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Agent skill

Qe Performance Testing

by proffesor-for-testing in proffesor-for-testing/agentic-qe

Test application performance, scalability, and resilience. An agent skill from proffesor-for-testing/agentic-qe.

MITAuto-check passedTesting & QA

Install Qe Performance Testing

skills CLI
$ npx skills add proffesor-for-testing/agentic-qe --skill qe-performance-testing -a claude-code

Project install by default; add -g for ~/.claude/skills/.

GitHub CLI
$ gh skill install proffesor-for-testing/agentic-qe qe-performance-testing --agent claude-code

Project scope by default; add --scope user for a personal install. Needs GitHub CLI 2.90.0 or later (public preview).

Manual copy
$ git clone --depth 1 https://github.com/proffesor-for-testing/agentic-qe.git skills-src && mkdir -p .claude/skills && cp -r skills-src/.kiro/skills/qe-performance-testing .claude/skills/qe-performance-testing && rm -rf skills-src

Use ~/.claude/skills/ instead of .claude/skills for a personal install. The folder must contain SKILL.md.

Claude Code skills documentation · loads skills from .claude/skills/

Facts

Skill name
qe-performance-testing
GitHub stars
494
Token cost
~2k tokens
SKILL.md length
507 words
Files
1
Skills in repo
95
Repo updated
First seen
Licence
MIT

At a glance

Test application performance, scalability, and resilience. An agent skill from proffesor-for-testing/agentic-qe.

  • Works in 5 steps: DEFINE SLOs: p95 response time,… → IDENTIFY critical paths: revenue flows,… → CREATE realistic scenarios: user… → …
  • Planning load testing
  • SKILL.md covers Quick Reference Card, Defining SLOs, Realistic Scenarios and Common Bottlenecks, plus 8 more sections
  • Instructions only: no scripts, shell commands, URLs or credentials in SKILL.md

What it does

Qe Performance Testing is an agent skill from proffesor-for-testing/agentic-qe. Test application performance, scalability, and resilience. Use when planning load testing, stress testing, or optimizing system performance.

Its SKILL.md is about 2k tokens, which your agent loads only when the skill is triggered. It is a single SKILL.md file with no bundled scripts.

It sits in Testing & QA, covering Load testing. The repository describes itself as: Agentic QE Fleet is an open-source AI-powered QA/QE platform designed for use with Coding Agents (works best with Claude Code) featuring specialized agents and skills to support… The licence is MIT.

When your agent uses it

  • Planning load testing
  • Optimizing system performance

Example prompts

  • “/qe-performance-testing”

Workflow steps

5 steps, taken from the first numbered list in SKILL.md.

  1. DEFINE SLOs: p95 response time, throughput, error rate targets
  2. IDENTIFY critical paths: revenue flows, high-traffic pages, key APIs
  3. CREATE realistic scenarios: user journeys, think time, varied data
  4. EXECUTE with monitoring: CPU, memory, DB queries, network
  5. ANALYZE bottlenecks and fix before production

What it can do on your machine

Read from SKILL.md and the folder at commit 829d030. It shows what the files ask for, not the result of running them.

  • Tool permissions

    Pre-approves nothing: there is no allowed-tools line, so your agent's usual permission prompts apply.

    From allowed-tools in the SKILL.md frontmatter.

  • Runs code

    No scripts in the folder and no shell commands in SKILL.md (its code samples are javascript, typescript and yaml).

    From the folder's file list and the shell code blocks in SKILL.md.

  • Network

    No URLs in SKILL.md.

    From URLs in SKILL.md, links to its own repository left out.

  • Credentials

    Names no API keys, tokens, secrets or passwords.

    From names ending in _API_KEY, _TOKEN, _SECRET, _KEY or _PASSWORD in SKILL.md.

Context cost

Qe Performance Testing loads about 2k tokens when it runs. Until then it costs about 41 tokens; SKILL.md has 507 words of instructions outside code blocks.

Always · name and description, kept in context so the agent knows when to use it
~41
When it runs · the whole SKILL.md, loaded when a task matches
~2k

Estimates: characters ÷ 4, the usual rule of thumb; real counts depend on the model's tokenizer. Scripts and assets cost tokens only if the agent reads them.

Safety

Auto-check passed

The automated check found no risky patterns in SKILL.md.

Automated static check — not a guarantee. Review scripts before installing. It scans the text of SKILL.md for risky patterns (piping downloads into a shell, reading credential files, hidden Unicode, destructive commands); files beside SKILL.md are not scanned.

SKILL.md

The full file from proffesor-for-testing/agentic-qe at commit 829d030, republished under its MIT licence (© proffesor-for-testing). 507 words, ~1,977 tokens.

Download SKILL.mdSave it as .claude/skills/qe-performance-testing/SKILL.md (or your agent's skills folder).
name
qe-performance-testing
description
Test application performance, scalability, and resilience. Use when planning load testing, stress testing, or optimizing system performance.
inclusion
auto
tags
performance, load-testing, stress-testing, scalability, k6, bottlenecks

Performance Testing

<default_to_action> When testing performance or planning load tests:

  1. DEFINE SLOs: p95 response time, throughput, error rate targets
  2. IDENTIFY critical paths: revenue flows, high-traffic pages, key APIs
  3. CREATE realistic scenarios: user journeys, think time, varied data
  4. EXECUTE with monitoring: CPU, memory, DB queries, network
  5. ANALYZE bottlenecks and fix before production

Quick Test Type Selection:

  • Expected load validation → Load testing
  • Find breaking point → Stress testing
  • Sudden traffic spike → Spike testing
  • Memory leaks, resource exhaustion → Endurance/soak testing
  • Horizontal/vertical scaling → Scalability testing

Critical Success Factors:

  • Performance is a feature, not an afterthought
  • Test early and often, not just before release
  • Focus on user-impacting bottlenecks </default_to_action>

Quick Reference Card

When to Use
  • Before major releases
  • After infrastructure changes
  • Before scaling events (Black Friday)
  • When setting SLAs/SLOs
Test Types
TypePurposeWhen
LoadExpected trafficEvery release
StressBeyond capacityQuarterly
SpikeSudden surgeBefore events
EnduranceMemory leaksAfter code changes
ScalabilityScaling validationInfrastructure changes
Key Metrics
MetricTargetWhy
p95 response< 200msUser experience
Throughput10k req/minCapacity
Error rate< 0.1%Reliability
CPU< 70%Headroom
Memory< 80%Stability
Tools
  • k6: Modern, JS-based, CI/CD friendly
  • JMeter: Enterprise, feature-rich
  • Artillery: Simple YAML configs
  • Gatling: Scala, great reporting
Agent Coordination
  • qe-performance-tester: Load test orchestration
  • qe-quality-analyzer: Results analysis
  • qe-production-intelligence: Production comparison

Defining SLOs

Bad: "The system should be fast" Good: "p95 response time < 200ms under 1,000 concurrent users"

javascript
export const options = {
  thresholds: {
    http_req_duration: ['p(95)<200'],  // 95% < 200ms
    http_req_failed: ['rate<0.01'],     // < 1% failures
  },
};

Realistic Scenarios

Bad: Every user hits homepage repeatedly Good: Model actual user behavior

javascript
// Realistic distribution
// 40% browse, 30% search, 20% details, 10% checkout
export default function () {
  const action = Math.random();
  if (action < 0.4) browse();
  else if (action < 0.7) search();
  else if (action < 0.9) viewProduct();
  else checkout();

  sleep(randomInt(1, 5)); // Think time
}

Common Bottlenecks

Database

Symptoms: Slow queries under load, connection pool exhaustion Fixes: Add indexes, optimize N+1 queries, increase pool size, read replicas

N+1 Queries
javascript
// BAD: 100 orders = 101 queries
const orders = await Order.findAll();
for (const order of orders) {
  const customer = await Customer.findById(order.customerId);
}

// GOOD: 1 query
const orders = await Order.findAll({ include: [Customer] });
Synchronous Processing

Problem: Blocking operations in request path (sending email during checkout) Fix: Use message queues, process async, return immediately

Memory Leaks

Detection: Endurance testing, memory profiling Common causes: Event listeners not cleaned, caches without eviction

Show full SKILL.md (206 more words)Show less
External Dependencies

Solutions: Aggressive timeouts, circuit breakers, caching, graceful degradation

k6 CI/CD Example

javascript
// performance-test.js
import http from 'k6/http';
import { check, sleep } from 'k6';

export const options = {
  stages: [
    { duration: '1m', target: 50 },   // Ramp up
    { duration: '3m', target: 50 },   // Steady
    { duration: '1m', target: 0 },    // Ramp down
  ],
  thresholds: {
    http_req_duration: ['p(95)<200'],
    http_req_failed: ['rate<0.01'],
  },
};

export default function () {
  const res = http.get('https://api.example.com/products');
  check(res, {
    'status is 200': (r) => r.status === 200,
    'response time < 200ms': (r) => r.timings.duration < 200,
  });
  sleep(1);
}
yaml
# GitHub Actions
- name: Run k6 test
  uses: grafana/k6-action@v0.3.0
  with:
    filename: performance-test.js

Analyzing Results

Good Results
Load: 1,000 users | p95: 180ms | Throughput: 5,000 req/s
Error rate: 0.05% | CPU: 65% | Memory: 70%
Problems
Load: 1,000 users | p95: 3,500ms ❌ | Throughput: 500 req/s ❌
Error rate: 5% ❌ | CPU: 95% ❌ | Memory: 90% ❌
Root Cause Analysis
  1. Correlate metrics: When response time spikes, what changes?
  2. Check logs: Errors, warnings, slow queries
  3. Profile code: Where is time spent?
  4. Monitor resources: CPU, memory, disk
  5. Trace requests: End-to-end flow

Anti-Patterns

❌ Anti-Pattern✅ Better
Testing too lateTest early and often
Unrealistic scenariosModel real user behavior
0 to 1000 users instantlyRamp up gradually
No monitoring during testsMonitor everything
No baselineEstablish and track trends
One-time testingContinuous performance testing

Agent-Assisted Performance Testing

typescript
// Comprehensive load test
await Task("Load Test", {
  target: 'https://api.example.com',
  scenarios: {
    checkout: { vus: 100, duration: '5m' },
    search: { vus: 200, duration: '5m' },
    browse: { vus: 500, duration: '5m' }
  },
  thresholds: {
    'http_req_duration': ['p(95)<200'],
    'http_req_failed': ['rate<0.01']
  }
}, "qe-performance-tester");

// Bottleneck analysis
await Task("Analyze Bottlenecks", {
  testResults: perfTest,
  metrics: ['cpu', 'memory', 'db_queries', 'network']
}, "qe-performance-tester");

// CI integration
await Task("CI Performance Gate", {
  mode: 'smoke',
  duration: '1m',
  vus: 10,
  failOn: { 'p95_response_time': 300, 'error_rate': 0.01 }
}, "qe-performance-tester");

Agent Coordination Hints

Memory Namespace
aqe/performance/
├── results/*       - Test execution results
├── baselines/*     - Performance baselines
├── bottlenecks/*   - Identified bottlenecks
└── trends/*        - Historical trends
Fleet Coordination
typescript
const perfFleet = await FleetManager.coordinate({
  strategy: 'performance-testing',
  agents: [
    'qe-performance-tester',
    'qe-quality-analyzer',
    'qe-production-intelligence',
    'qe-deployment-readiness'
  ],
  topology: 'sequential'
});

Pre-Production Checklist

  • Load test passed (expected traffic)
  • Stress test passed (2-3x expected)
  • Spike test passed (sudden surge)
  • Endurance test passed (24+ hours)
  • Database indexes in place
  • Caching configured
  • Monitoring and alerting set up
  • Performance baseline established

Remember

Performance is a feature: Test it like functionality Test continuously: Not just before launch Monitor production: Synthetic + real user monitoring Fix what matters: Focus on user-impacting bottlenecks Trend over time: Catch degradation early

With Agents: Agents automate load testing, analyze bottlenecks, and compare with production. Use agents to maintain performance at scale.

© proffesor-for-testing, MIT. Rendered from Markdown: HTML in the file is shown as text, images as links, and headings moved down two levels. Raw file

Files

Just SKILL.md in .kiro/skills/qe-performance-testing of proffesor-for-testing/agentic-qe.

Open the folder on GitHubat commit 829d030

Compare with similar skills

Qe Performance Testing next to the 5 skills that share the most tags, products or categories with it. Stars are the repository's; “used in” counts other GitHub owners with a copy.

Qe Performance Testing compared with similar skills
SkillStarsUsed inTokensAuto-checkLicenceRepo updated
Qe Performance Testing this skillproffesor-for-testing/agentic-qe494—~2kAutomated safety check: PassMIT
Writing Livekit Scenarioslivekit-examples/agent-starter-python2641 repos~2.5kAutomated safety check: PassMIT
Go Testingcxuu/golang-skills1701 repos~1.3kAutomated safety check: PassApache-2.0
Goalcraftgrp06/goalcraft102—~3.8kAutomated safety check: PassMIT
Thinking Partnermattnowdev/thinking-partner206—~4.4kAutomated safety check: PassMIT
Visionkunchenguid/vision329—~2.9kAutomated safety check: PassMIT

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Categories

Questions about Qe Performance Testing

What does Qe Performance Testing do?

Test application performance, scalability, and resilience. An agent skill from proffesor-for-testing/agentic-qe. Qe Performance Testing is an agent skill from proffesor-for-testing/agentic-qe. Test application performance, scalability, and resilience.

When should I use Qe Performance Testing?

Qe Performance Testing fits situations like: planning load testing; optimizing system performance.

How do I install Qe Performance Testing in Claude Code?

Run `npx skills add proffesor-for-testing/agentic-qe --skill qe-performance-testing -a claude-code`. Or copy the skill folder (.kiro/skills/qe-performance-testing in proffesor-for-testing/agentic-qe) into .claude/skills/qe-performance-testing in your project. Claude Code loads it when a task matches its description.

How do I install Qe Performance Testing in Codex?

Run `npx skills add proffesor-for-testing/agentic-qe --skill qe-performance-testing -a codex`. Or copy the skill folder (.kiro/skills/qe-performance-testing in proffesor-for-testing/agentic-qe) into .agents/skills/qe-performance-testing in your project. Codex loads it when a task matches its description.

Can I use Qe Performance Testing in Cursor, Gemini CLI or GitHub Copilot?

Cursor, Gemini CLI, GitHub Copilot and OpenCode also load SKILL.md folders. With the skills CLI, run `npx skills add proffesor-for-testing/agentic-qe --skill qe-performance-testing -a cursor` (or -a gemini-cli, github-copilot or opencode for the others). To copy it by hand, put the folder in .cursor/skills/qe-performance-testing, .gemini/skills/qe-performance-testing, .github/skills/qe-performance-testing and .opencode/skills/qe-performance-testing in your project.

What does Qe Performance Testing need to run?

SKILL.md names no scripts, command-line tools or credentials: Qe Performance Testing is instructions for the agent only.

Does Qe Performance Testing access the network?

SKILL.md contains no URLs. Any network use would come from the scripts or tools the agent runs. This is read from the text; nothing was executed.

Is Qe Performance Testing safe to install?

Our automated static check of SKILL.md found no risky patterns, such as piping downloads into a shell, reading credential files or hidden Unicode. It is not a guarantee. Review the folder before installing.

What licence does Qe Performance Testing use?

Qe Performance Testing is published under the MIT licence (the repository's licence). It allows redistribution, so the full SKILL.md is shown on this page.

How many tokens does Qe Performance Testing use?

About 2k tokens (SKILL.md is roughly 7.9k characters). Agents keep only the skill's name and description in context until a task matches; then they load SKILL.md in full.

What are the alternatives to Qe Performance Testing?

Skills that share tags, products or a category with Qe Performance Testing: Writing Livekit Scenarios (livekit-examples/agent-starter-python, 264 stars), Go Testing (cxuu/golang-skills, 170 stars), Goalcraft (grp06/goalcraft, 102 stars) and Thinking Partner (mattnowdev/thinking-partner, 206 stars). The comparison table on this page puts their stars, adoption, token cost, safety result and licence side by side.

Who maintains Qe Performance Testing?

proffesor-for-testing (a GitHub user) maintains it in proffesor-for-testing/agentic-qe, which has 494 GitHub stars. The repository holds 95 skills in this directory. The repository was last updated on October 4, 2026.

Source: proffesor-for-testing/agentic-qe on GitHub. Facts on this page come from the repository at the commit we read; the author's words are quoted as theirs.