菜单
Performance and load testing patterns — k6 load tests, Locust stress tests, pytest execution optimization (xdist parallel, plugins), test type classification, and performance benchmarking. Use when writing load tests, optimizing test execution speed, or setting up pytest infrastructure.
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基于 SOC 职业分类
| name | testing-perf |
| license | MIT |
| compatibility | Claude Code 2.1.220+. |
| description | Performance and load testing patterns — k6 load tests, Locust stress tests, pytest execution optimization (xdist parallel, plugins), test type classification, and performance benchmarking. Use when writing load tests, optimizing test execution speed, or setting up pytest infrastructure. |
| tags | ["testing","performance","k6","locust","pytest","load-testing","benchmarking"] |
| context | fork |
| agent | test-generator |
| version | 2.1.0 |
| author | OrchestKit |
| user-invocable | false |
| disable-model-invocation | false |
| complexity | medium |
| persuasion-type | reference |
| targets | [{"library":"k6","version":">=1.0.0"},{"library":"locust","version":">=2.40.0"}] |
| metadata | {"category":"document-asset-creation"} |
| allowed-tools | ["Read","Glob","Grep","WebFetch","WebSearch"] |
Focused skill for performance testing, load testing, and pytest execution optimization. Covers k6, Locust, pytest-xdist parallel execution, custom plugins, and test type classification.
| Area | File | Purpose |
|---|---|---|
| k6 Load Testing | rules/perf-k6.md | Thresholds, stages, custom metrics, CI integration |
| Locust Testing | rules/perf-locust.md | Python load tests, task weighting, auth flows |
| Test Types | rules/perf-types.md | Load, stress, spike, soak test patterns |
| Execution | rules/execution.md | Coverage reporting, parallel execution, failure analysis |
| Pytest Markers | rules/pytest-execution.md | Custom markers, xdist parallel, worker isolation |
| Pytest Plugins | rules/pytest-plugins.md | Factory fixtures, plugin hooks, anti-patterns |
| k6 Patterns | references/k6-patterns.md | Staged ramp-up, authenticated requests, test types |
| xdist Parallel | references/xdist-parallel.md | Distribution modes, worker isolation, CI config |
| Custom Plugins | references/custom-plugins.md | conftest plugins, installable plugins, hook reference |
| Perf Checklist | checklists/performance-checklist.md | Planning, setup, metrics, load patterns, analysis |
| Pytest Checklist | checklists/pytest-production-checklist.md | Config, markers, parallel, fixtures, CI/CD |
| Test Template | scripts/test-case-template.md | Full test case documentation template |
Set up a load test with thresholds and staged ramp-up:
import http from 'k6/http';
import { check, sleep } from 'k6';
export const options = {
stages: [
{ duration: '30s', target: 20 }, // Ramp up
{ duration: '1m', target: 20 }, // Steady state
{ duration: '30s', target: 0 }, // Ramp down
],
thresholds: {
http_req_duration: ['p(95)<500'], // 95th percentile under 500ms
http_req_failed: ['rate<0.01'], // Less than 1% error rate
},
};
export default function () {
const res = http.get('http://localhost:8000/api/health');
check(res, {
'status is 200': (r) => r.status === 200,
'response time < 200ms': (r) => r.timings.duration < 200,
});
sleep(1);
}
Run: k6 run --out json=results.json tests/load/api.js
k6 run tests/load/api.ts — no compilation step needed.k6 run pulls required extensions automatically; manual xk6 build is superseded for most workflows.import browser from 'k6/browser' — the old k6/experimental/browser path was removed in v0.52+. Any generated code using /experimental/ will fail.k6 run --out experimental-opentelemetry=... — stream results straight to your tracing backend.import http from 'k6/http'
import browser from 'k6/browser'
import { check } from 'k6'
export const options = { vus: 5, duration: '30s' }
export default async function () {
const page = await browser.newPage()
await page.goto('https://example.com')
check(page, { 'title present': async p => (await p.title()).length > 0 })
await page.close()
}
| Type | Duration | VUs | Purpose | When to Use |
|---|---|---|---|---|
| Load | 5-10 min | Expected traffic | Validate normal conditions | Every release |
| Stress | 10-20 min | 2-3x expected | Find breaking point | Pre-launch |
| Spike | 5 min | Sudden 10x surge | Test auto-scaling | Before events |
| Soak | 4-12 hours | Normal load | Detect memory leaks | Weekly/nightly |
Speed up test suites with pytest-xdist:
# pyproject.toml
[tool.pytest.ini_options]
addopts = ["-n", "auto", "--dist", "loadscope"]
markers = [
"slow: marks tests as slow",
"smoke: critical path tests for CI/CD",
]
# Run with parallel workers and coverage
pytest -n auto --dist loadscope --cov=app --cov-report=term-missing --maxfail=3
# CI fast path — skip slow tests
pytest -m "not slow" -n auto
# Debug mode — single worker
pytest -n 0 -x --tb=long
When running parallel tests with databases, isolate per worker:
@pytest.fixture(scope="session")
def db_engine(worker_id):
db_name = f"test_db_{worker_id}" if worker_id != "master" else "test_db"
engine = create_engine(f"postgresql://localhost/{db_name}")
yield engine
engine.dispose()
| Metric | Target | Tool |
|---|---|---|
| p95 response time | < 500ms | k6 |
| p99 response time | < 1000ms | k6 |
| Error rate | < 1% | k6 / Locust |
| Business logic coverage | 90% | pytest-cov |
| Critical path coverage | 100% | pytest-cov |
| Scenario | Recommendation |
|---|---|
| JavaScript/TypeScript team | k6 for load testing |
| Python team | Locust for load testing |
| Need CI thresholds | k6 (built-in threshold support) |
| Need distributed testing | Locust (built-in distributed mode) |
| Slow test suite | pytest-xdist with -n auto |
| Flaky parallel tests | --dist loadscope for fixture grouping |
| DB-heavy tests | Worker-isolated databases with worker_id |
ork:testing-unit — Unit testing patterns, pytest fixturesork:testing-e2e — End-to-end performance testing with Playwrightork:performance — Core Web Vitals and optimization patternsChain patterns for multi-phase pipelines: MCP detection, handoff files, checkpoint-resume, worktree agents, CronCreate monitoring. Use when building or debugging a pipeline skill.
OrchestKit doctor for health diagnostics across manifest integrity, hook configuration, skill validation, agent frontmatter, MCP server connectivity, CC version compatibility, and permission rules. Reports issues with severity levels and auto-remediation suggestions. Validates component counts, detects orphaned entries, and checks CC version matrix compliance. Use when diagnosing plugin health, troubleshooting configuration issues, or running pre-release checks.
Inspects the OrchestKit telemetry pipeline for the current project — lists all known telemetry files with write counts, sizes, schema status, growth trend, and orphan detection. Use when verifying the observability pipeline is healthy, debugging a missing writer, or auditing which files have schema locks vs. which are drift-vulnerable. Read-only — never modifies telemetry files.