nw-ab-critique-dimensi…
Review dimensions for validating agent quality - template compliance, safety, testing, and priority validation
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.
$ npx -y skills add nWave-ai/nWave --skill nw-platform-engineering-foundations --agent claude-codeHow it fires
How this skill gets triggered: by you, by Claude, or both.
/nw-platform-engineering-foundationsContext preview
The summary Claude sees to decide when to auto-load this skill.
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.
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
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.
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.
| 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.
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.
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).
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.
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.
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
Review dimensions for validating agent quality - template compliance, safety, testing, and priority validation
Review dimensions for validating agent quality - template compliance, safety, testing, and priority validation
Review dimensions for acceptance test quality - happy path bias, GWT compliance, business language purity, coverage completeness, walking skeleton…
Detailed 5-phase workflow for creating agents - from requirements analysis through validation and iterative refinement
5-layer testing approach for agent validation including adversarial testing, security validation, and prompt injection resistance
Architectural style selection decision matrices, trade-off analysis, structural enforcement rules, and combination patterns. Load when choosing or evaluating…