Engineering Advanced PlatformDeveloper ExperienceGolden Paths

Platform Engineer

Internal developer platform design: golden paths, self-service infrastructure, Backstage/Port catalogs, developer portal, and platform-as-a-product principles.

A platform engineering skill focused on treating infrastructure as a product for internal developers. It covers golden path templates, self-service provisioning, developer portal setup with Backstage or Port, platform metrics (DORA, developer NPS), and the team topologies required to operate an effective platform without becoming a bottleneck.

Added Mar 20, 2026

$npx skills add johnefemer/skillfish --skill platform-engineer

What This Skill Can Do

Concrete capabilities you get when you install this skill.

Design golden path templates for new services with pre-wired CI/CD, observability, and security

Set up self-service infrastructure provisioning with Backstage, Port, or Crossplane

Define platform team operating model: what to own, what to delegate, how to avoid bottlenecks

Measure platform adoption and developer experience with DORA metrics and NPS surveys

Implement service catalog with component metadata, dependencies, and ownership

Build paved roads for database provisioning, secret management, and environment creation

Real Conversations

See how the Platform Engineer skill responds to actual requests. These are representative examples of what your AI agent will produce.

You

We want developers to be able to spin up a new microservice in under 10 minutes with all our standards pre-applied. What should a golden path template include?

Agent

A production golden path template for a new microservice should include everything a team needs to ship safely from day one:

Minimum viable golden path — what to include:

service-template/
├── .github/workflows/
│   ├── ci.yml          # lint, test, build, push image
│   └── deploy.yml      # deploy to dev on merge, prod on tag
├── k8s/
│   ├── deployment.yaml  # resource limits pre-set, liveness/readiness probes
│   ├── service.yaml
│   └── hpa.yaml        # horizontal pod autoscaler
├── src/
│   └── main.ts         # health endpoint at /healthz already wired
├── Dockerfile           # multi-stage, non-root user, .dockerignore
├── opentelemetry.ts    # tracing + metrics pre-configured for your stack
├── CODEOWNERS          # ownership pattern
└── backstage-template.yaml  # Backstage Software Template descriptor

The non-negotiables:

  • Health endpoint (/healthz) responding before template is accepted
  • Resource requests and limits set (prevents node starvation)
  • Default network policy: deny-all ingress except from service mesh
  • Secret injection via Vault/AWS Secrets Manager (no env vars with plaintext secrets)
  • Observability bootstrapped: metrics endpoint, structured logging, trace context propagation

Backstage scaffolder action to wire it up:

steps:
  - id: fetch-template
    action: fetch:template
    input:
      url: ./skeleton
      values:
        serviceName: ${{ parameters.serviceName }}
        owner: ${{ parameters.owner }}
  - id: create-repo
    action: publish:github
    input:
      repoUrl: github.com?owner=myorg&repo=${{ parameters.serviceName }}

Target: developer runs backstage scaffold, fills 5 fields, gets a repo with first CI passing in <5 minutes.

Get Started

How to install and use this skill in your preferred environment.

Skills are designed for AI coding agents (Claude Code, Cursor, Windsurf) and IDE-based workflows where the agent can read files, run scripts, and act on your codebase.

Models & Context

Which AI models and context windows work best with this skill.

Recommended Models

Works well with Claude Sonnet or GPT-4o. YAML template generation and platform design benefit from models with strong code and architecture reasoning.

Context Window

SKILL.md is ~10KB. Include current team topology, tool inventory, and sample templates in context for tailored recommendations.

Pro tips for best results

1

Be specific

Include numbers — users, budget, RPS — so the skill can size the architecture.

2

Share constraints

Compliance needs, team size, and existing stack all improve the output.

3

Iterate

Start with a high-level design, then ask follow-ups for IaC, cost analysis, or security review.

4

Combine skills

Pair with companion skills below for end-to-end coverage.

Works Great With

These skills complement Platform Engineer for end-to-end coverage. Install them together for better results.

$ skillfish add johnefemer/skillfish --all # install all skills at once

Ready to try Platform Engineer?

Install the skill and start getting expert-level guidance in your workflow — any agent, any IDE.

$npx skills add johnefemer/skillfish --skill platform-engineer
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