Design and implement Internal Developer Platforms (IDPs) with self-service capabilities, golden paths, and developer experience optimization. Covers platform strategy, IDP architecture (Backstage, Port), infrastructure orchestration (Crossplane), GitOps (Argo CD), and adoption patterns. Use when building developer platforms, improving DevEx, or establishing platform teams.
npx skills add https://github.com/ancoleman/ai-design-components --skill platform-engineering
Build Internal Developer Platforms (IDPs) that provide self-service infrastructure, reduce cognitive load, and accelerate developer productivity through golden paths and platform-as-product thinking.
Platform engineering represents the evolution beyond traditional DevOps, focusing on creating product-quality internal platforms that treat developers as customers. The discipline addresses the developer productivity crisis where engineers spend 30-40% of time on infrastructure and tooling instead of features.
Trigger this skill when:
Treat internal platforms with the same rigor as customer-facing products:
Product Management Approach:
Key Differences from Traditional DevOps:
Three-Layer Architecture:
1. Developer Portal (Frontend)
2. Platform Orchestration (Backend)
3. Integration Layer (Glue)
For detailed architecture patterns and component breakdowns, see references/idp-architecture.md.
Golden Path Principle:
Provide opinionated templates that handle 80% of use cases while allowing escape hatches for the remaining 20%.
Template Components:
Constraint Mechanisms:
For template design patterns and examples, see references/golden-paths.md.
Cognitive Load Reduction:
Key Metrics:
DORA Metrics:
SPACE Framework:
Platform-Specific Metrics:
Assess current platform capabilities using a 5-level maturity model:
Level 0: Ad-Hoc - Manual provisioning, no standardization
Level 1: Basic Automation - Some IaC and CI/CD, limited self-service
Level 2: Paved Paths - Golden path templates, early portal, limited coverage
Level 3: Self-Service Platform - Comprehensive portal, 80%+ self-service
Level 4: Product-Driven Platform - Data-driven, product team structure, FinOps integration
Level 5: AI-Augmented Platform - AI-assisted troubleshooting, predictive optimization
For detailed assessment framework, gap analysis, and improvement roadmap, see references/maturity-model.md.
Choose Open Source (Backstage) when:
Choose Commercial IDP (Port, Humanitec, Cortex) when:
Choose Hybrid Approach when:
For complete decision tree, selection criteria, and ROI calculations, see references/decision-frameworks.md.
Spectrum of Control:
High Standardization (Regulated Industries):
Balanced Approach (Recommended for Most):
High Flexibility (Innovative Organizations):
For detailed guidance on choosing the right balance and enforcement strategies, see references/decision-frameworks.md.
Centralized Model:
Federated Model:
Hub-and-Spoke Model:
For team sizing, roles, responsibilities, and governance models, see references/decision-frameworks.md.
Backstage (Open Source, CNCF)
Port (Commercial)
Cortex (Commercial SaaS)
Crossplane (Open Source, CNCF)
Humanitec (Commercial)
Terraform Cloud (Commercial)
Argo CD (Open Source, CNCF) - RECOMMENDED
Flux (Open Source, CNCF)
For detailed tool comparisons, integration patterns, and selection criteria, see references/tool-recommendations.md.
Foundation Phase (Months 1-3):
Pilot Phase (Months 4-6):
Expansion Phase (Months 7-12):
Maturity Phase (Year 2+):
For detailed implementation steps and bootstrapping code, see references/implementation-backstage.md.
Template Design Process:
Template Categories:
For template examples, scaffolding code, and customization patterns, see references/golden-paths.md and examples/ directory.
Evangelization Strategies:
Incentive Alignment:
For adoption metrics, tracking dashboards, and success patterns, see references/maturity-model.md.
Strategy and Vision:
IDP Foundation:
Self-Service Capabilities:
Security and Compliance:
Operations and Support:
Building Too Much Upfront:
Ignoring Developer Feedback:
Over-Standardization:
Under-Measuring Success:
Treating Platform as IT Project:
Related Skills:
kubernetes-operations: Cluster operations, namespace management, RBAC, network policiesinfrastructure-as-code: Terraform, Pulumi for infrastructure provisioning integrated with platformgitops-workflows: GitOps principles, Argo CD / Flux implementation patternsbuilding-ci-pipelines: CI/CD pipeline design integrated into platform templatessecurity-hardening: Security best practices enforced through golden pathssecret-management: Secrets management integrated into platform (Vault, cloud providers)observability: Monitoring, logging, tracing integrated into developer portalCross-Skill Workflows:
Platform Bootstrapping:
infrastructure-as-code to provision platform infrastructurekubernetes-operations to configure clustersgitops-workflows (Argo CD) for continuous deliveryobservability integrations (Prometheus, Grafana plugins)Golden Path Creation:
building-ci-pipelines patterns for CI/CD configurationsecurity-hardening best practices (SAST, container scanning)secret-management (Vault, encrypted configs)observability instrumentation (metrics, logging, tracing)Context: 300-engineer e-commerce company, microservices architecture, manual provisioning causing bottlenecks.
Approach: Deploy Backstage, create 3 golden paths, integrate Argo CD, pilot with 3 teams, expand to 20 teams over 6 months.
Results: Onboarding time 2 days → 2 hours, deployment frequency 2x/week → 10x/day, developer NPS +35.
Context: 1500-engineer bank, strict compliance, legacy infrastructure, fragmented tooling.
Approach: Adopt Port (commercial), high standardization golden paths, OPA Gatekeeper, federated model, Terraform Cloud.
Results: Compliance audit prep 3 weeks → 3 days, infrastructure drift incidents 90% reduction, per-service cost attribution.
Context: 50-engineer startup, rapid growth, need fast developer onboarding.
Approach: Lightweight Backstage (2 engineers), 2 golden paths, GitHub Actions, PaaS infrastructure (Fly.io), documentation focus.
Results: New engineer to production 1 day (vs. 2 weeks), 100% self-service, 2 engineers supporting 50 developers.
For code examples and template structures, see examples/ directory.
Assess Kubernetes workloads and cluster configuration for AKS Automatic compatibility. Identifies incompatibilities, generates fixes, and guides migration from AKS Standard to AKS Automatic. WHEN: migrate to AKS Automatic, check AKS Automatic readiness, validate manifests for Automatic, assess cluster for Automatic compatibility, fix deployment for Automatic compatibility, identify AKS Automatic migration blockers, is my cluster ready for AKS Automatic.
Discovers available Azure OpenAI model capacity across regions and projects. Analyzes quota limits, compares availability, and recommends optimal deployment locations based on capacity requirements. USE FOR: find capacity, check quota, where can I deploy, capacity discovery, best region for capacity, multi-project capacity search, quota analysis, model availability, region comparison, check TPM availability. DO NOT USE FOR: actual deployment (hand off to preset or customize after discovery), quota increase requests (direct user to Azure Portal), listing existing deployments.
Interactive guided deployment flow for Azure OpenAI models with full customization control. Step-by-step selection of model version, SKU (GlobalStandard/Standard/ProvisionedManaged), capacity, RAI policy (content filter), and advanced options (dynamic quota, priority processing, spillover). USE FOR: custom deployment, customize model deployment, choose version, select SKU, set capacity, configure content filter, RAI policy, deployment options, detailed deployment, advanced deployment, PTU deployment, provisioned throughput. DO NOT USE FOR: quick deployment to optimal region (use preset).
Unified Azure OpenAI model deployment skill with intelligent intent-based routing. Handles quick preset deployments, fully customized deployments (version/SKU/capacity/RAI policy), and capacity discovery across regions and projects. USE FOR: deploy model, deploy gpt, create deployment, model deployment, deploy openai model, set up model, provision model, find capacity, check model availability, where can I deploy, best region for model, capacity analysis. DO NOT USE FOR: listing existing deployments (use foundry_models_deployments_list MCP tool), deleting deployments, agent creation (use agent/create), project creation (use project/create).
Intelligently deploys Azure OpenAI models to optimal regions by analyzing capacity across all available regions. Automatically checks current region first and shows alternatives if needed. USE FOR: quick deployment, optimal region, best region, automatic region selection, fast setup, multi-region capacity check, high availability deployment, deploy to best location. DO NOT USE FOR: custom SKU selection (use customize), specific version selection (use customize), custom capacity configuration (use customize), PTU deployments (use customize).
This skill should be used when working with LaminDB, an open-source data framework for biology that makes data queryable, traceable, reproducible, and FAIR. Use when managing biological datasets (scRNA-seq, spatial, flow cytometry, etc.), tracking computational workflows, curating and validating data with biological ontologies, building data lakehouses, or ensuring data lineage and reproducibility in biological research. Covers data management, annotation, ontologies (genes, cell types, diseases, tissues), schema validation, integrations with workflow managers (Nextflow, Snakemake) and MLOps platforms (W&B, MLflow), and deployment strategies.
Latch platform for bioinformatics workflows. Build pipelines with Latch SDK, @workflow/@task decorators, deploy serverless workflows, LatchFile/LatchDir, Nextflow/Snakemake integration.
Run Python code in the cloud with serverless containers, GPUs, and autoscaling. Use when deploying ML models, running batch processing jobs, scheduling compute-intensive tasks, or serving APIs that require GPU acceleration or dynamic scaling.
Take ancoleman/platform-engineering from the repository into ~/.claude/skills for personal
use, or into .claude/skills inside a project.
The agent identifies a skill by the name field in its header. Two skills with the
same name cannot sit side by side — one of them will be ignored.