huawei-cloud-cce-pressure-test

Runs and evaluates Huawei Cloud CCE workload pressure tests using hcloud, kubectl-cce, and k6.

1|Updated May 27, 2026
One-click install
npx skills add https://github.com/pancake0001/huawei-cloudnative-skills --skill huawei-cloud-cce-pressure-test-pancake0001
Or copy as Structured Prompt for Agent▼
Please help me install this Agent Skill.
Skill: huawei-cloud-cce-pressure-test
Source: https://github.com/pancake0001/huawei-cloudnative-skills/tree/main/releases/container/cce/huawei-cloud-cce-pressure-test
Command: npx skills add https://github.com/pancake0001/huawei-cloudnative-skills --skill huawei-cloud-cce-pressure-test-pancake0001

SYSTEM DOCUMENTATION & REQUIREMENTS

💡 This Skill includes references (resource) components.

What problem does it solve? Running a controlled pressure test against a Huawei Cloud CCE workload requires coordinating cluster discovery, Kubernetes route verification, traffic generation, and evidence collection across multiple tools, and mistakes can affect production traffic or create billable resources. This Skill orchestrates that entire workflow with strict approval gates and produces an evidence-backed bottleneck report. ## Core Features & Use Cases - End-to-end pressure test orchestration: Discovers CCE clusters with hcloud, verifies routes and RBAC with the kubectl-cce plugin, and generates traffic with local k6 or an approved in-cluster k6 Job. - Staged traffic with safety gates: Enforces smoke-before-load phases, explicit approval for any mutation or traffic, stop conditions, and cleanup rules that never touch user resources. - Bottleneck analysis and reporting: Correlates k6 latency/error results with Pod metrics, HPA behavior, Events, and optional ELB/VPC evidence into a structured Markdown report with ranked findings. - Use Case: Ask the agent to run a ramp load test against an Ingress-fronted Deployment in cluster cn-north-4, and it will preflight the route, run a smoke phase, execute the approved k6 test, and report whether the bottleneck is the application, HPA lag, node capacity, or the ELB layer. ## Quick Start Ask the agent to run a smoke pressure test with k6 against your CCE workload by providing the region, cluster ID, namespace, workload name, and target URL.

Frequently Asked Questions about huawei-cloud-cce-pressure-test

High-intent search queries and answers about installing and using this skill.

FAQPage Schema
How do I run a pressure test on a Huawei Cloud CCE workload?▼

Provide the region, cluster ID, namespace, workload name, and target URL, then the skill verifies cluster access with hcloud and kubectl-cce, runs a low-volume k6 smoke test, and only proceeds to larger approved phases after smoke succeeds.

How to run k6 load tests inside a Kubernetes cluster?▼

Use the in-cluster k6 Job mode when the target is only reachable internally. The skill generates a ConfigMap and Job manifest, shows it for explicit approval, applies it via kubectl cce, waits for completion, and collects the k6 summary from Job logs.

Does this skill use the Huawei Cloud Python SDK or kubeconfig?▼

No. It explicitly prohibits the Python SDK dispatcher and kubeconfig generation. Kubernetes access goes through the kubectl-cce plugin against the CCE API Gateway, and cloud metadata uses hcloud CLI commands only.

Why does my in-cluster k6 Job fail with ImagePullBackOff?▼

The k6 image could not be pulled, so no traffic reached the target. Check the Job Pod Events, then mirror the k6 image to regional SWR or switch to local k6 before rerunning the smoke phase.

Can the skill scale workloads or create ELB resources automatically?▼

No. Scaling, Service or Ingress changes, ELB creation, and any traffic generation are classified as R2/R3 risk actions that require explicit user approval after previewing the exact command or manifest, impact, and rollback plan.

What happens when kubectl top reports Metrics API unavailable?▼

The skill records it as a data gap rather than inventing CPU or memory trends. HPA-driven elasticity conclusions are deferred until metrics-server or the observability add-on recovers.