/nw-platform-engineering-foundations
Foundational platform engineering knowledge from key references -- Continuous Delivery, SRE, Accelerate, Team Topologies, Chaos Engineering, and Secure Delivery. Load when contextual grounding in platform engineering theory is needed.
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/nw-platform-engineering-foundations
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Foundational platform engineering knowledge from key references -- Continuous Delivery, SRE, Accelerate, Team Topologies, Chaos Engineering, and Secure Delivery. Load when contextual grounding in platform engineering theory is needed.
SKILL.md
nw-platform-engineering-foundations.SKILL.mdname: nw-platform-engineering-foundations
description: Foundational platform engineering knowledge from key references -- Continuous Delivery, SRE, Accelerate, Team Topologies, Chaos Engineering, and Secure Delivery. Load when contextual grounding in platform engineering theory is needed.
user-invocable: false
disable-model-invocation: true
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}Read more
name: nw-platform-engineering-foundations description: Foundational platform engineering knowledge from key references -- Continuous Delivery, SRE, Accelerate, Team Topologies, Chaos Engineering, and Secure Delivery. Load when contextual grounding in platform engineering theory is needed. user-invocable: false disable-model-invocation: true
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}AI agents that guide you from idea to working code, with human judgment at every gate. nWave runs inside Claude Code. It breaks feature delivery into seven waves (discover, diverge, discuss, design, devops, distill, deliver).
Repo: nWave-ai/nWave
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