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Get Started Free →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.
.claude/skills/ancoleman-platform-engineering/SKILL.md| Test case | Without → With | Effect | Δ tokens | Δ turns |
|---|---|---|---|---|
| case-03 | ✗→✓ | ▲ Improved | 121% | 0% |
| case-08 | ✓→✗ | ▼ Worse | 118% | 0% |
| case-18 | ✓→✗ | ▼ Worse | 115% | 0% |
| case-05 | ✓→✓ | ＝ Same ✓ | 111% | 0% |
| case-01 | ✓→✓ | ＝ Same ✓ | 109% | 0% |
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.
| Case | Status | Duration (ms) | Turns | Tokens | Tool calls | ||||||||
|---|---|---|---|---|---|---|---|---|---|---|---|---|---|
| Without | With | Δ | Without | With | Δ | Without | With | Δ | Without | With | Δ | ||
case-05 | pass→pass | 17,719 | 11,143 | -37% | 1 | 1 | 0% | 2,507 | 5,292 | +111% | 0 | 0 | — |
case-03 | fail→pass | 18,200 | 14,229 | -22% | 1 | 1 | 0% | 2,703 | 5,967 | +121% | 0 | 0 | — |
case-01 | pass→pass | 26,658 | 32,221 | +21% | 1 | 1 | 0% | 4,343 | 9,064 | +109% | 0 | 0 | — |
case-02 | pass→pass | 16,580 | 21,155 | +28% | 1 | 1 | 0% | 2,552 | 5,479 | +115% | 0 | 0 | — |
case-04 | fail→fail | 20,742 | 15,349 | -26% | 1 | 1 | 0% | 3,411 | 6,372 | +87% | 0 | 0 | — |
case-06 | fail→fail | 17,534 | 16,587 | -5% | 1 | 1 | 0% | 2,550 | 6,187 | +143% | 0 | 0 | — |
case-07 | pass→pass | 12,329 | 18,351 | +49% | 1 | 1 | 0% | 1,856 | 5,145 | +177% | 0 | 0 | — |
case-08 | pass→fail | 19,390 | 20,734 | +7% | 1 | 1 | 0% | 3,220 | 7,028 | +118% | 0 | 0 | — |
case-09 | pass→pass | 15,903 | 10,408 | -35% | 1 | 1 | 0% | 2,251 | 5,284 | +135% | 0 | 0 | — |
case-10 | fail→fail | 15,976 | 32,917 | +106% | 1 | 1 | 0% | 2,515 | 6,688 | +166% | 0 | 0 | — |
case-11 | pass→pass | 19,522 | 19,317 | -1% | 1 | 1 | 0% | 3,053 | 6,525 | +114% | 0 | 0 | — |
case-12 | fail→fail | 14,753 | 10,403 | -29% | 1 | 1 | 0% | 2,496 | 5,239 | +110% | 0 | 0 | — |
case-13 | pass→pass | 23,293 | 15,664 | -33% | 1 | 1 | 0% | 2,213 | 6,031 | +173% | 0 | 0 | — |
case-14 | pass→pass | 18,694 | 22,655 | +21% | 1 | 1 | 0% | 3,125 | 7,310 | +134% | 0 | 0 | — |
case-15 | fail→fail | 16,485 | 9,309 | -44% | 1 | 1 | 0% | 2,388 | 4,921 | +106% | 0 | 0 | — |
case-16 | fail→fail | 17,698 | 21,351 | +21% | 1 | 1 | 0% | 2,846 | 5,948 | +109% | 0 | 0 | — |
case-17 | pass→pass | 15,540 | 15,987 | +3% | 1 | 1 | 0% | 2,281 | 6,046 | +165% | 0 | 0 | — |
case-18 | pass→fail | 17,053 | 12,167 | -29% | 1 | 1 | 0% | 2,574 | 5,526 | +115% | 0 | 0 | — |
case-19 | pass→pass | 12,588 | 10,762 | -15% | 1 | 1 | 0% | 1,853 | 5,491 | +196% | 0 | 0 | — |
case-20 | pass→pass | 19,350 | 15,296 | -21% | 1 | 1 | 0% | 2,245 | 6,202 | +176% | 0 | 0 | — |
case-21 | pass→pass | 4,294 | 5,093 | +19% | 1 | 1 | 0% | 926 | 4,769 | +415% | 0 | 0 | — |
case-22 | pass→pass | 4,270 | 5,693 | +33% | 1 | 1 | 0% | 859 | 4,813 | +460% | 0 | 0 | — |
case-23 | pass→pass | 7,634 | 7,252 | -5% | 1 | 1 | 0% | 1,307 | 5,047 | +286% | 0 | 0 | — |
DecimalAI ran this skill against gemini-3.6-flash twice over the same eval suite — once with the skill loaded and once without — and compared the two runs case by case. 23 cases were attempted. The headline lift of -4 percentage points is the difference between those two pass rates over the 23 comparable cases. 2 cases got worse with the skill loaded, and they are included in that figure.
Without the skill loaded, the model failed this case. With it loaded, the same prompt on the same model passed. This is one improved case from the latest verified run; every case, including any that regressed, is in the table above.
Other measured skills in the registry, with their headline benchmark lift.