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Nw Diverger Review Criteria

nwave-ai/nw-diverger-review-criteria

Review criteria for the nw-diverger-reviewer — validates JTBD rigor, research quality, option diversity, taste application correctness, and recommendation coherence in DIVERGE wave artifacts

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Install

one command, takes just this skill from the repository
npx skills add https://github.com/nWave-ai/nWave --skill nw-diverger-review-criteria

The instruction itself

22 sections, as written by the author

Diverger Review Criteria

Role

You are reviewing DIVERGE wave artifacts. Your job is adversarial: assume artifacts have problems until you prove they don't. Flag issues before the team commits to a design direction.

Four artifact files to review:

  • docs/feature/{id}/diverge/job-analysis.md
  • docs/feature/{id}/diverge/competitive-research.md
  • docs/feature/{id}/diverge/options-raw.md
  • docs/feature/{id}/diverge/taste-evaluation.md
  • docs/feature/{id}/diverge/recommendation.md

Dimension 1: JTBD Rigor

Check 1.1 — Abstraction Level

Requirement: Job must be at strategic or physical level, not tactical.

FAIL signals (quote from artifact when found):

  • Job statement describes a feature: "When I need to see status, I want a dashboard..."
  • Job statement contains a solution reference: "When using the app, I want to..."
  • Job reads like a user story: "As a developer, I want to..."

PASS signal: Job statement answers "what progress is being made?" without specifying how.

Check 1.2 — First-Principles Extraction

Requirement: Evidence of 5-Why or abstraction-layer navigation.

FAIL signals:

  • Job accepted as stated by user without elevation
  • No "why?" chain documented
  • Functional, emotional, and social jobs not distinguished

PASS signal: At least one level of elevation documented, from the raw request to the extracted job.

Check 1.3 — Outcome Statement Quality

Requirement: ODI-format outcome statements (Minimize + metric + object).

FAIL signals:

  • "Easy", "reliable", "good", "effective" in outcome statements
  • Solution references: "using AI", "via the dashboard"
  • Compound statements with "and"/"or"
  • Future-intent framing: "would reduce"

PASS signal: Each statement starts with "Minimize the [time/likelihood/effort]..." and is solution-agnostic.


Dimension 2: Research Quality

Check 2.1 — Evidence vs Opinion

Requirement: Competitive research cites real products, real behaviors, real data.

FAIL signals:

  • "Most users probably..." without source
  • "The market suggests..." without citation
  • Competitor descriptions without named products
  • Generic claims not tied to specific evidence

PASS signal: Each competitive insight names a real product or cites a real behavior/metric.

Check 2.2 — Prior Art Coverage

Requirement: Research covers at least 3 existing solutions to the validated job.

FAIL signals:

  • Research covers only direct competitors (ignores adjacent solutions)
  • "No existing solutions" claim without justification
  • Research treats the feature space, not the job space

PASS signal: Research includes at least one surprising or non-obvious alternative (a different category that does the same job).


Dimension 3: Option Diversity

Check 3.1 — Structural Diversity

Requirement: 6 options, each structurally different (different mechanism, different assumption, different cost profile).

FAIL signals:

  • Two or more options differ only in degree, not kind ("Option A: full dashboard" / "Option B: mini dashboard")
  • Options cluster around one approach with minor variations
  • No option represents a radical simplification (SCAMPER "Eliminate")
  • No option inverts the workflow (SCAMPER "Reverse")

PASS signal: Applying the 3-point diversity test to each pair of options — they differ in at least 2 of 3 dimensions (mechanism, assumption, cost).

Check 3.2 — Generation Discipline

Requirement: Options were generated before evaluation (separation principle).

FAIL signal: Options-raw.md contains evaluative language ("This is the best because...", "This won't work because...") mixed with generation content.

PASS signal: options-raw.md is purely descriptive; evaluation appears only in taste-evaluation.md.

Check 3.3 — HMW Framing Quality

Requirement: The HMW question doesn't embed a solution.

FAIL signals:

  • HMW question names a specific technology: "How might we use AI to..."
  • HMW question names a specific UI pattern: "How might we build a dashboard that..."
  • HMW question is narrower than the validated job

PASS signal: HMW question can be answered by options that don't share the same technology or UI pattern.


Dimension 4: Taste Application

Check 4.1 — Criteria Applied Consistently

Requirement: All four taste criteria (Subtraction, Concept Count, Progressive Disclosure, Speed-as-Trust) applied to all surviving options.

FAIL signals:

  • Some options scored on fewer criteria than others
  • Criteria added or removed mid-evaluation
  • DVF elimination not documented (options disappeared without reason)

PASS signal: Full scoring matrix present for all post-DVF-filter options with all criteria scored.

Check 4.2 — Cherry-Picking Prevention

Requirement: Weights locked before scoring begins; recommendation follows from scores.

FAIL signals:

  • Recommendation contradicts the highest-scoring option without documented weight adjustment
  • Weights not specified in artifact
  • "This option feels right" language in recommendation without score grounding

PASS signal: Recommended option has highest or second-highest weighted total; if second-highest, reason for not recommending top is documented.

Check 4.3 — Score Rubric Application

Requirement: Scores justified against rubric, not assigned freely.

FAIL signals:

  • Score of 5 for "Subtraction" on an option with multiple features, without justification
  • Score of 1 for "Speed-as-Trust" on a text-based tool without latency analysis
  • Scores assigned without quoting the rubric criterion

PASS signal: Each score accompanied by one sentence referencing the specific rubric level.


Dimension 5: Recommendation Coherence

Check 5.1 — Traceability

Requirement: Recommendation traceable to JTBD → Research → Scores.

FAIL signal: Recommendation could be made without reading job-analysis.md or taste-evaluation.md.

PASS signal: Recommendation references the validated job, cites competitive research findings, and derives from the highest-scoring option(s).

Check 5.2 — Dissent Documented

Requirement: "Runner-up" case documented — which option almost won and why.

FAIL signal: Only the winning option discussed in recommendation.

PASS signal: recommendation.md includes a "dissenting case" section naming the runner-up and the margin.

Check 5.3 — DISCUSS Handoff Readiness

Requirement: Recommendation ends with a clear decision statement for the DISCUSS wave.

FAIL signal: Recommendation ends with "both options are viable" or "the team should decide."

PASS signal: Explicit decision statement: "Proceed with [option], assuming [key risk] is acceptable."


Review Output Format

review_result:
  artifact_path: "docs/feature/{id}/diverge/"
  review_date: "{timestamp}"
  reviewer: "nw-diverger-reviewer"

  jtbd_rigor:
    status: "PASSED|FAILED"
    issues: [{check, location, quoted_evidence, remediation}]

  research_quality:
    status: "PASSED|FAILED"
    issues: [{check, location, quoted_evidence, remediation}]

  option_diversity:
    status: "PASSED|FAILED"
    issues: [{check, location, quoted_evidence, remediation}]

  taste_application:
    status: "PASSED|FAILED"
    issues: [{check, location, quoted_evidence, remediation}]

  recommendation_coherence:
    status: "PASSED|FAILED"
    issues: [{check, location, quoted_evidence, remediation}]

  approval_status: "approved|conditionally_approved|rejected_pending_revisions"
  blocking_issues: []
  recommendations: []

Approval thresholds:

  • approved: all dimensions PASSED
  • conditionally_approved: no FAILED dimensions, minor issues only
  • rejected: any dimension FAILED, with specific remediation required

How to use it

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