, Gatling, Artillery, and Lighthouse for web performance audits. Includes test type definitions, key metrics, thresholds, CI integration patterns, and performance budgets.
USE FOR: k6, JMeter, Gatling, Artillery, Lighthouse, load testing, stress testing, performance benchmarks, Core Web Vitals, throughput testing, spike testing, soak testing, capacity planning, performance budgets
DO NOT USE FOR: functional API testing (use api-testing), browser E2E tests (use e2e-testing), visual regression (use visual-testing)
الانتقال إلى التثبيت سوق المهارات اكتشف واستكشف مهارات الذكاء الاصطناعي التي بناها المجتمع.
التثبيت باستخدام Codex أو Claude انسخ هذا Prompt والصقه في Codex أو Claude أو مساعد آخر ليراجع صفحة Skill ويثبّتها لك.
عرض تفاصيل Prompt يتجاوز الأمر المباشر Prompt المخصّص للمراجعة. افحص المصدر قبل تشغيله.
npx skills add https://github.com/Tyler-R-Kendrick/agent-skills --skill performance-testingيبقى الأمر في سطر واحد. مرّر أفقيًا لمراجعته كاملًا قبل النسخ.
تفضّل نسخة محلية؟ نزّل الملفات المتاحة حاليًا لدى SkillsMP.
تحميل Zip جاري التحميل... المزيد من هذا المستودع Use when producing agent/LLM evals, synthetic simulation data, or self-improvement pipelines for prompts, code, skills, agents, harnesses, and workflows. Covers AgentEvals/AgentV, Agent Skills evals, ASSERT, GEPA, Trace, VISTA, Agent Lightning, SkillOpt, Simula-style data design, progressive disclosure, deterministic workspaces, and release evidence.
USE FOR: eval creation, EVAL.yaml, AgentEvals, AgentV, evals.json, ASSERT, judge-traces, behavior taxonomy, judges, graders, rubrics, synthetic data, simulation data, Simula, QDC, source-grounded generation, prompt optimization, agent improvement, skill improvement, harness hardening, progressive disclosure, deterministic workflows, GEPA, Trace, VISTA, Agent Lightning, SkillOpt
DO NOT USE FOR: ordinary unit/integration tests without AI quality criteria (use testing), refactoring without eval or trace feedback (use refactor), generic Agent Skills packaging without eval or improvement work (use agent-skills)
Use when working with AI agent protocols, standards, interoperability specifications, evaluation contracts, synthetic simulation data, improvement pipelines, and agent steering workflows. Covers MCP, A2A, ACP, Agent Skills, AGENTS.md, ADL, Improve, x402, AP2, MCP Apps, cagent, and learn.
USE FOR: agent protocol selection, comparing MCP vs A2A vs ACP, understanding agent standards ecosystem, choosing payment protocols, choosing eval standards, choosing improvement techniques, choosing synthetic data simulation techniques, steering from user feedback
DO NOT USE FOR: specific protocol, eval, or improvement implementation details (use the sub-skills: mcp, a2a, acp, improve, learn, x402, etc.)
Use when a user corrects, rejects, edits, or redirects an LLM/agent response and the correction should become a reusable reasoning strategy. Converts feedback into generalized learnings for ~/.agents/STEERING.md with linked RDF/Turtle evidence.
USE FOR: user corrections, preference feedback, rejected agent behavior, reasoning strategy updates, steering file maintenance
DO NOT USE FOR: storing task facts (use memory), ordinary skill authoring (use agent-skills), project instruction files unrelated to feedback (use agents-md)
استنادا إلى تصنيف SOC المهني
name performance-testing description Use for load, stress, and scalability testing of applications and APIs. Covers k6 (Grafana), JMeter, Gatling, Artillery, and Lighthouse for web performance audits. Includes test type definitions, key metrics, thresholds, CI integration patterns, and performance budgets.
USE FOR: k6, JMeter, Gatling, Artillery, Lighthouse, load testing, stress testing, performance benchmarks, Core Web Vitals, throughput testing, spike testing, soak testing, capacity planning, performance budgets
DO NOT USE FOR: functional API testing (use api-testing), browser E2E tests (use e2e-testing), visual regression (use visual-testing)
license MIT metadata {"displayName":"Performance Testing","author":"Tyler-R-Kendrick"} compatibility claude, copilot, cursor references [{"title":"k6 Documentation (Grafana)","url":"https://grafana.com/docs/k6/latest/"},{"title":"Apache JMeter","url":"https://jmeter.apache.org"},{"title":"Gatling Documentation","url":"https://docs.gatling.io"},{"title":"Lighthouse Documentation (Chrome Developers)","url":"https://developer.chrome.com/docs/lighthouse/"}]
Performance Testing
Overview
Performance testing validates that your application meets speed, stability, and scalability requirements under expected and extreme conditions. It answers questions like "How many concurrent users can we handle?", "What's the 99th percentile response time?", and "Where does the system break?".
Performance Test Types
Type Goal Pattern When to Use Load Validate expected traffic Ramp to target VUs, sustain, ramp down Before release, capacity planning Stress Find the breaking point Ramp beyond expected capacity Pre-launch, architecture validation Spike Handle sudden traffic bursts Jump to high VUs instantly Flash sales, event-driven traffic Soak Detect memory leaks / degradation Moderate load over hours After major changes, long-running services Breakpoint Determine absolute maximum Continuously increase until failure Capacity planning, SLA definition
Key Metrics
Metric Description Typical Thresholds Response Time (p50) Median latency < 200ms for APIs, < 1s for pages Response Time (p95) 95th percentile latency < 500ms for APIs, < 3s for pages Response Time (p99) 99th percentile latency < 1s for APIs, < 5s for pages Throughput (RPS) Requests per second Application-specific Error Rate % of failed requests < 1% under normal load VU Concurrency Active virtual users Application-specific TTFB Time to first byte < 200ms Core Web Vitals (LCP)
Core Web Vitals (INP) Interaction to Next Paint < 200ms
Core Web Vitals (CLS) Cumulative Layout Shift < 0.1
Cross-Platform Tools Tool Language Strengths k6 (Grafana) JavaScript Developer-friendly, CLI-native, thresholds, scenarios, k6 cloud, k6 browser JMeter Java (GUI + CLI) Mature, GUI test plan builder, extensive protocol support, plugins Gatling Scala / Java High performance, code-based DSL, detailed HTML reports Artillery YAML + JS Simple YAML config, plugin ecosystem, serverless mode Lighthouse CLI / Chrome Web performance audits, Core Web Vitals, accessibility, SEO
k6 (Grafana)
Load Test with Stages, Thresholds, and Checks
import http from "k6/http" ;
import { check, sleep, group } from "k6" ;
export const options = {
stages : [
{ duration : "2m" , target : 50 },
{ duration : "5m" , target : 50 },
{ duration : "2m" , target : 100 },
{ duration : "5m" , target : 100 },
{ duration : "2m" , target : 0 },
],
thresholds : {
http_req_duration : [
"p(50)<200" ,
"p(95)<500" ,
"p(99)<1000" ,
],
http_req_failed : ["rate<0.01" ],
checks : ["rate>0.99" ],
},
};
const BASE_URL = __ENV.BASE_URL || "http://localhost:3000" ;
export default function (group ("Homepage flow" , () => {
const homeRes = http.get (`${BASE_URL} /` );
check (homeRes, {
"homepage returns 200" : (r ) => r.status === 200 ,
"homepage loads under 500ms" : (r ) => r.timings .duration < 500 ,
});
const apiRes = http.get (`${BASE_URL} /api/products?limit=20` );
check (apiRes, {
"products API returns 200" : (r ) => r.status === 200 ,
"products returns array" : (r ) => Array .isArray (r.json ()),
});
});
sleep (1 );
}
k6 Scenarios (Advanced)
import http from "k6/http" ;
import { check } from "k6" ;
export const options = {
scenarios : {
constant_load : {
executor : "constant-arrival-rate" ,
rate : 100 ,
timeUnit : "1s" ,
duration : "5m" ,
preAllocatedVUs : 50 ,
maxVUs : 200 ,
},
ramping_users : {
executor : "ramping-vus" ,
startVUs : 0 ,
stages : [
{ duration : "2m" , target : 50 },
{ duration : "3m" , target : 50 },
{ duration : "1m" , target : 0 },
],
},
spike : {
executor : "ramping-arrival-rate" ,
startRate : 10 ,
timeUnit : "1s" ,
stages : [
{ duration : "10s" , target : 10 },
{ duration : "1m" , target : 500 },
{ duration : "10s" , target : 10 },
],
preAllocatedVUs : 200 ,
maxVUs : 500 ,
},
},
thresholds : {
http_req_duration : ["p(95)<500" ],
http_req_failed : ["rate<0.01" ],
},
};
export default function (const res = http.get (`${__ENV.BASE_URL} /api/health` );
check (res, { "status 200" : (r ) => r.status === 200 });
}
Running k6
k6 run tests/performance/load-test.k6.js
k6 run tests/performance/load-test.k6.js --env BASE_URL=https://staging.example.com
k6 run tests/performance/load-test.k6.js \
--out json=results.json \
--out influxdb=http://localhost:8086/k6
k6 cloud tests/performance/load-test.k6.js
Artillery
YAML Configuration Example
config:
target: "https://staging-api.example.com"
phases:
- name: "Warm up"
duration: 60
arrivalRate: 5
- name: "Ramp up"
duration: 120
arrivalRate: 5
rampTo: 50
- name: "Sustained load"
duration: 300
arrivalRate: 50
defaults:
headers:
Authorization: "Bearer {{ $processEnvironment.AUTH_TOKEN }} "
Content-Type: "application/json"
ensure:
thresholds:
- http.response_time.p95: 500
- http.response_time.p99: 1000
- http.codes.200: 95
plugins:
expect: {}
scenarios:
- name: "Browse and purchase flow"
flow:
- get:
url: "/api/products"
expect:
- statusCode: 200
- hasProperty: "body.length"
capture:
- json: "$[0].id"
as: "productId"
- think: 2
- get:
url: "/api/products/{{ productId }} "
expect:
- statusCode: 200
- think: 1
- post:
url: "/api/cart"
json:
productId: "{{ productId }} "
quantity: 1
expect:
- statusCode: 201
Running Artillery
npm install -g artillery
artillery run tests/performance/artillery-config.yml
artillery run tests/performance/artillery-config.yml --target https://staging.example.com
artillery run tests/performance/artillery-config.yml --output results.json
artillery report results.json --output report.html
artillery quick --count 10 --num 5 https://staging-api.example.com/api/health
JMeter
Overview Apache JMeter is a mature load testing tool with a GUI for building test plans and a CLI mode for CI execution.
Key Concepts Concept Description Test Plan Root container for all test elements Thread Group Defines VUs (threads), ramp-up time, loop count Samplers HTTP Request, JDBC Request, FTP, etc. Assertions Response assertions (status, body, duration) Listeners Results viewers (Summary Report, Graph, JTL files) Config Elements CSV Data Set, HTTP Header Manager, User Variables Timers Think time between requests
CLI Mode for CI
jmeter -n -t test-plan.jmx -l results.jtl -e -o report/
jmeter -n -t test-plan.jmx \
-Jthreads=100 \
-Jrampup=60 \
-Jduration=300 \
-Jhost=staging-api.example.com \
-l results.jtl
jmeter -g results.jtl -o report/
GitHub Actions Integration
jobs:
performance-test:
runs-on: ubuntu-latest
steps:
- uses: actions/checkout@v4
- name: Run JMeter Tests
uses: rbhadti94/apache-jmeter-action@v0.5.0
with:
testFilePath: tests/performance/test-plan.jmx
outputReportsFolder: reports/
args: >
-Jthreads=50 -Jrampup=30 -Jduration=120
-Jhost=${{ secrets.STAGING_HOST }}
- uses: actions/upload-artifact@v4
if: always()
with:
name: jmeter-report
path: reports/
Gatling
Overview Gatling uses a code-based DSL (Scala or Java) for defining simulations, producing detailed HTML reports automatically.
Scala DSL Example // src/test/scala/simulations/BasicSimulation.scala
import io.gatling.core.Predef._
import io.gatling.http.Predef._
import scala.concurrent.duration._
class BasicSimulation extends Simulation {
val httpProtocol = http
.baseUrl("https://staging-api.example.com")
.acceptHeader("application/json")
.authorizationHeader("Bearer ${authToken}")
val feeder = csv("test-data/users.csv").random
val browseScenario = scenario("Browse Products")
.feed(feeder)
.exec(
http("List Products")
.get("/api/products")
.check(status.is(200))
.check(jsonPath("$[0].id").saveAs("productId"))
)
.pause(1, 3)
.exec(
http("Get Product Detail")
.get("/api/products/${productId}")
.check(status.is(200))
)
setUp(
browseScenario.inject(
rampUsers(50).during(2.minutes),
constantUsersPerSec(10).during(5.minutes),
rampUsers(0).during(1.minute)
)
).protocols(httpProtocol)
.assertions(
global.responseTime.percentile(95).lt(500),
global.successfulRequests.percent.gt(99.0)
)
}
Running Gatling
mvn gatling:test
gradle gatlingRun
mvn gatling:test -Dgatling.simulationClass=simulations.BasicSimulation
Lighthouse
Overview Lighthouse audits web performance, accessibility, best practices, and SEO. It measures Core Web Vitals and provides actionable improvement suggestions.
CLI Usage
npm install -g lighthouse
lighthouse https://example.com \
--output json,html \
--output-path ./results/lighthouse \
--chrome-flags="--headless --no-sandbox"
lighthouse https://example.com \
--only-categories=performance \
--output json \
--output-path ./results/perf.json
lighthouse https://example.com \
--budget-path=budgets.json \
--output html
Performance Budget File
[
{
"path" : "/*" ,
"timings" : [
{ "metric" : "interactive" , "budget" : 3000 } ,
{ "metric" : "first-contentful-paint" , "budget" : 1500 } ,
{ "metric" : "largest-contentful-paint" , "budget" : 2500 }
] ,
"resourceSizes" : [
{ "resourceType" : "script" , "budget" : 300 } ,
{ "resourceType" : "total" , "budget" : 1000 }
]
}
]
CI Integration with Lighthouse CI
jobs:
lighthouse:
runs-on: ubuntu-latest
steps:
- uses: actions/checkout@v4
- uses: actions/setup-node@v4
with:
node-version: 20
- run: npm install -g @lhci/cli
- run: |
lhci autorun \
--collect.url=https://staging.example.com \
--collect.numberOfRuns=3 \
--assert.preset=lighthouse:recommended \
--assert.assertions.largest-contentful-paint=warn:2500 \
--assert.assertions.interactive=error:5000
CI Integration Patterns
When to Run Each Test Type Test Type Trigger Duration Gate Smoke (minimal load)Every PR 1-2 min Fail PR if errors Load (expected traffic)Nightly or pre-release 10-20 min Alert on threshold breach Stress (beyond capacity)Pre-release 20-30 min Report, don't gate Soak (extended duration)Weekly or pre-release 2-8 hours Alert on degradation Lighthouse Every PR 1-2 min Warn on budget violation
k6 CI Pipeline Example
name: Performance Tests
on:
pull_request:
branches: [main ]
schedule:
- cron: "0 2 * * *"
jobs:
smoke-test:
if: github.event_name == 'pull_request'
runs-on: ubuntu-latest
steps:
- uses: actions/checkout@v4
- uses: grafana/k6-action@v0.3.1
with:
filename: tests/performance/smoke.k6.js
env:
BASE_URL: ${{ secrets.STAGING_URL }}
load-test:
if: github.event_name == 'schedule'
runs-on: ubuntu-latest
steps:
- uses: actions/checkout@v4
- uses: grafana/k6-action@v0.3.1
with:
filename: tests/performance/load-test.k6.js
env:
BASE_URL: ${{ secrets.STAGING_URL }}
- uses: actions/upload-artifact@v4
if: always()
with:
name: k6-results
path: results/
Best Practices
Test Design
Start with a smoke test (minimal load) to validate the test script works before scaling up.
Use realistic think times (sleep() / pause()) to simulate actual user behavior.
Use data-driven tests with CSV feeders or dynamic data generation to avoid caching skew.
Test the same scenario at different load levels: smoke, load, stress, spike.
Metrics and Thresholds
Always define thresholds — tests without pass/fail criteria are just logs.
Focus on percentiles (p95, p99), not averages — averages hide tail latency.
Track error rate alongside response time — fast errors are still failures.
Baseline before optimizing — run tests against a known-good build first.
CI Integration
Run smoke tests on every PR (fast, catches regressions early).
Run full load tests nightly or pre-release (comprehensive, takes time).
Store results as artifacts for trend analysis over time.
Set thresholds as CI gates: fail the pipeline if p95 exceeds the budget.
Infrastructure
Run performance tests against a dedicated staging environment, not shared dev.
Ensure the load generator has sufficient resources (CPU, network) to avoid bottlenecking the test tool itself.
Use distributed load generation (k6 cloud, JMeter distributed mode) for large-scale tests.
Monitor the system under test (CPU, memory, DB connections) alongside the k6/Artillery metrics.
Reporting
Generate HTML reports for human review (Gatling, JMeter, Artillery all support this).
Export machine-readable results (JSON, JTL) for trend tracking and dashboards.
Compare results against previous runs to catch performance regressions.
Document performance baselines and SLAs in the repository alongside the test scripts.
