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Kubernetes workload design

Skill planifest/planifest-framework/planifest-framework/external-skills/kubernetes-workload-design

A specification framework for agentic development. Agents build from complete specs - not guesses.

Install
npx -y skills add planifest/planifest-framework --skill kubernetes-workload-design

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Kubernetes workload design workflow for resource sizing, autoscaling behavior, and safe rollout strategy. Use when workload specs need concrete sizing and resilience decisions to meet reliability/performance targets; do not use for API contract design or requirement prioritization.

SKILL.md

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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.

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