Agent Skills: DevOps and Platform Engineering

DevOps and platform engineering patterns: Kubernetes, Terraform, GitOps, CI/CD, observability, incident response, and cloud-native ops.

UncategorizedID: vasilyu1983/ai-agents-public/ops-devops-platform

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pnpm dlx add-skill https://github.com/vasilyu1983/AI-Agents-public/tree/HEAD/frameworks/shared-skills/skills/ops-devops-platform

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frameworks/shared-skills/skills/ops-devops-platform/SKILL.md

Skill Metadata

Name
ops-devops-platform
Description
"Designs DevOps and platform engineering systems. Use when planning Kubernetes, Terraform, GitOps, CI/CD, observability, incident response, or cloud-native operations."

DevOps and Platform Engineering

Use this skill for platform, infrastructure, CI/CD, GitOps, observability, and incident-operating-model design. Keep the output operational: target architecture, rollout path, guardrails, ownership, and artifacts.

Quick Reference

| Need | Starting Direction | |------|--------------------| | infrastructure provisioning | Terraform, OpenTofu, Pulumi, or cloud-native IaC | | cluster or app deployment | GitOps first for steady-state, direct tooling for local iteration | | CI/CD | protected pipelines plus workload identity and supply-chain controls — see supply-chain-security | | observability | OpenTelemetry plus metrics, logs, traces, and SLO-based alerting | | platform engineering | golden paths, policy-as-code, and self-service interfaces | | incident operations | runbooks, severity model, escalation, and postmortems |

Workflow

  1. classify the dominant problem:
    • provisioning
    • deployment
    • CI/CD
    • observability
    • platform engineering
    • security hardening
    • incident operations
  2. choose the smallest viable toolchain that matches the runtime and team skill
  3. load the relevant reference and template set
  4. verify version-sensitive or vendor-sensitive claims before final guidance
  5. finish with concrete operational outputs: plan, controls, owners, and artifacts

Decision Rules

| Situation | Rule | |-----------|------| | any infrastructure change | IaC first; no clickops | | steady-state production reconciliation | GitOps (Argo CD / Flux) over push-based deploys | | CI credentials | workload identity (OIDC) over long-lived secrets | | alerting | SLO burn-rate alerts; suppress raw host-metric noise | | new environments | platform template + policy guard; no snowflakes | | supply-chain integrity | SLSA build track + cosign keyless signing | | drift | detect via reconciler or terraform plan in CI; never discover by accident |

Related Routing


Guardrails

| Domain | Do | Anti-pattern to avoid | |--------|----|-----------------------| | Provisioning | all material changes in IaC; explicit promotion gates | clickops drift; untagged infrastructure | | Delivery | protected pipelines; artifact provenance; rollback + smoke checks | pipelines without identity boundaries | | Platform | golden paths before self-service; policy-as-code that reduces variation | tools shipped without adoption path or ownership | | Observability | define SLOs first; join logs/traces/metrics on shared trace ID | alert fatigue from raw host-metric thresholds | | Incidents | postmortems feed runbooks and platform changes | postmortems that stop at narrative | | Cost | tagging + budget alerts at resource creation; monthly right-sizing | unmanaged snowflake environments; unreviewed reservations |


Navigation

Reference routing

| Load when… | Reference | |------------|-----------| | supply-chain, SBOM, signing, SLSA | references/supply-chain-security.md | | DORA's five metrics and team archetypes (Elite/High/Medium/Low tiers are retired), AI-adoption instability tax, general DevOps best practices | references/devops-best-practices.md | | GitLab CI — parent/child pipelines, MR variable traps, env-export pattern | references/gitlab-ci-patterns.md | | choosing a tool (IaC, GitOps, CI, policy, observability) | references/tool-landscape.md | | golden paths, internal developer portal, platform maturity, when NOT to build an IDP, platform-vs-product boundary, CI/IaC/GitOps adoption sequencing | references/platform-engineering-patterns.md | | GitOps multi-env promotion, Argo CD / Flux patterns | references/gitops-workflows.md | | on-call, severity model, escalation, postmortems | references/sre-incident-management.md | | day-2 operational runbooks, environment hygiene | references/operational-patterns.md | | AIOps alert correlation, automated triage | references/aiops-patterns.md | | Kalman canary, cost autoscaler, CI capacity stabiliser | references/control-theory-applied.md | | capacity planning, saturation SLO, pipeline bottleneck hunt | references/queueing-theory-applied.md | | CI/CD throughput recovery, constraint surfacing, spend reallocation | references/theory-of-constraints-applied.md | | platform-team charter, algedonic escalation, PRR audit | references/cybernetics-vsm-applied.md | | MTBF/MTTR, availability budgets, FMEA | references/reliability-theory-applied.md | | CAP/PACELC, consensus, idempotency, quorums | references/distributed-systems-applied.md | | source URLs and release trackers | data/sources.json |

Templates

AWS / GCP / Azure

Kubernetes

Docker / Kafka

Terraform / IaC

CI/CD and GitOps

Monitoring / Observability

Incident response

Security / Cost

Shared utilities

Related Skills

Trend Awareness Protocol

When users ask for current tool recommendations, verify:

  • current supported Kubernetes and ecosystem versions
  • active IaC and GitOps tool state
  • current observability and policy-engine capabilities
  • current CI/CD and platform-tool support windows

Prefer official docs and release notes over blogs or rankings.

Fact-Checking

  • Verify current versions, deprecations, support windows, pricing, and cloud limits before final answers.
  • Prefer official docs and release notes for named tools and platforms.
  • If web access is unavailable, mark version-sensitive guidance as unverified.

Learnings Loop

Before applying this skill on a non-trivial task, read learnings.consolidated.md in this directory (and learnings.md if present).

After applying it, if you encountered a pattern worth remembering, a mistake worth preventing, or a domain fact that surprised you, append one dated bullet to learnings.md via agents-skills-feedback-loop/scripts/append_learning.py. Do not modify SKILL.md itself.