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/nw-review-output-format

YAML output format and approval criteria for platform design reviews. Load when generating review feedback.

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$ npx -y skills add nWave-ai/nWave --skill nw-review-output-format --agent claude-code

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How this skill gets triggered: by you, by Claude, or both.

  • Fires itselfAuto-invocation. Claude auto-loads it when your prompt matches the work.Auto-invocation is when the right skill fires by itself at the right moment, driven by a FLOW.md router and a hook, instead of you invoking it by name. It is the difference between a skill being installed and a skill actually getting used.Read the full definition →
  • You can call itInvoke it directly when you want it.
  • Slash command/nw-review-output-format

Context preview

The summary Claude sees to decide when to auto-load this skill.

YAML output format and approval criteria for platform design reviews. Load when generating review feedback.

SKILL.md

nw-review-output-format.SKILL.md
name: nw-review-output-format
description: YAML output format and approval criteria for platform design reviews. Load when generating review feedback.
user-invocable: false
disable-model-invocation: true

Platform Review Output Format

YAML Structure

review_id: "platform_rev_{timestamp}"
reviewer: "nw-platform-architect-reviewer (Atlas)"
artifact_reviewed: "{path to platform design documents}"
review_date: "{ISO 8601 timestamp}"

external_validity_check:
  deployment_path_complete: "{PASS/FAIL}"
  observability_enabled: "{PASS/FAIL}"
  rollback_capability: "{PASS/FAIL}"
  security_gates_present: "{PASS/FAIL}"
  overall_status: "{PASS/FAIL/BLOCKER}"
  blocking_issues:
    - "{issue description if any}"

strengths:
  - category: "{pipeline/infrastructure/deployment/observability/security}"
    description: "{what is done well}"
    evidence: "{specific example from design}"

issues_identified:
  - id: 1
    category: "{pipeline/infrastructure/deployment/observability/security}"
    dimension: "{critique dimension name}"
    severity: "{blocker/critical/high/medium/low}"
    description: "{clear description}"
    impact: "{consequence if not addressed}"
    recommendation: "{specific, actionable fix}"
    evidence: "{where in the design this was found}"

dora_metrics_assessment:
  deployment_frequency_enabled: "{yes/no/partial}"
  lead_time_achievable: "{yes/no/partial}"
  change_failure_rate_trackable: "{yes/no/partial}"
  time_to_restore_measurable: "{yes/no/partial}"
  assessment_notes: "{specific observations}"

priority_validation:
  largest_bottleneck_addressed: "{YES/NO/UNCLEAR}"
  simple_alternatives_documented: "{ADEQUATE/INADEQUATE/MISSING}"
  constraint_prioritization: "{CORRECT/INVERTED/NOT_ANALYZED}"
  verdict: "{PASS/FAIL}"

recommendations:
  immediate:
    - "{must fix before approval}"
  short_term:
    - "{should fix soon}"
  long_term:
    - "{consider for future improvement}"

approval_status: "{approved/rejected_pending_revisions/conditionally_approved}"
conditions_for_approval:
  - "{condition if conditionally_approved}"

Approval Criteria

**Approved**: No blocker or critical issues. High issues acknowledged with timeline.

**Conditionally approved**: No blockers. Critical issues have mitigation plan. High issues documented for follow-up.

**Rejected (pending revisions)**: Any blocker present | multiple critical issues without mitigation | external validity check failed.

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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).

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