Kubernetes Workload Design

Overview

Use this skill to design Kubernetes workloads that scale predictably and roll out safely under real traffic behavior.

Scope Boundaries

• Use this skill when the task matches the trigger condition described in description.
• Do not use this skill when the primary task falls outside this skill’s domain.

Shared References

• Autoscaling and rollout decision rules:
  • references/autoscaling-and-rollout-decision-rules.md

Templates And Assets

• Workload sizing template:
  • assets/workload-sizing-template.md
• Rollout strategy checklist:
  • assets/rollout-strategy-checklist.md

Inputs To Gather

• Traffic profile and latency/SLO targets.
• CPU/memory/concurrency characteristics.
• Rollout risk tolerance and availability requirements.
• Observability signals for scaling and rollback decisions.

Deliverables

• Workload sizing and scaling plan.
• Rollout strategy with guardrails and rollback triggers.
• Resilience assumptions and saturation behavior notes.
• Verification plan for load and deployment behavior.

Workflow

1. Define resource and scaling assumptions in assets/workload-sizing-template.md.
2. Choose scaling/rollout strategy using references/autoscaling-and-rollout-decision-rules.md.
3. Validate rollout readiness via assets/rollout-strategy-checklist.md.
4. Run representative load and rollout verification.
5. Publish residual capacity and rollout risks with owners.

Quality Standard

• Resource sizing reflects measured workload behavior.
• Autoscaling avoids oscillation and delayed recovery.
• Rollout controls match service criticality.
• Rollback criteria are objective and monitored.

Failure Conditions

• Stop when workload design lacks safe rollout or capacity guarantees.
• Stop when autoscaling signals do not correlate with user impact.
• Escalate when saturation risk remains unmitigated.