Platform Engineering Foundations

Continuous Delivery (Humble and Farley)

Key principles: Build quality in | Work in small batches | Automate almost everything | Pursue continuous improvement | Everyone is responsible (shared ownership).

Pipeline progression: Commit -> Acceptance -> Capacity -> Production stages. For detailed stage definitions and quality gates, see cicd-and-deployment skill.

Site Reliability Engineering (Google -- Beyer et al.)

Key principles: SLOs over SLAs (internal targets stricter than external) | Error budgets (balance reliability and velocity) | Toil elimination (automate repetitive manual work) | Embrace risk (calculate risk, do not eliminate it).

Observability: Four Golden Signals (latency, traffic, errors, saturation) | SLI -> SLO -> Error Budget -> Alerting chain | Dashboards for investigation, not monitoring.

Accelerate (Forsgren, Humble, Kim)

DORA Metrics

• Deployment frequency: how often code deploys to production
• Lead time for changes: time from commit to production
• Change failure rate: % of deployments causing failure
• Time to restore: time to recover from production failure

Performance Levels

Metric	Elite	High
Deployment frequency	Multiple times/day	Daily to weekly
Lead time	< 1 hour	1 day to 1 week
Change failure rate	0-15%	16-30%
Time to restore	< 1 hour	< 1 day

Use DORA metrics as baselines when assessing current state and setting improvement targets.

Team Topologies (Skelton and Pais)

Team Types

• Stream-aligned: delivers value to customer, owns full lifecycle
• Platform: provides self-service capabilities, reduces cognitive load
• Enabling: helps teams adopt new practices, temporary engagement
• Complicated subsystem: owns complex technical domain

Platform Principles

Platform as a product (internal developer platform) | Self-service with guardrails | Reduce cognitive load on stream-aligned teams | Thinnest viable platform.

Use when designing platform team structures and determining which capabilities to centralize vs delegate.

Chaos Engineering (Rosenthal et al.)

Principles: Build hypothesis about steady state | Vary real-world events | Run experiments in production | Automate experiments continuously.

Practices: GameDays (scheduled chaos experiments) | Fault injection (network latency, failures) | Chaos monkey (random instance termination).

Secure Delivery (Building Secure and Reliable Systems)

Principles: Least privilege (minimal permissions) | Defense in depth (multiple security layers) | Zero trust (verify explicitly, assume breach).

Pipeline security: SAST in CI | DAST pre-production | SCA for dependency vulnerabilities | Secrets scanning | SBOM for supply chain transparency.

GitOps (GitOps and Kubernetes)

Principles: Declarative desired state in Git | Automated reconciliation | Drift detection and correction | Pull-based deployments.

Tools: ArgoCD (Kubernetes-native GitOps CD) | Flux (GitOps toolkit for Kubernetes).

Patterns: App of Apps for multi-environment management | Helm with GitOps for parameterization | Kustomize overlays for environment differences.

Constraint Impact Analysis Template

Use when assessing platform constraints before designing infrastructure.

## Platform Constraint Impact Analysis

| Constraint | Source | % Delivery Affected | Priority |
|------------|--------|---------------------|----------|
| {constraint} | {architecture/ops/security} | {X}% | {HIGH/MEDIUM/LOW} |

### Constraint-Free Baseline
- Maximum theoretical deployment frequency: ___
- Components that can proceed without constraints: ___ ({X}%)
- Quick wins available now: ___

### Decision Rules
- Constraint affects > 50% of delivery: address as primary focus
- Constraint affects < 50% of delivery: address as secondary
- Constraint affects < 20% of delivery: consider deferring

### Recommendation
Primary focus should be: {constraint-free opportunities or primary constraint}