Nw Devops

NW-DEVOPS: Platform Readiness and Infrastructure Design

Wave: DEVOPS (wave 4 of 6) | Agent: Apex (nw-platform-architect) | Command: /nw-devops

Overview

Execute DEVOPS wave: platform readiness|CI/CD pipeline setup|observability design|infrastructure preparation. Positioned between DESIGN and DISTILL (DISCOVER > DISCUSS > SPIKE > DESIGN > DEVOPS > DISTILL > DELIVER), ensures infrastructure is ready before acceptance tests and code.

Apex translates DESIGN architecture decisions into operational infrastructure: CI/CD pipelines|logging|monitoring|alerting|observability.

Output Tiers (per D2)

Provenance: feature lean-wave-documentation — D2 (schema-typed sections), D10 (one-line expansion descriptions). Tier-1 [REF] sections (always emitted) + Tier-2 EXPANSION CATALOG items (lazy, on-demand) are the two output bands. Full contract: nWave/skills/nw-density-resolution-contract/SKILL.md.

Tier-1 [REF] — always emitted

Under ## Wave: DEVOPS / [REF] <Section> headings:

• Environment matrix — table of target environments with platform + preconditions
• CI/CD pipeline outline — stage list with trigger rules per branch
• Monitoring contracts — KPI-to-instrument mapping (one row per outcome KPI)
• Deployment strategy — chosen strategy + rollback contract (one paragraph)
• Mutation testing strategy — selected mode (per-feature/nightly-delta/pre-release/disabled)
• Observability stack — chosen tools per signal class (logs/metrics/traces)
• Branching strategy — selected model + CI trigger alignment
• Coexistence matrix — tools that must continue to work alongside deployment
• Pre-requisites — DESIGN constraints the platform must satisfy

Tier-2 EXPANSION CATALOG — lazy, on-demand (per D10)

Rendered under ## Wave: DEVOPS / [WHY|HOW] <Section> only when requested via --expand <id> (DDD-2), the wave-end menu (expansion_prompt = "ask"), mode = "full" auto-expansion, or an ad-hoc user request mid-session.

Density resolution (per D12)

Call resolve_density(global_config) from scripts/shared/density_config.py after reading ~/.nwave/global-config.json (missing/malformed = empty dict). Returns mode ("lean" | "full") + expansion_prompt ("ask" | "always-skip" | "always-expand" | "smart") per the D12 cascade (resolver-internal, DDD-5 — do NOT replicate locally). Branch on density.mode for what to emit; branch on density.expansion_prompt at wave end for menu behaviour. Full cascade detail, branch semantics, ad-hoc override workflow: nWave/skills/nw-density-resolution-contract/SKILL.md.

Telemetry (per D4 + DDD-6)

Every expansion choice emits a DocumentationDensityEvent (dataclass at src/des/domain/telemetry/documentation_density_event.py) via event.to_audit_event() → JsonlAuditLogWriter().log_event(...). Schema fields per D4: feature_id, wave, expansion_id, choice, timestamp. For this wave the schema declares "wave": "DEVOPS". Use helper scripts/shared/telemetry.py:write_density_event(...) — do NOT write JSONL directly.

Wave-specific signal: DISTILL consuming a lean DEVOPS environment matrix — downstream --expand requests for runbook drafts or alternative deploy targets indicate the [REF] baseline was insufficient. Full emission rules: nWave/skills/nw-density-resolution-contract/SKILL.md.

Interactive Decision Points

Before proceeding, the orchestrator asks:

Decision 1: Deployment Target

Question: What is the deployment target? Options:

1. Cloud-native -- AWS, GCP, Azure managed services
2. On-premise -- self-hosted infrastructure
3. Hybrid -- mix of cloud and on-premise
4. Edge -- distributed edge deployment
5. Other -- user provides custom input

Decision 2: Container Orchestration

Question: Container orchestration approach? Options:

1. Kubernetes -- full orchestration
2. Docker Compose -- lightweight container management
3. Serverless -- function-as-a-service, no containers
4. None -- bare metal or VM-based deployment

Decision 3: CI/CD Platform

Question: CI/CD platform preference? Options:

1. GitHub Actions
2. GitLab CI
3. Jenkins
4. Azure DevOps
5. Other -- user provides custom input

Decision 4: Existing Infrastructure

Question: Is there existing infrastructure or CI/CD to integrate with? Options:

1. Yes, both -- describe existing infrastructure and CI/CD (user provides details)
2. Existing infra only -- infrastructure exists, CI/CD is greenfield
3. Existing CI/CD only -- CI/CD exists, infrastructure is greenfield
4. No -- greenfield, design everything from scratch

Decision 5: Observability and Logging

Question: What observability and logging approach? Options:

1. Prometheus + Grafana (metrics) with structured JSON logs
2. Datadog (full-stack observability including logs)
3. ELK stack (Elasticsearch, Logstash, Kibana for logs and metrics)
4. OpenTelemetry (vendor-agnostic telemetry) with provider of choice
5. CloudWatch (AWS-native metrics and logging)
6. Custom -- user provides details
7. None -- defer observability setup

Decision 6: Deployment Strategy

Question: What deployment strategy? Options:

1. Blue-green -- zero-downtime with environment swap
2. Canary -- gradual traffic shifting
3. Rolling -- incremental pod/instance replacement
4. Recreate -- simple stop-and-replace

Decision 7: Continuous Learning (conditional)

Question: Is there existing monitoring/alerting infrastructure in place? Options:

1. Yes -- include continuous learning and experimentation capabilities
2. No -- focus on foundational monitoring setup first

If Yes to Decision 7: Follow-up: Which continuous learning capabilities to include? Options:

1. A/B testing framework
2. Feature flags (LaunchDarkly, Unleash, custom)
3. Canary analysis (automated rollback on metrics)
4. Progressive rollout (percentage-based deployment)
5. All of the above

Decision 8: Git Branching Strategy

Question: What Git branching strategy should the project follow? Options:

1. Trunk-Based Development -- single main branch, short-lived feature branches (<1 day), continuous integration. Requires robust CI gates on every commit.
2. GitHub Flow -- feature branches from main, pull requests, merge to main after review. Balanced CI with PR-triggered pipelines.
3. GitFlow -- develop/main branches, feature/release/hotfix branches, formal release process. Requires branch-specific pipelines (develop CI, release candidate, hotfix fast-track).
4. Release Branching -- long-lived release branches, cherry-pick fixes between branches. Requires per-branch pipelines and cross-branch validation.
5. Other -- user provides custom strategy

This directly influences CI/CD pipeline design: trigger rules|branch protection|environment promotion|release automation.

Decision 9: Mutation Testing Strategy

Question: When should mutation testing run? Options:

1. per-feature (default) -- Runs after each feature delivery (refactoring + review), scoped to modified files. Best for small/medium projects where per-feature overhead is acceptable. Fastest feedback loop but adds ~5-15 min per delivery.
2. nightly-delta -- Runs in CI nightly on files modified that day. Best for large projects where per-feature mutation testing is too slow. Delays feedback but keeps delivery fast.
3. pre-release -- Runs before each release on the entire solution. Best for projects with long release cycles where comprehensive mutation coverage matters most at release boundaries. Slowest feedback but most thorough.
4. disabled -- No mutation testing. Only appropriate for prototypes, spikes, or projects where test quality is validated through other means.

After selection, Apex asks permission to write to project CLAUDE.md under ## Mutation Testing Strategy:

per-feature: This project uses **per-feature** mutation testing. Runs after refactoring during each delivery, scoped to modified files. Kill rate gate: >= 80%.

nightly-delta: This project uses **nightly-delta** mutation testing. CI runs on files modified each day. NOT run during feature delivery.

pre-release: This project uses **pre-release** mutation testing. Runs on entire solution before each release. Delivery not blocked.

disabled: Mutation testing is **disabled**. Test quality validated through code review and CI coverage.

Default if not chosen: per-feature.

Prior Wave Consultation

Before beginning DEVOPS work, read targeted prior wave artifacts:

1. DISCOVER (skip): DESIGN already synthesizes DISCOVER+DISCUSS into architecture. Not needed for infrastructure design.
2. DISCUSS (KPIs only): Read docs/feature/{feature-id}/discuss/outcome-kpis.md — drives observability and instrumentation design.
3. DESIGN (primary input): Read all files in docs/feature/{feature-id}/design/ — architecture drives infrastructure decisions.

READING ENFORCEMENT: Read every file listed above using the Read tool before proceeding. After reading, output a confirmation checklist (✓ {file} for each read, ⊘ {file} (not found) for missing). Do NOT skip files that exist — skipping causes infrastructure decisions disconnected from architecture.

After reading, check whether any DEVOPS decisions would contradict DESIGN architecture. Flag contradictions and resolve with user before proceeding. Example: DESIGN specifies "single-region deployment" but DEVOPS discovers latency requirements from outcome-kpis.md that demand multi-region — this must be resolved.

Document Update (Back-Propagation)

When DEVOPS decisions change assumptions from prior waves:

1. Document change — Add a ## Changed Assumptions section at the end of the affected DEVOPS artifact. Gate: section present in artifact.
2. Reference original — Quote the original prior-wave document and the original assumption. Gate: quote included.
3. State new assumption — Write the new assumption and rationale for the change. Gate: rationale documented.
4. Flag upstream changes — If infrastructure constraints require architecture changes, write them to docs/feature/{feature-id}/devops/upstream-changes.md for the architect to review. Gate: file created if architecture impact exists.

Agent Invocation

1. Dispatch — Invoke @nw-platform-architect with the feature-id and configuration below. Gate: agent accepts invocation.
2. Provide context — Pass all prior wave consultation files (see Prior Wave Consultation). Gate: context files attached.
3. Pass configuration — Include all Decision 1-9 selections in the invocation:
   • deployment_target: {Decision 1} | container_orchestration: {Decision 2}
   • cicd_platform: {Decision 3} | existing_infrastructure: {Decision 4}
   • observability_and_logging: {Decision 5} | deployment_strategy: {Decision 6}
   • continuous_learning: {Decision 7} | git_branching_strategy: {Decision 8}
   • mutation_testing_strategy: {Decision 9}
4. KPI-driven observability — If outcome-kpis.md exists in the feature's discuss directory, Apex MUST read it and design instrumentation to collect the defined KPIs. Each KPI's "Measured By" and "Measurement Plan" sections drive: data collection infrastructure (events, logs, analytics), dashboard design (which metrics to visualize), alerting rules (guardrail metric thresholds). Gate: all KPIs have corresponding instrumentation design.

Mandatory Deliverable: Environment Inventory

BEFORE completing the DEVOPS wave, produce the environment inventory:

1. Create file — Write docs/feature/{feature-id}/devops/environments.yaml with the structure below. Gate: file written.
2. Populate target environments — List all deployment environments with name, description, platform, and preconditions. Gate: at least one environment entry present.
3. Define coexistence matrix — List tools that must not break alongside the deployment (e.g., pre-commit, husky). Gate: matrix present.
4. Specify platform coverage — List OS/platform versions to support. Gate: coverage table complete.
5. Document deployment assumptions — List idempotency, uninstall safety, and hook coexistence requirements. Gate: assumptions enumerated.

# environments.yaml — consumed by DISTILL for Mandate 4 (Environmental Realism)
target_environments:
  - name: clean
    description: "Fresh install, no prior state"
    platform: [linux, macos, wsl]
    preconditions: []
  - name: with-pre-commit
    description: "Pre-commit hooks installed and active"
    platform: [linux, macos, wsl]
    preconditions: ["pre-commit installed", "core.hooksPath set to .git/hooks"]
  - name: with-stale-config
    description: "Outdated configuration from prior version"
    platform: [linux, macos]
    preconditions: ["legacy config present", "version mismatch"]

coexistence_matrix:
  - tool: pre-commit
    must_not_break: true
  - tool: husky
    must_not_break: true

platform_coverage:
  macOS: [12.x, 13.x, 14.x]
  Linux: [Ubuntu 22.04, Ubuntu 24.04]
  WSL: [WSL2]
  CI: [GitHub Actions ubuntu-latest]

deployment_assumptions:
  - "Installation MUST be idempotent (safe to run twice)"
  - "Uninstall MUST remove only nWave artifacts"
  - "Hooks MUST survive alongside existing hook managers"

For features that do NOT install into systems (pure business logic), the environment inventory contains only target_environments: [{name: clean, platform: [linux, macos]}].

DISTILL reads this file to parametrize acceptance scenarios over target environments. If this file is missing, DISTILL uses defaults (clean, with-pre-commit, with-stale-config) — but coverage gaps are the PA's responsibility.

Peer Review Gate (OPTIONAL — per-wave; mandatory at end of DISTILL)

Per-wave Forge review is opt-in. Default: skip and proceed to DISTILL. The mandatory consolidated review covering DISCUSS+DESIGN+DEVOPS+DISTILL fires at end of DISTILL where Eclipse + Architect + Forge + Sentinel run in parallel against the full feature-delta.md (all 4 waves visible — catches cross-wave inconsistencies that per-wave review misses).

Structural-correctness reviewer never skips: rigor.reviewer_model: "skip" applies to scale-sensitive cost-driven reviewers (Eclipse / Architect / Forge) only; the structural-correctness reviewer at the end of DISTILL (Sentinel / @nw-acceptance-designer-reviewer) ALWAYS dispatches — silent skip masks the bug class issue #52 fixed.

Invoke per-wave Forge review explicitly via /nw-review nw-platform-architect-reviewer only if:

• Novel deployment target not in prior coexistence matrix
• New CI/CD framework introduced (e.g., switching from GitHub Actions to GitLab)
• Observability stack rewrite (not extension)
• Security posture change (new secrets management, new RBAC layer)
• Maintainer explicitly flags uncertainty

When triggered, the reviewer covers: CI/CD pipeline correctness and completeness, environment inventory coverage, observability design alignment with outcome KPIs, infrastructure security and deployment strategy soundness. On REJECTION: revise artifacts per findings and re-submit (max 2 revision cycles before escalation). Gate: optional unless triggered.

Success Criteria

• Environment inventory produced (environments.yaml with target environments and coexistence matrix)
• CI/CD pipeline design finalized and documented
• Logging infrastructure design complete (structured logging|aggregation)
• Monitoring and alerting design complete (metrics|dashboards|SLOs/SLIs)
• Observability design complete (distributed tracing|health checks)
• Infrastructure integration assessed (if existing infra)
• Continuous learning capabilities designed (if applicable)
• Git branching strategy selected and CI/CD triggers aligned
• Mutation testing strategy selected and persisted to project CLAUDE.md
• Outcome KPIs instrumentation designed (if outcome-kpis.md exists)
• Data collection pipeline documented for each KPI
• Dashboard mockup or spec includes all outcome KPIs
• Per-wave peer review (OPTIONAL — invoked only on trigger; mandatory consolidated review fires at end of DISTILL)
• Handoff accepted by nw-acceptance-designer (DISTILL wave)

Next Wave

Handoff To: nw-acceptance-designer (DISTILL wave) Deliverables: Infrastructure design documents + environments.yaml (mandatory for DISTILL Mandate 4)

Examples

Example 1: Cloud-native greenfield

/nw-devops payment-gateway

User selects: cloud-native, Kubernetes, GitHub Actions, no existing infra, OpenTelemetry, blue-green, trunk-based development. Apex designs full infrastructure from scratch with robust CI gates on every commit to main.

Example 2: Brownfield with existing CI/CD

/nw-devops auth-upgrade

User selects: hybrid, Docker Compose, GitLab CI (existing), existing CI/CD only, Datadog, rolling, GitFlow. Apex extends existing pipelines with branch-specific stages for develop, release, and hotfix branches.

Wave Decisions Summary

Before completing DEVOPS, produce docs/feature/{feature-id}/devops/wave-decisions.md:

# DEVOPS Decisions — {feature-id}

## Key Decisions
- [D1] {decision}: {rationale} (see: {source-file})

## Infrastructure Summary
- Deployment: {target + strategy}
- CI/CD: {platform + branching strategy}
- Observability: {stack}
- Mutation testing: {strategy}

## Constraints Established
- {infrastructure constraint}

## Upstream Changes
- {any DESIGN assumptions changed, with rationale}

Outputs

Single narrative file: docs/feature/{feature-id}/feature-delta.md — environment matrix, CI/CD outline, monitoring contracts, deployment strategy, mutation strategy, observability stack, branching strategy, coexistence matrix all become ## Wave: DEVOPS / [REF|WHY|HOW] <Section> headings.

Machine artifacts (declared, parseable by downstream):

• docs/feature/{feature-id}/environments.yaml — target environments + coexistence matrix + platform coverage + deployment assumptions. DISTILL parses this to parametrize acceptance scenarios over environments (Mandate 4 / Environmental Realism).

SSOT updates (per Recommendation 3 / back-propagation contract):

• docs/product/kpi-contracts.yaml — instrumentation deltas: per-KPI data collection (event names, log fields, metric labels), dashboard mapping, alerting thresholds. Created if absent; extended otherwise.
• docs/product/architecture/brief.md — append/update deployment topology subsection if the chosen platform changes the system-context diagram (e.g. new managed services, new region).

Legacy multi-file outputs (platform-architecture.md, ci-cd-pipeline.md, observability-design.md, monitoring-alerting.md, infrastructure-integration.md, branching-strategy.md, continuous-learning.md, kpi-instrumentation.md, wave-decisions.md as separate files) are NOT produced — that content lives in feature-delta.md. Only environments.yaml survives as a separate machine artifact because it has a parseable downstream consumer. Validator: scripts/validation/validate_feature_layout.py.

Source: nWave-ai/nWave → nWave/skills/nw-devops/SKILL.md